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A Methodology for Near-Real Time Spatial Estimation of Evaporation

Report to the Water Research Commission

by

C Jarmain1, W Bastiaanssen2, M G Mengistu3, G Jewitt4 & V Kongo4

1 CSIR Natural Resources and the Environment, P.O. Box 320, Stellenbosch, 7599, South Africa

2 WaterWatch, Generaal Fouikesweg 28 6703 BS Wageningen, The Netherlands

3 CSIR Natural Resources and the Environment,

c/o Soil-Plant-Atmosphere Continuum Research Unit, Agrometeorology Discipline, School of Environmental Sciences,

University of KwaZulu-Natal, Private Bag X01, Scottsville, 3209, South Africa

4 School of Bioresources Engineering and Environmental Hydrology,

University of KwaZulu-Natal, Private Bag X01, Scottsville, 3209, South Africa

WRC Report No. 1751/1/09 ISBN 978-1-77005-725-8

November 2009

A M e t h o d o l o g y f o r N e a r - R e a l T i m e S p a t i a l E s t i m a t i o n o f E v a p o r a t i o n

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DISCLAIMER

This report has been reviewed by the Water Research Commission (WRC) and approved for

publication. Approval does not signify that the contents necessarily reflect the views and policies of the WRC, nor does mention of trade names or commercial products constitute endorsement or

recommendation for use.


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Acknowledgements The research in this project was funded by the Water Research Commission, for whose assistance we are sincerely grateful. We also wish to acknowledge contributions made by members of the steering committee:

Dr R Dube and Mr M C Moseki Water Research Commission (Chairman)

Prof GS Pegram University of KwaZulu-Natal

Dr N Lecler South African Sugarcane Research Institute

Prof CS Everson CSIR

Prof MJ Savage University of KwaZulu-Natal

Dr J van Aardt / Dr R Mathieu CSIR

Dr C Eloff CSIR

We also wish to acknowledge the Water Research Commission and CSIR for making available validation data used in this project. Prof Rick Allen from University of Idaho, the developer of the METRIC model, and his assistants Dr Aureo Oliviera and Dr Jeppe Kjaersgaard are thanked for their help with the METRIC modelling and specifically for doing a run at their offices. Dr Bob Su and Dr Lichun Wang from ITC are thanked for their help with the SEBS modelling and for making the latest version of this model available for use in this project. Mrs Lesley Gibson from the Agricultural Research Council is also thanked for her inputs relating to the SEBS modelling. Prof Wim Bastiaanssen from WaterWatch, The Netherlands, and developer of SEBAL, is thanked for making available the code of the SEBAL model and training and assistance in the use of this model. Dr Wouter Meijninger from WaterWatch is also thanked for assistance in the use of SEBAL.


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Executive Summary

1. MOTIVATION AND BACKGROUND

In a water-scarce country like South Africa with a number of large consumers of water, it is important to estimate evaporation with a high degree of accuracy. This is especially important in the semi-arid regions where there is an increasing demand for water and a scarce supply thereof. Evaporation varies regionally and seasonally, so knowledge on evaporation is fundamental to save and secure water for different uses, and to guarantee that water is distributed to water consumers in a sustainable manner. Furthermore, water quality is increasingly being compromised, which negatively impacts on the supply of water. Many of the conventional methods used locally and internationally to estimate evaporation can only be applied at field scale and provide point- based estimates of evaporation. Evaporation estimates are often also required at scales larger than field scale, e.g. at catchment scale. Therefore, spatial estimates of evaporation and other components of the water balance are still difficult to obtain. Recent developments in the use of remote sensing data to estimate evaporation hold great potential for improved water resources management. Remote sensing data can be used in evaporation estimation methods to extend point measurement of evaporation, to much larger areas, even areas where measured meteorological data may be sparse.

2. PROJECT OBJECTIVE

The project aims were as follows: Aim 1: Review methodologies

available to determine evaporation utilising remote sensing images,

Aim 2: Recommend a methodology or methodologies (based on the review and first order validation) that has potential application in South Africa in terms of evaporation estimation and other water resources management applications,

Aim 3: Recommend infrastructure and other arrangements required to run models based on remote sensing imagery operationally, and

Aim 4: Expose and inform stakeholders in the water management field of the potential of remote sensing products combined with advances in information technology, to support water management.

3. METHODS

A number of models are available for estimating evaporation using remote sensing data. These models follow two main approaches to estimate evaporation. In the first approach, evaporation is estimated as the residual of the shortened energy balance equation. The second approach uses a water use efficiency relationship to determine evaporation. Although a number of remote-sensing based models varying in complexity are available to estimate evaporation spatially, only four internationally applied models were selected and evaluated for different land covers and geographical regions within South Africa. The four models evaluated are the Surface Energy Balance Algorithm for Land (SEBAL) model, the Surface Energy Balance System (SEBS) model, the Mapping EvapoTranspiration with high Resolution and Internalised Calibration (METRICtm) model and the Vegetation Index / Temperature Trapezoid (VITT) model. The first three models estimate evaporation from surface


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temperature estimates and the shortened energy balance. In the fourth model (VITT) evaporation is estimated from surface temperature and vegetation cover using four different reference conditions. Four different research sites for which energy balance and evaporation data are available, were selected for this study. The sites were situated in the KwaZulu-Natal and the Eastern Cape provinces, and are representative of different climatic regions. Seven Oaks Acacia mearnsii site

The first research site, which was situated in the Sevenoaks area (KwaZulu-Natal), was a six hectare compartment planted with Acacia mearnsii trees. A year-long data set of evaporation and energy balance data was available for Acacia mearnsii. Four different models were parameterised for this site – the SEBAL, SEBS, METRIC and VITT model. The SEBAL model was parameterised for five different days, using five Landsat 5 images spanning the period of September 2006 to July 2007. Instantaneous, daily and monthly evaporation rates were modelled and compared with field data. The SEBS, METRIC and VITT models were also parameterised for this site, but not for the entire 11-month period. The SEBS model was parameterised for the Landsat image collected on 14/12/2006 whereas METRIC was parameterised for two Landsat images collected on 09/09/2006 and 14/12/2006. Daily evaporation was also modelled using the VITT model for all five Landsat images. Midmar open water body

The second selected site was the Midmar Dam, a relatively small water body situated in the KwaZulu-Natal Midlands. For this site a week-long open water evaporation and energy balance data set was available. Since a cloud-free Landsat 5 image was not available during the period of field data collection, an image taken prior to the onset of evaporation measurements was therefore used in the spatial evaporation modelling. The SEBAL model was the only

model used to model the components of the shortened energy balance and evaporation from this site. Average energy balance field data collected at the nearest 30 minute interval to the time of the satellite image acquisition were used to validate the instantaneous estimates of the energy balance components simulated using SEBAL. The daily evaporation modelled using SEBAL was also compared to the evaporation measured over a six day period. St Lucia sites

The third research area, consisting of three dominant vegetation types – a swamp forest, grassland and a sedges wetland – was situated in the iSimangaliso Wetland Park, close to St Lucia (KwaZulu-Natal). These three sites were situated within close proximity to each other. Concurrent evaporation and energy balance data sets were available for these three different vegetation types. Since a satellite image was not available during the period of field data collection, a cloud-free Landsat 7 image collected directly after this field campaign was used in the evaporation and energy balance modelling. Average energy balance data collected at the nearest 30 minute interval to the time of satellite image acquisition were used to validate the instantaneous estimates of the energy balance components using the SEBAL and SEBS models. Daily average evaporation measured for the three sites was also compared to the daily evaporation estimates modelled using the SEBAL, SEBS and the VITT models. Kirkwood sites

The fourth research area was situated in the Kirkwood area (Eastern Cape) and consisted of two sites, an area with Spekboom thicket and an area of degraded veld. The two sites were located adjacent to each other. Concurrent week-long evaporation and energy balance data sets were available for both sites. A Landsat 7 satellite image was acquired for this study area during the period of field


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measurements. This image was used in the modelling of evaporation and energy balance fluxes. Energy balance fluxes collected at the nearest 30 minute interval to the time of the satellite image acquisition were used to validate the instantaneous estimates of the energy balance components using the SEBAL and SEBS models. The daily evaporation was also compared to the daily evaporation estimates modelled using the SEBAL, SEBS and the VITT models. In addition, since a long-term evaporation data set was available, evaporation estimates using SEBAL over a week overlapping the date of satellite image collection, were also compared to the field data for each site.

4. RESULTS AND DISCUSSION

Instantaneous energy fluxes

The instantaneous energy flux estimates, i.e. the energy flux estimates from each model at the time of the satellite overpass, or a comparable time, were compared to field measured data. For the comparison, the satellite pixels around the position of the actual field sites were selected as Areas of interest, and only the data within these pixels were used in the data comparison and analysis. Data averages of between 8 (degraded site) and 136 (Midmar open water body) pixels were used in the comparisons. At the Acacia site, the instantaneous estimates of net radiation (Rn) simulated using SEBAL, SEBS and METRIC, agreed well with the measured Rn data – for all five Landsat images. The SEBAL estimates of instantaneous Rn slightly exceeded the measured Rn (5%) except for the May 2007 estimates. For the May image, Rn was underestimated by 7%. Similarly SEBS and METRIC slightly overestimated the instantaneous Rn (SEBS up to 11%, and METRIC between 7 and 10%). The Rn estimates are highly dependent on the accuracy of the albedo and the transmissivity data. Solar radiation data obtained for each research site and the extraterrestrial estimates of solar radiation were used to estimate transmissivity values which were subsequently used in the modelling.

At the two Kirkwood sites, Rn simulated using SEBAL and SEBS, also compared well with the measured Rn data (to within 9%) but it was slightly underestimated. The instantaneous estimates of Rn were 424 and 404 Wm-2 for the Spekboom and degraded sites respectively. The higher Rn at the Spekboom thicket site reflects the lower average albedo at this site (0.12) compared to the degraded site (0.15). Net radiation simulated using SEBAL for Midmar Dam, also compared well to the measured Rn, but was also slightly underestimated – 212 vs. 202 Wm-2 (validation vs. SEBAL estimates respectively). At the three St Lucia sites, the simulated Rn using both SEBAL and SEBS were less accurate. However, for these sites the instantaneous Rn estimates were compared to the average estimates of Rn (at the time interval closest to the time of the satellite overpass), since field estimates for the simulation day were not available. The Rn estimates simulated using SEBS were consistently higher than the measured Rn on sunny days (up to 46% at the swamp forest site). Similarly, the Rn estimates simulated using SEBAL for the swamp forest were also higher than the measured Rn (up to 23%). The net radiation estimates were more accurate for both the grassland and sedges sites, where the Rn estimates simulated using SEBAL were only 1 and 4% lower than the measured Rn respectively. Soil heat flux density and heat storage in water are difficult to estimate and based on the heat storage results for Midmar Dam, also difficult to simulate accurately. The soil heat flux (G) (or in the case of Midmar Dam, the heat storage in water) is estimated in SEBAL and METRIC as a function of albedo, surface temperature, NDVI and Rn (according to Bastiaanssen, 2000). In SEBS G is a function of Rn, fraction of vegetation, and a constant ratio of G to Rn for surfaces with a full vegetative cover, and bare soil (according to Su et al., 1999 and 2001). Therefore any errors in the simulated Rn will be carried through to the simulated G.


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At the Acacia site, the instantaneous estimates of G obtained using SEBAL, SEBS and METRIC differed greatly from the field estimated values. The G estimated using SEBAL exceeded that measured G by up to 82%. The actual measured G for the five days of simulation were generally small and ranged between 57 Wm-2 (December) and -3 Wm-2 (May). The simulated values ranged between 98 Wm-2 (December) and 2 Wm-2 (June). Similarly, SEBS and METRIC failed to simulate soil heat flux densities comparable to those measured. The SEBS instantaneous G estimate for December was 134 Wm-2 compared to the measured 57 Wm-2, and 121 Wm-2 for the METRIC estimate. At the Kirkwood sites, both SEBS and SEBAL also failed to simulate G accurately, with the simulated values consistently exceeding the measured G. At the Spekboom site, the G estimates simulated using SEBAL and SEBS overestimated the measured G by 41 and 63% respectively. At the degraded site, the simulated G estimates using SEBAL and SEBS overestimated the measured G by 27 and 6% respectively. SEBAL however succeeded in simulating higher G values for the degraded site which has a lower leaf area index (0.16) compared to the Spekboom site (0.46). SEBS failed to simulate the relative differences in G accurately, with instantaneous G estimates at the Spekboom site exceeding those at the degraded site. Similarly, both SEBAL and SEBS also failed to simulate G accurately at the St Lucia sites. For both the forest and sedges sites with their high vegetative covers, large differences existed between the measured and simulated G, with the Gs simulated exceeding those measured. The Gs simulated for the grassland were more accurate with the SEBAL G estimates 12% lower than the measured Gs, and the SEBS estimates 21% higher than the measured Gs. For Midmar Dam, heat storage validation data were available and were compared to the Gs simulated using SEBAL. The heat storage in the water (G) was therefore calculated using SEBAL and was greatly affected by the water depth (here simulated at an average depth of 5 m). The

average SEBAL estimates of heat storage in water were 18% lower than the measured. The way in which the sensible heat flux density (H) is estimated in SEBAL, SEBS and METRIC differs and is a crucial component of these models. The accuracy of H estimates and the evaporative fraction estimated using these models, and the H estimates for the different sites varied greatly. At the Acacia site both METRIC and SEBAL were unable to simulate H accurately for most of the simulation days. The measured H was less than the Hs modelled using SEBAL, by as much as 65%, and as much as 64% with METRIC (December run only). With the exception of the May simulations, all SEBAL estimates of H exceeded the field measurements (sometimes significantly). In contrast, the instantaneous H estimates using SEBS compared favourably with those measured, to within 3%. But, despite these large differences in the measured and simulated H fluxes at the Acacia site, the evaporative fraction (EF) for the five simulation days agreed reasonably well with those estimated from the field data (between 5 and 31%). The METRIC estimates of EF for December were within 27% of the measured EF. Both the SEBAL and METRIC EF estimates were however lower than those measured. The good agreement between the measured H and simulated H using SEBS, resulted in a good agreement between the measured and estimated EFs (to within 3%). At the Spekboom site at Kirkwood, the SEBAL and SEBS estimates of H were both lower than that measured H (by as much as 32 and 61% respectively). Similarly H was also underestimated using SEBS for the degraded veld (by 46%), but SEBAL did simulate H more accurately, to within 4% of that measured for the degraded veld. Subsequently, both SEBS and SEBAL failed to accurately simulate EF at this site. The EFs simulated using SEBS were higher than the measured EFs, e.g. by up to 75% for the Spekboom site.


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The H estimated for the St Lucia sites using SEBAL consistently overestimated the fluxes by 79, 62 and 1% for the forest, grass and sedges sites respectively. The ratios of sensible heat flux to net radiation (H/Rn) (based on measurements) were 12, 65 and 58% for the forest, grass and sedges sites respectively, whereas the H/Rn ratio for SEBAL estimates were 46, 174 and 61%. The high H/Rn fraction estimated from the SEBAL data and exceeding 1 for the grass site might suggest advective conditions. The SEBS simulations of H show H/Rn of 0.4, 66 and 13% for the forest, grass and sedges sites respectively. This implies high evaporation rates for both the forest and sedges sites. The actual EF values for the forest and sedges sites confirm these high evaporation rates (EF > 0.7 and > 0.42 respectively). In contrast, the EFs for the grassland site were lower (<0.11) and suggest low evaporation rates. The H estimates for an open water body are generally low since evaporation is the dominant process under these conditions. The H/Rn calculated from the measurements and the SEBAL estimates were similar (10 and 13% respectively). The SEBAL estimates of H were within 77% of the measured H, with the measured estimates slightly exceeding the simulated H using SEBAL. As a result, the EFs measured and modelled were greater than 0.78, agreeing to within 7%. Daily and period evaporation

The spatial estimates of the energy balance components at the time of the satellite overpass were converted into spatial estimates of evaporation at a daily time step. Evaporation from Midmar Dam was calculated for both one day and a 6-day period. Evaporation estimates for SEBAL were done for a day prior to the field measurements and this daily average evaporation of 4.4 mm/d exceeded the average daily evaporation measured using the eddy covariance system (2.34 mm/d) by 47%. The evaporation measurements took place amid extremely cold weather and the significantly colder conditions experienced during the

measurement period, compared to the time of evaporation simulations could have contributed significantly to the higher simulated evaporation rates. The daily average air temperature on the day of evaporation simulation was 16.8oC compared to the 11.8oC average daily air temperature over the simulation period. The accuracy of the simulated evaporation from Midmar Dam using SEBAL, improved over a longer period. Over a six day period, and using a fixed evaporative fraction, the SEBAL evaporation estimate was only 18% (or 2.7 mm) less than the measured. For vegetation with a low Normalized Difference Vegetation Index (NDVI) or vegetation cover (VC), the daily evaporation estimated using the SEBAL and VITT models for the degraded veld site compared favourably to the evaporation measured – to within 0.53 mm/d. However, the evaporation estimated using the SEBS model exceeded the measured evaporation rates by up to 1.89 mm/d. However, none of these models (SEBAL, SEBS and VITT) could simulate evaporation from the Spekboom thicket within an acceptable degree of accuracy. The measured evaporation (0.79 mm/d) was significantly less than the simulated (1.82, 3.21, 2.99 and 2.89 mm/d for the SEBAL, SEBS, VITT1 and VITT2 models respectively). Spekboom thicket contains the CAM plant, Spekboom, which has the ability to shut stomata during the day if stress conditions occur (and also subsequently open stomata during the night). The differences in evaporation measured and modelled could potentially be explained by the constant evaporative fraction used in the SEBAL evaporation modelling. The evaporative fraction estimated using SEBAL for the Spekboom thicket exceeded those measured in the field and so the higher evaporation rates estimated for the Spekboom thicket directly reflect the higher estimated EFs. SEBAL takes advective conditions into account, but not variable EFs, therefore it will compensate somewhat to stress conditions experienced by plants.


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In contrast to the Midmar site, the accuracy of evaporation estimates from the Spekboom thicket estimated using SEBAL over a longer period (7 days) was not improved. Over a 7 day period the evaporation estimates using SEBAL for the degraded and Spekboom sites were 0.18 and 15.25 mm/d respectively. Evaporation measured over the same period at the degraded veld was 3.35 mm and exceeded the SEBAL estimate. The evaporation measured at the Spekboom was 6.69 mm and significantly different from that modelled – 57% lower. At the St Lucia sites, measured evaporation (ET) compared favourably to modelled ET using SEBAL despite the fact that the evaporation was calculated for the day after the measurements ended. Evaporation measured and simulated using SEBAL differed by a maximum of 0.5 mm/d (or up to 16% for the forest and sedges sites). Evaporation estimated using SEBS, similarly compared favourably to the measured ET – to within 0.81 mm/d (or 32%) for the forest and sedges sites. There was a big difference between the measured and simulated evaporation at the grassland, with a larger difference at the grassland site (up to 44%), however the actual evaporation estimates were very low (0.5 mm/d) and within the range of measurement error. In contrast, the evaporation estimates from both the VITT models compared well with the measured ET for the grassland site, but less favourably for the forest and sedges sites – with evaporation differences of up to 45% estimated. The daily evaporation modelled using SEBAL, METRIC and VITT models also differed from the measured ET at the Acacia site for the five different days selected over a period of 11 months. The SEBAL evaporation estimates, with the exception of the September simulation, were generally lower than the measured ET (up to 80% in winter (June simulation) and 15% in summer (December simulation)). Nevertheless, there is a good agreement of the energy balance components and evaporative fractions, between the measured and simulated. Similarly, the METRIC daily evaporation estimate (for September) for the Acacia site was less than the

measured by 1.41 mm/d. In contrast to the low SEBAL and METRIC evaporation estimates, the SEBS daily evaporation estimate for December exceeded the measured slightly (by 9% or 0.48 mm/d). The five daily SEBAL estimates of evaporation for the Acacia site, plotted against the 11 month time series of scintillometer evaporation measurements showed favourable comparison. At this site (Acacia), all evaporation estimates of the VITT model were also lower than the measured – often significantly (by up to 2.48 mm/d in May), as was the case for SEBAL and METRIC estimates. However, the VITT evaporation estimates for June were not different from the measured evaporation. The daily evaporation rates for the Acacia site were further up-scaled to period (monthly or two weekly) estimates of evaporation. The up-scaled evaporation estimates using SEBAL, were consistently lower that the measured, by up to 1.86 mm/d or 44% (excluding the June image). These lower evaporation rates simulated using SEBAL reflect lower daily evaporation estimates. A good agreement between the measured and modelled evaporation is however not necessarily to be expected here since data from a single image was used to estimate evaporation over 31 days, assuming that the EF will remain constant over this entire period, which will generally not be the case. These differences in evaporation strongly suggest that when evaporation is up-scaled to longer periods, more than one image per month should be used to estimate the monthly evaporation estimates, and it is expected that the use of more than one image, will significantly improve the evaporation estimates significantly.

5. CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE

RESEARCH

The accurate estimation of evaporation still remains a challenge to researchers in the field of micrometeorology, hydrology as well as for


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water resources managers and planners. Frequently-used methodologies still generally estimate evaporation at field scale, but internationally it is now recognised that remote-sensing based models hold great potential for the spatial estimation of evaporation at both field and catchment scale. In this study it was shown that remote-sensing data can be used in evaporation estimation methods to extend point measurement of evaporation, to much larger areas, even areas where measured meteorological data may be sparse. In this study, the accuracy of the components of the energy balance as well as evaporation, estimated using the SEBAL, METRIC, SEBS and VITT models were evaluated for different land surfaces, ranging from a water body, forestry plantation, wetlands and native vegetation under semi-arid environments varying in vegetative cover. Most of the models used in this study (SEBAL, METRIC, and SEBS) quite easily simulated net radiation accurately, but the estimation of soil heat flux and heat storage of a water body was more complex and variable. Similarly, the estimation of sensible heat flux density (H) at the time of satellite image overpass for various land uses and using different models remains a complex process. Accurate estimates of the simulated H, when compared to that measured, were not always achieved. Evaporative fraction (EF) estimates, however were simulated accurately in many cases, and for such instances, the daily evaporation rates measured compared favourably to the simulated evaporation. The VITT model generally yielded the least accurate evaporation estimates. This could be attributed to the inability to locate the four reference conditions required in the VITT model, within the satellite image. The accuracy of evaporation estimates was occasionally improved over longer simulation periods, as was the case for the Midmar site. However, the period of extrapolation should not be too long since a constant evaporative fraction was assumed. The opposite can also be the case, as was shown at the Acacia site where longer term evaporation estimates using SEBAL

differed greatly from that measured. These increased differences in evaporation over longer periods, were likely the result of the constant EFs assumed in the modelling. The literature review has shown that a great variety of models exist that can be used for the estimation of evaporation. The models based on the simplified energy balance hold great potential. A number of these models are used operationally for water resources management and planning. It would therefore be possible to establish a remote sensing unit in South Africa that will assist in the estimation of spatially distributed evaporation, if supported by some of the model developers. In recent years, the accuracy of evaporation estimates produced by these remote sensing models have been reviewed by numerous researchers. A number of papers have subsequently appeared where the evaporation estimates from these models were compared with evaporation measured using different techniques. Although remote-sensing based evaporation estimation techniques hold great potential, locally and internationally, there are still some shortcomings that will need to be addressed in the future. The first most general shortcoming of remote-sensing based evaporation models currently is the limited availability of high resolution thermal infra-red (TIR) images. Landsat 5 is not acquiring data constantly anymore and Landsat 7 has a scan-line correction problem that causes 22% of the pixels to be without data. Furthermore, it is not definite that Landsat 8 will be equipped with a TIR channel. The second major challenge is the scattering and absorption of radiation by clouds. More research into the use of microwave measurements for evaporation estimation is needed. Some disadvantages of this approach are that it cannot be directly related to the evaporation process, but only indirectly via soil moisture and generally has a coarse spatial resolution. But, a major advantage is that evaporation estimation can take place under all climatic conditions, including both cloudy and clear skies.


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Thirdly, evaporative processes in mountainous areas are very complex due to the presence of strong lateral movements of heat and the three dimensional flow of air. International experimental data bases are also limited and often inadequate to facilitate the improvement of model formulations for these terrains. Lastly, water bodies can have significant non-beneficial evaporation losses. The evaporative fluxes over water bodies are difficult to estimate because a large component of the energy balance is related to the heat storage and release through the water. The estimation of this component also needs to be improved.

6. EXTENT TO WHICH THE CONTRACT OBJECTIVES HAVE

BEEN MET

In the first aim, different methodologies available to determine evaporation using remote sensing images were reviewed. This aim was successfully met. An extensive literature review was conducted on various aspects. Aspects reviewed included different remote sensing data sources, seven remote sensing models, methods of up-scaling instantaneous evaporation estimates to daily evaporation estimates, potential applications of spatial estimates of evaporation, operational examples of the application of remote sensing based evaporation estimation models and general short comings and research opportunities in this field. The second aim had, as an outcome, recommendations for a methodology or methodologies (based on the review and first order validation) that have potential application in South Africa in terms of evaporation estimation and other water resources management applications. In order to do this, four different models (SEBAL, SEBS, METRIC, VITT) were parameterised for seven different land uses and the energy balances and evaporation rates simulated were compared to field data collected with a range of methods including the Eddy covariance method and Scintillometry. This aim was successfully met.

In aim three, we were to make recommendations on the infrastructure and other arrangements required to run models based on remote sensing imagery operationally. For an operational unit to run successfully for the estimation of evaporation, certain human resource capabilities and infrastructure need to be in place. This aim was also successfully met, and practical examples of what would be required to run such a unit were given. Aim 4 was to expose and inform stakeholders in the water management field of the potential of remote sensing products combined with advances in information technology, to support water management. This aim was also successfully achieved. During two research workshops conducted, three scientists (of which two are internationally well known) discussed this topic. The workshops were attended by up to 50 people. Two popular articles on this topic were also published.

7. CAPACITY BUILDING AND TECHNOLOGY EXCHANGE

The use of remote sensing technologies for estimating evaporation is a very new field in South Africa and little capacity currently exists in this field. Companies like WaterWatch, the developers and owners of the SEBAL model, has conducted a number of studies in South Africa over the past years, specifically focussing on the field of irrigated agriculture. But, they did most of the modelling for these projects. This WRC project presented unique capacity building and training opportunities since all the energy balance and evaporation modelling were done in South Africa. In November 2007, Caren Jarmain attended a TIGER training workshop on Remote sensing in Kenya. This visit was funded by the TIGER initiative, and the training course exposed Caren to the basic principles of remote sensing. A version of SEBS, run in ILWIS, was also used in one of the practical sessions to estimate evaporation.


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In June 2008, Caren visited WaterWatch in Wageningen, The Netherlands, and received personal training in the use of SEBAL. The training was funded by a research project funded by Working for Water, where SEBAL is currently applied to estimate alien invasive water use. During this visit Prof Wim Bastiaanssen also helped Caren to write code for the METRIC model, as well as the VITT model, that was used in this project. In July 2008, Victor Kongo visited the developers of SEBS, as part of his post-doc project, and received training in the use of SEBS. Lastly in January 2009, a number of CSIR employees also received training in the use of SEBAL, under the Memorandum of Understanding signed between the CSIR and WaterWatch. The participants of the training course were Caren Jarmain, Colin Everson, Michael Mengistu, Alistair Clulow, Mark Gush, Marilyn Govender, Vivek Naiken, Wesley Roberts and Ashton Maherry.

8. DATA

All processed data used for this report have been catalogued. The raw data are stored at the Natural Resources and the Environment Unit, CSIR, P.O. Box 320, Stellenbosch, 7599. Contact Person: Dr. C. Jarmain.

The raw data are stored on a hard drive and DVDs. All data can be supplied on DVDs if required.

9. PUBLICATIONS

Two popular articles were published focussing on this project. In The Water Wheel January / February 2009 edition, Lani van Vuuren who attended the stakeholder workshop held as part of this report, reported on the use of Satellites to assist SA in Determining Evaporation. The article can be accessed at http://www.wrc.org.za/publications_waterwheel_jan-feb09.htm Also, in the December 2008 issue of e-News of the CSIR, the CSIR reported on the signing of an MOU between the CSIR and WaterWatch which will facilitate the future use of remote sensing models to estimate evaporation in support of water resources management. The article can be accessed at http://www.csir.co.za/enews/2008_dec/nre_03.html


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Contents

Acknowledgements iii Executive Summary v List of Figures xvi List of Tables xix List of Symbols and Abbreviations xxi List of Definitions xxiv

CHAPTER 1: BACKGROUND 1

CHAPTER 2: MODELS USED TO ESTIMATE EVAPORATION SPATIALLY 8

2.1 Surface Energy Balance Algorithm for Land (SEBAL) model 8 2.2 Surface Energy Balance System (SEBS) model 10 2.3 Mapping EvapoTranspiration with high Resolution and Internalised Calibration

(METRICtm) model 16 2.4 Vegetation Index/Temperature Trapezoid (VITT) model 19

CHAPTER 3: SITES AND DATA USED TO ASSESS SPATIAL EVAPORATION MODELS 22

3.1 Site information 24 3.1.1 Seven Oaks site 24 3.1.2 Midmar site 29 3.1.3 St Lucia site 29 3.1.4 Kirkwood site 30

3.2 Spatial and ancillary data 31 3.2.1 Image source 31 3.2.2 Pre-processing of images 31 3.2.3 Ancillary data 32

3.3 Models and modelling code 32

CHAPTER 4: ASSESSMENT OF MODELS USED FOR SPATIAL ESTIMATION OF EVAPORATION 36

4.1 Instantaneous energy balance fluxes 38 4.2 Daily evaporation and evaporation over longer periods 52

CHAPTER 5: CONCLUSIONS 58

CHAPTER 6: RECOMMENDATIONS FOR AN OPERATIONAL REMOTE SENSING UNIT 62

6.1 Human Resources (the team and the users) 62 6.2 Infrastructure requirements (hardware and software) 64 6.3 Remote sensing and meteorological data 65 6.4 Auxiliary geographical databases 66

CHAPTER 7: REFERENCES 67


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List of Figures Figure 1 Satellite remote sensing and GIS for better water management (Taken

from Bastiaanssen and Harshadeep, 2005) 2

Figure 2 Example of an operational ET product for The Netherlands that can be publicly accessed through the website www.boerinbeeld.nl. Shown is the weekly total ET for 13 to 19 July 2000 (in Dutch). 5

Figure 3 Example of an operational ET product over Africa developed by the Princeton University group (courtesy Dr. Eric Wood) 6

Figure 4 The geographical distribution of the research sites used in this study. These include the Seven Oaks, Midmar, Kirkwood and St Lucia sites. 23

Figure 5 Graphical representation of the VITT model – showing the relationship between Ts-Ta (surface temperature – air temperature difference) and fractional vegetation cover fc (assumed to 1 for full cover vegetation and 0 for bare soil), for a “wet” and “dry” branch respectively 34

Figure 6 A screen dump of some of the code of the VITT model, programmed in Spatial modeller, ERDAS Imagine 35

Figure 7 Schematic representation of the process followed to estimate energy fluxes spatially using remote sensing (RS) and other ancillary data (e.g. meteorological, meteo data). 36

Figure 8 Energy balance and evaporation data for the A. mearnsii site, as measured and estimated using the SEBAL, SEBS, and METRIC models. The instantaneous data estimated using the models are shown, together with the time series measured data for five days (DOY’s 252, 348, 79, 143, 175 corresponding to 9 September 2006, 14 December 2006, 20 March 2007, 23 May 2007 and 24 June 2007 are shown for the period 0700 to 1800. 39

Figure 9 Energy balance and evaporation data for the Kirkwood Spekboom thicket site, as measured and estimated using the SEBAL and SEBS models. Only the instantaneous data estimated using the models are shown, whereas the measured data are shown for the period 0700 to 1700. Rn refers to net radiation, G to soil heat flux, H to sensible heat flux, and EF to the evaporative fraction. Also, val refers to the validation data, _ave refers to the average value of the specific parameter, _std to the standard deviation, and /val refers to the fraction of the estimated value to the validation value. 39

Figure 10 Energy balance and evaporation data for the Kirkwood Spekboom thicket site, as measured and estimated using the SEBAL and SEBS models. Only the instantaneous data estimated using the models are shown, whereas the measured data are shown for the period 0700 to 1700. Rn refers to net radiation, G to soil heat flux, H to sensible heat flux, and EF to the evaporative fraction. Also, val refers to the validation data, _ave refers to the average value of the specific parameter, _std to the standard deviation, and /val refers to the fraction of the estimated value to the validation value. 40


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Figure 11 Energy balance and evaporation data for Midmar Dam, as measured and estimated using the SEBAL model. Only the instantaneous data estimated using the models are shown, whereas the measured data are shown for the period 0800 to 1700. Rn refers to net radiation, G to heat flux in the water, H to sensible heat flux, and EF to the evaporative fraction. Also, val refers to the validation data, _ave refers to the average value of the specific parameter, _std to the standard deviation, and /val refers to the fraction of the estimated value to the validation value. 41

Figure 12 Energy balance and evaporation data for the St Lucia swamp forest site, as measured and estimated using the SEBAL and SEBS models. Only the instantaneous data estimated using the models are shown, whereas the measured data are shown for the period 0600 to 1800. Rn refers to net radiation, G to soil heat flux, H to sensible heat flux, and EF to the evaporative fraction. Also, val refers to the validation data, _ave refers to the average value of the specific parameter, _std to the standard deviation, and /val refers to the fraction of the estimated value to the validation value. 42

Figure 13 Energy balance and evaporation data for the St Lucia grassland site, as measured and estimated using the SEBAL and SEBS models. Only the instantaneous data estimated using the models are shown, whereas the measured data are shown for the period 0600 to 1800. Rn refers to net radiation, G to soil heat flux, H to sensible heat flux, and EF to the evaporative fraction. Also, val refers to the validation data, _ave refers to the average value of the specific parameter, _std to the standard deviation, and /val refers to the fraction of the estimated value to the validation value. 43

Figure 14 Energy balance and evaporation data for the St Lucia sedges wetland site, as measured and estimated using the SEBAL and SEBS models. Only the instantaneous data estimated using the models are shown, whereas the measured data are shown for the period 0600 to 1800. Rn refers to net radiation, G to soil heat flux, H to sensible heat flux, and EF to the evaporative fraction. Also, val refers to the validation data, _ave refers to the average value of the specific parameter, _std to the standard deviation, and /val refers to the fraction of the estimated value to the validation value. 44

Figure 15 The spatial distribution of the daily evaporation estimated using e.g. SEBAL for the (a) Acacia site on 9 Sep 2006, (b) for the Midmar site on 24 Jul 2007, (c) for the St Lucia sites on 14 Aug 2008 and (c) the Kirkwood sites on 27 Sep 2008. The dotted lines show the outline of the Areas of interest at each site (AOIs). Colour range – darker colours represent lower evaporation rates and lighter colours represent higher evaporation rates. 52

Figure 16 Daily evaporation rates over a 10 day period (DOY 181-193) measured using an eddy covariance system, and estimated using SEBAL (SEBAL) for DOY 175, and also calculated over a six day period (DOY 188 TO 193) (Validation). The total evaporation summed over the six day period (measured and estimated using SEBAL) is also compared. The evaporation totals are given in brackets (15.28 mm measured and 12.72 mm simulated using SEBAL). 53

Figure 17 Total evaporation (mm/d) estimated at the Spekboom thicket and the Degraded veld sites at Kirkwood. Val refers to the validation data, _ave refers to the average value of the specific parameter, _std to the


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standard deviation, and -val refers to the difference between the model estimated evaporation and the measured evaporation. The top graphs show the evaporation in relation to a seven day long evaporation time series. 54

Figure 18 Total evaporation estimates for the Spekboom thicket and the degraded veld sites at Kirkwood over a period of 7 days using the SEBAL model, and calculated from eddy covariance field data. 55

Figure 19 Total evaporation (mm/d) as estimated for the forest, grassland and sedges sites at St Lucia. Val refers to the validation data, _ave refers to the average value of the specific parameter, _std to the standard deviation, and -val refers to the difference between the model estimated evaporation and the measured evaporation. The top graphs show the evaporation in relation to a seven day long evaporation time series. 55

Figure 20 Daily total evaporation at the Acacia mearnsii site measured using the LAS system (Validation) and estimated using the SEBAL, SEBS, METRIC, and the VITT models. Evaporation modelling was performed for five days (9 Sept 06, 14 Dec 06, 20 March 07, 23 May 07 and 24 June 07). 56

Figure 21 Daily total evaporation measured at the Acacia mearnsii site using the LAS for the period 22 August 2006 to 30 June 2007, and simulated using SEBAL for five days (9 Sept 2006, 14 Dec 2006, 20 March 2007, 23 May 2007 and 24 June 2007) 57

Figure 22 Period evaporation estimated for Acacia mearnsii, where four Landsat images were used to upscale instantaneous data to monthly estimate of evaporation. Data from the fifth image was upscaled to a two week estimate of evaporation (see Table 5 for more information) 57

Figure 23 Disciplines and sequence of procedures needed to interpret satellite spectral measurements for use in practical water management (taken from Bastiaanssen et al. (2000)) 63


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List of Tables Table 1 A limited list of evapotranspiration assessment methods based on

Earth Observation techniques. A summary of (some) model parameters is given, as well as model (dis-)advantages (Taken from Verstreaten et al. (2008)) 3

Table 2 Description of sites used to evaluate the accuracy of models that estimate evaporation spatially using remote sensing data. The models evaluated include the Surface Energy Balance Algorithm for Land (SEBAL), Mapping EvapoTranspiration with High Resolution and Internalized Calibration (METRIC), Vegetation Index / Temperature Trapezoid (VITT) and Surface Energy Balance System (SEBS) models. 25

Table 3 Photos of the research sites used to evaluate the accuracy of the spatial evaporation models 26

Table 4 Information on the images acquired for the evaporation modelling at the Seven Oaks, Midmar, St Lucia and Kirkwood sites 27

Table 5 Information on validation data used in this study for the four sites 28

Table 6 An example of the VITT modelling (option 1) where four reference conditions are described 34

Table 7 Specific areas of interest (AOI) used to evaluate the accuracy of the simulated energy balance and evaporation data at the Seven Oaks, Midmar, St Lucia and Kirkwood sites are shown by the dotted lines. Only the energy balance and evaporation data contained within the AOIs were used in the analysis and comparison of the validation data 37

Table 8 Components of the energy balance and evaporation for the Seven Oaks Acacia mearnsii sites as estimated using the SEBAL, SEBS, METRIC and the VITT model(s), and those measured using micrometeorological methods. Minimum (Min), Maximum (Max), Mean (Mean) and Standard deviations (Stdev) values are shown. The variables shown include: Instantaneous net radiation (Rn_i), Instantaneous soil heat flux (G_i), Instantaneous sensible heat flux (H_i), Instantaneous evaporative fraction (EF_i), daily evaporation (ET24), daily reference evaporation (ETr24), the evaporation estimate over a period (ET_per), albedo (alb) and leaf area index (LAI). **Instantaneous (_i) refers to the data estimated for the time of the satellite overpass 45

Table 9 Components of the energy balance and evaporation for the Kirkwood sites: degraded veld and Spekboom thicket, as estimated using the SEBAL, SEBS and the VITT model(s), and those measured. Minimum (Min), Maximum (Max), Mean (Mean) and Standard deviations (Stdev) values are shown. The variables shown include: Instantaneous net radiation (Rn_i), Instantaneous soil heat flux (G_i), Instantaneous sensible heat flux (H_i), Instantaneous evaporative fraction (EF_i), daily evaporation (ET24), daily reference evaporation (ETr24), daily potential evaporation (ETp24), evaporation over a period (ET_per), albedo (alb) and leaf area index (LAI). **Instantaneous refers to the time of the satellite overpass 47


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Table 10 Components of the energy balance and evaporation for the Midmar

site as estimated using the SEBAL model, and those measured. Minimum (Min), Maximum (Max), Mean (Mean) and Standard deviations (Stdev) values are shown. The variables shown include: Instantaneous net radiation (Rn_i), Instantaneous soil heat flux (G_i), Instantaneous sensible heat flux (H_i), Instantaneous evaporative fraction (EF_i), daily evaporation (ET24), daily reference evaporation (ETr24), daily potential evaporation (ETp24), and evaporation over a period (ET_per). 48

Table 11 Components of the energy balance and evaporation for the St Lucia sites: swamp forest, grassland and sedges, as estimated using the SEBAL, SEBS and the VITT model(s), and those measured. Minimum (Min), Maximum (Max), Mean (Mean) and Standard deviations (Stdev) values are shown. The variables shown include: Instantaneous and 24 hr average net radiation (Rn_i, Rn_24), Instantaneous and 24 hour average soil heat flux (G_i, G_24), Instantaneous and 24 hour average sensible heat flux (H_i, H_24), Instantaneous evaporative fraction (EF_i), daily evaporation (ET24), daily reference evaporation (ETr24), daily potential evaporation (ETp24), evaporation over a period (ET_per), albedo (alb) and leaf area index (LAI). **Instantaneous refers to the time of the satellite overpass 49

Table 12 Minimum human resource requirements for operating a basic remote sensing ET unit 63


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List of Abbreviations and Symbols

SYMBOL DESCRIPTION UNIT

(where applicable)

ALEXI Atmosphere-Land Exchange Inverse

ASL Atmospheric Surface Layer

BAS Bulk Atmospheric boundary layer Similarity

CWSI Crop Water Stress Index

DEM Digital Elevation Map

DN Digital Number of satellite image

DSTV Diurnal surface temperature variation

LAI Leaf Area Index

METRIC Mapping Evapotranspiration with high Resolution and

Internalised Calibration

MOST Monin-Obukhov Similarity Theory

NDVI Normalized Difference Vegetation Index

NIR Near-infrared

PBL Planetary boundary layer

SEBAL Surface Energy Balance Algorithm for Land

SEBS Surface Energy Balance System

TIR Thermal Infrared

TSEB Two Source Energy Balance

VITT Vegetation Index Temperature Trapezoid

VMC Vegetation and Moisture Coefficient

W Soil moisture

WDI Water deficit index

sR Incoming short wave radiation W m-2

cf Fractional canopy coverage / vegetation cover Dimensionless

z Reference height above the surface m

*u Friction velocity m s-1

d Zero plane displacement height m

omz Roughness height for momentum transfer m

ohz Roughness height for heat transfer m

L Obukhov length m

g Acceleration due to gravity m s-2

h Vegetation or canopy height m 1B Inverse Stanton number (dimensionless heat transfer

coefficient)

Dimensionless

sf Complement of fractional canopy coverage Dimensionless

dC Drag coefficient of foliage

tC Heat transfer coefficient of leaf

wetH Sensible heat flux at the wet limit W m-2

dryH Sensible heat flux at the dry limit W m-2


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(where applicable)

e Actual measured vapour pressure Pa

se Saturation vapour pressure Pa

ewr External resistance at the wet limit s m-1

instET Instantaneous evaporation mm hr-1

Cp Specific heat capacity of air (≈ 1004) at constant pressure J kg-1 K-1

Edaily Daily evaporation mm day-1

EF Evaporative Fraction Unitless

ET Evapotranspiration mm

ETa Actual evaporation mm

ETp Potential evaporation mm

ETr Reference evaporation mm

fg Greenness fraction

G Soil heat flux W m-2

H Sensible heat flux W m-2

Hc Sensible heat flux of vegetated canopy W m-2

Hs Sensible heat flux of bare soil W m-2

k von Karman’s constant (0.4)

K Canopy extinction

LE Latent Energy flux W m-2

Ma Soil moisture availability index

PT Priestly-Taylor

rah Aerodynamic resistance for heat transport s m-1

Rl↑ Outgoing long wave radiation W m-2

Rl↓ Incoming long wave radiation W m-2

Rn Net radiation W m-2

Rn24 Daily (24 hours) net radiation W m-2

Ta Air temperature K

To Land surface temperature K

TR Radiometric temperature K

Ts surface temperature K

u Wind speed at height z m s-1

Wmax Maximum soil moisture holding capacity

Surface reflectance (albedo)

Evaporative Fraction

c Ratio of soil heat flux to net radiation for full vegetation

canopy

s Ratio of soil heat flux to net radiation for bare soil

nir Atmospheric corrected ground reflectance in the near infrared

band

red Atmospherically corrected ground reflectance in the red band

Density of air kg m-3

o Potential temperature at the surface K

m Stability correction functions for momentum transfer

h Stability correction functions for sensible heat transfer


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(where applicable)

v Potential virtual temperature near the surface K

Psychrometric constant

Rate of change of saturation vapour pressure with

temperature

r Relative evaporation fraction

Daily average evaporative fraction

0 Broad-band surface emissivity

ΔT Temperature difference K

Surface emissivity

εa Apparent atmospheric emissivity (air emissivity)

λ Latent heat of vaporization J kg-1

ρw Density of fresh water kg m-3

Σ Stefan-Boltzmann constant (5.67*10-8) W m-2 K-4

Ω(0) Directional clumping factor

Apparent view angle


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List of Definitions

Evaporation Evaporation is the “physical process by which a liquid or solid is transferred to the gaseous state” (Huschke, 1959).

Total evaporation

Total evaporation (ET) can be defined as the total process of water movement into the atmosphere. Soil evaporation (E) and transpiration (T) occur simultaneously and are determined by the atmospheric evaporative demand (available energy and water vapour pressure deficit), soil (soil water availability), windspeed and canopy characteristics (canopy resistances) (Rosenberg et al., 1983). Others (Kite and Droogers, 2000) refer to total evaporation as evapotranspiration.

In this report the term evaporation will be used – for vegetative covers evaporation to describe the total evaporation or the sum of (a) evaporation from the soil surface, (b) transpiration by vegetation, and (c) evaporation of water intercepted by vegetation and for open water bodies to describe the evaporation of water as defined above.

Transpiration

Transpiration can be defined as evaporation of water that has passed through the plant. Transpiration therefore consists of vaporization of liquid water contained in the plant tissues and vapour removal to the atmosphere (Allen et al., 1998).

Reference evapotranspiration Allen et al. (1998) defines reference evapotranspiration (ETo) as “The evapotranspiration from a reference surface, not short of water …The reference surface is a hypothetical grass reference crop with specific characteristics…The only factors affecting ETo are climatic parameters. Consequently, ETo is a climatic parameter and can be computed from weather data. ETo expresses the evaporating power of the atmosphere at a specific location and time of the year and does not consider the crop characteristics and soil factors.” Other definitions specify that the reference surface should fully cover the soil surface. In this document we refer to it as reference evaporation.

Simplified energy balance

The simplified version of the energy balance of a specific surface is given by the equation

0 HLEGRn

where Rn is the net irradiance, LE the latent (evaporation) energy flux density, H the sensible heat flux density and G the soil heat flux density. All terms are in W m-2. The specific latent energy of vapourisation L is (2.501-0.00237 Tz) (MJ kg-1), where Tz is the air temperature (oC) at height z.


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Evaporative fraction

The evaporative fraction is defined as the fraction of energy partitioned into latent heat flux density (LE) compared to the available energy (Rn - G).

Remote sensing

Thoreson et al. (2004) defines remote sensing as the collection of information about an object from a distance. In applications where evaporation is estimated, remote sensing generally refers to measurement of spectral radiances sensed by satellites or planes (Thoreson et al., 2004). Satellites remotely sense naturally reflected or emitted radiation from the earth’s surface. Most imaging systems on satellites take images of visible, near infrared, thermal and microwave (radar) energy, and for that reason a single satellite “image” generally consist of many different images (often called bands), where each of these images correspond to a different wavelength (Thoreson et al., 2004).


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Chapter 1: Background In a water-scarce country like South Africa with a number of large consumers of water, it is important to estimate evaporation with a high degree of accuracy. Especially in the semi-arid regions where there is an increasing demand for water and a scarce supply thereof. In these areas, water lost due to total evaporation is significant and crop water demand usually exceeds rainfall and have to be supplemented by surface water or groundwater (Gowda et al., 2007). Evaporation also varies regionally and seasonally, so knowledge on evaporation is fundamental to save and secure water for different uses, and to guarantee that water is distributed to water consumers in a sustainable manner. Many of the conventional methods still used locally and internationally to estimate evaporation can only be applied at field scale and provides point based evaporation estimates. Generally these methods do not allow for the direct estimation of evaporation over large geographical areas. Quite often point based estimates obtained using these methods feed into models operating at a catchment scale or are used to validate results from models operating at large scales. Most existing catchment scale models estimating evaporation require point data and not spatial data. Some of these involve methods to disaggregate point data for spatial representation, but are still based on the point data. Therefore, spatial estimates of evaporation and other components of the water balance are still difficult to obtain. However, evaporation estimates are more often required at scales larger than field scale e.g. at catchment scale. Methods based on remote sensing data hold great potential for the estimation of evaporation and answering questions related to water resources management, at near real time and over large spatial scales. Firstly, remote sensing data can be used to extend point measurement, to much larger areas, even areas where measured meteorological data may be sparse. Secondly, remote sensing data can be used to estimate components of the energy and water balances that can be used to determine evaporation. Bastiaanssen and Harshadeep (2005) described how spatially distributed evapotranspiration data from remote sensing, in conjunction with other ancillary data, can help to build the knowledge base for integrated basin scale water management (Fig. 1). Remote sensing is not a solution, but it provides key data that is difficult to access by conventional field data collection methods. Figure 1/...


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Figure 1 Satellite remote sensing and GIS for better water management (taken from Bastiaanssen and

Harshadeep, 2005) Research into evaporation modelling with remote sensing data already started towards the end of the seventies. Dr. Ray Jackson and his colleagues at the United States Department of Agriculture (USDA) laboratory in Phoenix, Arizona started some of the experimental work in the seventies. The first generation evaporation models were superseded gradually by the more complex second generation evaporation models. The team of Dr. Bill Kustas at the USDA laboratory in Beltsville, Maryland has substantially contributed to this international development. Over the years a variety of remote sensing (RS) based models have been developed. In Verstraeten et al. (2008), different methods used to estimate evaporation are listed according to four broad classes. These classes include methods based on the parameterisation of the energy balance, on the Penman-Monteith formulation, methods based on the water balance and Vegetation index / radiative temperature relationships. Table 1 provides details of the different methods used as well as advantages and disadvantages of each method.


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Table 1 A limited list of evapotranspiration assessment methods based on Earth Observation techniques.

A summary of (some) model parameters is given, as well as model (dis-)advantages (taken from Verstreaten et al. (2008)).


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Whereas some of the one-layer and two-layer ET models developed are often tested intensively at experimental field sites, they are hardly exposed to practical implementations in hydrology and agriculture. One of the plausible reasons for this is that these models require calibration of critical model parameters, such as the roughness length for heat transport and the derivation of daily scale evaporative fluxes from instantaneous evaporation estimates. Although their physics is rather strong some computational schemes are at the same time a burden in terms of data demand. Moreover, Thermal infra-red (TIR) imagery require cloud free conditions and this cannot be fulfilled for rainy seasons to which many climate systems are plagued with. Therefore of the remote sensing (RS) based evaporation models developed over the years, most have been developed for research applications with only a few used internationally for operational application. The MODIS science team under the aegis of NASA has made attempts to prepare an operational evaporation product based on MODIS data, however to the authors’ knowledge this is not operationally available yet. A recent paper published by Mu et al. (2007) demonstrates that the University of Montana prepared a prototype model for this sake. Most updated information can be found at http://secure.ntsg.umt.edu/projects/index.php/ID/9a3dae27/fuseaction/projects.detail.htm. Spatial evaporation modelling development is also still underway at the University of Wisconsin (Dr. Norman) and the USDA in Beltsville (Dr. Anderson). The Atmosphere-Land Exchange Inverse (ALEXI) model was developed as an auxiliary means for estimating surface fluxes over large regions using primarily remote sensing data. This flux model is unique in that no information regarding antecedent precipitation or moisture storage capacity is required. The surface moisture status is deduced from a radiometric temperature change signal. ALEXI provided independent information for updating soil moisture variables in more complex regional models (Anderson et al., 1997). Regional fluxes for the continental USA can be viewed at http://www.soils.wisc.edu/alexi/us.html. Between January 2000 and February 2005, The Idaho Department of Water Resources (IDWR) and the University of Idaho (UI) Department of Biological and Agricultural Engineering worked on a NASA Synergy grant to develop an efficient and accurate method of mapping evapotranspiration. IDWR and UI worked at first with the Surface Energy Balance Algorithm for Land (SEBAL) model. SEBAL was gradually modified into METRIC (Mapping EvapoTranspiration with High Resolution and Internalized Calibration). Both SEBAL and METRIC are energy balance models that use satellite image data to compute a complete radiation and energy balance, sensible heat, and evaporation (ET) for each pixel of the satellite image. Some of the evaporation algorithms of METRIC have been developed for the US, since METRIC is currently mainly applied in the USA. Evaporation maps can be downloaded for Idaho State from http://www.idwr.idaho.gov/gisdata/mapserver.htm. Some commercial companies provide remotely sensed evaporation maps upon request. EARS in Delft (NL) based firm sells evapotranspiration maps for South Africa (http://www.ears.nl/evapotranspiration_field.php?lang=en). Their calculations are based on Meteosat images and the spatial resolution is approximately 3 km.


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WaterWatch in Wageningen (NL) also provides evaporation maps which is generated through the SEBAL model. The SEBAL model can use both low resolution MODIS images (25 to 1000 m) and high resolution images like ASTER, Landsat, IRS, SPOT and ALOS. The spatial resolution of their evaporation maps varies between 10 m to 1000 m. Regional scale weekly water resources applications maps can be viewed publicly in the Netherlands via Google Maps (www.waterwatch.nl/flevoland and www.boerinbeeld.nl) (Fig. 2) for subscribers to this service. A commercial firm, BasFood, manages this service. High resolution SEBAL generated evaporation maps assist farmers in the understanding of the rate of water depletion from their farm plots. Farmers can subscribe for individual fields instead of the entire farm. See more information at www.mijnakker.nl. As subscribers to these services, farmers / consultants get weekly updates of evaporation and other parameters like soil moisture, crop water stress, leaf nitrogen content, leaf area index, biomass production which will assist the farmers in their management. The service is offered for areas of 10,000 ha being encompassed in a spatial window of 60 km x 60 km. (The latter is the image dimension of SPOT and ASTER). Four of these “image blocks” are guaranteed for the growing season in The Netherlands. Also, a new evaporation disaggregation procedure (unpublished) is applied here to make SEBAL evaporation modelling more independent from the high resolution satellite images.

Figure 2 Example of an operational ET product for The Netherlands that can be publicly accessed

through the website www.boerinbeeld.nl. Shown is the weekly total ET for 13 to 19 July 2000 (in Dutch).

Princeton University is computing the terrestrial water cycle (evaporation, runoff, soil moisture, snow) using the VIC land surface model, forced by observed and remotely sensed precipitation and temperature (Fig. 3). This seems to be an operational product although little information is available on it.


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Figure 3 Example of an operational ET product over Africa developed by the Princeton University

group (courtesy Dr. Eric Wood) Although remote sensing based evaporation estimation techniques hold great potential, locally and internationally, there are still some shortcomings that will need to be addressed in future. These can be viewed as current and / future research question and some international research groups in this field are already addressing some of these. The most general shortcoming of RS based evaporation models at this moment is the limited availability of high resolution TIR images (30 to 60 m pixel size). TIR images available from Landsat-5 and Landsat-7 imagery are no longer acquired. Landsat-5 is running out of fuel and acquires images for specific locations only. Landsat-7 has a scan-line correction problem that causes 22% of the pixels to be without data. Though there is a possibility that newly developed Landsat-8 satellite will be equipped with a TIR channel, the developers have not confirmed yet the inclusion of this channel. That leaves the hydrological remote sensing community only with high resolution TIR images from the ASTER satellite, and these images are not archived systematically. Using the available MODIS TIR images on the Terra (morning) and Aqua (afternoon) satellites provide a number of challenges in estimating evaporation. The problem is that the nominal pixel size of 1000 m is too coarse for many water resources management applications. Using these MODIS images therefore requires downscaling procedures to be developed. Different procedures can be developed to break down the evaporation values from 1 km x 1km pixels into smaller units, using high resolution data as a guiding mechanism. Different approaches ranging from simple NDVI corrections to land cover related un-mixing procedures and more sophisticated bio-physical un-mixing schemes. It is expected that the international research community will come up with more solutions in the near future. The second challenge is the scattering and absorption of radiation by clouds. More research into the use of microwave measurements for evaporation estimation is needed. A disadvantage of microwave data is that it cannot be related directly to evaporation processes, but indirectly


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via soil moisture. The relationship between soil moisture and evaporation is reasonably well understood, and soil moisture information jointly with micro-meteorological data may yield acceptable evaporation results. The use of microwave data for evaporation estimation, will provide evaporation maps under all weather conditions (cloudy or not). One major challenge at this stage though, is the conversion of the radar backscatter coefficients into soil moisture of the root zone. It is expected that the SMOS satellite will have an L-band for soil moisture detection, but this system needs to be launched and experience needs to be build-up for operational moisture retrieval. Passive microwave measurements already provide estimates of soil moisture in the top soil layer, and although the data seems reasonably accurate, the spatial resolution of 25 km is too coarse for substituting high resolution TIR images with this. Besides pitfalls in the calibration of the evaporation algorithms, the decay of high resolution TIR imagery, the obstacle of cloud cover and the absence of good quality local weather data are the general shortcomings in the evaporation modelling process. While automatic weather stations have an excellent performance at the point scale, the spatial interpolation of these data is not receiving sufficient attention. Although evaporation is estimated successfully spatially using remote sensing data, it needs to be noted that some RS based methods use constant surface meteorological parameters in their calculations to extrapolate instantaneous evaporation estimates to daily evaporation estimates. These methods generally fail to simulate fluxes and evaporation accurately for large scales where meteorological parameters are not longer constant (Su, 2002). The evaporative process in mountains is very complex due to the presence of strong lateral movements of heat and the three dimensional flow of air. The turbulent fluxes are extremely difficult to model, and most one dimensional energy balance models fail to accurately represent the energy fluxes under these conditions. The international experimental databases are also too limited to facilitate the improvement of model formulations of fluxes in mountainous terrain. Water bodies can have significant amounts of non-beneficial evaporation losses. The evaporative fluxes over water bodies are difficult to estimate because a large component of the energy balance is related to heat storage / release through the water. These heat storage processes vary with the depth of the water body, the time of the year and with the turbidity of water. Although there are some good experimental evaporation datasets available, the overall accuracy of evaporation from water bodies is lower than for land-based ecosystems.


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Chapter 2: Models used to estimate evaporation spatially

A number of models are available for estimating evaporation using remote sensing data. These models use different approaches and can be divided into different groups. Choudhury (1997) describes two main approaches for estimating ET from remotely sensed data. Firstly, estimating evaporation as the residual of the shortened energy balance equation, and secondly using a water use efficiency relationship to determine evaporation. Models estimating evaporation as a residual of the shortened energy balance equation have been widely applied whether for operational or research purposes, and include the Surface Energy Balance Algorithm for Land (SEBAL) model, the Surface Energy Balance System (SEBS) model, the Two Source Energy Balance (TSEB) model, the Atmosphere-Land Exchange Inverse (ALEXI) model, and the Mapping EvapoTranspiration with high Resolution and Internalised Calibration (METRICtm) model. A number of simple vegetation index models that use the water use efficiency relationship to determine evaporation are also available and include the Vegetation Index/Temperature Trapezoid (VITT) model and the NDVI-DSTV (Normalised Difference Vegetation Index Diurnal surface temperature variation) triangle model. The SEBAL, SEBS, METRIC and VITT models will be discussed below briefly. Descriptions of the TSEB, ALEXI and the NDVI-DSTV models are provided in Appendix 2.

2.1 Surface Energy Balance Algorithm for Land (SEBAL) model

The formulation of the Surface Energy Balance Algorithm for Land (SEBAL) is discussed in detail by Bastiaanssen et al. (1998a). SEBAL uses remotely sensed images, from satellites measuring Thermal Infrared (TIR) radiation in addition to visible and Near-infrared (NIR), to compute both the instantaneous and 24-hr integrated surface heat flux for each pixel of a satellite image where the latent heat flux, representing the energy required for evapotranspiration, is computed as a residual of the shortened energy balance which is expressed as:

GHRLE n 1

where LE is the latent heat flux (W m-2); which is readily converted to ET (mm), Rn is the net radiation (W m-2), H is the sensible heat flux (Wm-2) convected into the air, and G is the soil heat flux (Wm-2) conducted into the ground. The algorithm computes most essential hydro-meteorological parameters (e.g. surface albedo, Normalized Difference Vegetation Index (NDVI), surface temperature) and requires limited field information (air temperature, relative humidity and wind speed). It is useful to note that the


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actual resolution of LE (or ET) is impacted by the resolutions of both shortwave and the thermal band(s) of the respective satellite images used in the analysis (Tasumi et al., 2005). In SEBAL, the net radiation (Rn) is computed using the expression described in Bastiaanssen et al. (1998a), Timmermans et al. (2007) and Koloskov et al. (2007), i.e.:

lllssn RRRRR 11

444 11 air'

oair'

ss TTTR 2

where Rl↓ and Rl↑ are the incoming and outgoing long wave radiation respectively (W m-2), ε´ is the apparent atmospheric emissivity (air emissivity), ε is the surface emissivity, σ is the Stefan-Boltzmann constant (5.67*10-8 W m-2K-4), Tair is the air temperature (K) and To is the land surface temperature (K). The soil heat flux G (W m-2) is empirically estimated using a function by Bastiaanssen (2000) based on albedo, surface temperature and Normalized Difference Vegetation Index (NDVI), i.e.:

ns RNDVI

TG

)98.01)(0074.00038.0(

16.273 42

3

where Ts is surface temperature in oK and α is the surface albedo. The sensible heat flux H (W m-2) is estimated from wind speed and surface temperature using an internal calibration process of the near surface Ts (

oK) to air temperature Ta (oK) difference (ΔT)

as described by Bastiaanssen et al. (1998a), i.e.:

ah

spair

r

TbaCH

)*(

4

where ρair is air density (kg m-3); which is a function of atmospheric pressure, Cp is the specific heat capacity of air (≈ 1004 J kg-1 K-1) at constant pressure, rah is aerodynamic resistance to heat transport (s m-1) between two near surface heights (generally 0.1 and 2 m) computed as a function of estimated aerodynamic roughness of the particular pixel and using wind speed extrapolated to some blending height above the ground surface (typically 100 to 200 m), with an iterative stability correction scheme based on the Monin-Obukhov functions (Allen et al., 1996, Koloskov et al. 2007) and “a” and “b” are empirical coefficients calibrated for each image. The definition of the coefficients “a” and “b” requires the selection of two extreme pixels within the scene (image). These extreme pixels are termed “cold” and “hot” pixels, where the ΔT values can be back-calculated using known H at the two pixels. According to Bastiaanssen et al. (1998a, 1998b), the “cold” pixel is assigned Ts from a wet surface pixel while the “hot” pixel is typically assigned to a dry surface pixel. The sensible heat is assumed zero for the “cold”

pixel, and equal to GRn for the “hot” pixel. The coefficients “a” and “b” are calibrated for


page 10

each image using linear interpolation based on surface temperature (Ts) between these two extreme pixels. However, there is no absolute method for the user to select wet/dry pixels in a satellite image and hence experience in the SEBAL computational approach is useful in this regard. Once latent heat flux (LE) at the image acquired time is estimated, an Evaporative Fraction (EF) for each pixel can be calculated (Bastiaanssen, 2000) as:

GR

LEEF

n 5

Several authors including Mohammed et al. (2004) have shown that EF is fairly constant during the day time thus allowing estimation of daytime evaporation from one or two estimates of only of EF (Courault et al., 2005). The daily total evaporation can then be estimated using the following expression:

324 10*86400w

n GRET

6

where ET is actual evaporation (mm d-1), Rn24 is the daily (24 hours) net radiation (W m-2), λ is the latent heat of vaporization (J kg-1) and ρw is the density of fresh water (kg m-3).

2.2 Surface Energy Balance System (SEBS) model

The Surface Energy Balance System (SEBS) estimates the atmospheric turbulent fluxes using remote sensing data. SEBS consists of a set of tools for the determination of the land surface physical parameters, such as albedo, emissivity, temperature, vegetation cover from spectral reflectance and radiance (Su et al., 1999), an extended model for the determination of the roughness length for heat transfer (Su et al., 2001) and a new method for the determination of the evaporative fraction on the basis of energy balance at limiting cases (Su, 2002). The SEBS requires three sets of information or data. The first set of data consists of land surface albedo, emissivity, temperature, fractional vegetation coverage and leaf area index, and the height of the vegetation. If vegetation information is not available, the Normalized Difference Vegetation Index (NDVI) is used as a surrogate. These input data can be derived from remote sensing data in conjunction with other information about the surface of interest. The second set includes meteorological data, such as air pressure, temperature, humidity, and wind speed at a reference height. The reference height is the measurement height for point application and the height of the planetary boundary layer (PBL) for regional application. This data set can be variables estimated by large scale meteorological models. The third data set includes downward solar radiation, and downward long wave radiation which can either be measured or estimated as model output or parameterization. The SEBS model also applies the surface energy balance equation (eq. 1) and partition the available energy into sensible and latent heat flux density. In SEBS the net radiation is calculated as

llsn RRRR )1( 7


page 11

where is the surface reflectance (albedo), Rs is the downward solar radiation, Rl is the

downward long wave radiation, is the surface emissivity, and Rl is the upward long wave radiation. The downward long wave radiation is determined as

4al TR 8

where is the Stefan-Bolzmann constant, ’ is the emissivity of air described in Campbell and Norman (1998) as:

26 )15.273(102.9' aT 9

where aT is the air temperature at the reference height.

The upward long wave radiation is determined as a function of surface temperature ( oT ) and

surface emissivity () as

4ol TR 10

The equation to calculate soil heat flux G is parameterized as

csccn fRG )(1( 11

in which it is assumed that the ratio of soil heat flux to net radiation c is 0.05 for full

vegetation canopy by Monteith cited in Su (2002) and s is 0.315 for bare soil (Kustas and

Daughtry, 1989). An interpolation is then performed between these limiting cases using the

fractional canopy coverage cf .

The fractional vegetation cover ( cf ) is used to separate non-vegetated, partially vegetated and

densely vegetated land surfaces. The parameter cf can be determined as (Choudhury et al.,

1994)

max

max min

1p

c

NDVI NDVIf

NDVI NDVI

12


page 12

where the exponent p represents the ratio of the leaf angle distribution and taken as a constant

0.625, maxNDVI is the NDVI value of the full vegetation cover, minNDVI is the NDVI value of

the bare soil, and NDVI is the NDVI value of the current pixel (NDVI map). The normalized difference vegetation index (NDVI) is a good indicator of photosynthetic activity on the vegetation surface. Due to the strong spectral absorption of chlorophyll in the visible region (0.475 to 0.65 m) and the high reflectance of vegetation in the near infrared (NIR) part, the reflectance value in these bands is used to provide the information of the vegetation status. The NDVI is computed from the reflectance in the red and NIR channels as:

nir red

nir red

NDVI

13

where nir and red are atmospherically corrected ground reflectance in the near infrared and

red bands respectively. In order to estimate the sensible heat and latent heat fluxes, the use of similarity theory is necessary. Models of energy and mass transfer between the land surface and atmosphere usually use a bulk parameterization based on Monin-Obukhov similarity theory (MOST). MOST relates surface fluxes to surface variables and variables in the atmospheric surface layer (ASL) (Su et al., 2001). The bulk atmospheric boundary layer similarity (BAS) proposed by Brutsaert (1982, 1999) relates surface fluxes to surface variables and the mixed layer atmospheric variables. In the ASL, the similarity relationships for the mean wind speed u and the mean temperature

difference o a are usually written as:

* ln omm m

om

zu z d z du

k z L L

14

*

ln oho a h h

p oh

zH z d z d

ku C z L L

15

where z is the reference height above the surface, *u is the friction velocity, is the density of

air, 0.4k von Karman’s constant, d is the zero plane displacement height, omz is the

roughness height for momentum transfer, o is the potential temperature at the surface, a is

the potential air temperature at height z , ohz is the scalar roughness height for heat transfer,

m and h are the stability correction functions for momentum and sensible heat transfer

respectively, L is the Obukhov length defined as:


page 13

3*p vC u

LkgH

16

where g is the acceleration due to gravity and v is the potential virtual temperature near the

surface. The stability length L depends on H , which in turn depends on *u and is determined

in equation 16 as a function of and *u , hence iterative procedures need to be used to estimate

H and *u .

In the above derivations, the aerodynamic ( d and omz ) and thermal dynamic roughness

parameters ( ohz ) need to be known. If near surface wind speed and vegetation parameters

(height and leaf area index) are available, the aerodynamic parameters, d and omz can be

estimated. When wind speed and vegetation parameters are not available, the aerodynamic parameters can be related to vegetation indices derived from satellite data (Su, 2002). The

roughness height for momentum transfer ( omz ) is taken as reference height for momentum flux

calculations, and can be estimated using empirical relationships with NDVI (Su and Jacobs, 2001) as:

2.5

max

0.005 0.5om

NDVIz

NDVI

17

The vegetation height ( h ) and the zero plane displacement height d can be estimated respectively using empirical relationships (Brutsaert, 1982) as:

0.136omz

h 18

2

3d h 19

The scalar roughness height for heat transfer ( ohz ) is calculated as

1exp( )om

oh

zz

kB 20

where 1B is the inverse Stanton number, a dimensionless heat transfer coefficient. The

extended physical based model of Su et al. (2001) estimates 1kB as follows


page 14

1 2 2 2 1 2**

/ 2*

/ ( ) /

4 (1 )( )

d omc c s s s

nec tt

kC k u u h z hkB f f f kB f

u CC eu h

21

where cf is the fractional canopy coverage and sf is its complement, dC is the drag

coefficient of the foliage assumed to be as constant 0.2, and tC is the heat transfer coefficient of

the leaf. For most canopies and environmental conditions, tC is bound as

0.005 0.075tN C N (N is the number of sides of a leaf to participate in heat exchange).

The heat transfer coefficient of the soil is given by * 2/3 1/ 2*Pr RetC , where Pr is the Prandtl

number and Re is the roughness Reynolds number.

The actual sensible heat flux ( H ) is constrained by the sensible heat flux at the wet limit, wetH ,

and the sensible heat flux at the dry limit dryH in SEBS. Under the dry-limit, the latent heat

(evaporation) becomes zero due to the limitation of soil moisture and the sensible heat flux is at its maximum value. The dry limit is given as:

GRH ndry 22

dryndry HGRLE 23 Under the wet-limit, where evaporation takes place at potential rate, LEwet (evaporation is limited only by the energy available under the given surface and atmospheric conditions), the

sensible heat flux takes its minimum value, wetH , i.e.

)1/())(

)((

ew

snwet r

eeCpGRH 24

wetnwet HGRLE 25

where e is the actual measured vapour pressure, se is the saturation vapour pressure, is the

psychrometric constant, is the rate of change of saturation vapour pressure with temperature,

and ewr is the external resistance at the wet limit which is determined as:

*

1ln oh

ew h hoh w w

zz d z dr

ku z L L

26

The wet-limit stability length can be determined as:


page 15

/)(61.0

3

*

GRkg

uL

nv 27

The relative evaporation then can be given as:

wet

wet

wetr LE

LELE

LE

LE 1 28

Substitution of equations (1), (22-25) in equation (28) after some algebra yields

1 wetr

dry wet

H H

H H

29

The evaporative fraction is then given by:

GR

LE

GR

LE

LEH

LE

n

wet

n

30

The actual sensible heat and latent heat fluxes can be finally obtained by inverting equation (30) as

)(

))(1(

GRLE

GRH

n

n

31

When the evaporative fraction is known, the daily evaporation (mm/day) can be determined as:

)(1064.8 7

w

ndaily

GRxxE

32

where is the daily average evaporative fraction, and w is the density of water. Since the

daily soil heat flux is close to zero because the downward daytime and upward flux at night balance each other approximately, the daily evaporation is determined by assuming the daily evaporative fraction is approximately equal to the instantaneous value as:

w

ndaily

RxxE 71064.8 33

By summing up the corresponding daily evaporation for a certain period the actual evaporation for a week, month, season, and year can be determined. However errors will occur due to cloud


page 16

effects, and such effects can be removed by using the time series processing or by data assimilation procedures (Su et al., 2003).

2.3 Mapping EvapoTranspiration with high Resolution and Internalised Calibration (METRICtm) model

METRICtm as described by Allen et al. (2007) uses SEBAL2000 as it foundation. The main difference between METRIC and SEBAL is that METRIC uses the alfalfa reference ET (based on ground weather data) to establish the energy balance conditions of the “cold pixel” and is therefore partly calibrated with ground-based alfalfa reference ET. METRIC is a hybrid model that uses both remote sensing and ground-based data. The weather data is used in the calculation of sensible heat flux density (wind speed), the calibration of the cold pixel (Reference ET) and also to extrapolate instantaneous evaporation to daily and monthly data (Reference ET). METRICtm also uses the surface energy balance equation (eq. 1) where Rn is net irradiance, G is the soil heat flux density, H is the sensible heat flux density and LE is the latent heat flux density. Since the latent heat flux density is calculated as the residual of the surface energy balance equation, the estimate of evaporation is very dependent on accuracy of the other energy balance components. In short, in METRIC Rn is calculated from satellite measured narrow-band reflectance and surface temperature, G is calculated from estimates of net radiation, surface temperature and vegetation indices, H is estimated from surface temperature ranges, surface roughness, and windspeed using buoyancy corrections and LE is calculated as the residual of the shortened energy balance, and extrapolated to daily ET using alfalfa reference evapotranspiration. Net radiation is calculated according to

LLLssn RRRRRR )1( 34 where Rs

is the incoming short wave solar irradiance, is the surface albedo which is the ratio of reflected solar radiation to the incident solar (short wave) radiation at the surface, RL

is the outgoing long-wave radiation, RL

is the incoming long-wave radiation and is the broad-band surface emissivity. For an image with little slope (fairly flat surface) this can be calculated as a constant for the whole image if the image area is smaller than a Landsat image (25000km2) and if clear skies exist over the entire image. However, for mountainous areas, it must be computed for each pixel of the scene using the relevant solar incidence angle which can be calculated from the solar constant, solar incidence angle, square of the relative earth-sun distance and the broad-band atmospheric transmissivity. Shortwave incoming radiation calculated for clear skies should have a similar / better accuracy than that estimated using an automatic weather station. In METRIC, the bidirectional at-surface reflectance is corrected (functions by Tasumi et al. (2007)) for scattering and absorption of incoming and reflected solar radiation from the surface. Alternatively atmospheric corrections can be done with MODTRAN (Berk et al., 1999) which


page 17

take into account the effects of humidity, temperature and aerosol profiles on scattering and absorption. The long wave outgoing radiation RL

, which is affected mainly by the surface emissivity and

surface temperature is calculated from the Stephan-Boltzmann equation 4sL TR , where is

the broad-band surface emissivity, is the Stefan-Boltzmann constant and Ts the surface temperature. In METRIC is computed using an empirical equation based on soil and vegetation thermal spectral emissivities by Tasumi (2003). Surface temperature is calculated using a modified Planck equation (after Markham and Barker, 1986), modified specifically for Landsat images. RL

is the downward thermal long-wave radiation originating from the atmosphere which is computed from Stephan Boltzmann equation, using an effective atmospheric emissivity (a) and near-surface air temperature (Ta). In METRIC Ts is used as a surrogate (for every pixel) for Ta. As an alternative, for homogenous areas, a single Ta value had been used over an area. The soil heat flux density describes the rate of heat storage change in soil and vegetation as a result of conduction. METRIC calculates G as a ratio of Rn according to Bastiaanssen (2000) representing values at mid-day. Generally G / Rn is a function of surface temperature, surface albedo and NDVI and therefore G is calculated by multiplying this ratio of G / Rn, to Rn. Alternatively an approach developed by Tasumi (2003) can be used. Here it is assumed that G / Rn is a function of LAI for LAI > 0.5, but where G / Rn is a function of Ts and Rn for LAI < 0.5. This approach works well for irrigated crops in Idaho, and in general for agricultural soils. The approach to estimate sensible heat flux in METRIC differs from the approach followed in SEBAL, specifically in how the sensible heat flux function is calibrated for each specific satellite image. In both SEBAL and METRIC, H is calculated from aerodynamic function

ahp r

dTCH 35

where is air density, Cp is specific heat of the air at constant pressure, rah is aerodynamic resistance between two near surface heights (z1, z2) (computed as a function of estimated aerodynamic roughness of the particular pixel) and dT represents the near surface temperature difference between z1 and z2 (generally 0.1 and 2m). The near surface temperature difference dT is used rather than Ts, because it is very difficult to accurately estimate Ts because of unknowns in atmospheric attenuation or contamination and radiometric calibration of the sensor. Ts (basically a radiometric or kinetic temperature) can also be different from aerodynamic T that drives heat transfer processes. dT is calculated at a height beyond sensible heat roughness (zoh) and zero plane displacement – i.e. float above the surface. This dT can be estimated as a simple function (often linear) of Ts as shown by Bastiaanssen, 1995

sdatumbTadT 36


page 18

where a, b are empirical constants for a specific satellite and Tsdatum is the surface temperature adjusted to a common elevation for each pixel using a digital elevation map (DEM) and customized lapse rate to do this. This is done to correct for the cooling impacts on Ts due to increased elevation with an image that are not related to dT and H. In SEBAL, the dT function is solved by assuming that for a local water body (or a well-vegetated field) where the evaporation will be maximum and H will be close to 0 (cold pixel), that dTcold will be 0. For the opposite extreme condition, where evaporation will be negligible or LE = 0 (hot pixel), that dThot is estimated from

pairhot

ahhotnhot C

rGRdT

37

where rahhot and airhot is calculated for the hot pixel. In METRIC a similar approach is followed to determine dThot. However a simple surface soil water balance model is run for bare soil to confirm that LE = ET = 0, or to make ET = 0 if there is residual evaporation from antecedent precipitation/wetting. For a cold pixel it is assumed that

coldcoldncold LEGRH 38 For the coldest and wettest agricultural fields (LEcold) with a LAI > 4, evaporation rates are often 5% higher than alfalfa ET. In METRIC LEcold is based on a representative cold pixel selected from the satellite scene to yield

rcold ETLE 05.1 39 An exception for the estimation of LEcold are for non-growing seasons / early growing seasons where vegetation cover is less than half that of alfalfa reference. Here a relationship developed by the operator and based on the local data can be developed according to

)(NDVIfETET

rcold 40

In southern Idaho ETcold / ETr 1.25NDVI to 1.30NDVI where NDVI is top of the atmosphere value, and a, b coefficients are determined using the two pairs of values for dT and Ts (from the wet and dry pixel) according to

sdatumcoldsdatumhot

coldhot

TT

dTdTa

41

sdatumhothot TadTb / 42


page 19

The instantaneous latent heat flux density is subsequently calculated as the residual of the shortened energy balance equation for each pixel.

winst

LEET

3600 in mm hr-1 43

where 3600 for conversion from seconds to hours, density of air and latent heat of vaporization. In METRIC the instantaneous evaporation is also up-scaled to daily estimates of evaporation according to

24_24 rrrad ETFETCET 44 where Crad is a correction term used in sloping terrain to correct for variation in 24hr versus instantaneous energy availability which is a function of clear-sky solar radiation, and where reference ET fraction, ETrF is given by

r

instr ET

ETFET 45

where ETr is the reference evaporation from a standard 0.5 m alfalfa reference crop. It should be noted that since the ETrF and EF are assumed to be constant over a day, evaporation can be underestimated under dry / advective conditions. The daily evaporation is further up-scaled to a seasonal estimate of evaporation by interpolating the reference ETrF (similar to crop coefficient) between processed images and multiplying it with daily ETr values. METRIC assumes that a change in evaporation for an entire image / area is proportional to the reference alfalfa evaporation ETr at the AWS. Generally one satellite image per month is normally sufficient to construct a good ETrF curve to calculate seasonal ET. However, in areas with a rapid vegetation change, more than one mage a month is suggested. Also, if pixels need to be masked out because of clouds, and alternative image (at later stage) must be used to estimate ETrF, else if will reduce the accuracy of ETrF and ET.

2.4 Vegetation Index/Temperature Trapezoid (VITT) model

Kalma (in prep.) refers to methods using vegetation indices and surface temperature to estimate evaporation. Correlations (negative) exist between e.g. NDVI and Trad for a range of surface due to evaporative cooling associated with transpiration. But for most landscapes there will be a considerable scatter in these relationships due to variations in fractional cover and soil moisture. This method is described in Yang et al. (1997).


page 20

Moran et al. (1994) proposed the Vegetation index temperature (VIT) trapezoid. VITT combines vegetation indices with composite surface temperature measurements to allow for the application of CWSI theory, specifically to partially vegetated fields. In this method a soil moisture availability index (Ma) is defined based on the VITT concept. Landsat TM data is used to estimate NDVI and Ts, where after Ma and ET is calculated. The assumption of the VITT concept is that if Ts-Ta is plotted against vegetation cover (fc), the plot would resemble a trapezoidal shape. The four vertices of the trapezoid would respond to four specific conditions – (a) well-watered vegetation, fully covering the soil, (b) water stressed vegetation, fully covering the soil, (c) saturated bare soil and (d) bare dry soil. Values for the four vertices for a given time and crop can be calculated according to Jackson et al., 1981 and Moran et al., 1994. Instead of plotting vegetation cover vs. Ts-Ta, Ts-Ta can also be plotted against NDVI. NDVI can be calculated from remote sensing data. For the relationship between fc and Ts-Ta, linear relationship can be used to estimate slope (a1, b1) and offset (a0, b0) for two relationships describing different conditions:

NDVIaaTaTs 10max 46

NDVIbbTaTs 10min 47 The soil moisture availability index (Ma) is a function of the water deficit index (WDI). Moran et al., 1994 describes WDI as

Ep

ET

TaTsTaTs

rTaTsTaTsWDIMa

maxmax

max1 48

where r subscript refers to field measured, and Ep can be calculated from one of the standard methods (e.g. Doorenbos and Pruitt, 1977). Alternatively Ma can also be calculated from soil moisture (W) and maximum soil moisture holding capacity (Wmax)

Ep

ET

W

WfM

max 49

Therefore, if Ts is available, Ma and ET can be calculated and used to calculate the instantaneous evaporation. Both NDVI and Ts can be computed from Landsat TM data. NDVI is computed as

RNIR

RNIRDN kDNDN

kDNDNNDVI

50

where NIR refers to near infra-red and R to red, k is a satellite specific calibration coefficient (e.g. 0.801 for Landsat 5), and DN the digital number for each image. Ts is calculated from channel 6 (for Landsat 5) digital numbers. The DN has to be converted first to spectral radiance


page 21

and then to at-sensor-temperature and path radiance. Transmittance is then calculated using LOWTRAN7 code. In addition, meteorological and atmospheric profile data (e.g. Ta, RH) at the time of model over pass is also used. Subsequently the corrected spectral radiance and surface temperature was calculated. The NDVIs from ground reflectance measurements are compared with those from Landsat (NDVIDN) and NDVI is calculated as function of NDVIDN according to

DNNDVINDVI 049.1125.0 51 The surface temperature is related to NDVIDN according to

DNNDVITs 0.92.21 52

Daily evaporation is calculated from the instantaneous estimate of evaporation and is highly dependent on Ma and potential evaporation.


page 22

Chapter 3: Sites and Data used to Assess Spatial Evaporation Models

The primary aim of this project was to recommend a methodology(ies), based on a model review and first order validation, that can potentially be used South Africa to estimate evaporation spatially in order to improve water resources management. Although a number of remote-sensing based models varying in complexity are available to estimate evaporation spatially, four internationally applied models were selected and evaluated for different land covers and geographical regions within South Africa. The four models evaluated included the Surface Energy Balance Algorithm for Land (SEBAL) model, the Surface Energy Balance System (SEBS) model, the Mapping EvapoTranspiration with high Resolution and Internalised Calibration (METRICtm) model and the Vegetation index / Temperature trapezoid (VITT) model. The basis of the former three models is the estimation of evaporation from surface temperature estimates and the shortened energy balance. In the latter model evaporation is estimated from surface temperature and vegetation cover for four different reference conditions. Four different research sites for which energy balance and evaporation data from seven different land uses were available, were selected for this study. The sites were situated in the KwaZulu-Natal and Eastern Cape provinces (Fig. 4), and are representative of different climatic regions. At the Seven Oaks site, a year-long data set of evaporation and energy balance data was available for Acacia mearnsii and at the Midmar site a week-long open water evaporation and energy balance data set was available. At the Kirkwood site, concurrent week-long evaporation and energy balance data sets were available for both Spekboom thicket and degraded veld. Lastly, at the St Lucia sites, concurrent evaporation and energy balance data sets were available for three different vegetation types, including a swamp forest, grassland and a sedges wetland.


page 23

Figure 4 The geographical distribution of the research sites used in this study. These include the Seven Oaks, Midmar, Kirkwood and St Lucia sites.


page 24

3.1 Site information

3.1.1 Seven Oaks site

A six hectare compartment planted with Acacia mearnsii trees and situated within the Two Streams catchment in the Seven Oaks area was selected. The surrounding compartments are also planted with A. mearnsii but are at different stages in their development. More than two years long evaporation and energy balance estimates are available for this site. At this site, energy balance and evaporation data were collected with a Kipp & Zonen Large Aperture Scintillometer (LAS) at 10 minute intervals. The LAS system provides path averaged estimates of sensible heat flux density. A description of the LAS system can be found in Appendix 1. Energy balance measurements started in August 2006. At the start of the measurement and also the evaporation simulation period (August 2006) the average canopy height was < 1 m. At the time, the A. mearnsii trees did not cover the compartment completely, and grasses and weeds covered the inter-row areas. Towards June 2007 the canopy height was around 4.5 m and the compartment was mainly covered by A. mearnsii trees. Table 2 provides site specific information and in Table 3 a photo of the A. mearnsii compartment is shown. Energy fluxes and total evaporation were modelled at this site with all four models evaluated in this study. The SEBAL model was parameterised for five different dates spanning the period September 2006 to July 2007 (Table 2 and 4). Five Landsat 5 images were selected for this purpose (Table 4). Instantaneous, daily and monthly evaporation rates were modelled. For example, the instantaneous estimates of the energy balance components modelled using SEBAL were compared to the LAS estimates of these components at the 10 minute time interval closest to the date and time of the satellite image acquisition. Subsequently, the daily energy balance and evaporation estimates from the SEBAL model, were compared with the data from the LAS system. The SEBS, METRIC and VITT models were also parameterised for this site, but not for the entire 11 month period. The SEBS model was only parameterised for the Landsat image collected on 14/12/2006 (Table 4) whereas METRIC was parameterised for two Landsat images collected on 09/09/2006 and 14/12/2006. The evaporation modelling with METRIC, for the September image, was done by the project team in South Africa without any assistance from the METRIC team, whereas the evaporation modelling for the December images was done at the University of Idaho by the METRIC team. Daily evaporation was also modelled using the VITT model for all five Landsat images, using two approaches which are described in section 3.3. The validation data, which formed part of Mr AD Clulow’s MSc dissertation, was collect in a project funded by the Water Research Commission. More detailed information about the study is presented in Clulow (2008).

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, 25

°22.

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E


page 26

Table 3 Photos of the research sites used to evaluate the accuracy of the spatial evaporation models

Seven Oaks – Acacia mearnsii with grass Midmar – water body

St Lucia – Swamp forest St Lucia – burnt grassland

St Lucia – sedges wetland Kirkwood – Spekboom veld

Kirkwood – degraded land

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ta


page 29

3.1.2 Midmar site

The Midmar Dam, a relatively small water body situated in the KwaZulu-Natal Midlands was used in this study. The reservoir has a surface area of 1793.15 ha and a net capacity of 235.42 million m3 (DWAF, 2007). The Midmar Dam is surrounded with agricultural, recreational and residential areas. Table 3 shows a photo of the site and Tables 2 and 4 provide more information on this research site. Energy balance and evaporation data was collected from Midmar Dam from 29 June to 13 July 2007 using a variety of techniques, including the Scintec BLS900 scintillometer, the eddy covariance technique (one and two sensor type) and the surface renewal method. Appendix 1 describes these techniques. All the sensors were mounted onto a 3 m tall scaffolding tower situated about 50 m from the dam shore line. Data was collected at time intervals ranging from 10 to 30 minutes. Unfortunately a cloud free Landsat 5 image was not available during the period of field data collection. A Landsat 5 image taken prior to the onset of evaporation measurement, on 24 June 2007, was therefore used in the spatial evaporation modelling (Table 2, 4, 5). The SEBAL model only was used to model the components of the energy balance and evaporation from this site. The SEBS model was not applied since it is not designed to estimate evaporation from water bodies. METRIC could also not be applied since the algorithms used to estimate evaporation from water bodies have been calibrated for conditions specific to Idaho, US. The VITT model could also not be applied since it relates surface temperature to vegetation cover to estimate evaporation, and is therefore not ideally suited to estimate evaporation from water bodies. One Landsat 5 image was used in the SEBAL modelling. Average energy balance field data collected at the nearest 30 minute interval to the time of satellite image acquisition was used to validate the instantaneous estimates of the energy balance components simulated using SEBAL. The daily evaporation modelled using SEBAL was also compared to the daily average evaporation measured over the period 29 to 13 July 2007. The Water Research Commission funded the validation work as part of a previous research project, and the experimental results are discussed in detail in Jarmain et al. (2009).

3.1.3 St Lucia site

Three research sites situated in the iSimangaliso Wetland Park (previously known as the St Lucia Wetland Park) and in close proximity to Cape Vidal, were chosen (Fig. 4). The iSimangaliso Wetland Park situated along the Zululand cost, contains a range of vegetation types, including grasslands, forests, wetlands, mangroves and vegetated dunes. Three dominant vegetation types: a swamp forest, grassland and a wetland consisting of sedges, studied in this project. The swamp forest, a narrow strip of pristine swamp forest with an average canopy height of 17 m and a leaf area index of 4.7 was situated adjacent to a short grassland that was burnt prior to the energy balance and evaporation measurements (Table 3). At


page 30

the time of evaporation measurement, the grassland therefore had a low leaf area index (0.67) and the average canopy height was only 0.19 m. The third site, a wetland consisted of sedges in a water body of variable depth. This site was within a few kilometers of the other two sites (Table 3). The sedges canopy was on average 1 m tall and the average leaf area index was 2.78. Tables 2, 4 and 5 provide more information on these sites. Energy balance and evaporation data were collected at the three sites in the iSimangaliso Wetland Park. An eddy covariance system and a surface renewal method were used to collect energy balance and evaporation data from the swamp forest. A Scintec BLS900 large aperture scintillometer was used to collect data at the sedges wetland and a Kipp &Zonen large aperture scintillometer and the surface renewal method were used to collect data at the grassland. At the swamp forest site, point based energy balance and evaporation data were collected every 30 minutes. However, at the grassland and sedges site, path averaged energy balance and evaporation data were collected every 10 minutes. The equipment used for the field measurements are described in Appendix 1. Since a satellite image was not available during the period of field data collection which took place from 8 to 13 August 2008 (Table 5), a cloud-free Landsat 7 image collected directly after this field campaign (14 August 2008) was used in the evaporation and energy balance modelling. Average energy balance data collected at the nearest 30 minute interval to the time of satellite image acquisition was used to validate the instantaneous estimates of the energy balance components using the SEBAL and SEBS models. Daily average evaporation measured for the three sites was also compared to the daily evaporation estimates modelled using the SEBAL, SEBS and the VITT models. The field work was commissioned by Mr Piet-Louis Grundling and was funded by the University of Waterloo in Canada. This project compliments an ongoing research project funded by the Water Research Commission in the St Lucia wetland park. The unpublished results were made available by Prof Colin Everson from the CSIR ([email protected]).

3.1.4 Kirkwood site

In the Kirkwood area two research sites were selected on the Blaauwkrantz farm, which is situated approximately 15 km from Kirkwood. One of the sites consisted of Spekboom thicket and the other was an area of degraded veld. The two sites were located adjacent to each other. The degraded veld was sparsely vegetated compared to the Spekboom thicket. The vegetation cover at the degraded veld consisted of a few Pappea capensis trees, shrubs, and “opslag” or short weeds. The average canopy height (taking into account both Pappea capensis trees, shrubs and grasses), was around 1.4 m and the leaf area index was 0.32. The Spekboom thicket consisted of a wide variety of plant species, including the CAM plant Portulacaria afra. This site had an average canopy height of 2.2 m and an average leaf area index of 1.1. Table 3 shows photos of the Spekboom and degraded sites and Table 2 provides more information on the two sites.


page 31

Energy balance, evaporation and carbon dioxide fluxes were measured at the Spekboom thicket and degraded veld sites over the period 17 September 2008 to 7 October 2008, using two In situ flux eddy covariance systems (two sensor type). The equipment used in this study is described in Appendix 1. Point based energy balance and evaporation data were collected (averaged) at both sites every 30 minutes, though the raw data were collected at high frequency (100 Hz). A Landsat 7 satellite image was acquired for this study area for 27 September 2008 (Table 4 and 5), which was within the period of field measurements. This image was used in the modelling of evaporation and energy balance fluxes. Energy balance fluxes collected at the nearest 30 minute interval to the time of satellite image acquisition was used to validate the instantaneous estimates of the energy balance components using the SEBAL and SEBS models (Table 4 and 5). The daily evaporation measured on 27 September 2008 at the two sites was also compared to the daily evaporation estimates modelled using SEBAL, SEBS and the VITT models. Since a long-term evaporation data set was collected, the evaporation estimated using SEBAL over a week overlapping the date of satellite image collection, was also compared to the field data for each site. The field data collection forms part of a CSIR research project and was funded by the CSIR. The unpublished data from this field campaign was made available by Dr Caren Jarmain ([email protected]).

3.2 Spatial and ancillary data

3.2.1 Image source

Measurements of evaporation are generally made at field scale, and therefore it was decided to use high resolution Landsat satellite images in this study. The Satellite Application Centre (SAC) from the CSIR is a supplier of satellite images and they assisted the project team in obtaining near cloud free Landsat images. These Landsat images were subsequently obtained at reduced cost through SAC. For the Acacia mearnsii site, few completely cloud-free images were available to choose from, for the duration of the 12 month period considered in this study. Details on the Landsat images used are shown in Table 4.

3.2.2 Pre-processing of images

Both the Landsat 5 tm and Landsat 7 tm images obtained were in the EOSAT fast format and not geometrically corrected (geo-referenced or projected). Upon overlaying the five images obtained and to be used in the A. mearnsii evaporation modelling, large variations in the positions of the pixels of these raw uncorrected images were observed. Therefore for each of the Landsat images used a reference geo-referenced and projected image were downloaded from the Global Land Cover Facility (GLCF) website (http://www.landcover.org). Each of the Landsat images were then geometrically corrected and projected, using an available remote sensing package, the ERDAS Imagine software. Following these corrections, e.g. the positions of the pixels of the five images used in the A. mearnsii modelling overlapped very well, and these images were ready for use in the evaporation modelling.


page 32

3.2.3 Ancillary data

In addition to the satellite images, other ancillary data are required for the modelling of evaporation. These include hourly and daily climatic data, land use information and digital elevation (DEM) data. The digital elevation data was downloaded in the form of a DEM map for each Landsat image. DEM data were downloaded from the Global Land Cover Facility (GLCF), Shuttle Radar Topography Mission (SRTM) website (http://glcf.umiacs.umd.edu/data/srtm). Instantaneous and daily weather data were obtained from different sources in cases where the weather data were not collected as part of the field experiments. For the Kirkwood site, daily and hourly weather data were obtained from the Agricultural Research Council (ARC). Missing data for the Seven Oaks site were obtained from the South African Sugar Association (SASA) website for the Warburg weather station which is the closest weather station to the Seven Oaks site. Weather data used in the modelling at St Lucia were also obtained from the SASA website from the St Lucia weather station. Land cover information from the National land cover (NLC) classification of 2000 was used for the modelling at St Lucia, whereas unsupervised classification of land use and cover was done in ERDAS imagine for the other sites.

3.3 Models and modelling code

Two software packages were used to perform most of the calculations. The different algorithms used in SEBAL, METRIC and the VITT model and described in Chapter 2, were coded into different “sub-models” using the Spatial modeller in the ERDAS Imagine software. These “sub-models” of each of these three models were run successively for each of these models. For all three these models, some supporting Excel spreadsheets were also used. Initially ERDAS version 6.1 was used, but later version 6.2. Prof Wim Bastiaanssen the developer of SEBAL agreed to provide one copy of the SEBAL code programmed in Erdas Imagine Spatial modeller, to the project team (specifically Caren Jarmain) for evaluation purposes. Most but not all of the algorithms of SEBAL have been published and are therefore available in the public domain. A confidentiality agreement was signed between Caren Jarmain (CSIR) and WaterWatch, for this code to be made available, to protect WaterWatch’s Intellectual property from being distributed and used unfairly. The SEBAL code did not include the Meteolook programming code, which is used in SEBAL to spatially extrapolate the point-based climatic data obtained from weather stations. In this study, fixed meteorological variables across the landscape and not spatially variable estimates thereof were used in SEBAL, as was also the case for all the other models. As part of the TIGER training course, which was attended by Caren Jarmain in December 2007, the SEBS model was made available to participants. SEBS is run in the ILWIS software package, developed by ITC in the Netherlands. Prof Bob Sue from


page 33

ICT in the Netherlands made available to the project team the latest version of SEBS (run in ILWIS 3.4), for evaluation in this project. Most of the algorithms used in SEBS, have also been published and are described in Chapter 2. METRICtm is another model evaluated in this project. METRIC, a derivative of SEBAL, have been modified over the years for application in irrigated areas in Idaho. Two version of METRIC were used in this project. Initially a version of METRIC was coded up in Spatial modeller with extensive help from Prof Bastiaanssen, using recently published literature (Allen et al., 2007). Later on, after a visit by Prof Allen to South Africa in October 2007, and a closer working relationship was established. Prof Allen then offered that his team in the US will do some simulations for us using the official version of the METRIC model, for one Landsat image of our choice5. The algorithms applied in the VITT modelling approach have also been published in the international literature and are described in Chapter 2. These algorithms were coded up in ERDAS Spatial modeller, using extensive help from Prof Bastiaanssen. Supporting Microsoft Excel spreadsheets were also used. Two approaches were followed in the application of the VITT algorithms. In each of these approaches, a simple excel spreadsheet, as well as one Spatial modeller coded sub-model, were used. In approach one, later referred to as VITT_1, the four conditions referred to in the VITT model (full cover, well-watered vegetation; full cover, no available water; saturated bare soil and dry bare soil), were defined in a spreadsheet (Table 6) for the specific Landsat image to be analysed. Available climatic data and Ts-Ta (surface temperature to air temperature difference) related to vegetation cover fc (assumed as 1 for full cover vegetation and 0 for bare soil), are used for both “wet” and “dry” branches (Fig. 5). These relationships were subsequently used in the ERDAS Spatial modeller model (Fig. 6) to calculate daily evaporation. In the second approach, VITT_2, a similar approach was followed. However, the four conditions were not defined as in Table 6. The surface to air temperature differences Ts-Ta, for the four different conditions were simply calculated and related to the actual vegetation covers identified from an image for these four conditions.

5 In the modelling results section we refer to the METRIC results obtained from the official version of METRIC,

as METRIC, and to the modelling results obtained from the version of METRIC coded up in South Africa as METRIC(SA).


page 34

Table 6 An example of the VITT modelling (option 1) where four reference conditions are described

FOR WET BRANCH Full cover, well-watered vegetation #1 FOR DRY BRANCH Full cover, no available water #2

Air temperature C 18.98 Air temperature C 18.98

Air humidity % 55.53 Air humidity % 55.53

Net radiation W/m2 511 Net radiation W/m2 511

Soil heat flux W/m2 11.5 Soil heat flux W/m2 11.5 minimum canopy resistance s/m 50

canopy resistance at near complete closure s/m 2000

aerodynamic resistance s/m 20 aerodynamic resistance s/m 60

Air density kg/m3 1.05 Air density kg/m3 1.05 Air specific heat at constant pressure J/kg/K 1004

Air specific heat at constant pressure J/kg/K 1004

saturated vapour mbar 21.94 saturated vapour mbar 21.94 slope saturated vapour pressure curve Mbar/C 1.36

slope saturated vapour pressure curve mbar/C 1.36

vpd Mbar 9.75 Vpd mbar 9.75

(Ts-Ta)1 3.35 (Ts-Ta)2 3.35

Saturated bare soil #3 Dry bare soil #4

Air temperature C 18.98 Air temperature C 18.98

Air humidity % 55.53 Air humidity % 55.53

Net radiation W/m2 511 Net radiation W/m2 511

Soil heat flux W/m2 11.5 Soil heat flux W/m2 11.5 minimum canopy resistance s/m 0 minimum canopy resistance s/m 100000

aerodynamic resistance s/m 80 aerodynamic resistance s/m 80

Air density kg/m3 1.05 Air density kg/m3 1.05 Air specific heat at constant pressure J/kg/K 1004

Air specific heat at constant pressure J/kg/K 1004

saturated vapour mbar 21.95730976 saturated vapour mbar 21.95730976 slope saturated vapour pressure curve mbar/C 1.370003423

slope saturated vapour pressure curve mbar/C 1.370003423

vpd mbar 9.764415651 Vpd mbar 9.764415651

(Ts-Ta)3 7.662871098 (Ts-Ta)4 37.83203397

y = -0.2321x + 1.7789

y = 0.6629x - 7.0494

-0.2

0

0.2

0.4

0.6

0.8

1

0 10 20 30 40

Ts - Ta (C)

Fra

cti

on

al v

eg

eta

tio

n c

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er

(-)

Wet branch

Dry branch

Figure 5 Graphical representation of the VITT model – showing the relationship between Ts-

Ta (surface temperature – air temperature difference) and fractional vegetation cover fc (assumed to 1 for full cover vegetation and 0 for bare soil), for a “wet” and “dry” branch respectively


page 35

Figure 6 A screen dump of some of the code of the VITT model, programmed in Spatial

modeller, ERDAS Imagine


page 36

Chapter 4: Assessment of Models used for Spatial Estimation of Evaporation

As described in Chapter 3, alltogether seven sites from four different areas (Seven Oaks, Midmar, Kirkwood, St Lucia) were used to evaluate the accuracy of four different remote-sensing based models (SEBAL, SEBS, METRIC, VITT) used to estimate energy fluxes and evaporation spatially. As illustrated in Fig. 7, remote sensing data (e.g. the Landsat images) together with other ancillary data (meteorological, DEM, land classification, etc.) were used to estimate instantaneous energy fluxes (Rn, G and H) and derivatives such as the evaporative fraction (EF) in SEBAL, SEBS and METRIC. Then using the available daily climatic data and average climatic data over a specific period (meteo data_24 and meteo data_per), the daily evaporation rates as well as evaporation over a period can be estimated. In the subsequent sections, the instantaneous energy flux estimates, i.e. the energy flux estimates from the different models at the time of the satellite overpass, are compared with the measured fluxes. Similarly, the daily measured evaporation are compared with the evaporation estimated using the various models, and where possible evaporation over a longer period. For the analysis of the simulated energy balance and evaporation data, the satellite pixels around the position of the field sites have been selected as Areas of interest (AOI), and only the data within these pixels will be used in the data comparison and analysis. These AOIs are shown in Table 7.

Figure 7 Schematic representation of the process followed to estimate energy fluxes spatially

using remote sensing (RS) and other ancillary data (e.g. meteorological, meteo data).

A M

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r N

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r-R

ea

l T

ime

Sp

ati

al

Est

ima

tio

n o

f E

va

po

rati

on

page

37

Tab

le 7

S

peci

fic

area

s of

inte

rest

(A

OI)

use

d to

eva

luat

e th

e ac

cura

cy o

f th

e si

mul

ated

ene

rgy

bala

nce

and

evap

orat

ion

data

at t

he S

even

Oak

s, M

idm

ar, S

t Luc

ia a

nd

Kir

kwoo

d si

tes

are

show

n by

the

dotte

d lin

es.

Onl

y th

e en

ergy

bal

ance

and

eva

pora

tion

data

con

tain

ed w

ithin

the

AO

Is w

ere

used

in th

e an

alys

is a

nd c

ompa

riso

n of

the

valid

atio

n da

ta

Sev

en O

aks

Aca

cia

mea

rnsi

i M

idm

ar O

pen

wat

er b

ody

St L

ucia

Sw

amp

fore

st, g

rass

land

and

sed

ges

Kir

kwoo

d Sp

ekbo

om th

icke

t and

Deg

rade

d ve

ld


page 38

4.1 Instantaneous energy balance fluxes

The components of the simplified energy balance equation (equation 1), were estimated for each of the Landsat images processed in this study with the SEBAL, SEBS and METRIC models. In addition, the evaporative fraction (the fraction of the available energy partitioned into evaporation, LE/(Rn-G)) was also calculated. The instantaneous energy flux estimates at the time of the satellite overpass (whether with SEBAL, SEBS or METRIC), were compared to the field estimates at the time interval closest to the date and time of the satellite image acquisition for the Acacia mearnsii, Spekboom thicket and degraded veld sites. For the Midmar Dam, and the St Lucia sites the average field estimates of the various energy balance components at the time interval closest to the time of the satellite image acquisition were compared to the instantanous estimates. At the Acacia site, the instantaneous estimates of net radiation (Rn) simulated using SEBAL, SEBS and METRIC compared well with the field measurements of Rn for all five Landsat images. In Figure 8, the diurnal trends of the measured Rn for the five different days studied are shown. Maximum daily Rn increased from spring (DOY 272) towards summer (DOY 348), and then decreased towards winter (DOY 175). The SEBAL instantaneous Rn estimates slightly (5%) exceeded the measured Rn (Fig. 8, Table 8), except for May 2007 (DOY 143). For the May image, Rn was underestimated by 7%. Similarly SEBS and METRIC slightly overestimated the instantaneous Rn (SEBS up to 11% and METRIC between 7-10%) (Fig. 8, Table 8). The net radiation estimates are highly dependent on the accuracy of albedo and transmissivity. Solar radiation data obtained from each research site and the extraterrestrial estimates of solar radiation were used to estimate the transmissivity values which were subsequently used in the modelling. At the two Kirkwood sites, Rn was simulated using SEBAL and SEBS. The Rn estimated using these models compared well with the measured Rn (to within 9%). Figures 9 and 10 show Rn estimates for 27 September 2008 (07h00 to 17h00), for the Spekboom thicket and degraded veld sites respectively. The instantanous Rn values estimated using SEBAL averaged over the respective AOIs were 424 and 404 Wm-2 for the Spekboom and degraded sites respectively (Fig. 9 and 10, Table 9). The Rn value was higher for the Spekboom thicket compared to the degraded site. This is due to the lower estimated average albedo value (0.12) compared to the degraded site which was 0.15. The instantaneous Rn estimates from SEBAL were within 9% of the measured Rn (Fig. 9, 10). SEBS also yielded an accurate estimate of Rn, within 6% of the measured Rn (Fig. 9, 10). In general, both SEBAL and SEBS therefore slightly underestimated Rn.


page 39

Acacia Net radiation

-200

0

200

400

600

800

252.3 252.5 252.7 348.4 348.7 79.4 79.6 143.3 143.5 143.8 175.5 175.7

Date

Energ

y flu

x (W

m

Validation

sebal

metric_Sa

sebs

metric_us

Acacia Soil heat flux

-50

0

50

100

150

252.3 252.5 252.7 348.4 348.7 79.4 79.6 143.3 143.5 143.8 175.5 175.7

Date

Energ

y flu

x (W

m

Validation

sebal

metric_Sa

sebs

metric_us

Acacia Sensible heat flux

0

50

100

150

200

250

252.3 252.5 252.7 348.4 348.7 79.4 79.6 143.3 143.5 143.8 175.5 175.7

Date

Energ

y flu

x (W

m

Validation

sebal

metric_sa

sebs

metric_us

Acacia Evaporative fraction

-1

-0.5

0

0.5

1

1.5

252.3 252.5 252.7 348.4 348.7 79.4 79.6 143.3 143.5 143.8 175.5 175.7

Date

Evapora

tive fra

cti

Validation

sebal

metric_sa

sebs

metric_us

Figure 8 Energy balance and evaporation data for the A. mearnsii site, as measured and estimated using the SEBAL, SEBS, and METRIC models. The instantaneous data estimated using the models are shown, together with the time series measured data for five days (DOY’s 252, 348, 79, 143, 175 corresponding to 9 September 2006, 14 December 2006, 20 March 2007, 23 May 2007 and 24 June 2007 are shown for the period 0700 to 1800.

Spekboom: Rn_i

-100

0

100

200

300

400

500

600

700

271.3 271.4 271.4 271.5 271.5 271.6 271.7

Net

irr

adia

nce

Wm

Validation

SEBAL

SEBS

Spekboom: G_i

-40

-20

0

20

40

60

80

100

120

271.3 271.4 271.4 271.5 271.5 271.6 271.7

Soil h

eat flux

Wm

Validation

SEBAL

SEBS

Spekboom: H_i

-100

0

100

200

300

400

500

600

271.3 271.4 271.4 271.5 271.5 271.6 271.7

Sen

sible

hea

t flux

Wm

Validation

SEBAL

SEBS

Spekboom: EF_i

-0.2

0

0.2

0.4

0.6

0.8

1

271.3 271.4 271.4 271.5 271.5 271.6 271.7

Eva

pora

tive

fra

cti

Validation

SEBAL

SEBS

Spekboom Rn (Wm-2) G (Wm-2) H (Wm-2) EF (Unitless)

Val 468.50 68.45 340.51 0.15 sebal_ave 424.08 96.51 233.08 0.29 sebal_std 8.28 2.86 2.92 0.02 sebal/val 0.91 1.41 0.68 1.94 sebs_ave 439.28 111.79 133.18 0.59

sebs_stdev 2.73 1.66 2.84 0.01 sebs/val 0.94 1.63 0.39 3.99

Figure 9 Energy balance and evaporation data for the Kirkwood Spekboom thicket site, as

measured and estimated using the SEBAL and SEBS models. Only the instantaneous data estimated using the models are shown, whereas the measured data are shown for the period 0700 to 1700. Rn refers to net radiation, G to soil heat flux, H to sensible heat flux, and EF to the evaporative fraction. Also, val refers to the validation data, _ave refers to the average value of the specific parameter, _std to the standard deviation, and /val refers to the fraction of the estimated value to the validation value.


page 40

Degraded: Rn_i

-100

0

100

200

300

400

500

600

271.3 271.4 271.4 271.5 271.5 271.6 271.7

Net

irr

adia

nce

Wm

Validation

SEBAL

SEBS

Degraded: G_i

-80

-40

0

40

80

120

160

271.3 271.4 271.4 271.5 271.5 271.6 271.7Soil h

eat flux

Wm

Validation

SEBAL

SEBS

Degraded: H_i

0

50

100

150

200

250

300

350

400

271.3 271.4 271.4 271.5 271.5 271.6 271.7

Sen

sible

hea

t flux

Wm

Validation

SEBAL

SEBS

Degraded: EF_i

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

271.3 271.4 271.4 271.5 271.5 271.6 271.7

Eva

pora

tive

fra

cti

Validation

SEBAL

SEBS

Degraded Rn (Wm-2) G (Wm-2) H (Wm-2) EF (Unitless)

Val 402.70 101.76 272.82 0.09 sebal_ave 404.63 129.36 284.19 0.00 sebal_std 6.80 2.98 3.08 0.00 sebal/val 1.00 1.27 1.04 0.00 sebs_ave 386.72 107.37 146.29 0.48


Figure 10 Energy balance and evaporation data for the Kirkwood Spekboom thicket site, as


For Midmar Dam, the components of the energy balance and evaporation was only simulated using SEBAL. Figure 11 shows the diurnal trend of the measured Rn for Midmar Dam for Days of Year (DOY) 181 to 193, as well as the average Rn values measured for cloud free days only (DOYs 181-188) (Val_ave) and the instantaneous SEBAL estimates of Rn. Simulated Rn compared favourably to the average measured values – 212 vs. 202 Wm-2 (validation vs. SEBAL estimates respectively) – or to within 5% (Fig. 11, Table 10). At the three St Lucia sites, the simulated Rn using both SEBAL and SEBS were less accurate compared to some of the other sites discussed above. However, it is worth noting that the SEBAL and SEBS instantaneous Rn estimates were compared to average estimates of Rn (for the time interval closest to the time of the satellite overpass), since field estimates for the simulation day were not available. The simulated Rn using SEBS was consistently higher than the measured Rn on sunny days (up to 46% at the swamp forest site) (Fig. 12-14, Tab. 11). Similarly, Rn simulated using SEBAL for the swamp forest was also higher compared to the measured Rn (up to 23%) (Fig. 12). However,


page 41

the Rn estimates were more accurate for both the grassland and sedges sites where the Rn values simulated using SEBAL were only 1 and 4% lower than the measured Rn respectively (Fig. 13, 14, Table 11).

Midmar: Water_Rn

-200

-100

0

100

200

300

400

500

800 900 1000 1100 1200 1300 1400 1500 1600 1700

Net

rad

iation W

m

Validation

SEBAL

Val_Ave

Midmar: Water_G

-300

-200

-100

0

100

200

300

800 900 1000 1100 1200 1300 1400 1500 1600 1700

Soil h

eat flux

Wm

Validation

SEBAL

Val_Ave

Midmar: Water_H

-20

0

20

40

60

80

100

120

140

800 900 1000 1100 1200 1300 1400 1500 1600 1700

Sen

sible

hea

t flux

Wm

Validation

SEBAL

Val_Ave

Midmar: Water_EF

-1.5

-1

-0.5

0

0.5

1

1.5

800 900 1000 1100 1200 1300 1400 1500 1600 1700

Eva

pora

tive

fra

cti

Validation

SEBAL

Val_ave

Rn (Wm-2) G (Wm-2) H (Wm-2) EF (Unitless) val_ave 212.73 77.85 28.38 0.78

val_stdev 3.59 18.52 11.91 0.12 sebal_ave 202.69 64.07 21.89 0.83

sebal_stdef 16.17 32.30 12.24 0.11 sebal/val 0.95 0.82 0.77 1.07

Figure 11 Energy balance and evaporation data for Midmar Dam, as measured and estimated

using the SEBAL model. Only the instantaneous data estimated using the models are shown, whereas the measured data are shown for the period 0800 to 1700. Rn refers to net radiation, G to heat flux in the water, H to sensible heat flux, and EF to the evaporative fraction. Also, val refers to the validation data, _ave refers to the average value of the specific parameter, _std to the standard deviation, and /val refers to the fraction of the estimated value to the validation value.

The soil heat flux (G) and heat storage in water (G) is difficult to estimate and to simulate accurately. The soil heat flux is estimated in SEBAL and METRIC as a function of albedo, surface temperature, NDVI and net radiation (Bastiaanssen, 2000). In SEBS, G is a function of Rn, fraction of vegetation, and a constant ratio of soil heat flux to net radiation for surfaces with a full vegetative cover, and bare soil (Su et al., 1999 and 2001). Therefore any errors in the simulated Rn will be carried through to the simulated G. At the Acacia site, the soil heat flux (G) obtained using SEBAL, SEBS and METRIC differed greatly from the field estimated values (Fig. 8, Table 8). Soil heat flux estimated using SEBAL exceeded the measured G by up to 82%. Measured G values were generally small, and ranged between 57 Wm-2 (December) and -3 Wm-2 (May). The simulated values ranged between 98 Wm-2 (December) and 2 Wm-2 (June). Similarly, SEBS and METRIC failed to simulate soil heat flux densities comparable to those measured. The SEBS instantaneous G estimate for December was 134 Wm-2 compared to measured estimate of 57 Wm-2. The METRIC estimate for the same date was 121 Wm-2 (Fig. 8, Table 8).


page 42

At the Kirkwood sites, both SEBS and SEBAL also failed to simulate G accurately, with the simulated G consistently exceeding the measured G. At the Spekboom site, G simulated using SEBAL and SEBS was overestimated by 41 and 63% respectively. At the degraded site, the simulated G estimates, using SEBAL and SEBS exceeded the measured G by 27 and 6% respectively (Fig. 8). SEBAL succeeded in simulating higher G values for the degraded site compared to the spekboom site (Table 9), because of the lower average leaf area index (0.16) at the degraded site, compared to the Spekboom site (0.46) (Table 9). SEBS failed to do so, and the instantaneous G estimate at the Spekboom site exceeded the G at the degraded site (Table 8).

Forest: Rn_i

-200

-100

0

100

200

300

400

500

600

600 800 1000 1200 1400 1600 1800

Net

irr

adia

nce

(W

m

Validation

SEBAL

SEBS

Forest: G_i

-10

0

10

20

30

40

50

60

70

80

600 800 1000 1200 1400 1600 1800

Soil h

eat flux(

Wm

Validation

SEBAL

SEBS

Forest: H_i

-200

-100

0

100

200

300

400

600 800 1000 1200 1400 1600 1800

Sen

sible

hea

t flux

(Wm

Validation

SEBAL

SEBS

Forest: EF_i

-1

-0.5

0

0.5

1

600 800 1000 1200 1400 1600 1800

Eva

pora

tive

fra

cti

Validation

SEBAL

SEBS

Forest Rn (Wm-2) G (Wm-2) H (Wm-2) EF (Unitless)

Val 254.37 -0.50 30.71 0.70 sebal_ave 312.06 19.38 145.13 0.50 sebal_std 3.25 1.15 10.87 0.04 sebal/val 1.23 -38.98 4.73 0.72 sebs_ave 370.74 66.81 1.57 0.99

sebs_stdev 2.07 2.21 3.98 0.01 sebs/val 1.46 -134.39 0.05 1.43

Figure 12 Energy balance and evaporation data for the St Lucia swamp forest site, as



page 43

Grass: Rn_i

-100

0

100

200

300

400

500

600 800 1000 1200 1400 1600 1800

Net

irr

adia

nce

(W

m

Validation

SEBAL

SEBS

Val_AVe

Grass: G_i

-100

-50

0

50

100

150

200

600 800 1000 1200 1400 1600 1800Soil h

eat flux

(Wm

Validation

SEBAL

SEBS

Val_Ave

Grass: H_i

-100

0

100

200

300

400

500

600 800 1000 1200 1400 1600 1800

Sen

sible

hea

t flux

(Wm

Validation

SEBAL

SEBS

Val_Ave

Grass: EF_i

-0.5

-0.3

-0.1

0.1

0.3

0.5

600 800 1000 1200 1400 1600 1800

Eva

pora

tive

fra

cti

Validation

SEBAL

SEBS

Val_Ave

Grass Rn (Wm-2) G (Wm-2) H (Wm-2) EF (Unitless)

val 260.95 69.77 171.17 0.11 sebal_ave 258.22 61.51 451.43 0.00 sebal_std 3.91 1.96 9.22 0.00 sebal/val 0.99 0.88 2.64 0.00 sebs_ave 309.14 84.27 205.82 0.08


Figure 13 Energy balance and evaporation data for the St Lucia grassland site, as measured

and estimated using the SEBAL and SEBS models. Only the instantaneous data estimated using the models are shown, whereas the measured data are shown for the period 0600 to 1800. Rn refers to net radiation, G to soil heat flux, H to sensible heat flux, and EF to the evaporative fraction. Also, val refers to the validation data, _ave refers to the average value of the specific parameter, _std to the standard deviation, and /val refers to the fraction of the estimated value to the validation value.

Both SEBAL and SEBS also failed to simulate G accurately at the St Lucia sites (Figures 12-14, Table 11)6. For both the forest and sedges sites, with high vegetative covers, large differences existed between measured and simulated G, with the Gs simulated exceeding those measured (Table 11). The Gs simulated for the grassland were more accurate, with the SEBAL G estimate 12% lower than the measured G, and the SEBS estimate 21% higher than the measured G (Table 11, Figures 12-14). For Midmar Dam, heat storage validation data were available. The heat stored in the water (G) was therefore calculated using SEBAL and was greatly affected by the water depth (here simulated at an average depth of 5 m). The heat storage simulated using SEBAL is shown in Figure 11. The average SEBAL estimate of G was 18% lower than the measured G. Since the energy fluxes were simulated for winter, the measure and

6 The SEBAL and SEBS instantaneous estimates were compared to average estimates of the energy

fluxes (at the time interval closest to the time of the satellite overpass), since field estimates for the simulation day were not available.


page 44

simulated G values were low compared to Rn (36 and 31% respectively for the measurements and simulations).

Sedges: Rn_i

-100

0

100

200

300

400

500

600 800 1000 1200 1400 1600 1800

Net

irr

adia

nce

(W

m

Validation

SEBAL

SEBS

Sedges: G_i

-100

102030405060708090

600 800 1000 1200 1400 1600 1800

Soil h

eat flux

(Wm

Validation

SEBAL

SEBS

Sedges: H_i

-200

-100

0

100

200

300

400

600 800 1000 1200 1400 1600 1800

Sensib

le h

eat flux (W

m

Validation

SEBAL

SEBS

Sedges: EF_i

-1

-0.5

0

0.5

1

600 800 1000 1200 1400 1600 1800E

vapora

tive

fra

cti

Validation

SEBAL

SEBS

Sedges Rn (Wm-2) G (Wm-2) H (Wm-2) EF (Unitless)

Val 306.20 -1.01 177.64 0.42 sebal_ave 293.09 28.84 179.50 0.32 sebal_std 9.06 2.57 34.74 0.13 sebal/val 0.96 -28.61 1.01 0.77 sebs_ave 354.24 79.19 47.12 0.83

sebs_stdev 8.91 6.02 9.65 0.04 sebs/val 1.16 -78.55 0.27 1.98

Figure 14 Energy balance and evaporation data for the St Lucia sedges wetland site, as


A M

eth

od

olo

gy

fo

r N

ea

r-R

ea

l T

ime

Sp

ati

al

Est

ima

tio

n o

f E

va

po

rati

on

page

45

Tab

le 8

C

ompo

nent

s of

the

ener

gy b

alan

ce a

nd e

vapo

ratio

n fo

r th

e S

even

Oak

s A

caci

a m

earn

sii s

ites

as

esti

mat

ed u

sing

the

SE

BA

L, S

EB

S, M

ET

RIC

and

the

VIT

T

mod

el(s

), a

nd th

ose

mea

sure

d us

ing

mic

rom

eteo

rolo

gica

l met

hods

. M

inim

um (

Min

), M

axim

um (

Max

), M

ean

(Mea

n) a

nd S

tand

ard

devi

atio

ns (

Std

ev)

valu

es a

re

show

n. T

he v

aria

bles

sho

wn

incl

ude:

Ins

tant

aneo

us n

et r

adia

tion

(Rn_

i), I

nsta

ntan

eous

soi

l hea

t flu

x (G

_i),

Ins

tant

aneo

us s

ensi

ble

heat

flu

x (H

_i),

Ins

tant

aneo

us

evap

orat

ive

frac

tion

(E

F_i

), d

aily

eva

pora

tion

(E

T24)

, dai

ly r

efer

ence

eva

pora

tion

(ET

r24)

, the

eva

pora

tion

esti

mat

e ov

er a

per

iod

(ET_

per)

, alb

edo

(alb

) an

d le

af

area

inde

x (L

AI)

. **

Inst

anta

neou

s (_

i) r

efer

s to

the

data

est

imat

ed f

or th

e ti

me

of th

e sa

tell

ite

over

pass

Dat

e V

aria

ble

Un

it

SE

BA

L

SE

BS

M

ET

RIC

tm

VIT

T1

VIT

T2

Val

idat

ion

dat

aIn

pu

t d

ata

Max

M

in

Mea

n S

tdev

Max

Min

Mea

nS

tdev

Max

Min

Mea

nS

tdev

Mea

nS

tdev

M

ean

Std

evA

vera

ge

Dat

e 09

/09/

06

09/0

9/06

Rn_

i W

m-2

48

5.48

363.

0940

4.71

29.

53

49

3.99

370.

0541

2.43

29.6

7

38

3.80

T

ime

(GM

T)

07:4

4:30

09/0

9/06

G_i

W

m-2

94

.20

57.0

3 80

.68

7.27

92.4

864

.38

74.0

57.

05

28

.75

Ta_

i (oC

) 22

.20

09/0

9/06

H_i

W

m-2

16

2.44

51.9

2 13

0.54

24.

81

26

9.46

124.

7722

9.87

30.3

8

82

.36

RH

_i (

%)

40.2

2

09/0

9/06

EF

_i

Uni

tles

s0.

88

0.45

0.

59

0.10

0.70

0.

16

0.31

0.

12

0.

77

u_i (

m/s

) 1.

96

09/0

9/06

ET

24

mm

/d

5.57

2.

29

3.24

0.

78

3.

78

0.68

1.

46

0.68

0.78

1.

00

1.07

0.

67

2.87

R

s_i (

Wm

-2)

n/a

09/0

9/06

ET

r24

mm

/d

4.35

4.

04

4.18

0.

07

5.

20

Ta_

24 (

oC)

19.5

2

09/0

9/06

ET

_per

mm

/mth

91.0

8 31

.39

45.3

4 12

.94

76.4

8 R

H_2

4 (%

) 50

.95

09/0

9/06

alb

Uni

tles

s0.

14

0.13

0.

14

0.00

u_

24 (

m/s

) 2.

08

09/0

9/06

LA

I U

nitl

ess

0.76

0.

47

0.62

0.

08

Rs_

24 (

Wm

-2)

223.

70

14/1

2/06

Rn_

i W

m-2

70

5.80

602.

1363

9.34

24.

4675

1.69

686.

0769

8.14

15.2

070

9.36

680.

9969

3.04

4.71

631.

30

Dat

e 14

/12/

06

14/1

2/06

G_i

W

m-2

11

6.94

54.5

6 98

.15

13.7

315

3.57

114.

6313

4.29

8.91

135.

6410

0.58

121.

418.

58

57

.26

Tim

e (G

MT

) 07

:45:

29

14/1

2/06

H_i

W

m-2

24

6.12

91.1

1 21

4.02

38.

5496

.83

20.6

573

.47

22.2

325

3.27

61.3

620

8.66

46.3

5

75

.79

Ta_

i (oC

) 30

.68

14/1

2/06

EF

_i

Uni

tles

s0.

86

0.52

0.

60

0.09

0.97

0.

82

0.87

0.

04

0.

87

RH

_i (

%)

32.9

3

14/1

2/06

ET

24

mm

/d

6.50

4.

06

4.59

0.

636.

60

5.57

5.

87

0.28

3.10

1.

41

0.28

0.

91

5.39

u_

i (m

/s)

2.41

14/1

2/06

ET

r24

mm

/d

6.29

6.

19

6.24

0.

02

7.

90

Rs_

i (W

m-2

) 91

0.00

14/1

2/06

ET

_per

mm

/mth

105.

6566

.51

74.7

4 10

.09

132.

28

Ta_

24 (

oC)

24.4

0

14/1

2/06

alb

Uni

tles

s0.

15

0.13

0.

14

0.00

R

H_2

4 (%

) 66

.14

14/1

2/06

LA

I U

nitl

ess

2.75

1.

09

1.66

0.

36

u_24

(m

/s)

1.44

Rs_

24 (

Wm

-2)

315.

30

20/0

3/07

Rn_

i W

m-2

58

1.60

495.

5853

3.75

21.

64

51

0.70

D

ate

20/0

3/07

20/0

3/07

G_i

W

m-2

62

.28

24.1

1 48

.78

6.55

11.5

4 T

ime

(GM

T)

07:4

5:42

20/0

3/07

H_i

W

m-2

17

4.69

56.8

1 12

1.59

19.

14

10

7.29

T

a_i (

oC)

18.9

8

20/0

3/07

EF

_i

Uni

tles

s0.

89

0.62

0.

75

0.05

0.79

R

H_i

(%

) 55

.33

A M

eth

od

olo

gy

fo

r N

ea

r-R

ea

l T

ime

Sp

ati

al

Est

ima

tio

n o

f E

va

po

rati

on

page

46

Dat

e V

aria

ble

Un

it

SE

BA

L

SE

BS

M

ET

RIC

tm

VIT

T1

VIT

T2

Val

idat

ion

dat

aIn

pu

t d

ata

Max

M

in

Mea

n S

tdev

Max

Min

Mea

nS

tdev

Max

Min

Mea

nS

tdev

Mea

nS

tdev

M

ean

Std

evA

vera

ge

Dat

e 09

/09/

06

20/0

3/07

ET

24

mm

/d

4.35

2.

95

3.70

0.

27

4.

16

0.04

1.

92

0.19

4.

64

u_i (

m/s

) 2.

23

20/0

3/07

ET

r24

mm

/d

3.51

3.

29

3.39

0.

05

4.

20

Rs_

i (W

m-2

) 20

1.80

20/0

3/07

ET

_per

mm

/mth

109.

8773

.80

93.2

3 6.

83

10

9.06

T

a_24

(oC

) 16

.21

20/0

3/07

alb

Uni

tles

s0.

14

0.12

0.

13

0.00

R

H_2

4 (%

) 73

.00

20/0

3/07

LA

I U

nitl

ess

4.50

1.

23

2.09

0.

58

u_24

(m

/s)

1.47

Rs_

24 (

Wm

-2)

278.

90

23/0

5/07

Rn_

i W

m-2

34

8.49

249.

5829

0.50

24.

51

31

3.30

D

ate

23/0

5/07

23/0

5/07

G_i

W

m-2

9.

10

2.50

3.

23

1.03

-3.0

1 T

ime

(GM

T)

07:4

5:12

23/0

5/07

H_i

W

m-2

90

.93

26.4

8 53

.41

15.0

2

86

.96

Ta_

i (oC

) 8.

40

23/0

5/07

EF

_i

Uni

tles

s0.

92

0.69

0.

81

0.06

0.73

R

H_i

(%

) 42

.43

23/0

5/07

ET

24

mm

/d

2.38

0.

84

1.48

0.

36

1.

24

0.29

2.

48

u_i (

m/s

) 3.

20

23/0

5/07

ET

r24

mm

/d

1.55

1.

33

1.44

0.

05

1.

77

Rs_

i (W

m-2

) 49

6.40

23/0

5/07

ET

_per

mm

/mth

101.

2241

.87

69.4

4 14

.73

73.1

7 T

a_24

(oC

) 6.

47

23/0

5/07

alb

Uni

tles

s0.

20

0.13

0.

18

0.01

R

H_2

4 (%

) 56

.38

23/0

5/07

LA

I U

nitl

ess

8.22

0.

98

3.70

1.

65

u_24

(m

/s)

1.78

Rs_

24 (

Wm

-2)

163.

10

24/0

6/07

Rn_

i W

m-2

28

9.56

203.

9224

2.61

21.

29

24

1.20

D

ate

24/0

6/07

24/0

6/07

G_i

W

m-2

2.

90

2.04

2.

43

0.21

20.6

6 T

ime

(GM

T)

07:4

4:38

24/0

6/07

H_i

W

m-2

62

.44

23.1

1 39

.30

8.94

25.9

9 T

a_i (

oC)

16.5

4

24/0

6/07

EF

_i

Uni

tles

s0.

91

0.73

0.

84

0.04

0.88

R

H_i

(%

) 28

.58

24/0

6/07

ET

24

mm

/d

1.05

0.

00

0.42

0.

26

2.

20

0.00

2.

20

0.00

2.

21

u_i (

m/s

) 2.

56

24/0

6/07

ET

r24

mm

/d

3.18

2.

90

3.03

0.

07

2.

17

Rs_

i (W

m-2

) 41

4.80

24/0

6/07

ET

_per

mm

(14

)5.

87

0.10

2.

30

1.45

26.3

3 T

a_24

(oC

) 16

.87

24/0

6/07

alb

Uni

tles

s0.

17

0.12

0.

15

0.01

R

H_2

4 (%

) 25

.01

24/0

6/07

LA

I U

nitl

ess

4.50

0.

70

2.20

0.

81

u_24

(m

/s)

2.32

A M

eth

od

olo

gy

fo

r N

ea

r-R

ea

l T

ime

Sp

ati

al

Est

ima

tio

n o

f E

va

po

rati

on

page

47

Tab

le 9

C

ompo

nent

s of

the

ener

gy b

alan

ce a

nd e

vapo

ratio

n fo

r th

e K

irkw

ood

site

s: d

egra

ded

veld

and

Spe

kboo

m th

icke

t, as

est

imat

ed u

sing

the

SE

BA

L, S

EB

S a

nd th

e V

ITT

mod

el(s

), a

nd th

ose

mea

sure

d. M

inim

um (

Min

), M

axim

um (

Max

), M

ean

(Mea

n) a

nd S

tand

ard

devi

atio

ns (

Std

ev)

valu

es a

re s

how

n. T

he v

aria

bles

sho

wn

incl

ude:

Ins

tant

aneo

us n

et r

adia

tion

(Rn_

i), I

nsta

ntan

eous

soi

l hea

t flu

x (G

_i),

Ins

tant

aneo

us s

ensi

ble

heat

flu

x (H

_i),

Ins

tant

aneo

us e

vapo

rati

ve f

ract

ion

(EF

_i),

da

ily e

vapo

ratio

n (E

T24

), d

aily

ref

eren

ce e

vapo

rati

on (

ET

r24)

, dai

ly p

oten

tial e

vapo

ratio

n (E

Tp24

), e

vapo

ratio

n ov

er a

per

iod

(ET

_per

), a

lbed

o (a

lb)

and

leaf

ar

ea in

dex

(LA

I).

**In

stan

tane

ous

refe

rs to

the

time

of th

e sa

telli

te o

verp

ass

Dat

e S

ite

Mod

el

Var

iab

le

Un

it

SE

BA

L

SE

BS

VIT

T1

VIT

T2

Val

idat

ion

dat

a In

pu

t d

ata

Max

M

in

Mea

n

Std

ev

Max

M

in

Mea

n

Std

ev

Mea

n

Std

ev

Mea

n

Std

ev

Ave

rage

D

ate

27/0

9/20

08

27/0

9/20

08

Deg

rade

d S

EB

AL

R

n_i

Wm

-2

416.

93

393.

90

404.

63

6.80

39

1.81

38

1.80

38

6.72

3.

94

402.

70

Tim

e (G

MT

) 0

8:00

:00

27/0

9/20

08

Deg

rade

d S

EB

AL

G

_i

Wm

-2

134.

09

125.

98

129.

36

2.98

10

9.54

10

5.16

10

7.37

1.

48

101.

76

Ta_

i (oC

) 15

.71

27/0

9/20

08

Deg

rade

d S

EB

AL

H

_i

Wm

-2

288.

38

280.

01

284.

19

3.08

16

0.72

13

1.13

14

6.29

9.

86

272.

82

RH

_i (

%)

10.8

0

27/0

9/20

08

Deg

rade

d S

EB

AL

E

F_i

U

nitl

ess

0.00

0.

00

0.00

0.

00

0.53

0.

43

0.48

0.

04

0.09

u_

i (m

/s)

3.23

27/0

9/20

08

Deg

rade

d S

EB

AL

E

T24

m

m/d

0.

00

0.00

0.

00

0.00

2.

71

2.19

2.

42

0.19

0.

56

0.09

1.

60

0.03

0.

40

Rs_

i (W

m-2

) 65

3.00

27/0

9/20

08

Deg

rade

d S

EB

AL

E

Tr2

4 m

m/d

4.

05

3.99

4.

02

0.02

Ta_

24 (

oC)

12.1

0

27/0

9/20

08

Deg

rade

d S

EB

AL

E

Tp2

4 m

m/d

0.

87

0.53

0.

69

0.11

RH

_24

(%)

31.7

1

27/0

9/20

08

Deg

rade

d S

EB

AL

al

b U

nitl

ess

0.16

0.

14

0.15

0.

01

0.18

u_

24 (

m/s

) 2.

24

27/0

9/20

08

Deg

rade

d S

EB

AL

L

AI

Uni

tles

s 0.

21

0.11

0.

16

0.03

0.

32

Rs_

24 (

Wm

-2)

272.

20

27/0

9/20

08

Deg

rade

d S

EB

AL

E

T_p

er

mm

0.

17

0.18

0.

18

0.00

3.

35

27/0

9/20

08

Spe

kboo

m

SE

BA

L

Rn_

i W

m-2

44

0.78

41

1.65

42

4.08

8.

28

443.

62

434.

94

439.

28

2.73

46

8.50

C

ell c

ount

27/0

9/20

08

Spe

kboo

m

SE

BA

L

G_i

W

m-2

10

0.94

93

.00

96.5

1 2.

86

115.

06

109.

04

111.

79

1.66

68

.45

Deg

rade

d 8

27/0

9/20

08

Spe

kboo

m

SE

BA

L

H_i

W

m-2

23

9.26

22

9.02

23

3.08

2.

92

138.

57

129.

94

133.

18

2.84

34

0.51

S

pekb

oom

12

27/0

9/20

08

Spe

kboo

m

SE

BA

L

EF

_i

Uni

tles

s 0.

31

0.24

0.

29

0.02

0.

61

0.57

0.

59

0.01

0.

15

Spe

kboo

m (

SE

BS

) 10

27/0

9/20

08

Spe

kboo

m

SE

BA

L

ET

24

mm

/d

2.04

1.

48

1.82

0.

15

3.27

3.

07

3.21

0.

06

2.99

0.

60

2.89

0.

68

0.47

27/0

9/20

08

Spe

kboo

m

SE

BA

L

ET

r24

mm

/d

4.05

3.

98

4.01

0.

03

27/0

9/20

08

Spe

kboo

m

SE

BA

L

ET

p24

mm

/d

2.04

1.

55

1.83

0.

13

27/0

9/20

08

Spe

kboo

m

SE

BA

L

alb

Uni

tles

s 0.

12

0.11

0.

12

0.00

0.

11

27/0

9/20

08

Spe

kboo

m

SE

BA

L

LA

I U

nitl

ess

0.56

0.

33

0.46

0.

06

1.29

27/0

9/20

08

Spe

kboo

m

SE

BA

L

ET

_per

m

m

12.5

4 16

.97

15.2

5 1.

19

6.69

A M

eth

od

olo

gy

fo

r N

ea

r-R

ea

l T

ime

Sp

ati

al

Est

ima

tio

n o

f E

va

po

rati

on

page

48

Tab

le 1

0

Com

pone

nts

of th

e en

ergy

bal

ance

and

eva

pora

tion

for

the

Mid

mar

site

as

esti

mat

ed u

sing

the

SE

BA

L m

odel

, and

thos

e m

easu

red.

Min

imum

(M

in),

Max

imum

(M

ax),

Mea

n (M

ean)

and

Sta

ndar

d de

viat

ions

(S

tdev

) va

lues

are

sho

wn.

The

var

iabl

es s

how

n in

clud

e: I

nsta

ntan

eous

net

rad

iatio

n (R

n_i)

, Ins

tant

aneo

us s

oil h

eat

flux

(G

_i),

Ins

tant

aneo

us s

ensi

ble

heat

flu

x (H

_i),

Ins

tant

aneo

us e

vapo

rativ

e fr

acti

on (

EF

_i),

dai

ly e

vapo

ratio

n (E

T24)

, dai

ly r

efer

ence

eva

pora

tion

(E

Tr2

4),

daily

pot

entia

l eva

pora

tion

(ET

p24)

, and

eva

pora

tion

over

a p

erio

d (E

T_pe

r).

Dat

e S

ite

Var

iab

le

Un

it

SE

BA

L

Val

idat

ion

dat

a In

pu

t d

ata

Max

M

in

Mea

n

Std

ev

Ave

rage

D

ate

24/0

6/20

07

24/0

6/20

07

Wat

er b

ody

Rn_

i W

m-2

39

1.52

36

1.75

37

3.24

4.

13

210.

83

Tim

e (G

MT

) 07

:44:

38

24/0

6/20

07

Wat

er b

ody

G_i

W

m-2

19

5.76

18

0.88

18

6.62

2.

07

53.8

5 T

a_i (

oC)

16.5

4

24/0

6/20

07

Wat

er b

ody

H_i

W

m-2

-1

.59

-15.

12

-11.

50

2.93

7.

64

RH

_i (

%)

28.5

8

24/0

6/20

07

Wat

er b

ody

LE

_i

Wm

-2

209.

72

182.

46

198.

12

4.80

14

9.34

u_

i (m

/s)

2.56

24/0

6/20

07

Wat

er b

ody

EF

_i

Uni

tles

s 1.

08

1.01

1.

06

0.02

0.

95

Rs_

i (W

m-2

) 41

4.80

24/0

6/20

07

Wat

er b

ody

ET

24

mm

/d

4.79

4.

10

4.40

0.

10

2.34

T

a_24

(oC

) 16

.87

24/0

6/20

07

Wat

er b

ody

ET

r24

mm

/d

3.50

3.

40

3.40

0.

01

N/A

R

H_2

4 (%

) 25

.01

24/0

6/20

07

Wat

er b

ody

ET

p24

mm

/d

4.79

4.

10

4.40

0.

10

N/A

u_

24 (

m/s

) 2.

32

24/0

6/20

07

Wat

er b

ody_

per

ET

_per

m

m

13.7

4 12

.52

12.5

6 0.

14

15.2

8 R

s_24

(W

m-2

) N

/A

C

ount

13

6.00

A M

eth

od

olo

gy

fo

r N

ea

r-R

ea

l T

ime

Sp

ati

al

Est

ima

tio

n o

f E

va

po

rati

on

page

49

Tab

le 1

1 C

ompo

nent

s of

the

ener

gy b

alan

ce a

nd e

vapo

ratio

n fo

r th

e S

t Luc

ia s

ites:

sw

amp

fore

st, g

rass

land

and

sed

ges,

as

estim

ated

usi

ng th

e S

EB

AL

, SE

BS

and

the

VIT

T m

odel

(s),

and

thos

e m

easu

red.

Min

imum

(M

in),

Max

imum

(M

ax),

Mea

n (M

ean)

and

Sta

ndar

d de

viat

ions

(S

tdev

) va

lues

are

sho

wn.

The

var

iabl

es s

how

n in

clud

e: I

nsta

ntan

eous

and

24

hr a

vera

ge n

et r

adia

tion

(Rn_

i, R

n_24

), I

nsta

ntan

eous

and

24

hour

ave

rage

soi

l hea

t flu

x (G

_i, G

_24)

, Ins

tant

aneo

us a

nd 2

4 ho

ur

aver

age

sens

ible

hea

t flu

x (H

_i, H

_24)

, Ins

tant

aneo

us e

vapo

rativ

e fr

actio

n (E

F_i

), d

aily

eva

pora

tion

(ET2

4), d

aily

ref

eren

ce e

vapo

rati

on (

ET

r24)

, dai

ly p

oten

tial

ev

apor

atio

n (E

Tp2

4), e

vapo

ratio

n ov

er a

per

iod

(ET

_per

), a

lbed

o (a

lb)

and

leaf

are

a in

dex

(LA

I).

**In

stan

tane

ous

refe

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04

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0.

06

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4 (oC

) 20

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ges

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4 W

m-2

54

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%)

69.90

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ges

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tles

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(m/s)

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0

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ges

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24

mm

/d

0.00

2.

82

1.69

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2.34

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98

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0.

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0.

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4 (W

m-2)

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r24

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/d

3.74

3.

82

3.79

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ount

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dges

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312.

95

309.

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58

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6 61

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1.96

82

.26

87.1

7 84

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1.49

64

.60

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46

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45

1.43

9.

22

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52

218.

92

205.

82

10.4

9

18

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0.

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0.00

0.

03

0.18

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08

0.05

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136.

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06

0.07

0.

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0.00

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ss

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m-2

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2.54

14

1.98

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page

50

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a

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ax

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n

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n

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n

Std

ev

Ave

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D

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14/0

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14/0

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ss

ET

24

mm

/d

0.00

0.

00

0.00

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0.10

0.

60

0.28

0.

16

0.49

0.

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0.16

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0.50

14/0

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ss

ET

r24

mm

/d

3.76

3.

84

3.79

0.

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14/0

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ET

p24

mm

/d

0.71

1.

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0.97

0.

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14/0

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alb

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0.11

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0.15

0.

30

0.22

0.

05

0.67

14/0

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08

For

est

Rn_

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30

8.15

31

6.05

31

2.06

3.

25

367.

97

373.

35

370.

74

2.07

28

7.00

14/0

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For

est

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m-2

17

.61

21.4

9 19

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1.15

63

.87

70.0

6 66

.81

2.21

-0

.58

14/0

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08

For

est

H_i

W

m-2

13

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16

3.78

14

5.13

10

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-3.1

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1.57

3.

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47.0

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0.55

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1.01

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-2

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139.

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m-2

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14/0

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For

est

H_2

4 W

m-2

49

.20

69.7

9 57

.65

7.41

14/0

8/20

08

For

est

EF

_24

Uni

tles

s 0.

50

0.65

0.

58

0.05

14/0

8/20

08

For

est

ET

24

mm

/d

2.46

3.

15

2.83

0.

25

3.26

3.

37

3.31

0.

04

3.10

0.

00

1.49

0.

07

2.50

14/0

8/20

08

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est

ET

r24

mm

/d

3.80

3.

81

3.80

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00

14/0

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ET

p24

mm

/d

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3.

95

3.77

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14

14/0

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alb

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s 0.

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0.10

0.

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0.00

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est

LA

I U

nitl

ess

1.67

2.

09

1.88

0.

15

4.69


page 51

The way in which the sensible heat flux density (H) is estimated in SEBAL, SEBS and METRIC differs and is a crucial component of these models. The differences are described in Chapter 2. The accuracy of estimates of H and the evaporative fraction (EF) measured with the different models and for the different sites, varied greatly. At the Acacia site both METRIC and SEBAL were unable to simulate H accurately for most of the simulation period. The measured H was less than the H modelled using SEBAL by as much as 65%, and by as much as 64% using METRIC (December run only) (Fig. 8, Table 8). All the SEBAL estimates of H exceeded the field measurements, sometimes significantly, except for the May simulation. In contrast, the instantaneous H estimated using SEBS compared favourably with the measured H, to within 3%. Despite the big differences in the measured and simulated H at the Acacia site, the evaporative fraction (EF) for the five simulation days agreed reasonably well with those estimated from the field data (between 5 and 31%) (Fig. 8, Table 8). The METRIC estimate of EF for December was within 27% of the measured EF. Both the SEBAL and METRIC EF estimates were however lower than those measured. The good agreement between the measured H and simulated H using SEBS (within 3%), resulted in a good agreement of the EFs measured and estimated (to within 3%) (Fig. 8, Table 8). At the Spekboom site (Kirkwood), the SEBAL and SEBS estimates of H were both lower than the measured estimates H (by as much as 32 and 61% respectively). Similarly H was also underestimated using SEBS for the degraded veld (by 46%). SEBAL simulated H more accurately, to within 4% of the measured H for the degraded veld. Subsequently, both SEBS and SEBAL failed to accurately simulate the evaporative fraction. The EFs simulated using SEBS were greater than those measured, e.g. by up to 75% for the Spekboom site (Fig. 8, Table 8). Estimated Hs using SEBAL for the St Lucia sites, were consistently higher than the fluxes measured (by 79, 62 and 1% for the forest, grass and sedges sites respectively) (Figures 13 to 15). The ratio of sensible heat flux to net radiation (H/Rn) (based on measurements) was 0.12, 0.65 and 0.58 for the forest, grass and sedges sites respectively, whereas from the SEBAL estimates these ratios were 0.46, 1.74 and 0.61 respectively. The high H/Rn fraction estimated from the SEBAL data and exceeding 1 for the grass site might suggest advective conditions. Using the H and Rn data simulated with SEBS the H/Rn ratios were 0.004, 0.66 and 0.13 for the forest, grass and sedges sites respectively. This implies high evaporation rates for both the forest and sedges sites. The actual EF values for the forest and sedges sites confirm these high evaporation rates (EF > 0.7 and > 0.42 respectively) (Tab. 11). In contrast, the EF for the grassland site was low (< 0.11) and therefore imply low evaporation rates. Lastly, H estimates for an open water body are generally low since evaporation is the dominant process under these conditions. This is shown in Figure 11 and Table 10. The H/Rn calculated from the measurements and the SEBAL estimates were similar (10 and 13% respectively). The SEBAL estimate of H was within 77% of that measured, with the measured estimate of H exceeding the simulated H. As a result, the measured and modelled EFs agreed to within 7%.


page 52

4.2 Daily evaporation and evaporation over longer periods

As illustrated in Fig. 7, the spatial estimates of the the energy balances at the time of the satellite overpass can be converted into spatial estimates of evaporation at a daily time step. Spatially averaged evaporation estimates from the Seven Oaks Acacia site using the SEBAL, SEBS, METRIC and VITT models are compared with field measurements in the section below (Fig. 15). The SEBAL model only was used to estimate open water evaporation from Midmar Dam, and these spatially averaged evaporation estimates are also compared to the measured evaporation (Fig. 15). The SEBAL, SEBS and VITT models were also used to estimate the evaporation from both the St Lucia and Kirkwood sites. The evaporation estimates are compared to field measurements in Fig. 15. Since the VITT model is strongly based on vegetative cover, this model was not applied to the Midmar site. Also, since longer term field data sets were available for the Seven Oaks Acacia, Kirkwood and Midmar sites, long-term estimates of evaporation were calculated. For the Acacia site, monthly estimates of evaporation was calculated and for the Kirkwood and Midmar sites, week long and 6-day evaporation estimates were calculated respectively.

a b c d

Figure 15 The spatial distribution of the daily evaporation estimated using e.g. SEBAL for the (a)

Acacia site on 9 Sep 2006, (b) for the Midmar site on 24 Jul 2007, (c) for the St Lucia sites on 14 Aug 2008 and (c) the Kirkwood sites on 27 Sep 2008. The dotted lines show the outline of the Areas of interest at each site (AOIs). Colour range – darker colours represent lower evaporation rates and lighter colours represent higher evaporation rates.

Acacia

Midmar

Sedges

Grassland Forest

Degraded

Spekboom


page 53

The daily spatial evaporation estimates using SEBAL for the four study areas (with the red dotted lines showing the various AOIs), are shown in Fig. 15. Within some of these AOIs a variation in colour, representing different daily evaporation rates are clear – an example is the evaporation at the Acacia site in Fig. 15 a. Lighter colours represent higher evaporation rates, whereas darker colours represent lower evaporation rates. For example, Fig. 15 d clearly shows differences in the evaporation rates between the swamp forest, grassland and sedges sites at St Lucia (with the evaporation rates from the forest site exceeding the evaporation rates of the sedges site and that of the grassland site (also shown in Table 11). In Fig. 16, the daily evaporation (average for AOI) for Midmar Dam is shown – both for 1 day and a 6-day period. For this site, the evaporation estimate using SEBAL was done for a date prior to the field measurements. The daily average evaporation estimated using SEBAL (4.4 mm/d) exceeded the average daily evaporation measured using the eddy covariance system (2.34 mm/d) (Fig. 16). The daily evaporation simulated was therefore 43% less than the daily evaporation measured. It should be noted that the evaporation measurements took place amid exceptionally cold weather. The very cold conditions experienced during the measurement period, compared to the time of evaporation simulations could have contributed significantly to the higher evaporation rates simulated. The daily average air temperature on the date of evaporation simulation was 16.8oC and the average daily air temperature over the simulation period was 11.8oC. The accuracy of the simulated evaporation from Midmar Dam using SEBAL improved over a longer period. Over a six day period, the SEBAL evaporation estimate was 18% (or 2.7 mm) less than the measured evaporation (Fig. 16, Table 10). The SEBAL evaporation estimates also compared favourably to the Symon’s pan evaporation estimates. The daily evaporation estimate from these pans were 2.5 mm/d and the total evaporation over the six day period was 15 mm.

Midmar

4.40

12.56

2.34

15.28

0

2

4

6

8

10

12

14

16

18

Water body Water body_per

Eva

pora

tion (m

m/

Sebal

validation

Evaporation: Midmar dam

Validation, 15.28

SEBAL, 12.56

0

2

46

8

10

1214

16

18

175 177 179 181 183 185 187 189 191 193 188-193

Eva

pora

tion m

m

Validation

SEBAL

Figure 16 Daily evaporation rates over a 10 day period (DOY 181-193) measured using an eddy

covariance system, and estimated using SEBAL (SEBAL) for DOY 175, and also calculated over a six day period (DOY 188 TO 193) (Validation). The total evaporation summed over the six day period (measured and estimated using SEBAL) is also compared. The evaporation totals are given in brackets (15.28 mm measured and 12.72 mm simulated using SEBAL).

Daily evaporation estimated using the SEBAL and VITT models for the degraded veld site compared favourably to the measured evaporation (within 0.53 mm/d) (Table 9, Fig. 20). However, evaporation estimated using the SEBS model exceeded the measured evaporation by up to 1.89 mm/d (Fig. 20).


page 54

None of the models (SEBAL, SEBS, VITT) could however simulate the evaporation from the Spekboom thicket within an acceptable degree of accuracy. The measured evaporation (0.79 mm/d) was significantly less than the simulated evaporation (1.82, 3.21, 2.99 and 2.89 mm/d for the SEBAL, SEBS, VITT1 and VITT2 models respectively). Spekboom thicket contains the Spekboom plant, which is a CAM (Crassulacean acid metabolism) plant (http://en.wikipedia.org/wiki/CAM_photosynthesis), and therefore has the ability to shut stomata during the day if stress conditions occur (and also subsequently open stomata during the night time). The differences in evaporation could potentially be explained by the evaporative fraction. The evaporative fraction (which is the ratio between the latent heat flux density and the available energy) is assumed to be constant during the day by SEBAL, but for Spekboom this might not be the case since EF might decline under stress conditions. The evaporative fraction estimated using SEBAL data for the Spekboom thicket exceeded the evaporative fraction calculated from field data (Fig. 9, Table 9), consequently the higher evaporation rates estimated for the Spekboom thicket directly reflect the higher estimated EF values. SEBAL currently takes advective conditions into account, but not variable EFs, as a result it would compensate somewhat to stress conditions experienced by plants. Evaporation from the Spekboom thicket estimated using SEBAL over a longer period (7 days), did not agree well with the measured evaporation (Fig. 17). Over a 7 day period, evaporation estimates using SEBAL for both degraded and Spekboom sites, were 0.18 and 15.25 mm/d respectively (Fig. 18). The degraded veld measurements of evaporation over the same period was 3.35 mm and exceeded the SEBAL estimates. The Spekboom evaporation measurements (6.69 mm) were significantly different to those modelled – 57% lower.

Evaporation: Spekboom veld

0

0.5

1

1.5

2

2.5

3

3.5

268 269 270 271 272 273 274

Eva

pora

tion m

m

Validation

SEBAL

VITT1

VITT2

SEBS

Evaporation: Degraded land

0

0.5

1

1.5

2

2.5

3

268 269 270 271 272 273 274

Eva

pora

tion m

m

Validation

SEBAL

VITT1

VITT2

SEBS

Evaporation: Spekboom veld

0.47

1.82

2.99 2.893.21

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

27-Sep

Eva

pora

tion m

m

Validation

SEBAL

VITT1

VITT2

SEBS

Evaporation: Degraded land

0.40

0.00

0.56

1.60

2.42

0.00

0.50

1.00

1.50

2.00

2.50

3.00

27-Sep

Eva

pora

tion m

m

Validation

SEBAL

VITT1

VITT2

SEBS

Sites val Sebal _ave

Sebal _std

Sebal -val

Sebs _ave

Sebs _stdev

Sebs -val

vitt1 _ave

vitt1 _std

vitt1 -val

vitt2 _ave

vitt2 _std

vitt2 -val

Spekboom 0.79 1.82 0.15 1.03 3.21 0.06 2.42 2.99 0.60 2.20 2.89 0.68 2.10 Degraded 0.53 0.00 0.00 -0.53 2.42 0.19 1.89 0.56 0.09 0.03 1.60 0.03 1.07

Figure 17 Total evaporation (mm/d) estimated at the Spekboom thicket and the Degraded veld sites at

Kirkwood. Val refers to the validation data, _ave refers to the average value of the specific parameter, _std to the standard deviation, and -val refers to the difference between the model estimated evaporation and the measured evaporation. The top graphs show the evaporation in relation to a seven day long evaporation time series.


page 55

Kirkwood

0.18

15.25

3.35

6.69

0

2

4

6

8

10

12

14

16

18

Degraded Spekboom

Eva

pora

tion (m

m

sebal per

validation per

Figure 18 Total evaporation estimates for the Spekboom thicket and the degraded veld sites at

Kirkwood over a period of 7 days using the SEBAL model, and calculated from eddy covariance field data.

At the St Lucia sites, despite the fact that the evaporation was calculated for the day after the measurements ended, the average daily evaporation measured compared favourably to the modelled evaporation using SEBAL (Fig. 19, Table 11), despite differences in the evaporative fractions (Figures 12-13, Table 11). The measured evaporation and simulated evaporation using SEBAL differed by a maximum of 0.5 mm/d (or up to 16% for the forest and sedges sites). Evaporation estimates using SEBS similarly compared favourably (within 0.81 mm/d or 32%) to the measured evaporation for the forest and sedges sites (Fig. 19). There was a big difference between the measured and simulated evaporation at the grassland site (up to 44%), however the actual evaporation estimates were very low (0.5 mm/d) and within the range of measurement error.

St Lucia

2.00

0.50

2.50

1.69

0.00

2.832.67

0.28

3.31

2.90

0.49

3.10

1.44

0.16

1.49

0

0.5

1

1.5

2

2.5

3

3.5

Sedges Grass Forest

Eva

pora

tion (m

m

validation

sebal

sebs

vitt1

vitt2

Sites Val Sebal _ave

Sebal _std

Sebal -val

Sebs _ave

Sebs _stdev

Sebs -val

vitt1 _ave

vitt1 _std

vitt1 -val

vitt2 _ave

vitt2 _std

vitt2 -val

forest 2.50 2.83 0.25 0.33 3.31 0.04 0.81 3.10 0.00 0.60 1.49 0.07 -1.01 grass 0.50 0.00 0.00 -0.50 0.28 0.16 -0.22 0.49 0.11 -0.01 0.16 0.03 -0.34

sedges 2.00 1.69 0.70 -0.31 2.67 0.12 0.67 2.90 0.12 0.90 1.44 0.11 -0.56

Figure 19 Total evaporation (mm/d) as estimated for the forest, grassland and sedges sites at St Lucia.

Val refers to the validation data, _ave refers to the average value of the specific parameter, _std to the standard deviation, and -val refers to the difference between the model estimated evaporation and the measured evaporation. The top graphs show the evaporation in relation to a seven day long evaporation time series.


page 56

The evaporation estimates from both VITT models (both VITT1 and VITT2) also compared well with the measured evaporation for the grassland site (Fig. 19), but less favourably for the forest and sedges sites, with evaporation differences of up to 45% estimated. The daily evaporation modelled using SEBAL, METRIC and the VITT models also differed from the measured evaporation at the Acacia site for the five different days selected over a period of 11 months (Fig. 20, Table 8). The SEBAL evaporation estimates, with the exception of the September simulation, were generally lower than the measured evaporation (up to 80% in winter (June simulation) and 15% in summer (December simulation)). Nevertheless, there was a good agreement of the energy balance components and evaporative fractions that was measured and simulated. Similarly, the METRIC daily evaporation estimate (for September) for the Acacia site was less than the measured evaporation (by 49% or 1.41 mm/d). In contrast to the lower SEBAL and METRIC evaporation estimates, the SEBS daily evaporation estimate for December exceeded the measured evaporation slightly (by 9% or 0.48 mm/d). The five daily SEBAL estimates of evaporation for the Acacia site, plotted along the 11 month time series of evaporation estimates from a scintillometer, show a favourable comparison (Fig. 21). All evaporation estimates of the VITT model for the Acacia site were also lower than the measured – often significantly (by up to 2.48 mm/d in May). However, the VITT evaporation estimate for June was not different from the evaporation measured (Fig. 20, Table 8).

Acacia Evaporation

0.0

1.0

2.0

3.0

4.0

5.0

6.0

Date

Evapora

tion m

m

Validation

sebal

metric_sa

sebs

vitt1

vitt2

Validation 2.87 5.26 4.64 2.48 2.21

sebal 3.24 4.59 3.70 1.48 0.42

metric_sa 1.46

sebs 5.87

vitt1 0.78 3.10 4.16 2.20

vitt2 1.07 0.28 1.92 1.24 2.20

252 348 79 143 175

Figure 20 Daily total evaporation at the Acacia mearnsii site measured using the LAS system

(Validation) and estimated using the SEBAL, SEBS, METRIC, and the VITT models. Evaporation modelling was performed for five days (9 Sept 06, 14 Dec 06, 20 March 07, 23 May 07 and 24 June 07).


page 57

Acacia Total evaporation

2.87

5.39

2.21

4.64

2.48

0

1

2

3

4

5

6

7

22-Aug

12-Sep

03-Oct

24-Oct

14-Nov

05-Dec

26-Dec

16-Jan

06-Feb

27-Feb

20-Mar

10-Apr

01-May

22-May

12-Jun

Date (2006-7)

Tota

l ev

apora

tion (m

m

Validation

SEBAL

Figure 21 Daily total evaporation measured at the Acacia mearnsii site using the LAS for the period

22 August 2006 to 30 June 2007, and simulated using SEBAL for five days (9 Sept 2006, 14 Dec 2006, 20 March 2007, 23 May 2007 and 24 June 2007)

The daily evaporation rates were further up-scaled to period estimates of evaporation for the Acacia mearnsii site (Fig. 22). Monthly evaporation was estimated using SEBAL from the first four images used, whereas evaporation from the fifth image was up-scaled to a 2 week estimate of evaporation (Fig. 22, Tables 5 and 8). Evaporation estimates up-scaled using SEBAL were consistently lower than the measured, by up to 1.86 mm/d or 44% (excluding the June image). These lower rates reflect the lower daily evaporation estimates shown in Figure 20. A good agreement is however not necessarily to be expected here, since data from a single image was used to estimate evaporation over 31 days, assuming that the EF will remain constant over this entire period, which will generally not be the case. These differences in evaporation strongly suggest that when evaporation is up-scaled to longer periods, more than one image per month should be used to estimate monthly evaporation, and it is expected that the use of more than one image, will significantly improve monthly evaporation estimates.

Acacia - Period evaporation

76.48

132.28

109.06

73.17

26.33

45.34

74.74

93.23

2.30

69.44

0

20

40

60

80

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140

Sep-06

Oct-06

Nov-06

Dec-06

Jan-07

Feb-07

Mar-07

Apr-07

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Jun-07

Date

Evapora

tion m

Validation

SEBAL

Figure 22 Period evaporation estimated for Acacia mearnsii, where four Landsat images were used to

upscale instantaneous data to monthly estimate of evaporation. Data from the fifth image was upscaled to a two week estimate of evaporation (see Table 5 for more information)


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Chapter 5: Conclusions The accurate estimation of evaporation still remains a challenge to researchers in the field of micrometeorology, hydrology as well as for water resources managers and planners. Frequently-used methodologies, both locally and internationally, still generally estimate evaporation at field scale. However, it is now recognised internationally that remote-sensing based models hold great potential for the spatial estimation of evaporation at both field and catchment scale. In this study, the accuracy of the components of the energy balance including evaporation estimated using the SEBAL, METRIC, SEBS and VITT models were evaluated for different surfaces including an open water surface, forestry plantation, wetlands and native vegetation under semi-arid environments varying in vegetative cover. Most of the models used in this study (SEBAL, METRIC and SEBS) quite easily simulated net radiation accurately, but the estimation of soil heat flux and heat storage of a water body is more complex and variable. Similarly, the estimation of sensible heat flux density (H) at the time of satellite overpass for various land uses and with different models remains a complex process. Accurate estimates of simulated H was not always achieved compared to the measured H. Evaporative fraction (EF) estimates however, were simulated accurately in many cases, and for such instances, the daily evaporation rates measured compared favourably to the simulated evaporation rates. The VITT model generally yielded the least accurate evaporation estimates. This could be attributed to the inability to locate the four reference conditions within the satelite image, as required by the VITT model. The accuracy of evaporation estimates were occasionally improved over longer simulation periods, as was the case for the Midmar site. However, the period of extrapolation should not be too long since a constant evaporative fraction was assumed. The opposite can also be the case, as was shown at the Acacia site where longer term estimates of evaporation for SEBAL differed greatly from the measured evaporation. These increased differences in evaporation over longer periods, were likely the results of the constant EFs assumed in the modelling. In this study it was shown that remote sensing data can be used in evaporation estimation methods to extend point measurement of evaporation, to much larger areas, even areas where measured meteorological data may be sparse. The literature review has shown that a great variety of models exist that can be used for the estimation of evaporation. The models based on the simplified energy balance hold great potential. A number of these models are used operationally for water resources management and planning. It would therefore be possible to establish a remote sensing unit in South Africa that will assist the estimation of spatially distributed evaporation, if supported by some of the model developers. In recent years the accuracy of these remote sensing models and the evaporation estimates produced have been reviewed by numerous researchers. A number of papers have been


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published subsequently, where evaporation estimates from these models were compared with evaporation measured using different methods. These research papers to a great extent reflect what was found in this study. Below of number of these studies are discussed in brief.

Timmermans et al. (2007) compared the outputs from SEBAL and TSEB with field data. Data from the Southern Great Plains experiment of 1997 (SGP '97) with an area covering sub-humid grassland near EL Reno, Oklahoma was used as well as data collected as part of the Monsoon '90 field experiment, conducted in a semi-arid rangeland area within the USDA-ARS Walnut Gulch Experimental Watershed near Tucson, Arizona. In general, the fluxes simulated using both models compared reasonably well with the fluxes measured. However, when comparing the model outputs spatially relatively large differences in the sensible heat flux densities were observed. Areas under soil and sparsely vegetated areas yielded the largest differences and for these conditions the TSEB fluxes showed a better agreement with the field based estimates. Timmermanss et al. (2007) suggest that good agreement of the modelled fluxes with few field based measurements does not guarantee that the models compute consistent and/or reliable fluxes across a landscape.

Timmermans et al. (2005) also compared flux estimates from SEBAL, TSEB and SEBS with

field estimates. Data from the SPARC2004 site in Barrax, Spain was used. Comparisons of net irradiances and soil heat flux densities estimated using the three models compared favourably with differences being negligible. Sensible heat flux densities estimated using the TSEB model, however, differed from those estimated using the single source models SEBAL and SEBS. For more arid areas, the sensible heat flux densities estimated using TSEB exceeded those estimated using SEBAL.

Bashir et al. (2006) compared evaporation from irrigated sorghum fields estimated using

SEBAL and that estimated using field based methods for the Gezira scheme, Sudan. The seasonal ET estimated for the irrigated sorghum using SEBAL was within 5% of the measured ET in the field over a period of 92 days.

In Su (undated), fluxes estimated using SEBS are compared to tower flux measurements, for

three different sites: cotton, shrub and grass. At the cotton site, the uncertainties of the estimated flux values (here expressed as the ratio of the root mean squared difference between the estimated and the observed values) were all within 20% of the mean observed values, except the sensible heat flux which had a 30% uncertainty. At the shrub and the grass sites, the results from SEBS and four different algorithms including the two-source model of Norman et al. (1995) were also compared. The accuracy of the SEBS estimates were compared to the best case analyzed by Zhan et al. (1996) who only reported the statistics for the sensible heat fluxes estimated using the four different algorithms. Su (undated) concluded that SEBS can be applied to different sites and different atmospheric stability situations while maintaining the same parameterizations. However, this needs to be proven, as this is important and critical when application to a large scale is desired where no sufficiently detailed information on the site characteristics or the local atmospheric stability regime is available.

The Surface Energy Balance Algorithm for Land (SEBAL) model has been applied in

different countries and to estimate evaporation from various land uses. On the WaterWatch website (http://www.sebal.nl/waterwatch/pagina/pagina.php?page=7031&kl=1) Bastiaanssen (undated) has listed studies where the accuracy of SEBAL outputs (aerial patterns of evaporation determined from remote sensing) was assessed against independently collected


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field measurements. Field measurements were obtained from lysimeters, scintillometers, Bowen ratio energy balance systems as well as eddy covariance systems.

Bastiaanssen et al. (1998) compared surface flux data obtained using SEBAL with data

available from the large-scale field experiments EFEDA (Spain), HAPEX-Sahel (Niger) and HEIFE (China). In 85% of the cases where field scale surface flux ratios were compared with SEBAL-based surface flux ratios, the differences were within the range of instrumental inaccuracies. Without any calibration procedure, the root mean square error of the evaporative fraction Λ (latent heat flux/net available radiation) for footprints of a few hundred meters varied from ΛRMSE=0.10 to 0.20. Bastiaanssen et al. (1998) found that when aggregating several footprints to a length scale of a few kilometers, the overall error in the estimation of the outputs was reduced to five percent. For the EFEDA experiment, fluxes measured by aircraft were used to study the accuracy of remote sensed watershed fluxes (1 000 000 ha). The overall difference in evaporative fraction (sensible to sensible plus latent heat flux density) was negligible. Bastiaanssen et al. (1998) found that for the HAPEX-Sahel campaign, observed flux differences were larger (15%) than estimated using SEBAL, which could be attributed to the rapid moisture depletion of the coarse textured soils between the moment of image acquisition (18 September 1992) and the moment of in situ flux analysis (17 September 1992). For HEIFE, the average difference in SEBAL estimated and ground verified surface fluxes was 23 W m−2 and considering that surface fluxes were not used for calibration, was encouraging. Furthermore, SEBAL estimates of evaporation from the sub-sea level Qattara Depression in Egypt (2 000 000 ha) were consistent with the numerically predicted discharge from the groundwater system. In Egypt’s Nile Delta, the evaporation from a distributed field scale water balance model at a 700 000 ha irrigated agricultural region led to difference of 5% with daily evaporative fluxes obtained from SEBAL. Bastiaanssen et al. (1998) concluded that for all the study areas, the errors in the fluxes estimated in these arid zones will be averaged out, if a larger number of pixels are considered.

Sanchez et al. (2008) compared surface energy fluxes calculated over an extensive area with a

large variety of land uses, with ground measurements (lysimetry). For the remote sensing calculations two Landsat7-ETM and one Landsat 5-TM images were used, together with a simplified version of a two-source energy balance (STSEB) method. When comparing daily evaporation data, an accuracy of close to 1 mm/d was obtained. These errors are in agreement with the uncertainties reported in the literature.

In Marx et al. (2008) three methods for estimating instantaneous sensible heat flux are

compared over a Savannah canopy in West Africa. Sensible heat fluxes were estimated from SEBAL, large-aperture scintillometer and high resolution meso-scale meteorological simulations MM5. LAS measurements were performed at two sites. The satellite-derived sensible heat flux was based on seven NOAA-16 AVHRR images acquired for a 2-week period in December 2001 (dry season). Total computed relative uncertainty in H was 15% for the Tamale test site and 20% for the Ejura site. Uncertainties in instantaneous evaporation were however much smaller than the uncertainties of H. An uncertainty analysis due to input data was performed which showed relative uncertainty of 8% for the Tamale site and 7% for Ejura. It was found that satellite derived net radiation (Rn) was underestimated in comparison to ground measurements which finally caused an underestimation of H. Satellite estimates of H using spatially interpolated ground based measurements of net radiation showed good agreement with the LAS data. MM5-computed latent heat flux showed very low values for the entire region. This caused a serious relative MM5-overestimation of sensible heat flux in comparison to LAS and satellite derived estimates.


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Kite and Droogers (200) published data from a study where estimates of ET using satellites, hydrological models and field data were compared for a site in Gediz river in western Turkey. The aim of the study was to determine whether if newly developed techniques provide data which are comparable to data from more traditional methods that rely either on field measurements or methods where ET and T are calculated as the residual of the water balance. Most of the comparisons were based on data for two days of satellite overpasses, and in some instances longer data sets and data from larger areas were used. Three groups of methods were compared: (1) methods that use field measured meteorological data (FAO-24, FAO-56 and scintillometry), (2) hydrological models at both field and catchment scale, where E and T is calculated as part of the hydrological balance and (3) methods using remotely sensed data including satellite derived feedback mechanisms, biophysical processes and energy balance techniques. Kite and Droogers (2000) observed a wide range in the estimated ET with no patterns evident amongst the various methods. They concluded that it is difficult to judge which method produced the most accurate estimate of ET. They further concluded that the assumption that field methods are the most reliable is difficult to justify as there were differences between the different field methods.


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Chapter 6: Recommendations for an Operational Remote Sensing Unit

In the project “Development of appropriate techniques for evaporation monitoring”, a number of remote-sensing based models currently used internationally to estimate total evaporation spatially, were evaluated. For these models to be used operationally in support of water resources management, certain human resource capabilities and infrastructure need to be in place. The following section is aimed at highlighting some of these required capabilities and infrastructural and institutional arrangements.

6.1 Human Resources (the team and the users)

The use of remote sensing technologies for total evaporation (ET) estimation requires the establishment of a Remote Sensing (RS) unit consisting of people familiar with a broad range of aspects related to image processing. Such a RS unit will be required to collect appropriate images, make various corrections to these images, apply or run dedicated models such as ET algorithms and also market the products (outcomes) for new and challenging applications. It is important to note that the RS unit described in this context will require specialized knowledge of surface energy balances and evaporation from various surfaces. Therefore, the required human resources described here do not apply to general remote sensing work. Figure 23 summarizes some of the human resource requirements and procedures needed for spatial ET modelling. The success of any modelling activity depends on the knowledge and commitment of the modeller. In the case of spatial ET modelling, the modeller should have a good background and familiarity with surface energy balance processes. While it is necessary to have one or two senior image processors in a RS unit, routine image processing could be done by a junior image processor (Table 12). Experience from other countries where such RS units have been established showed that the majority of time spent prior to the actual ET modelling will go towards data preparation. For the actual ET modelling, both meteorological and satellite input data are required and the collection and standardization of these datasets takes a considerable amount of time. The size of the RS unit is related to the volume of image processing. Generally the minimum sized (basic) team will consist of (i) a project leader who can communicate with clients and define the technical approach and perform quality control, supported by (ii) a junior image processor who can prepare all data and (iii) a senior image processor that has a background in earth sciences and has the capability to interpret the image results and adapt the bio-physical models, if so required. The human resource requirements for such a RS unit are summarized in Table 12.


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Figure 23 Disciplines and sequence of procedures needed to interpret satellite spectral measurements

for use in practical water management (taken from Bastiaanssen et al. (2000))

Table 12 Minimum human resource requirements for operating a basic remote sensing ET unit

Function Task Background required Junior image

processor Retrieval of raw satellite data; geometric correction, radiometric correction, atmospheric correction, computation of surface reflectance, spectral vegetation indices and surface temperature; retrieval of raw weather station data; extrapolation of point data to weather grids; assistance with ET modelling

B.Sc. or M.Sc. in remote sensing / GIS and/or a training in computer sciences

Senior image processor

Selection of the required images; selection of weather data; ET modelling; modelling of other physical land and water related processes; improvement of models; development of data fusion techniques; comparison with ancillary data; studying literature; website maintenance; data dissemination; provision of training;

M.Sc. or Ph.D. in hydrology, agriculture, geology, geography or other related geosciences with interest in remote sensing

Team leader Daily leadership to the team; weekly assignment of tasks; relationship with clients; doing user needs assessments; presentation of results; public relations; supervision of the image processing, quality control; definition of technical approach; developing new applications; provision of training

M.Sc. or Ph.D. in hydrology, agriculture, geology, geography or other related geosciences with interest in remote sensing and business development


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The Team Leader of the RS unit has the challenging task to acquire a sufficient work load to at least break-even financially. The team leader is therefore required to stay in close contact with potential clients and be informed on the financing and subsidy systems of various potential funders (e.g. Government departments, etc.). In addition ample attention should be given to the development of new and attractive applications using spatial ET data and derivatives thereof. These applications could be related to e.g. the monitoring of irrigation system performance, agricultural production, on-farm management, catchment scale water balances, land use changes and stream flow, and droughts and floods. Feedback from the users of the RS data products is fundamental for improving the models, and satisfying the needs of the user. The users confidence with the technology, and the RS units’ response to the evolving needs of the users must both be addressed continually. Making operational products available will enable users (e.g. farmers, advisors, etc.) to have direct access to these products so they can use it for spatial water management. It is important to develop operational products in close association with users so the demands of the users can be addressed appropriately. Regular feedback sessions with the user community are recommended. If not there is the risk that a very useful product is available, but that few people are aware of its existence.

6.2 Infrastructure requirements (hardware and software)

The RS unit should be equipped with sufficient capacity to retrieve, store, process, and archive all RS data. In terms of hardware, a Local Area Network (LAN) server with a capacity of at least 1 terabyte is required. For example, one MODIS reflectance image has 7 individual bands of 1000 m pixel size, and the file size when stored as signed 16 bit data varies between 70 Mb (Transverse Mercator Projection) to 120 Mb (Geographic Projection). For each 8 days, minimally 5 MODIS products need to be downloaded, such as NDVI, LAI, etc. A single band NDVI file with a pixel size of 250 m will need a memory of 120 Mb for signed 16 bit data. An equivalent quantity of data is to be received from other satellites, and this brings the total capacity of primary RS data to 1.2 GB. After running the various algorithms and models, the number of output data layers will be double the amount of input data layers. For one period of 8 days, a total capacity of 2.5 GB is required. An annual cycle with 45 periods of 8 days will occupy approximately 20 GB. This will be over 0.1 Terabyte per year. Because of the size of the images it is recommended that image processing be done on desktop computers. The satellite input data needs to be transferred from the server to the desktop computer where all image processing takes place. No data processing should be done on the LAN directly. During routine image processing raw RS data is coupled with auxiliary data, which results in the rapid accumulation of the number of data layers. Such temporary data layers are needed to bridge the gap between input and output data. The input and output data will be archived together with the models on the server. Every sub-model will likely have specific coefficients for each application and it is therefore recommended to store models jointly with their input data. That leaves the option to re-compute certain outputs or time series of certain parameters, if necessary. The server is thus the central unit for data retrieval, data storage and for dissemination.


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We estimate that in the end a total capacity of 1 terabyte should be made available for the RS unit. A proper back-up system should be put in place. In addition a strong central server and at least 4 modern desktop computers should be made available for image processing. Computers with a memory of 2 to 4 GB RAM and a hard disk capacity of 120 GB are recommended. The screen size should be minimally 19 inch for a proper display of the images. Nowadays flat screen monitors have sufficient quality for the processing of images. It is suggested to use the Erdas Image software for the processing of the satellite images. Erdas is the world leading software and it has several standard options built in, as well as convenient options for data exchange with ArcGIS. Both ERDAS and ARC software originated from ESRI and this rules out compatibility problems. For instance it is easy to import the raster-data from Erdas into ArcGIS. One of the strong features of Erdas is the model generator. This allows the user to set up a model with graphical interactions. Since modelling is a dominant process – and it needs to be done in a flexible mode because models require adjustments – a user friendly modelling interface is essential. The purchase of floating licenses that can be installed on the server and accessed from various working nodes is recommended. It will be handy (though not essential) to have a central plotter for the printing of large maps and posters.

6.3 Remote sensing and meteorological data

With the human resources and software and hardware infra-structure in place, the most important remaining requirements for an operational RS unit are (1) the remotely sensed images (with thermal band) and (2) meteorological data. For an operational RS unit to run at a weekly (or sub-weekly) time step, the data should be available for download and processing within these time steps. Individual satellites spatial and temporal resolutions will determine its usefulness for operational applications or research applications. Generally satellite data can be downloaded directly from websites. Weather data collected with automatic weather station will also have to be ordered from sources like the SA Weather services or research councils (e.g. ARC), or downloaded directly from websites where data is freely available. Meteorological data, used for the instantaneous ET modelling and for extrapolation of ET to longer time periods (day, week, month, etc.), should also be in a spatial format like the radiances and thermal data from a satellite image. Early in 2008 a decision was made by USGS to provide all Landsat images for Africa free of charge. There is a 35-years record of the earth’s surface which is of great value for land degradation assessment and forest management. The first Landsat satellite, Landsat-1, was launched in 1972. The full archive of historical Landsat-7 data acquired since the launch in 1999 will become available for selection and downloading by the end of September 2008. It is planned to have an automatic processing facility for concurrent Landsat-7 images by February 2009. Any image selected by a user will be processed to a standard product recipe and staged for electronic retrieval. Images can be selected using the USGS Earth Explorer tool at http://earthexplorer.usgs.gov.


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Other useful spatial data for ET modelling and ET interpretation, that can be downloaded freely from the internet include shortwave atmospheric transmissivity and rainfall maps. These will have to be downloaded prior to the ET modelling.

6.4 Auxiliary geographical databases

The interpretation of the spatial ET results can be supported by means of the auxiliary GIS databases. Topographical maps with inventories of roads, canals, infrastructure, and land cover classes, etc., are traditionally used to support water management. Such maps can be complemented further by elevation maps, soil maps, geological maps, aquifer maps, catchment boundaries, climate maps, population density maps, etc. All of these are generally constructed from intensive field surveys and measurements. The digital character of the RS and GIS datasets, and the common coordination system, makes it feasible to couple these. Digital Elevation Models (DEM) describe terrain elevation, terrain slope and terrain aspect. The SRTM-DEM from NASA is a good data base and is available for entire South Africa. The DEM could be used for the delineation of catchments and sub-catchment. These water divides are key for the separation of water flows across the network of streams and rivers. In addition, there are websites available that provide global shape files of streams, rivers, country boundaries, location of urban areas, bridges, etc. These databases have a very general character, and are considered to be useful as an overlay when presenting and interpreting spatial ET data. Figure 1 shows the relationship between the spatial data originating from satellites and GIS data in support of Sustainable water resources planning, management and service delivery in a basin context.


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Appendix 1 Energy balance and evaporation data from a number of different techniques where used in the validation process. A brief description of these validation techniques is given below. Net irradiance and soil heat flux

Net irradiance was generally measured using NR-Lite net radiometers (Model 240-110, Kipp & Zonen). Soil and water temperatures were measured using type E thermocouples, and heat flux in the soil and water were measured with REBS heat flux plates. The sensors were all connected to a CR23X datalogger (Campbell Scientific, Logan, Utah, USA) and measurements were performed every 1.0 s and averages obtained every 2 minutes which were in turn used to calculate 30 min averages for the latent energy flux calculations. Open path Eddy covariance systems

Two types of In Situ Flux open path eddy covariance systems were used for these experiments. The In Situ Flux systems open path eddy covariance system (In Situ Flux systems, Sweden), and an Applied Technologies Inc. (Applied Technologies Inc., USA) eddy covariance system that was later modified into the In Situ Flux systems format. The former system is referred to as the In Situ Flux system and the latter as the Applied Technologies Inc. or ATI system. Both systems are described below. In Situ Flux systems open path eddy covariance system components The In Situ Flux systems eddy covariance system, consists of a number of units that were integrated into a complete ready-to-run system: Gill Solent R3 three dimensional sonic anemometer with inclinometer for remote

levelling of the anemometer. Analogue Signal Input Unit, (SIU) for interfacing the Gill and other sensors. Li-Cor Li-7500 open path gas analyzer was interfaced through the SIU.

A platinum resistance thermometer was included to provide more accurate measurements of the sonic anemometer derived temperatures. The System box was insulated and cooled to offer the optimal environment for the enclosed components. In Situ Flux open path system (Gill R3 Anemometer) The open path flux system GR3-L7500 was a complete system for measurement of momentum, heat, CO2

and H2O fluxes. The sensors were a Gill R3 anemometer and Li-Cor 7500 Analyzer (Li-cor Inc., Lincoln, Nebraska, USA). The system was designed for continuous monitoring in harsh environments and included transient protection, with 12 VDC for safe and flexible power supply. Components inside the system box included AC/DC Converters, Deep Discharge Protection, transient protection and thermostat overheat protection. The box temperature sensor was a Vaisala PTB101B. The Westermo MD-54LV Flux Computer (12 VDC) with display and keyboard included a CD-RW drive and 4xRS232 serial card with an on board microprocessor.


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There were four USB ports and a 20 Gbyte 2.5” auto heated hard drive. An internal battery prevented unplanned power shut downs. The Flux card with on board microprocessor, cooperated with the PC software for control of data processing, system shutdown, system auto start and operating temperatures. Data were copied once a day during the night. Two mobile USB drives were used for downloading the processed and raw data from the flux system computer. EcoFlux software package: The EcoFlux software handled the collection and storage of raw data, calculation and storage of mean fluxes, variances, co-variances, wind direction and wind speed, stability and friction velocity. The software made all the necessary corrections, filtering and co-ordinate rotations that were needed for accurate measurements. The software included long term rotation angles and average tilt angle for every wind direction (planar fit). Software included a view of instantaneous values and graphs. In Situ Flux open path system (Applied Technologies Inc. anemometer) The Applied Technologies Inc. eddy covariance system was identical to the In Situ Flux system described above with the exception that an Applied Technologies Inc. 3-D SATI-3VX sonic anemometer replaced the Gill R3 and a model PAD-802 data packer replaced the SIU. Scintec boundary layer scintillometer

Scintec AG is the supplier and manufacturer of the BLS900 Boundary Layer Scintillometer used in this study. This sophisticated scintillometer system, evaluates the atmospheric scintillation caused by refractive index fluctuations, which is linked to sensible heat flux density. The BLS900 system can be operated over distances ranging from 0.5 to 5 km. The BLS900 consists of (a) an optical transmitter, (b) an optical receiver, (c) an SPU and (d) data evaluation software (BLSRUN). Both the optical transmitter and receiver units are equipped with positioning devices. The signal processing unit is equipped with an integrated datalogger, and the evaluation software runs in Microsoft Windows based operating systems. The BLS900 transmitter sensor emits radiation through 924 light emitting diodes (LED) on two disks. The LEDs can emit radiation in 4 different pulse repetition rates (1, 5, 25 and 125 Hz). A pulse rate of 125 Hz provides maximum accuracy and transverse wind speed measurement capability. A pulse rate of 1 Hz results in a very low power consumption. The two-disk configuration of the BLS900 allows for a correction of absorption fluctuations which is performed in the BLSRun software and increases the accuracy of the measurement. Although the two-disk configuration could provide crosswind measurement capability, this feature was not used in this project. In the BLS900 receiver radiation is collimated by a lens onto two photodiodes. The lens is convex and made of glass. One of the photodiodes is used for sensing the turbulence-induced fluctuations, and the auxiliary detector is used as an alignment aid. For alignment purposes both the transmitter and receiver sensors were mounted onto 3 axis-positioning devices and the receiver sensor were also equipped with a mounted telescope. The receiver electronics pre-


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amplifies and filters the signals. The transmitter and receiver sensors were mounted on standard surveyors’ tripods. The SPU houses two plugged-in cards: (a) a signal processing card that filters, demodulates and digitises the received signals, and (b) a microprocessor card for evaluating and storing the converted data. The microprocessor also handles the communication to a PC via a serial interface. The SPU is also equipped with non-volatile flash memory for storing up to approximately 700 days of measurement data. The BLSRUN software is used in part to configure the system, and also reads the measured data either in real-time, from volatile SPU memory or from the non-volatile SPU/DL storage. BLSRUN requires input on a number of parameters. For the open water field campaign, the path length was set to 2.5 km, the path averaged height of the sensors to 8.99 m and the data averaging period to 10 minutes. More information on the BLS900 scintillometer can be obtained from the Users manual (Scintec, 2006). The spatial estimates of sensible heat flux density obtained with this system were integrated with the net irradiance and soil heat flux density estimates to estimate evaporation as a residual of the simplified energy balance equation. Kipp & Zonen Large aperture scintillometer

Kipp & Zonen is the supplier and manufacturer of the Large aperture Scintillometer (LAS). The data collected with this system was used to validate the energy balance and evaporation data simulated in this study. The BLS900 scintillometer evaluates the atmospheric scintillation caused by refractive index fluctuations, which is linked to sensible heat flux density. The Kipp & Zonen LAS system can be operated over distances ranging from 0.25 to 4.5 km. The Kipp & Zonen LAS consists of a transmitter and receiver sensor each with pan and tilt adjuster for leveling and sighting telescopes for optical alignment. The light beam emitted by the transmitter is at near-infrared wavelength (880 nm) at a frequency of 7 kHz. At this wavelength, the observed scintillations are caused primarily by turbulent temperature fluctuations. The transmitter and receiver sensors use Fresnel lenses with aperture diameters of 0.152 m. The receiver sensor is directly connected to a Campbell Scientific CR23X data logger on which data is stored. Data (demodulated signal and structure parameter of the refractive index) measurements took place at 1 second intervals, which were then averaged at 10 minute intervals and stored on the logger. This data is later integrated with net irradiance and soil heat flux and meteorological data on a computer using the WINLAS software provided by Kipp & Zonen.


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Appendix 2 Two Source Energy Balance (TSEB) model

The Two Source Energy Balance (TSEB) model uses a two-source approach that separates the soil and vegetation component, aiming at a more physical description of heterogeneous surfaces when dealing with radiative and aerodynamic properties. The model uses remotely sensed surface temperature as a primary boundary condition for providing estimates of instantaneous fluxes of net radiation (Rn), soil (G), sensible (H) and latent heat (LE) to solve the energy balance equation: The net radiation is estimated from

Rn = RS+RS+ RL+ RL = (1 -) RS + a T4

a - Ta4

1

where Rn is radiation, RS referring to net shortwave and RL is net long wave, with the superscripted arrows indicating incoming and outgoing flux directions. The Greek letters ρ, ε, and σ represent albedo, emissivity and the Stefan–Boltzmann constant, respectively, Ta is air temperature and the subscripts R and a refer to radiative surface and atmospheric level. The TSEB model requires the following input information – maps of kinetic temperature, Normalized Difference Vegetation Index (NDVI), land use, incoming solar and thermal radiation, as well as estimates of air temperature, relative humidity, air pressure and wind speed to solve the energy balance equation. Fractional vegetation cover is computed using a scaled NDVI. Leaf Area Index (LAI) is deduced form the fractional vegetation cover using a logarithmic function. Vegetation height is deduced from the land use map along with a look up table calibrated from field observations. In TSEB the roughness length momentum z0m is estimated as a fraction of the canopy height, h. Where z0m=1/8 the vegetation height (h) while the displacement height is d0=2/3 h, which is reasonable for vegetated canopies. While c can be obtained from remote sensing data using the scaled NDVI approach where the end-member NDVI values, NDVImax and NDVImin, represent a surface fully covered by vegetation and completely bare vegetation respectively. The following equation is used to calculate c.

c (0) = 1- [(NDVImax−NDVI)/(NDVImax−NDVImin)]p 2


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The parameter p represents the ratio of a leaf angle distribution term to canopy extinction. This is computed using the equation p=Λ/K, where Λ is leaf angle distribution term, K is canopy light extinction coefficient. Radiative transfer inside canopy is modelled considering the multiple scattering between the soil and vegetation. The energy balance terms Rn, H and LE are computed for both soil and vegetation. While leaf area index (LAI) is used to determine net radiation and wind speed decay through the canopy layer which is related to c (0) were

LAI = [ln(1-c (0))]/Λ 3 with the cover fraction at view angle can then be computed from:

c (0) = 1-exp[(-Λ Ω( LAI)/cos 4

Canopy radiative properties are computed using the divergence of net radiation method, along with radiative properties of soil and vegetation that are set to nominal values. The model initially assumes that the vegetation is unstressed and transpiring at the potential rate. The canopy, soil components of H and soil evaporation is computed from the radiometric surface temperature. If soil evaporation rate result is negative then vegetation stress occurs and the canopy transpiration component of LE is reversed. The TSEB model uses a physically-based temperature gradient/resistance approaches to model H and uses an estimate of fractional vegetation cover, fc (), apparent at view angle, , is needed to partition the ensemble directional radiometric temperature, TR (), into the temperatures for the soil and vegetation components from equation:

TR() [c().T4c+ (1-c()).T4

s]1/4 5

The partition of available energy between H and LE are then defined by the vegetation (Tc) and soil temperatures (Ts). A modified parallel canopy resistance formula developed by Norman et al. (1995) is used to calculate the sensible heat from the soil (Hs) and vegetated canopy (Hc). The modified formula is:

H = Hs + Hc 6

Hs = Cp[(Ts – Ta)/(rs +ra)] 7

Hc = Cp[(Tc – Ta)/rah] 8 where rs is the aerodynamic resistance to heat transfer from the soil, ra is the aerodynamic resistance from the canopy air layer above the soil, Ta is measured soil temperatures, Tc is


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vegetation temperature, rah is the bulk aerodynamic resistance, is the density of air and Cp is specific heat capacity of air. The directional clumping factor Ω(0) depends on canopy architecture. The scaled NDVI approach to estimating fc and LAI is not the only method in TSEB, other techniques also exist for estimating these surface parameters from remote sensing data. One such technique is the inversion of the bi-directional reflectance function. The TSEB model requires additional specification to canopy architecture of several parameters for more detailed description of radiation and turbulent flux exchange. These parameters are namely canopy height, leaf width, and vegetation clumping factor which are typically assigned by land cover classes in the model. In situations where the local meteorological data is unavailable, a nested modelling approach can be used to supply these inputs (Anderson et al., 2004; Norman et al., 2003). The estimation of sensible heat flux (G) and turbulent exchange coefficients are treated as a dual source in the land surface where the radiative and convective exchange processes between soil and vegetative canopies and the atmosphere are explicitly parameterized. According to Campbell and Norman (1998) the soil heat flux is determined as a fraction of the net radiation just above the soil surface and by modelling the divergence of net radiation within the canopy using a simplified two-stream radiative transfer approach. When using a two-source remote sensing approach along with single direction observations some assumptions are required to compute soil and vegetation temperatures. This is performed deducing the vegetation latent heat flux from vegetation net radiation using the Priestly-Taylor (PT) relation. Further, composite radiative temperature is expressed as a weighted sum of vegetation and soil temperatures, the weights being the fractional vegetation cover and its complementary to unity, respectively. Finally, vegetation sensible heat flux and soil latent heat flux are computed as the residues of the vegetation and soil energy balance respectively. The latent heat flux for soil can be overridden if the temperature difference between the soil – canopy system and the atmosphere is large as the PT relation only provides an initial calculation which can result in erroneous flux estimates. PT approximation will tend to overestimate the canopy transpiration rate, if the estimated radiometric temperature is less than the measured temperature as there is inadequate water supply to the root zone. Hence the computation of latent heat fluxes goes through an iteration procedure, were the vegetation below estimates given by the PT estimation until values of canopy and soil temperature agree with the measured radiometric temperature. The Atmosphere-Land Exchange Inverse (ALEXI) Model Description

The development of the Atmosphere-Land Exchange Inverse (ALEXI) model was for the estimation of surface fluxes over large regions using primarily remote-sensing data. In addition the model requires no information regarding antecedent precipitation or moisture storage capacity. The surface moisture is calculated from radiometric temperature change signal this further allows the model to provide independent information for soil moisture variables in complex regional models. The model structure is a one dimensional, 2 source land surface atmospheric boundary layer model with thermal IR observations taken twice in the morning to compute the lower boundary


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conditions. These conditions are then related to the rise in air temperature above the canopy and the growth in the atmospheric boundary layer to include the influx of sensible heating from the surface. In addition all major components of the energy budget system are estimated in the ALEXI model. The general the energy balance equation (Rn = H + LE + G) is further divided into the canopy and soil energy budget components for net radiation. The canopy energy budget and soil energy budget equation are:

Rnc = Hc + LEc 9

Rns = Hs + LEs + G 10 Where Rn is the net radiation, LE is the latent heat flux, G is the soil heat flux, and the subscripts

c and s refer to the canopy and soil components of the system respectively. The model net radiation is given by

Rn = (Rl↓ - Rl↑) + (Rs↑ + Rs↓) 11 Where Rl and Rs are long and shortwave radiation fluxes respectively, in the downward (↓) and upward (↑) directions. Downward and upward fluxes are derived from visible near infrared and thermal imagery with the upward long wave emission dependent on the soil and canopy temperatures computed by the two source model as well as the emissivity component while shortwave fluxes depends on the albedo of the surface. Hence surface emissivity and albedo are related to land cover class and fractional vegetation cover. The soil heat flux fraction for the net radiation at the surface is a simple approximation (G = 0.3 Rns) that does not account for the phase difference between the diurnal Rn and G curves. However an adequate estimate is obtained were G is typically small in comparison with the other energy budget components. The Priestley-Taylor approximation applied to the divergence of net radiation within the green portion of the canopy is used to calculate canopy evapotranspiration:

LEc = 1.3 fg Rnc (/+γ ) 12 Where fg is the greenness fraction, is the slope of the saturation vapour pressure vs. temperature curve, and γ is the psychrometric constant. This provides a reasonable approximation of the canopy ET as long as the vegetation is not stressed and the canopy is transpiring at its potential rate. All components of the energy budget can be solved for, with LEs as the final residual as measurement of the composite surface radiometric temperature, estimates of G, Rnc and LEc, and an initial estimate of H are given. The assumption that the modified Priestley-Taylor is a good representation of LEc is checked by using this final residual to make sure that the canopy is not stressed as stress causes an increase in sensible heat flux and canopy temperature. One of the draw backs of the ALEXI model is that it cannot be applied within the modelling domain where it was cloudy at, or between, the times of the two radiometric temperature


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measurements. For this reason, Due to this modelled output data was unreliable resulting to non-favourable analyses of long-term flux data sets. Continuous flux estimates across the modelling domain was achieved by using a flux interpolation algorithm to predict fluxes under cloudy conditions by comparing estimates of soil and canopy latent heat fluxes to potential fluxes given by the Priestley-Taylor approximation partitioned between the soil and canopy components in the system. A relation between the fractions of potential plant uptake rate to the available water fraction in the root zone is shown by Campbell and Norman (1998). The available water fraction in the soil surface layer from the ratio of "actual" to potential soil evaporation is predicted using a similar relationship thus allowing the surface soil moisture and root zone moisture to be updated on a daily basis. On cloudy day this procedure is reversed to estimate the actual/potential latent heat flux ratio from the current estimates of available water fraction and sensible heat fluxes solved as the residuals to the component energy budgets. This allows day to day continuity in flux partitioning during cloudy intervals while still responding to large-scale climatic controls on evaporation such as radiation load and atmospheric demand. NDVI-(Diurnal surface temperature variation) DSTV triangle

The NDVI-DSTV triangle method is described by Chen et al. (2002). The NDVI-DSTV (where DSTV stands for Diurnal surface temperature variation) approach assumes that when one plot the vegetation index against DSTV, one would find a triangle shape. The top of this triangle will correspond to an area fully covered by vegetation, where as the bottom will correspond to an area with a low vegetation coverage/index e.g. bare soil or built-up urban areas. The area in between represents areas covered with a partial vegetation cover. DSTV is defined as the difference between surface temperatures obtained at day and at night. DSTV shows a relationship to soil moisture. Surface temperature will be influenced by the NDVI, and the NDVI will likely contain fractions of wet soil, dry soil and vegetation and represent these conditions. The NDIV-DSTV triangle relationship is described by surface temperature (Ti) and the NDVI (NDVIi):

TdwTwwTvwTi 321 13

DSTVdwDSTVwwDSTVvwDSTVi 321 14 where TinightTidayDSTV and

TddaywTwdaywTvdaywTiday 321 15

TdnightwTwnightwTvnightwTinight 321 16

NDVIdwNDVIwwNDVIvwNDVIi 321 17


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where the w parameters are weighing functions. Combining these equations for NDVI and DSTV, one can solve for w1, w2 and w3. The vegetation and moisture coefficient (VMC) is the ratio of actual to potential ET, and describes natural land covers. VMC is dependent on the vegetation type and soil moisture and is therefore affected by different land cover conditions (wet soil, dry soil and vegetation cover). VMC is described by

ETp

ETadrylandVMCwwetlandVMCwvegetationVMCwNDVIDSTVVMC )(3)(2)(1),( 18

Actual evaporation (ETa) is subsequently computed from values for these “extreme” cases (full vegetation, dry soil, wet soil) as a function of ETp and VMC

ETpVMCETa 19 This approach had been applied using NOAA AVHRR data.

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