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.
Top Related