A GIS Based Water Balance Study of Africa - Physical Land Resources

4LIST OF FIGURESFigure 1.1: Flowchart of research methodology---------------------------------------------------------9Figure 1.2: Schematic water balance of a hypothetical raster cell------------------------------------14Figure 3.1: Topographic map of Africa-------------------------------------------------------------------23Figure 3.2: Slope map of Africa---------------------------------------------------------------------------24Figure 3.3: USDA-based reclassified Soil map of Africa ---------------------------------------------25Figure 3.4: Histogram of soil texture classes------------------------------------------------------------25Figure 3.5: Landuse map of Africa------------------------------------------------------------------------26Figure 3.6: Histogram of regrouped landuse classes for WetSpass-----------------------------------27Figure 3.7 Histogram of relative proportion of Landuse classes--------------------------------------27Figure 3.8 Annual rainfall map of Africa-----------------------------------------------------------------28Figure 3.9 PET map of Africa------------------------------------------------------------------------------30Figure 4.1: Average annual evapotranspiration values from WetSpass-----------------------------34Figure 4.2: Annual average evapotranspiration values-------------------------------------------------35Figure 4.3: Average annual evapotranspiration values across Africa --------------------------------36Figure 4.4: Annual average trend of surface runoff values across Africa----------------------------37Figure 4.5: Annual runoff trends --------------------------------------------------------------------------38Figure 4.6: Groundwater recharge map of Africa-------------------------------------------------------40Figure 4.7: Groundwater recharge map of Africa (From Döll et al. 2002) --------------------------41Figure 4.8: Interception map of Africa--------------------------------------------------------------------42Figure 4.9: Surface evaporation map of Africa----------------------------------------------------------43Figure 4.10: Transpiration map of Africa ----------------------------------------------------------------44

5LIST OF TABLESTable 3.1: Summary of annual minimum, maximum and mean values of precipitation ----------29Table 3.2: Summary of PET values on a seasonal and annual base-----------------------------------30Table 3.3: Statistical values of seasonal and annual temperature values-----------------------------31Table 4.1: Summary of previously computed water balance values of Africa ----------------------47Table 4.2: Summary of computed water balance components----------------------------------------50

6SUMMARYNkezeah, A.J., 2010. A GIS Based Water Balance Study of Africa. M.Sc Thesis, VrijeUniversiteit Brussel, Belgium, 55pp.The GIS-based water balance Model WetSpass was applied to the continent of Africa toinvestigate the amount and variability of annual water balance components for an area ofapproximately 30 million Sq. Km. Average annual spatial patterns of surface runoff, actualevapotranspiration and ground water recharge were computed. Gridded 1km spatial resolutionmaps of elevation, soil, landuse/landcover, slope, and climatological variables were used. Theresults show variability in water balance components across the continent. These changescorrelate with local and regional changes of the parameters above.Estimates of Evapotranspiration (ET) go beyond 1000mm/yr around equatorial Africa. The aridregions of the Sahara and the western tip of southern Africa fall below 100mm/yr. In the semiarid subtropics of southern Africa the value ranges between 200 to 700mm/yr. Very little or nosurface runoff occurs around the northern Sahel except for the mountainous areas further northwest with values of 10-50mm/yr. Higher values of 100-400mm/yr occur around the ruggedterrain of east Africa. The groundwater recharge estimates are higher around the humid regionsof the tropics than further north and south. This obviously is related to higher amounts of rainfallthat this region receives annually.Generally there is a good agreement between the calculations of this study and others that havebeen carried out with other approaches/ methods e.g. Nicholson et al. (1997). The average annualsurface runoff for Africa is computed at 57mm/yr, ET 588mm/yr, groundwater recharge34mm/yr. interception 104mm/yr, transpiration 383mm/yr and soil evaporation 72mm/yr.While these values are not exact but only estimates, they do provide useful insights on thevariability of these critically important water balance components across the continent.

7CHAPTER 1INTRODUCTION1.1. GENERAL INTRODUCTIONWater is a resource of critical importance for sustaining life on this planet. In Africa especially,where a greater part of the population depend on agriculture as a source of livelihood, theimperative to properly manage water resources has become even more demanding. The rapidpopulation growth and climatic changes throughout the continent in recent years have furtherhighlighted this concern.While there is a growing demand for water resources in Africa, there is limitation to currentmanagement arising from amongst others lack of sufficient knowledge about the resource. Theestimation and quantification of hydrologic parameters such as surface runoff, actualevapotranspiration and ground water recharge are key factors to the sustainable management ofwater resources. Unfortunately, parameters such as evapotranspiration or runoff are rarelymeasured directly (Nicholson et al., 1997). The few measurements that are available are at localor basin scale and out of scale with the current reality. Evapotranspiration is generally calculatedfrom empirical formulas or energy balance approaches. Runoff is usually calculated as aresidual. The problem for Africa is the general lack of data for continental scale comparisons ofwater cycle studies.With the advent of Geographic Information Systems (GIS), physical-based hydrologic modelinghas become important in contemporary hydrology for assessing these parameters as well as theimpact of human intervention and/or possible climatic change on basin hydrology and waterresources (Alemaw and Chaoka, 2003). Information gathered from these models can greatlyenhance the decision making process in these regions. Unfortunately, poor knowledge aboutthese technologies in addition to the expensive nature of some GIS software packages hasretarded progress on the Continent as a whole. However, most Governments have begundedicating many resources in acquiring these software and hardware packages for researchpurposes. This research is partly motivated by these policy changes.

81.2. OBJECTIVE OF THE RESEARCHThe specific objectives of this research are threefold:1. To estimate in quantitative terms the annual average water balance components (surfacerunoff, actual evapotranspiration, groundwater recharge, soil evaporation etc) for theentire continent of Africa using a GIS-based approach.2. To present a general overview of the variability of these components across Africa.3. To demonstrate that despite the general lack of hydrological datasets on the continentresearch on the water cycle is still possible with the few data available.

91.3. REPORT OVERVIEWFigure 1.1 shows a 4-stage summary of the steps followed to undertake this thesis.GENERAL STEPS- state problem- state researchobjectivesACTIVITY PROCEDURES / PRODUCTSINVOLVED- review literature - discuss with promoter & advisor- read journals & books- discuss with peers- build spatial datasets - search maps- download maps- topography- soil- temperature- precipitation, etc- process spatial data -modify data tomodel compatibleformat- analyze spatial data -set up WetSpass-run model- Grids of:- landuse- soil- topography- slope etc- report results- make conclusions- make recommendationsFigure 1.1: Flowchart of research methodology

101.4. ORGANISATION OF THE REPORTThis report has been organized into 5 chapters. Chapter 1 is a general introduction that outlinesthe research objectives and gives an overview of the methodology.Chapter 2 is a presentation of the review of some consulted literature on GIS-based hydrologicalmodelling. It includes details of the WetSpass methodology employed in the analysis of the datafor this research.Chapter 3 deals with the materials and methods.Chapter 4 is a presentation of the results of the analysis accompanied by some discussion.Finally the report ends with chapter 5 which draws some conclusions and recommendations.

11CHAPTER 2LITERATURE REVIEW2.1. GIS-BASED HYDROLOGICAL MODELLINGGIS is a system of hardware, software and procedures designed to support the capture,management, manipulation, analysis, modeling and display of spatially referenced data forsolving complex planning and management problems (Rhind, 1989).Owing to its sophisticated analysis and graphic capabilities, GIS has become a powerful tool inhydrological modeling. Almoutaz (2002) highlights some of its capabilities to include the abilityto:• Make the information in the data base available in the same screen during datapreparation.• Save and analyze spatial data and results as any GIS layers.• Provide a graphical error check in continuous bases.• Integrate data layers and performs spatial operations on data in a flexible manner.Some random citing is made of some GIS-based water balance modelling approaches.Jyrkama et al. (2002) developed a methodology for incorporating the hydrologic model HELP3to generate a physically based and highly detailed recharge boundary condition for groundwatermodelling. The model uses daily precipitation and temperature records in addition to landuse/landcover and soil data.Stone et al. (2001) used a method that involves the combination of GIS analysis, empiricalsurface runoff modelling and water balance calculations to estimate the groundwater rechargewithin the hydrographic basins in the Great Basin region of the South West United States.

14independent water balances. Thus the total water balance of a given area is calculated as asummation of the water balances of each raster cell as shown by the following equations.ET raster = a v ET v + a s E s + a o E o + a i E i (2.1)S raster = a v S v + a s S s + a o S o + a i S i (2.2)R raster = a v R v + a s R s + a o R o + a i R i (2.3)Where ET raster , S raster , R raster are the total evapotranspiration, surface runoff, and groundwaterrecharge of a raster cell respectively, each having a vegetated, bare-soil, open-water andimpervious area component denoted by a v , a s , a o , and a i , respectively.

16dependency on soil and vegetation type. In the second step, actual surface runoff is calculatedfrom the S v-pot by considering the differences in precipitation intensities in relation to soilinfiltration capacitiesS v =C Hor S v – pot (2.6)Where, C Hor is a coefficient that parameterizes the fraction of seasonal precipitation contributingto the Hortonian overland flow. For groundwater discharge areas C Hor is equal to 1 since allintensities of precipitation contribute to surface runoff. On the other hand, only high intensitystorms can generate surface runoff in infiltration areas.EVAPOTRANSPIRATIONThis refers to the sum of evaporation and plant transpiration from the Earth's land surface toatmosphere. Evaporation accounts for the movement of water to the atmosphere from sourcessuch as the soil, canopy interception, and water bodies. Transpiration accounts for the movementof water within a plant and the subsequent loss of water as vapor through stomata in its leaves.In order to obtain a seasonal distributed evapotranspiration value, WetSpass converts the openwater evaporation value to a reference transpiration value ( Federer, 1979) based on a vegetationcoefficient.T rv = cE o (2.7)Where, T rv is the reference transpiration of a vegetated surface [LT -1 ]E o is the potential evaporation of open water [LT -1 ], and

17c is the vegetation coefficient [-].The vegetation coefficient (c) is determined as the ratio of the reference vegetation transpirationas given by the Penman-Monteith equation.1+γc = Λ⎛ r+⎜ +c1γ1Λ⎝ ra⎞⎟⎠(2.8)Where, γ is the psychrometric constant [ML -1 T -2 C -1 ]Λ , is the slope of the first derivative of the saturated vapour pressure curve (slope of saturatedvapour pressure at the prevailing air temperature) [ML -1 T -2 C -1 ]r c is the canopy resistance [TL -1 ] andr a is the aerodynamic resistance [TL -1 ] and given by the equation:2r 1 ⎡ z ⎤a− da= ln2 ⎢ ⎥k ua⎣ z(2.9)o ⎦Where k is the Von Karman constant = 0.4 [-]u a is the wind speed [LT -1 ] at the measurement level z a = 2md is the zero plane displacement length [L], and

18z 0 is the roughness length for the vegetation or soil [L]For vegetated recharge areas, the actual transpiration (T v ) is equal to the reference transpirationdue to the reason that the availability of soil water is not limiting.T v = T rv if (G d –h t ) ≤ R d (2.10)Where G d is groundwater depth [L], h t is the tension saturated height [L], and R d is the rootingdepth [L].For a vegetated area where the groundwater level is below the root zone the actual transpirationis given by:T v = f(θ)T rv if (G d - h t ) > R d (2.11)f(θ) is a function of the water content. For a time variant simulation it is defined as:f (θ) = 1- a 1w/Trvwith w = P + ( θ fc - θ pwp )R d (2.12)Where a 1 is the calibrated parameter related to sand content of a soil [-], w is the water availablefor transpiration [LT -1 ], and θ fc - θ pwp is the plant available water content [T -1 ] and stated as thedifference in water content at field capacity and at permanent wilting point.

19GROUNDWATER RECHARGERecharge refers to the process where water moves downward from surface water to groundwater(vadose) zone below plant roots. WetSpass calculates groundwater recharge as a residual termof the water balance. That is:R v = P-S v - ET v -I (2.13)ET v is the actual evapotranspiraton [LT -1 ] given as the sum of transpiration T v and E s (theevaporation from the bare soil in between the vegetation)By applying the above equations and relations spatially distributed recharge can thus beestimated from the vegetation type, soil type, slope, groundwater depth, precipitation, potentialevapotranspiration, temperature and wind speed.WATER BALANCE FOR BARE SOIL, OPEN WATER AND IMPERVIOUS SURFACESRecharge is estimated from the water balance for bare soil as:P = S s + E s + R s (2.14)Where P is the precipitation , S s, is the surface runoff, E s is the actual evapotranspiration, and R s isthe groundwater recharge. All have the unit [LT- 1 ] Equation (2.14) is the same as Equation (2.4)except that there is no interception and transpiration term. The ET v in this case becomes E s . Thesurface runoff is simulated in a similar way to the vegetated area fraction in two stages. In the

20first stage, the potential surface runoff, S v – pot , is calculated and in the second stage, the actualsurface runoff is calculated using Equation (2.6)The water balance for the open water fraction of a pixel is defined as:P = S o + E o + R o (2.15)Where P is the precipitation, E o is the evaporation from the water surface, S o is the part ofprecipitation that runs over the water surface and R o is the groundwater recharge from openwater and all have unit of [LT -1 ]Finally the water balance for impervious surfaces is also given by:P = S i + E i + R i (2.16)Where the index i refer to the impervious surfaces.

21CHAPTER 3MATERIALS AND METHODS3.1. DESCRIPTION OF STUDY AREAAfrica is the second largest continent after Asia covering a total surface area of about 30.2million km 2 (11.7 million sq miles) (Wikipedia, 2010). The study area is bounded by Longitudes-17.5 o W and 51.5 o E and Latitudes 37.4 o N and -34.8 o S. For reasons of lack of complete data theIsland region of Madagascar was masked out. Africa’s relief is characterized by two broadelevated regions of eastern and southern Africa having an average topography of 1015m (asl)while a more subdued low-lying area dominates the central western region with an averageelevation of 450m (asl). Both regions are punctuated in places by elevated areas of about 2000-5000m e.g. Mt Cameroon (4095m) and Mt Kilimanjaro (5893m). Africa straddles the equatorand encompasses numerous climate areas. The central region has a tropical rain forest climate.Here, the average annual temperature is about 26.7° C and the average annual rainfall is about1780 mm/yr. The north and south of the equator is characterized by a drier steppe climate zonehaving an average annual rainfall between 250 and 500mm/yr. The arid regions such as theSahara in the north, the Horn of Africa in the east, and the Kalahari and Namib deserts in thesouthwest have less than 250 mm of rainfall annually.Regions of tropical Africa located north of the equator experience the rainy season from April-September and the dry season from October-March while those south of the equator experiencethe rainy season from October-March. The dry season occurs from April-September. Potentialevapotranspiration which is the combined evaporation from the soil surface and transpirationfrom plants when the water supply is unlimited ranges averagely from 400-1400 on an annualscale (Balek, 1979).

223.2. DRAINAGE AND HYDROGEOLOGYAfrica has a vast array of drainage networks. Amongst the major ones are the river Nile whichdrains northeast and empties into the Mediterranean Sea. The Congo River drains much ofcentral Africa and empties into the Indian Ocean. Southern Africa is drained by the ZambeziRiver. Lake Chad constitutes one of the largest inland drainage areas of the continent. Othermajor lakes located in the east of Africa include Lake Tanganyika and Lake Victoria.The geological structure of Africa, with lowlands in the north, central and western regions andhighlands in the east and south, contributes to the uniformity of the groundwater regimes on thecontinent. Generally, large water-bearing units correspond to large geological bodies.Precambrian formations at low attitudes in West Africa and the high altitudes of the East Africanhighlands have water bearing aquifers in weathered, fractured or combined sections. Aquifers ofthe calcero-dolomitic and limestone-shale series are found south of the equator and parts of WestAfrica. The large sedimentary basins of central Africa are filled with soft deposits of Paleozoic,Jurassic and cretaceous origin (Balek, 1979). In the Sahel region intermediate and deep aquifersare found in the continental sandstones.3.3. THE INPUT DATAThis study was performed using the WetSpass model whose detail methodology is explainedabove (2.2). The model uses a combination of both gridded files and parameter tables (dbf files).Arcview (version 3.2) together with its spatial analyst extensions were used to process all of theinput data. The model was applied to the study area using grid cells of 1km x 1km spatialresolution. WetSpass operates on the bases of two seasonal datasets (winter and summer). Theyear was divided into two seasons with summer months running from April to September andwinter from October to March. Each season requires a separate gridded file of landuse,precipitation, potential evapotranspiration, wind speed, temperature, and groundwater depth. Themodel also uses separate dbf attribute tables for soil, landuse, and runoff coefficients. Due to thelack of monthly data for landuse, wind speed and groundwater depth, same maps were used asinput for both seasons. This of course makes assumption to the fact that these parameters stay thesame during both seasons. The following data sets were used as input to the model.

233.3.1. TOPOGRAPHY AND SLOPEThe topographical data used in this study for characterizing the horizontal hydrologicalcharacteristics of the land surface are derived from the HYDRO1k geographic databasedeveloped by the US Geological Survey's EROS Data Center in cooperation with the UnitedNations Environment Programme/Global Resource Information Database (UNEP/GRID) locatedin Sioux Falls, South Dakota USA. The slope data layer describes the maximum change in theelevations between each cell and its eight neighbors. The slope is expressed in integer degrees ofslope between 0 and 90.The elevation of the study area varies from below sea level to 5825m above sea level. Theaverage is approximately 651m (Fig 3.1). The slope varies from 0 to 44 percent with average0.80 percent (Fig 3.2).30° 30° 30° 10° 10° 30° 50° 70°10° 10°10° 10°Topography(m)Below sea level0 - 100100 - 250250 - 500500 - 10001000 - 15001500 - 20002000 - 2500>2500No DataNWE3000 0 3000 6000 MilesSFigure 3.1: Topographic Map of Africa30° 30° 10° 10° 30° 50° 70° 30°

30° 30° 30° 10° 10° 30° 50° 70°10° 10°24Slope of Topography0 - 0.5 ( % )0.5 - 11 - 1.51.5 - 22 - 55 - 77 - 10>10No DataNWE3000 0 3000 6000 MilesS10° 10°Figure 3.2: Slope Map of Africa30° 30° 10° 10° 30° 50° 70° 30°3.3.2. SOILThe soil map of major soil types in the area of study came from the United Nations’ FAO1.5,000,000 Digital Soil Map of the World. The soil code system that WetSpass uses is based onthe soil texture triangle developed by the United States Department of Agriculture (USDA).USDA soil classification takes into account the percentages of clay, silt and sand in the soil.Soils dominated by fine textures are classified as clay. Those with intermediate textures loam,coarse texture soils are sand. The original soil map consisting of 4904 soil classes based on FAOnomenclature were reclassified to the USDA soil system (Fig 3.3). We must admit here thaterrors of misclassification could have occurred during this painstaking and laborious process,however, without undermining one’s determination to achieve maximum accuracy the outcomeof such minor error will be minimal taking into account the 1km by 1km pixel size. The soil

25textural types were dominated by loam followed by sandy loam and sand in decreasing order(Fig 3.4).30°10°10°30° 30°10° 10°10° 10°30°50°70°Soil_reclasssandloamy sandsandy loamsilty loamloamsandy clay loamclay loamsilty clayclayNo DataNWE3000 0 3000 6000 MilesSFigure 3.3: USDA-based Soil Classification Map of Africa30° 30° 10° 10° 30° 50° 70° 30°Figure 3.4: Histogram plot of the soil classes in the catchment (x-axis = pixel number)

263.3.3. LANDUSE/LANDCOVERLand use information is an important input to the WetSpass model because each landuse typecontributes differently to the simulated output. For example there are variations in leave areaindexes and rooting depths from one type of vegetation to the next. Therefore, one would expectthe simulated outcome to vary with respect to these changes. The landuse/ landcover map wasobtained from the United States Geological Survey (http://edc2.usgs.gov/glcc/afdoc1_2.php).The data is a 1-km nominal spatial resolution map which is based on 1-km AVHRR dataspanning April 1992 through March 1993. The landuse / landcover classes are grouped intonineteen (Fig 3.5). WetSpass classes distinguish from each other on the basis of their effect onhydrological processes. The nineteen classes were therefore reclassified into one of fivedominant classes namely Grass, Forest, Crop, Bare soil and Open water (Fig 3.6). Each of theseclasses is characterized by quantitative attributes for vegetated, impervious, bare and open waterareas that sum up to one.30°10°10°30° 30°10° 10°10° 10°30°50°70°Landuseurban/buidup landdryland/cropland/pasturecropland/pasturecropland/grasslandcropland/wetlandgrasslandshrublandshrubland/grasslandsavanadeciduous broadleaf forestevergreen broad leaf forestevergreen needleleaf forestmixed forestwater bodiesherbaceous w etlandwooded wetlandbarren/sp arsely vegetatedNo DataNWE2000 0 2000 4000 MilesSFigure 3.5: Landuse/ Landcover Map of Africa.30° 30° 10° 10° 30° 50° 70° 30°

283.3.4 PRECIPITATION INPUT DATAThe precipitation data used for this study are based on the Rainfall Estimate (RFE) imagesprepared by the National Oceanic and Atmospheric Administration’s (NOAA) climate predictioncenter for the United States Agency for International Development (USAID) Faming earlyWarning System (FEWS). The images are archived and disseminated by USGS(http://earlywarning.usgs.gov/adds/index.php). They are compiled on a nominal 10 day time stepfrom thermal infra red images from Meteosat, acquired every 30 minutes. Each month iscomposed of 3 maps. These were added up to construct a single map for each month of the year.The compilations were done for a period of 3 years (2007-2009). The maps were then resampledfrom their initial 8km x 8km resolution to 1km x 1km. The average annual precipitation forAfrica from 2007-2009 is presented in Fig 3.8. Table 2 shows a summary statistics ofprecipitation values.30° 30° 30° 10° 10° 30° 50° 70°10° 10°10° 10°(mm)Rainfall_yrly1 - 100100 - 500500 - 10001000 - 15001500 - 2000>2000No DataNWE3000 0 3000 6000 MilesSFigure 3.8: Mean annual Rainfall Map of Africa (2007-2009)30° 30° 10° 10° 30° 50° 70° 30°

29Table 3.1 Summary of annual minimum, maximum and mean values of PrecipitationPrecipitation (mm)SeasonMinimum Maximum AverageWinter 0 1606 317Summer 0 2633 354Annual 0 3667 6713.3.5 POTENTIAL EVAPOTRANSPIRATION (PET)PET is a measure of the ability of the atmosphere to remove water through the process ofevaporation and transpiration. The FAO introduced the definition of PET as theevapotranspiration of a reference crop under optimal conditions, having the characteristics ofwell watered grass with an assumed height of 12 centimeters, a fixed surface resistance of 70seconds per meter and an albedo of 0.23 (Allen et al. 1998). Among several equations to estimatePET, the FAO application of the Penman-Monteith equation (Allen et al. 1998), is currentlywidely considered as a standard method (Walter et al. 2000). Data on referenceevapotranspiration used for this study is available from the Consortium for Spatial Information(CGIAR-CSI) Global Aridity Index (Global-Aridity) and Global Potential Evapotranspiration(Global-PET) Climate Database. The dataset is a representation of monthly average Potentialevapotranspiration estimates covering the period 1950-2000. It is distributed through theCGIAR-CSI GeoPortal (http://www.csi.cgiar.org). Fig 3.9 and Table 3.2 respectively show theaverage annual potential evapotranspiration map and some statistical values.

30° 30° 30° 10° 10° 30° 50° 70°10° 10°30(mm)Pet_annual0 - 100100 - 500500 - 10001000 - 15001500 - 20002000 - 3000No DataNWE3000 0 3000 6000 MilesS10° 10°Fig 3.9: Map of annual average Potential Evapotranspiration of Africa30° 30°10° 10° 30° 50° 70°Table 3.2 Summary of PET values on a seasonal and annual base30°PET (mm)SeasonMinimum Maximum AverageWinter 223 1285 874Summer 202 1463 971Annual 449 2383 1845

313.3.6. TEMPERATURETemperature maps were obtained from the online directory of spatial information maintained byUnited Nations Environment Programme (http://geodata.grid.unep.ch/). The data representestimates of monthly minimum and maximum temperatures for the period running from 1950-2000. Processing of these temperature images involved summing together the minimum andmaximum temperatures then taking an average between them. A presentation of derived statisticsof the temperature data is shown on Table 3.3.Table 3.3 Statistical values of seasonal and annual temperatureTemperature ( o C)SeasonMinimum Maximum AverageWinter -5 30 21Summer -7 34 24Annual -6 29 223.3.7 DBF PARAMETER TABLESLanduse, parameter tables were prepared similar to that of Batelaan and Woldeamlak (2003).Each of the 19 landuse/landcover type were re-assigned as either crop, forest, grass, baresoil oropen water and given numerical IDs from 1-5 (Fig 3.6) . The table also contain values for rootingdepth, leaf area index and vegetation height. The soil attribute table and runoff coefficient ofBatelaan and Woldeamlak were used after modification to tie with our data.

32CHAPTER 4RESULTS AND DISCUSSION4.1. INTRODUCTIONIn addition to computing the long-term average spatially distributed recharge of a basin,WetSpass also simulates the following out put on both seasonal and annual basis:- Evapotranspiration- Runoff- Interception- Transpiration- Soil evaporation, and- Error in water balance.Annual variations of actual evapotranspiration, runoff, groundwater recharge, soil evaporation,interception and transpiration across the continent will be highlighted and the results comparedwith those of previous researchers on the subject. A tabular summary of the seasonal values willalso be presented.4.2. YEARLY RESULTSThe simulation process took approximately 5-6 hours to complete. The following yearly out putmaps were produced:4.2.1. EVAPOTRANSPIRATION (ET)ET is a key factor in water balance computations. The surface of vegetation provides atemporary storage for rainfall water, as an interception. It is then lifted back to the air in the formof evaporation. Plants also loose water through transpiration at the leave surface. WetSpasscalculates the total actual ET as the sum of the evaporation of intercepted water by vegetation,transpiration and evaporation from bare soil in between the vegetation cover.

33The simulated values of the annual evapotranspiration for Africa are presented in Fig 4.1The evapotranspiration values range from 0 to 2162mm/yr with an average of 588mm/yr andstandard deviation of 455mm. The result shows a fluctuation in evapotranspiration that correlateswith vegetation cover and annual rainfall. In the Sahel region which is mostly barren or sparselyvegetated, ET values are generally below 100mm/yr. Here the intense penetration of solar heatenergy favours evaporation from the soil surface but not transpiration since the vegetation coveris sparse.On the contrary regions around the tropics located approximately 10 o N to 10 o S, have annualevapotranspiration that exceeds 1000mm/yr. At the humid zone around the equator in West-Central Africa, where there are three varieties of forest types: tropical rainforest, mixeddeciduous forest and savannah forest, ET values exceed 1500mm/yr, reaching up to 2000mm/yrin the wettest areas. The eastern and southern margins do show a reduction of theevapotranspiration value generally between 500 to 700mm/yr. Further eastwards around the hornof Africa the value drops further to the range 100- 250 mm per year reflecting the aridconditions of these areas. In the semi arid subtropics of southern Africa, the values range from200 to 700mm/yr.

30° 30° 10° 10° 30° 50° 30°34Etyear0 - 100100 - 200200 - 500500 - 750750 - 10001000 - 15001500 - 20002000 - 3000No Data70°(mm)10° 10°NWE3000 0 3000 6000 MilesS10° 10°Figure 4.1: Average annual Evapotranspiration Map of Africa30° 30° 10° 10° 30° 50° 70° 30°

Figure 4.2: Average Annual Evapotranspiration Map of Africa (From Nicholson et al., 1997)35

30° 30° 10° 10° 30° 50° 30°36Et_fao0 - 100100 - 200200 - 500500 - 750750 - 10001000 - 15001500 - 20002000 - 3000No Data70°(mm)10° 10°NWE3000 0 3000 6000 MilesS10° 10°Fig 4.3: Average annual Evapotranspiration Map of Africa (From FAO, 2010).30° 30° 10° 10° 30° 50° 70° 30°4.2.2. SURFACE RUNOFFWetSpass estimates surface runoff by using runoff coefficients. These coefficients are based onvegetation type, soil texture class and slope value. The simulated runoff ranges from 0-1205mm/yr. The average value is 57mm/yr (Fig 4.4). The Sahel regions (approx. 15 o N) havevery little or no surface runoff. Values here are generally less than 10mm/yr except for the littlemountainous area further North West which has runoff of between 10-50mm/yr. Some parts ofthe horn of Africa also have very low runoff values of 0-10mm/yr. In the semi arid regions ofsouthern Africa runoff ranges between 50mm to 200mm/yr decreasing to less than 10mm peryear in the desert landscapes around Namibia. Within the tropical latitudes the range is more orless between 100 to 500mm per annum.

30° 30° 10° 10° 30° 50° 30°37Royear0 - 11 - 1010 - 5050 - 100100 - 200200 - 500500 - 1500No Data70°(mm)10° 10°NWE3000 0 3000 6000 MilesS10° 10°Figure 4.4: Annual average Runoff Map of Africa.30° 30° 10° 10° 30° 50° 70° 30°

Figure 4.5: Annual average Runoff Map of Africa (From Nicholson at al., 1997)38

394.2.3. GROUNDWATER RECHARGERecharge is a naturally occurring process whereby permeable soil or rock allows water toreadily seep into the aquifer. This takes place intermittently during and immediately followingperiods of rain. This depends on the rate and duration of rainfall, the conditions at the upper landsurface boundary, soil moisture conditions, the water table depth and the soil type. Monitoring ofgroundwater recharge is very important because it allows for estimation of its temporalvariability and areal distribution.The estimated groundwater recharge simulated by WetSpass is presented on Fig 4.6. The valuesrun from 0-1856mm/yr with means of 34mm/yr and standard deviation 107mm. The variabilityof groundwater recharge is influenced by precipitation and runoff. The equatorial region appearsto be a high groundwater recharge area. In the drier areas, most of the little short annual rainfallcan not infiltrate to the vadose zone. Higher values are simulated for regions with lowtopography and permeable soils such as the Central and Western parts of Africa. The relief hereis gentler than in the eastern and southern parts. Normally, the recharge around the Congo basinshould have been much higher. This is certainly not the case probably because the thick tropicalrainforest sucks up much of the water that comes from precipitation and transpires it back to theatmosphere. Also, the huge forest canopy intercepts much of the rainfall from reaching the soilsurface preventing it from infiltrating into the aquifer.

30° 30° 10° 10° 30° 50° 30°40Reyear0 - 11 - 1010 - 2020 - 5050 - 200200 - 400400 - 2000No Data70°(mm)10° 10°NWE3000 0 3000 6000 MilesS10° 10°Figure 4.6: Groundwater recharge Map of Africa30° 30° 10° 10° 30° 50° 70° 30°

41Figure 4.7: Groundwater recharge Map of Africa (From Döll et al., 2002)4.2.4. INTERCEPTIONThe vegetation cover significantly affects interception. Much of the total annual rainfall receivedby the forest never reaches the forest floor. Instead it is intercepted retained on vegetation for ashort duration, absorbed by plants, or evaporated from the canopy. Therefore, vegetation servesas a bridge between the gap of rainfall and infiltration into the soil. Interception is very muchassociated with vegetation and varies with the forest type as shown by the result of this analysis(Fig 4.8). Generally, the values range from 0 mm to 1077mm/yr. The average is 104mm/yr.Regions of the tropical rainforest have the highest values. The result shows that the evergreenbroadleaf forest typically intercepts as much as 400-700mm/yr of the total annual precipitation.The desert regions of the Sahara, the horn of Africa and Southern Africa intercepts very little orno rainfall throughout the year. Values of between 0 and 120mm/yr of rainfall are interceptedhere. Interception by forest cover is more significant to water balance computation than othervegetative covers as they tend to intercept more rainfall.

30° 30° 10° 10° 30° 50° 30°42Inyear0 - 11 - 1010 - 5050 - 100100 - 200200 - 500500 - 1100No Data70°(mm)10° 10°NWE3000 0 3000 6000 MilesS10° 10°Figure 4.8: Interception Map of Africa30° 30° 10° 10° 30° 50° 70° 30°4.2.5. SOIL EVAPORATIONEvaporation is the process whereby liquid water is converted to water vapour then removed fromthe evaporating surface. The annual values of the simulated soil evaporation are shown on (Fig4.9). The trend shows higher values of 100mm to about 500mm per year for arid regions of theSahara, eastern Africa and the Namib and Kalahari deserts of southern Africa compared to therest of the continent.This trend is not surprising because these areas are characterized by:• Very high daily and seasonal temperature variations,• Low relative humidity,• High evaporation intensity,• Strong dry winds, and,

43• Intense solar radiation.All of which help speed up evaporation from these arid land surfaces.On the contrary, lower values of soil evaporation mark the tropical regions with average valuesranging from about 0 to 10mm per year. This should most likely be accounted for by the degreeof shading of the soil surface by the forest vegetation canopy that occupies a huge fraction of thisarea. The high humidity of the air as well as the lower wind speed (

70°(mm)444.2.6. TRANSPIRATIONTranspiration is the vaporization of liquid water contained in plant tissues. This mainly occursthrough the stem and leaf stomata. Transpiration, like direct evaporation, depends on factors suchas radiation, air temperature, air humidity and wind speed. In addition, the soil water content andthe ability of the soil to conduct water upwards play a role. The length of the plant’s root systemalso determines the transpiration rate. The vegetation type is equally important in assessingtranspiration.The average annual transpiration map of Africa is shown below (Fig 5). Generally, the valuesrange from 0-1600mm per year. High values of between 700 to 1000mm are located around theCongo basin dominated by is forests. The barren and sparsely vegetated regions of northern,eastern and southern Africa have low values of less than 100mm per year.30° 30° 30° 10° 10° 30° 50°10° 10°10° 10°Tryear0 - 1010 - 5050 - 200200 - 500500 - 10001000 - 1700No DataNWE3000 0 3000 6000 MilesSFigure 4.10: Transpiration Map of Africa30° 30° 10° 10° 30° 50° 70° 30°

454.2.7. COMPARISON OF RESULTS WITH THOSE FROM OTHER STUDIES.Although this analysis did not utilize the same datasets employed by previous workers, we dofind it interesting to highlight some agreements or disagreements between this work and those ofothers.MEAN MAPS :ET WetSpass vs. ET Nicholson et al. (1997)Nicholson’s results (Fig 4.2) show excellent agreement with ours. Their maximum ET valuesare found around the equatorial region and range from 1000-1500mm/yr. For us, this range is1000-2000mm/yr. Their core area with ET values in excess of 1000mm/yr agrees perfectlywith ours. In the central part of the Sahel about 15 0 N both their calculations and ours give500mm/yr. Closer to the Southern limits of the Sahara located about 18 0 N, both studies alsoagree on a value of 200mm/yr. The same value occurs around the Namid desert of SouthernAfrica. The southern edge of the equatorial maximum (about 5 0 S) shows a range from 750-1000mm/yr on both maps.ET WetSpass vs. ET FAOThe results of this study also perfectly align with those of FAO, (FAO, 2010) Fig 4.3. Theirannual average evapotranspiration reach the 2000mm/yr value around the equatorial region.The same is the case with ours. They also had ET values of 200mm/yr and 500mm/yrrespectively around 18 0 N and 15 0 N Latitudes. Around the Namid desert the 200mm/yr valueis also present on both maps. The southern limits of the equatorial maximum do show valuesof 750-1000mm/yr for both studies.Surface runoff WetSpass vs. Nicholson et al. (1997)Runoff values don’t seem to agree perfectly for both cases. Generally, the values estimatedby our model are lower. For example the equatorial region has values of runoff within therange 200-500mm/yr on their map while with us this region has runoff of only 10-50mm/yr.Regions having such high runoff values occur only on the eastern part of Africa and few

46areas of Cental-West Africa. The high runoff values on our map around east and centralAfrica is justified by its higher elevation. At the arid regions where runoff is almostnegligible coincide on both maps. South of the Sahel, around 12 0 – 15 0 N, the situation is stillsimilar with values from 10-50mm/yr. It would therefore appear that the region with the mostdiscrepancy is the area around the equator. One would tend to agree more with their higherestimates around the Congo basin because the soils there tend to be heavier owing to themuch higher precipitation they receive. This kind of texture would generally act againstinfiltration thus favouring runoff.Groundwater rechargeWetSpass vs. Döll et al. (2002)Our groundwater recharge map seems not to perfectly align with that of Döll et al. (2002).Though the ranges of recharge values do agree it is the exact location that changes. The highregions of recharge with values of 400-2000mm/yr seem to have been shifted a bit awayfrom the area around the equator (Congo and Gabon) toward the West of Africa aroundCameroon or Nigeria on our map (Fig 4.6). The other regions with lower recharges of lessthan 50mm/yr do however, agree on both maps.NUMERICAL VALUESSeveral attempts have been made by various researchers to numerically quantify the longtermannual values of Precipitation (P), Evapotranspiration (ET) and Surface runoff (Ro) forthe Continent of Africa.

47Shahin, (2002) has summarized these estimates as follows:Table 4.1: Summary of previously computed water balance values of Africa (From Shahin,2000)Data sourceP(mm)ET(mm)Ro(mm)Runoffcoef. (r)%Kalinin, (1971) 728 525 203 28.0Baumgartner and Reichel, (1975) 696 582 114 16.4Korzun, (1978) 740 587 153 20.7L’vovich, (1979) 686 547 139 20.3Willmott et al. (1985) 639 492 114 22.5Budyoko, (1986) 720 580 140 19.4Our value from WetSpass 671 588 57 8.4Previous estimates for precipitation and evapotranspiration closely tie with the results of thiswork. The case however, is somewhat different with runoff. Our model appears to haveunderestimated the annual average runoff. The runoff coefficient has been computed as theratio of runoff to precipitation. Higher coefficients imply limited loss from infiltration andtherefore more runoff.4.3. SEASONAL RESULTSWetspass also simulated average estimates of runoff, evapotranspiration, groundwater recharge,soil evaporation, interception and transpiration on seasonal bases (winter and summer). The

48variations of these components are intricately linked with changes in landuse, soil texture, slopeand other hydrometeorological parameter during the course of the year. A summary of averagevalues of the water components are presented in table 4.4.4. AFRICA’S NET WATER BALANCEWater balance computation is essentially a representation of the net result of the inflow andoutflow of water. Precipitation is the most significant inflow component. The most importantoutflow components of water balance are surface runoff, evapotranspiration and groundwaterrecharge. An area of the world would have water surplus if it receives more rainfall than theamount that it looses mainly through the process of evapotranspiration. Similarly, regions withwater deficit would get fewer rains than the amount that they lost through evapotranspiration.Meanwhile, those which neither get surpluses nor deficits will experience some sort of a balance.A summary of the numerical contribution to this net balance of the various componentsdiscussed earlier is presented below.

49Table 4.2: Summary of computed water balance components for AfricaAnnualWinterSummerComponentMean(mm)Runoff (Ro) 57 98Evapotranspiration (Et) 588 455Interception (In) 104 120Transpiration (Tr) 383 369Soil evaporation (Se) 72 141Recharge (Re) 34 107Error water balance (Er)Precipitation (P) 671 541Water balance (WB) = P-Ro-Et-ReRunoff (Ro) 22 47Evapotranspiration (Et) 311 320Interception (In) 59 83Transpiration (Tr) 225 244Soil evaporation (Se) 12 30Recharge (Re) -13 76Error water balance (Er)Precipitation (P) 317 325Water balance (WB) = P-Ro-Et-ReRunoff (Ro) 35 66Evapotranspiration (Et) 276 322Interception (In) 45 67Transpiration (Tr) 158 253Soil evaporation (Se) 59 132Recharge (Re) 47 123Error water balance (Er)Precipitation (P) 354 411Water balance (WB) = P-Ro-Et-Restandard Dev.(mm)

50CHAPTER 5CONCLUSION AND RECOMMENDATIONS5.1. CONCLUSIONA GIS-based water balance study was carried out for the entire continent of Africa. Theobjectives were to numerically quantify the water balance components and report their variabilityfrom one region of this vast landscape to the next. To achieve this, the model WetSpass wasused. The model computes long-term average spatial patterns of surface runoff, actualevapotranspiration, ground water recharge, interception, transpiration and soil evaporation. Thesimulations were based on gridded 1km by 1km spatial resolution maps of soil texture,landuse/landcover, slope, Potential evapotranspiration, groundwater depth, precipitation,temperature and wind speed. The results show variability in water balance components thatcorrelate with local and regional changes of the above parameters across the continent.Evapotranspiration estimates were above 1000mm per year around equatorial Africa but fellbelow 100mm per year in the arid areas while the semi arid areas had values between 200 and700mm/yr. Runoff averages about 200 to 400/yr in the equatorial latitudes. This value drops toless than 50mm/yr in the semiarid landscapes of the continent. The recharge estimates are higherin regions which receive high amounts of rainfall annually with the equatorial regions havingmore recharge than the drier areas.Overall, the average annual surface runoff computed for Africa is 57mm per year. The averageevapotranspiration and recharge were 588mm and 34mm per annum respectively. Interceptionaverages 104mm/yr, transpiration 383mm/yr and soil evaporation 72mm/yr.The non-uniformity of the climate and changing topography, together with physiography andlanduse/landcover are responsible for this variation of water balance components.

51Generally there is a good agreement between the calculations of this study and those from otherse.g. Nicholson et al. (1997).Though these values are only estimates, they could be very useful to water policy makerstowards a more sustainable management of the resource both now and in the future.5.2 RECOMMENDATIONSThe results from this study are obtained on data sets with a scale factor of 1km by 1km pixelsize. This may be considered too large if one were to investigate hydrological processes ingreater detail.WetSpass is designed originally for use in temperate climates where attributes characterizing thesimulation conditions are based on temperate measurements. The situation may be different inthe tropics.What is feasible at the local scale where parameters may be derived from careful observations orcalibrated using observed data, is generally not feasible over an area as large as the entirecontinent of Africa.The absence of certain grids on seasonal time scale namely wind speed, depth to groundwatertable and landuse prompted same maps to be used for both seasons with the assumption thatthese parameters stay unchanged throughout the year. In reality, however, this may not be thecase. This is certainly one of the shortcomings of this analysis.This analysis was 100 percent based on input data that was freely available. This means ourresult can be improved upon by using higher quality data which, most of the time is priced.Despite the above shortcomings the research marks a positive beginning at the dawn of GISapproaches to problem solving on the continent.

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55CITED INTERNET SITESFAO Geonetwork of spatial data. Global Actual Evapotranspiration Maphttp://www.fao.org/geonetwork/srv/en/main.home (Accessed July 2010)Wikipedia general information on Africahttp://www.wikipedia.org/ (Accessed August 2010)CGIAR-CSI GeoPortal for PET datahttp://www.csi.cgiar.org (Accessed July 2010)United States Geological Survey. Information on Landuse/Landcover Map of Africa.http://edc2.usgs.gov/glcc/afdoc1_2.php (Accessed June 2010)