1

Modeling the impact of Climate Change on Water Resources: a case study of Sasumua Catchment in Kenya

Field Work Report

Francis Omondi Oloo

(University of Salzburg and OeAD grant holder)

September, 2012

Supervisors:

Prof. Josef Strobl (University of Salzburg)

Dr. Luke Olang’ (Kenyatta University)

i

Contents

Acknowledgements ........................................................................................................ ii

ACRONYMS ................................................................................................................. iii

List of Figures ............................................................................................................. iv

List of tables .............................................................................................................. iv

1.0 Introduction ....................................................................................................... 1

2.0 Description of the area of study ............................................................................... 2

2.1 Sasumua reservoir location .................................................................................. 2

2.2 Altitude ......................................................................................................... 3

2.3 Soils .............................................................................................................. 3

2.4 Land cover ...................................................................................................... 4

2.6 Population ...................................................................................................... 6

3.0 Spatial data capture ............................................................................................. 7

4.0 Watershed delineation ........................................................................................ 10

5.0 Downscaling Global Climate Models ........................................................................ 11

5.0 Hydrological model selection ................................................................................ 12

6.0 Observed weather and hydrology data from Sasumua ................................................... 13

6.1 Conversion of dam levels to water volume ............................................................. 17

7.0 Simulated Climate data for Sasumua Catchment ......................................................... 18

Conclusion ................................................................................................................ 20

References ................................................................................................................ 21

ii

Acknowledgements

I sincerely wish to express my gratitude to OeAD for granting me the scholarship to pursue a Masters

Degree course in Applied at the University of Salzburg and for funding my trip to carry out the field

work for my upcoming thesis. I wish to highlight my specific appreciation to Madam Elke Stinnig and

Madam Tanja Vogl for their continued support and understanding.

Secondly, I wish pass my gratitude to my supervisors Prof. Josef Strobl and Dr. Luke Olang’ for their

continued guidance and support as I continue in this journey. In view of this report, I am particularly

grateful to Dr. Olang’ for his regular guidance and the very insightful contributions as I continued with

the field studies in Kenya.

Finally but not least, I am grateful to Dr. Eike Luedeling of the World Agroforestry Centre (ICRAF) for

assisting me to generate the synthetic climate data for the synthetic weather data points within

Sasumua catchment.

iii

ACRONYMS

ASTER Advanced Spaceborne Thermal Emission and Reflection

AWC Available Water Capacity

BSAT Base Saturation

CCAFS Climate Change, Agriculture and Food Security

GCM General Circulation Model/ Global Climate Model

GIS Geographic Information System

GWP Global Water Partnerships

ICPAC IGAD Climate Prediction and Application Center

IPCC Intergovernmental Panel on Climate Change

OeAD Austrian Agency for International Mobility in Education, Science and Research

PRECIS Providing Regional Climate for Impact Studies

RCM Regional Circulation Model/Regional Climate Model

SRTM Shuttle Radar Topography Mission

SWAT Soil and Water Assessment Tool

TOTC Total Organic Carbon

iv

List of Figures

Figure 1: Map of the area of study ..................................................................................... 2

Figure 2: Soil classes in Sasumua catchment ......................................................................... 3

Figure 3: Land cover map for the Sasumua catchment ............................................................. 5

Figure 4: Population density in the catchment area ................................................................ 6

Figure 5: GPS waypoints and tracks captured in Sasumua ........................................................ 7

Figure 6: Kiburu intake tunnel heading to the reservoir ........................................................... 8

Figure 7: Chania intake tunnel, closed during rainy seasons ...................................................... 9

Figure 8: Watershed elements for the Sasumua catchment ..................................................... 10

Figure 9: Trends of rainfall from Sasumua dam weather station and stream inflows from Sasumua

stream, Kiburu intake channel and Mungutiu stream from1st January, 2011 and 9th September, 2012 14

Figure 10: Trends of rainfall in Sasumua dam station and stream flow in Sasumua stream in the period

1st January, 2011 to 9th September, 2012 ......................................................................... 15

Figure 11: Trends of rainfall in Sasumua dam station and stream flow in Kiburu intake channel in the

period 1st January, 2011 to 9th September, 2012 ................................................................ 15

Figure 12: Trends of rainfall in Sasumua dam station and stream flow in Mingutiu stream in the period

1st January, 2011 to 9th September, 2012 ......................................................................... 16

Figure 13: Trends of rainfall in Sasumua dam station and dam levels of Sasumua reservoir in the period

1st January, 2011 to 9th September, 2012 ......................................................................... 16

Figure 14: Monthly dam level and rainfall for the years 2011 and 2012 ....................................... 17

Figure 15: Trend line describing the relationship between dam levels (feet) and water volumes (cubic

meters) in Sasumua reservoir ......................................................................................... 18

Figure 16: Spatial distribution of the synthetic weather stations .............................................. 20

List of tables

Table 1: Soil characteristics in Sasumua catchment ................................................................ 4

Table 2: Land cover classes in Sasumua catchment ................................................................. 4

1

1.0 Introduction

Water is a primary medium through which climate change will have impact on people, ecosystems and

economies. Improved understanding of the dynamics of climate change and how it affects water supply

and demand and the broader impacts on all water-using sectors will guide better water resource

management (Global Water Partnerships, 2009). By using the global General Circulation Models (GCMs),

different studies have been carried out to model the potential impact of climate change on water

resources. According to the 6th technical report of IPCC (Bates, Kundzewicz, Wu, & Palutikof, 2008), it

was noted that fresh water resources are considerably vulnerable and have the potential of being

adversely affected by climate change. (Turral, Burke, & Faures, 2011) also reported that climate

change will impact the extent and productivity of irrigated and rain-fed agriculture across the globe.

(Xu, 1999) reviewed different downscaling methods and their application in hydrological modeling. (Al

Zawad, 2008) applied GIS to RCMs generated by the PRECIS model to simulate the impact of climate

climate change on water resources in Saudi Arabia.

The objective of this study is to use the statistical downscaling models to downscale GCM to a level

where they can then be reasonably used to model the impact of climate change on water resources.

The area of study is Sasumua catchment in central Kenya. The catchment hosts the Sasumua dam, one

of the five reservoirs that supply Nairobi city and its environs with the domestic water needs. This

report was compiled after the field work exercise that was undertaken in the catchment as from July-

September, 2012. The main objective for the field study was to collect the data that would be needed

for the analysis in the study and to also meet the different stakeholders who have worked on different

aspects of natural resource management in the catchment. This report outlines different bioclimatic

and demographic characteristics of the field of study, and also some of the tools and the

methodologies that will be utilized to meet the specific objectives of this study.

The cost of travel and the different logistics during the field exercise were met through a grant from

OeAD Scholarship. During the duration of the field work, I was under the direct supervision of Dr. Luke

Olang of Kenyatta University.

2

2.0 Description of the area of study

2.1 Sasumua reservoir location

Sasumua reservoir is situated at the tip of Sasumua River which is a temporary river in the greater

Sasumua catchment. Due to the fact that Sasumua River is non-perennial, the reservoir receives

additional water from Chania and Kiburu rivers through underground tunnels. Chania and Kiburu intakes

are mainly utilized in the dry seasons when the inflows from Sasimua River reduce drastically in

volume. The reservoir catchment is therefore made up of three minor catchments which are the

Sasumua dam catchment, Chania catchment and the Kiburu catchment with a combined spatial area of

approximately 111 square kilometers, all these three drain to a lower sub-catchment of approximately

25 square kilometers as portrayed in the figure 1. The entire combination of sub-catchments lies

between longitudes 36⁰35′E and 36⁰43′E and latitudes 0⁰39′S and 0⁰47′S in Nyandarua County, Kenya

Figure 1: Map of the area of study

3

2.2 Altitude

The altitude of the catchment lies at an approximate range of 2200m and 3880m above the mean sea

level. The highest parts of the catchment are towards the north eastern sections with are located

within the Aberdare forest, the south western sections of the catchment are fairly flat and are mainly

used for horticultural farming and for dairy keeping. The area lies in a rich agricultural zone with mean

annual rainfall values between 800mm and 1600m

2.3 Soils

According to the FAO soil classification extracted from the world soil maps (Batjes and Gicheru, 2004),

the catchment is composed of six different soil classes which are distributed as shown in figure 2.

Figure 2: Soil classes in Sasumua catchment

4

Of the six soil classes in the catchment, terric histosols and eutric planosols are imperfectly drained

while the other soil classes are well drained. Apart from the drainage characteristics of the soil classes,

the other characteristics which are necessary for hydrological modeling are the level of organic carbon

(TOTC), base saturation (BSAT), available water capacity (AWC) and the percentage composition of

clay, sand and silt. For the area of study, these characteristics are as outlined in table 1 below

Table 1: Soil characteristics in Sasumua catchment

Soil class % Sand % Silt % Clay TAWC BSAT TOTC

Humic Nitisols 32 46 22 12 32 19

Terric Histosols -1 -1 -1 35 40 80

Haplic Acrisols 52 16 32 8 45 9

Eutric Planosols 24 52 24 18 50 21

Haplic Phaeozem 14 67 19 13 49 36

Mollic Andosols 26 24 50 15 80 46

2.4 Land cover

In order to create a land cover map for the catchment, ASTER imagery for the month of June 2007 was

used. Maximum likelihood classification method was applied to come up with 11 major land cover

classes in the catchment. Additionally, GPS coordinates of points of interest and a 2.4m resolution

QuickBird image were used to confirm the spatial accuracy of the different land cover classes. In

summary, the predominant land cover classes in the catchment are as outlined in the table 2 below

and the spatial distribution of the same are shown in figure 3

Table 2: Land cover classes in Sasumua catchment

Land cover class Area (km2) Area( Acres) % cover

Mooreland 3.4 847 2.5

Bareland 0.1 13.4 0.1

Degraded forest 6.8 1670 5

Broad leaved forest 60.5 14938 44.2

Agricultural fields 56 13826 40.9

Woodlots 0.4 94 0.3

Roads 1.2 297 0.9

Settlements 0.3 84 0.3

Riverine vegetation 1.5 361 1.1

Grassland 5.6 1389 4.1

Water 1.3 312 1

Total 137.1 33831.4 100

5

According to these statistics, it is evident that the main land cover in the catchment is the forest cover

and particularly towards the eastern side which is situated on the Aberdare range. This land cover type

occupies slightly more than 40% or the area of study. The second major land cover class in the area is

the agricultural fields, this particular so since the catchment is a rich agricultural area with the main

crops propagated being maize, potatoes and vegetables.

Figure 3: Land cover map for the Sasumua catchment

6

2.6 Population

In the 1999 Kenya national population census, the 10 sub-locations (smallest administrative units) in

the vicinity of the catchment had a combined population of 101,236 persons with a mean population

density of 181 persons per square kilometers. In the 2009 national population census however, the

same administrative units had a combined population of 125,276 persons with a mean population

density of 222 persons per square kilometers. This signifies a 24% increase in population within a period

of 10 years or 2.4% increase in population. Figure 4 shows population density map of the administrative

units within the catchment as enumerated in the 2009 Kenya national population census.

Figure 4: Population density in the catchment area

7

3.0 Spatial data capture

During my stay in Kenya, I made a visit to the catchment, the main objectives for these visits were (i)

to collect hydrological and associated data from the NCWSC offices which are situated in close

proximity to the dam (ii) to capture coordinates of points of interest in the catchment which would

then be used to geo-reference other GIS datasets and to modify and “groundtruth” the land cover map

developed for this study, and (iii) to have a feel of the general setting of the study site and to take

note of any unique aspects of the area.

Figure 5: GPS waypoints and tracks captured in Sasumua

During the visit, a handheld GPS was used to capture the coordinates of the points of interest, there

then mapped and overlaid on Quickbird image of the area. At the same time the dominant land use and

land cover classes within the vicinity of the points of reference were also noted. The resulting field

8

notes were later used to update the land cover maps. One of the unique information that came up

during the field exercise was that the Sasumua dam does not depend on the natural stream flows but

actually receives some water from underground tunnels from Chania and Kiburu rivers. Coordinates and

the photographs of the intake tunnels were captured. Figure 5 is a map of the GPS waypoints and the

tracks captured during the field visit.

Figure 6: Kiburu intake tunnel heading to the reservoir

9

Figure 7: Chania intake tunnel, closed during rainy seasons

10

4.0 Watershed delineation

From the 90m SRTM digital elevation model, watershed elements including the sub-basins, drainage

lines and drainage points were delineated using ArcHydro tools in ArcGIS 10. From the analysis, 42

minor sub-basins were generated with the smallest having an area of 0.055 square kilometers and the

largest having an area of 12.869 square kilometers. Figure 8 shows the generated watershed elements

overlaid on the hillshade of the area

Figure 8: Watershed elements for the Sasumua catchment

11

5.0 Downscaling Global Climate Models

The main source of information for climate change studies are the global General Circulation Models

(GCMs) (Sunyer, Henrik, & Keiko, 2010). These are mathematical representations of atmospheric

motions and changes in moisture and are used to model the current and future climate scenarios.

Although the spatial extents of the GCMs cover the entire earth, their spatial resolution is course,

ranging from 200km to 300km (Hewitson & Crane, 1996). As a result, they cannot be directly applied to

monitor the impact of climate change on hydrology or on agriculture at a landscape scale. In order to

apply the GCMs to impact studies at micro scales, they need to be downscaled to represent climate

variables at the local level of application.

Downscaling is a term that is used to describe the techniques that are used to relate the local and

regional climate variable to the large scale atmospheric models (Hewitson & Crane, 1996). There are

two broad categories of downscaling approaches; these are (i) Dynamical downscaling and (ii)

Statistical downscaling (Sunyer, Henrik, & Keiko, 2010).

In dynamical downscaling, Regional Circulation Models (RCMs) are nested in GCMs in order to simulate

the regional climatic variables at spatial resolutions which are much finer than those of the GCMs. At

higher spatial resolution, the RCMs capture climate features related to the regional forcings such as the

topography, lakes, complex coastlines and heterogeneous land cover/use classes hence they are able

to represent local climatic variables more accurately than the GCMs. Additionally, actual observations

at the local and regional level can also be included in the downscaling procedure to further refine the

outcome from the RCMs, this process is referred to as reanalysis. However, due to the higher demand

for computational power and also since the RCMs depend on the boundary conditions inherited from

the GCMs; the RCMs can only be generated at spatial resolutions in the range of 50-10km which are still

not fine enough for accurate impact studies in hydrology at watershed and sub-catchment scales.

Statistical downscaling on the other hand relies on the mathematical relationships between the large

scale climate models and the local scale climatic variables (Clement, Mathieu, Sovan, & Andrew,

2010). Once the relationship between large scale climate variables and the local climate variables has

been accurately defined, the relationship can then be used to predict current and future climate

variables at the local scale. Statistical downscaling models can generally be divided into three types of

approaches, these include; regression models, weather typing schemes and weather generators (Vrac &

Naveau, 2007). In the first method, the relationship between large scale variables and location specific

variable are directly estimated using parametric and nonparametric linear and non-linear methods

including multiple linear regression, kriging and neural networks (Vrac & Naveau, 2007). The weather

typing scheme method involves a recurrent clustering and classification procedures that are aimed at

refining the relationship between the land scale variables and the local estimates. Stochastic weather

12

generators on the other hand are statistical models that are able to simulate weather data for specific

locations based on the statistical relationships in the characteristics between the large scale climate

variables and the local climate variables (Sunyer, Henrik, & Keiko, 2010).

In this study, LARS-WG (Semenov, 2002) will be used to generate daily climate variables for different

points within the study site. The main variables that will be required for hydrological modeling include

precipitation, maximum and minimum temperature and potential evapotranspiration.

5.0 Hydrological model selection

In order to select an appropriate model to be used in this study, the main classes of hydrological

models were looked at as described below:

i. Lumped models

These are hydrological models that treat the entire catchment as a single unit and thus the resulting

catchment variables represent averages over the entire catchment (Pechlivanidis, Jackson, MCintyre, &

Wheater, 2011). Such models are not appropriate for prediction of single events (Cunderlik, 2003) but

can be used to predict long term catchment variables and processes including discharge and sediment

load.

ii. Semi-distributed models

The parameters of these models are allowed to be partially distributed by dividing the entire

catchment into smaller sub-basins. There are two main types of semi-distributed hydrological models.

These are the kinetic wave models and the probability models (Cunderlik, 2003)

iii. Distributed models

The parameters of distributed parameters are allowed to be spatially distributed across the network at

the users desired spatial resolution for instance at a pixel level. Although the distributed models tend

to require large quantities of data for parametization at the pixel level, if properly applied they lead to

more accurate results (Cunderlik, 2003).

iv. Time-scale based classification models

Hydrological models can also be classified based on whether the model in question is intended for

analysis of a continuous time series data or whether the model is intended for analysis of single storm

event (Pechlivanidis, Jackson, MCintyre, & Wheater, 2011).

With the above outline, three fundamental criteria were considered in determining the most

appropriate hydrological modeling tool that will be used in this study. The criteria were as follows:

13

The tool/model should be able to handle time series climate (daily climate variables)

preferably for a number of points within the study area.

The model should be able to simulate daily variation in hydrological processes including

discharge, infiltration and sediment load for a catchment of which is slightly above 100 square

kilometers in area.

The model should also be able to simulate the soil water balance for the area of study when

precipitation, temperature and potential evapotranspiration data are available for input.

Where possible to model should show spatial variability in the catchment variables.

Since this study aims at modeling the potential impact of climate change on various hydrological

processes in Sasumua catchment, the Soil and Water Assessment Tool (SWAT) model was preferred as

the most appropriate to be applied in this exercise. SWAT is a semi-distributed which uses specific

input data on weather, vegetation, topography, land use and land management practices to model the

physical processes associated with the water movement, sediment movement, crop growth and

nutrient recycling within a watershed (Neitsch, Arnold, Kiniry, & Williams, 2009).

The SWAT model has been applied in various climate change related studies in Kenya. Githui et al,

2009 carried out a study on the impact of climate change on simulated streamflow in Nzoia River

catchment in western Kenya. Mango et al, 2011 applied SWAT to investigate the combined impact of

climate change and land use on the headwater hydrology of the Mara River.

6.0 Observed weather and hydrology data from Sasumua

Nairobi Water and Sewerage Company has a water treatment plant located within the Sasumua

catchment, apart from the daily water treatment and the regular reservoir maintenance works, the

team at the plant carries out regular recording of weather data for one of the stations located at the

dam site . Additionally daily dam levels and inflows of Sasumua stream and Kiburu intake tunnel are

also recorded. Manual records have been recorded from October 2008 while the process of digitally

recording the hydrology and weather data started in January 2011. In the course of the field work, both

the available digital data and hard copy data was accessed. Some of the graphs drawn from the data

are present in the sections below

14

Figure 9: Trends of rainfall from Sasumua dam weather station and stream inflows from Sasumua stream, Kiburu intake channel and Mungutiu stream from1st January, 2011 and 9th September, 2012

According to figure 9, it is evident that in the year 2011, there were two main rainfall patterns in the

catchment; these were between March and May and also between September and December. This is in

agreement with the general rainfall patterns in Kenya where short rain period occurs in March, April

and May (commonly referred to as MAM period) and the long rains occur between September and

December (commonly referred to as SOND). Additionally from figure 1, it is evident that there is a

positive correlation between rainfall patterns and the amount of inflows in the three streams. An

increase in rainfall results in an increase of inflows into the reservoir. Of the data from the three

streams, it was noted that there were many data gaps in the inflow data for the Mingutiu stream.

Apart from the combined graph of rainfall against the inflows in the three streams, individual graphs

relating the daily precipitation to the daily inflow record for the three streams were plotted as shown

in figures 10-12

15

Figure 10: Trends of rainfall in Sasumua dam station and stream flow in Sasumua stream in the period 1st January, 2011 to 9th September, 2012

Figure 11: Trends of rainfall in Sasumua dam station and stream flow in Kiburu intake channel in the period 1st January, 2011 to 9th September, 2012

16

Figure 12: Trends of rainfall in Sasumua dam station and stream flow in Mingutiu stream in the period 1st January, 2011 to 9th September, 2012

Apart from the inflow data, daily dam levels were also plotted together with the rainfall data the

result of which is shown in figure 13

Figure 13: Trends of rainfall in Sasumua dam station and dam levels of Sasumua reservoir in the period 1st January, 2011 to 9th September, 2012

17

Since the reservoir was designed to have a maximum capacity of approximately 15.9 million cubic

meters, this is equivalent to 8190feet (approximately 2496 meters). This is evident in the figure 5 as

the dam level tends to flatten at this level.

Further, the average monthly dam levels were plotted against the rainfall values for the years 2011 and

2012 as shown in figure 14

Figure 14: Monthly dam level and rainfall for the years 2011 and 2012

Apart from the rainfall values, other weather data sets which were obtained from NCWSC offices

include maximum and minimum temperatures for the Sasumua dam station, wind speed and

evaporation. All these will be very useful in the hydrological modeling process.

6.1 Conversion of dam levels to water volume

From NCWSC office in Sasumua, a copy of the conversion table that is used to translate specific dam

levels (from 8100 ft to 8190 ft) to volume (in cubic meters) was obtained. In order to develop a generic

model that can be used to translate any dam level (for Sasumua reservoir) to volumes, the values were

typed and then plotted using Tableau software. Using the analysis function within the software, a trend

line was drawn on the data points and a model for the trend line retrieved. Figure 15 shows the plot of

the water volume plotted against the dam levels and the associated trend line.

18

Figure 15: Trend line describing the relationship between dam levels (feet) and water volumes (cubic meters) in Sasumua reservoir

From figure 15, it is evident that the water volume in Sasumua catchment can be described as a second

order polynomial function of the dam levels. From the analysis menu in Tableau, the descriptive model

of the trend line was obtained as

This model will be used to compute the water volume for all the other observed dam level

7.0 Simulated Climate data for Sasumua Catchment

Due to the lack of temporally consistent and spatially well distributed weather data for the catchment,

LARS-WG, which is a stochastic weather data generator was used to generate synthetic weather data

for regularly generated points within the catchment. Using Quantum GIS software 25 points were

generated within the catchment at a spatial resolution of 0.02⁰ which is approximately 2.3km on the

ground.

Three General Climate Models (GCMs) were used as the basis of generated the synthetic climate data

for the 25 regularly selected points. The three models used in the exercise were

19

HADCM3 - Hadley Centre Coupled Model, version 3

CCCMA CGCM2 - Canadian General Circulation Model 2 by the Canadian Centre for Climate

Modelling and Analysis

CSIRO Mk2 - CSIRO Atmospheric Research Mark 2b

For all models, the statistically downscaled versions provided by the CGIAR Research Program on

Climate Change, Agriculture and Food Security (CCAFS; http://ccafsclimate.org/download_allsres.html

) were used for analysis. These projections have a spatial resolution of 2.5 min (approx. 25 km in the

study region), and are available for two IPCC greenhouse gas emissions scenarios (A2a - 'business as

usual' emissions; and B2a – reduced emissions), and three points in time (2020s, 2050s and 2080s).

CCAFS also provides baseline climatology for the time span 1950-2000 (Hijmans et al., 2005), which was

used as a reference scenario.

20

Figure 16: Spatial distribution of the synthetic weather stations

The weather generator was used to produce 100 years of synthetic daily weather data for each scenario

and for each of the 25 points. These 100-year records are not time series, they rather constitute 100

replicates of a given year’s weather, spanning the range of weather situations that can plausibly be

expected.

Conclusion

Based on the objectives set out for the field studies, it is my considered view that despite the

challenges faced during field exercise, the exercise was generally a success. By visiting the project

site, I now have a better understanding of the various aspects of the site, additionally very valuable

field related spatial data was also obtained in the field and these have been very useful in generating

21

the land-use map for the study site and for validating different other datasets. Apart from field related

data, other auxiliary data were also obtained.

Secondly, the visit also provided me with a good opportunity to interact with various stakeholders in

the project site and with key players in the hydrology sector in Kenya. In particular, the interactions

with the staff at the Nairobi City Water and Sewerage Company (NCWSC) and Water Resource

Management Authority (WRMA) provided with the opportunity to understand various challenges faced

by water management stakeholders in Kenya. Further, the interactions with scientists and researchers

at the World Agroforestry Center (ICRAF) and IGAD Climate Prediction and Application Center (ICPAC)

were very beneficial especially in understanding the various tools that are available to studying the

impact of climate change on hydrological processes.

In view of the inconsistencies and inadequacy of the climate and hydrology data for the project site, it

is recommended that the main project objectives should be edited so that more weight is given to

modeling the impact of climate change on water resources based on the simulated climate data.

Finally it was noted that there is poor record keeping of hydrology data in Kenya and especially in the

Sasumua catchment. Apart from the water level readings that are being taken by the NCWSC staff, no

other hydrology data sets exist for the catchment, in fact a visit to Water Resource Management

Authority revealed that of all the gauges within the catchment (4CA6, 4CA5, 4CA13 and 4CA12) did not

have any useful data and it appeared none of them was still operationally. Additionally, even the water

level data has been only continuously for only 3 years and most of the data is yet to be digitized. Worst

still, the measurements do not include volumes and therefore it is not possible to convert the water

level data into discharge. It is therefore recommended that there should be collaboration between

different water management organs in the catchment (and in the country) to ensure that water related

data is properly recorded, archived and made accessible to different users.

References

1. Al Zawad, F. M. (2008). Using GIS Technology to assess the impact of climate change on water

resources. Dammam, Saudi Arabia: Saudi National GIS Achive.

2. Bates, B., Kundzewicz, Z. W., Wu, S., & Palutikof, J. (2008). Climate Change and Water: IPCC

Technical Paper VI. Geneva: Intergovernmental Panel on Climate Change.

3. Clement, T., Mathieu, V., Sovan, L., & Andrew, J. W. (2010). Statistical downscaling of river

flows. Journal of Hydrology vol 385 , 279-291.

4. Cunderlik, J. (2003). Hydrologic model selection for CFCAS project: Assessment of water

resources risk and vulnerability to changing climatic conditions. Ontario, Canada: University of

Western Ontario.

22

5. Githui, F., Gitau, W., Mutua, F., & Bauwens, W. (2009). Climate change impact on SWAT

simulated streamflow in western Kenya. International Journal of Climatology 29 , 1823-1834.

6. Global Water Partnerships. (2009). Perspectives on water and climate change

adaptation:Better water resources management-Greater resilience today, more effective

adaptation tomorrow. Global Water Partnerships Technical Committee.

7. Hewitson, B. C., & Crane, R. G. (1996). Climate downscaling: techniques and applications.

Climate Research , 85-95.

8. Mango, L. M., Melesse, A. M., McClain, M. E., & Setagan, S. G. (2011). Land use and climate

change impacts on the hydrology of the upper Mara River Basin, Kenya: results of a modeling

study to support better resource management. Hydrology and Earth Systems Science, Vol 15 ,

2245-2258.

9. Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2009). Soil and Water Assessment

Tool: Theoretical Documentation, version 2009. Texas, USA: Texas Water Resources Institute.

10. Pechlivanidis, I. G., Jackson, B. M., MCintyre, N. R., & Wheater, H. S. (2011). CATCHMENT

SCALE HYDROLOGICAL MODELLING: A REVIEW OF MODEL TYPES, CALIBRATION APPROACHES AND

UNCERTAINTY ANALYSIS METHODS IN THE CONTEXT OF RECENT DEVELOPMENTS IN TECHNOLOGY

AND APPLICATIONS. Global Nest Journal Vol 12, No 3 , 193-214.

11. Semenov, M. A. (2002). LARS-WG: Stochastic Weather Generators for Climate Impact Studies.

Hertsfordshire, UK: Rothamsted Research.

12. Sunyer, M. A., Henrik, M., & Keiko, Y. (2010). On the use of statistical downscaling for

assessing climate change impacts on hydrology. International Workshop ADVANCES IN

STATISTICAL HYDROLOGY, (pp. 1-11). Taormina, Italy.

13. Turral, H., Burke, J., & Faures, J. M. (2011). Climate change, Water and Food Security. Rome,

Italy: Food and Agricutural Organization (FAO) of United Nations.

14. Vrac, M., & Naveau, P. (2007). Stochastic downscaling of precipitation: From dry events to

heavy rainfalls. Water Resources Research, Vol 43, W07402 , 1-13.

15. Xu, C.-y. (1999). From GCM to river flow: a review of downscaling methods and hydrological

modelling aproaches. Progress in Physical Geography, Vol 23, 2 , 229-249.