SysTem for Analysis, Research, and Training Assessing the ... · Assessing the impacts of climate change and variability on water resources in Uganda: developing an integrated approach

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 theInternational Geosphere-Biosphere Programme (IGBP)

World Climate Research Programme (WCRP), andInternational 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

Assessing the impacts of climate change and variability on water resources in Uganda: developing an integrated approach at the sub-regional scale –FINAL REPORT

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Table of Contents

Executive Summary ...........................................................................................................................................2

1. Project rationale and objectives...................................................................................................................41.1 Project aims and objectives ............................................................................................................6

2. Progress - regional climate modelling (PRECIS) ......................................................................................72.1 installation of PRECIS and training.....................................................................................................72.2 Climatic factors and patterns in Uganda – meteorological datasets for RCM validation.............8

2.2.1 Main influencing atmospheric meteorological systems in Uganda .................................82.2.2. The ITCZ.................................................................................................................................82.2.3. The Monsoons ........................................................................................................................92.2.4. Meso–Scale Circulations........................................................................................................92.2.5 Teleconnections (El Niño Southern Oscillation) ..............................................................92.2.6 Mean annual rainfall .............................................................................................................102.2.6. Seasonal rainfall distribution ...............................................................................................102.2.7. Delineation of “Climatological Zones”.............................................................................11

2.3 RCM experiments ..........................................................................................................................132.3.1 Model domain .......................................................................................................................142.3.2 Simulations ............................................................................................................................14

3. Progress - Hydrological modelling............................................................................................................153.1 Development of soil-moisture balance models ................................................................................16

3.1.1. Simulation of soil moisture - Wobulenzi...........................................................................183.1.2. Simulation of runoff.............................................................................................................193.1.3. Simulation of recharge .........................................................................................................19

3.2 Development of temperature-based estimates potential evapotransipration for East Africa ...223.2.1. PET derived from evaporation pans .................................................................................223.2.2. Climate-based methods........................................................................................................233.2.3. Temperature-based methods ..............................................................................................243.2.4. Estimation of potential evapotranspiration ......................................................................253.3.1. Study rationale & objective .................................................................................................273.3.2 Results ....................................................................................................................................273.3.3. Summary ................................................................................................................................28

4 Report Summary .....................................................................................................................................29

5 Way forward ............................................................................................................................................30

6 References................................................................................................................................................31

Appendix 1 List of participants – FINAL WORKSHOP ...................................................................33Appendix 2 Daily rainfall data for validation of RCM experiment.....................................................35


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Executive 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 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, indigenouscapacity 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.


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List of figuresFigure 1 Recent water-level fluctuations in Lake Victoria (thin line) and in groundwater

(thick line) at Entebbe, Uganda (32.5ºE, 0.2ºN) 4Figure 2 Location of groundwater monitoring sites in Uganda 3Figure 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) 4

Figure 4 Rainfall (mm) map of Uganda 8Figure 5 Delineated climatological zones of Uganda 10Figure 6 Model domain (10º to 53ºE, 22ºS to 10ºN) and grid (100x100) selected for RCM

experiments 11Figure 7 Total precipitation over RCM study area for 12 days (December 4 to 15, 1972) of

an 11-year simulation (1971-1982) 12Figure 8 Representation of the different components of the SMBM 13Figure 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) 14

Figure 10 Soil-moisture deficit and precipitation (7-day running means) at Katikamu during calendar year of 2001 15

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 200 16

Figure 12 Daily SMBM – recharge and water levels (obs. well 2) at Katikamu year 2001 17Figure 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) 21

Figure 14 Monthly PET derived from pan-evaporation data and Thornthwaite method (1961-1968) 23

Figure 15 Water table fluctuations and daily precipitation from July 1998 to present at Entebbe (station 17 in Fig. 2) in southern Uganda 24

Figure 16 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 tablesTable 1 Model Sensitivity analysis – reconciling SMBM estimates of recharge with water

level observations.21

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

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Table 3 Water-level changes, estimated recharge and preceding precipitation of identified recharge events (Entebbe)

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Table 4 Water-level changes, estimated recharge and preceding precipitation of identified recharge events (Apac Town)

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1. Project rationale and objectives

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 hydro-electric 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 affectedthe 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.

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l)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


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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 low-flow 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


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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).

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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.


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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)


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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.


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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 bi-model 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 meso-scale 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


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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-assessedusing 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 thecountry 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.


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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.


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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. Zoneboundaries 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.


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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


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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


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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.


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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 - ROR = Peffective - PET when SMD = 0R = 0 when SMD > 0SMD = PET - P

P : precipitationRO : runoffR : rechargePET : potential evapotranspirationSMD : soil-moisture deficit


17

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).


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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 DispensaryRunoff 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

Model validation:Runoff - daily stream discharge on River KigwaRecharge - water-level fluctuations @ Katikamu

0

30

60

90

120

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180

1 31 61 91 121 151 181 211 241 271 301 331 361

Time (days)

SM

D (

mm

)

0

10

20

30

40

50

60

P (

mm

)

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


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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 Stationresides. RO estimated as 5% of rainfall events exceeding P events of 7mmday-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.

0

2

4

6

8

10

12

14

14 19 24 29 34

Time (10 day periods)

RO

(m

m)

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 smaller-scale 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


20

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).

0

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1 31 61 91 121 151 181 211 241 271 301 331 361

day (year (2001)

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17.0

17.5

18.0

wat

er l

evel

(m

bel

ow

gro

un

d l

evel

)

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 10mmday-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).

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

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


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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).


23

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 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec YearlyBangui PET 95 105 117 106 87 63 63 83 96 98 93 87 1239Bangui (f) 0.82 0.78 0.79 0.83 0.80 0.79 0.81 0.73 0.73 0.76 0.74 0.80 0.78Intermediate(f) 0.74 0.71 0.72 0.80 0.80 0.87 0.88 0.90 0.88 0.82 0.74 0.75 0.80Equatorial(f) 0.89 0.91 0.92 0.94 0.96 0.93 0.96 0.90 0.92 0.94 0.92 0.91 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).


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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)

A

B


25

I = annual thermal index = sum of monthly indices ii = (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


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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)

0

50

100

150

1 2 3 4 5 6 7 8 9 10 11 12

PE

T (

mm

)

ET (Thw) ET (pan)

Figure 14: Monthly PET derived from pan-evaporation data and Thornthwaite method (1961-1968)


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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

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th t

o w

ater

(m

bg

l)

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20

40

60

80

100

120

Dai

ly r

ain

fall

(mm

)

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 Δh est. R preceding rainfall driving recharge event

1 Nov 12-Dec 18, 1999 1.9 570 3 days (65.6mm), 1 day (55mm), 4 days (151mm)2 May 9 - 17, 2000 0.39 117 4 days (62.8mm)3 Mar 28 - May 22, 2001 2.23 669 3 days (82mm), 4 days (98mm), 4 days (95.5mm)4 May 22 - Jul 15, 2003 0.98 294 3 days (56.8mm), 6 days (154.9mm)5 Oct 31-Dec 22, 2003 0.52 156 4days with alternation(103.5mm), 3days(48.8mm), 8days(162.4mm)


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5

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ater

(m

)

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100

120

140

Dai

ly R

ain

fall

(m

m)

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 preceding rainfall driving recharge event

1 Aug 06 - Dec 13, 2000 1.28 1923 days (88.8mm), 5days (79.2mm), 3days (142.08mm), 5days with alternation(135.84mm)

2 aug 16- Nov 18, 2002 0.57 85.51day(53.3mm), 1day(44.6mm), 4days(165.9mm), 2days(42.4mm), 1day(60.5mm), 12days(73.3mm)

3 Jun 12 - Oct 23, 2003 0.54 81

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)

4 Aug 13 - Dec 11, 2004 0.66 99

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.


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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 waterresources 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 consideringgroundwater 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 experimentwhich 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


30

has been very helpful funds are required to train a local expert not only to help run thePRECIS 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.


31

6 References

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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,

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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


33

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, UgandaDr. Richard Taylor, University College London, UKMr. A. W. Majugu, Uganda Meteorology Department, UgandaMr. Mukwaya Paul, Geography Department, Makerere University, UgandaThe Commissioner, Uganda Meteorology Department, UgandaMr. Muwembe Khalid, Uganda Meteorology Department, UgandaMr. Nimusiima Alex, Geography Department, Makerere University, UgandaMr. A. K. Kagoro, Uganda Meteorology Department, UgandaMr. Magezi Akiiki, Uganda Meteorology Department, UgandaMs. Nanteza Jamiat, Geography Department, Makerere University, UgandaMr. Sabiiti Geoffrey, Geography Department, Makerere University, UgandaMr. 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, UgandaDr. Richard Taylor, University College London , UKThe Head, Geography Department, Makerere University, UgandaThe Dean, Faculty of Arts, Makerere University, UgandaMr. A. W. Majugu, Uganda Meteorology Department, UgandaThe Assistant Commissioner, Water Development Department, UgandaMr. Mukwaya Paul, Geography Department, Makerere University, UgandaThe Commissioner, Uganda Meteorology Department, UgandaMr. Eza John, Uganda Meteorology Department, UgandaMrs. Lubega, Uganda Meteorology Department, UgandaMr. Mujuni R. Godfrey, Uganda Meteorology Department, UgandaMs. Mulinde Catherine , Geography Department, Makerere University, UgandaMr. Muwembe Khalid, Uganda Meteorology Department, UgandaMr. Nimusiima Alex, Geography Department, Makerere University, UgandaMr. Ogwanga Bob Alex, Uganda Meteorology Department, UgandaMr. Otim Faustine, Uganda Meteorology Department, Uganda


34

Mr. Eneku John Paul, Uganda Meteorology Department, UgandaMr. A. K. Kagoro, Uganda Meteorology Department, UgandaMr. Magezi Akiiki, Uganda Meteorology Department, UgandaMr. Mugume Isaac, Uganda Meteorology Department, UgandaMr. Mulindwa M. Matovu, Uganda Meteorology Department, UgandaMs. Nanteza Jamiat, Geography Department, Makerere University, UgandaMs. Ninyesiga Annie, Uganda Meteorology Department, UgandaMr. Omonyi William George, Uganda Meteorology Department, UgandaMr. Sabiiti Geoffrey, Geography Department, Makerere University, UgandaMr. George Obua, Uganda Meteorology Department, Uganda, UgandaMr. Ayesiga Godwin, Uganda Meteorology Department , Uganda


35

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

19711 2 3 4 5 6 7 8 9 10 11 12

1 0.7 2.1 0.3 0.6 7.7 0.0 4.8 0.0 1.4 5.3 0.0 0.52 0.0 0.0 0.0 0.5 4.4 3.0 0.1 1.2 0.0 4.0 0.8 2.63 0.0 0.0 0.0 1.9 0.0 14.5 0.5 0.0 6.2 5.4 5.3 0.04 0.0 0.0 0.0 2.2 0.1 8.0 0.0 0.0 3.4 9.7 1.0 0.05 0.0 0.0 0.0 2.6 1.5 1.3 2.9 0.0 8.5 2.0 8.3 5.96 0.0 0.0 0.0 0.0 5.7 0.7 3.2 7.2 8.8 0.7 0.0 0.07 0.0 2.6 0.0 0.5 11.3 2.4 0.0 1.3 2.6 7.4 0.0 0.08 0.0 2.4 0.0 0.0 14.0 0.0 0.9 0.0 5.7 2.1 0.0 9.29 0.0 0.3 2.8 6.7 3.4 0.0 0.8 0.0 3.6 0.0 9.1 4.9

10 0.0 0.0 0.0 0.4 9.8 0.0 0.0 1.4 0.0 7.0 6.6 0.311 0.0 0.0 2.2 17.2 3.2 0.0 0.0 12.1 8.9 8.7 0.2 0.012 5.0 0.0 9.4 7.1 2.9 2.6 0.0 0.0 2.2 3.7 10.5 0.013 2.6 0.0 2.4 2.0 11.2 0.3 0.0 1.3 6.9 6.2 8.4 0.114 19.0 0.0 0.0 1.7 0.0 0.0 3.5 2.3 0.0 1.4 3.0 0.015 17.5 0.0 4.1 8.6 0.3 2.6 30.1 1.1 0.4 0.9 0.5 0.016 11.6 0.0 6.0 4.4 4.5 3.9 0.0 0.0 0.1 1.7 0.0 8.817 15.2 0.0 0.6 0.3 5.7 0.0 18.4 0.6 2.9 6.5 0.0 0.718 1.4 4.6 1.3 7.9 5.7 1.5 6.1 0.0 10.5 6.6 2.5 0.019 0.7 6.2 0.0 14.8 17.0 0.1 11.8 3.0 3.7 2.5 0.0 0.020 0.4 1.7 0.0 4.0 23.7 0.8 2.3 16.3 0.9 0.0 0.0 0.021 0.7 2.5 0.6 7.0 0.8 0.5 8.8 3.9 0.0 0.1 0.0 0.022 0.0 0.4 0.0 0.0 3.2 1.4 0.0 9.4 2.2 3.0 0.8 0.023 0.0 1.0 0.2 2.0 2.5 0.0 0.8 3.3 10.7 0.0 2.0 0.024 0.0 0.1 0.0 3.1 9.7 0.0 1.1 7.4 0.0 0.7 3.0 0.025 0.0 0.0 4.0 11.5 4.1 0.1 0.0 3.1 0.2 0.0 0.0 0.026 0.0 0.0 0.0 3.8 1.8 0.7 3.8 0.0 7.9 1.9 5.0 0.027 0.0 0.0 0.0 6.9 4.8 0.5 0.7 4.0 0.5 2.2 4.4 12.2


36

28 0.0 0.0 4.8 4.0 0.0 0.0 8.2 3.6 1.9 0.3 21.5 3.029 0.0 3.2 0.0 0.0 9.9 9.0 6.1 0.2 3.6 0.0 3.730 0.0 13.1 0.0 1.1 0.0 0.0 0.4 0.5 15.9 0.0 2.931 0.0 14.2 4.3 0.4 1.0 0.1 0.0

19721 2 3 4 5 6 7 8 9 10 11 12

1 0.1 0.5 1.4 0.0 15.4 7.4 0.0 0.0 12.4 0.4 0.0 3.82 0.0 6.7 3.7 0.0 4.1 14.4 1.6 0.0 0.0 0.0 0.0 0.03 0.0 3.4 4.8 0.3 0.3 6.1 0.1 0.2 0.9 0.5 0.0 0.04 4.7 0.2 1.8 0.0 2.9 13.4 0.0 0.0 7.1 1.3 4.4 3.95 4.7 0.0 0.4 0.8 11.7 0.0 0.1 0.0 2.8 11.5 4.9 7.56 0.0 0.0 5.1 1.2 0.0 4.3 0.0 0.0 8.6 6.1 5.4 0.47 0.0 0.0 0.0 1.4 1.0 0.0 0.0 0.0 1.1 3.7 5.4 4.28 3.5 0.0 6.1 0.0 1.5 1.2 0.0 0.0 1.3 1.6 23.9 0.19 0.4 0.0 0.0 0.4 4.8 0.0 0.0 0.0 2.5 0.0 12.4 11.0

10 2.1 0.0 1.8 7.8 7.2 0.2 0.0 0.1 2.6 0.0 0.1 0.011 5.3 14.3 0.4 14.8 9.8 0.0 5.6 3.9 2.6 0.4 16.0 9.512 3.2 0.0 0.3 22.0 0.0 0.1 1.9 0.9 0.2 0.3 10.3 6.713 2.8 0.1 0.0 0.0 1.1 1.8 0.0 0.1 1.0 4.5 10.9 1.914 0.0 5.9 0.5 6.3 0.0 0.0 0.9 0.0 0.0 4.2 14.5 0.015 1.7 15.2 0.8 5.7 0.0 0.5 0.4 3.2 0.5 0.2 1.1 0.916 0.0 0.0 12.5 0.0 0.0 0.9 0.0 17.1 0.2 0.8 1.3 6.317 6.1 1.2 1.6 0.6 4.5 0.2 0.8 6.2 0.0 6.8 9.7 4.818 10.3 0.0 0.0 3.2 0.0 3.0 1.0 5.4 0.0 6.5 3.1 2.019 0.3 0.0 1.1 11.6 0.0 24.4 2.0 5.3 0.0 2.0 2.1 1.520 0.7 0.0 0.0 0.4 3.1 0.0 0.1 0.3 2.7 3.5 4.7 5.121 4.7 0.0 1.9 0.2 0.2 5.8 0.2 0.1 0.4 6.6 12.1 0.022 0.8 3.8 0.0 4.7 4.0 0.0 3.0 6.7 6.1 0.8 2.8 0.023 1.4 0.3 5.9 0.0 0.0 2.6 0.8 13.9 0.1 0.0 0.0 0.024 12.8 0.0 0.0 0.0 1.2 7.6 4.9 6.6 8.0 13.3 0.0 0.025 0.8 0.0 1.7 0.0 4.2 17.3 0.0 1.1 0.3 3.0 0.4 0.026 0.2 8.7 0.0 2.4 1.4 3.7 1.4 2.0 3.0 2.1 5.4 1.827 0.0 3.8 0.0 2.1 0.8 7.7 0.1 10.2 0.3 3.5 6.8 0.028 0.0 0.7 0.0 1.3 3.2 2.5 0.0 2.2 14.8 8.4 2.6 0.029 0.0 0.0 0.0 0.3 0.2 0.3 1.2 0.0 4.2 13.1 0.3 1.930 0.0 0.0 0.0 0.4 0.0 0.0 0.5 1.1 19.3 0.0 0.031 11.7 0.0 0.0 1.9 0.2 0.1 0.0

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

19711 2 3 4 5 6 7 8 9 10 11 12


37

1 0.0 0.9 0.0 5.3 7.2 0.5 5.7 2.0 0.3 13.2 0.0 0.52 0.0 0.1 0.0 6.5 9.4 9.0 1.0 3.8 21.5 7.5 0.3 0.03 0.0 0.0 0.0 7.5 1.8 0.3 0.0 0.0 11.2 0.0 0.0 0.04 0.0 0.0 0.0 15.1 19.5 0.0 0.0 0.0 2.9 8.2 5.0 0.05 0.0 0.0 0.0 6.8 0.6 4.8 6.4 2.5 1.5 1.1 7.0 0.06 0.0 0.0 0.0 7.7 11.6 5.4 0.0 8.2 5.2 7.4 3.9 0.07 0.0 0.0 0.0 0.8 19.1 2.4 1.2 11.6 7.0 4.9 0.4 0.08 0.0 0.0 4.4 5.5 17.8 0.7 1.8 7.1 7.5 1.0 3.0 0.09 0.0 0.0 10.7 20.6 6.9 1.8 5.4 23.6 0.9 0.0 2.3 0.0

10 0.3 0.0 0.9 19.8 10.8 10.5 0.0 0.0 8.8 0.1 4.9 0.011 0.0 0.0 2.0 11.2 3.9 4.3 14.1 8.3 13.0 0.7 8.4 0.012 2.2 0.0 0.0 8.1 0.3 0.2 1.4 2.8 4.7 2.2 17.0 0.013 4.6 0.0 0.0 21.3 1.9 2.1 0.0 2.0 0.8 0.4 12.7 0.014 6.3 0.0 3.3 1.5 0.9 6.6 0.0 0.5 5.0 0.1 0.4 0.015 0.4 0.0 1.0 0.6 2.3 5.4 2.7 15.9 6.1 0.0 0.0 0.716 0.9 0.2 6.9 2.1 9.8 2.9 2.5 0.4 1.0 0.4 0.0 4.617 5.0 0.0 2.9 0.4 7.5 1.4 13.1 3.1 0.1 8.8 0.0 0.118 0.0 0.0 0.0 3.2 7.0 0.5 2.7 2.9 6.2 10.4 0.2 0.019 3.5 0.9 0.0 6.2 1.9 4.5 18.6 0.0 5.0 2.8 0.0 0.020 0.0 0.0 0.0 9.6 20.2 1.6 7.2 0.5 4.4 2.6 2.3 0.021 0.9 2.2 0.0 4.2 16.0 2.7 3.5 2.4 0.0 9.5 0.0 3.722 0.0 4.3 0.0 1.2 3.7 4.2 0.9 5.8 2.1 0.3 0.0 0.023 0.0 4.7 5.4 5.4 3.1 0.0 0.0 6.6 0.0 0.1 2.3 0.524 0.0 0.6 1.5 17.8 8.8 0.0 0.3 4.9 0.1 0.4 2.2 0.025 0.0 0.0 2.6 12.3 5.6 16.7 8.1 9.0 7.2 0.0 0.0 0.126 0.0 0.0 5.1 7.5 5.6 18.5 2.5 0.5 21.0 0.9 0.2 0.027 0.0 0.5 8.1 5.0 2.9 0.6 0.9 9.2 5.5 2.2 3.0 3.028 0.0 0.0 8.5 0.4 2.8 11.1 2.7 7.6 0.0 15.7 1.3 1.829 0.0 7.1 0.8 5.3 1.3 7.5 3.9 1.7 3.5 8.1 0.330 0.0 4.7 10.1 0.8 4.5 5.7 3.1 0.6 0.6 1.4 0.831 0.0 0.0 0.0 0.6 10.9 0.0 0.0

19721 2 3 4 5 6 7 8 9 10 11 12

1 0.0 2.0 7.1 0.1 4.3 8.1 0.1 12.2 0.0 0.0 0.7 0.02 0.0 2.9 0.3 0.9 1.9 3.9 2.6 0.6 0.0 0.0 18.2 0.03 2.5 24.2 0.0 0.6 2.2 6.9 2.7 1.0 0.0 0.1 4.6 3.14 0.0 5.0 0.7 1.3 16.5 2.9 3.5 0.0 2.5 2.0 8.5 0.15 0.0 1.1 0.0 1.1 29.1 0.0 4.2 5.6 2.7 0.0 9.1 3.16 0.3 1.8 0.0 6.0 14.5 0.1 4.0 0.5 1.1 4.0 14.9 0.37 23.2 4.3 0.9 22.1 2.4 0.0 3.0 0.0 2.4 6.7 17.0 0.58 7.4 0.0 0.5 1.4 0.0 0.2 1.0 2.3 1.6 2.3 9.0 0.09 1.1 0.0 0.4 8.0 0.0 2.0 0.0 0.0 1.2 5.5 20.2 0.0

10 7.6 1.6 27.6 11.8 6.9 13.8 4.0 1.1 2.3 3.7 3.6 0.011 8.2 4.3 11.7 1.8 3.2 0.6 0.2 0.2 9.6 0.0 10.5 0.512 0.0 0.9 0.3 16.7 0.2 6.2 0.0 0.0 9.9 2.3 9.9 0.013 0.0 0.5 6.5 1.6 0.0 0.0 2.0 4.1 16.8 0.0 8.9 2.614 0.0 0.1 1.4 0.6 0.0 0.0 0.0 0.9 0.9 3.6 11.4 0.0


38

15 0.0 10.6 14.9 1.8 7.3 4.8 3.4 7.3 7.5 4.9 1.8 0.016 0.0 9.5 6.2 0.0 12.8 0.4 11.7 14.4 6.0 11.9 0.2 0.617 0.0 2.7 5.1 0.0 1.1 16.6 2.7 0.0 0.0 16.3 20.0 14.818 0.5 0.0 3.6 2.3 2.4 5.3 13.6 3.2 0.0 18.7 0.7 3.119 0.0 0.0 2.3 8.6 18.2 11.6 3.4 6.2 0.0 12.2 7.1 8.320 0.7 0.0 0.1 7.7 0.9 1.4 0.6 1.2 0.3 6.0 3.0 0.121 4.5 0.0 4.2 2.5 5.1 2.8 0.2 13.4 2.2 5.0 0.0 0.022 0.0 0.6 1.4 2.3 2.3 25.1 10.0 7.8 16.4 15.4 7.6 0.023 4.7 4.4 0.0 4.9 1.7 2.5 2.5 7.8 3.0 10.2 2.4 0.024 5.3 3.9 0.0 2.8 5.0 12.2 2.9 16.4 18.8 27.6 1.3 0.025 0.0 0.0 8.4 0.0 23.3 11.9 0.0 0.2 1.6 9.0 16.4 0.026 0.0 0.6 0.0 2.8 7.9 19.1 1.9 2.1 4.9 6.6 7.6 0.027 0.0 3.4 0.1 33.6 7.4 5.3 0.0 6.6 4.7 2.9 3.9 0.028 0.0 11.7 0.0 13.0 0.0 1.8 0.9 4.4 7.8 1.4 0.0 0.029 1.1 8.3 0.0 5.8 3.8 4.6 1.5 13.9 3.4 10.3 0.0 0.030 0.6 0.0 6.8 8.5 5.0 5.9 2.8 12.0 3.6 0.0 0.031 1.1 0.0 16.4 0.0 0.0 4.1 0.4

ZONE CE(3 STATIONS USED)90300030 30.683 -0.600

90310000 31.567 -0.633

90310060 31.150 -0.383

19711 2 3 4 5 6 7 8 9 10 11 12

1 0.5 0.0 0.0 4.0 3.5 0.0 0.9 0.0 2.2 4.5 0.0 0.02 0.6 10.2 0.0 2.7 0.0 0.0 0.0 0.0 0.0 4.5 4.8 0.03 0.1 0.0 0.0 2.5 0.2 0.0 0.0 0.0 2.7 14.1 4.1 0.04 0.0 0.0 0.0 6.7 0.0 0.0 0.0 0.0 0.6 0.2 1.0 0.05 0.0 0.0 0.0 1.0 0.3 0.0 0.0 0.0 0.7 0.0 0.4 0.06 0.0 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.9 0.0 0.1 0.07 0.0 0.4 0.0 1.1 23.8 0.0 0.0 1.7 0.0 0.8 9.3 0.78 0.0 0.3 12.7 0.0 10.8 0.0 0.0 0.0 8.1 0.0 0.0 0.09 0.0 0.1 17.1 8.6 10.1 0.0 0.0 0.0 0.1 0.0 6.3 0.0

10 0.0 0.0 0.0 0.0 13.4 0.0 0.0 0.0 0.0 0.0 1.7 3.211 3.3 0.0 1.4 2.4 0.2 0.0 0.0 0.9 0.8 4.3 0.0 0.012 0.1 0.0 0.0 12.9 5.2 0.0 0.0 2.5 1.7 1.1 14.1 0.013 0.0 0.0 1.1 14.1 0.1 0.0 0.0 0.0 0.6 0.8 4.1 0.014 11.9 0.0 0.0 5.5 0.0 0.0 0.1 0.0 0.1 0.0 4.4 0.015 6.2 0.0 0.0 0.0 0.0 0.0 12.4 0.0 0.0 0.0 1.6 0.016 14.2 0.0 17.2 10.2 3.6 0.0 1.3 0.0 0.0 0.0 0.0 0.217 2.4 0.0 11.3 0.0 0.2 0.0 0.2 0.0 0.0 12.2 0.1 0.118 6.1 0.0 1.7 4.1 0.0 0.0 3.9 0.0 0.2 3.0 0.1 0.019 0.0 0.2 0.0 7.6 1.3 0.0 6.9 3.9 0.0 9.6 0.5 0.020 0.1 6.8 0.0 11.9 0.1 0.0 0.1 1.3 0.0 1.2 0.1 0.021 0.0 9.8 0.0 11.3 13.4 0.0 0.0 4.2 0.0 0.3 0.5 0.022 0.0 1.3 0.0 0.0 0.7 0.0 0.0 15.0 0.0 2.2 1.2 0.023 0.0 1.7 0.3 0.0 0.5 0.0 0.0 14.8 3.4 0.0 0.4 0.3


39

24 0.0 0.2 0.5 0.0 0.0 0.0 0.0 4.7 4.1 0.6 4.6 0.025 0.0 0.0 2.3 17.1 0.0 0.0 0.5 0.4 3.4 1.7 1.2 1.626 0.0 0.0 1.4 0.2 0.0 0.1 0.4 12.4 4.4 5.8 0.0 0.027 0.0 0.0 6.7 0.0 0.0 0.1 1.1 0.2 0.0 2.2 0.8 0.028 0.0 0.0 1.1 8.3 0.1 0.0 0.0 0.5 2.1 20.7 10.3 0.029 0.0 3.4 0.5 0.0 0.0 0.0 0.7 5.1 0.0 0.3 0.230 0.0 11.9 0.0 3.8 0.0 0.0 0.7 5.3 1.9 0.0 0.631 0.0 10.9 4.0 0.0 0.5 2.2 1.4

19721 2 3 4 5 6 7 8 9 10 11 12

1 1.0 3.0 1.7 1.2 0.6 0.0 0.0 0.0 0.0 0.0 2.1 7.02 1.5 8.4 8.7 0.0 7.0 0.0 0.0 0.7 0.0 0.0 0.0 8.13 1.9 0.0 5.3 0.0 0.1 2.2 0.0 0.0 7.7 0.0 0.0 8.24 2.1 0.3 0.5 0.0 9.7 0.0 0.0 0.0 0.3 0.0 9.7 1.55 3.5 0.6 0.4 0.0 5.2 0.0 0.0 0.0 9.7 0.0 2.2 10.26 0.0 0.0 0.6 9.2 1.7 0.0 0.0 0.0 12.5 1.8 12.6 2.27 0.5 0.0 0.0 22.0 0.1 4.1 0.7 0.0 0.6 0.0 1.6 0.38 6.0 0.0 4.6 15.3 1.3 0.1 0.0 0.0 0.0 0.0 13.1 5.69 0.6 0.0 0.0 3.8 1.9 0.0 0.0 0.0 0.1 2.3 12.8 0.0

10 16.8 0.0 0.2 0.2 0.9 0.0 0.0 0.0 19.4 0.0 0.0 4.511 0.4 1.2 0.0 0.0 3.3 1.1 0.0 0.0 3.3 3.4 0.0 4.712 2.7 0.0 2.6 18.3 0.0 0.0 0.0 2.9 0.8 1.9 13.5 5.013 0.1 0.0 0.3 1.2 1.2 2.1 0.0 0.0 0.0 1.3 0.5 0.414 0.0 4.2 10.8 7.4 2.9 0.0 0.8 0.0 0.0 13.0 4.0 0.415 0.0 6.7 1.1 6.1 4.4 0.1 0.0 4.0 0.0 11.0 0.0 1.016 0.0 0.5 1.6 0.0 0.0 4.2 0.0 10.4 0.0 19.2 0.0 0.017 0.0 1.3 1.0 4.8 0.0 4.8 0.0 0.0 0.0 0.9 0.7 9.118 1.0 0.7 2.9 0.0 0.0 8.7 0.0 1.2 0.0 10.5 9.5 0.119 2.6 0.0 0.0 5.9 20.1 7.6 0.0 0.0 0.0 1.1 2.8 0.020 9.0 0.0 0.1 26.1 8.3 3.9 0.0 0.1 10.0 1.2 2.2 0.121 0.9 0.0 2.1 3.5 0.9 0.0 0.0 0.1 10.6 15.7 0.8 2.022 1.8 0.0 1.5 8.5 0.0 4.8 0.0 22.6 23.5 15.7 6.4 0.023 1.5 1.4 0.1 23.2 1.5 7.9 0.0 5.5 10.4 20.1 7.9 0.024 2.8 0.0 11.8 0.0 4.9 2.4 0.0 7.3 1.3 0.3 7.2 0.025 0.2 0.0 3.2 0.0 12.2 4.4 0.0 0.1 29.0 0.6 0.5 0.026 0.0 5.2 0.2 23.6 17.2 7.2 0.0 0.8 3.4 4.6 6.8 0.027 7.8 2.2 5.5 10.1 16.5 4.2 0.0 5.7 0.0 2.2 0.0 0.128 2.9 1.1 0.2 24.2 0.1 0.0 0.0 6.8 5.1 0.0 1.0 0.029 2.2 0.0 0.1 7.9 4.3 0.0 0.0 0.7 0.2 10.3 0.6 0.030 0.0 0.0 0.9 2.1 0.0 0.0 0.0 0.0 7.7 0.0 1.431 7.0 0.0 0.0 0.0 0.0 2.4 0.5

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