SysTem for Analysis, Research, and Training Assessing the impacts

Transcription

SysTem for Analysis, Research, and Training
Assessing the impacts of climate change and variability
on water resources in Uganda: developing an
integrated approach at the sub-regional scale
a START project co-sponsored by the
International Geosphere-Biosphere Programme (IGBP)
World Climate Research Programme (WCRP), and
International Human Dimensions Programme on global environmental change (IHDP)
FINAL REPORT
February 2006
Meteorology Unit of the Department of Geography, Makerere University (Uganda)
Water Resources Management Department (Uganda)
Department of Geography, University College London (UK)
Meteorology Department (Uganda)
PROJECT NUMBER 202 457 5859
Table of Contents
Executive Summary...........................................................................................................................................2
1. Project rationale and objectives...................................................................................................................4
1.1
Project aims and objectives ............................................................................................................6
2. Progress - regional climate modelling (PRECIS) ......................................................................................7
2.1 installation of PRECIS and training.....................................................................................................7
2.2 Climatic factors and patterns in Uganda – meteorological datasets for RCM validation.............8
2.2.1
Main influencing atmospheric meteorological systems in Uganda .................................8
2.2.2.
The ITCZ.................................................................................................................................8
2.2.3.
The Monsoons ........................................................................................................................9
2.2.4.
Meso–Scale Circulations........................................................................................................9
2.2.5
Teleconnections (El Niño Southern Oscillation) ..............................................................9
2.2.6
Mean annual rainfall .............................................................................................................10
2.2.6.
Seasonal rainfall distribution ...............................................................................................10
2.2.7.
Delineation of “Climatological Zones”.............................................................................11
2.3
RCM experiments..........................................................................................................................13
2.3.1
Model domain .......................................................................................................................14
2.3.2
Simulations ............................................................................................................................14
3. Progress - Hydrological modelling............................................................................................................15
3.1 Development of soil-moisture balance models ................................................................................16
3.1.1.
Simulation of soil moisture - Wobulenzi...........................................................................18
3.1.2.
Simulation of runoff.............................................................................................................19
3.1.3.
Simulation of recharge .........................................................................................................19
3.2 Development of temperature-based estimates potential evapotransipration for East Africa ...22
3.2.1.
PET derived from evaporation pans .................................................................................22
3.2.2.
Climate-based methods........................................................................................................23
3.2.3.
Temperature-based methods ..............................................................................................24
3.2.4.
Estimation of potential evapotranspiration ......................................................................25
3.3.1.
Study rationale & objective .................................................................................................27
3.3.2
Results ....................................................................................................................................27
3.3.3.
Summary ................................................................................................................................28
4
Report Summary .....................................................................................................................................29
5
Way forward ............................................................................................................................................30
6
References................................................................................................................................................31
Appendix 1
Appendix 2
List of participants – FINAL WORKSHOP ...................................................................33
Daily rainfall data for validation of RCM experiment.....................................................35
Assessing the impacts of climate change and variability on water resources in Uganda: developing an integrated
approach at the sub-regional scale –FINAL REPORT
1
Executive Summary
A one-year project to enhance indigenous scientific capacity in Uganda to undertake integrated assessment of the subregional impact of climate change and climate variability on water resources has been completed. A PC-based regional
climate model (RCM), PRECIS v1.2 (UK Meteorological Office) has been successfully established in the Meteorology
Unit of the Geography Department, Makerere University; and soil-moisture balance models of the terrestrial water
balance have been constructed and applied in the Water Resources Management Department.
The project did not achieve the overall aim of using the output of precipitation and temperature from validated RCM
experiments to drive constructed soil-moisture balance models of the terrestrial water balance. However, indigenous
capacity for such work which will critically form future water resources management strategies, has been established.
The project also enabled focused dialogue in Uganda between those engaged in the management of water resources (e.g.,
hydrogeologists, hydrologists) and those engaged in the study and assessment of climate variability and change (e.g.,
climatologists, meteorologists).
Progress has been made in four keys areas:

Regional climate experiment (SRES HadAM3) over tropical Africa has been run for 2 years

A temperature-based model of potential evapotranspiration in East Africa based on a modification of the
Thornthwaite equation has been developed.

Soil-moisture balance models of the terrestrial water balance that explicitly considering groundwater and are able
to be driven by the validated output (precipitation, temperature) of RCMs have been developed and tested

Training of meteorologists, hydrologists and hydrogeologists (academic and professional) on the application and
relevance of the PRECIS RCM climate variables. Such climate variables outputs provide early warning of
possible climate variability or change that enable the development of mitigation measures of their impacts.
2
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
List of figures
Recent water-level fluctuations in Lake Victoria (thin line) and in groundwater
(thick line) at Entebbe, Uganda (32.5ºE, 0.2ºN)
Location of groundwater monitoring sites in Uganda
Dr. Andre Kamga (ACMAD) discussing PRECIS Regional Climate Model to
workshop participants in the new Meteorology Unit, Makerere University
(Kampala) under the START project (January 14th, 2005)
Rainfall (mm) map of Uganda
Delineated climatological zones of Uganda
Model domain (10º to 53ºE, 22ºS to 10ºN) and grid (100x100) selected for RCM
experiments
Total precipitation over RCM study area for 12 days (December 4 to 15, 1972) of
an 11-year simulation (1971-1982)
Representation of the different components of the SMBM
Actual change in soil-moisture loss versus potential (uncorrected) soil-moisture
loss as constrained by root constant (e.g., C=200mm) and wilting point (e.g.,
D=251mm)
Soil-moisture deficit and precipitation (7-day running means) at Katikamu during
calendar year of 2001
Ten-day totals of SMBM-predicted runoff (5% of rainfall events exceeding
threshold P of 7mm) and observed discharge of River Kigwe during the calendar
year of 200
Daily SMBM – recharge and water levels (obs. well 2) at Katikamu year 2001
(A) Map showing lines of equal potential evapotranspiration requirement, mm/yr,
for some parts of Rwanda-Burundi, Uganda, Tanzania, and Kenya, (B) Ten-day
means of Penman evaporation at four stations in Southern Uganda (Hanna, L.W., 1971)
Figure 14
Figure 15
Figure 16
Table 1
Table 2
Table 3
Table 4
4
3
4
8
10
11
12
13
14
15
16
17
21
Monthly PET derived from pan-evaporation data and Thornthwaite method
(1961-1968)
23
Water table fluctuations and daily precipitation from July 1998 to present at
Entebbe (station 17 in Fig. 2) in southern Uganda
24
Water table fluctuations and daily precipitation from July 1998 to present at Apac
Town (station 13 in Fig. 2) in central Uganda
25
List of tables
Model Sensitivity analysis – reconciling SMBM estimates of recharge with water
level observations.
Estimated potential evapotranspiration in Bangui using evaporation pan data and
corresponding pan coefficients, and pan coefficients (f) for Bangui and the
intermediate and equatorial climatic zones (Adapted from Riou, 1984
Water-level changes, estimated recharge and preceding precipitation of identified
recharge events (Entebbe)
Water-level changes, estimated recharge and preceding precipitation of identified
recharge events (Apac Town)
21
23
24
25
3
1. Project rationale and objectives
7
8
-0
5
Ja
n
3-
4Ju
l
-0
4
Ja
n
3-
4Ju
l
-0
3
Ja
n
2-
3Ju
l
1Ja
n
2Ju
l
D
31
-
1Ju
l
D
31
-
1Ju
l
groundwater level @ Entebbe (mbgl)
7
5Ju
l-0
5
8
-0
4
6
-0
3
9
-0
2
5
-0
2
10
-0
1
4
ec
-0
0
11
-0
0
3
ec
-9
9
12
-9
9
lake level @ Entebbe (mad)
The populations of East Africa are particularly vulnerable to climate change and climate variability,
including extreme climatic events such as drought and flooding, due, in part, to reliance upon hydroelectric power (HEP), localised (untreated) water supplies and rainfall-fed agriculture. Recent decline
in the water level of Lake Victoria (Fig. 1) has led to a significant reduction in the capacity of the
Owen Falls Dam at the northern outlet of Lake Victoria at Jinja to generate electricity. As the dam
generates almost all of Uganda’s electricity, the reduction in the lake’s level has dramatically affected
the livelihoods of people throughout Uganda. Due to the shallowness of the second largest lake in
world (<90m at it deepest) and low topographical gradients, shorelines have retreated up to 50m and
consequently affected shipping and fishing industries. Widespread debate persists as to the relative
contributions of climate and dam management to water-level decline.
Figure 1: Recent water-level fluctuations in Lake Victoria (thin line) and in groundwater (thick line) at Entebbe,
Uganda (32.5ºE, 0.2ºN)
Populations in Uganda primarily depend upon groundwater for a source of potable water on
account of its improved distribution and microbiological quality relative to surface waters. Current
efforts to supply safe water to rapidly expanding towns and cities also commonly target groundwater
as it necessarily avoids high treatment costs associated with surface-water sources (Tindimugaya,
2000; Taylor and Howard, 2000; Taylor et al., 2004). This demand for groundwater is expected to
increase significantly in the next few decades as the UN (2002) estimates that the current population
of 25.8 million is expected to more than double by the year 2025. Despite rapid population growth
and dependence upon localised water resources and groundwater in particular, the impacts of
climate change on water resources in tropical Africa have been subject of relatively few studies
(Hulme et al., 2001) and remain poorly resolved. Moreover, the assessment of the impact of climate
change and climate variability on water resources (Fig. 1) is in its infancy in Uganda. There is a need
4
for enhanced capacity to develop integrated assessment of water resources and the response to
climate events.
The climate of East Africa is particularly interesting due to the combination of (i) its relationship to
the Indian Ocean circulation system (including the Indian Ocean Zonal Mode and ENSO
teleconnections, providing the basis for seasonal forecasting, e.g. Mutai and Ward, 1998) (ii)
complex topography (associated with the African rift and the Ethiopian highlands to the north), (iii)
the existence of large lakes in the region, and (iv) marked climatic gradients. Predictions of climate
change (and natural variability) are (largely) dependent on General Circulation Models (GCMs).
Predictions from GCMs of climate change in East Africa through the 21st century tend to suggest:
1) an increase (10-20%) in rainfall; 2) a change in the seasonal distribution of rainfall with an
increase from December to February and a decrease from June to August; 3) an increase in air
temperature of 0.3°C to 0.5°C per decade (IPCC, 2001). There is, however, a great deal of
uncertainty in GCM predictions. Notably, the resolution of climate information provided by GCMs
is too coarse (typically about 300kmx300km) to enable assessment of the regional impacts of climate
change. This problem is particularly pronounced in regions such as East Africa with a complex
regional climate. Regional climate models (RCMs) with a higher grid resolution (typically tens of km)
provide more detailed and more realistic simulations of current climate and predictions of climate
variability and climate change for particular areas of interest. The finer spatial scale of RCMs also
allows greater ability to detect and predict high frequency variations including extreme climatic
events. RCMs are the state-of-the-art tool with which to downscale the coarse scale GCM
predictions for climate impact assessment.
Indigenous capacity to evaluate climate variability and predict future climate change in Uganda is
currently limited. A PC-based RCM, PRECIS (Providing Regional Climates for Impacts Studies) has
been developed by the Hadley Centre (UK) specifically to address the need for countries to make
regional-scale climate predictions. In Uganda, a dense and extensive network of meteorological
stations exists to assess rainfall variability and, along with high-resolution satellite rainfall estimates
(Todd et al., 2001) and reanalysis data (e.g. CRU2), can be used to evaluate output from a locally
based RCM at appropriate scales. Output from the PRECIS RCM can also be tested against that
from other RCMs already implemented over East Africa such as the NCSU-RegCM2-POM coupled
model (Song et al., 2004).
Water resource assessment and management requires climate-driven hydrological models. In Uganda
it is vital that such models incorporate of groundwater recharge. Indeed, the role of groundwater in
the terrestrial water balance, often ignored, may be significant particularly during drought or lowflow periods (Taylor and Tindimugaya, 1996). Soil-moisture balance models (SMBMs) that simulate
changes in soil moisture and groundwater flux and are thus highly relevant to rainfall-fed agriculture
(e.g., crop models), have been successfully employed in Uganda to estimate the terrestrial water
balance (Howard and Karundu, 1992; Taylor and Howard, 1996; 1999; Tindimugaya, 2000). This
work indicates that such models perform well only at high temporal resolution (daily). SMBMs
possess a major advantage over other, commonly employed hydrological models (e.g., Pitman,
PRMS) in that they provide estimates of groundwater recharge. Input variables to SMBMs
(precipitation, soil and vegetation types, pan evaporation data, and air temperature) are widely
available in East Africa and can be obtained also from RCMs. Model outputs of recharge and runoff
can be constrained respectively by water-level fluctuations and baseflow separation of streamflow
discharges (Taylor and Howard, 1999). The Water Resources Management Department of Uganda
5
possesses extensive networks of river gauging stations and groundwater monitoring sites. The latter,
instituted throughout Uganda in 1998 with records for some stations since 1994 (Taylor and
Howard, 1999), is the most extensive in East Africa (Fig. 2).
Groundwater Monitoring sites
Stn. ID
Station Name
001 Nkokonjeru
002 Pallisa -Asera's Home
003 Kangole
004 Moroto Prisons
004
#
003
012
005 Bombo Barracks
#
006 Hoima Hospital
#
013
007 Rakai Civic center
#
014
008 Lyantonde Kyabazala
#
006
009 Mbarara UNICEF Camp
#
002
#
010 Rukungiri
011 Luzira -Portbell
019
#
012 Apac - Loro CPAR offices
005
#
001
018
013 Apac DWD offices
#
#
015
#
017
#
008
015 Nkozi University
#
009
#
014 Soroti UNICEF Camp
007
#
010
016 Busia
017 Entebbe WRMD Offices
018 Kasese Cobalt Co. Ltd.
#
019 Wobulenzi
Figure 2: Location of groundwater monitoring sites in Uganda.
1.1
Project aims and objectives
The overall aim of the project is to develop the capacity for integrated assessment of climate change
and variability on water resources. Specifically, the project objectives are:
1. to implement a PC-based regional climate model (PRECIS) and initiate evaluation of model
output using land-based observations and reanalysis data;
2. to develop soil-moisture balance models of the terrestrial water balance;
3. to develop the capacity to assess the impacts of climate change and climate variability on the
terrestrial water balance by integrating soil-moisture balance models and output from the
regional climate model; and
4. to provide training in each of these modelling tools and consolidate dialogue between
relevant agencies.
6
2. Progress - regional climate modelling (PRECIS)
Significant progress was made toward the project’s first objective of implementing a PC-based
regional climate model (PRECIS) and evaluating model output using land-based observations and
reanalysis data.
2.1 installation of PRECIS and training
Following the installation of workstation and PRECIS in the offices of the newly instituted
Department of Meteorology at Makerere University, a project inception training programme in
regional climate modelling was implemented in January 2005 by Dr. Andre Kamga (Fig. 3) from the
African Centre for Meteorological Applications to Development (ACMAD). Emphasis during this
training was placed upon capacity building for regional climate change scenarios generation. The
training programme included an introduction to climate models, climate model set up as well as
analysis, interpretation and use of climate scenarios. Meteorological data processing and visualization
software (GRADS) was also downloaded and installed. In addition, instruction pertaining to the
design of climate change experiments and uncertainties in climate scenarios was provided. Extensive
datasets that included boundary conditions from GCMs for historical analyses (1961-1990) and
future scenarios (2050-2080) were provided by the UK Met Office.
Figure 3: Dr. Andre Kamga (ACMAD) discussing PRECIS Regional Climate Model to workshop participants in
the new Meteorology Unit, Makerere University (Kampala) under the START project (January 14th,
2005)
7
2.2 Climatic factors and patterns in Uganda – meteorological datasets for RCM validation
2.2.1
Main influencing atmospheric meteorological systems in Uganda
Uganda, enclosed within about 300 to 350 E and 10 S to 40 N, is an inland country in the East African
region. The climate of East Africa is broadly controlled by large-scale easterly trade winds, which are
responsible for the transport of moisture from the neighbouring oceans (advection of moisture
inland). The moisture transported from the neighbouring oceans makes up over 75% of the
moisture forming the inland rainfalld. The space-time state and reliability of weather and climate
within the East African region in general and Uganda in particular is controlled by a number of large
to medium scale atmospheric meteorological systems which include:




The Inter Tropical Convergence Zone (ITCZ)
Monsoons
Meso – Scale Circulations
Teleconnections (El Nino/ Southern Oscillation) ENSO
2.2.2. The ITCZ
The Inter Tropical Convergence Zone is the main synoptic scale system that controls the intensity
and migration of the seasonal rainfall over the East African region. The ITCZ is a broad zoneof low
surface pressure into which the low-level equator-ward moving air masses from both hemispheres
converge. It is closely linked to the position of the overhead sun, due to the heating of the overhead
sun a wide belt of low level pressure develops and the subsequent tendency of the air zone of
convergence behind the overhead sun. The characteristics of the ITCZ over East Africa are rather
complex; it consists of a North – South dynamic arm, which is locally referred to as the meridional
arm and the East – West arm called the zonal arm. The meridional arm is a zone of convergence
between the westerlies from the Atlantic Ocean and the easterlies from the Indian Ocean, while the
zonal arm is the convergence between the North East and south East monsoons/trades.
The two components of the ITCZ, the meridional and the zonal brings about two rain seasons in
most parts of the country - the so called “long rains of March to May” and the “short rains” in
October to November (EAMD, 1962). During the long rains, the zonal component of the ITCZ
which runs from West Africa through Central Africa to East Africa, progresses from the southern
hemisphere towards the northern hemisphere passing over Uganda. As a result, the rains begin from
the southern parts of the country and spread northwards. The meridional component of the ITCZ
is very variable and oscillates from west to east, normally running from Namibia, Angola through
Central Africa to the Sudan, depending on the strength of the St. Helena anticyclone situated over
the Atlantic Ocean. When the St. Helena intensifies, the meridional arm of the ITCZ moves
eastwards, giving rain to parts of Uganda especially the western, northern and central high ground
areas.
During the season June to August/September meridional component of the ITCZ is normally
located over eastern Uganda and the western Kenya rift valley highlands, this implies that much of
Uganda is under the influence of the moist Congo basin low level westerly flow.
8
The zonal arm of the ITCZ has maximum influence over East Africa during the months of March
to May and October to December at the start of the build up of the south easterly and north easterly
monsoons respectively. Thus, large areas of Uganda, including the Western, Central, the Lake Basin
and Eastern areas have two rain seasons (bi-model season). However, the northern region has a
strongly uni-model rainy season from April to October.
2.2.3. The Monsoons
The most fully developed phases of monsoons that affect East Africa are the North East (December
to February) and the South East monsoons (June to August). These phases correspond to the
maximum positions of the ITCZ to the South (Southern summer) and to the North (Northern
summer). Unlike the West Africa and the Asians, monsoons, the fully developed East African
monsoons are associated with relatively little rainfall and coincide with the dry period within the bimodel areas. Both monsoon currents are generally diffluent in the low levels and flow parallel to the
coast and thus they do not advect much moisture inland. They are also relatively shallow, extending
up to about 600 hpa and are capped aloft by an easterly flow. The South East monsoons are cool
and moist and the persistent inversion near 600 hpa inhibits cloud development leading to extensive
low-level cloud cover especially over the east facing slopes of the East African rift valley mountain
ranges.
2.2.4. Meso–Scale Circulations
Due to the proximity of East Africa to the Indian Ocean, the highly variable topography and the
existence of the large Lake Victoria basin the region experiences vigorous meso-scale circulations.
Thus, the spatial distribution of weather in East Africa is to a large extent determined by the
interaction between the quasi-stationary meso-scale local circulation systems and the seasonally
varying large-scale monsoon/trade flows.
Further the region experiences marked diurnal variation of precipitation due to the vigorous mesoscale circulations as they contribute substantially to the distribution and intensity of rainfall over the
region. This is accomplished through their interaction with the large-scale synoptic system leading to
preferred areas of mass convergence and therefore precipitation. Over Uganda there are broadly two
diurnal time periods of intense precipitation. The first is in the morning period, especially between
0400hrs – 0900hrs when the Lake Victoria basin experiences its maximum precipitation while the
second period is between 1500hrs and 1700hrs when the inland areas, especially the high grounds,
experience their most intense precipitation.
The Lake Victoria influence is due to its large body of water, the temperature contrasts between the
Lake and land during the day and night which result in a Lake breeze being advected towards the
land during the day and a land breeze towards the Lake during the night. Overall, this land - sea
breeze phenomenon results in the lake basin region getting some rains almost throughout the year.
However, this rainfall is significantly enhanced during the main rainy seasons.
2.2.5
Teleconnections (El Niño Southern Oscillation)
The El Niño / southern oscillation (ENSO) is the principal mode of inter-annual variability in the
global tropics. To a first approximation, ENSO can be viewed as a modulation of the global
monsoon / trade-wind system. This modulation is manifested in modification and displacement of
9
large-scale precipitation patterns and includes episodes of both floods and droughts, which may
occur at various phases of the ENSO evolution and in particular during the opposite extremes of
the ENSO cycle. Regions of the world where significant impacts of ENSO are experienced include:
India, Northern Australia, Equatorial Central Pacific, Eastern Equatorial Africa (including Uganda in
particular), South Eastern, South America and Gulf coast of the United States.
2.2.6 Mean annual rainfall
The mean annual rainfall patterns over Uganda shown in figure 4 have recently been re-assessed
using data of over 200 stations for the period 1943 to 1982 (Water Resources Management
Department (WRMD), 2003). Relatively low rainfall areas (400 to 1000mm) are dominated by the
so-called ‘cattle corridor axis’ running from the Karamoja region in the northeast to the Ankole region
in the Southwest, Kisamba-Mugerwa (2001). The other rather elongated area of low rainfall is along
the western rift valley running through Lake Albert. The main area of relatively high rainfall
(>1400mm) is over the central and western parts of the Lake Victoria basin and highland regions of
Mount Elgon and Rwenzori Mountains.
2.2.6. Seasonal rainfall distribution
There is a spatial and temporal seasonal migration of the dry and wet seasons across the country and
within the year. The season December to February is the driest period over most parts of the
country and especially over the northern (5 - 10%) and parts of central region (10 – 15%). It is only
the south-western region that receives moderate rains (20 – 25%). It should be noted that during
the season December to February the Inter-tropical Convergence Zone (ITCZ) is over southern
Africa and most of Uganda is dominated by the hot and dry north-to-northern easterly flow. On the
other hand the season March to May is the main wet season over the most parts of Uganda with the
percentage levels highest over the south-eastern areas (40 –45%) and lowest over the north-western
areas (20 – 25%). During this period, the zonal arm of the ITCZ lies within the equatorial areas and
the country is dominated by the moist south-easterly flow.
During the season June to August, the main wet season is now centred over the northern parts of
the country while the southern parts experience its secondary dry season. Over the northern region,
the highest percentages (40 – 45%) are recorded over the north-eastern parts of Karamoja while
over the southern regions the lowest percentages (5 – 15%) are reported over the south-western
parts of the country. During the season June to August the main zonal arm of the ITCZ is within
the vicinity of the northern region. Finally the season September to November is the secondary
rainy season over most parts of the Western and central parts of the country with the main wet
season centred over the western parts of the country and extending into the central areas (30 – 40%)
while the areas with lowest percentages (15 – 20%) are recorded over the north-eastern parts of
Karamoja. During the season September to November, the main dominant synoptic feature is the
meridional arm of the ITCZ.
10
Figure 4: Rainfall (mm) map of Uganda
2.2.7. Delineation of “Climatological Zones”
Similar to the seasonal and time-series analyses described above, it is desirable to summarise
meteorological observations in a manner that not only highlights the underlying variations in climate
across the country but also divides the country into regions to test the output from regional climate
models. This is particularly useful in regions like Uganda where there are distinct variations in
climate across the country and where external global factors appear to influence the time-series of
variations about the mean according to location.
11
Two approaches to this generalisation are possible. One is to divide the country into zones within
which the rainfall at all the stations in each zone can be regarded as sharing a tendency to vary in the
same way. The alternative is to select a set of stations, usually those with the most reliable and
longest records that might be considered representative of the area or region around them.
Each method has its advantages and drawbacks. If zones can be effectively defined, computation of
zonal rainfall characteristics is straightforward. Averaging the information from many stations in a
zone minimises the uncertainties due to different record lengths and possible errors in the data,
especially when the data are standardised in the averaging process. However, the use of zones
implies some sudden change in the characteristics of rainfall as we pass from one zone to another.
In reality, the defined rainfall characteristics will be slowly varying across the country. Zone
boundaries are, therefore, somewhat arbitrary; areas near the boundary of zones might have rainfall
characteristics that lie midway between those estimated for the two adjacent zones. The process of
averaging over many stations may also suppress variations in characteristics that are the ultimate
object of the analysis.
Selection of representative stations is no less difficult than the definition of zones. ‘Good’ stations
might not be well distributed across the country and the areas that they are considered to represent
might not be readily definable. The use of mapping as a basis for presentation of the results tends to
offset some of these difficulties. Incomplete records from representative stations can be enhanced
by deriving ‘composite’ records in which information from neighbouring stations is used to fill gaps
in the record or to extend the record to a common period.
Both of these approaches can be employed to facilitate validation of RCM experiments. The choice
in approaches is primarily conditioned by the length and completeness of the records that puts
greater weight on the benefits of spatial averaging, and the advantage of simplicity in the
presentation of complication variations in climate that are observed in the historical record. Zones
adopted in this study were initially delineated by Basalirwa et al. (1993) in his study of the Design of
a regional minimum rain gauge network. The method used was based on the principal component
analysis (PCA). The spatial patterns of the dominant principal components were used to classify
Uganda into 14 homogeneous zones. Further work on zoning refined the zones into 16
homogeneous zones (Fig. 5), zones M and C over the southwestern region were subdivided along
longitude 30.750 in order to reflect the relatively dry column to the east, along the so-called cattle
corridor more conspicuously. Precipitation data divided into climatological zones for RCM
validation is given in Appendix 2.
12
4
J
N
G
3
H
I
K
2
E
L
1
D
B
ME
MW
0
A2
A1
CE
CW
30
F
-1
0
31
0
32
0
33
0
34
0
35
0
Scale 1:4,500,000
40
0
40
80
120
160
200
240 Kilometers
Figure 5: Delineated climatological zones of Uganda
2.3
RCM experiments
A regional climate model (RCM) is a high resolution climate model that covers a limited area of the
globe, typically 5,000 km x 5,000 km (in this case latitude 21.350 N to 22.210 S and longitude 9.860 E
to 53.220 E; 43.36 degrees square) with a typical horizontal resolution of 50 km (in this case 0.420
longitude/latitude). The dominant synoptic features within this domain include the ITCZ, the
Congo basin west, the Atlantic Ocean west, the Indian Ocean to the east, and the Arabian ridge to
the northeast. The RCMs are limited area models that need to be driven at their boundaries by time
13
dependent large scale fields such as those of temperature, pressure, etc. These fields are provided by
global climate models (GCMs) integrations in the buffer area.
2.3.1
Model domain
Initially, a 15-year (1978-1993) RCM experiment in PRECIS was initiated at the project’s inception
workshop in January 2005 (Fig. 5). However, the experiment was regularly interrupted by irregular
power supplies despite a generator backup and eventually abandoned after running for a period of
about four months. A new experiment to run for 11 years was later set up, in consultation with the
UK Meteorological Office personnel, who developed the PRECIS, using a SRES HadAM3: baseline
scenario starting on December 1, 1970 (Fig.5). This period, where there is a greater availability of
meteorological station data, was chosen to permit improved evaluation of model output.
Figure 6: Model domain (10º to 53ºE, 22ºS to 10ºN) and grid (100x100) selected for RCM experiments .
2.3.2
Simulations
The experiment successfully run and generated data for a number of meteorological parameters such
air pressure, total precipitation rate, evapotranspiration, etc for 2 years (1971 and 72). The
experiment generated 6-hourly precipitation rates which could be animated. These animated
precipitation results indicated a good correspondence of the of the most intense rainfall regions with
the expected position of the ITCZ. During the months of March, April and May, for example, when
the ITCZ is centred over equatorial areas the region of high intensity precipitation appeared
concentrated in the middle of the domain of interest (over Uganda). The area of the highest
intensity of precipitation was observed to migrate northwards in the months of June and July, the
14
north of the experimental domain, and thereafter beginning to move southwards. A sample of the
simulation images from 4 to 15 December 1972 shown in Figure 7 demonstrate the close
correspondence between the seasonal positions of the most intense precipitation regions and the
position of the ITCZ. Furthermore it indicates the likely presence of a tropical cyclone over
Madagascar which also corresponds with the seasonal expectation of tropical cyclone development
in this region.
However, detailed analyses of validation of the model output with actual observations (Appendix 2)
are still essential and will be carried out in the next phase.
Figure 7: Total precipitation over RCM study area for 12 days (December 4 to 15, 1972) of an 11-year simulation
(1971-1982)
3. Progress - Hydrological modelling
Soil-moisture balance models of the terrestrial water balance (Figure 8) were constructed and applied
to selected monitoring stations in Uganda (section 3.1). A temperature-based estimation of potential
evapotranspiration, applicable to local climatic conditions in Uganda, was also developed and tested
using estimates of PET using limited, locally derived records pan evaporation (section 3.2). Finally,
due to concern as to whether output from regional climate models is able to represent effectively
daily distribution of rainfall and subsequently permit reliable predictions of water resources, the
impact of rainfall distribution on groundwater recharge was assessed.
15
Figure 8: Representation of the different components of the SMBM
3.1 Development of soil-moisture balance models
Translation of the predictions of climate change and climate variability using the PRECIS regional
climate model (25km x 25km grid scale) to the terrestrial water balance in Uganda was conducted
using a soil-moisture balance model. This step is critical in order to assess the impact of climate
change and variability not only on freshwater availability (quality and quantity) for domestic,
agricultural and industrial use (e.g., hydro-electric power generation) but also on the integrity of
aquatic ecosystems. Key advantages of soil-moisture balance models (SMBMs) are that they can be
constructed and tested using commonly available field observations of both meteorological and
hydrological parameters in Uganda. SMBMs have, furthermore, been successfully applied in
catchment water balances in Uganda (Howard and Karundu, 1992; Taylor and Howard, 1996; 1999;
Tindimugaya, 2000). SMBMs simulate changes in soil moisture and provide estimates of rainfall-fed,
groundwater recharge and runoff. The basic formulation of the model is given below:
Peffective = P - RO
R = Peffective - PET
R=0
SMD = PET - P
when SMD = 0
when SMD > 0
P : precipitation
RO : runoff
R : recharge
PET : potential evapotranspiration
SMD : soil-moisture deficit
16
Runoff (RO) is typically estimated as infiltration excess (Hortonian overland flow) and represented
as a fraction (e.g., 4%) of a minimum threshold precipitation event (e.g., 10mm). More sophisticated
routines for estimating RO can, however, be applied that consider saturation overland flow (Hewlett
hypothesis). PET in Uganda is usually estimated using pan evaporation records and applying a pan
factor of 0.9 and a climate factor of 1.0 (Taylor and Howard, 1996; Tindimugaya, 2000). SMD is
constrained by the depth of the root zone and the porosity of the soil and reflected in applied root
constants (C) and wilting points (D) (Figure 9).
Explicit consideration of groundwater and soil-moisture in SMBMs is significant as climate studies
to date typical assume basin storage is static (ΔS=0, eq. 1) and use variations in river discharge to
evaluate changes in climate (eq. 1). Significant, dynamic stores of water exist, however, as
groundwater, surface water, and soil moisture.
Qriver = P - ET ± ΔS
(eq. 1)
The SMBM is a lumped parameter model but is converted into a semi-distributed model by
explicitly evaluating for different land-surface conditions (soil, slope, vegetation). As a result,
scenarios involving changes in the vegetative cover as a result of deforestation or urbanisation can
be considered in the model. Of critical importance is that constructed SMBMs can be driven using
the output from PRECIS: daily rainfall and air temperature where potential evapotranspiration is
estimated using the Thornthwaite Method (see section 3.2).
Figure 9: Actual change in soil-moisture loss versus potential (uncorrected) soil-moisture loss as constrained by root
constant (e.g., C=200mm) and wilting point (e.g., D=251mm).
17
3.1.1. Simulation of soil moisture - Wobulenzi
A soil-moisture balance model was constructed and run from the calendar years of 2000 to 2002. at
Katikamu Monitoring Station (site 19 in Figure 2) in Wobulenzi (0°42’N, 32°34’E) where both input
parameters (meteorological data, soil and vegetation types) and land-based observations (streamflow
and water-level monitoring data) are available to construct and test soil-moisture balance models (see
below). The conceptual framework is given in Figure 8. Figure 10 shows the variation in predicted
soil moisture (7-day running means), as a soil-moisture deficit (SMD), and the influence of seasonal
rainfall through the calendar year of 2001. The SMD clearly decreases in response to bimodal
precipitation.
Input data:
Precipitation – daily records from Katikamu Dispensary
Runoff co-efficient - 4% of daily rainfall (Taylor and Howard, 1999)
Potential evapotranspiration – 10-day measurements converted to daily data (Tindimugaya, 2000)
Root depth – 1.2m (from field study)
Specific yield – 0.25 (estimated)
C = 150mm, D = 251mm
180
60
150
50
120
40
90
30
60
20
30
10
0
P (mm)
SMD (mm)
Model validation:
Runoff - daily stream discharge on River Kigwa
Recharge - water-level fluctuations @ Katikamu
0
1
31
61
91
121
151
181
211
241
271
301
331
361
Time (days)
7 per. Mov. Avg. (Preciptn mm)
7 per. Mov. Avg. (Actual Current SMD)
Figure 10: Soil-moisture deficit and precipitation (7-day running means) at Katikamu during calendar year of 2001
18
3.1.2. Simulation of runoff
RO was simulated as a fraction of rainfall events exceeding threshold P and compared with field
observations of the discharge of River Kigwe within which the Katikamu Monitoring Station
resides. RO estimated as 5% of rainfall events exceeding P events of 7mmday-1 from day 140 to 365
in the calendar year of 2001 are plotted with available records of river discharge in Fig. 10. During
the beginning of the second rainy season (Figure 10), SMBM-predicted RO events are not reflected
in river discharge (Figure 11) but agreement between simulated RO and observed river discharge
improves during middle and latter part of the second rainy season in 2001. These very preliminary
data suggest that RO generation is influenced by soil-moisture conditions as RO is minimal when
SMD is very high > 120mm (Figure 10). The relationship between predicted RO and observed river
discharge improves during the second rainy season when soils are at or closer to saturation (field
capacity). These observations suggest that saturation overland flow (Hewlett Hypothesis) may be a
more important mechanism for runoff generation than Infiltration excess (i.e., Hortonian Overland
Flow) as simulated by the SMBM.
14
12
RO (mm)
10
8
6
4
2
0
14
19
24
29
34
Time (10 day periods)
SMBM runoff
obs. Q
Figure 11: Ten-day totals of SMBM-predicted runoff (5% of rainfall events exceeding threshold P of 7mm) and observed
discharge of River Kigwe during the calendar year of 2001.
3.1.3. Simulation of recharge
Daily SMBM-predicted R and water-level changes in the shallow regolith aquifer (obs. well no. 2) at
Katikamu Monitoring Station are plotted in Figure 12 for the calendar year of 2001. A strong
temporal correlation between predicted R events and water-level rises is apparent though smallerscale fluctuations as a sharp water-level decline between estimated recharge events are not explained.
The proximity of monitoring well to production borehole means that abstraction patterns also
19
induce water-level fluctuations (i.e., they are not restricted to recharge inputs). Barometric effects on
water-level fluctuations are expected to be small (<10cm) and are not considered at this stage.
Restriction of recharge inputs to heavy rainfall events during the two rainy seasons is consistent with
evidence from other catchments in Uganda (Taylor and Howard, 1996; 1999).
100
15.0
90
15.5
70
16.0
R (mm)
60
50
16.5
40
17.0
water level (m below ground level)
80
30
20
17.5
10
0
18.0
1
31
61
91
121
151
181
211
241
271
301
331
361
day (year (2001)
Figure 12: Daily SMBM – recharge and water levels (obs. well 2) at Katikamu year 2001.
The sensitivity of SMBM-predictions of recharge (Figure 12) in the calendar year of 2001 to
uncertainty in input parameter was tested (Table 1). Analyses indicate that the SMBM is relatively
insensitive to variations in the starting SMD (50 to 150 mm) and threshold P (5 to 10mmday-1) for
RO events. In contrast, the SMBM is sensitive to variations in Root constant, C (100 to 200mm) and
wilting point, D and RO factor (0 to 10 %). If SMBM is correctly estimating R, the specific yield of
the shallow aquifer necessarily ranges from 0.15 to 0.20. This is similar to the results of groundwater
tracing experiments in the regolith aquifer in Iganga that indicate a specific yield of 0.23 (Taylor et
al., 2001).
20
Table 1: Model Sensitivity analysis – reconciling SMBM estimates of recharge with water level observations.
parameters
SMB - 1
SMB-2
SMB-3
SMB-4
SMB-5
SMB-6
SMB-7
SMB-8
SMB-9
SMB-10
SMB-11
SMB-12
SMB-13
C=
150.00
150.00
150.00
100.00
200.00
150.00
150.00
200.00
100.00
150.00
150.00
150.00
150.00
D=
201.00
201.00
201.00
151.00
251.00
201.00
201.00
251.00
151.00
201.00
201.00
201.00
201.00
F=
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
0.10
RO=
5.00
5.00
5.00
5.00
5.00
5.00
5.00
5.00
5.00
3.00
0.00
8.00
10.00
PAN=
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
Starting SMD
150.00
100.00
50.00
50.00
50.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
RO Threshold
7.00
7.00
7.00
7.00
7.00
5.00
10.00
10.00
10.00
10.00
10.00
10.00
10.00
SMB R1
229.50
229.50
229.50
279.50
179.49
229.50
229.49
179.49
279.49
240.75
257.64
212.59
201.33
Obs R1 (Sy=0.25)
347.50
347.50
347.50
347.50
347.50
347.50
347.50
347.50
347.50
347.50
347.50
347.50
347.50
Obs R1 (Sy=0.20)
278.00
278.00
278.00
278.00
278.00
278.00
278.00
278.00
278.00
278.00
278.00
278.00
278.00
Obs R1 (Sy=0.15)
208.50
208.50
208.50
208.50
208.50
208.50
208.50
208.50
208.50
208.50
208.50
208.50
208.50
SMB R2
33.30
33.30
33.30
83.30
0.00
33.30
33.30
0.00
83.30
38.93
47.37
24.86
19.23
Obs R2 (Sy=0.25)
77.50
77.50
77.50
77.50
77.50
77.50
77.50
77.50
77.50
77.50
77.50
77.50
77.50
Obs R2 (Sy=0.20)
62.00
62.00
62.00
62.00
62.00
62.00
62.00
62.00
62.00
62.00
62.00
62.00
62.00
Obs R2 (Sy=0.15)
46.50
46.50
46.50
46.50
46.50
46.50
46.50
46.50
46.50
46.50
46.50
46.50
46.50
Assessing the impacts of climate change and variability on water resources in Uganda: developing an integrated approach at the sub-regional scale –FINAL REPORT
21
3.2
Development of temperature-based estimates potential evapotransipration for East
Africa
It is only in mountainous areas of the Rwenzori, Kigezi and Elgon highlands that annual rainfall
exceeds annual potential evapotranspiration (PET) in east Africa. Elsewhere, potential
evapotranspiration increases from about 1500 mm on the shores of Lake Victoria to over 2200 mm
per annum in northern Uganda (Hanna, 1971). It is usual in the wetter areas of Uganda for each
rainy season to restore the soil to field capacity yet if deep-rooting plants extract moisture from
greater depths, as observed for tea in Kenya (Dagg, 1968) and Malawi (Laycock and Wood, 1963),
the soil profile may take several wet years to replenish the losses. Although there was particular
interest in the estimation of potential evapotranspiration in the 1960s, there has since been a dearth
of studies in east Africa.
Development of a soil moisture balance model for basins in Uganda requires the estimation of
reference crop evapotranspiration (ET0). Reliable, accurate estimation of evapotranspiration is critical to
soil moisture balance modelling (SMBM) in the tropics as it can represent between more than 80%
of the water budget (e.g. Howard and Karundu, 1992; Taylor and Howard, 1996; 1999;
Tindimugaya, 2000). ET0 is commonly derived as a function of PET where it is defined as the
“consumptive water use of a field situation where the soil is not under moisture stress” and can be estimated using
pan evaporation from a free water surface (Epan). Open water evaporation is related to ET0 by an
empirical factor, f, which varies in response to daylight throughout the year (eq. 2). In equatorial
regions, the consistency in daylight hours yields conversion factors that deviate less than 5%
throughout the year (Riou, 1984). Thus, a single factor can be applied throughout the year without
encountering significant error.
ET0 = f Epan
(eq. 2)
3.2.1. PET derived from evaporation pans
Evaporation pans are used extensively throughout the world to measure free-water evaporation and
to estimate reference evapotranspiration. Reliable estimation of ET0 using pan evaporation depends
on the accurate determination of pan coefficients (f). Pan coefficients vary with latitude and season
and are dependant on the climatology of the region.
In central Africa (Chad, Central African Republic, Congo) estimates of potential evapotranspiration
carried out by Riou (1984) indicate that the ratio between annual PET and open water evaporation
calculated by, Penman (1948, 1950) is ~0.80 in the dry zones and ~0.75 in the rainy zones. The
coefficient f decreases in the cool and rainy seasons and reaches a maximum in the warm and dry
seasons, but shows a rather small (<10 %) variation. A constant value of 0.8 in the dry zone and
0.75 in the rainy zone would produce no major error in PET computation (Riou, 1984). Studies
carried out in Bangui in the Central African Republic, which experience a climate comparable to that
of Uganda used pan factors ranging from 0.79-0.83 (Table 2), yet Riou (1984) suggests the use of f
coefficients of between 0.89-0.96 in equatorial regions (Table 2).
22
Table 2: Estimated potential evapotranspiration in Bangui using evaporation pan data and corresponding pan
coefficients, and pan coefficients (f) for Bangui and the intermediate and equatorial climatic zones (Adapted
from Riou, 1984)
Zone
Bangui PET
Bangui (f)
Intermediate(f)
Equatorial(f)
Jan
95
0.82
0.74
0.89
Feb
105
0.78
0.71
0.91
Mar
117
0.79
0.72
0.92
Apr
106
0.83
0.80
0.94
May
87
0.80
0.80
0.96
Jun
63
0.79
0.87
0.93
Jul
63
0.81
0.88
0.96
Aug
83
0.73
0.90
0.90
Sep
96
0.73
0.88
0.92
Oct
98
0.76
0.82
0.94
Nov
93
0.74
0.74
0.92
Dec
87
0.80
0.75
0.91
Yearly
1239
0.78
0.80
0.92
A mean monthly f coefficient of 0.9 is consistent with pan factors used in previous work in east
Africa (Dagg and Blackie, 1965; Hanna, 1970; Edwards and Waweru, 1979; Eeles, 1979; Shanin,
1985; Taylor and Howard, 1996, 1999). Hanna (1971) carried out studies of tea plantations in
Uganda and found that ET0 from tea cover is approximate to 0.85 times Penman evaporation, whilst
the evergreen forests have an evapotranspiration ratio of 0.9 as an annual average (Dagg, 1972). A
mean monthly f coefficient is thought to be appropriate in Uganda and the Central African Republic
where it has been demonstrated that pan evaporation from class A pans has a mean monthly
deviation of less than 20% during the years of observation in both Nyabisheki and Aroca
catchments, indicating a relatively consistent evaporative flux from year to year (Taylor and Howard,
1999). In contrast, the use of a mean annual pan value has been found to be misleading in Kenya as
studies exhibit considerable seasonal variations (Dagg and Blackie, 1965).
3.2.2. Climate-based methods
If pan evaporation data are unavailable locally, theoretical or empirical methods are often used to
estimate evaporation from meteorological variables. The most widely applied empirical method is
the Penman-Monteith equation (Penman, 1948, 1949). The method demands, however, numerous
input variables (e.g., temperature, relative humidity, wind, saturation vapour pressure and net
radiation) that are seldom available in remote areas of low-income countries and this requirement
limits its use in these areas including Uganda (Pereira et al., 2004). For regions where water supply is
adequate to maintain full transpiration throughout the year Penman Evaporation (Epen) calculated
from meteorological data has been consistently related to measured transpiration, ET0 (Pereira et al.,
1962, cited in Hanna, 1970). In 1963-64, a network of seven meteorological stations across Uganda
were used to produce the first map of Penman derived PET (Rijks and Owen, 1965). This map has
since been improved upon, by a longer time series and a more extensive network of station data
(Figure 13a). As a basis for converting evaporative demand to a map of PET, a conversion factor of
0.9 was used apart from in areas of swamp where a value of 0.8 was used (Dagg, 1972). The network
of Entebbe, Kabanyolo, Jinja and Kituza stations for which the ten-day means of Penman
evaporation was reported by Hanna (1970) is considered to yield a reasonably accurate estimate of
the potential evapotranspiration within a narrow zone adjacent to Lake Victoria (Figure 13b)
(Shanin, 1985).
23
B
A
Figure 13 (A) Map showing lines of equal potential evapotranspiration requirement, mm/yr, for some parts of
Rwanda-Burundi, Uganda, Tanzania, and Kenya, (B) Ten-day means of Penman evaporation at four
stations in Southern Uganda (Hanna, L.W., 1971)
3.2.3. Temperature-based methods
The need to estimate evapotranspiration estimation in regions with limited meteorological records
has resulted in the development of several simplified versions of the Penman formula, and the
development of a number of temperature-based methods. These include the Blanley-Criddle (1950),
Linacre (1977), Hamon (1961), Hargreaves (1975), Kharrufa (1985), Romanenko (1961) and
Thornthwaite (1948) equations. Thornthwaite is the most widely used of these methods due to its
simplicity and minimal input data requirements. The Thornthwaite equation calculates monthly
PET based on mean monthly temperature (eq. 3). Use of the Thornthwaite equation has been widely
criticised in the tropics (Dupriez, G.L. 1959; Dagg and Blackie, 1965; Ward, 1971) as it is considered
to underestimate evaporative demand at high altitudes in the tropics and fails to represent seasonal
variations in evaporation. This arises from the poor correlation between radiation and temperature
near the equator where the day length is nearly constant. Moreover, temperature decreases with
altitude more rapidly than does radiation. Consequentially temperature measurements near the
equator fail to be such good indicators of radiation, the prime determinant of evaporation in
temperate regions (Dagg and Blackie, 1965, Ward, 1971). Evaporation estimates based on the
Thornthwaite and Blaney-Criddle equations also display very little variation in monthly values unlike
the Penman method which has a strong seasonal component.
E = 1.6(10T / I)a
(eq. 3)
E = monthly potential evaporation (in cm)
T = mean temperature for the month (in C)
24
I = annual thermal index = sum of monthly indices i
i = (T/5)1.514
a = 0.49239 + 0.01792 I – 0.0000771 I2 + 0.000000675 I3
Evidence from field studies shows that the Thornthwaite equation (Eq. 2) grossly underestimates by
100%, 34% and 225% respectively evaporation in the Kericho catchment, Kenya (Dagg and Blackie,
1965), the Amazon region, Brazil (Pereira et al, 2004), and Zambia (Sharma, 1988). Lysimeter studies
from Kisozi and Musas which are located in Rwanda-Burundi territory found that Penman produced
a much more accurate representation ET0 with Penman representing 95% of ET0 (Elsevier science
publication – photocopy). It can be concluded that although the method of Thornthwaite water
balance and classification (1948) has particular reference in east Africa, because of the paramount
importance of moisture rather than temperature, the evaporative demand was seriously
underestimated (Hanna, 1970). Sharma (1988) indicates further that estimates of monthly ET in the
Zambian environment vary significantly when using the Thornthwaite and Penman methods, with
Thornthwaite significantly underestimating the values given by Penman. The ET0 component in the
wet season was found to be 1.5 times larger than the Penman evapotranspiration values and 2.25
times larger than the Thornthwaite method. A ratio of ET0/PET of 1.5 however is not surprising
for tropical basins, where grasses and shrubs grow luxuriantly in the dambos, swamps and flooded
regions. On an annual basis the Penman method tends to overestimate the evapotranspiration
component for the tropical environments of central Africa.
An adjustment to the Thornthwaite equation was proposed by Carmargo et al., (1999) using an
effective temperature instead of the original mean temperature (Pereira et al., 2004) (eq. 4). The
equation is preferentially weighted towards maximum temperature. Studies in Brazil (Pereira et al.,
2004) gave an almost identical mean relationship as that obtained with the same data set using the
Penman-Monteith FAO-56 estimates. The Thornthwaite estimates represent 76% with the average
temperature, 97% with the newly defined effective temperature and 101% with Penman-Monteith,
of lysimeter measurements. The proposed adapted Thornthwaite had a performance almost identical
to that of the more robust and highly recommended Penman-Monteith FAO56 model (Pereira et al.,
2004). Improvement of PET estimates using Thornthwaite by weighting the temperature towards
maximum rather than minimum or mean temperatures was also suggested by Riou (1984) who
proposed an evapotranspiration equation based on mean maximum temperature (eq. 5). The
equation is effective at expressing an approximate value of PET in the rainy zone.
Tef =½ k(3Tmax-Tmin)
(eq. 4)
k=0.69 provides the best estimates for ET0
PET (mm∙day-1) = 0.30 * mean Max T -5.9
(eq. 5)
3.2.4. Estimation of potential evapotranspiration
Comparison of PET estimated using pan-evaporation data ar Namulonge (Tindimugaya, 2000) and
daily air temperature using the Thornthwaite Method (eq. 2) is shown in Figure 14. A correction
factor, applied to adjust for seasonal insolation that is important in mid and high latitudes, was not
used. Mean monthly PET from 1961 to 1968 is plotted in Figure 13 and shows that pan-derived
estimates of PET (pan) are, on average, 35% greater than those estimated using Thornthwaite
25
method. Thornthwaite PET exhibits less of a seasonal trend than pan-derived PET estimates. Use of
a simple weighting factor to maximum daily temperature to estimate Tef (eq. 6) for use in the
Thornthwaite equation (eq. 3) was able to closely match (<5% difference) pan-derived estimates of
monthly PET and the Thornthwaite approach.
Tef = (2Tmax-Tmin)/3
(eq. 6)
Monthly PET estimates (pan vs.
Thornthwaite)
ET (Thw)
ET (pan)
PET (mm)
150
100
50
0
1
2
3
4
5
6
7
8
9
10 11 12
Figure 14: Monthly PET derived from pan-evaporation data and Thornthwaite method (1961-1968)
26
3.3
Evaluation of rainfall distribution on the terrestrial water balance - assessing the
impact of rainfall distribution on groundwater recharge
3.3.1. Study rationale & objective
Recognising that annual potential evapotranspiration (PET) exceeds annual precipitation (P)
throughout most of Uganda, the nation’s water resources depend upon poor distribution in
precipitation (e.g. rainy seasons) where precipitation temporarily exceeds PET. Taylor and Howard
(1996) demonstrate that the annual recharge to groundwater depends more upon number of heavy
rain events that occur than total volume of P. The ability of regional climate models to represent the
daily distribution of P remains poorly tested. The objective of this was to assess the dependency of
observed recharge events on the distribution of P using data from groundwater and precipitation
monitoring stations throughout Uganda
3.3.2
Results
Of the 19 monitoring stations in Uganda, analyses of groundwater-level changes, estimated recharge
and preceding precipitation of identified recharge events at Entebbe (Figure 15, Table 3) and Apac
Town (Figure 16, Table 4) are presented below..
3
120
4
100
80
6
7
60
8
40
9
20
10
-0
6
M
ay
n06
Ja
Se
p05
-0
5
M
ay
n05
Ja
Se
p04
-0
4
M
ay
n04
Ja
-0
3
Se
p03
M
ay
n03
Ja
Se
p02
-0
2
M
ay
n02
Ja
Se
p01
-0
1
M
ay
n01
Ja
-0
0
Se
p00
M
ay
Ja
n00
0
Se
p99
M
ay
-9
9
11
Figure 15: Water table fluctuations and daily precipitation from July 1998 to present at Entebbe (station 17 in Fig.
2) in southern Uganda
Table 3: Water-level changes, estimated recharge and preceding precipitation of identified recharge events (Entebbe).
Event
Date
1
2
3
4
5
Nov 12-Dec 18, 1999
May 9 - 17, 2000
Mar 28 - May 22, 2001
May 22 - Jul 15, 2003
Oct 31-Dec 22, 2003
Δh
1.9
0.39
2.23
0.98
0.52
est. R
570
117
669
294
156
preceding rainfall driving recharge event
3 days (65.6mm), 1 day (55mm), 4 days (151mm)
4 days (62.8mm)
3 days (82mm), 4 days (98mm), 4 days (95.5mm)
3 days (56.8mm), 6 days (154.9mm)
4days with alternation(103.5mm), 3days(48.8mm), 8days(162.4mm)
27
Daily rainfall (mm)
Depth to water (mbgl)
5
5
140
6
7
120
8
80
10
11
60
Daily Rainfall (mm)
Depth to Water (m)
100
9
12
40
13
20
14
c t06
O
Fe
b06
-0
5
M
ay
04
Se
p-
4
Ja
n0
-0
3
ay
M
Se
p-
02
-0
1
D
ec
1
r-0
Ap
ug
-0
0
A
D
ec
-9
9
ar
M
Ju
l-9
-9
9
0
8
15
Figure 16: Water table fluctuations and daily precipitation from July 1998 to present at Apac Town (station 13 in
Fig. 2) in central Uganda
Table 4: Water-level changes, estimated recharge and preceding precipitation of identified recharge events (Apac Town)
Event
DATE
Δh
est. R
1
Aug 06 - Dec 13, 2000
1.28
192
2
aug 16- Nov 18, 2002
0.57
85.5
3
Jun 12 - Oct 23, 2003
0.54
81
4
Aug 13 - Dec 11, 2004
0.66
99
preceding rainfall driving recharge event
3 days (88.8mm), 5days (79.2mm), 3days (142.08mm), 5days with
alternation(135.84mm)
1day(53.3mm), 1day(44.6mm), 4days(165.9mm), 2days(42.4mm),
1day(60.5mm), 12days(73.3mm)
2days(56.64mm), 3days(25.76mm), 3days(46.88mm),
6days(52.16mm), 3days(38.88mm), 1day(49.92mm),
5days(58.88mm), 5days(48.32mm), 2days(40.98mm)
3days(50.08mm), 3days(100.16mm), 5days(89.44mm),
1day(80.96mm), 3days(35.36mm), 1day(24.64mm),
13days(99.68mm), 6days(80.16mm)
3.3.3. Summary
Major recharge events, observed from water-level fluctuations at Entebbe and Apac Town depend
upon consecutive (3 to 4 day) and heavy (>10mm/day) rainfall events. Although this conclusion will
be further tested by soil-moisture balance modeling, the results highlight the need for regional
climate models to represent effectively daily distribution in P in order to predict accurately future (or
past) groundwater resources.
28
4
Report Summary
A one-year project to enhance indigenous scientific capacity in Uganda to undertake integrated
assessment of the sub-regional impact of climate change and climate variability on water
resources has been completed. A PC-based regional climate model (RCM), PRECIS v1.2 (UK
Meteorological Office) has been successfully established in the Department of Meteorology,
Makerere University; soil-moisture balance models of the terrestrial water balance have been
constructed and applied in the Water Resources Management Department..
The project did not achieve the overall aim of using the output of precipitation and temperature
from validated RCM experiments to drive constructed soil-moisture balance models of the
terrestrial water balance. However, indigenous capacity for such work which will critically
inform future water resources management strategies, has been established. The project also
enabled focused dialogue in Uganda between those engaged in the management of water
resources (e.g., hydrogeologists, hydrologists) and those engaged in the study and assessment of
climate variability and change (e.g., climatologists, meteorologists).
Progress has been made in four keys areas:

Regional climate experiment (SRES HadAM3) over tropical Africa has been run for 2 years

A temperature-based model of potential evapotranspiration in East Africa based on a
modification of the Thornthwaite equation has been developed.

Soil-moisture balance models of the terrestrial water balance explicitly considering
groundwater and are able to be driven by the validated output (precipitation, temperature) of
RCMs have been developed and tested

Training of meteorologists, hydrogeologists and hydrologists (academic and professional) on the
application and relevance of the PRECIS RCM climate variables outputs. Such predicted climate
variables outputs provide early warning of possible climate variability or change that enable the
development of mitigation measures of their impacts.
However, there has been some difficulties in the execution of this project which have included:
(a) The quite often irregular power supply has frustrated the running of the PRECIS experiment
which must run continuously without power interruption for months. Although Makerere
University is doing its best to address the steady power problem, the irregular power
problem persists due to limited funds to purchace and fuel stand by generators.
(b) After the initial successful training given Dr. Andre Kamga from the African Centre of
Meteorological Aplications for Development (ACMAD) in Niamey, Niger at the launch of
our project in January 2005, it was clear that a local expert on UNIX trained in computer
based GCM and RCM simulations was needed. Unfortunately, there were no funds for
training such a person. The consequences were to send e-mails to ACMAD to start with; and
later to the UK Met Office for help to restart the experiment model. Although everybody
29
has been very helpful funds are required to train a local expert not only to help run the
PRECIS model but also to interprete the model outputs.
(c) Funds were limited for workshops and seminars to facilitate continuous consultation and
dissemination of project activities and results among the hydrologists and the meteorologists.
In spite of the early initiation of a website for this purpose, there was limited use of the site
and its development owing to lack of funds.
5
Way forward
Climate modelling




Further training in UNIX (locally sourced?)
Further training in PRECIS by the UK Met Office in Accra, Ghana in June 2006
Extraction and provision of regional simulations for water resources assessments
PRECIS validation over stations in Uganda using long records in all the rainfall
climatological zones of the country.
Hydrological modelling


Develop SMBMs for each of the groundwater monitoring stations in Uganda
Test sensitivity of the hydrological response of catchments to anomalous and extreme
climatic events
Future collaborative research & method development
Funds for continued integrated, collaborative research with project partners to investigate the impact
of climate variability and change on the availability and use of water resources in Uganda are
currently being sought from a variety of sources including NERC (QUEST programme) and EU
Water Facaility.
30
6
References
Basalirwa, C.P.K., Ogallo, L.J., and Mutua, F. M. (1993) The Design of a Regional Minimum
Raingauge Network. Int. J. of Water Res. Development, Vol. 9 No.4, 411-424.
Blaney, H.F., Criddle, W.D. (1950) Determining water requirements in irrigated areas for
climatological irrigation Data. Technical Paper No. 96, US Department of agriculture, soil
conservation service, Washington, D.C., 48pp.
Camargo, A.P., Marin, F.R., Sentelhas, P.C., and Picini, A.G. (1999) Adjust of the Thorthwaites method to
estimate the potential evapotranspiration for arid and superhumid climates, based on daily temperature amplitude,
Rev. Bras. Agrometeorology. 7, 2, 251-257 (In Portuguese with English summary).
Camberlin, P., S. Janicot, and I. Poccard (2001) Seasonality and atmospheric dynamics of the teleconnection
between African rainfall and tropical sea-surface temperature: Atlantic vs. ENSO. International Journal of
Climatology, 21, 973-1005.
Dagg, M. (1968) Hydrological implications of grass root studies at Mugaga, Proc 4th Specialist Meeting on applied
Meteorology (E.A.A.F.R.O).
Dagg, M. (1972) East Africa its peoples and resources (edited by W.T.W Morgan), chapter 10 water
requirements of crops, 119-125, oxford university press, London, New York.
Dagg, M. and Blackie, J.R. (1965) Estimates of evaporation in East Africa, in relation to climatological
classification.
Dagg, M., Woodhead, T., and Rijks, D.A. (1970) Evaporation in East Africa. Bull, IASH 15 (1), 61- 67.
Dupiez, G.L. (1959) La Curve lysimeter de Thornthwaite comme instrument de mesure de l’evapotranspiration en
regions equatorial. IASH symposium of Hannoversch - Munden, Vol.11 (lysimeters) no 49, 84-98.
EAMD (1962) Climatic seasons of East Africa. EA. Met. Dept. Report No.8, 4pp.
Edwards, K.A., and Waweru, E.S. (1979) The performance of evaporation pans in the experimental catchments,
East African Agricultural and forestry journal, Vol. 43, special issue, 296-305.
Eeles, C.W.O. (1979) Soil moisture deficits under montane rain forest and tea. East African agricultural and
forestry Journal, 43, 128-137.
Hamon, W.R. (1961) Estimating potential evapotranspiration, Journal of Hydraulics division, proceedings
of the American society of civil engineers, 871:107-120.
Hanna L.W. (1970) In chapter 9: Climate and crop potential in Uganda, in Studies in East African Geography
and Development, Ominde, H., Heinmann: London.
Hanna L.W. (1971) The effects of water availability on tea yields in Uganda, J. Appl. Ecol, 111, 791-813.
Hargreaves, G.H. (1975) Estimation of potential evapotranspiration, Journal of irrigation and drainage
division, proceedings of the American society of civil engineers, 108:223-230.
Howard, K.W.F. and Karundu, J. (1992) Constraints of the development of basement aquifers in East Africa –
water balance implications and the role of the regolith. Journal of Hydrology, 139, 183-196.
Hulme, M., R.M. Doherty, T. Ngara, M.G. New and D. Lister (2001) African climate change: 19002100. Climate Research, 17, 145-168.
IPCC (2001) Climate Change 2001: Impacts, Adaptation, and Vulnerability. CUP.
Kisamba-Mugerwa, W. (2001) Rangelands Management Policy in Uganda Int. Conf. on Policy and Inst.
Options Management of Rangelands in dry Areas, Hammamet, Tunisia May 7-1, 2001
Laycock, D.H., and Wood, R.A. (1963) Some observations on soil moisture under tea in Nyasaland, Tropical
Agriculture, 40, 1, 35-42.
Linacre, E.T. (1977) A simple formula for estimating evaporation rates in various climates, using temperature data
alone. Agricultural meteorology, 18:409-424.
31
Mutai, C.C. and Ward, M.N. (1998) Proc. 23rd Annual Climate Diagnostics Workshop, Miami, Florida,
Oct 26-30.
Penman, H.L. (1948) Natural evaporation from open water, bare soil and grass. Proceedings of the Royal
Society of London A, 193, 120-145.
Penman, H.L. (1950) The water balance of the Stour catchment area. Journal of the Institution of Water
Engineers, 4, 457-469.
Pereira, A.R., Pruitt, W.O. (2004) Adoption of the Thornthwaite scheme for estimating daily reference
evapotranspiration, Agricultural Water management, 66, 251-257.
Pereira, F.R.S. (1973) Landuse and water resources in temperate and tropical climates, Cambridge University
press.
Rijks, D.A. (1969) Evaporation from a papyrus swamp, Quart. J. Roy. Met. Soc., no 95: 643-649.
Rijks, D.A., and Owen, W.G. (1965) Hydro-meteorological records from areas of potential
agricultural development in Uganda, (Ministry of Mineral and Water Resources, Uganda
Government).
Riou, C. (1984) Experimental study of potential evapotranspiration (PET) in Central Africa, Journal
of hydrology, 72, 275-288.
Romanenko, V.A. (1961) Computation of the autumn soil moisture using a universal relationship for a large area.
Proceeding Ukrainian Review 100:81-92.
Shanin, M.M. (1985) Hydrology of the Nile Basin, Elesevier, Amsterdam.
Sharma, T.C. (1988) An evaluation of evapotranspiration in Tropical central Africa, Hydrological sciences –
Journal – des sciences hydrologiques, 33, 1, 2.
Song,Y., Semazzi, F.H.,M., Xie, L. and Ogallo, L. (2004) A coupled regional climate model for the Lake
Victoria basin of East Africa International Journal of Climatology, 24, 57-75.
Sutcliffe, J.V. and Parks, Y.P. (1989) Comparative water balances of selected African wetlands, Hydrological
sciences Journal, 34, 1, 49-62.
Taylor, R.G. and Howard, K.W.F. (1996) Groundwater recharge in the Victoria Nile basin of East Africa:
support for the soil-moisture balance method using stable isotope and flow modelling studies. Journal of
Hydrology, 180, 31-53.
Taylor, R.G. and Howard, K.W.F. (1999) The influence of tectonic setting on the hydrological characteristics of
deeply weathered terrains: evidence from Uganda. Journal of Hydrology, 218, 44-71.
Taylor, R.G. and Howard, K.W.F. (2000) A tectono-geomorphic model of the hydrogeology of deeply weathered
crystalline rock: evidence from Uganda. Hydrogeology Journal, 8, 279-294.
Taylor, R.G. and Tindimugaya, C. (1996) In: Proceedings of the IVth Nile 2002 Conference, Kampala
(Uganda), Vol. A, pp. 87 to 94.
Thornthwaite, C.W. (1948) An approach toward a rational classification of climate, Geographical Review,
Vol 38, 55-94.
Tindimugaya, C. (2000) MSc. Dissertation (IHE Delft, the Netherlands)
Todd, M.C , Kidd, C.K., Kniveton, D.R. and Bellerby, T.J. (2001) A combined satellite infrared and
passive microwave technique for estimation of small scale rainfall over the global tropics and subtropics. Journal of
Atmospheric and Oceanic Technology, 18, 742-755
UN (2002) World Population 2002. Rep. ST/ESA/SER.A/224.
Ward, R.C. (1971) Measuring evapotranspiration; A review, Journal of Hydrology, 13, 1-21.
WRMR (2003) Hydro-Climatic Study Report WRMD,Entebbe 36-38
32
Appendix 1
List of participants – START PROJECT WORKSHOPS
The first Workshop conducted for 5 days to launch the project, for capacity building and training on
the use of PRECIS was hosted by the Meteorology Unit, Geography Department. It was held in the
Meteorology Research room at the Faculty of Computing and Information Technology Building.
One of the objectives of the workshop was to introduce the participants to the PRECIS desk top
based computer model to forcast climate variables. The resource person at the workshop was Dr.
Andre Kamga from ACMAD.
List of participants in the first workshop on 10th – 14th January 2005
Dr. Andre Kamga, ACMAD,
Dr. C. P. K. Basalirwa, Geography Department, Makerere University, Uganda
Dr. Richard Taylor, University College London, UK
Mr. A. W. Majugu, Uganda Meteorology Department, Uganda
Mr. Mukwaya Paul, Geography Department, Makerere University, Uganda
The Commissioner, Uganda Meteorology Department, Uganda
Mr. Muwembe Khalid, Uganda Meteorology Department, Uganda
Mr. Nimusiima Alex, Geography Department, Makerere University, Uganda
Mr. A. K. Kagoro, Uganda Meteorology Department, Uganda
Mr. Magezi Akiiki, Uganda Meteorology Department, Uganda
Ms. Nanteza Jamiat, Geography Department, Makerere University, Uganda
Mr. Sabiiti Geoffrey, Geography Department, Makerere University, Uganda
Mr. Saul Daniel Ddumba, Geography Department Makerere University, Uganda
The second Workshop held to report and assess the findings of the PRECIS model output and the
uses it could be put. This was hosted also by the Meteorology Unit, Geography Department at the
Faculty of Computing & Information Technology in the Meteorology Lecture room on the 20th
January 2006.
List of participants in the final workshop on 20th February 2006
Dr. C. P. K. Basalirwa, Geography Department, Makerere University, Uganda
Dr. Richard Taylor, University College London , UK
The Head, Geography Department, Makerere University, Uganda
The Dean, Faculty of Arts, Makerere University, Uganda
Mr. A. W. Majugu, Uganda Meteorology Department, Uganda
The Assistant Commissioner, Water Development Department, Uganda
Mr. Mukwaya Paul, Geography Department, Makerere University, Uganda
The Commissioner, Uganda Meteorology Department, Uganda
Mr. Eza John, Uganda Meteorology Department, Uganda
Mrs. Lubega, Uganda Meteorology Department, Uganda
Mr. Mujuni R. Godfrey, Uganda Meteorology Department, Uganda
Ms. Mulinde Catherine , Geography Department, Makerere University, Uganda
Mr. Muwembe Khalid, Uganda Meteorology Department, Uganda
Mr. Nimusiima Alex, Geography Department, Makerere University, Uganda
Mr. Ogwanga Bob Alex, Uganda Meteorology Department, Uganda
Mr. Otim Faustine, Uganda Meteorology Department, Uganda
33
Mr. Eneku John Paul, Uganda Meteorology Department, Uganda
Mr. A. K. Kagoro, Uganda Meteorology Department, Uganda
Mr. Magezi Akiiki, Uganda Meteorology Department, Uganda
Mr. Mugume Isaac, Uganda Meteorology Department, Uganda
Mr. Mulindwa M. Matovu, Uganda Meteorology Department, Uganda
Ms. Nanteza Jamiat, Geography Department, Makerere University, Uganda
Ms. Ninyesiga Annie, Uganda Meteorology Department, Uganda
Mr. Omonyi William George, Uganda Meteorology Department, Uganda
Mr. Sabiiti Geoffrey, Geography Department, Makerere University, Uganda
Mr. George Obua, Uganda Meteorology Department, Uganda, Uganda
Mr. Ayesiga Godwin, Uganda Meteorology Department , Uganda
34
Appendix 2
Daily rainfall data for validation of RCM experiment
Precipitation estimated by PRECIS is generated on a 0.44º grid mesh, a distance of between 50
and 60 km. As a result, observed data should be transformed from point, station, values to mean
areal values. Mean areal values have been worked out using demarcated homogeneous
climatological zones. For the project’s preliminary experiment, mean homogeneous daily
rainfall has been worked out for zones CE, B and D over the southern region of the country.
Daily rainfall data has been extracted for the years 1971 and 1972 for which PRECIS output is
available for the above three zones.
ZONE B(4 STATIONS
USED)
89320280
32.617
0.617
89320520
32.533
0.417
89320670
32.617
0.533
89320300
32.750
0.450
1
0.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
5.0
2.6
19.0
17.5
11.6
15.2
1.4
0.7
0.4
0.7
0.0
0.0
0.0
0.0
0.0
0.0
2
2.1
0.0
0.0
0.0
0.0
0.0
2.6
2.4
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
4.6
6.2
1.7
2.5
0.4
1.0
0.1
0.0
0.0
0.0
1971
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
3
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2.8
0.0
2.2
9.4
2.4
0.0
4.1
6.0
0.6
1.3
0.0
0.0
0.6
0.0
0.2
0.0
4.0
0.0
0.0
4
0.6
0.5
1.9
2.2
2.6
0.0
0.5
0.0
6.7
0.4
17.2
7.1
2.0
1.7
8.6
4.4
0.3
7.9
14.8
4.0
7.0
0.0
2.0
3.1
11.5
3.8
6.9
5
7.7
4.4
0.0
0.1
1.5
5.7
11.3
14.0
3.4
9.8
3.2
2.9
11.2
0.0
0.3
4.5
5.7
5.7
17.0
23.7
0.8
3.2
2.5
9.7
4.1
1.8
4.8
6
0.0
3.0
14.5
8.0
1.3
0.7
2.4
0.0
0.0
0.0
0.0
2.6
0.3
0.0
2.6
3.9
0.0
1.5
0.1
0.8
0.5
1.4
0.0
0.0
0.1
0.7
0.5
7
4.8
0.1
0.5
0.0
2.9
3.2
0.0
0.9
0.8
0.0
0.0
0.0
0.0
3.5
30.1
0.0
18.4
6.1
11.8
2.3
8.8
0.0
0.8
1.1
0.0
3.8
0.7
8
0.0
1.2
0.0
0.0
0.0
7.2
1.3
0.0
0.0
1.4
12.1
0.0
1.3
2.3
1.1
0.0
0.6
0.0
3.0
16.3
3.9
9.4
3.3
7.4
3.1
0.0
4.0
9
1.4
0.0
6.2
3.4
8.5
8.8
2.6
5.7
3.6
0.0
8.9
2.2
6.9
0.0
0.4
0.1
2.9
10.5
3.7
0.9
0.0
2.2
10.7
0.0
0.2
7.9
0.5
10
5.3
4.0
5.4
9.7
2.0
0.7
7.4
2.1
0.0
7.0
8.7
3.7
6.2
1.4
0.9
1.7
6.5
6.6
2.5
0.0
0.1
3.0
0.0
0.7
0.0
1.9
2.2
11
0.0
0.8
5.3
1.0
8.3
0.0
0.0
0.0
9.1
6.6
0.2
10.5
8.4
3.0
0.5
0.0
0.0
2.5
0.0
0.0
0.0
0.8
2.0
3.0
0.0
5.0
4.4
12
0.5
2.6
0.0
0.0
5.9
0.0
0.0
9.2
4.9
0.3
0.0
0.0
0.1
0.0
0.0
8.8
0.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
12.2
35
28
29
30
31
1972
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
0.0
0.0
0.0
0.0
0.0
4.8
3.2
13.1
14.2
4.0
0.0
0.0
0.0
0.0
1.1
4.3
0.0
9.9
0.0
8.2
9.0
0.0
0.4
3.6
6.1
0.4
1.0
1.9
0.2
0.5
0.3
3.6
15.9
0.1
21.5
0.0
0.0
3.0
3.7
2.9
0.0
1
0.1
0.0
0.0
4.7
4.7
0.0
0.0
3.5
0.4
2.1
5.3
3.2
2.8
0.0
1.7
0.0
6.1
10.3
0.3
0.7
4.7
0.8
1.4
12.8
0.8
0.2
0.0
0.0
0.0
0.0
11.7
2
0.5
6.7
3.4
0.2
0.0
0.0
0.0
0.0
0.0
0.0
14.3
0.0
0.1
5.9
15.2
0.0
1.2
0.0
0.0
0.0
0.0
3.8
0.3
0.0
0.0
8.7
3.8
0.7
0.0
3
1.4
3.7
4.8
1.8
0.4
5.1
0.0
6.1
0.0
1.8
0.4
0.3
0.0
0.5
0.8
12.5
1.6
0.0
1.1
0.0
1.9
0.0
5.9
0.0
1.7
0.0
0.0
0.0
0.0
0.0
0.0
4
0.0
0.0
0.3
0.0
0.8
1.2
1.4
0.0
0.4
7.8
14.8
22.0
0.0
6.3
5.7
0.0
0.6
3.2
11.6
0.4
0.2
4.7
0.0
0.0
0.0
2.4
2.1
1.3
0.3
0.0
5
15.4
4.1
0.3
2.9
11.7
0.0
1.0
1.5
4.8
7.2
9.8
0.0
1.1
0.0
0.0
0.0
4.5
0.0
0.0
3.1
0.2
4.0
0.0
1.2
4.2
1.4
0.8
3.2
0.2
0.4
0.0
6
7.4
14.4
6.1
13.4
0.0
4.3
0.0
1.2
0.0
0.2
0.0
0.1
1.8
0.0
0.5
0.9
0.2
3.0
24.4
0.0
5.8
0.0
2.6
7.6
17.3
3.7
7.7
2.5
0.3
0.0
7
0.0
1.6
0.1
0.0
0.1
0.0
0.0
0.0
0.0
0.0
5.6
1.9
0.0
0.9
0.4
0.0
0.8
1.0
2.0
0.1
0.2
3.0
0.8
4.9
0.0
1.4
0.1
0.0
1.2
0.0
1.9
8
0.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
0.0
0.1
3.9
0.9
0.1
0.0
3.2
17.1
6.2
5.4
5.3
0.3
0.1
6.7
13.9
6.6
1.1
2.0
10.2
2.2
0.0
0.5
0.2
9
12.4
0.0
0.9
7.1
2.8
8.6
1.1
1.3
2.5
2.6
2.6
0.2
1.0
0.0
0.5
0.2
0.0
0.0
0.0
2.7
0.4
6.1
0.1
8.0
0.3
3.0
0.3
14.8
4.2
1.1
10
0.4
0.0
0.5
1.3
11.5
6.1
3.7
1.6
0.0
0.0
0.4
0.3
4.5
4.2
0.2
0.8
6.8
6.5
2.0
3.5
6.6
0.8
0.0
13.3
3.0
2.1
3.5
8.4
13.1
19.3
0.1
11
0.0
0.0
0.0
4.4
4.9
5.4
5.4
23.9
12.4
0.1
16.0
10.3
10.9
14.5
1.1
1.3
9.7
3.1
2.1
4.7
12.1
2.8
0.0
0.0
0.4
5.4
6.8
2.6
0.3
0.0
12
3.8
0.0
0.0
3.9
7.5
0.4
4.2
0.1
11.0
0.0
9.5
6.7
1.9
0.0
0.9
6.3
4.8
2.0
1.5
5.1
0.0
0.0
0.0
0.0
0.0
1.8
0.0
0.0
1.9
0.0
0.0
8
9
ZONE D(4 STATIONS
USED)
88330070
33.783
1.200
88330120
33.250
1.100
89330140
33.583
0.950
89340190
34.167
0.683
1
2
1971
3
4
5
6
7
10
11
12
36
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.3
0.0
2.2
4.6
6.3
0.4
0.9
5.0
0.0
3.5
0.0
0.9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.9 0.0 5.3 7.2 0.5 5.7 2.0 0.3 13.2 0.0
0.1 0.0 6.5 9.4 9.0 1.0 3.8 21.5 7.5 0.3
0.0 0.0 7.5 1.8 0.3 0.0 0.0 11.2 0.0 0.0
0.0 0.0 15.1 19.5 0.0 0.0 0.0 2.9 8.2 5.0
0.0 0.0 6.8 0.6 4.8 6.4 2.5 1.5 1.1 7.0
0.0 0.0 7.7 11.6 5.4 0.0 8.2 5.2 7.4 3.9
0.0 0.0 0.8 19.1 2.4 1.2 11.6 7.0 4.9 0.4
0.0 4.4 5.5 17.8 0.7 1.8 7.1 7.5 1.0 3.0
0.0 10.7 20.6 6.9 1.8 5.4 23.6 0.9 0.0 2.3
0.0 0.9 19.8 10.8 10.5 0.0 0.0 8.8 0.1 4.9
0.0 2.0 11.2 3.9 4.3 14.1 8.3 13.0 0.7 8.4
0.0 0.0 8.1 0.3 0.2 1.4 2.8 4.7 2.2 17.0
0.0 0.0 21.3 1.9 2.1 0.0 2.0 0.8 0.4 12.7
0.0 3.3 1.5 0.9 6.6 0.0 0.5 5.0 0.1 0.4
0.0 1.0 0.6 2.3 5.4 2.7 15.9 6.1 0.0 0.0
0.2 6.9 2.1 9.8 2.9 2.5 0.4 1.0 0.4 0.0
0.0 2.9 0.4 7.5 1.4 13.1 3.1 0.1 8.8 0.0
0.0 0.0 3.2 7.0 0.5 2.7 2.9 6.2 10.4 0.2
0.9 0.0 6.2 1.9 4.5 18.6 0.0 5.0 2.8 0.0
0.0 0.0 9.6 20.2 1.6 7.2 0.5 4.4 2.6 2.3
2.2 0.0 4.2 16.0 2.7 3.5 2.4 0.0 9.5 0.0
4.3 0.0 1.2 3.7 4.2 0.9 5.8 2.1 0.3 0.0
4.7 5.4 5.4 3.1 0.0 0.0 6.6 0.0 0.1 2.3
0.6 1.5 17.8 8.8 0.0 0.3 4.9 0.1 0.4 2.2
0.0 2.6 12.3 5.6 16.7 8.1 9.0 7.2 0.0 0.0
0.0 5.1 7.5 5.6 18.5 2.5 0.5 21.0 0.9 0.2
0.5 8.1 5.0 2.9 0.6 0.9 9.2 5.5 2.2 3.0
0.0 8.5 0.4 2.8 11.1 2.7 7.6 0.0 15.7 1.3
7.1 0.8 5.3 1.3 7.5 3.9 1.7 3.5 8.1
4.7 10.1 0.8 4.5 5.7 3.1 0.6 0.6 1.4
0.0
0.0
0.6 10.9
0.0
0.5
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.7
4.6
0.1
0.0
0.0
0.0
3.7
0.0
0.5
0.0
0.1
0.0
3.0
1.8
0.3
0.8
0.0
1972
1
2
3
4
5
6
7
8
9
10
11
12
13
14
1
2
3
4
5
6
0.0 2.0 7.1 0.1 4.3 8.1
0.0 2.9 0.3 0.9 1.9 3.9
2.5 24.2 0.0 0.6 2.2 6.9
0.0 5.0 0.7 1.3 16.5 2.9
0.0 1.1 0.0 1.1 29.1 0.0
0.3 1.8 0.0 6.0 14.5 0.1
23.2 4.3 0.9 22.1 2.4 0.0
7.4 0.0 0.5 1.4 0.0 0.2
1.1 0.0 0.4 8.0 0.0 2.0
7.6 1.6 27.6 11.8 6.9 13.8
8.2 4.3 11.7 1.8 3.2 0.6
0.0 0.9 0.3 16.7 0.2 6.2
0.0 0.5 6.5 1.6 0.0 0.0
0.0 0.1 1.4 0.6 0.0 0.0
7
8
9
0.1 12.2 0.0
2.6 0.6 0.0
2.7 1.0 0.0
3.5 0.0 2.5
4.2 5.6 2.7
4.0 0.5 1.1
3.0 0.0 2.4
1.0 2.3 1.6
0.0 0.0 1.2
4.0 1.1 2.3
0.2 0.2 9.6
0.0 0.0 9.9
2.0 4.1 16.8
0.0 0.9 0.9
10
0.0
0.0
0.1
2.0
0.0
4.0
6.7
2.3
5.5
3.7
0.0
2.3
0.0
3.6
11
0.7
18.2
4.6
8.5
9.1
14.9
17.0
9.0
20.2
3.6
10.5
9.9
8.9
11.4
12
0.0
0.0
3.1
0.1
3.1
0.3
0.5
0.0
0.0
0.0
0.5
0.0
2.6
0.0
37
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
0.0 10.6 14.9 1.8 7.3
0.0 9.5 6.2 0.0 12.8
0.0 2.7 5.1 0.0 1.1
0.5 0.0 3.6 2.3 2.4
0.0 0.0 2.3 8.6 18.2
0.7 0.0 0.1 7.7 0.9
4.5 0.0 4.2 2.5 5.1
0.0 0.6 1.4 2.3 2.3
4.7 4.4 0.0 4.9 1.7
5.3 3.9 0.0 2.8 5.0
0.0 0.0 8.4 0.0 23.3
0.0 0.6 0.0 2.8 7.9
0.0 3.4 0.1 33.6 7.4
0.0 11.7 0.0 13.0 0.0
1.1 8.3 0.0 5.8 3.8
0.6
0.0 6.8 8.5
1.1
0.0
16.4
4.8 3.4 7.3 7.5 4.9 1.8
0.4 11.7 14.4 6.0 11.9 0.2
16.6 2.7 0.0 0.0 16.3 20.0
5.3 13.6 3.2 0.0 18.7 0.7
11.6 3.4 6.2 0.0 12.2 7.1
1.4 0.6 1.2 0.3 6.0 3.0
2.8 0.2 13.4 2.2 5.0 0.0
25.1 10.0 7.8 16.4 15.4 7.6
2.5 2.5 7.8 3.0 10.2 2.4
12.2 2.9 16.4 18.8 27.6 1.3
11.9 0.0 0.2 1.6 9.0 16.4
19.1 1.9 2.1 4.9 6.6 7.6
5.3 0.0 6.6 4.7 2.9 3.9
1.8 0.9 4.4 7.8 1.4 0.0
4.6 1.5 13.9 3.4 10.3 0.0
5.0 5.9 2.8 12.0 3.6 0.0
0.0 0.0
4.1
0.0
0.6
14.8
3.1
8.3
0.1
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.4
ZONE CE(3 STATIONS USED)
90300030
30.683
-0.600
90310000
31.567
-0.633
90310060
31.150
-0.383
1971
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
1
2
3
4
5
0.5 0.0 0.0 4.0 3.5
0.6 10.2 0.0 2.7 0.0
0.1 0.0 0.0 2.5 0.2
0.0 0.0 0.0 6.7 0.0
0.0 0.0 0.0 1.0 0.3
0.0 0.0 0.0 0.0 0.6
0.0 0.4 0.0 1.1 23.8
0.0 0.3 12.7 0.0 10.8
0.0 0.1 17.1 8.6 10.1
0.0 0.0 0.0 0.0 13.4
3.3 0.0 1.4 2.4 0.2
0.1 0.0 0.0 12.9 5.2
0.0 0.0 1.1 14.1 0.1
11.9 0.0 0.0 5.5 0.0
6.2 0.0 0.0 0.0 0.0
14.2 0.0 17.2 10.2 3.6
2.4 0.0 11.3 0.0 0.2
6.1 0.0 1.7 4.1 0.0
0.0 0.2 0.0 7.6 1.3
0.1 6.8 0.0 11.9 0.1
0.0 9.8 0.0 11.3 13.4
0.0 1.3 0.0 0.0 0.7
0.0 1.7 0.3 0.0 0.5
6
7
8
0.0 0.9 0.0
0.0 0.0 0.0
0.0 0.0 0.0
0.0 0.0 0.0
0.0 0.0 0.0
0.0 0.0 0.0
0.0 0.0 1.7
0.0 0.0 0.0
0.0 0.0 0.0
0.0 0.0 0.0
0.0 0.0 0.9
0.0 0.0 2.5
0.0 0.0 0.0
0.0 0.1 0.0
0.0 12.4 0.0
0.0 1.3 0.0
0.0 0.2 0.0
0.0 3.9 0.0
0.0 6.9 3.9
0.0 0.1 1.3
0.0 0.0 4.2
0.0 0.0 15.0
0.0 0.0 14.8
9
10
11
2.2 4.5 0.0
0.0 4.5 4.8
2.7 14.1 4.1
0.6 0.2 1.0
0.7 0.0 0.4
0.9 0.0 0.1
0.0 0.8 9.3
8.1 0.0 0.0
0.1 0.0 6.3
0.0 0.0 1.7
0.8 4.3 0.0
1.7 1.1 14.1
0.6 0.8 4.1
0.1 0.0 4.4
0.0 0.0 1.6
0.0 0.0 0.0
0.0 12.2 0.1
0.2 3.0 0.1
0.0 9.6 0.5
0.0 1.2 0.1
0.0 0.3 0.5
0.0 2.2 1.2
3.4 0.0 0.4
12
0.0
0.0
0.0
0.0
0.0
0.0
0.7
0.0
0.0
3.2
0.0
0.0
0.0
0.0
0.0
0.2
0.1
0.0
0.0
0.0
0.0
0.0
0.3
38
24
25
26
27
28
29
30
31
1972
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1
1.0
1.5
1.9
2.1
3.5
0.0
0.5
6.0
0.6
16.8
0.4
2.7
0.1
0.0
0.0
0.0
0.0
1.0
2.6
9.0
0.9
1.8
1.5
2.8
0.2
0.0
7.8
2.9
2.2
0.0
7.0
0.2
0.0
0.0
0.0
0.0
0.5 0.0
2.3 17.1
1.4 0.2
6.7 0.0
1.1 8.3
3.4 0.5
11.9 0.0
10.9
0.0
0.0
0.0
0.0
0.1
0.0
3.8
4.0
0.0
0.0
0.1
0.1
0.0
0.0
0.0
0.0 4.7
0.5 0.4
0.4 12.4
1.1 0.2
0.0 0.5
0.0 0.7
0.0 0.7
0.0 0.5
4.1 0.6 4.6
3.4 1.7 1.2
4.4 5.8 0.0
0.0 2.2 0.8
2.1 20.7 10.3
5.1 0.0 0.3
5.3 1.9 0.0
2.2
2
3
4
5
3.0 1.7 1.2 0.6
8.4 8.7 0.0 7.0
0.0 5.3 0.0 0.1
0.3 0.5 0.0 9.7
0.6 0.4 0.0 5.2
0.0 0.6 9.2 1.7
0.0 0.0 22.0 0.1
0.0 4.6 15.3 1.3
0.0 0.0 3.8 1.9
0.0 0.2 0.2 0.9
1.2 0.0 0.0 3.3
0.0 2.6 18.3 0.0
0.0 0.3 1.2 1.2
4.2 10.8 7.4 2.9
6.7 1.1 6.1 4.4
0.5 1.6 0.0 0.0
1.3 1.0 4.8 0.0
0.7 2.9 0.0 0.0
0.0 0.0 5.9 20.1
0.0 0.1 26.1 8.3
0.0 2.1 3.5 0.9
0.0 1.5 8.5 0.0
1.4 0.1 23.2 1.5
0.0 11.8 0.0 4.9
0.0 3.2 0.0 12.2
5.2 0.2 23.6 17.2
2.2 5.5 10.1 16.5
1.1 0.2 24.2 0.1
0.0 0.1 7.9 4.3
0.0 0.9 2.1
0.0
0.0
6
0.0
0.0
2.2
0.0
0.0
0.0
4.1
0.1
0.0
0.0
1.1
0.0
2.1
0.0
0.1
4.2
4.8
8.7
7.6
3.9
0.0
4.8
7.9
2.4
4.4
7.2
4.2
0.0
0.0
0.0
7
8
9
10
0.0 0.0 0.0 0.0
0.0 0.7 0.0 0.0
0.0 0.0 7.7 0.0
0.0 0.0 0.3 0.0
0.0 0.0 9.7 0.0
0.0 0.0 12.5 1.8
0.7 0.0 0.6 0.0
0.0 0.0 0.0 0.0
0.0 0.0 0.1 2.3
0.0 0.0 19.4 0.0
0.0 0.0 3.3 3.4
0.0 2.9 0.8 1.9
0.0 0.0 0.0 1.3
0.8 0.0 0.0 13.0
0.0 4.0 0.0 11.0
0.0 10.4 0.0 19.2
0.0 0.0 0.0 0.9
0.0 1.2 0.0 10.5
0.0 0.0 0.0 1.1
0.0 0.1 10.0 1.2
0.0 0.1 10.6 15.7
0.0 22.6 23.5 15.7
0.0 5.5 10.4 20.1
0.0 7.3 1.3 0.3
0.0 0.1 29.0 0.6
0.0 0.8 3.4 4.6
0.0 5.7 0.0 2.2
0.0 6.8 5.1 0.0
0.0 0.7 0.2 10.3
0.0 0.0 0.0 7.7
0.0 0.0
2.4
11
2.1
0.0
0.0
9.7
2.2
12.6
1.6
13.1
12.8
0.0
0.0
13.5
0.5
4.0
0.0
0.0
0.7
9.5
2.8
2.2
0.8
6.4
7.9
7.2
0.5
6.8
0.0
1.0
0.6
0.0
0.0
1.6
0.0
0.0
0.0
0.2
0.6
1.4
12
7.0
8.1
8.2
1.5
10.2
2.2
0.3
5.6
0.0
4.5
4.7
5.0
0.4
0.4
1.0
0.0
9.1
0.1
0.0
0.1
2.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
1.4
0.5
39

SysTem for Analysis, Research, and Training Assessing the impacts

Transcription

Similar documents

Feature December 6..Mutesa.indd

Uganda National Archives – Entebbe, Uganda

Child`s Destiny of Hope CDHope

:30 Joseph Molnar, Nelly Isyagi, John Walakira, Gertrude Atakunda

Karen L. Veverica Dept of Fisheries and Allied Aquacultures Auburn

Vol 9(1) - canhave

Presentation - EWB-UMN - Engineers Without Borders – University

Welcome Home Ministries – Africa June 2006

2013 Impact Report - Children of Uganda

Freezerworks Users Group Meeting