Assessment of water fluxes in semi-arid environments...Assessment of water fluxes in semi-arid...

Assessment of water fluxes in semi-arid

environments Serowe case study (Botswana)

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Assessment of water fluxes in semi-arid environments (Serowe case study (Botswana)

by

Nanyonjo Cate Zziwa

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation (Groundwater Resources and Environmental Management) Degree Assessment Board Chairman: Prof. Dr.A.M.J.Meijerink (ITC) Supervisor: Dr. Maciek Lubczynski (ITC) Member: Dr. Ambro Gieske (ITC) External Examiner: Drs. J.W.A. Foppen (IHE)

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

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Disclaimer

This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute

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Abstract

Semi arid areas are characterised by low and erratic rainfall. Surface water is available only during

and shortly after the rainy season; therefore groundwater is the main supply of water. To address, the

challenges of sustainable management, assessment of water fluxes becomes essential. In this study,

various methodologies for computing water fluxes are presented for Botswana, a case study of Se-

rowe.

Transpiration of Savannah vegetation species (Terminalia Sericea, Acacia leuderitzii, Burkea Afri-

cana, Acacia erioloba, Ochna pulchra, Dichrostachys cinerea Acacia fleckii, Boscia albitrunca and

Lonchocarpus nelsii were evaluated by sapflow measurements using Granier’s Thermal Dissipation

Probe method. Stem diameters were the best predictor of sap wood area in all tree species having cor-

relations over 80% (78 – 93%) The crown diameter also explained (75 – 90%) but showed as a poor

indicator of sapwood area in Dichrostachys cinerea. From the statistical analysis, sap velocity was

proved to be independent of crown area and stem area. Daily average sap velocities varied widely

among species: Terminalia Sericea (1.68 cm hr-1), Acacia leuderitzii (0.61 cm hr-1), Burkea africana

(1.326 cm hr-1), Acacia erioloba (1.02 cm hr-1), Ochna pulchra (1.41 cm hr-1), Dichrostachys cinerea

(0.85 cm hr-1) Acacia fleckii (2.50 cm hr-1) Boscia albitrunca (3.84 cm hr-1) and Lonchocarpus nelsii

(3.35 cm hr-1). Regarding temporal variability of tree transpiration, high normalised sapflow rates dur-

ing summer were observed for Lonchocarpus nelsii, Ochna pulchra, Acacia fleckii, Burkea africana

and Dichrostachys cinerea. However, during winter periods when there was a water deficit, Boscia

albitrunca, Terminalia sericea and Acacia erioloba showed considerable amounts of normalised sap-

flow. To monitor temporal variability of Actual Evapotranspiration, at GS05 ADAS site, two energy

balance methods are presented. The temperature profile method indicated an average of 0.89 mm/day

and the Bowen ratio Energy balance method 0.29 mm/day. Computation of aerodynamic resistance

provided an average daily value of 48.2 sm-1. Daily average surface resistance by applying ETa from

temperature profile method was 1817 s m-1, and by applying sapflow method it was 4730 sm-1. Daily

actual evapotranspiration estimated by Penman-Monteith showed values ranging from 0.08mm/day to

0.64 mm/day. Sensitivity analysis of aerodynamic resistance and surface resistance indicated that the

use of a fixed aerodynamic resistance as temporal average value does not influence much, ETa (P-M)

while the same ETa appeared to be sensitive to temporal variability of aerodynamic resistance.

A lumped parameter hydrologic model, EARTH was used to model fluxes in the unsaturated zone Fi-

nally the fluxes evaluated with various methods were compared.

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Acknowledgements

I would like to express my sincere and heartfelt gratitude to the Netherlands Government for awarding

me a scholarship without which I would not have realized my dream to further my studies. I am grate-

ful to my employer, Rural Water and Sanitation Eastern Uganda Project (RUWASA) who through the

Project Coordinator Mr. Disan Ssozi complemented my efforts by supporting me and helping me fulfil

my dreams. Thanks are also extended to Dr. Erasmus Barifaijo, Head of Department for Geology,

Makerere University for his willingness to recommend me for this study.

Many thanks go to all staff members of WREM, for the support and guidance throughout the modules.

I am deeply indebted to my first supervisor Dr. Maciek Lubczynski, for his patience, guidance, en-

couragement and critical comments that made this research a success right from fieldwork to comple-

tion of this work. To my second supervisor Dr Ambro Gieske, I am very grateful for your valuable

inputs especially in the Evapotranspiration chapter. I am also grateful to my classmates in WREM

with whom we shared moments of joy as well as tense periods. To the Ugandan colleagues in En-

schede, thank you for making me feel at home.

I thank the Department of Geological Survey of Botswana for providing me with data for my research

and facilitating my fieldwork. Special thanks go to Mr. Obolokile Obakeng for his advice guidance

throughout this study and quick response in providing me with extra data even after fieldwork. To Mr

Ramatsoko, thank you for putting up a great camp for us. I cannot forget all the field assistants and

technicians who made data collection possible. To all my colleagues at the camp Esther, Walter, Er-

mius Ola seven!!!!

My heartfelt gratitude goes to my husband Darius who sacrificed his interests and wished for my suc-

cess. Darius, those special and lovely words especially through the hardest times gave me a lot of en-

couragement and inspiration. To my son Danny, I always missed you; I hope you understand that leav-

ing you at such a tender age would be beneficial to all of us.

I am forever grateful to my beloved parents, Mr. and Mrs. E. Zziwa who taught me the principles of

life and educated me this far. They have been my source of strength, mentors always supportive and

urging me on. I thank you very much for taking care of my baby Danny while I was away. To my sis-

ters and brothers thank you for the affection. To my friends, Sanyu, Rhoda, Elizabeth, Celia, Damalie,

Zuwena, Stella, Harriet, thank you for the endless e-mails and support. To Silvie, the hearty calls that

always made my days great cannot be forgotten, thank you very much for the advice and continuous

encouragement.

Finally all praises to the almighty GOD, the father, without whom this work would have remained a

dream!!

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List of acronyms ADAS Automatic Data Acquisition System

ANOVA Analysis of Variance

ASCII American Standard code for Information Exchange

BH Borehole

BREB Bowen Ratio Energy Balance method

Cum. prob cumulative probability

EARTH Extended model for Aquifer Recharge and soil moisture Transport through the un-

saturated Hardrock.

ET Evapotranspiration

ETa Actual evapotranspiration

ETo Reference crop evapotranspiration

FAO Food and Agricultural organisation

GIS Geographical Information System

GPS Global Positioning System

ILWIS Integrated Land and Water Information system

ITC International Institute for Geo-information and Earth observation Science

LAI Leaf Area Index

Landsat TM Land Satellite Thematic Mapper

m.a.s.l meters above sea level

NDVI Normalized Difference Vegetation index

PET Potential Evapotranspiration

P-M Penman-Monteith

SEBAL Surface Energy Balance Algorithm for Land

SE Standard Error

SGC Swedish Geological Company

TDP Thermal Dissipation Probe

UTM Universal Transverse Mercator

WCS Well field Consulting Services

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Table of contents

Disclaimer ............................................................................................................................ i

Abstract ................................................................................................................................ ii

Acknowledgements ....................................................................................................... iii

List of acronyms .............................................................................................................. v

Table of contents ............................................................................................................ vi

List of Figures .................................................................................................................. ix

List of Tables..................................................................................................................... x

List of Plates ...................................................................................................................... x

List of Appendices......................................................................................................... xi

1. GENERAL INTRODUCTION ...............................................................................................1

1.1. Background.................................................................................................................... 1 1.2. Research Problem and importance of research.............................................................. 1 1.3. Objectives ...................................................................................................................... 3 1.4. Research Questions........................................................................................................ 3 1.5. Previous work ................................................................................................................ 3 1.6. Outline of thesis............................................................................................................. 4

2. DESCRIPTION OF STUDY AREA.......................................................................................5

2.1. Location ......................................................................................................................... 5 2.2. Climate........................................................................................................................... 6 2.2.1. Rainfall ...................................................................................................................................6 2.2.2. Temperature ...........................................................................................................................7 2.2.3. Relative Humidity...................................................................................................................7 2.2.4. Wind speed .............................................................................................................................7 2.2.5. Evapotranspiration ................................................................................................................8 2.3. Topography and drainage density ................................................................................ 10 2.4. Land use and Land cover ............................................................................................. 10 2.5. Soils ............................................................................................................................. 11 2.6. Vegetation.................................................................................................................... 11 2.6.1. Vegetation and groundwater................................................................................................12 2.6.2. Vegetation density ................................................................................................................13 2.6.3. Species characteristics .........................................................................................................14 2.7. Hydrogeology............................................................................................................... 14 2.7.1. Formations ...........................................................................................................................15 2.8. Flow system analysis ................................................................................................... 18

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2.8.1. Head Distribution.................................................................................................................18 2.8.2. Depth to water table.............................................................................................................18 2.8.3. Hydraulic conductivity of the aquifer ..................................................................................19 2.8.4. Storage parameters ..............................................................................................................19 2.8.5. Recharge Potential...............................................................................................................20

3. MATERIALS AND METHODS..........................................................................................21

3.1. Materials ...................................................................................................................... 21 3.2. Methods ....................................................................................................................... 21 3.3. Preliminary preparation ............................................................................................... 21 3.4. Fieldwork..................................................................................................................... 23 3.4.1. Sapflow measurements .........................................................................................................23 3.4.2. Sapwood area estimation .....................................................................................................28 3.4.3. Mobile Mast for Canopy resistance .....................................................................................30 3.4.4. Measurements for Landsat 7 TM image...............................................................................30 3.4.5. Meteorological data acquisition ..........................................................................................31 3.4.6. Field data for 1-D modelling ...............................................................................................31 3.5. Post-field work............................................................................................................. 31

4. TRANSPIRATION OF TREES: SHORT TERM AND LONG TERM MONITORING USING SAPFLOW METHODS.......................................................................................................32

4.1. Data Distribution ......................................................................................................... 33 4.2. Descriptive statistics .................................................................................................... 33 4.2.1. Short term measurements .....................................................................................................33 4.2.2 Long term measurements .....................................................................................................34 4.3. Statistical analysis........................................................................................................ 35 4.4. Relationships between stem area, sapwood area and crown area................................ 36 4.4.1. Short-term measurements.....................................................................................................36 4.4.2. Long term measurements – Sapwood area estimation.........................................................40 4.5. Sapflow calculations.................................................................................................... 40 4.6. Relationship between crown area,sapwood area and sap velocity............................... 43 4.7. Temporal variability of sapflow .................................................................................. 47 4.8. Sapflow correlation with climatic and non climatic variables variables ..................... 48 4.8.1. Correlation with Potential evapotranspiration ...................................................................49 4.8.2. Correlation with soil moisture .............................................................................................50

5. EVAPOTRANSPIRATION ..................................................................................................52

5.1. Theoretical background ............................................................................................... 52 5.2. Penman-Monteith equation.......................................................................................... 52 5.3. Estimation of parameters for the Penman-Monteith equation ..................................... 53 5.4. Estimation of the aerodynamic resistance.................................................................... 54 5.5. Estimation of the surface resistance using Penman-Monteith formular ..................... 56 5.5.1. ETa computed from Temperature profile method................................................................57 5.5.2. Actual Evapotranspiration computed from Bowen ratio energy balance method...............60

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5.5.3. Comparison of BREB method and temperature profile method ..........................................60 5.5.4. Sensitivity analysis on the Temperature profile method ......................................................61 5.5.5. Results for surface resistance ..............................................................................................61 5.6. Estimation of surface resistance from sapflow measurements ................................... 62 5.6.1. Stand sapflow estimation......................................................................................................62 5.6.2. Results from sapflow ............................................................................................................63 5.7. Temporal variability of actual evapotranspiration....................................................... 63 5.7.1. Sensitivity analysis on estimates of ra and rs........................................................................64 5.8. Comparison of ETa from Different methods............................................................... 64

6. SOIL MOISTURE AND WELL LEVEL FLUCTUATIONS MODELLING..................66

6.1. Measurement of soil moisture in the unsaturated zone ............................................... 66 6.2. Groundwater level monitoring..................................................................................... 68 6.2.1. Water level calculation ........................................................................................................68 6.3. Modelling well level fluctuations using 1D EARTH model ....................................... 73 6.3.1. Description of model ............................................................................................................73 6.3.2. Modules of the model ...........................................................................................................74 6.3.3. Model Input data ..................................................................................................................75 6.3.4. Model Input parameters.......................................................................................................76 6.3.5. Model calibration.................................................................................................................76 6.3.6. Model results........................................................................................................................77

7. DISCUSSION, CONCLUSION AND RECCOMENDATIONS........................................80

7.1. Discussion.................................................................................................................... 80 7.1.1. Tree Transpiration and biometric variables........................................................................80 7.1.2. Evapotranspiration ..............................................................................................................82 7.1.3. Soil Moisture and well level fluctuations.............................................................................83 7.2. Conclusions ................................................................................................................. 83 7.3. Recommendations........................................................................................................ 84

REFERENCES ...................................................................................................................................86

IX

List of Figures Figure 2-1: Location of the study area ..................................................................................................5 Figure 2-2: Monthly precipitation for Serowe from 1986 to 2002 .......................................................6 Figure 2-3: Monthly average precipitation for a hydrological year ......................................................6 Figure 2-4: Daily average temperature at Mokongweng station...........................................................7 Figure 2-5: Average relative humidity at Mokongweng station ...........................................................7 Figure 2-6: Daily wind speed at Mokongweng station .........................................................................8 Figure 2-7: Daily reference crop evapotranspiration for Mokongweng station....................................9 Figure 2-8: Monthly reference ETo and rainfall for Mokongweng station ..........................................9 Figure 2-9: Topography and drainage network for Serowe ................................................................10 Figure 2-10: Distribution of soil units in the study area ......................................................................11 Figure 2-11: Vegetation map of Serowe (after Hernandez, 2002).......................................................12 Figure 2-12: NDVI derived from Landsat 7 TM satellite images........................................................13 Figure 2-13: Hydrogeological cross-section of Serowe study area (after Lubczynski, 2000) .............15 Figure 2-14: Thickness of the Ntane sandstone [m] ............................................................................16 Figure 2-15: Degree of confinement in Serowe in [m] ........................................................................16 Figure 2-16: Thickness of the Stormberg basalt in [m] .......................................................................17 Figure 2-17: Thickness of the Kalahari beds .......................................................................................17 Figure 2-18: Hydraulic head distribution [m] for June 2001 ...............................................................18 Figure 2-19: Depth to groundwater [m] ...............................................................................................19 Figure 3-1: Research Methodology.....................................................................................................22 Figure 3-2: Location of the sapflow stations in the study area ...........................................................24 Figure 3-3: Location of permanent stations in the study.....................................................................27 Figure 4-1: Xylem in a cross section of woody stem..........................................................................32 Figure 4-2: Normal probability plot of crown diameter data for Dichrostachys cineria....................33 Figure 4-3: Regressions of stem area, crown area and sapwood area for the monitored species .......39 Figure 4-4: Sap velocity rates for 6 trees monitored at station 3 on 24/09/02....................................41 Figure 4-5: Normalised sapflow rates for 9 trees monitored at station 3 on 24/09/02 .......................43 Figure 4-6: Crown area and sapwood area with Sap velocity relationship for monitored specie........46 Figure 4-7: Diurnal variability of (Qn) and rainfall for Lonchocarpus nelsii at GS04 station...........47 Figure 4-8: Diurnal variability of (Qn) and rainfall for Boscia albitrunca at GS06 station...............47 Figure 4-9: Daily pattern between Sapflow and PET for Terminalia sericea (132) at station 6........49 Figure 4-10: Qs Vs PET with 1 hr lag in (1) and 2 hr lag in (2) for Acacia Leuderitzii at station 1 ...49 Figure 4-11: Qs Vs PET correlation without and with 2hr lag accounting for tree 74 at station 3 .....50 Figure 4-12: Daily Qs pattern for station 3 (monitored) and station 4 (extrapolated).........................50 Figure 4-13: Soil moisture measurements at GS01..............................................................................51 Figure 5-1: Aerodynamic resistance for ADAS site GS05 .................................................................56 Figure 5-2: Computed Rn, H, LE and G using the temperature profile method.................................60 Figure 5-3: Surface resistance by applying ETa from different methods ...........................................62 Figure 5-4: Canopy resistances from sapflow measurements for GSO5 site......................................63 Figure 5-5: Calculated ETa at station GSO5 for the period December 2001 to September 2002 ......63 Figure 5-6: Constant versus variable estimates of ra and rs on ETa....................................................64 Figure 5-7: Actual evapotranspiration obtained from different methods for mobile station GS05....65

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Figure 6-1: Soil moisture content at the permanent stations...............................................................67 Figure 6-2: Location of boreholes with divers in Serowe...................................................................68 Figure 6-3: Water level fluctuations in boreholes 4743 and 4742and barometric fluctuations..........69 Figure 6-4: Water level fluctuations in boreholes 5306 and 5326 and barometric fluctuations.........70 Figure 6-5: Water level fluctuations in boreholes 5336 and 5337 and barometric fluctuations.........71 Figure 6-6: Water level fluctuations in boreholes 5343, 8493, 8449 and barometric fluctuations.....73 Figure 6-7: Flow chart of EARTH model after (Van der lee and Gehrels, 1990) ..............................74 Figure 6-8: EARTH model output for BH 8403 .................................................................................78 Figure 6-9: EARTH model output for BH 4742 .................................................................................79

List of Tables Table 3-1: Tree species and sampled individuals measured at the respective stations.......................23 Table 4-1: Descriptive statistics of tree species monitored for short-term measurements .................34 Table 4-2: Biometric variables of tree species monitored for long-term measurements ....................35 Table 4-3: Regression equations for stem area (x) and sapwood area (y) for the different species ...36 Table 4-4: Predicted sapwood area (As) for trees in the permanent stations......................................40 Table 4-5: Average velocities for the monitored species (short term measurement) .........................42 Table 4-6: Sapflow (Qs) and normalised sapflow (Qn) for the monitored trees (short term) ............42 Table 4-7: Summary of the Normalised sapflow during the wet and dry season ...............................48 Table 5-1: Displacement height and surface roughness calculation for GS05 after Parodi (2000)....54 Table 5-2: Leaf area index for permanent stations .............................................................................55 Table 5-3: ETa values for different values of Zom.............................................................................61 Table 6-1: EARTH model calculated parameters ................................................................................78

List of Plates Plate 1: Mobile stations at GS05.........................................................................................................24 Plate 2: Set up of sapflow sensors.......................................................................................................26 Plate 3: Sapflow ADAS at Station 3 and data Logger ........................................................................26 Plate 4: Permanent station at GS05.....................................................................................................27 Plate 5: Tree cutting experiment and stem discs for acacia erioloba..................................................29 Plate 6: Sapwood area estimation using the dielectric “blanket” experiment ....................................30 Plate 7: Acacia Leuderitzii (13 and 2) and presence of fungi on Acacia fleckii .................................. II Plate 8: Ochna pulchra (79)................................................................................................................. II Plate 9: Acacia erioloba (79 and 80) .................................................................................................. III Plate 10: Multi stemmed Dichrostachys cineria and inset sapwood area compared to heartwood..... III Plate 11: Terminalia sericea (125 and 126) and Burkea africana (77) ...............................................IV Plate 12: Sparse vegetation of Serowe.................................................................................................IV

XI

List of Appendices A1: Names of the monitored trees ......................................................................................................... I A2: Photographs of the monitored species............................................................................................ II A3: Biometric variables for the monitored species...............................................................................V B1: Normal probability plots for the monitored species......................................................................IX B2: Pearson correlation matrix of the Data set ....................................................................................XI B3: ANOVA for the different species............................................................................................... XIII B4: Biometric variables and water fluxes for the monitored trees ...................................................XIV B6: Diurnal variation of Qn and rainfall in the permanent stations............................................... XVIII C: 1 Graphical output of EARTH model ........................................................................................ XXII D1: Measurements during Landsat 7 over pass ......................................................................... XXVIII

ASSESSMENT OF WATER FLUXES IN SEMI-ARID ENVIRONMENTS SEROWE CASE STUDY (BOTSWANA) CHAPTER ONE

INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION 1

1. General introduction

1.1. Background

Lack of surface water is a major threat to human livelihood in semi arid areas. The problem is even

worsened by increasing human population. Groundwater is then left as the only permanent and safe

source of drinking water in these areas. The Semi arid Botswana Kalahari is notable for its lack of

permanent or even seasonal water. It is characterized by a very low and unpredictable rainfall pattern.

According to Gieske, (1992), water resources management is more difficult in highly variable semi

arid climates where years with good rains are followed by droughts.

Botswana is the largest world exporter of gemstone diamonds as well as a large exporter of beef to

the European community. Livestock farming, agriculture, domestic and industries are all dependent on

this water. Not only population growth but also mining of diamonds has increased the water require-

ments during the last 30 years (Pierce, 1993). The government of Botswana has evoked a policy of

long-term management of nationwide water resources and has designed to increase supply systems in

Botswana in order to meet a projected demand for the area up to 2020 (Van Dijk et al., 1996) . This

valuable source is subject to exploitation with no sense of sustainability of the catchments and the

future life of Tswanas.

In this research, the integrated use of acquired data from ADAS are explained for monitoring the spa-

tial and temporal variability of soil moisture content, transpiration, evapotranspiration and groundwa-

ter levels and will be applied to Serowe. It is therefore expected to contribute directly to better under-

standing of the assessment of water balance terms, which lead to greater understanding of the hydro-

geology in semi arid Botswana.

1.2. Research Problem and importance of research

Serowe study area is the capital city of the central district, one of the ten districts in Botswana. With

the growth and urban development of Serowe there has been an ever-increasing demand for water.

Serowe is faced with a rapid increase in human population. The population of Serowe has risen from

23,000 inhabitants in 1981 to around 35000 inhabitants by 1996 (WCS, 2000). The water demand at

present is approximately 3500 m3/day yet the existing wells can supply only 1000 m3/day. The current

situation in Serowe appears to be reaching a critical stage (WCS, 2000). This widening gap between

demand and availability has prompted the Botswana government to find ways of managing and moni-

toring a sustainable water supply for Serowe. Locating and developing new sources, is now the imme-

diate objective of Botswana government. There is need to establish sustainable water supply for Se-

rowe until the year 2020 (WCS, 2000).

However, this goes with quantitative assessment of fluxes in the area and appropriate monitoring and

management systems for the study area. Monitoring schemes in Serowe originally involved monitor-



ing of groundwater levels and rainfall, manually and the data had to be transformed into digital form

either by digitising or typing. This came with numerous errors; the data was not very reliable. Moni-

toring schemes provide a framework for the long term, planning, development and management of

water resources. On the 15th October 2001 to January 2002, the Kalahari Research project undertook a

major field campaign to establish a hydrological monitoring network of automatic data acquisition

systems (ADAS). This consisted of logger operating sensors: groundwater level monitoring; pressure

transducers for atmospheric pressure; soil moisture and soil suction pressure sensors; soil temperature

sensors; relative humidity and temperature sensors; Solar meter; anemometer; tipping bucket rain

gauge. The ADAS station is advantageous over previous monitoring schemes because of a better tem-

poral frequency of measurement acquisition, which ranges from less than 30 minutes to an hour and

the data is directly in digital form. Analysis and quantification of fluxes from these stations could help

to contribute towards the dynamics of groundwater in the study area. The fluxes, which can be as-

sessed, include evapotranspiration, transpiration and recharge.

Accurate transpiration data for trees is usually needed to assess the hydrologic implication of the spe-

cies in any area. The use of sapflow methods in studies of plant water use is becoming increasingly

widespread, so that they are likely to play a prominent role in future efforts to devise solutions to hy-

drological problems encountered by farmers, foresters and conservationists. Measurements of transpi-

ration by individual branches or whole trees using sapflow methods hold important advantages over

other techniques. Granier’s Thermal Dissipation Probe (TDP) gives an accurate, easy to install, simple

calculations and inexpensive estimate of sapflow (Granier et al., 1994). Further sapflow estimates

provide a specific estimate of transpiration per se as opposed to total evaporation, thereby minimising

additional experimental measurements or analysis required to isolate this component (Hatton T et al.,

1995). Granier’s Thermal Dissipation Probe (TDP) method has been used in the present study to in-

vestigate transpiration in a semi arid environment of Botswana. With the installation of ADAS sta-

tions in the study area, continuous records of plant water use with high time resolution have been ob-

tained with little disturbance; transpiration at tree level between species and its temporal variability

has been quantified. Another aspect is the depth of the root zone. It seems that normally the rooting

depth is much less than 10m, but it has been observed that roots of trees particularly desert species,

often go to depths of even more than 60 m (Scott D.F. and Maitre D.C.L., 1998)

Evapotranspiration is a major source of water depletion in arid and semi arid environments and is an

important component of water balance. Globally about 72,000 km3/year of precipitation received on

the continents is returned back to the atmosphere through evapotranspiration, (Dingman, 1994). The

importance of evapotranspiration to groundwater resource assessment and its significance to the

groundwater budget necessitates the assessment of evapotranspiration and its spatial and temporal

variability (Magombedze, 2002).

The most significant influencing factor on groundwater recharge in the sandveld area to the west of

the Kalahari escarpment is the thickness of the Kalahari bed. However, infiltration process through

this unsaturated zone is poorly understood (WCS, 2000). According to Timmermans and Meijerink

(1999), depletion of water by plant use in the unsaturated zone will reduce recharge because unsatu-



rated conductivity values will be reduced, hindering the passage of water through the upper zone.

ADAS stations set up under the Kalahari research project with the echo probes, enable measurement

of soil moisture at various depth.

There is therefore a need for understanding transpiration by means of sapflow measurements in Se-

rowe. In this study focus is on tree transpiration, its variability with time and between species. A dis-

cussion on the comparison of transpiration with microclimatic conditions is also presented. An at-

tempt is made to assess actual evapotranspiration. Unsaturated zone modelling was carried out in or-

der to understand the flow mechanisms through the unsaturated zone. It is hoped that the findings of

this study will increase the understanding of groundwater fluxes in the study area.

1.3. Objectives

The main objectives of the present work are: -

• To assess transpiration at tree level through sapflow measurement over the tree species repre-

senting Kalahari Savannah vegetation.

• To analyse biometric relationships of the different species in order to determine the temporal

variability of tree transpiration.

• To assess actual evapotranspiration in selected ADAS site (GS05) using various energy bal-

ance methods.

• To define surface resistance at selected ADAS (GS05) sites using various methods.

• To model subsurface fluxes in the unsaturated zones at selected ADAS sites (GS01, GS02,

GS03, GSO4, GSO5, GSO6, GS07)

1.4. Research Questions

The research was aimed at answering the following questions:

• How much water do trees in Serowe transpire?

• How do the results of the tree transpiration fluxes compare with climatic factors and ETa

from energy balance methods?

• What are the evapotranspiration rates in Serowe and how do they vary with time?

• What are the fluxes in Serowe subsurface zone and how do they vary with time?

1.5. Previous work

Consistent with the need for supply of water, most of the pioneering works carried out in Serowe were

for groundwater exploration. Also work on structural geology, hydrochemistry and hydrogeology has

been carried out by various researchers.

From 1985 to 1988, the “Serowe groundwater resources evaluation” project undertaken by Swedish

geological company, embarked on identifying and developing water resources in Serowe. Surveys

were carried out to establish whether 35,000 m3/d from a wellfield located in Ntane sandstone aquifer

was possible. Complex geological structures and hydrogeologically important elements in the region

were identified using mainly airborne magnetic methods.



Lubczynski (2000) discussed groundwater evapotranspiration as an underestimated component of the

ground water balance in Serowe study area. He noted that recharge values could be much higher,

though commonly assumed as 1- 5 mm/year being depleted by groundwater evapotranspiration.

1.6. Outline of thesis

Chapter 1: Gives a general introduction and outlines the Research problem, objectives and Ques-tions answered.

Chapter 2: A brief description of Serowe study area is presented. A brief overview on the physi-

cal location of Serowe, climate, topography, land use and land cover, Soils, vegeta-

tion, hydrogeology and drainage are discussed. Literature review is also incorporated

in this chapter.

Chapter 3: Describes the research methods from pre-fieldwork, field data collection and post

fieldwork. The materials used in the research are defined in this section.

Chapter 4: Focuses on the assessment of transpiration using sapflow methods. The acquired data

set is examined using statistics. Biometric relationships for the monitored trees are

calculated and transpiration fluxes are estimated for both long-term and short-term

measurements. An analysis of potential evapotranspiration for the short-term meas-

urements is made and the seasonal pattern of sapflow is discussed for the long-term

measurements and related to rainfall events.

Chapter 5: Focuses on temporal variability of actual evapotranspiration using the Penman-

Monteith method. The temperature profile method and Bowen ratio energy balance

method are applied in estimation of the actual evapotranspiration for the mobile sta-

tions. Computation of aerodynamic resistance and canopy resistance is also discussed

here.

Chapter 6: Discusses soil moisture measurements taken at 2m, 4m 6m and 8m and groundwater

modelling using 1D-EARTH in the unsaturated zone and estimates fluxes (recharge,

evapotranspiration, soil moisture, percolation).

Chapter 7: Discusses the results obtained, gives a synthesis of the research, conclusions and rec-

ommendations

ASSESSMENT OF WATER FLUXES IN SEMI-ARID ENVIRONMENTS SEROWE CASE STUDY (BOTSWANA) CHAPTER TWO


2. Description of study area

2.1. Location

Serowe, one of the largest villages in Africa is situated in the Central District of Botswana at the east-

ern fringe of the Kalahari basin and is about 275 km north east of the capital city of Botswana, Gabo-

rone. It lies in UTM zone 35, between UTM coordinates 400000E to 497000E and 7545000N to

7501000N and covers an area of 2444 km2 (Figure 2-1). The most prominent topographic feature of

the region is the 90 – 150 m high, NNW –SSE escarpment which splits the area into two. In the west-

ern part we have the Kalahari sand cover and the eastern part the where the Kalahari sand is reduced

and vegetation remains green is the hardveld (WCS, 2000)

Figure 2-1: Location of the study area



2.2. Climate

In common with Botswana, the climate of the study region is semi arid and is characterized by cool

dry winters and hot moist summers, which is influenced in its variability by the movement of the Inter

Tropical Convergence Zone (ITCZ).

2.2.1. Rainfall

According to WCS (2000) rainfall varies from 400 mm/yr in the western part of the study area to 450

mm/yr in the east. Based on a 66-year record, the mean annual rainfall for Serowe is 447 mm (SGC,

1988). Rainfall in the study area is seasonal with the highest rainfall in summer, which stretches from

October to April, and lowest in winter from May to September. Examination of monthly records from

1986 to 2002 from Serowe meteorological station (Figure 2-2) shows that the highest amount of rain-

fall was received in the hydrological year 1999 to 2000 (1038.1 mm/yr) with the least amount in 1993

to 1994 (291.6 mm/yr). Monthly average records (Figure 2-3) were calculated for the period 1986 to

September 2002. The wettest month was February with an average rainfall of 102 mm and July was

the driest with no rainfall.

Figure 2-2: Monthly precipitation for Serowe from 1986 to 2002

Figure 2-3: Monthly average precipitation for a hydrological year



2.2.2. Temperature

The lowest temperatures are experienced during winter season during June, July or August of each

year. Temperatures as high as 40 oC are experienced immediately before the rainy season in October

or November when soil deficits are at their peak (SGC, 1988) . Figure 2-4 below shows average tem-

peratures measured at Mokongweng ADAS in Serowe for the period January 1998 to November 2002.

Figure 2-4: Daily average temperature at Mokongweng station

2.2.3. Relative Humidity

In Serowe, it is found that minimum and maximum relative humidity is at 8:00 hrs and 14:00 hrs re-

spectively. This variation is because the saturation vapour pressure is dependent on the air pressure.

Relative humidity is measured at 2 m in Mokongweng station and Figure 2-5 below represents RH

from January 1998 to November 2002

Figure 2-5: Average relative humidity at Mokongweng station

2.2.4. Wind speed

Wind speed is one of the parameters measured at Mokongweng station. It is measured at 2m and 10m

to enable an accurate estimate of turbulence. Turbulence is the driving force for removing water va-



pour from the evaporating surfaces. Figure 2-6 represents wind speed measured at 2m from August

2001 to November 2002.

Figure 2-6: Daily wind speed at Mokongweng station

2.2.5. Evapotranspiration

Evapotranspiration occurs at the maximum possible rate when there is no shortage of water and this is

referred to as potential evapotranspiration (PET) or as actual evapotranspiration (ETa), which is the

actual amount of water leaving an evaporating surface or as reference cop evapotranspiration (ETo),

which is the rate from a reference surface that is not short of water. ETa is discussed in detail in chap-

ter 5 and reference crop evapotranspiration (ETo) also referred to as Potential evapotranspiration is

discussed in this section.

( )( )z

aszn

u

eeuT

GRETo

34.01273

900)(408.0

++∆

−+

+−∆=

γ

γ (2.1)

where ETo is Potential evapotranspiration [mm/day]

Rn is net radiation at the crop surface [MJ/m2/day]

G is soil heat flux [MJ/m2/day]

T is air temperature at 2 m height [oC]

z is height [m]

uz is wind speed at 2 m height [m/s]

es is saturation vapour pressure [kPa]

ea is actual vapour pressure (kPa)

∆ is slope of vapour pressure [kPa]

γ is psychometric constant

Input data from Mokongweng ADAS station in the study area from 1986 to 2002 is used to calculate

reference evapotranspiration using the Penman-Monteith formula. This method has the advantage of



taking into account most parameters that affect evapotranspiration (air temperature, relative humidity,

wind speed and net solar radiation). Daily ETo values were calculated as shown in Figure 2-6 with

ETo values ranging from 0.9 mm/d to 9 mm/day with an average rate of 3.82 mm/d. The graph shows

that daily potential evapotranspiration rises and falls periodically.

Figure 2-7: Daily reference crop evapotranspiration for Mokongweng station

Serowe experiences high evapotranspiration rates in summer when temperatures are high and soil

moisture is available just like other arid areas. Low evapotranspiration occurs during winter when

soils become drier (Obakeng, 2000). A graph of monthly ETo (Figure 2-8) shows that in most cases

evapotranspiration is higher than rainfall. Because surface runoff is almost non-existent in the area, it

can be concluded that part of the precipitation that is not lost by evapotranspiration infiltrates into the

unsaturated zone and may reach the groundwater table as effective recharge. This means that there is

moisture available to the soil despite high evapotranspiration.

Figure 2-8: Monthly reference ETo and rainfall for Mokongweng station



2.3. Topography and drainage density

The most prominent topographic feature in the area is the Kalahari escarpment that extends approxi-

mately NNE-SSW on the western side of Serowe (WCS, 2000). The escarpment varies in height from

1060 m.a.s.l to a maximum of 1240 m.a.s.l. To the west of the escarpment, the land is fairly flat and

featureless as a result of the Kalahari sand cover while the eastern section of the area dips gently away

from the escarpment, and a dendritic surface drainage is evident (Figure 2-9). Along the escarpment

significant outcrops of Stormberg basalt occur, with more isolated outcrops of both basalt and Ntane

Sandstone common to the east.

The river network is limited to the area east of the escarpment. All rivers in the area are ephemeral

and flow occurs a short time after exceptionally high rainfall events of the wet season (Obakeng,

2000). Thus the rivers are dry most of the year. Water levels below the riverbeds are very shallow

which support vegetation growth in the eastern part of the area.

Figure 2-9: Topography and drainage network for Serowe

2.4. Land use and Land cover

Settlements are concentrated around Serowe where rapid urbanization and modern infrastructure de-

velopment is taking place. Settlements are also found along the escarpment where there used to be in

the past springs for water supply. The main form of agriculture is cattle rearing, which is facilitated by

savannah vegetation and water availability from boreholes and shallow hand dug wells. Crop farming

is mainly practised at subsistence level around the study area. However there is a serious threat to

crop cultivation due to erratic rainfall coupled with poor soils.



Most of Serowe area is covered by the natural vegetation and is mainly used for grazing cattle. The

area to the west of the escarpment is dotted with cattle ‘posts’ where there are boreholes to supply

drinking water for cattle.

2.5. Soils

The study area can be divided into two parts with regards to soils, the sandveld in the west and hard-

veld in the east (WCS, 2000). The sandveld consists of fine Kalahari sands while the hardveld con-

sists of calcic luvisols and luvic arenosols (Figure 2-10)

Arenosols are the most common soil type and are sandy with minor silt and clay. Such composition

explains their high permeability and low water retention capacity. Arenosols are not subject to crust-

ing like other soils in the study area, therefore facilitating recharge to the underlying aquifers. Re-

gosols consist of sandy loams to clay loams with minor coarse sand. They are less drained than the

arenosols although they have big enough pore spaces to allow easy movement of water through the

sand matrix.

Lixisols are well drained with sand content of about 20% and clay content of greater that 20%

(Obakeng, 2000). Luvisols on the other hand have nearly equal proportions of coarse fine sand but

with similar sand and clay content as the Lixisols. Luvisols and Lixisols have good water holding ca-

pacity. Vertisols are the most poorly drained soils in the area with clay content in excess of 50%

(Obakeng, 2000).

Figure 2-10: Distribution of soil units in the study area

2.6. Vegetation

The Serowe biomass resource depicts a typical semi arid savannah environment with average tree

height ranging between 3 m to 5 m. (Namayanga, 2002). Although the dominant type of vegetation in

Serowe is open savannah, this may be divided into bushland, grassy woodland, dense woodland,

dense wooded bushland, Forest, wooded Bushland, dense bushland, grassy bushland and woodland



(Hernandez, 2002) as shown in Figure 2-11. Vegetation distribution is strongly associated with sand-

veld, hardveld and escarpment. The escarpment and hardveld show relatively higher plant densities

than the sandveld.

On the plateau covered by the Kalahari sands, vegetation is mainly shrubs showing community homo-

geneity but with least density. The major vegetation species on the sandveld are Acacia fleckii, Boscia

albitrunca, Acacia erioloba, Lonchocarpus nelsii, Terminalia sericea, Burkea africana and Ochna

pulchra.

Close to the escarpment, vegetation becomes green, dense, tall and big in terms of stem diameter. The

existence of such dense woodland on the escarpment is poorly understood (Timmermans and Meijer-

ink, 1999). The riverine woodlands are along the alluvial valleys of the escarpment where shallow

water is found. The main species at the escarpment are Croton gratissimus, Acacia erioloba, Termina-

lia sericea and Peltoforum africanum. The escarpment consists mainly of the sample species but with

heights around 3 m and canopy coverage of 14%.

In the hardveld, the vegetation is typically multi stemmed and leafless. Soils and soil moisture condi-

tions vary greatly causing a mixture of habitants with a large number of different plant communities.

Spiny acacias with mosaics of other tree/shrub species dominate this area. Common species are Aca-

cia tortilis and acacia nilotica.

Figure 2-11: Vegetation map of Serowe (after Hernandez, 2002)

2.6.1. Vegetation and groundwater

There is a growing appreciation that vegetation and groundwater systems are but expressions of the

same precious resource. Researchers have been developing works on understanding the relationship

between groundwater and vegetation. Groundwater-vegetation interactions can be considered from

two aspects:



��The effects of vegetation on groundwater recharge and extraction of groundwater by vegeta-

tion.

��The effects that abstraction of groundwater may have on vegetation communities (habitats)

the character of which is determined to some extent by groundwater.

The two aspects cannot be completely separated. For example the extraction of water from the soil

profile by roots reduces water flux to groundwater. Manipulation of vegetation cover by altering den-

sity or species, changes water use, infiltration and soil evaporation and thus ultimately influence the

amounts and patterns of surface runoff and recharge (Scott and Maitre, 1998).

2.6.2. Vegetation density

The Normalized Difference Vegetation index (NDVI) is known to be highly correlated with vegeta-

tion parameters such as green leaf biomass and green leaf area (Griend and Gurney, 1987). The mag-

nitude of NDVI is related to the level of photosynthetic activity in the observed vegetation. In general,

higher values of NDVI indicate greater vigour and amounts of vegetation (Parodi, 2000). NDVI val-

ues range from –1 to +1 with –1 values representing dry vegetation or bare land cover and for a com-

plete healthy green vegetation cover its value is equal to +1. It is defined as

34

34

rrrr

NDVI+−

= (2.1)

where r4 is reflectance for the near infrared band

r3 is reflectance for the red band

Landsat 7 TM image for 1st October 2001 was used to map out the vegetation density in the study area

using the NDVI. The calculations where performed in ILWIS GIS programme using a script according

to equation 2.1. Figure 2-12 shows NDVI values ranging from 0.12 to 0.32. High NDVI values indi-

cate healthy vegetation and low NDVI values indicate stressed vegetation.

Figure 2-12: NDVI derived from Landsat 7 TM satellite images



2.6.3. Species characteristics

Many Savannah trees are deep rooted with legumes such as Acacias reaching depths of 3-20 m and

even >53 m (Canadell et al., 1996). Eucalyptuses also have deep roots reaching 60 m. Studies of root-

ing systems of some trees found in a typical Savannah at Nylsvley were done by Scott and Maitre

(1998) and these can be compared with the same species found in the Serowe as described below.

��Acacia erioloba, is dependent on readily available groundwater

��Terminalia sericea is a fairly common shrub or small tree and grows up 6 to 9m tall but indi-

vidual trees may reach 23 m height. It thrives in deep sandy soil. Terminalia sericea occurs in

the woodlands. The root system is primarily shallow but can spread out from 0.12 – 0.35 m

depth and 3.6 – 6.6 m from the stem

��Burkea africana: A fairly common tree on deep sand, it grows up to 12 m tall. It is the last

large tree to lose its leaves during the dry season. It flowers in October with unspectacular

creamy-white to green spikes and can easily be identified by its dark-red velvety young shoot.

It is a drought resistant a perennial shrub africana roots spread up to 20.5 m from the tree,

most roots were found in the upper 0.4 m.

��Boscia albitrunca: According to Cannadell et al., (1996), this specie is reported to have had

very deep roots of up to 68 m in the central Kalahari

��Acacia fleckii: A multi-stemmed shrub, which is mainly found in the omurambas. It flowers in

white spikes from November to January.

��Lonchocarpus nelsii: A small, common tree up to 5 m tall, widely distributed on deep sands

as well as loamy sands. It flowers from September onwards

��Ochna pulchra: It is a small tree, which grows up to 4 m in height. It is widely distributed on

deep sands as well as loamy sands. It flowers together with the new leaves from September

onwards and is amongst the first trees to flower after the dry season. Ochna pulchra had roots

extending up to 4 m from the stem and “individuals” are often connected to others by lateral

roots to form clones. Most roots stop at the bedrock (2.2 m depth).

��Dichrostachys cinerea: This is a small, deciduous drought resistant tree.

��Acacia leuderitzii It is a large, fast growing species and grows up to a height of 5m. Accord-

ing to Barnes et al as cited by (Scott and Maitre, 1998), Acacia leuderitzii is one of the most

widespread southern African acacias that will only grow where it has access to permanent un-

derground water as it is not drought tolerant.

2.7. Hydrogeology

The geology and hydrogeology of Botswana and hence that of Serowe, has extensively been described

by various researchers. Carney et al, (1994), SGC (1988) and WCS (2000) described the geology of

Serowe, based on geophysical interpretations and cores from exploration works. The following sec-

tions will therefore focus only on the hydrogeological significance of the geological formations and

aquifer system and groundwater recharge.



2.7.1. Formations

The identified layers in the project area are Kalahari superficial deposit layer, Stormberg basalt layer,

Ntane sandstone layer and Ecca layer (Figure 2-13). In the study area, Ntane sandstone forms the main

aquifer, which is isolated by physical boundaries.

Figure 2-13: Hydrogeological cross-section of Serowe study area (after Lubczynski, 2000)

Ntane Sandstone

Ntane Sandstone is the principle aquifer of the region and consists of an upper arenaceous “Massive

member” and a lower more argillaceous “Transition member”. The Ntane sandstone thickness varies

from 0 to 120 m as seen in Figure 2-14 below and is overlain unconformably by a thickness of the

Stormberg Basalt. The sandstone is often poorly cemented with a metamorphosed band at the contact

with the overlying Stormberg and a few occasional, thin-cemented bands. Groundwater conditions in

the Ntane sandstone are either confined (where the overlying Stormberg basalt is present) or uncon-

fined (where basalts have been removed by erosion). Confined conditions are dominant in the north

and west of the up thrown block where basalt cover is thickest. Unconfined conditions predominate in

the south and east of the study area (Figure 2-14).



Figure 2-14: Thickness of the Ntane sandstone [m]

The confinement of the Ntane sandstone aquifer is dependent on the basalt thickness, its degree of

fracturing and also on the altitude of the base of the basalt layer with respect to the elevation of the

piezometric surface. Wherever the piezometric surface is higher than the elevation of the basalt base

the Ntane sandstone is confined, which is the case, over most of the area with basalt cover. However

in the western and southern part of the study area (Figure 2-15), the Ntane sandstone piezometric sur-

face is lower than the elevation of the basalt base such that unconfined conditions occur even below

the basalt. In Figure 2-15 the elevation of the bottom of the basalt has been subtracted from the

groundwater heads of 1997. Values less than or equal to zero indicate unconfined layers while posi-

tive values show confined values.

Figure 2-15: Degree of confinement in Serowe in [m]

Stormberg Basalt The Stormberg Basalt varies in thickness from less than 0 to more than 120 m and is structurally con-

trolled by faults (Figure 2-16). It is thickest in the centre of the study area and shallow in the east,

south, southwest and in the north. In the eastern part of the area the basalt is thin or non-existent due



to erosion leaving the Ntane sandstone exposed. Here the Ntane sandstone is directly recharged by

rainfall. In the western part of the escarpment, the basalt thickness forms a confining layer to the

Ntane sandstone where it is not fractured. This inhibits recharge to the Ntane sandstone, however,

where it is thin and deeply fractured, water is able to move freely into and out of the basalt enhancing

recharge.

Figure 2-16: Thickness of the Stormberg basalt in [m]

Kalahari sands Kalahari sands overlie the Stormberg basalt and occur only in the area to the west of the escarpment.

They vary considerably in thickness from 0 to 75 m as shown in figure 2-17. Together with the Ntane

sandstone, it is assumed to comprise a continuous hydrostratigraphic profile (WCS, 2000). Discon-

tinuous isolated aquifers occur in the superficial Kalahari sediments at a number of localities and to

the east of the escarpment where they are most often exploited by shallow hand dug wells (SGC,

1988).

Figure 2-17: Thickness of the Kalahari beds



2.8. Flow system analysis

2.8.1. Head Distribution

With respect to the principle aquifer of Serowe (Ntane sandstone) general piezometric gradient for

much of the region is from east to west from a zone that lies slightly to the west of the escarpment

(Figure 2-18). This zone represents a piezometric mound (or divide), centred approximately on Serwe

pan and trending NNW – SSE along the alignment of the escarpment WCS, (2000), SGC, (1998),

Magombedze, (2002). From this mound groundwater flows radially out in all directions WCS, and

SGC, (1998). Maximum elevation in the area exceeds 1180 m. To the west it falls to less than 1070 m.

At the boundary, eastward elevations decrease to below 1030 m where the aquifer outcrops. Steeper

gradients occur on the east. These hydraulic heads influence the groundwater flow direction.

Active recharge presently occurs to maintain this piezometric mould, although there is much debate

concerning the mechanisms of recharge especially in areas where Kalahari sand cover is substantially

thick. Direct discharge (in form of springs) is said to have occurred historically along the escarpment

and significant evapotranspiration from shallow groundwater is also believed to occur in this zone

(WCS, 2000).

Figure 2-18: Hydraulic head distribution [m] for June 2001

2.8.2. Depth to water table

The study area is characterised by relatively deep water tables ranging from 20 to 80m (Figure 2-19).

Groundwater levels in the Ntane sandstone lie partly above and partly below the base of the basalt

cover. The surface is thus a composite feature being a piezometric surface where it occurs above the

basalt and a water table where it is below or where there is no basalt. Groundwater levels in the Ntane

sandstone and further to the west in the Sanakoma are generally 30 – 70 m. The minimum recorded

value is 11.87 m at BH 7102 (WCS, 2000). Below the escarpment the water table is much shallower

and water is between 10 and 40 m depth SGC, (1988). On the eastern side of the piezometric divide,



groundwater heads are very much shallower of the order of 15 m below groundwater surface. How-

ever, no records of artesian flowing conditions exist in the study area. Figure 2-19 below show the

depth to groundwater based on water level data of 1997.

Figure 2-19: Depth to groundwater [m]

2.8.3. Hydraulic conductivity of the aquifer

The upper massive member of the Ntane sandstone aquifer, which consists of fine to medium grained

sandstone and is poorly cemented, has higher primary permeability than the lower siltier transition

member. However in some localities, the transition member contributes significantly to the overall

yield of the Ntane sandstone aquifer, particularly where fractures have been developed along bedding

planes (WCS, 2000).

Hydraulic conductivity of the Ntane sandstone ranges from 0.11 m/day to 0.30 m/day with an average

value of 0.19 m/day SGC,(1988). Pumping tests also carried out by WCS, (2000) indicated hydraulic

conductivity values ranging from 0.0127 m/day derived at a low yielding exploration borehole BH

8451 to an exceptionally high value of 0.464 m/day derived from a high yielding production borehole

BH 8673.

2.8.4. Storage parameters

Storage parameters represent the amount of water that is released from or taken into storage and are

influenced by porosity. Groundwater in the Ntane sandstone occurs partly in primary porosity consist-

ing of intergranular spaces in the sandstone matrix and partly as secondary porosity provided by the

fractures and fissures(WCS, 2000). Ntane sandstone storativity values range from 0.00007 to 0.04,

indicating both confined and unconfined aquifer response. However, boreholes located within the

thick basalt zones show confined response with a lower range of storage values 0.01 or less. With re-

spect to unconfined storativity (specific yield), a storativity value of 0.08 is representative of the



Ntane sandstone aquifer (WCS, 2000). From earlier work, by Department of Geological Survey, other

values between 0.7 and 2 have been obtained.

2.8.5. Recharge Potential

Quantitative estimates of recharge to groundwater table in the Serowe area have been made from vari-

ous techniques (chloride mass balance evaluation, unsaturated zone soil moisture measurements, wa-

ter balance modelling, GIS modelling and environmental isotopes).

Methods have shown that recharge occurs in the study area. All evidence indicates that recharge is

taking place both below the main groundwater mound and in regions where unconfined conditions

exist even in areas with basalt cover. Approximately half the study area is subject to recharge (SGC,

1988). Direct discharge in form of springs appears historically to have taken place along the foot of

the Kalahari escarpment. Recharge rates from various studies indicate rates of 0.1 mm/yr to 13.5

mm/yr.

Recharge potential for an area can be influenced by factors such as vegetation density, soils, geology,

Kalahari beds thickness and lineament density maps. However, according to Obakeng (2001), prefer-

ential flow needs to be accounted for when delineating recharge potential maps. The works of

Obakeng (2001) emphasized the need to include width and depth to which lineaments (fractures and

joints) extend to the unsaturated zone. Lack of this can lead to over estimation of recharge rates de-

spite few fractures in an area and a poor lithological unit (example fresh basalt may show high re-

charge rates despite the few fractures

ASSESSMENT OF WATER FLUXES IN SEMI-ARID ENVIRONMENTS SEROWE CASE STUDY (BOTSWANA) CHAPTER 3


3. Materials and methods

3.1. Materials

The following materials were used in this research

• Landsat 7 TM image 2001

• Software; Microsoft excel for spreadsheet calculations, ILWIS 3.1 for GIS interpretations,

SPSS, Microsoft word for word processing, AWSET, Tirtaharapan

• GPS for taking coordinates at different sampling points.

3.2. Methods

In order to achieve the objectives the following methodology was adopted and included: Pre-

fieldwork, Fieldwork and Post fieldwork. The methodology followed in this research is presented in

Figure 3-1 below.

3.3. Preliminary preparation

This was the initial step and included the following:

Literature review of reports, journals and publications was carried out in order to get acquainted to the

research topic. For the study area, literature on topography, hydrogeology, climate, geology vegeta-

tion, water balance and soils in the study area was reviewed.

By using satellite images, a familiarization of the area was carried out. Preliminary interpretation of

the images helped to map out the sampling areas for sapflow measurements during fieldwork.

A database with available data was organized, screened and pre-processed in excel spreadsheets.



Literature review

Problem formulation

Research objectives

Research proposal

Sapflow data

Groundwater levels

Secondary data

Evapotranspiration data

Raw data Data collection

Discussion, conclusions andrecommendations

Fiel

d w

ork

Pos

t fie

ldw

ork SM studies

Data analysis

Data entry

Pre

fied

wor

k

Preparation for field work

Preparation ofdatabase Image analysis Gathering available

material

Meteorological dataBiometric data

Sapwood area data

Manual measurements ofGWL, data from DIVERS

Pre

fied

wor

kFi

eld

wor

kP

ost f

ield

wor

k

Soil moisture data Soil moisture,rainfallmeasurements,calibration parameter

Air temperature, Rainfalldata and wind speed,

Relative humidity,Radiation

Database

Lobatse GeologicalSurvey

Botswanameteorological

Department

Transpirationflux Earth modelEvapotranspiration flux

Figure 3-1: Research Methodology



3.4. Fieldwork

Fieldwork was carried out from the 9th September 2002 to 12th October 2002 and consisted of the fol-

lowing activities.

3.4.1. Sapflow measurements

Sapflow measurements were made using the UP sapflow sensor developed according to Granier prin-

ciple (1985,1987). There were 3 major sites were sapflow measurements were ongoing:

• 6 stations were set up during fieldwork and for 3 consecutive days measurements were carried

out on 6 species (Short term measurements)

• At 2 mobile stations, originally set up to monitor canopy resistance, 6 trees were also moni-

tored for sapflow.

• At the permanent stations set up in November 2001,measurements of sapflow have been taken

since then till date.

Spatial sapflow assessment Short-term measurements were carried out to determine the spatial distribution of transpiration in the

study area. Measurements were carried out for a period of three days and also at the mobile stations

(Plate 1). The research target species selected for measurement at each vegetated site included: Ter-

minalia sericea, Acacia leuderitzii, Burkea africana, Acacia erioloba, Ochna pulchra, and Dichro-

stachys cineria (Appendix A1 -A2). Also data from species monitored 11th September 2001 to 4th Oc-

tober 2001 by Fregoso, (2002) are presented (Table 3-1)

Table 3-1: Tree species and sampled individuals measured at the respective stations

Scientific name Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 GS04 GS05 13-17 /

09 18- 21 /09

21/09-25/10

25-29/09

29/09-4/10

4 – 8 /10

3-7/10

24–28 /09

Total Trees

Acacia erioloba - - - 23 - - - - 23 Terminalia sericea - 11 1 - 13 1 - 6 32 Acacia leuderitzii 15 - - - - - - - 15 Burkea africana - 7 19 1 3 3 - - 33 Ochna pulchra, - - 3 - 4 10 - - 17 Dichrostachys cineria - 5 - - 2 10 6 - 23

Site 1 Site 2 Site 3 Site 4 By Fregoso (2002)

11-14 /9/01

4-19 /9 /01

19-29 /9/01

29/9-4/10/01

Acacia fleckii - 9 5 5 19 Boscia albitrunca 1 1 7 2 11 Lonchocarpus nelsii 11 - - 3 14 Site description A number of factors had to be considered in selecting suitable sites for the measurements. This was

done using satellite images, existing maps and field reconnaissance. Besides being easily accessible,

individual sites had to have a homogeneous terrain unit and no research should have been carried out

in the area before. Following this criterion, 7 sites were selected as shown in Figure 3-2 and sapflow

was continuously monitored from September 2002 to October 2002. The experimental sites were all



located in the western landscape unit (sandveld) of Serowe area. The sites are characterised by a

sandy plateau covered by open savannah vegetation. These sites represented a variety of species that

are found in the IKONOS and permanent station and there were no marked differences in the soil

type.

Figure 3-2: Location of the sapflow stations in the study area

Plate 1: Mobile stations at GS05



Tree selection The criteria used to select the species were:

• The species had to have stem diameters greater than 5cm.

• Abundance of the specie in the study area.

• Good representation of the size distribution of the stems and crowns of the stand

• Its occurrence in the permanent stations and dominance in the IKONOS area.

• Feasible to work in terms of tree weight

For each individual tree to be measured, the criteria used

• Height and weight of the tree would be assessed since heavy trees would be difficult to ma-

nipulate for the tree cutting experiment.

• The tree health was considered. Trees with no evidence of damage due to fire, pruning or

cracks were selected.

• The stem of the tree should be at least 50 cm above the ground and the shape should be almost

regular.

• The crown of the respective stem should be clearly identified from other neighbouring

crowns.

• The trees in the vicinity should be at least 6 as the sensor system can operate with at least 6

sensors.

• The individual tree should not be more than 20 m from the other trees due to maximum length

of sensor wires.

Installation and principle of Sap sensor operation For the short-term measurements, part of the stem of a tree (southern hemisphere) was identified in

order to avoid direct solar radiation and its effect on the thermal gradient between the probes. The

bark of the stem was cleared using a machete taking care that the sapwood is not damaged. A hole of

2 mm diameter, about 23 mm deep was drilled 0.5 m above the ground. A second hole 10 cm below

the first one was drilled and aluminium tubes were inserted into the holes. Two cylindrical probes (UP

Thermal Dissipation Probes (TDP) of 2 mm diameter, were covered with a thin layer of silicon fat and

inserted 2 cm into the sapwood of the tree stem taking care that the needle with the yellow band is in

the upper hole. Terostat was put around the needle to prevent sensor from water running down the

tree (Plate 2). After electrode installation, a radiation protection shield was placed around the sensor

to protect probes from solar radiation and down running water. The shield extended along the stem

where the probes were located and was attached to the bark with tape (Plate 3).



Plate 2: Set up of sapflow sensors

Plate 3: Sapflow ADAS at Station 3 and data Logger

The upper probe was continuously heated by a constant power (0.2 W) from the solar panel and bat-

tery. The lower one is not heated but together with the upper one provides the reference temperature

difference (∆T) between the two probes, which is inversely correlated with sap velocity (Granier,

1985). Higher sapflow cools down the heated electrode so ∆T becomes lower. Measurements of sap-

flow were recorded and stored every 30 minutes using automatic data loggers (Plate 3). Also moni-

tored with sapflow were the effects of weather through measurements of incoming short wave radia-

tion (Kin), air temperature (Ta), relative humidity (RH), and wind speed (Ws) at heights of 0.50 m and

2.05 m above ground and recorded at the ADAS system with a time resolution of 30 minutes. The

measurements were carried out from 12th September 2002 to 8th October 2002 on 6 station sites. In-

stallation of the 24-channel system required 8 – 10 hours with a good working team. This includes all

the processes described above. De-installation of the equipment at the end of the 3 monitoring days

usually took 2 to 3 hours.

Temporal assessment Continuously heated 6 sapflow meters consisting of two probes inserted in the sapwood of the trunk

(Granier, 1985,1987) were installed at the permanent stations. They are located on either single stem



or double stem. In total, 52 sapflow sensors at 8 stations were installed within Kalahari project and

have been monitoring sapflow since September 2001 (Figure 3-3). Besides sapflow, measurements,

relative humidity and air temperature at 2m, rainfall measurements and Soil moisture (2 m, 4 m, 6 m

and 8 m) are recorded regularly using automatic data loggers every 30 minutes (Plate 4). Net radiation

in the study area is recorded at a Paje meteorological station, located within the study area.

Figure 3-3: Location of permanent stations in the study

Plate 4: Permanent station at GS05

Biometric variables Once the sapflow measurements were done, stem diameter ds [cm] for each tree was measured using a

calliper, with measurements carried out in the middle of the previous location of the probes in the

north-south and east-west direction. The crown diameter (dc) [m] was also measured in the north-

south and east-west direction from the centre of the station. Each tree was given a unique number for



later identification and its botanical and local name noted down (Appendix A: A1). Coordinates of the

stations were taken using a garmin GPS.

Only crown diameters were measured from the permanent stations to avoid interruption of the system

as it records sapflow. The crown diameter dc [m] for each tree was measured in the north- south and

east–west direction from the centre of the station. Existing data on stem diameters in these stations

measured before installation of the sapflow sensors is used in this study.

3.4.2. Sapwood area estimation

Tree cutting experiment After sapflow measurements where made, trees were harvested in order to determine the conductive

xylem area (Sapwood area) in the stems. The experiments were conducted under clear skies from

08:00 and 14:00 hours when transpiration rates are high. A water-soluble dye (Eosine “C” or “B”)

was prepared in buckets to form a coloured solution. The trees were cut in the middle of the previous

location of the probes and immediately put in the buckets (Plate 5) with the dye solution for 3 hours to

make evident of difference between the conductive xylem areas from the dead heartwood. A strong

and abrupt colour change was observed between sapwood and heartwood. This was possible due to

the fact that trees still transpire and move water up through the conductive xylem tissue and hence

causing the colouring. It was assumed that the colour change boundary (Plate 5) delineates the con-

ducting wood from the non-conducting wood.

Sapwood cross sectional area (As) of each sample tree was determined from measurements on wood

discs, cut at 0.5 m heights. Bark thickness; sapwood thickness and heartwood diameter around each

wood disc were measured using a ruler used to compute sapwood area using equation 3.1 below. Only

3 species were difficult to delineate the sapwood and as such were left out of the analysis. Estimated

sapwood area for the different species is listed in Appendix A: A3.

As = Stem diameter – ((bark area) – (heartwood area)-(non coloured area) [cm2] (3.1)



Plate 5: Tree cutting experiment and stem discs for acacia erioloba

Di-electric “Blanket” experiment A non-destructive method was explored in an attempt to determine the sapwood area. The Dielectric

sap meter uses the contrast in dielectric property between tree sap and dry wood. The tree was first

cleared of branches, ensuring that the stem is smooth and cylindrical (Plate 6). After wrapping the

sensor sheet around the tree stem a measurement of dielectric constant of the tree was taken and af-

terwards another measurement of the dielectric constant (empty value) with the same folding of the

sensor sheet was taken. Using the calliper, the minimum and maximum diameter of the tree was also

taken. Additionally, the temperature of the bark was also measured using a thermometer, which was

inserted 2 cm into the tree (Plate 6).



Plate 6: Sapwood area estimation using the dielectric “blanket” experiment

3.4.3. Mobile Mast for Canopy resistance

Mobile stations were set up during the fieldwork to acquire data necessary for determining aerody-

namic resistance and canopy resistance by measurement of micrometeorology parameters. The sta-

tions were set up close to the permanent stations where other measurements (section 3.21) have been

continuously measured to supplement this data set, which can be used in analysis of the data. These

measurements were carried out for a period of 10 days on the assumption that this data was represen-

tative of the station’s tree characteristics. The measurements included wind speed, air temperature and

relative humidity (Plate 1). Soil temperature was also measured at 5 cm and 20 cm. Data from these

stations was downloaded during fieldwork.

3.4.4. Measurements for Landsat 7 TM image

On the 18th of September 2002 and 4th October 2002 at 10:00pm, day of the Landsat 7 overpass meas-

urements were carried out. A representative sample area 100 m x 100 m in area was selected. This

area was subdivided into 16 small plots each 25 m x 25 m. All measurements were conducted inside

this plot, which was used as a validation for future calculations and included

��Instantaneous incoming short-wave radiation and Extraterrestrial radiation were measured us-

ing a hand held pyranometer

��Surface temperature measured using a hand held thermal infrared radiometer.

��Ground control points for georeferencing and classifying the satellite image using the GPS.

Details of the landuse and landcover were noted.

However, this dataset (Appendix D1) was not used in the current study but can be used by another

person in future to assess actual evapotranspiration using remote sensing methods.



3.4.5. Meteorological data acquisition

Archived data for the study area was acquired from the Meteorological department of Botswana, Ga-

borone. Daily rainfall data for Serowe and Mahalapye, temperature and relative humidity measured at

2 m, sunshine hours and wind speed were obtained. The data set collected was from 2001 September

to September 2002 to update the existing database, which had data from 1986 till 2001.

Data from the permanent and mobile stations was also downloaded.

3.4.6. Field data for 1-D modelling

Groundwater level data from the monitoring boreholes located in the study area were downloaded

from the divers. Manually taken measurements for the ground water levels were also collected. This

data was used as input in the EARTH model.

3.5. Post-field work

This was the major phase of the research and consisted of data integration from the field, analysis and

interpretations using GIS, statistics, excel spreadsheets and modelling software. The results are the

contents of this thesis.

ASSESSMENT OF WATER FLUXES IN SEMI-ARID ENVIRONMENTS. SEROWE CASE STUDY (BOTSWANA) CHAPTER 4


4. Transpiration of trees: short term and long term monitoring using sapflow methods

Transpiration process Transpiration consists of the vaporization of liquid water contained in plant tissues and the vapour

removal to the atmosphere (Allen et al., 1998). Sapflow techniques measure transpiration alone; thus

sapflow methods are a useful and complimentary tool in studies of water or energy budgets of land

surfaces, as they can be used to partition evapotranspiration between plant and soil evaporation and to

divide estimates of transpiration among the component species of plant mixtures (Smith and Allen,

1996). Transpiration depends on the water potential in the leaves and micrometeorological parameters

(wind, radiation, humidity and air temperature) as well as on soil moisture.

Transpiration accounts for most of the vapour losses from land.Plant transpiration consists of three

processes; absorption of soil water by plant roots; translocation in liquid form through the vascular

system of roots, stems and branches to the leaves; and through the vascular system of leaves to the

tiny stomata cavities, where evaporation takes place (Dingman, 1994). Since translocation of water is

a continuous process in stems and branches of a tree, the quantity of flow of water from the stem is

proportional to that being evaporated from the surface of leaves. Hence measurement of sapflow can

be made at the stem of a tree for assessment of transpiration for a particular tree. The effective cross

sectional area of the stem is calculated for the computation of the water flux considering the flow of

water is only through the xylem. The xylem is a woody tissue found in higher plants that conducts

water and inorganic salts throughout the plant and provides it with mechanical support. (Figure 4-1)

Figure 4-1: Xylem in a cross section of woody stem



4.1. Data Distribution

Normal probability plots are recommended for assessing normality (Moore and McCabe, 1998). The

acquired data set from the field (stem diameter, crown diameter and sapwood area) for the six moni-

tored tree species was analysed to determine if there is a normal distribution using SPSS statistical

software. The points lie close to the straight line for the whole data set collected, indicating that the

data are normally distributed. Figure 4-2 shows probability plot of crown diameter for Dichrostachys

cineria, the rest are presented in Appendix B: B1.

Figure 4-2: Normal probability plot of crown diameter data for Dichrostachys cineria

4.2. Descriptive statistics

The biometric characteristics include (sapwood area (As), stem diameter (ds)] and crown diameter

(dc) for all the monitored trees of which descriptive characteristics like the mean, standard deviation,

minimum and maximum values where defined. A statistical analysis performed (ANOVA) on the data

set from the stations shows a wide range of variability between individuals of trees and within trees of

the same species. The biometric characteristics of the species measured are significantly different:

stem diameters (F = 7.80, P <0.05); crown diameters (F = 3.84, P <0.05) and sapwood area (F = 9.81,

P = < 0.05). These variations can be related to different dimensions of the sampled trees and to type

of species. The biometric variables measured for short-term measurements included: (sapwood area

(As), stem diameter (ds)] and crown diameter (dc) while long-term measurements, stem diameter and

crown diameter were measured. Sapwood area estimation as discussed in section 3.2.2. Biometric data

on 3 monitored species by (Fregoso, 2002) are also presented.

4.2.1. Short term measurements

Dichrostachys cinerea presents the smallest stem diameter and sapwood area ranging from 5.00 cm

to 10.55 cm and 0.0010 m to 0.00047 m2 respectively. Ochna pulchra presents the smallest crown di-

ameter (1.20 m to 4.60 m) while Acacia leuderitzii showed the largest sap wood area (0.00271 m2 to

0.01912 m2) and averaged 0.01074 m2 (Table 4-1)



Table 4-1: Descriptive statistics of tree species monitored for short-term measurements

Species n Biometric Characteristics

Mean Minimum Maximum Standard deviation

Acacia erioloba 23 ds [cm] 10.45 5.80 16.20 3.27 dc [cm] 2.67 1.45 3.50 0.60 310−As [m2] 4.20 1.07 8.90 2.45

Terminalia sericea 32 ds [cm] 8.66 5.60 15.70 2.52 dc [cm] 3.39 1.95 5.90 1.12 310−As [m2] 2.71 0.76 7.90 0.82

Acacia leuderitzii 15 ds [cm] 16.07 8.55 23.60 4.62 dc [cm] 3.80 2.55 5.50 0.89 310−As [m2] 10.74 2.71 19.12 5.67

Burkea africana 33 ds [cm] 11.07 5.65 19.25 4.59 dc [cm 2.52 1.15 5.50 1.22 310−As [m2] 5.98 1.15 16.17 4.73

Ochna pulchra 17 ds [cm] 11.974 5.65 20.11 4.90 dc [cm] 2.56 1.20 4.60 0.80 310−As [m2] 4.50 0.57 11.22 3.28

Dichrostachys cinerea 33 ds [cm] 6.63 5.00 10.55 1.38 dc [cm] 3.38 2.15 4.50 0.642 310−As [m2] 1.00 0.47 1.91 0.44

Acacia fleckii 11 ds [cm] 3.8 18.0 11.8 7.0 dc [cm] 2.45 1.43 3.55 1.95 310−As [m2] 80.7 23.3 163.6 46.1

Lonchocarpus nelsii 11 ds [cm] 15.3 6.6 23 6 dc [cm] 3.93 0.88 3.02 1.69 310−As [m2] 159.5 24.4 360.2 114.7

Boscia albitrunca 11 ds [cm] 19.6 8 29.0 7.8 dc [cm] 3.30 2.1 6 4.36 310−As [m2] 252 34.8 499.5 164.2

(Number of trees (n), Stem diameter (ds), crown diameter (dc) and sapwood area (As))

4.2.2 Long term measurements

Average crown diameters and stem diameter are presented in Table 4-2 below. Boscia albitrunca pre-

sented the largest diameter of 29.30 cm and the smallest being Ochna pulchra 3.50 cm. The biggest

crown diameter was observed in Lonchocarpus nelsii 6.95 m and the smallest in Terminalia sericea

with a crown diameter of 1.62 m.



Table 4-2: Biometric variables of tree species monitored for long-term measurements

STATION ID Botanical name Type of stem Stem diameter [cm] Crown diameter [m]

1 Acacia fleckii 1 20.06 4.95

2 Burkea africana 1 20.06 5.05



5a Ochna pulchra 2 3.18 1.13

GSO1

5b Ochna pulchra 2 6.69 2.25

6 Lonchocarpus nelsii 1 25.16 4.88

7 Ochna pulchra 1 15.61 3.31




GSO4


12 Acacia erioloba 1 10.83 3.30

13a Terminalia sericea 2 8.92 3.28

13b Terminalia sericea 2 5.73 3.05

14 Terminalia sericea 1 8.60 3.72

15 Dichrostachys cinerea 1 10.51 3.93

GS05

16 Boscia albitrunca 1 27.71 5.79

17a Acacia fleckii 2 15.13 5.62

17b Acacia fleckii 2 9.55 3.31




GSO7





GS06


4.3. Statistical analysis

Correlation coefficient (r) measures the strength and direction of the linear association between two

quantitative variables x and y (Moore and McCabe, 1998). Correlations were performed to ascertain if

certain measurable variables could have a relationship that can be used to predict sapwood area in

trees where it has not been determined. The variables included, stem area, crown area (derived from

stem diameters and crown diameters) and sapwood area for which correlation coefficients were ob-

tained using the Pearson- moment correlations.(Appendix B: B2).

The data showed all species having a strong positive correlation between stem diameter and sapwood

area (r = 0.88 to 0.96), crown diameter and sapwood area (r = 0.65 to 0.95) with the lowest from Di-

chrostachys cineria (r = 0.65 to 0.75), stem area and sapwood area (r = 0.86 to 0.96), crown area and

sapwood area (r = 0.68 to 0.92).



4.4. Relationships between stem area, sapwood area and crown area

Easily measurable parameters can be compared to other parameters, which are not easily measured by

means of equations. Since a relationship exists between sapwood area (Ax) [cm2], stem area (As)

[cm2] and crown area (Ca) [m2], relationships per species that allow estimating sapwood area are

therefore relevant in this type of studies. Regression lines were used to define the equations of pre-

dicting sapwood area from either crown area or stem area.

4.4.1. Short-term measurements

The biometric relationships in trees between crown area (Ac), stem area (As) and sapwood area (As)

for the 3 species in the study are known (Fregoso, 2002), however for the 6 species these relationships

were unknown. For that purpose, a regression model between each biometric variable was explored in

order to find the unique species relationships. A linear or polynomial curve was fitted into the data set

until the best curve, which fits the model, was obtained ensuring the regression line passes through

zero to avoid the prediction of negative sapwood area for very small stems. Figure 4-3 gives regres-

sion models for all specie monitored in the study area.

Dichrostachys cinerea, Acacia erioloba, Terminalia Sericea, Acacia leuderitzii and Acacia fleckii had

the polynomial curve fit the data set while Burkea africana, Ochna pulchra, Lonchocarpus nelsii and

Boscia albitrunca data set fitted well with the linear curve. A strong statistical association between

sapwood area and stem area was found for most species with r2 values exceeding 0.83 % in 8 species

apart from Dichrostachys cinerea (r2 =0.79, n = 21). Crown areas also explained 74 –90% of the

variation in sapwood area in all the five tree species except Dichrostachys cinerea (r2= 0.42, n= 21).

From the correlation coefficient, results showed that stem area was a better predictor of sapwood area

than crown area but also crown areas can be used for most of the species

Table 4-3: Regression equations for stem area (x) and sapwood area (y) for the different species

Species name Regression equations n r2 S.E Curve type Acacia erioloba 0004.0106108 528 −×+×−= −− xxy 23 0.94 0.0007 Polynomial

*Acacia fleckii 0005.0108109 528 −×+×= −− xxy 11 0.96 0.001 Polynomial

Acacia leuderitzii 003.0109109 528 −×+×−= −− xxy 15 0.91 0.0021 Polynomial

*Lonchocarpus nelsii 0009.0108 5 −×= − xy 11 0.98 0.0015 Linear

Burkea africana 0003.0105 5 +×= − xy 33 0.90 0.0015 Linear

*Boscia albitrunca 0002.0108 5 −×= − xy 11 0.99 0.00098 Linear

Dichrostachys cinerea 0004.0105103 527 −×+×−= −− xxy 33 0.79 0.00022 Polynomial

Ochna pulchra 0004.0103 5 +×= −y 17 0.83 0.0014 Linear

Terminalia Sericea 0004.0105102 528 −×+×−= −− xxy 32 0.86 0.00069 Polynomial

n = number of observations (* by Fregoso, 2002), SE (standard error)



Acacia Leuderitzii

Burkea africana

Terminalia sericea



Dichrostachys cinerea

Ochna pulchra

Boscia albitrunca



Lonchocarpus nelsii

Acacia fleckii

Acacia erioloba

Figure 4-3: Regressions of stem area, crown area and sapwood area for the monitored species



4.4.2. Long term measurements – Sapwood area estimation

Sapwood area estimates for 24 trees in the permanent stations were made based on the equations ob-

tained from the regressions (Table 4-3). Also existing relationships between stem area and sapwood

area reported by Fregoso (2002) for 3 different species; Acacia fleckii, Boscia albitrunca and Loncho-

carpus nelsii have been used to predict sapwood area for the various species in the permanent sta-

tions.

Data from stations GS08 had to be rejected due to breakdown of sapflow sensors from this station. At

GS02 and GS03 sapflow sensors developed problems and were recording incorrect data. The rest of

the species, relationships have not been determined as yet to enable estimate of sapwood area. The

actual numbers of trees that have reliable data and were included in the analysis are shown in Table 4-

4 below and predicted sapwood area for the different species.

Table 4-4: Predicted sapwood area (As) for trees in the permanent stations

Station ID Botanical name Type of stem Sapwood area (m2)

1 Acacia fleckii 1 0.0158 2 Burkea africana 1 0.0160 3 Acacia fleckii 1 0.0104 4 Acacia fleckii 1 0.0061 5a Ochna pulchra 2 0.0006

GSO1

5b Ochna pulchra 2 0.0015 6 Lonchocarpus nelsii 1 0.0389 7 Ochna pulchra 1 0.0061 8 Lonchocarpus nelsii 1 0.0419 9 Lonchocarpus nelsii 1 0.0252 10 Lonchocarpus nelsii 1 0.0359

GSO4

11 Lonchocarpus nelsii 1 0.0425 12 Acacia erioloba 1 0.0044 13a Terminalia sericea 2 0.0028 13b Terminalia sericea 2 0.0011 14 Terminalia sericea 1 0.0026 15 Dichrostachys cinerea 1 0.0023

GS05

16 Boscia albitrunca 1 0.0480 17a Acacia fleckii 2 0.0110 17b Acacia fleckii 2 0.0048 18 Terminalia sericea 1 0.0040 19 Terminalia sericea 1 0.0003 20a Acacia fleckii 2 0.0025

GSO7

20b Acacia fleckii 2 0.0101 21 Boscia albitrunca 1 0.0055 22 Boscia albitrunca 1 0.0139 23 Boscia albitrunca 1 0.0537

GS06

24 Dichrostachys cinerea 1 0.0009 Single stem (1); double stem (2)

4.5. Sapflow calculations

The Granier method is based on sapwood heat dissipation method, which increases with sap velocity.

The temperature differences between the two probes, is influenced by the sap velocity in the vicinity

of the heated probe obtained by



231.10119.0 KV ×= (4.1)

where V = sap velocity [cm hr-1]

K is a dimensionless empirical factor defined by Granier (1985) and calculated from the

measured temperature difference as

( )T

TTK

∆∆∆

= - max (4.2)

Where: �T is the temperature difference between the probes [oC]

�Tmax is the maximum temperature difference between the probes [oC]

It is assumed that the temperature difference is maximum when sapflow velocity is zero or minimum,

which takes place in the night when the stomata are closed and sapflow ceases. Results of average sap

velocity for each tree are presented in Appendix B: B4.

Figure 4-4 shows diurnal variation of sap velocity for 6 trees. A symmetrical curve about midday is

observed with a rapid increase after sunrise and a decrease in the late afternoon for 6 monitored trees.

Figure 4-4: Sap velocity rates for 6 trees monitored at station 3 on 24/09/02

The results reflect the different behaviour of sap velocity rates among the trees selected. Ochna pul-

chra tree 72 exhibited a high mean sap velocity rate of 3.8 cm/hr; Burkea africana presented interme-

diate rates (2.10 cm/hr) and Terminalia sericea the least rate of (1.52 cm/h).

However, Sap velocity monitored for these species in other sapflow stations, gave different sap veloc-

ity rates and not necessarily the same trend. Table 4-5 summarizes the sap velocity rates for the3 day

period. Boscia albitrunca exhibited the highest sap velocities (3.84 cm/hr) while the least rates are

observed in Acacia leuderitzii (2.5 – 2.9 cm/hr).



Table 4-5: Average velocities for the monitored species (short term measurement)

Type of specie Minimum (cm/hr) Maximum (cm/hr) Mean

(cm/hr)

Standard deviation

Acacia erioloba 0.473 1.767 1.023 0.385

Terminalia Sericea 0.190 4.455 1.683 1.079

Acacia leuderitzii 0.225 1.559 0.608 0.428

Burkea Africana 0.290 3.974 1.326 0.879

Ochna pulchra, 0.372 3.940 1.408 1.178

Dichrostachys cineria 0.091 3.044 0.857 0.743

Acacia fleckii 0.90 6.76 2.50 1.95

Boscia albitrunca 1.26 9.97 3.84 2.29

Lonchocarpus nelsii 1.08 9.99 3.35 3.15

The sapflow rate (Qs) is calculated from the following expression:

1003600×××= VAsQs [l/day] (4.3)

Where As is the Sapwood area at the heating probe in [m2]

V is the sap velocity [cm/hr]

Sapflow behaviour for trees varies significantly depending on the tree dimensions. A low velocity but

with a bigger sapwood area can lead to a higher sapflow value. Average sapflow values for the

monitored species is shown in Table 4-6 but the values are much related to the tree dimensions (range

of diameter and hence conductive xylem area) and type of specie. The highest mean daily total

sapflow is observed in Boscia albitrunca (26 l/day) while the lowest sapflow was observed in

Dichrostachys cinerea (0.571 l/day).

Table 4-6: Sapflow (Qs) and normalised sapflow (Qn) for the monitored trees (short term)

Type of Qs (l/day) Qn (mm/day) species Minimum Maximum Average Minimum Maximum Average Acacia erioloba 0.3049 2.893 0.953 0.058 0.360 0.165 Terminalia Sericea 0.123 8.481 1.367 0.011 0.310 0.113 Acacia leuderitzii 0.0001 4.972 0.9534 0.00002 0.4042 0.073 Burkea Africana 1.039 10.586 1.982 0.00001 0.891 0.364 Ochna pulchra, 0.149 10.878 3.326 0.059 2.170 0.660 Dichrostachys cineria 0.061 3.212 0.516 0.0051 0.276 0.059 *Acacia fleckii 0.58 13.700 5.071 0.08 0.64 0.26 *Boscia albitrunca 2.43 80.03 26.32 0.29 4.87 1.55 *Lonchocarpus nelsii 0.69 30.95 11.31 0.21 2.04 0.88 Normalised Sapflow Qn

For this study, it is necessary to convert flux estimates expressed as volume per unit time to more use-

ful hydrological expressions such as mm/day. In this conversion an expression of the effective area

occupied by the tree is used. To scale tree water use in space as well as time, the relationship must

reflect the changing availabilities of energy and water supply (Hatton and Hsin-Iwu, 1995). Scaling of

sapflow to the tree can be done using the projected crown area (Oren, 1998) sapflow normalised (Qn)

is therefore deduced from the crown area (Ac) and sapflow (Qs) measurements as



AcQs

Qn = (4.4)

Normalised sapflow calculations were made for both short term (142 trees) and long-term measure-

ments (24 trees). Long-term measurements results are presented in section 4.7. For short-term meas-

urements, Figure 4-5 presents Qn for 9 trees. Qn is highest during the day and lowest during the night.

Figure 4-5: Normalised sapflow rates for 9 trees monitored at station 3 on 24/09/02

A summary of Qn for all species is listed in table 4-6 below. Boscia albitrunca presented the highest

Qn (1.55 mm/day) and the lowest Qn by Dichrostachys cineria (0.059mm/day) Details of all the moni-

tored trees and their respective Qs and Qn are listed in Appendix B: B5.

4.6. Relationship between crown area,sapwood area and sap velocity

In order to test if a relationship exists between sap velocity rates, crown area and sapwood area of the

monitored species, a correlation analysis was performed using SPSS. For this analysis the sapwood

area of each tree and mean sap velocity rates were used as input data. The Pearson moment

correlations were used. Appendix B: B2.

Terminalia sericea shows a weak positive correlation with sap velocity and sapwood area (r = 0.41)

and crown area (r = 0.37). Acacia erioloba shows a weak negative correlation with sap velocity and

sapwood area (r = -0.36,) and crown area (r = -0.49). Acacia leuderitzii shows a weak positive

correlation with sap velocity and sapwood area (r = -0.03) and crown area (r = -0.08,) Ochna pulchra

shows a weak negative correlation with sap velocity and sapwood area (r = -0.43,) and crown area (r =

0.28). 0.37). Burkea africana shows a weak positive correlation with sap velocity and sapwood area

(r = -0.17) and crown area (r = -0.09). Dichrostachys cineria shows a weak negative correlation with

sap velocity and sapwood area (r = -0.56) and crown area (r = -0.26).



Furthermore, a regression analysis was also performed between sapwood area and crown area. The

results show low regression coefficient between sap velocity, crown area and sapwood area for all

species Figure 4-6. Very low regression coefficient r2 < 0.4 for crown area and sap velocity and for

sapwood area and sap velocity showed that sap velocity is independent of sapwood area or crown

area.

Terminalia sericea

Acacia erioloba

Acacia Leuderitzii



Burkea africana

Ochna pulchra

Dichrostachys cineria



Acacia fleckii

Boscia albitrunca

Lonchocarpus nelsii

Figure 4-6: Crown area and sapwood area with Sap velocity relationship for monitored specie

A significance test (one way ANOVA) on the data set also showed no significant difference between

sapwood area and sap velocity (p >0.05) and crown area (p > 0.05) in all the species monitored at a

significance level of 0.05. The data shows high values of sap velocity and at times very low velocities.

However, on analysis of the raw data, these differences cannot be related to any direct cause.



4.7. Temporal variability of sapflow

Diurnal courses of tree water uptake reveal the key information on water status of a tree and soil mois-

ture conditions, and they also show the progression and degree of water deficit (Granier and Loustau,

1994). Temporal variability of sap flow for trees in the permanent stations was obtained from equa-

tion 4.11 to 4.4. (Appendix B: B6). Typical evolution of daily sapflow measured for the monitored

species through the measurement period 2001 to 2002 is presented. To obtain an impression of the

climatic conditions in the study area, rainfall data has been plotted.

Species react differently to water stress as seen from the graphs. The rainfall season of Serowe is from

October to April. Usually rainfall stimulates the growth of leaves and hence an increase in Transpira-

tion. This was clearly reflected by the daily rise in sapflow rates over this period in Figure 4-7. Sap-

flow rates then gradually decrease as rainfall events end. During the dry season that stretches from

May to September sapflow rates are very low, as the trees become increasingly water stressed and

shed off their leaves. This seasonal pattern in Qn was found in Lonchocarpus nelsii, Ochna pulchra,

Acacia fleckii, Burkea africana and Dichrostachys cinerea as presented in Appendix B6.

However results in Figure 4-8 indicate that during the dry season, high quantities of water are tran-

spired by Boscia albitrunca. Other species that present the same characteristics are Terminalia

Sericea, Acacia erioloba and, Dichrostachys cinerea

Figure 4-7: Diurnal variability of (Qn) and rainfall for Lonchocarpus nelsii at GS04 station

Figure 4-8: Diurnal variability of (Qn) and rainfall for Boscia albitrunca at GS06 station



Analysis of the normalised sapflow in the wet season (October to April) and the dry season (May to

September) (Table 4-7), Lonchocarpus nelsii (8) showed the highest Qn in both the wet (5.22

mm/day) and dry season (0.84 mm/day), it transpires more in the wet season when there is more water

available and less in the dry season. Acacia fleckii (4) had the lowest Qn on average with very low

transpiration rates in the wet season (0.00028 mm/day) and high Qn in the dry season (0.0029

mm/day).

Table 4-7: Summary of the Normalised sapflow during the wet and dry season

STATION ID Botanical name Type of stem Qn (mm/day)

Wet season

Qn (mm/day)

Dry season


2 Burkea africana 1 0.1910 0.1490


4 Acacia fleckii 1 0.00028 0.00293

5a Ochna pulchra 2 0.0980 0.175

GSO1

5b Ochna pulchra 2 0.1540 0.058


7 Ochna pulchra 1 0.190 0.040




GSO4


12 Acacia erioloba 1 0.079 0.129

13a Terminalia sericea 2 0.010 0.022

13b Terminalia sericea 2 0.023 0.038



GS05







GSO7





GS06


4.8. Sapflow correlation with climatic and non climatic variables variables

Transpiration, like direct evapotranspiration, depends on the energy supply, vapour pressure gradient

and wind (Allen et al., 1998). Hence, radiation air temperature, relative humidity and rainfall terms

are considered when assessing transpiration. Most experiments have shown good relationships be-



tween sapflow and climatic factors (radiation, vapour pressure deficit) for individual trees, however

these relationships can vary from one tree to another when comparing for example trees with different

crown status (Granier, 1996).Comparison with climatic variables measured over forests; also indicate

whether there is a bias in the estimate of transpiration (Granier, 1996). Climatic data was obtained

from measurements carried out near the monitored trees.

4.8.1. Correlation with Potential evapotranspiration

Climatological data measured over the 3 day period included wind speed (Ws), air temperature (Ta),

Relative humidity (RH), Net radiation (Rn) and solar radiation (Rs), were used as input to the AWSET

software to calculate Potential evapotranspiration (PET). 30 minutes PET was estimated using FAO-

Penman Monteith equation 2.1 presented in chapter 2. In Figure 4-9 the same pattern for PET and

sapflow are depicted.

Figure 4-9: Daily pattern between Sapflow and PET for Terminalia sericea (132) at station 6

However this pattern was not present in all the monitored trees as seen in Figure 4-10. Sap flow

lagged PET by 1 hour in tree1 and 2 hours in tree 3. The lags observed are presumably the result of

the uptake and transpiration of water stored in tree tissues. In such data sets, cross correlation analysis

is used to find the best correlation by finding their respective lag between the two series. For the trees

that showed lags, a cross correlation was performed with SPSS and the best lag for the tree was de-

fined.

Figure 4-10: Qs Vs PET with 1 hr lag in (1) and 2 hr lag in (2) for Acacia Leuderitzii at station 1



Relationships were then derived between PET and Qs (Figure 4-11). The correlation pattern between

Q and PET before and after the cross correlation analysis. A lower correlation is observed before and

after lagging this correlation is greatly improved. The explained variability in the measured tree sap-

flow varied at 79% after the cross correlation.

Figure 4-11: Qs Vs PET correlation without and with 2hr lag accounting for tree 74 at station 3

With the improved relationship, it was possible to determine water usage pattern for each tree even

where measurements of sapflow had not been recorded from the derived equations. For tree 74,

equation 4.5 was used to extrapolate tree fluxes to the following day where measurements of sapflow

had not been done. Figure 4-12 shows extrapolated values of sapflow originally measured up to 25th

September and the extrapolated sapflow up to 28th September 2002

3894.1738.39582.53 2 ++−= xxy [4.5]

Where y is sapflow (l/day)

x is Potential evapotranspiration (mm/day)

Figure 4-12: Daily Qs pattern for station 3 (monitored) and station 4 (extrapolated)

4.8.2. Correlation with soil moisture

Reduction in soil moisture affects the dynamics of water flux in stems and increases the contribution

of water stored in stems (Oren, 1998). Qn is correlated with soil moisture, seasonally as also shown

with Figure 4-7 and 4-8. Figure 4-7 presents the same seasonal pattern as Figure 4-13 below. Meaning



that high Qn occurs when soil moisture content is high. However, Figure 4-8 despite the low soil

moisture content up to 8m, Qn is high.

Figure 4-13: Soil moisture measurements at GS01



5. Evapotranspiration

5.1. Theoretical background

Evapotranspiration (ET) is a collective term for all the processes by which water in the liquid or solid

phase at or near the earth’s land surface becomes atmospheric water vapour. The two process involved

are evaporation and transpiration. Evaporation (E) occurs when water is converted into water vapour

from wet surfaces into the atmosphere. The variety of surfaces includes: lakes, rivers, pavements, soils

and wet vegetation. Transpiration (T) consists of the vaporization of liquid water contained in plant

tissues and the vapour removal to the atmospheres. Evapotranspiration and transpiration occur simul-

taneously and there is no easy way of distinguishing between the two processes and hence the total

vaporisation process is called ET (Dingman, 1994).

Actual evapotranspiration (ETa) is the actual amount of water leaving an evaporating surface by the

process of evaporation and transpiration. It is a very important parameter of the water budget in semi

arid areas where there is often a shortage of water and plants transpire at the actual rate and not at the

potentially highest rate called potential evapotranspiration (PET). PET or reference crop evapotran-

spiration (ETo) is the water loss that will occur if there is no deficiency of water in the soil for use of

vegetation (Dingman, 1994).Concerning original location of the processes ETa can be divided into

surface evapotranspiration, groundwater evapotranspiration and unsaturated evapotranspiration

5.2. Penman-Monteith equation

In 1948, Penman-Monteith combined the energy balance with mass transfer method and derived an

equation for computing actual evapotranspiration. This equation allows the calculation of actual

evapotranspiration (ETa) from meteorological variables and resistances which are related to the sto-

mata and aerodynamic characteristics of the crop and has the form given in equation 5.1 (Maidment,

1993). To determine the temporal variability of ETa in the study area this equation is used and the

different parameters are estimated as discussed in the sections 5.3 below of which aerodynamic resis-

tance (ra) and surface resistance (rs) are one of the problematic parameters to define (Bruin, 1988). To

estimate rs and ra micrometeorological data from the mobile and permanent stations has been used and

the methods are presented in section below.

��

��

�++∆

��

��

� −+−∆

=

a

s

a

asan

rr

ree

CpGR

ETa

1

)(

γ

ρλ (5.1)

Where Rn is the net radiation [MJ m-2 day-1]

G is soil heat flux [MJ m-2 day-1]

ρa is the mean air density at constant pressure =1 [kg m-3]

Cp is the specific heat capacity of air = 1013 [kJ kg-1 oC-1]



∆ is the slope of the saturation vapour pressure-temperature relationships [kPa oC-1]

γ is the psychometric constant [kPa oC-1]

rs is the bulk surface resistance [s m-1]

ra is the aerodynamic resistance [s m-1]

es is the saturation vapour pressure [kPa]

ea is the actual vapour pressure [kPa]

5.3. Estimation of parameters for the Penman-Monteith equation

Slope of the saturation vapour pressure (∆∆∆∆) The slope of the relationship between saturation vapour pressure and temperature is given by

( )23.237

3.23727.17

exp6108.04098

+

��

�

��

��

�

+=∆

T

TT

(5.2)

where ∆ is slope of saturation vapour pressure curve at air temperature T [kPa oC-1]

T is air temperature [oC]

Mean saturation vapour pressure (es) Saturation vapour pressure is related to air temperature as

2min)(max)( TeTe

eoo

s

+= (5.3)

where eo(T) saturation vapour pressure at the air temperature [kPa]

��

�

�

+=

3.23727.17

exp6108.0)(T

TTeo (5.4)

Tmax and Tmin are daily maximum and daily minimum air temperature respectively [oC]

Actual vapour pressure (ea) Actual evaporation is usually calculated from relative humidity data (RH)

2100

)(100

)( minmax

maxmin

RHTe

RHTe

e

oo

a

+= (5.5)

ea actual vapour pressure [kPa]

eo(Tmin) saturation vapour pressure at daily minimum temperature [kPa]

eo(Tmax) saturation vapour pressure at daily maximum temperature [kPa]

RHmax maximum relative humidity [%]

RHmin minimum relative humidity [%]

Vapour pressure deficit (es –ea) Vapour pressure deficit is the difference between the saturation (es) and actual vapour pressure for a

given time period

Psychometric constant γγγγ

λεγ CpP= (5.6)

where λ = 2.501 – 2.361T [MJkg-1] (5.7)



ε is ratio of molecular weight of water vapour to that for dry air = 0.622 [ ]

Cp is specific heat at constant pressure 1.013[JKg-1 oC-1]

P is the atmospheric pressure given by P= 26.5

2930065.0293

3.101 ��

��

� − z [kPa] (5.8)

Where z is elevation above sea level = 1205 [m]

5.4. Estimation of the aerodynamic resistance

The rate of water vapour transfer away from the ground by turbulent diffusion is controlled by aero-

dynamic resistance (ra) which is inversely proportional to wind speed and changes with the height

covering the ground (Maidment, 1993) and is given by equation 5.9 as

z

LzhLzm

a ukZoh

dzZom

dz

r 2

)/()/( lnln ��

�

� Ψ−��

��

� −��

�

� Ψ−��

��

� −

= (5.9)

Where ra is aerodynamic resistance

k is the von Karman constant = 0.41 [ ]

Zoh is the surface roughness length for heat transfer ≈ 0.1Zom

z is measurement height = 10 [m]

uz is the wind speed at height z =10 [m]

Surface roughness length for momentum transfer (Zom) To estimate Zom, an attempt was made to derive it from other parameters. Table 5-1 presents a sim-

plified model by Rapauch, (1994) later modified with an optimisation procedure by Verhoef et al.

(1997) as cited by Parodi (2000) which can lead to calculation of Zom (equation 5.10) and displace-

ment height (d) as per equation 5.15. The model is based on the LAI and requires input data that was

adjusted to fit a range of field results. A calculated value of Zom = 0.0061 m and d = 0.022 m was

obtained and later used in all the calculations for this station.

Table 5-1: Displacement height and surface roughness calculation for GS05 after Parodi (2000)

Vegetation data Vegetation height 3.08 Field observation Leaf area Index 0.0001 From Landsat image Aerodynamic data Free parameter cd 20.6 0<cd<100 Dimensionless cw 2 1<cw<2.5 Drag coefficient unobstructed bare soil cs

0.01 Sparse savannah

Overstory drag coefficient cr 0.35 Results Displacement height 0.022 Influence of vegetation height α 0.193 Auxiliar = (cs + cr*LAI) 0.5 0.100 Wind speed friction velocity uh/u* 10.00 Surface roughness (Zom) 0.061



( )( )( )huuhke

dhZom Ψ−

−= )/ * (5.10)

where h is the vegetation height (L. nelsii 3.08) [m]

d is the displacement height [m]

k is the Von Karman constant = 0.41 [ ]

Ψh is a vegetation influence function given as 11ln −+−=Ψ cwcwh [ ] (5.11)

where cw = 2 5.0

*

2��

��

� ×+= LAIcrcs

uhu

(5.12)

cs is the drag coefficient for unobstructed bare soil = 0.01 [ ]

cr is the overstorey drag coefficient = 0.35 [ ]

u* is the friction velocity [m/s]

uh is the wind speed at the top of the canopy [m/s]

LAI is the Leaf Area Index

LAI was derived from Landsat 7 TM 2001 satellite image using formula 5.12 and 5.13 in the ILWIS

script for the study area. Table 5-2 presents the LAI for all monitoring ADAS sites in the study area.

Once LAI was calculated it was substituted in table 5-1. However for station GS05, LAI was equal to

0 and the map was filtered to adopt a value of 0.00001.

)ln(1

2

1

3 cSAVIc

cLAI

−×−= (5.13)

where coefficients c1= 0.69 c2 = 0.59 and c3 = 0.91 [ ]

)5.0()(5.1

34

34

++−

=rr

rrSAVI (5.14)

r4 and r3 are reflectances of bands 4 and 3 respectively

Table 5-2: Leaf area index for permanent stations

Station LAI

GS01 0.077

GS02 0.150

GS03 0.003

GS04 0.077

GS05 0.000

GS06 0.000

GS07 0.130

GS08 0.093

Displacement height (d) Displacement height is a major fraction of the plant vegetation height (h) and is calculated according

to equation 5.15.



��

�

�

��

�

�

×−=

×−

LAIcd

ehd

LAIcd1 (5.15)

where h is vegetation height [m]

cd is a free parameter = 20.6 [ ]

LAI is the Leaf Area Index [ ]

Stability function for momentum transfer (ΨΨΨΨm (z/L) and stability function for heat transfer (ΨΨΨΨh

(z/L)) The integrated stability corrections are recommended usually in dry areas, avoiding them may lead to

underestimation of ETa (Allen et al, 1998). They were estimated from equations 5.21 to 5.23 dis-

cussed in the later sections.

Results Estimated parameters were substituted in equation 5-9. Figure 5-1 presents the daily values of ra. The

graph presents almost similar values with little variation between 40 s/m and 65 s/m with an average

value of 48 s/m.

Figure 5-1: Aerodynamic resistance for ADAS site GS05

5.5. Estimation of the surface resistance using Penman-Monteith formular

The surface resistance (rs) (for mixed canopies and soil elements) describes the resistance of vapour

flow through stomata openings, total leaf and soil surface (Allen et al., 1998). Surface resistance is

computed from equation 5.16 as

effs LAI

rr 1= (5.16)

where rl is surface resistance of a well-illuminated leaf area (upper side)[s/m]

LAIeff is the effective leaf area index contributing to Et =2.13.0 +LAI

LAI [ ] (5.17)

However, this equation may not apply to sparse vegetation (Allen et al., 1998) and it is also difficult

to determine LAIeff with no direct method. rs was therefore obtained for GS05 station by inversion of

the P-M equation. Equation 5.1 was rearranged to obtain equation 5.18. All the other parameters have



been discussed in the sections above. However, E is determined from the Temperature profile method

and Bowen ratio energy balance method (BREB). Data obtained over the 10-day period from the mo-

bile station is used and the estimates of E and ra, which have been estimated for the same period, are

used in the calculation. Net radiation data from Paje station is used.

C

r

BEta

eeCp

AEta

GRnrr aasaa

s γγ

γλρ

γλ)()()(. +∆

−−

+−∆

= (5.18)

The equation below was used to derive a conversion factor of 86.4, which enables part B to be con-

verted to sm-1

CkPa

CkPa

CkPa

ms

CkPa

daymMJ

kPaCkg

MJmkg

CkPa

daymMJ

daymMJ

ms

CkPa

o

oo

o

o

o

o )(

2

3

2

2 +−

×

××+

×

××

5.5.1. ETa computed from Temperature profile method

Daily actual evapotranspiration (ETa) can be computed from latent heat of vapourisation (LE) by

solving the energy balance equation, (equation 5.19) having computed H by other means and measur-

ing Rn and G. One promising method is the temperature profile method, which uses air temperature at

some height z along with wind speed and is used to estimate actual evapotranspiration for the mobile

stations. According to Holtslag, (1983), heat and momentum fluxes can be obtained from observed

wind and temperature profiles using similarity relations for the atmospheric surface layer, which are

based on Monin-Obukhov similarity theory, that assumes stationary and horizontally homogeneous

conditions.

Calculation procedure Energy balance equation Evapotranspiration rate can be predicted by applying the principle of energy conservation. The energy

arriving at the surface must equal the energy leaving the surface for the same period (Allen et al.,

1998). The equation of the evaporating surface can be written as

Rn - H - G - LE = 0 (5.19)

where Rn is net radiation [Wm-2]

H is sensible heat flux [Wm-2]

G is soil heat flux [Wm-2]

LE is the latent heat flux [Wm-2]

Net radiation (Rn) The total energy available at the surface (Rn) was obtained from measurements of Incoming short-

wave radiation outgoing short wave radiation, incoming long wave radiation and outgoing long wave

radiation measured at Paje station located within the study area (Figure 2-1). The long wave and out-

going radiation are variable but were used in this study because they are easily available from the

nearest station and where computed to Rn estimates according to equation 5.20.



↑−↓+↑−↓= LLSS RRRRRn (5.20)

where RS↓ is incoming short-wave radiation [Wm-2]

RS↑ is outgoing short-wave radiation [Wm-2]

RL↓ is incoming short-wave radiation [Wm-2]

RL↑ is outgoing Long wave radiation [Wm-2]

Soil Heat Flux (G) The soil heat Flux (G) can be calculated if data on soil temperatures is adequate. Despite having soil

temperature data taken at 5 cm and 10 cm for each station, it was not possible to calculate G, as data

on soil properties was required which was not available. Holtslag and Ulden (1983) estimated G as

10% of net radiation for grass covered surfaces of Netherlands that gave good comparable results

with measured values of G. Also according to Burridge and Gadd (1977) as cited by Holstlag et al

(1980), approximated G as 0.1 of the net radiation and got good results for snow free surfaces.

In this study, for daytime temperature fluctuations, soil heat flux was calculated as

G = 0.1Rn (5.21)

While night time soil heat flux is calculated G = 0.5Rn (5.22)

Sensible heat flux (H) According to Nieuwstadt (1978) as cited by Holstlag et al., (1980), the momentum flux ( )τ is related

to the friction velocity ( )*u by the following equation:

2*uρτ = (5.23)

The flux of sensible heat H is related to the frictional velocity (u*) and the temperature scale (θ*) by

**θρ uCH p−= (5.24)

Where Cp is the specific heat capacity of air at constant pressure ≈ 1.013 [kJ kg oC-1]

θ* is the temperature scale [K]

ρ is the density of air =1 [Kg/m3]

u* is the frictional velocity [m/s]

The integrated flux profile relations of Dyer and Hick are used to calculate θ* and u* (equation 5.25

and 5.26 respectively) since they are more simplified than Nieuwstadt’s method and the results are

normally comparable (Holstlag et al, 1980).

1

1

2* 12ln

−

��

��

��

��

� ��

�

�Ψ+Ψ−��

�

��

�∆=

Lzh

Lzhz

zk θθ (5.25)

1

* ln−

��

��

��

��

� ��

�

� Ψ+Ψ−��

��

�=L

ZommLzmz Zom

zkuu (5.26)

where k is the von Karman constant = 0.41[ ]

Ψm is the stability function for momentum transfer[ ]

Ψh is the stability function for heat transfer[ ]

∆θ is the temperature difference [K]



z1, z2 are heights, in this case z1 = 2 m and z2 = 10 m respectively [m]

uz is the wind speed at height z = 10 m [m/s]

Zom is the surface roughness length for momentum transfer (equation 5.10)[m]

L is the Monin-Obukhov stability parameter [m]

Stability correction functions (ΨΨΨΨh) and (ΨΨΨΨm) The integrated function for heat transfer (ΨH) and the stability correction function for momentum

transfer (Ψm) for unstable conditions (L<0) were estimated from equation 5.27 and 5.28 but substitut-

ing different values of z/L as defined in the functions ( mΨ ��

��

�ΨL

Zomm ,��

��

�ΨLzm ,

��

��

�ΨLzh 1 ,

��

��

�ΨLzm 2 )

��

�

��

��

� +=Ψ2

1ln2

2xh (5.27)

2)arctan(22

1(ln)

21

ln22

π+−��

�

� ++��

�

��

��

� +=Ψ xxx

m (5.28)

Where ( ) 41

/161 Lzx −= (5.29)

And for L>0 (stable conditions)

)(2.5Lz

hm −=Ψ=Ψ (5.30)

Monin-Obukhov stability length (L)

θkgTu

L2

*= (5.31)

where g is acceleration due to gravity = 9.81[m/s]

T is the air temperature [K]

K is Von Karman constant = 0.41 [ ]

θ is temperature difference between two heights (z1 = 2 m and z2 = 10 m)

u* is friction velocity from equation 5.24

Iteration procedure to estimate H An iteration procedure was employed in order to calculate H. An initial value of L= –5.00 m in this

case was assumed. Ψh and Ψm are calculated from equations 5.26 and 5.25 respectively. u* and θ* are

then calculated from equations 5.24 and 5.23. L is computed from equation 5.31 by using the esti-

mated values of u* and θ*. The new value of L is substituted in equations 5.27 and 5.28 to get im-

proved values of u* and θ*. X is recalculated from equation 5.29.The procedure was repeated until the

value of L became numerically stable (4 iterations). H was then calculated from equation 5.24 using

the last values of u* and θ*.

The iteration was done for the entire measurement period. In this way the H was computed every 30

minutes for GS05. H was substituted in equation 5.19 to obtain the LE, which is used to compute ETa.

The results of Rn, H, LE and G are presented in Figure 5-1.

45.2LE

Eta = [W/m2] (5.32)



The daily values of ET were calculated using equation 5.31

45.28

)/()/(

2mWETdaymmET = (5.33)

Figure 5-2: Computed Rn, H, LE and G using the temperature profile method

5.5.2. Actual Evapotranspiration computed from Bowen ratio energy balance method

The Bowen ratio energy balance method (BREB) method rearranges the energy balance equation

(5.19) in order to cancel aerodynamic transport means (ASCE, 1996). This permits determination of

LE by measuring air temperature and vapour pressure at two elevations above the surface in addition

to Rn and G. The BREB equation for application to ETa from vegetation is

β

λ+−=

1GRn

E (5.34)

The Bowen ratio is dependent on the temperature and vapour pressure gradients implying determina-

tion of only temperature and relative humidity at two levels above the surface in addition to Rn and G.

If the transport coefficients under measurement conditions are considered equal for H and λET, then β

can be expressed in a finite difference form as:

( )[ ]12

1212

eaeazzTT

−−Γ+−

= γβ (5.35)

where z1 and z2 are measurement heights z1 = 2 and z2 = 10 [m]

T1 and T2 are air temperature at heights z1 and z2 [oC]

e1 and e2 are vapour pressure at heights z1 and z2 obtained from equation 5.5 [kPa]

Γ is the adiabatic lapse rate generally taken as 0.01 [oCm-1]

γ is the psychometric constant obtained from equation 5.6 [kPa/oC]

5.5.3. Comparison of BREB method and temperature profile method

The temperature profile method has been known to be less reliable than other micrometeorological

methods, however it has the advantage that the required sensors two for temperature two for wind

speed and one for net radiation are easily available and very reliable (Maidment, 1993). With the ac-



quired measurements, it has been shown that it is possible to calculate sensible heat (H) adequately

using a few simple functions of the differences in temperature. With the additional data for Rn and G

then ETa can be estimated. On the other hand, the BREB method has been recognized as one of the

most accurate ETa methods if Rn, G and the gradients of temperature and humidity can be accurately

measured (ASCE, 1996). However, the methodology has some numerical instability that develops as β

approaches –1.0. Angus and Watts (1984), in the same book, showed the potential for error in calcula-

tion of Latent heat of vaporization, as β drops below-0.2. An attempt to avoid this problem was to fix

the values of β within –0.9 to -1.1 outside these limits, however despite this, the results on some par-

ticular days are considered not reliable.

5.5.4. Sensitivity analysis on the Temperature profile method

To test the accuracy of the temperature profile method and some errors involved, adjustments were

made on one of the parameters. Assuming that the data from the loggers (air temperature, relative hu-

midity and wind speed) is accurate and the constants (k, g, Cp) are well known, then the parameter,

that can be under discussion is the surface roughness length (Zom), which was estimated from LAI

obtained from the satellite image. The values of Zom were adjusted and the final values of ETa ob-

served. Results indicate that values of ETa changed by less than 10% (Table 5-3)

Table 5-3: ETa values for different values of Zom

Zom Date 0.03 0.04 0.05 0.06 0.07 0.08 0.09 25/9/02 1.29 1.33 1.38 1.41 1.44 1.46 1.48 26/9/02 0.66 0.70 0.73 0.75 0.77 0.79 0.80 27/9/02 0.30 0.31 0.33 0.34 0.35 0.36 0.36 28/9/02 1.04 1.08 1.12 1.14 1.16 1.18 1.19 30/9/02 0.84 0.88 0.91 0.94 0.97 0.98 1.00 31/9/02 0.38 0.39 0.39 0.39 0.39 0.39 0.38 1/10/02 1.07 1.13 1.17 1.20 1.23 1.25 1.27 2/10/02 0.62 0.62 0.62 0.62 0.62 0.62 0.61

5.5.5. Results for surface resistance

The computed results of ETa from the temperature profile method; aerodynamic resistance and other

parameters from the sections above were substituted in equation 5.18 to obtain daily estimates of rs as

shown in the Figure below. A temporarily variable rs is observed. The results in Figure 5-3 show val-

ues ranging from 900 to 2800 with an average value of 1817 s/m .



Figure 5-3: Surface resistance by applying ETa from different methods

5.6. Estimation of surface resistance from sapflow measurements

At stand level, canopy resistance (rs) is considered to be the integration of the entire stomata resis-

tance in the canopy. Canopy resistance is a key parameter for modelling forest responses to climatic

factors and can be derived from direct latent flux measurement using eddy correlation method. It is

also often related to climatic variability (Granier, 1996). rs can also be estimated from sapflow

measurements as a basis to explore stomata effects on transpiration with respect to tree species and

canopy position (Kostner et al., 1992).

If transpiration and climatic variables are known over the same time-scale, surface resistances can be

derived from the P-M equation (Lu et al., 1994). This model assumes that the canopy can be consid-

ered as a single leaf characterised by only one value of canopy resistance to water vapour and that

stand sapflow is equal to tree transpiration (Granier, 1996). However with this approach, the key

problem is determining stand transpiration. In this study, stand transpiration was estimated from sap-

flow measurements for GS05 station. Canopy resistance was then calculated from stand sapflow and

from climatic variables measured above the canopy like net radiation, air temperature and vapour

pressure deficit.

5.6.1. Stand sapflow estimation

One plot with 15 trees was selected from measurements carried out by Mapanda (unpublished) near

GSO5. Sapflow estimates for a sample of individual trees are used in this study to upscale to a stand

of known area. The scaling process involves taking information at smaller spatial scales and using that

information to derive processes at larger spatial and longer temporal scales (Jarvis, 1995). Scaling up

was required in order to determine stand sapflow.

Sapflow rates of the analysed trees are up-scaled to a circular plot covering an area 1250 m2 based on

the crown cover and stem diameters of trees that were analysed by Mapanda, (Unpublished). In order

to evaluate the stand transpiration, a single calculation experiment is presented for Terminalia sericea

specie, which is dominant at station GS05. The assumption is made that trees in the analysed plot

transpire at the same rate as the monitored Terminalia sericea.The average sapflow for the plot is the

sum of sapflow for all trees and was equal to 187 l/day. Stand transpiration (Ec) for the plot was then

obtained by integration of sapflow estimates as given in equation 5.36.



p

ii

p

i

AAv

AQ

Ec

=

= (5.36)

where Ap = plot area [m2] v = sap velocity [m s-1]

Qi = sapflow for each tree [l day-1]

i = individual trees [ ]

Ec = stand transpiration [m day-1]

5.6.2. Results from sapflow

The canopy resistance was calculated from inverted Penman-Monteith equation (equation 5.18) and

the results are presented in Figure 5-4. The values range from 1000 s/m to 7000s/m.

Figure 5-4: Canopy resistances from sapflow measurements for GSO5 site

5.7. Temporal variability of actual evapotranspiration

In this study half hourly data for the input data into P-M was aggregated into daily averages. This was

to avoid using mean values for estimating parameters like vapour pressure. Using mean values of air

temperature and not maximum and minimum results in lower estimates of saturation vapour pressure

and hence an underestimation of the evapotranspiration(Allen et al., 1998)

Using the computed average daily values of aerodynamic resistance and surface resistance (fixed)

from the temperature profile method, actual evapotranspiration was computed for the permanent sta-

tions using P-M equation. The results are shown in Figure 5-5. It is observed that ETa varies season-

ally. It is high during the wet and hot summer months and low during the cold and dry winter months.

ETa at GS05 station varies from 0.08 mm/day to 0.64 mm/day.

Figure 5-5: Calculated ETa at station GSO5 for the period December 2001 to September 2002



5.7.1. Sensitivity analysis on estimates of ra and rs

For 24 hour or large calculation time steps use of a constant value of rs is adequate (Allen et al.,

1998). Sensitivity analysis was performed on the days when rs and ra was determined to see the effect

of using a constant value of rs and ra on the calculated ETa. Figure 5-6 shows that by having a fixed

average value of ra do not influence the results of ETa. However, ETa is sensitive to temporal variabil-

ity of rs.

Figure 5-6: Constant versus variable estimates of ra and rs on ETa

5.8. Comparison of ETa from Different methods

A comparison was madeComputed results of actual evapotranspiration from BREB method, tempera-

ture profile method and Penman-Monteith method (when both ra and rs variable) and were derived

from different methods Sapflow, BREB and temperature profile method are presented in Figure 5-7

from 27th September to 1st October 2002. The data was taken when all methods presented positive

values of ETa. From the graphs; the ETa values from P-M (sapflow) show the lowest daily average

rate of 0.14 mm/day. The temperature profile method presents the highest amounts (0.84 mm/day) and

the BREB method (0.97mm/day). BREB and P-M method present low and comparable results.

the temperature profile method and P-M are more realistic then BREB. The BREB method gives for

some days, negative values of ETa. Daily average ETa values from BREB gave an average value of

0.29 mm/day as compared to 0.90 mm/day from the temperature profile method. Evapotranspiration

calculated from the 3 methods are shown in figure 5-7.



Figure 5-7: Actual evapotranspiration obtained from different methods for mobile station GS05



6. Soil moisture and well level fluctuations modelling

In this chapter, soil moisture measurements using the ECHO probe are analysed and a description of

well level fluctuations using EARTH model is made.

6.1. Measurement of soil moisture in the unsaturated zone

Movement of moisture content in the unsaturated zone can in principle be determined by measuring

soil moisture content at various depth (Gieske and Selaolo, 1988). The ECHO probe measures the di-

electric constant of the soil in order to find its volumetric water content. Since the dielectric constant

of water is much higher than that of air or soil minerals, the dielectric constant of the soil is a sensitive

measure of water content (Decagon, 2002)

Determination of soil moisture in Serowe has been done by means of soil moisture (Decagon ECHO)

probes. Installations were carried out by the Kalahari research project at depths of 2 m, 4 m, 6 m and

8 m. The ECHO sensors are connected to data loggers, from where readings are taken every 30 min-

utes. The locations of the soil moisture probes are at the permanent stations (Figure 3-3) and data has

been collected since November 2001.

Calibration of the ECHO probe sensor was already determined by gravimetric moisture content by the

Kalahari research project staff for the individual soil types as:

23.0000695.0 −= mVθ (6.1)

where θ is the volumetric water content [m3/m3]

mV is the millivolt output of the ECHO probe.

Figure 6-1 shows the evolution of soil moisture content in the 4 layers of soil during November 2001

and November 2002 for the ADAS sites. Also indicated is daily rainfall data. At ADAS sites GS01,

GS02, GS03, GS04, GS05, GS06, there is an increase in soil moisture content within days following

periods of rainfall which indicates that there was very high infiltration during the rainy season. How-

ever the pattern is not clearly seen at site GS07. It is clear from the Figures that in Serowe, there are

large differences within short distances considering the actual amount of soil moisture content. As a

result of the rainy season from October to April, large amounts of rain infiltrated at GS05 and GS03.

GSO5 also presents high soil moisture content but at shallower depths of 2 m and 4 m and GS05 at

deeper depths of 6m and 8m. Very low moisture contents are observed at GS04, GS01, GS02, GS07

and GS06. All graphs also show a slow decrease in soil moisture content during the dry season as a

result of evapotranspiration and downward soil moisture transport. The short spells of heavy rainfall

are also marked by an increase in water content, although this is not clearly evident.



Figure 6-1: Soil moisture content at the permanent stations



6.2. Groundwater level monitoring

Water table fluctuations give a direct indication of the hydrogeological conditions affecting an aqui-

fer.To monitor groundwater level changes through time, pressure transducers (Diver) were installed in

several boreholes in the study area (Figure 6-2). Fluctuating water levels in the borehole are recorded

using data loggers at a frequency of one hour. The pressure measurement is automatically compen-

sated for the temperature fluctuations giving increased accuracy.

In Botswana, barometric, effects have been observed in a variety of wells (Vries and Gieske, 1988). A

barometric pressure increment at the surface is instantaneously transmitted to the boundary between

the aquifer and confining bed, where it is borne partly by the solid skeleton and partly by the water.

The water in the well, in contrast, has to carry the full pressure change. The resulting imbalance in

pressure between water in the well and in the aquifer causes the barometric effect (Vries and Gieske,

1988). For this purpose, in the same study area, barometric pressure is recorded at two locations

(BH5336 and BH 8403).

Figure 6-2: Location of boreholes with divers in Serowe

6.2.1. Water level calculation

The Diver, which is an absolute logger, accurately measures the total pressure, comprising atmos-

pheric pressure and pressure due to the height of the water column above the logger sensor in milli-

bars or cm of water column. The barometric pressure recorded at BH 5336 was subtracted from the

total pressure recorded by the logger in order to get the hydrostatic pressure only due to the height of

the column of water according to equation 6.2

aT PPh −=γ (6.2)

where γh is the pressure due to the height of the column of water above the logger [mbars]

PT is total pressure recorded by logger [mbars]



Pa is atmospheric pressure [mbars]

Assuming 1≈γ and 1 mbar ≈ 1 cm of water then equation 6.2 gives height of water in cm The proc-

essing of the data has been done in Tirtaharapan data viewer software and the graphs below show Tir-

taharapan output for boreholes BH8449, BH4742, BH5343, BH5336, BH4743, BH5306, BH5326,

BH5337 and BH8493 in the study area.

Borehole 4742

Borehole 4743

Figure 6-3: Water level fluctuations in boreholes 4743 and 4742and barometric fluctuations

Figure 6-3 for borehole 4742 indicates a general decline in well levels from October 2001 to June

2002. Groundwater level fluctuations are in the order of a few centimetres with the effect that minute

fluctuations could possibly be attributed to diurnal barometric influences. Borehole 4743 indicates an

almost stable groundwater level fluctuation over this period from October 2001 to September 2002.



Borehole 5306

Borehole 5326

Figure 6-4: Water level fluctuations in boreholes 5306 and 5326 and barometric fluctuations

Borehole 5306 has some missing data due to data lost during downloading, however, the general pat-

tern as seen in Figure 6-4 indicates a few cm of water level fluctuations. This behaviour is similar in

borehole 5326 but with higher fluctuations and ground water levels during the months of February

and March 2002. This can be attributed to the rainy season in those months leading to recharge in the

well and therefore an increase in groundwater levels.



Borehole 5336

Borehole 5337

Figure 6-5: Water level fluctuations in boreholes 5336 and 5337 and barometric fluctuations

Borehole 5337 shows ≈26 cm of water level fluctuations while borehole 5336 presents ≈ 22 cm. Like

the previous boreholes discussed there are fluctuations due to barometric effect. In borehole 5336,

there is a discontinuity in the original measurements; however this part of the graph was shifted to fit

the original trend (Figure 6-5).

Borehole 8493 indicates fluctuations of ≈ 30 cm, 8449 shows ≈17 cm while borehole 5343 indicates

≈17 cm fluctuations of groundwater levels.



Borehole 8449

Borehole 8493



Borehole 5343

Figure 6-6: Water level fluctuations in boreholes 5343, 8493, 8449 and barometric fluctuations

6.3. Modelling well level fluctuations using 1D EARTH model

Water level fluctuations give an indication of hydrogeological conditions affecting an aquifer. Re-

charge to the aquifer will result in an increase in water levels while a net loss will have the opposite

effect.

Recharge is often the most important factor in evaluating regional aquifer systems in arid and semi

arid environments and it is unfortunately the most difficult to quantify. Several methods for estimating

groundwater recharge have been developed and can be divided into the physically based method (wa-

ter level measurements, water balance method and lysimeters), chemical and isotopic methods. Re-

cently models were used to estimate groundwater recharge. In this study groundwater recharge is as-

sessed though water level measurements using 1-D modelling with EARTH model.

6.3.1. Description of model

EARTH (Extended model for aquifer recharge and soil moisture Transport through the unsaturated

Hard rock) is a lumped parameter hydrological model for the simulation of recharge and deep

groundwater level fluctuations. It is applied to semi arid climatic conditions of Botswana and ex-

tended to simulate also surface runoff and interflow of hilly catchments. The model has been tested in

semi arid areas with deep groundwater levels (up to 40m) and where the weathered hard rock is cov-

ered by a thin soil cover (Van der lee and Gehrels, 1990).

The model uses both direct and indirect methods of recharge modelling. The direct method refers to

the processes taking place above the water table such as percolation, soil moisture distribution and

evapotranspiration, which enable determining of recharge above the groundwater table. The indirect



part calculates the groundwater level with the estimated recharge of the direct part (Van der lee and

Gehrels, 1990). The model assumes redistribution of the soil moisture profile in one single time step,

which is determined by the thickness of the unsaturated zone. According to Van der lee and Ger-

hels,(1990) the model operates properly at a time step of one day.

MAXIL

ETaEoP

SOMOS

SUST

LINRES

SATFLOW

Pe

Rp

R

Qd

Qs

Eo

InterceptionEvaporation

PondingEvapotranspiration

Soil moisture storagePercolation

Unsaturated flow, temporal

redistribution of recharge

Groundwater level fluctuations,

Drainage

Figure 6-7: Flow chart of EARTH model after (Van der lee and Gehrels, 1990)

P = Precipitation, Pe = Precipitation excess, Eo = Evaporation, ETa = Actual evapotranspiration, Qs =

Surface runoff, Rp = Percolation, R = Recharge and Qd = Subsurface drainage

6.3.2. Modules of the model

Figure 6-7 shows the modules of the EARTH model. The first two modules of the programme,

MAXIL and SOMOS represent the agro-hydro meteorological zone of the modelling space where the

influence of vegetation and atmosphere are buffered while LINRES and SATFLOW represent the hy-

drogeological zone of the modelled space. For detailed outline of the mathematical equations applied

to each module, the reader is referred to Van der Lee and Gehrels (1990).



MAXIL (MAXimum Interception Loss) In this unit, the net infiltrating amount Pe of total precipitation P is calculated. An amount Eo is lost

immediately through interception and evaporation. MAXIL is estimated in the field by observations or

used as an optimisation parameter. It is given by

oe EMAXILPP −−=

where P is precipitation excess

Eo is open water evaporation

SUST (Surface Storage) When the amount of water in the soil reaches a certain threshold, ponding and runoff will occur. The

remaining part is the change in soil moisture storage. SOMOS (SOil MOisture Storage) The effective rain Pe infiltrates in the topsoil layers, from where it may be lost by evaporation or trans-

ported further down.

LINRES (Linear REServoir routing) This module therefore redistributes the output of SOMOS in time. Moisture, which is percolating

down from the soil reservoir, can no longer be lost by evaporation. However, the groundwater table

may be further down and therefore there is a delay before the soil moisture actually reaches the water

table. This delay is modelled by linear reservoirs (Gieske, 1992).

SATFLOW (SATurated FLOW model) When moisture reaches the groundwater table, it will affect the water level that is without recharge,

there will be an exponential recession of the water level. SATFLOW is a saturated flow model that

predicts groundwater levels with an estimated recharge, the outcome of LINRES.

6.3.3. Model Input data

Calibration of the model can be run with precipitation and PET data only although this makes the

calibration doubtful (Gieske, 1992). In the following calibration of the EARTH model, only ground-

water levels were used due to time shortage. An improvement could be made by applying the avail-

able soil moisture data.

Ground water levels 7 wells (BH8403, BH8449, BH4742, BH5343, BH5336, BH5337 and BH8493) located in the Sand

veld were selected for simulating groundwater level fluctuations and estimate fluxes at these sites us-

ing EARTH. These boreholes are not close to pumping wells meaning that ground water level fluc-

tuations in these boreholes are more or less natural. Hydrostatic pressure data obtained in section

6.2.1 was further, referenced to some manual groundwater table measurements obtained from Geo-

logical Survey of Botswana. The resulting referenced hourly data was then resampled to daily data

and used as an input to the EARTH model.

Potential evapotranspiration and Rainfall Daily Potential evapotranspiration calculated in chapter 2 and daily rainfall from Mokongweng station

also presented in chapter 2 are basic inputs to the model.



6.3.4. Model Input parameters

The parameters needed to run the simulation are listed below. The parameters were determined from

previous works carried out in the area. Maximum soil moisture content (sm) [mm]

Residual soil moisture content (Sr) [mm]

Initial soil moisture (Sin) [mm]

Soil moisture at field capacity (Sfc) [mm]

Maximum surface storage (SUSTmax) [ ]

Maximum interception loss (MAXIL) [mm]

Saturated conductivity (Ks) [mm/day]

Unsaturated recession constant (Rcunsat) [day]

Number of reservoirs (n) [ ]

Saturated recession constant (Rsat) [day]

Saturated recession constant [ ]

Storage coefficient (STO) [ ]

Initial groundwater level (Hi) [m]

Local base level (Ho) [m]

Output reductor [ ]

Output reductor [ ]

Simulation shift [ ]

6.3.5. Model calibration

An interactive process of adjusting the model parameters manually, to obtain an optimum fit of the

observed and simulated groundwater heads was carried out to calibrate the models initially. The con-

figuration file with the manually obtained model parameters was imported into an optimisation pro-

gram OPTIMIZE that enables the possibility of refining the manually obtained fit. However, since a

good fit was achieved with the manual calibration, the use of the programme did not significantly alter

the results obtained. The optimum calibrated parameters for the 7 sites are presented in Table 6-1 be-

low

Table 6-1: EARTH parameter values used for simulation of well level fluctuations

Symbol BH 8449 BH4742 BH 8403 BH5336 BH5326 BH 5343 BH 5337 Sm 500 650 215 400 500 550 500 Sr 40 50 9.63 15 40 80 50 Sin 80 90 10 70 80 100 120 Sfc 350 125 175 300 254 300 480

SUSTmax 2 3 1 3 3 3 3.1 MAXIL 8 8 1.5 2 4 4 2.5

Ks 300 500 150 1000 300 600 500 Rcunsat 50 30 21.63 5 15 10 100

n 1 1 2 3 2 3 2 Rcsat 6000 850 509.98 8000 100 8000 18000 STO 0.01 0.09 0.03 0.03 0.01 0.03 2

Hi 5 15 4.22 3 0.5 3 5 Ho 115.97 1150.95 1124.361 1114.65 1157.23 1099.64 1097.61



6.3.6. Model results

Modelling with EARTH was successful with boreholes, BH8403, BH4742, and BH5326. An obvious

misfit occurs toward the end of simulation at BH8403 and can be attributed to error in the input data.

Figure 6-8 shows the output of the EARTH model in 6 graphs for a simulation of borehole 8403. The

top diagram shows precipitation excess that is precipitation minus surface evaporation, interception

and runoff. The second diagram shows losses from the soil reservoir because of evapotranspiration.

Diagram 3 illustrates the seasonal soil moisture evolution. Usually during the dry season (May to Sep-

tember), there is low moisture, which eventually increases after the rainy season (October to April).

The next diagram shows the net downward percolation from the soil zone into the lower unsaturated

zone. Percolation occurs in sharply defined events within a couple of days after heavy rain. The next

diagram is the slow redistribution and delay of the moisture on its way to the groundwater table. Fi-

nally the last diagram shows the resulting water table fluctuations. It is clear from the diagram that,

groundwater level response to percolation and recharge occurred in the year 2000, when there was

high precipitation. The average values simulated by the model are given in table 6-1 for the modelling

period 8/16/97 to 10/1/02.



Figure 6-8: EARTH model output for BH 8403

Table 6-1: EARTH model calculated parameters

Parameter BH8403 BH4742

Mean actual evapotranspiration [mm] 0.894 0.409

Mean recharge [mm] 0.184 0.490

Soil Moisture storage [mm] 52.54 101.70

Mean Precipitation excess [mm] 1.080 0.849

Figure 6-9 shows the graphical output of EARTH model for borehole 4742. Recharge in this borehole

occurred during November and December 2001 rainy season that resulted from increased precipitation

excess. Using the parameters in the model, mean values of the water balance are calculated and area

presented in Table 6-1 above. The average values are valid for the period 8/ 16/1997 to 10/1/02:

Appendix C1 shows groundwater level fluctuations in boreholes 8493, 8449, 5326, 5343 and 5336.

Difficulty to model these groundwater level fluctuations was attributed to the small water table fluc-

tuations. Also the data available is for less than a year, which makes it even harder. Comparing these

graphs to borehole 8403 one can conclude that the models represent is the recession period when there

is no recharge, which is believed to occur once in many years/.



Figure 6-9: EARTH model output for BH 4742



7. Discussion, conclusion and reccomendations

7.1. Discussion

This section analyses the results of the present study within the framework of the objectives.

7.1.1. Tree Transpiration and biometric variables

Sapwood area (As) and Sap velocity are the two main components used in the determination of Sap-

flow that later leads to estimates of the transpiration flux.

Sapwood area In order to determine and afterwards be possible to extrapolate data, correlation of sapwood area with

stem parameters (area, diameter) and with crown parameter (area, diameter) were carried out.

Stem area presented over 90% of the correlation with sapwood area for all the monitored species (Ter-

minalia Sericea (86%), Acacia leuderitzii (91%), Burkea africana (90%), Acacia erioloba (94%),

Ochna pulchra (83%), and Dichrostachys cinerea (79%), Acacia fleckii (96%), Boscia albitrunca

(99%) and Lonchocarpus nelsii (98%). Also good relationships were observed between crown area

and sapwood area (75% to 90%) except Dichrostachys cineria (0.49 %). Good correlation between

stem area and sapwood area was expected because stem area substantially contributes to stem area,

closely related to sapwood area. But because stem diameters are easy to measure and are less prone to

measurement error than crown area or LAI (Hatton T.J. et al., 1995) (Kostner et al., 1998a). This way

then it is possible to make reliable estimates of sapwood area in trees where its measurement has not

been determined. Consistently, Vertessy et al.,(1995), Hatton T.J. et al.,(1995) reported that stem

areas explained 96% variations in sapwood area. The derived relationships were then used to predict

sapwood area for trees in Serowe permanent stations from biometric measurements of their stems.

Sap velocity When sap velocity for all tree species for short-term measurements was analysed using the Pearson

moment correlation, a weak relationship between sap velocity, crown area and sapwood area was ob-

served. Correlation coefficients showed very low values (r2 = 0.01 to r2 = 0.24). Furthermore,

ANOVA did not reveal a significant difference between sap velocity, crown area and sapwood area (p

>0.05) in all the 9 species monitored. The same observation was made by Vertessy et al, (1995) and

Wullscheleger and King (2000) who found no relationship between sap velocity and any biometric

variables. This means the sap velocity does not differ with the growing stage of the trees. Thus up

scaling to stand level or vegetation level is possible by applying average sap velocities characteristic

for the tree species. The average velocities obtained are: Terminalia Sericea (1.68 cm hr-1), Acacia

leuderitzii (0.61 cm hr-1), Burkea africana (1.326 cm hr-1), Acacia erioloba (1.02 cm hr-1), Ochna pul-

chra (1.41 cm hr-1), Dichrostachys cinerea (0.85 cm hr-1) Acacia fleckii (2.50 cm hr-1) Boscia albi-

trunca (3.84 cm hr-1) and Lonchocarpus nelsii (3.35 cm hr-1).



Sapflow/ Normalised sapflow The results of daily water use by trees (transpiration) for the monitored species showed that transpira-

tion varied between trees and among species. Difference between trees of the same specie is attributed

to different sapwood area while between species to different velocity and different sapwood area.

Sapflow quantity was highest in Boscia albitrunca (2.43 to 26.32 l/day) and lowest in Acacia erioloba

(0.30 to 0.93 l/day). The highest daily normalised sapflow was observed in Boscia albitrunca (0.29 –

1.55 mm/day) and the least in Dichrostachys cinerea (0.0051 to 0.276 mm/day). The variations in

transpiration can be explained by the fact that measurements for Boscia albitrunca were made when

the tree was in leaf compared to the other species. These variations can also be related to weather

variables explained by Oren and Pataki (2001) as due to differences in sensitivity to soil moisture

variations. Also from the field observations Dichrostachys cinerea specie had bigger heartwood

compared to the size of the sapwood area and was usually multi stemmed (Plate 10). Accurate meas-

urement of sapwood area is an essential matter as this can lead to the substantial over-estimation or

underestimation of sapflow. However, in this study most of the trees sampled, had good radial sym-

metry with clear heartwood apart from 4 trees that were not included in this analysis. Köstner et

al.(1998b), reported also other source of species specific variations in sapflow related to infections by

fungi, generally decreasing transpiration rates. This problem was common in Serowe area especially

among the Acacia fleckii species (Plate7).

Temporal variability of Sapflow (Qs)/Normalised sap flow (Qn) Similarly, Vertessy et al (1995)

reported that variation in transpiration was correlated with tree characteristics.

In an examination of the temporal behaviour of sapflow among different species, a strong similarity in

water use among tree species was noticed. For the monitored period, Lonchocarpus nelsii showed the

highest Qn in both the wet (5.22 mm/day) and dry season (0.84 mm/day). It transpires more in the wet

season when there is more water available and less in the dry season. Terminalia sericea had the low-

est transpiration rates on average with low Qn rates in the wet season (0.038 mm/day) and high Qn

rates in the dry season (0.067 mm/day). Qn was high during periods of high rainfall events and slowly

decreased throughout the dry season. This behaviour was observed by Lonchocarpus nelsii, Ochna

pulchra, Acacia fleckii, Burkea africana and Dichrostachys cinerea tree specie and also reported by-

Hatton and Vertessy (1990). However, results of Boscia albitrunca, Terminalia sericea and Acacia

erioloba indicate that during the dry season in Serowe, large quantities of water can be transpired at

rates higher than those during rainy season. Moreover, during dry season, soil moisture levels in the

soil are very low in the study area. Therefore, water necessary for growth water is taken from large

depth, perhaps even directly from the groundwater table. This can occur if roots can grow up to the

water table. Similar conclusions were made by Timmermans and Meijerink (1999), Fregoso (2002).

Scott and Le Maitre (1988) found a tree root in a borehole in central Botswana, with a perforated cas-

ing at a depth of 68m, possibly even exceeding deeper. The only tree in the vicinity was Boscia albi-

trunca. Also consistent with Jenning’s (1974) observations, in the same paper, fine roots were found

in fissured rock in mine shafts, east of the Mkagadikadi pans of Botswana at 30 m to 45 m.



Sapflow and PET Comparison of results for Sapflow and PET from FAO Penman-Monteith formula showed higher val-

ues of PET than sapflow. Diurnal pattern of sapflow followed the course of PET for some trees. Cien-

cial et al,(2000) explained this as an expression of soil water available during the measurement pe-

riod in sufficient amount. Plants are able to store water in the stem or canopy, which acts as a buffer

for the instantaneous transpiration loss during a day. However, some tree species like Acacia leude-

ritzii (3) showed a time lag of 2 hours. This time lag is interpreted as the time it takes for the sapwood

at 0.5 m above the ground level to come into equilibrium with the water potential of the canopy

(Hatton and Vertessy, 1990). This study demonstrated that sapflow behaviour is correlated to poten-

tial evapotranspiration. Therefore PET allowed extrapolating sapflow measurements for the period

where sapflow was not measured in some trees.

7.1.2. Evapotranspiration

The original Penman-Monteith formula used extensively by various researchers for estimation of

evapotranspiration has always been faced with the problem of definition and determination of aerody-

namic resistance ra and surface resistance rs (De Bruin and Holtslag, 1981). This study has presented

the different methodologies for establishing these two parameters and hence calculated ETa. Actual evapotranspiration (ETa) Different methods to estimate ETa are yielding different results. Although, the BREB method is theo-

retically sound and can be used to estimate actual evapotranspiration, difficulties in determining the

accurate correction factor for values when β approaches -1 values, limits its usefulness (ASCE, 1996).

This method gave an average ETa value of 0.23 mm/day after restricting β values out of the range (–

0.9 to –1.1). The temperature profile method gave an average daily value of to 0.90 mm/day and was

considered the better estimate of ETa than the BREB method.

Aerodynamic resistance (ra) and surface resistance (rs) Aerodynamic resistance, derived from micrometeorological data yielded values ranging from 40 s m-1

to 65 s m-1 with an average daily value of 48 s m-1. ETa from temperature profile method was used in

estimation of surface resistances by inversion of the Penman-Monteith equation and gave results rang-

ing from 900 s m-1 to 2800 s m-1. These values where highly temporarily variable as presented in chap-

ter 5. Sapflow measurements throughout up scaling to a plot level have shown that estimates of rs are

possible with this method. However, these results showed a higher value compared to estimates from

temperature Profile method (1000 s m-1 to 7000 s m-1). This is due to low values of actual evapotran-

spiration derived from sap plot measurements.

Temporal variability of actual evapotranspiration Average values of rs and ra used in computation of temporal variability of evapotranspiration have

shown high evapotranspiration fluxes in the rainy season and low values in the winter period when

incoming solar radiation and water availability are limited. The actual daily evapotranspiration as ob-

tained from original Penman-Monteith equation ranges from 0.08 mm/day to 0.64 mm/day. These val-

ues are consistent with those obtained by WCS (2000) in the same study area (0.4 mm/day) from sap-

flow measurements, 0.5 mm/day from hydrographs and 0.5 mm/day from BREB). However the results



are slightly lower than estimates of Magombedze, (2002) who reported actual evapotranspiration rates

of 0.17 mm/day to 3.2 mm/day from BREB and 0.41 mm/day to 2.87 mm/day from Temperature pro-

file method in the same area. Timmermans and Meijerink (1999) also reported estimates of 1.5

mm/day to 3 mm/day from SEBAL method which were high.

Although in ASCE,(1996), daily or longer calculations time steps, use of a constant value of rs was

adequate, validation of the Penman-Monteith computation of ETa in the period when rs and ra values

were available yielded different result. The effect on ETa when ra was fixed and rs variable did not

influence the ETa values. However, ETa results were sensitive to temporal variability of ra. The re-

sults indicate that the aerodynamic resistance has no effect on the ETa values. This is consistent with

Maidment,(1993) who reported that it is not realistic to represent the average effect of controlling

processes operating within the canopy in terms of a single surface resistance since rate of evaporation

can be very high when the canopy is wet, than when it is dry.

7.1.3. Soil Moisture and well level fluctuations

The soil moisture measurements at ADAS sites presented an understanding of the amount of water

infiltrated to the unsaturated zone and possibly to the groundwater table.According to Jennings as

cited by Gieske and Selaolo (1988), the soils of Botswana mainly of dark, red brown, medium tex-

tured and ferruginous materials favour rapid infiltration of rain water. Variability of soil moisture for

the different ADAS sites was high. ADAS site GS03 showed high soil moisture content at depths of 8

m and 6 m compare to other ADAS sites. This could be explained by the fact that rainfall events of 80

mm or less tend to infiltrate to shallow depths if no other flow enhancement processes are involved. In

cases where flows are enhanced by pans or depressions infiltration is high. According to SGC, (1988)

in such cases at 3 – 4 m depths evapotranspiration effects are reduced and likelihood of groundwater

recharge to the groundwater table is higher.

Hydrographs of boreholes presented in this study, show barometric effects. Groundwater levels oscil-

late between 17 cm and 30 cm. This is about the same range as observed by Vries and Gieske (1988),

who reported that the majority of boreholes in Botswana are subject to barometric and tidal effects

with fluctuations ranging up to 25cm. Most hydrographs showed little or no change in water level

fluctuations. Since replenishment of groundwater is episodic or absent in most of the country, these

fluctuations often form the only head variations for years (Vries and Gieske, 1988). Modelling with

the EARH model showed that recharge is possible when daily rainfall exceeds the daily evapotranspi-

ration BH8403, BH4742, and BH5326. Recharge in the study area occurs once in many years. How-

ever, most boreholes did not show any recharge after running the model (BH8493, BH5326 BH8449,

BH5343 and BH5336).

7.2. Conclusions

This study has attempted to make an assessment of water fluxes for Serowe (Transpiration, evapotran-

spiration and unsaturated zone water fluxes using the discussed methods. From this study, the follow-

ing conclusions are drawn.



This study has presented the different methodologies of establishing these two parameters from

micrometeorological and sap flow data, which are later used in assessment of the temporal variability

of actual evapotranspiration from Penman-Monteith method one of the objectives of the study.

The relationships obtained from the biometric variables of stem diameter and crown area with sap-

wood area makes possible to use stem diameter (stem area) and crown diameter (crown area) to de-

termine sapwood area and further transpiration fluxes for the 9 species without using the destructive

method of tree cutting.

The results achieved in this study confirm the competence of using sapflow measurements to deter-

mine transpiration of trees. The sapflow measurements identify the contributions of individual trees or

forest canopies to total water use. Continuous long-term measurements of tree sapflow provided esti-

mates of transpiration flux through the whole 10 to 11 months. Together with simultaneous weather

and soil moisture measurements, this data allow detailed analysis of the dependence of transpiration

upon climatic conditions. The study has shown that the temporal variability of transpiration depends

not only upon the climatic conditions but also upon characteristics of the trees. There is a significant

difference between Sap velocities and normalised sapflow within species depending on the growing

stage and among the different species.

Sapflow in individual stems showed strong positive relationships with potential evapotranspiration.

Therefore with knowledge of PET, estimates of sapflow for a particular species can be extrapolated

even where measurements have not been taken. Sapflow measurements also offer a way to estimate

surface resistance through up scaling of sapflow measurements.

The study has concluded that setting of aerodynamic resistance (ra) as time independent does not in-

fluence much ETa solution while the same applied to surface resistance (rs) oversimplifies the solu-

tion. Estimate of actual evapotranspiration using the original P-M equation is a good method

The monitoring of soil moisture content and groundwater levels at selected ADAS sites all point to

the presence of active recharge within the study area in particular areas. Recharge in the study is oc-

curs once in many years. The pattern is related to distribution of precipitation. SGC reported also that

in places where the Kalahari sand cover is reduced recharge is favoured.

7.3. Recommendations

During the course of this study, a number of factors and processes were identified that can influence

the final results and as a consequence, the following recommendations were drawn.

Investigation of the radial sapflow pattern across the sapwood for different species is vital to rule out

if there is any significant change in sap velocity at different depths. Also further research on the char-



acteristics of the different species should be carried out to relate normalised sapflow with tree charac-

teristics.

Data from the non-destructive method (dielectric experiment) tested in the field can be analysed for

future use.

Validation of the sapwood area determination should be carried out at least with another method for

the verification of the results. Problems of accuracy associated with sapwood area estimation are not

yet solved. More research is needed on this subject. The water soluble dye used in the field (eosine

dye B) produced better results compared with eosine C dye.

The ADAS station has proved to be a very useful tool for acquisition of data for evapotranspiration

and sapflow estimates. It is therefore recommended that data from these stations should be the respon-

sibility of a well-trained staff in order to avoid any losses. Data from the loggers should be checked

regularly to see if the trends in the data are okay and that the sensors are working well.

In order to get better results in future, it is recommended that mobile stations with measurements of

wind speed, temperature and humidity at levels higher than the canopy be continued at least through-

out different seasons. This is to avoid using a constant value for surface resistance. This will improve

the confidence of the evapotranspiration results. However also a relationship between ra and rs and

other variables like Potential evapotranspiration, vapour pressure could be explored in future so that

measurements of rs and ra can be extrapolated over the whole year.

A lot of data has been collected on the soil temperatures in the study area but has not been utilised in

this study for calculation of the Soil heat flux (G). At the moment there do not exist simple methods,

requiring routine input data only to estimate soil heat flux, whereas these quantities are extremely im-

portant in semi arid areas. The method requires additional information related to the soil characteris-

tics, which were not available. This aspect should be considered for future research in the study area.

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Gieske, A.S.M. 1992. Dynamics of groundwater recharge- A case study in semi arid eastern Bot-swana. Phd thesis, Amsterdam university, Amsterdam, Netherlands.

Granier, A. 1996. Transpiration of trees and forest stands : short and longterm monitoring using sap-flow methods. Global Change Biology 2:265-274.

Granier, A., and D. Loustau. 1994. Measuring and modelling the transpiration of a maritime pine can-opy from sap flow data. Agricultural and Forest Meteorology 71:61-81.

Griend, A.A.V.d., and R.J. Gurney. 1987. Satellite remote sensing and energy balance modelling for water balance assesment in Semi-arid regions, p. 89 -116, In I. Simmers, ed. Estimation of Natural Recharge of Groundwater (NATO Advanced Research Workshop), Vol. 222, Am-sterdam.

Hatton, J., and Hsin-Iwu. 1995. Scaling theory to extrapolate individual tree water use to stand water use. Hydrological Processes 9:527-540.



Hatton, J.T., and A.R. Vertessy. 1990. Transpiration of plantation Pinus radiata estimated by the heat pulse method and the Bowen ratio. Hydrological Processes 4:289 - 298.

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Hatton T.J., Moore S.J., and Reece P.H. 1995. Estimating stand transpiration in a eucalyptus populnea woodland with the heat pulse method: measurement errors and sampling strategies. Tree Physiology 15:219-227.

Holtslag, A.A.M., Van Ulden A.P. 1983. A simple scheme for daytime estimates of the surface fluxes from routine weather data. Journal of climate and applied metereology 22:571 - 529.

Jarvis, P.G. 1995. Scaling processes and problems. Plant, cell and environment 18:1079 - 1089.

Kostner, B., A. Granier, and J. Cermak. 1998a. Sapflow measurements in forest stands methods and uncertainities. Agricultural Science and Forestry 55:pp 13 - 27.

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Lu, P., P. Biron, N. Breda, and A. Granier. 1994. Water relations of adult Norway spruce (picea abies (L) Karst) under soil drought in the Vosges mountains: water potential, stomatal conductance and transpiration. Ann. Science For Elsevier 52:117 - 129.

Magombedze, L.M. 2002. Spatial and temporal variability of groundwater fluxes in a semi - arid envi-ronment, Serowe, Botswana. MSc, ITC, Enschede p. 160.

Maidment, D.R. 1993. Handbook of Hydrology McGraw-Hill, Texas, 650p.

Mapanda W. Unpublished. Scaling up tree trnspiration of Eastern Kalahari Sandveld of Botswana using remote sensing and GIS. Msc, ITC, Enschede.

Moore, D.S., and G.P. McCabe. 1998. Introduction to the practice of statistics. Third ed. Freeman, New York.

Namayanga, L.N. 2002. Estimating terrestrial carbon sequestered in aboveground woody biomass from remotely sensed data:the use of SEBAL and CASA algorithms in a semi-arid area of Se-rowe Botswana. Msc, ITC, Enschede p. 58.

Obakeng, O.T. 2000. Groundwater recharge and vulnerability : a case study at the margins of the south - east central Kalahari sub basin Serowe region Botswana. Msc, ITC, Enschede p 125.

Oren, R. 1998. Evapotranspiration: partitioning water flux in forests. Agricultural and Forest Meteor-ology 55:93-216.

Oren, R., and D.E. Pataki. 2001. Transpiration in response to variation in microclimate and soil mois-ture in southeasten deciduous forests [Online]. Available by Oecologia http://152.16.58.129/pdf/transpiration.pdf (posted 22 february 2001).



Parodi, G.N. 2000. AVHRR Hydrological analysis system, Algorithms and theory version 1.

Pierce, D.W. 1993. Developing Botswana's savannas, p. 205 - 220, In M. D. Young and O. T. Solbrig, eds. The world's Savannas, Vol. 12.

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SGC. 1988. Serowe Groundwater Resources Evaluation Project, final report. Ministry of Mineral Re-sources and Water Affairs, Department of Geological Survey, Lobatse, Botswana, pp. 286.

Smith, D.M., and S.J. Allen. 1996. Measurement of sapflow in plant stems. Journal of experimental Botany 47:1833-1844.

Timmermans, W.J., and A.M.J. Meijerink. 1999. Remotely sensed actual evapotranspiration: implica-tions for groundwater management in Botswana. Journal of Applied Meteorology 1:222-234.

Van der lee, J., and J.C. Gehrels. 1990. Modelling aquifer recharge:Introduction to the lumped pa-rameter model -EARTH, Amsterdam, pp. 28.

Van der lee J., and Gehrels J.C. 1990. Modelling aquifer recharge: Introduction to the lumped parame-ter model -EARTH, Amsterdam, pp. 28.

Van Dijk, P.M., M.W. Lubczynski, F. J.L., and G. G.G. 1996. Application of remote sensing, GIS and groundwater modelling techniques in recharge evaluation at Palla road, Botswana. Confer-ence on the application of remotely sensed data and GIS in environmental and natural re-sources assessment in Africa, Harare, Zimbabwe pp. 256 - 259.

Vertessy, R.A., R.G. Benyon, S.K.O. Sullivan, and P.R. Gribben. 1995. Relationships between stem diameter, sapflow area, leaf area and transpiration in a young mountain ash forest. Tree Physiology 15:559-567.

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ASSESSMENT OF WATER FLUXES IN SEMI-ARID ENVIRONMENTS SEROWE CASE STUDY (BOTSWANA) APPENDICES

INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION I

Appendix A Monitored trees

A1: Names of the monitored trees

Botanical name Sestwana name English name Acacia erioloba Mogotho Camel thorn Acacia fleckii Mohahu Blade thorn Acacia leuderitzii/Acacia karoo Moaka, Mokgwelekgwele Kalahari sand thorn Boscia albitrunca Motopi Shepherd’s tree Burkea Africana Mosheshe Wild syringa Dichrostachys cinerea Moselesele Sickle bush Lonchocarpus nelsii Mohata/Mhata Kalahari apple leaf Terminalia sericea Mogonono Silver cluster leaf Ochna pulchra Monyelenyele Peeling plane Securidaca Longepedunculata Maaba Violet tree Acacia mellifera Mookana Acacia galpinii Mokala Ziziphus mucronata Mokgalo Buffalo thorn


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION II

A2: Photographs of the monitored species

Plate 7: Acacia Leuderitzii (13 and 2) and presence of fungi on Acacia fleckii

Plate 8: Ochna pulchra (79)


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Plate 9: Acacia erioloba (79 and 80)

Plate 10: Multi stemmed Dichrostachys cineria and inset sapwood area compared to heartwood


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION IV

Plate 11: Terminalia sericea (125 and 126) and Burkea africana (77)

Plate 12: Sparse vegetation of Serowe


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A3: Biometric variables for the monitored species

Terminalia sericea

TREE ID Average Stem diameter (cm) Average crown diameter

(m) Sapwood area

(m2) 25 9.30 3.80 0.00270 26 8.20 3.30 0.00229 27 8.95 3.15 0.00284 28 10.10 4.25 0.00413 32 8.10 2.35 0.00183 33 6.90 2.10 0.00141 34 5.60 1.95 0.00098 36 6.20 2.95 0.00118 43 6.70 3.35 0.00197 44 5.80 2.15 0.00084 45 6.45 2.90 0.00161 49 5.95 2.20 0.00076 50 6.10 2.50 0.00135 51 5.65 2.40 0.00139 52 6.35 2.55 0.00154 53 6.90 2.90 0.00170 54 6.45 2.15 0.00148 67 9.00 3.15 0.00314

106 11.40 4.10 0.00384 109 10.50 4.05 0.00191 110 7.80 3.45 0.00093 111 15.70 5.90 0.00790 113 11.70 4.30 0.00386 116 6.55 2.10 0.00139 119 10.80 5.10 0.00524 120 8.65 4.50 0.00303 121 11.45 4.30 0.00446 123 9.10 2.95 0.00226 124 8.80 2.35 0.00151 125 11.95 5.00 0.00468 126 12.25 4.80 0.00669 132 11.90 5.55 0.00586


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION VI

Acacia erioloba

TREE ID Average Stem diameter

(cm) Average crown diameter

(m) Sapwood area

(m2) 79 12.3 3.25 0.00422 80 14.7 3.15 0.00890 81 15.25 3.45 0.00851 82 13.35 2.90 0.00631 83 11.05 2.90 0.00459 84 6.95 2.20 0.00167 85 9.05 2.55 0.00248 86 7.45 2.10 0.00248 87 6.55 1.85 0.00138 88 9.75 2.85 0.00331 89 10.3 2.70 0.00448 90 7.2 1.80 0.00154 91 11.65 2.95 0.00459 92 15.2 3.20 0.00689 94 8.4 2.50 0.00298 95 8.9 2.45 0.00277 96 6.3 1.85 0.00155 97 6.45 2.05 0.00154 98 5.8 1.45 0.00107 99 12.85 3.00 0.00613

100 13 3.50 0.00696 101 16.2 3.35 0.00771 102 11.7 3.35 0.00459

Acacia Leuderitzii



(m) Sapwood area

(m2) 1 13.10 3.55 0.00780 2 12.95 2.75 0.00543 3 13 2.65 0.00506 4 9.30 2.55 0.00271 5 11.35 3.15 0.00431 6 21.2 4.30 0.01300 7 23.6 5.50 0.01912 8 16.45 4.20 0.01346

10 8.55 3.10 0.00275 11 16.65 4.30 0.01300 13 21.75 5.30 0.01741 14 16 3.50 0.01217 15 20.2 4.20 0.01866 16 17.6 3.75 0.01193 17 19.3 4.15 0.01433


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION VII

Burkea africana



(m) Sapwood area

(m2) 31 9.1 1.75 0.00286 35 6.05 1.15 0.00164 38 9.95 1.90 0.00428 39 8 2.00 0.00681 40 19.25 4.50 0.01112 41 7.2 2.65 0.00478 42 9.3 1.80 0.00226 55 13.45 3.35 0.00891 56 13 2.40 0.00607 57 10.8 1.53 0.00413 58 6.1 1.50 0.00192 59 6.7 1.35 0.00187 60 12.85 2.38 0.00684 61 7.1 1.28 0.00216 62 7.45 1.50 0.00165 63 16.25 3.15 0.00921 64 18.3 4.20 0.01313 65 10.4 1.95 0.00425 66 5.95 1.35 0.00116 68 9.85 2.05 0.00340 69 8.2 2.10 0.00253 73 19.2 4.00 0.01497 74 15.6 4.05 0.01200 75 7.3 1.70 0.00172 76 5.65 1.60 0.00131 77 6.95 1.35 0.00150 93 12.7 2.90 0.00801

114 7.3 2.10 0.00192 118 17.95 5.50 0.01348 122 6.15 1.40 0.00115 134 15.45 3.65 0.01115 135 17.7 4.75 0.01617 137 18.15 4.45 0.01291


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION VIII

Ochna pulchra

TREE ID Average Stem diameter (cm) Average crown diameter (m) Sapwood area (m2) 70 5.65 1.70 0.00088 71 5.9 1.20 0.00081 72 5.65 1.60 0.00057

105 7.35 2.20 0.00217 108 11.35 2.75 0.00502 112 9.4 2.45 0.00188 115 6.45 1.95 0.00088 128 14.95 2.60 0.00460 129 10.85 2.65 0.00310 130 18.85 4.60 0.00926 131 14.45 2.30 0.00649 133 20.1 3.55 0.01122 136 18.2 2.90 0.00658 138 15.05 3.35 0.00939 144 17.1 2.90 0.00540 149 12.35 2.55 0.00509 150 9.9 2.20 0.00322

Dichrostachys cinerea

TREE ID Average Stem diameter (cm) Average crown diameter (m) Sapwood area (m2) 19 5 2.65 0.00047 20 5.05 2.85 0.00049 21 5.1 3.55 0.00061 22 6.15 2.95 0.00062 23 5.2 3.35 0.00052 24 5.3 2.80 0.00054 29 9.4 4.50 0.00188 30 5.95 2.85 0.00063 46 10.55 4.05 0.00191 47 7.35 3.90 0.00083 48 7.3 4.20 0.00114

103 7.15 3.85 0.00070 104 6.45 3.50 0.00118 127 6.8 3.65 0.00130 139 5.7 2.45 0.00074 140 7.1 3.00 0.00116 141 7.2 3.35 0.00125 142 6.35 2.80 0.00101 143 5.45 2.15 0.00083 145 7.45 4.50 0.00169 146 7.85 3.80 0.00151 147 5.95 3.80 0.00080 148 6.75 3.30 0.00122


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Appendix B B1: Normal probability plots for the monitored species


Ochna pulchra

Burkea africana


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Acacia leuderitzii

Acacia erioloba

Terminalia sericea


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B2: Pearson correlation matrix of the Data set

Terminalia sericea

Acacia erioloba

Acacia Leuderitzii

Burkea africana


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Ochna pulchra

Dichrostachys cineria ** Correlation is significant at the 0.01 levels

* Correlation is significant at the 0.05 levels


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B3: ANOVA for the different species

All species Sum of

squares

df Mean

square

F Signifi-

cance

Between groups 964 9 107.12 Stem diameter (cm)

Within groups 1827 133 13.74

7.80 0.0001

Between groups 32.17 9 3.573 Crown diameter (m)

Within groups 123.67 133 0.930

3.843 0.0001

Between groups 0.001 9 0.0011 Sapwood area


9.80 0.0001

ANOVA Sap velocity factor Sum of

squares

df Mean

square

F Signifi-

cance

Between groups 964 140 0.00002 Stem diameter (cm)

Within groups 1827 2 0.00001

1.69 0.445

Between groups 32.17 140 29.48 Crown diameter (m)


0.77 0.722


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XIV

B4: Biometric variables and water fluxes for the monitored trees

Terminalia sericea

TREE ID Stem area (cm2)

Crown area (m2)

Average Sap velocity (cm/hr)

Average sapflow (l/day)

Average sapflow nor-malised (mm/day)

25 67.93 11.34 0.190 0.123 0.011 26 52.81 8.55 0.704 0.389 0.045 27 62.91 7.79 0.631 0.424 0.054 28 80.12 14.19 0.332 0.326 0.023 32 51.53 4.34 0.552 0.238 0.055 33 37.39 3.46 0.863 0.290 0.084 34 24.63 2.99 1.608 0.386 0.129 36 30.19 6.83 1.011 0.291 0.043 43 35.26 8.81 1.384 0.726 0.082 44 26.42 3.63 3.128 0.560 0.154 45 32.67 6.61 0.412 0.157 0.024 49 27.81 3.80 0.791 0.190 0.050 50 29.22 4.91 1.966 0.472 0.096 51 25.07 4.52 2.305 0.553 0.122 52 31.67 5.11 2.006 0.482 0.094 53 37.39 6.61 2.383 0.572 0.087 54 32.67 3.63 1.950 0.468 0.129 67 63.62 7.79 1.524 1.149 0.147

106 102.07 13.20 1.136 1.164 0.088 109 86.59 12.88 1.183 0.542 0.042 110 47.78 9.35 0.780 0.174 0.019 111 193.59 27.34 4.455 8.481 0.310 113 107.51 14.52 4.331 4.013 0.276 116 33.70 3.46 0.796 0.263 0.076 119 91.61 20.43 3.023 3.801 0.186 120 58.77 15.90 1.861 1.353 0.085 121 102.97 14.52 2.020 2.162 0.149 123 65.04 6.83 2.235 1.212 0.177 124 60.82 4.34 2.292 0.830 0.191 125 112.16 19.63 2.814 3.161 0.161 126 117.86 18.10 2.314 3.717 0.205 132 111.22 24.19 0.865 5.068 0.209


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XV

Acacia erioloba

TREE ID Stem area

(cm2) Crown area

(m2) Average Sap velocity

(cm/hr) Average sapflow

(l/day) Average sapflow

normalised 79 118.82 8.30 0.473 0.480 0.058 80 169.72 7.79 0.611 1.304 0.167 81 182.65 9.35 0.942 1.924 0.206 82 139.98 6.61 0.841 1.274 0.193 83 95.90 6.61 0.557 0.614 0.093 84 37.94 3.80 1.292 0.518 0.136 85 64.33 5.11 1.022 0.606 0.119 86 43.59 3.46 1.767 1.051 0.304 87 33.70 2.69 1.483 0.491 0.183 88 74.66 6.38 0.495 0.393 0.062 89 83.32 5.73 1.131 1.213 0.212 90 40.72 2.54 1.076 0.398 0.156 91 106.60 6.83 0.630 0.694 0.102 92 181.46 8.04 1.750 2.893 0.360 94 55.42 4.91 1.411 1.009 0.206 95 62.21 4.71 1.262 0.839 0.178 96 31.17 2.69 1.473 0.548 0.204 97 32.67 3.30 0.868 0.321 0.097 98 26.42 1.65 1.187 0.305 0.185 99 129.69 7.07 0.598 0.880 0.124

100 132.73 9.62 0.864 1.444 0.150 101 206.12 8.81 0.998 1.850 0.210 102 107.51 8.81 0.795 0.877 0.100

Acacia Leuderitzii

TREE ID Stem area

(cm2) Crown area

(m2) Average Sap velocity

(cm/hr) Average sapflow


normalised 1 134.78 9.90 0.350 0.00018 0.000018 2 131.71 5.94 0.737 0.00027 0.000045 3 132.73 5.52 0.386 0.00013 0.000024 4 67.93 5.11 0.484 0.00009 0.000017 5 101.18 7.79 1.137 0.00033 0.000042 6 352.99 14.52 0.319 0.00028 0.000019 7 437.44 23.76 0.561 0.00075 0.000031 8 212.53 13.85 0.346 0.00030 0.000022

10 57.41 7.55 0.378 0.00007 0.000009 11 217.73 14.52 0.225 0.00019 0.000013 13 371.54 22.06 0.407 1.69830 0.076979 14 201.06 9.62 0.249 0.72820 0.075688 15 320.47 13.85 0.544 2.43540 0.175785 16 243.28 11.04 1.559 4.46440 0.404213 17 292.55 13.53 1.446 4.97198 0.367573


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XVI

Burkea africana

TREE ID Stem area

(cm2) Crown area (m2) Average Sap ve-

locity (cm/hr) Average sapflow


normalised 31 65.04 2.41 1.195 0.832 0.34591 35 28.75 1.04 2.409 0.925 0.89074 38 77.76 2.84 1.403 1.441 0.50834 39 50.27 3.14 1.259 2.055 0.65403 40 291.04 15.90 3.974 10.585 0.66557 41 40.72 5.52 3.974 4.578 0.82994 42 67.93 2.54 0.917 0.614 0.24120 55 142.08 8.81 1.372 2.931 0.33253 56 132.73 4.52 1.293 1.881 0.41573 57 91.61 1.83 0.816 0.803 0.43973 58 29.22 1.77 1.110 0.511 0.28944 59 35.26 1.43 1.485 0.667 0.46569 60 129.69 4.43 2.060 3.631 0.81967 61 39.59 1.28 0.884 0.458 0.35876 62 43.59 1.77 1.458 0.577 0.32670 63 207.39 7.79 0.734 1.622 0.20812 64 263.02 13.85 0.482 1.518 0.10960 65 84.95 2.99 0.827 0.843 0.28240 66 27.81 1.43 0.879 0.245 0.17106 68 76.20 3.30 1.817 1.483 0.44922 69 52.81 3.46 0.950 0.577 0.16659 73 289.53 12.57 1.156 4.153 0.33050 74 191.13 12.88 1.115 3.211 0.24924 75 41.85 2.27 1.414 0.577 0.25423 76 25.07 2.01 1.023 0.322 0.16000 77 37.94 1.43 0.776 0.279 0.19516 93 126.68 6.61 1.179 2.266 0.34308

114 41.85 3.46 0.800 0.366 0.10576 118 253.06 23.76 1.111 3.595 0.15132 122 29.71 1.54 2.920 0.806 0.52348 134 187.48 10.46 0.291 2.455 0.23463 135 246.06 17.72 0.398 6.440 0.36342 137 258.73 15.55 0.304 2.164 0.13914


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XVII

Ochna pulchra

TREE ID

Stem area

(cm2) Crown area (m2)

Average Sap ve-

locity (cm/hr)

Average sapflow

(l/day)

Average sapflow

normalised

(mm/day)

70 25.07 2.27 1.921 4.056 1.78694 71 27.34 1.13 2.203 0.428 0.37852 72 25.07 2.01 3.190 4.364 2.17028

105 42.43 3.80 3.034 1.580 0.41571 108 101.18 5.94 3.940 4.747 0.79926 112 69.40 4.71 2.717 1.225 0.25974 115 32.67 2.99 0.705 0.149 0.04986 128 175.54 5.31 0.595 2.738 0.51564 129 92.46 5.52 0.372 1.153 0.20899 130 279.07 16.62 1.175 10.878 0.65456 131 163.99 4.15 0.600 3.892 0.93683 133 317.31 9.90 0.475 5.326 0.53812 136 260.16 6.61 0.415 2.731 0.41352 138 177.89 8.81 1.079 6.420 0.72832 144 229.66 6.61 0.388 2.095 0.31724 149 119.79 5.11 0.608 3.097 0.60638 150 76.98 3.80 0.519 1.670 0.43922


TREE ID Stem area

(cm2) Crown area (m2) Average Sap ve-

locity (cm/hr) Average sapflow


normalised 19 19.63 5.52 0.899 0.101 0.01838 20 20.03 6.38 2.044 0.240 0.03769 21 20.43 9.90 1.132 0.166 0.01674 22 29.71 6.83 2.100 0.312 0.04571 23 21.24 8.81 3.047 0.380 0.04314 24 22.06 6.16 1.413 0.183 0.02974 29 69.40 15.90 0.828 0.378 0.02375 30 27.81 6.38 0.541 0.082 0.01282 46 87.42 12.88 0.505 0.219 0.01704 47 42.43 11.95 0.315 0.061 0.00512 48 41.85 13.85 0.091 0.131 0.00943

103 40.15 11.64 1.736 3.212 0.27586 104 32.67 9.62 0.772 0.219 0.02272 127 36.32 10.46 0.519 2.385 0.22795 139 25.52 4.71 0.735 0.544 0.11530 140 39.59 7.07 0.524 0.615 0.08705 141 40.72 8.81 0.598 0.628 0.07127 142 31.67 6.16 0.463 0.468 0.07601 143 23.33 3.63 0.498 0.413 0.11389 145 43.59 15.90 0.176 0.298 0.01875 146 48.40 11.34 0.174 0.262 0.02310 147 27.81 11.34 0.356 0.285 0.02512 148 35.78 8.55 0.243 0.296 0.03460


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XVIII

B6: Diurnal variation of Qn and rainfall in the permanent stations

Station GS01


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XIX

Station GSO4


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XX

Station GS05


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XXI

Station GS07

Station GS06


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XXII

Appendix C C: 1 Graphical output of EARTH model

BH5343

ASSESSMENT OF FLUXES IN A SEMI-ARID ENVIRONMENT OF BOTSWANA, A CASE STUDY OF SEROWE REFERENCES

INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XXIII

BH5337

Actual evapotranspiration

0

0.5

1

1.5

Nov-01 Dec-01 Feb-02 Mar-02 May-02 Jul-02 Aug-02Time (days)

(mm

)


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XXIV

BH8449


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XXV

BH8493

Soil moisture storage

0100200

300

Oct-01 Dec-01 Jan-02 Mar-02 May-02 Jun-02 Aug-02 Oct-02Time (days)

(mm

)

5326


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XXVI

5336


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XXVII


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XXVIII

D1: Measurements taken during Landsat 7 over pass

Date 4-Oct-02 Year: 2002.00 Sensitiv: 10:04 mV/(kW/m2) Mode: 0:00 Cnst: 1

Time

Incoming radiation

Outgoing radiation

Temperature

Count

Count Integral Average

STATION Ini Time

hh:mm:ss End Time hh:mm:ss Initial Actual

Integral kJ/m2

Average kW/m2 Initial Actual kJ/m2 kW/m2

Baresoil

Shrubs

Grass

1 8:05 8:05 2856 2857 0.09 0.001 2691 2711 1.75 -0.0146 16 19 20 2 9:10 8:07 3133 3249 10.16 -0.003 3279 3308 2.54 0.0007 19 21 21 3 9:15 8:10 3866 4006 12.26 -0.003 4039 4077 3.33 0.0009 19 21 22 4 9:20 8:15 4348 4458 9.63 -0.003 4490 4520 2.63 0.0007 19 22 21 5 9:25 8:20 4898 4997 8.67 -0.002 5015 5040 2.19 0.0006 20 21 22 6 9:30 8:26 5379 5494 10.07 -0.003 5523 5559 3.15 0.0009 23 19 22 7 9:34 8:30 6067 6172 9.19 -0.003 6195 6219 2.10 0.0007 20 21 21 8 9:40 8:35 6348 6449 8.84 -0.003 6533 6554 1.84 0.0006 21 21 21 9 9:45 8:42 7085 7166 7.09 -0.002 7189 7210 1.84 0.0006 19 22 22 10 9:50 8:46 7433 7564 11.47 -0.004 7600 7628 2.45 0.0008 25 19 24 11 9:55 8:54 8143 8297 13.49 -0.004 8362 8400 3.33 0.0011 25 29 27 12 9:58 9:01 8750 8913 14.27 -0.006 8956 9004 4.20 0.0018 25 26 28 13 10:05 9:05 9366 9580 18.74 -0.008 9649 9682 2.89 0.0013 30 23 28 14 10:10 9:19 11396 11615 19.18 -0.009 11670 11723 4.64 0.0021 29 27 31 15 10:14 9:28 12309 12560 21.98 -0.011 12624 12672 4.20 0.0022 33 26 34 16 10:17 9:34 13772 14013 21.10 -0.011 14105 14171 5.78 0.0031 33 34 33 1 10:27 9:42 15050 15196 12.78 -0.006 15248 15286 3.33 0.0015 33 25 34 2 10:30 9:46 16043 16207 14.36 -0.007 16253 16289 3.15 0.0015 33 32 29 3 10:35 9:51 16688 16919 20.23 -0.009 17007 17061 4.73 0.0022 38 34 34 4 10:40 9:55 19308 19588 24.52 -0.012 19658 19726 5.95 0.0028 45 34 34 5 10:45 9:59 20658 20920 22.94 -0.010 20988 21047 5.17 0.0023 42 30 41 6 10:50 10:05 22330 22559 20.05 -0.009 22614 22336 -24.34 -0.0104 35 27 33 7 10:55 10:07 23290 23498 18.21 -0.007 23555 23597 3.68 0.0015 36 33 34 8 10:59 10:11 24213 24452 20.93 -0.008 24546 24607 5.34 0.0021 44 23 39 9 11:05 10:14 24953 25178 19.70 -0.007 25240 25290 4.38 0.0017 39 30 36 10 11:10 10:17 25575 25888 27.41 -0.010 25970 26044 6.48 0.0024 46 36 38


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XXIX

Time

Incoming radiation

Outgoing radiation

Temperature

Ini Time End Time Count Integral Average Count Integral Average

STATION hh:mm:ss hh:mm:ss Initial Actual kJ/m2 kW/m2 Initial Actual kJ/m2 kW/m2 Baresoil Shrubs Grass 10 11:10 10:17 25575 25888 27.41 -0.010 25970 26044 6.48 0.0024 46 36 38 11 11:15 10:21 26385 26639 22.24 -0.009 26690 26762 6.30 0.0024 46 34 40 12 11:20 10:25 27361 27643 24.69 -0.009 27699 27760 5.34 0.0020 44 22 39 13 11:25 10:32 28750 29060 27.15 -0.010 29129 29197 5.95 0.0022 47 28 39 14 11:30 10:35 29688 30021 29.16 -0.011 30111 30170 5.17 0.0019 46 26 43 15 11:40 10:40 30788 31108 28.02 -0.009 31208 31269 5.34 0.0018 46 36 38 16 11:44 10:44 31633 31955 28.20 -0.010 32024 32084 5.25 0.0018 55 36 45 1 11:55 10:50 32819 33109 25.39 -0.007 33173 33242 6.04 0.0018 51 29 34 2 12:00 10:55 33849 34168 27.93 0.000 34325 34382 4.99 0.0000 50 40 41 3 12:04 10:58 34708 34989 24.61 -0.007 35065 35132 5.87 0.0016 51 40 41 4 12:08 0:00 35398 35578 15.76 -0.004 35846 35910 5.60 0.0015 51 42 38 5 12:11 11:03 36398 36711 27.41 -0.007 36802 36864 5.43 0.0015 52 37 46 6 12:14 11:06 37959 38296 29.51 -0.008 38361 38432 6.22 0.0017 45 28 40 7 12:19 11:10 39075 39338 23.03 -0.006 39395 39457 5.43 0.0014 49 40 42 8 12:21 11:14 39680 39899 19.18 -0.005 40092 40150 5.08 0.0014 47 27 42 9 12:25 11:16 40385 40640 22.33 -0.006 40736 40785 4.29 0.0012 50 32 46 10 12:30 11:20 41028 41269 21.10 -0.006 41322 41377 4.82 0.0013 49 35 41 11 12:35 11:25 41678 42072 34.50 -0.009 42207 42290 7.27 0.0019 50 39 43 12 12:40 11:29 42840 43100 22.77 -0.006 43184 43236 4.55 0.0011 55 36 40 13 12:45 11:32 43808 44098 25.39 -0.007 44230 44284 4.73 0.0012 53 32 49 14 12:50 11:34 44678 44952 23.99 -0.006 45005 45081 6.65 0.0016 55 40 40 15 12:55 11:40 45513 45814 26.36 -0.001 45937 45598 -29.68 -0.0006 50 34 40 16 13:00 11:42 46298 46607 27.06 -0.001 46738 46800 5.43 0.0001 53 32 48


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XXX

Date 18-Sep-02 Sensitiv: 10:04 mV/(kW/m2) Mode: 0:00 Cnst: 1

Incoming radia-

tion Outgoing radia-

tion Temperature

Ini Time End Time Count Integral Average

Count Integral Average STATION hh:mm:ss hh:mm:ss Initial Actual kJ/m2 kW/m2 Initial Actual kJ/m2 kW/m2 Baresoil Shrubs Grass

24 6.66 1 8:56 9:10 86384 86575 16.73 0.020 86640 86677 3.24 0.00 31 25 2 9:10 9:15 87476 87713 20.75 0.069 87809 87850 3.59 -0.01 23 10 22 3 9:15 9:20 89629 89864 20.58 0.069 81991 89964 698.16 -2.33 38 20 27 4 9:20 9:25 90638 90876 20.84 0.069 90926 90979 4.64 -0.02 33 25 26 5 9:25 9:30 91335 91588 22.15 0.074 91648 91708 5.25 -0.02 33 26 30 6 9:30 9:34 93052 93313 22.85 0.095 93360 93438 6.83 -0.03 36 35 29 7 9:34 9:40 94654 94900 21.54 0.060 94988 95057 6.04 -0.02 36 17 30 8 9:40 9:45 96236 96496 22.77 0.076 96550 96632 7.18 -0.02 37 22 28 9 9:45 9:50 97729 97888 13.92 0.046 97688 98047 31.44 -0.10 39 20 29 10 9:50 9:55 98554 98792 20.84 0.069 98598 99032 38.00 -0.13 40 20 35 11 9:55 9:58 99775 100054 24.43 0.136 100198 100275 6.74 -0.04 37 30 29 12 9:58 10:05 100613 100875 22.94 0.055 100937 101007 6.13 -0.01 40 22 29 13 10:05 10:10 101622 101898 24.17 0.081 101968 102047 6.92 -0.02 43 24 32 14 10:10 10:14 102555 102845 25.39 0.106 102998 103086 7.71 -0.03 40 38 32 15 10:14 10:17 103497 103828 28.98 0.161 103975 104023 4.20 -0.02 40 30 32 16 10:17 10:27 104376 104668 25.57 0.043 104795 104853 5.08 -0.01 41 14 32 1 10:27 10:30 106638 106940 26.44 0.147 107063 107149 7.53 -0.04 44 27 31 2 10:30 10:35 107765 108058 25.66 0.086 108147 108233 7.53 -0.03 44 29 35 3 10:35 10:40 108556 108850 25.74 0.086 108948 109033 7.44 -0.02 45 30 34 4 10:40 10:45 110520 110820 26.27 0.088 110928 111020 8.06 -0.03 43 29 34 5 10:45 10:50 111285 111579 25.74 0.086 111698 111787 7.79 -0.03 40 23 32 6 10:50 10:55 112096 112400 26.62 0.089 112510 112611 8.84 -0.03 46 31 33 7 10:55 10:59 113126 113418 25.57 0.107 113499 113593 8.23 -0.03 46 39 36 8 10:59 11:05 113970 114295 28.46 0.079 114418 114524 9.28 -0.03 46 35 37 9 11:05 11:10 114843 115149 26.80 0.089 115270 115368 8.58 -0.03 47 25 34 10 11:10 11:15 115947 116282 29.33 0.098 116420 116524 9.11 -0.03 47 25 37 11 11:15 11:20 116847 117185 29.60 0.099 117298 117405 9.37 -0.03 47 28 34


INTERNATIONAL INSTITUTE FOR GEO INFORMATION SCIENCE AND EARTH OBSERVATION XXXI

Incoming radia-

tion Outgoing radiation Temperature

Ini Time End Time Count Integral Average

Count Integral Average STATION hh:mm:ss hh:mm:ss Initial Actual kJ/m2 kW/m2 Initial Actual kJ/m2 kW/m2 Baresoil Shrubs Grass

12 11:20 11:25 117913 118235 28.20 0.094 118332 118430 8.58 -0.03 49 28 35 13 11:25 11:30 118795 119127 29.07 0.097 119228 119332 9.11 -0.03 48 20 28 14 11:30 11:40 119668 119997 28.81 0.048 120129 120232 9.02 -0.02 50 23 35 15 11:40 11:44 120738 121058 28.02 0.117 121186 121278 8.06 -0.03 49 22 32 16 11:44 11:55 121898 122218 28.02 0.042 122308 122408 8.76 -0.01 1 11:55 12:00 123408 123756 30.47 0.102 123828 123924 8.41 -0.03 2 12:00 12:04 124258 124598 29.77 0.124 124678 124777 8.67 -0.04 3 12:04 12:08 125438 125778 29.77 0.124 125900 126012 9.81 -0.04 56 28 35 4 12:08 12:11 126323 126656 29.16 0.162 126789 126890 8.84 -0.05 55 38 40 5 12:11 12:14 127042 127378 29.42 0.163 127500 127600 8.76 -0.05 53 30 38 6 12:14 12:19 127876 128205 28.81 0.096 128324 128415 7.97 -0.03 49 24 33 7 12:19 12:21 128713 129046 29.16 0.243 129166 129253 7.62 -0.06 53 30 32 8 12:21 12:25 129453 129780 28.63 0.119 129918 130007 7.79 -0.03 50 31 38 9 12:25 12:30 130230 130560 28.90 0.096 130643 130720 6.74 -0.02 52 20 40 10 12:30 12:35 130970 131300 28.90 0.096 131400 131466 5.78 -0.02 51 33 34 11 12:35 12:40 131760 132098 29.60 0.099 132249 132317 5.95 -0.02 49 27 38 12 12:40 12:45 133134 133467 29.16 0.097 133548 133620 6.30 -0.02 51 34 36 13 12:45 12:50 134232 134580 30.47 0.102 134685 134765 7.01 -0.02 52 26 42 14 12:50 12:55 136510 136838 28.72 0.096 136925 136993 5.95 -0.02 49 31 36 15 12:55 13:00 137215 137540 28.46 0.095 137635 137723 7.71 -0.03 54 28 44 16 13:00 0:00 137993 138307 27.50 -0.001 138405 138482 6.74 0.00 54 34 38

Assessment of water fluxes in semi-arid environments...Assessment of water fluxes in semi-arid...

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