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Modelling the water balance of Lake Victoria (East Africa), part 1:
observational analysis
Inne Vanderkelen^1 , Nicole P. M. van Lipzig^2 , and Wim Thiery1,
(^1) Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Brussels, Belgium (^2) Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium (^3) Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland Correspondence to: Inne Vanderkelen ([email protected])
Abstract. Lake Victoria is the largest lake in Africa and one of the two major sources of the Nile River. The water level of Lake Victoria is determined by its water balance, consisting of precipitation on the lake, evaporation from the lake, inflow from tributary rivers and lake outflow, controlled by two hydropower dams. Due to scarcity of in-situ observations, previous estimates of individual water balance terms are characterised by substantial uncertainties, which makes that the water balance is 5 often not closed independently. Here we present a water balance model for Lake Victoria, using state-of-the-art remote sensing observations, high resolution reanalysis downscaling and outflow values recorded at the dam. The uncalibrated computation of the individual water balance terms yield lake level fluctuations that closely match the levels retrieved from satellite altimetry. Precipitation is the main cause of seasonal and inter-annual lake level fluctuations, and on average causes the lake level to rise from May to July and to fall from August to December. Finally, our results indicate that the 2004-2005 drop in lake level can be 10 attributed about half to a drought in the Lake Victoria Basin and about half to an enhanced outflow, highlighting the sensitivity of the lake level to human operations at the outflow dam.
1 Introduction
Lake Victoria is the largest freshwater lake in Africa - and the second largest in the world - with a surface area of 68 000 km^2 spanning over Kenya, Tanzania and Uganda. Lake Victoria directly sustains the 30 million people living in its Basin and 15 the 200 000 fishermen operating from its shores (Semazzi, 2011). Being one of the major sources of the Nile, Lake Victoria supports natural resources that impact the livelihood of over 300 million people living in the Nile Basin (Semazzi, 2011). Fluctuations in the water level of the lake are therefore of major importance, as a drop in lake level may have massive im- plications for the ability of local communities to access water, to collect food via fishing and to transport goods (Semazzi, 2011). Moreover, a decreased outflow due to declining lake levels may have major consequences downstream. The Nile river is 20 already under immense pressure of various competitive uses and social, political and legislative conditions (Taye et al., 2011). In addition, lake level fluctuations also influence the amount of outflow released by the dam and by consequence the amount of hydropower generated and energy available in the region.
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Lake level fluctuations are determined by the Water Balance (WB) of the lake. Precipitation on the lake and inflow from tributary rivers coming from the Lake Victoria Basin provide the input. Water is lost by evaporation from the lake surface and by lake outflow, which is controlled by the Nalubaale dam complex for hydropower located at Jinja, Uganda. Water level fluctuations of Lake Victoria are therefore controlled by both climatic conditions and human management. 5 Given the high societal importance, several studies have attempted to reconstruct historical variations in the water levels of Lake Victoria based on observations (Kite, 1981; Piper et al., 1986; Sene and Plinston, 1994; Yin and Nicholson, 1998; Tate et al., 2004; Awange et al., 2007a; Swenson and Wahr, 2009; Hassan and Jin, 2014). In these studies, the WB components are typically modelled using in situ observations from lake shore stations, whereas more recent studies use various remote 10 sensing data. The most recurring aim of Lake Victoria WB models is to explain major changes in lake levels. However, these estimates are characterised by susbtantial uncertainties due to the lack of observations on the lake itself and the sparsity of in situ observations on land. Moreover, in most studies, one WB component is often directly or indirectly derived from the WB residual, which imposes a closure of the balance a priori. These limitations make it difficult to assess the role of the different WB terms on the one hand, and to force the WB model with other spatio-temporal data, like climate simulations, on the other 15 hand.
Here, we build an observational Water Balance Model (WBM) to reconstruct the observed Lake Victoria water levels from in situ and high-quality satellite observations and a high-resolution reanalysis downscaling. For the first time we independently calculate the individual water balance components and evaluate the resulting lake level against observations obtained from 20 satellite altimetry. Based on the observational WBM input, the role of the different terms within the WB is analysed. Next, the observational WBM is evaluated by comparing the calculated WB terms to values found in literature on the one hand and by comparing the resulting lake levels with the observed ones on the other.
2 Previous water balance studies
Lake Victoria has a surface area of approximately 68 000 km^2 , spanning over two thirds of its basin area (258 000 km^2 including 25 the lake; Awange et al., 2007a). Lake Victoria is a relatively shallow lake, with a mean depth of 40 m and a maximum depth up to 90 m. The Lake Victoria Basin spans over Uganda, Kenya, Tanzania, Rwanda and Burundi (Fig. 1). The lake levels of Lake Victoria fluctuated up to 3 meters over the last 65 years (Fig. 2). The rapid rise in the years 1961-1964 could not be attributed to the construction of the dam in 1951, but is linked to unusual heavy precipitation in East Africa as a similar rise is observed in the levels of other African Great Lakes (Lake Albert, Malawi and Tanganyika) in the early 60s (Kite, 1981). The lake level 30 remained high after the rapid rise, which is consistent with a sustained high rainfall amounts (Sene and Plinston, 1994). From 2004 to 2007, the lake level strongly dropped. This decline could be partially related to an increased outflow at the new dam complex (see Sect. 2.4; Kull, 2006; Sutcliffe and Petersen, 2007).
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WB studies accounted for this by rescaling onshore measured precipitation based on a relationship obtained by solving the water balance for precipitation in a period that all other terms were known (Piper et al., 1986; Sene and Plinston, 1994; Tate et al., 2004), or by calculating lake precipitation from basin precipitation with a weighting factor based on a satellite analysis of cloud occurrence (Yin and Nicholson, 1998). More recent studies use satellite data for estimating precipitation on the lake, 5 providing temporally and spatially homogeneous observations. In the Lake Victoria Basin, Awange et al. (2007b); Swenson and Wahr (2009); Hassan and Jin (2014) used products of the Tropical Rainfall Measuring Mission (TRMM), which provides monthly precipitation rates.
2.2 Evaporation from the lake
Annual evaporation shows little change (Kite, 1981) and is often assumed to be constant in previous water balance model stud- 10 ies (Kite, 1981; Piper et al., 1986; Sene and Plinston, 1994; Tate et al., 2004; Smith and Semazzi, 2014), as water availability is unlimited above a lake surface and temperature in the tropics is assumed to be nearly constant. In early water balance studies, it is not possible to differentiate between underestimations in precipitation and overestimations of evaporation. This makes the estimation of evaporation somewhat arbitrary. On the eastern shores of the lake, higher lake evaporation is recorded, while on the islands and western shore evaporation rates are lower (Lake Victoria Basin Commission, 2006). 15 Evaporation from the lake surface is typically calculated by the energy balance using bulk formulae for sensible and latent heat flux (Yin and Nicholson, 1998; Swenson and Wahr, 2009), or by Penman’s equation (Piper et al., 1986; Yin and Nichol- son, 1998). Both methods require multiple climatic input data like surface water temperature, surface vapour pressure, solar radiation, wind speed and cloudiness over the lake. Early water balance calculations using the Penman method, like Piper et al. 20 (1986), estimate these parameters based on lake shore station data and tend to make simplistic assumptions on the parameters (Yin and Nicholson, 1998). However, the calculation of evaporation is very sensitive to assumptions on the meteorological parameters. The various estimations for annual evaporation therefore range from 1350 to 1743 mm/year (Yin and Nicholson, 1998). Nevertheless, the evaporation estimation of Piper et al. (1986) (1595 mm/year) remains widely used in other water balance studies (e.g. Sene and Plinston, 1994; Sutcliffe and Parks, 1999; Tate et al., 2004).
25 2.3 Lake inflow
Approximately 25 major rivers flow from the basin into Lake Victoria, contributing to the inflow in the lake. The largest trib- utary is the river Kagera in the southwest of the basin. The river contributes to around 30% of the total inflow in the lake (Sutcliffe and Parks, 1999). Tributaries from the northeast like the Nzoia, Yala, Sondu and Awach Kaboun, have a relatively fast runoff, because they come from regions with a high and prolonged rainy season and steep slopes (Tate et al., 2004). The 30 southeastern tributaries like the Mara and the Mbalangeti come from regions with lower rainfall and runoff, contributing to a greater variability in inflow (Tate et al., 2004). The region in the northwest contains a lot of wetlands and therefore contributes less runoff.
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According to earlier water balance studies, inflow from the basin accounts for 20 % of the total lake input, while direct precipitation on the lake accounts for 80 % (Awange et al., 2007a). River inflow was measured in different periods from the 1950s until the 1980s during an hydrometeorological survey initiated by the World Meteorological Organisation (WMO) (WMO, 1981). Based on the total runoff to the lake and the derived lake precipitation series, a relationship between runoff and 5 lake precipitation was established. During periods when not all tributaries are measured, early studies used this relationship to estimate river inflow across the ungauged perimeter of the lake from over-lake precipitation (Kite, 1981; Piper et al., 1986; Sene and Plinston, 1994; Yin and Nicholson, 1998). Tate et al. (2004) derived inflow from lake precipitation with a parametrisation that calculates the inflow of five major tributaries from their catchment rainfall using a linear regression. The specific catchment precipitation in turn is calculated from the lake precipitation. The inflow of the 5 major tributaries is scaled up to represent 10 the total inflow (Tate et al., 2004). Swenson and Wahr (2009) are the first to not only account for overland flow, but also for baseflow in the WB of Lake Victoria. As direct observations of surface runoff by remote sensing techniques were not available, surface runoff is modelled proportional to the precipitation derived from satellite observations. Subsurface flow or baseflow, which can be related to groundwater storage, is calculated using data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission.
15 2.4 Lake outflow
Lake Victoria’s outlet is located at Jinja in Uganda and is since 1951 controlled by the Nalubaale dam complex. Since then, the outflow is regulated by the Agreed Curve, a rating curve relating lake level and outlfow in natural conditions. This relation is quantified by Sene (2000) as: Qout = 66.3(L − 7 .96)^2.^01 (1)
20 with Qout, the outflow and L, the lake level. This dam operating rule was closely followed until 2000, when increasing power demands in Uganda led to the construction of a second dam (called Kiira), parallel with the Nalubaale dam (Kull, 2006). The combination of the two dams facilitated deviation from the Agreed Curve by releasing more water (Awange et al., 2007b; Kull, 2006; Sutcliffe and Petersen, 2007). Consequently, the lake outflow and lake level began to diverge, resulting in a declin- ing lake level (Owor et al., 2011). Both Kull (2006) and Sutcliffe and Petersen (2007) pointed out that measured fall in lake 25 levels could be attributed for approximately 50% to over-release next to a decrease in rainfall amounts. Most water balance studies calculate the outflow based on the Agreed Curve and lake levels measured at the Jinja dam (Piper et al., 1986; Sene and Plinston, 1994; Yin and Nicholson, 1998; Tate et al., 2004; Smith and Semazzi, 2014).
2.5 Uncertainty and limitations
30 In previous water balance studies, the major source of uncertainty is attributed to different terms of the water balance (Kite, 1981; Piper et al., 1986; Sene and Plinston, 1994; Yin and Nicholson, 1998; Tate et al., 2004; Swenson and Wahr, 2009; Smith and Semazzi, 2014). Here we quantify the uncertainty by computing the relative difference between the lowest and highest
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3.3 Outflow data
Measured outflow at the Nalubaale dam complex is a key variable for this study, but a complete time series could not be obtained. After 2000, the releases from the dam were higher than allowed according to the Agreed Curve. Because of these deviations from the political agreement between Egypt and Uganda, dam discharges are not publicly available (Kull, 2006). 5 Therefore, a time series of outflow data is compiled from different data sources (Kull, 2006; Lake Victoria Basin Commission, 2006, Fig. 8) to use as outflow input in the water balance model (Fig. 3). From 2006 on, there is no outflow data available. Therefore, outflow is kept constant at the last measured level to complete the time series. We also tested outflow values based on the Agreed Curve and constant outflow equal to the average outflow recorded over the period 1950 to 2006, but these approaches reduced the correspondence between modelled and observed lake levels during the period where no outflow values 10 are available (2006-2014).
Figure 3. Daily outflow time series compiled from different data sources: monthly measurements for 1950 to 1997, digitzed values from (Lake Victoria Basin Commission, 2006, Fig. 8) for 2000 to 2004, daily measurements for 2004 to 2006, constant outflow at the 2006-value for 2006 to 2014, due to the lack of measurement data.
3.4 Inflow following the CN method
Inflow by tributary rivers is calculated using the Soil Conservation Service-Curve Number method (hereafter denoted as the CN method; NEH4, 2004a). This method relates daily precipitation (P ) to daily runoff (R) through the Curve Number (CN), obtained based on an empirical model, with parameters associated to land use, hydrological soil types and antecedent hydro- 15 logical conditions. The calculated runoff represents direct runoff, consisting of Hortonian overland flow and saturation excess flow (NEH4, 2004c). It has been shown that simple runoff models like the CN method with a few input parameters can provide results that are at least as good as more complex rainfall-runoff models (Van den Putte et al., 2013). Here, the CN method is applied on pixel level: each pixel is assigned a CN value, after which the resulting pixel runoff is calculated based on the pixel precipitation. First, the CN of each pixel is determined based on the land cover and soil type of the pixel. The land cover is de-
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rived from the Global Land Cover 2000 (GLC 2000) project (Mayaux et al., 2003). The original GLC 2000 land cover classes are grouped and reclassified in more general classes (see table A5). Each of these general classes is subsequently coupled to a corresponding land cover class with known CNs (see table A1). This coupling is based on the classification of Maetens (2013) who reclassified similar land cover classes into CN classes. The effect of soil texture is also included in the CN method through 5 the hydrological soil group (NEH4, 2004b). Based on runoff potential and soil texture, four different hydrological soil groups are defined (table A2). In the study area, the hydrological soil groups are defined based on soil data from the Soil Atlas of Africa (Dewitte et al., 2013), classified according to the World Reference Base soil classification and converted to hydrological soil group based on table A6. Using table A3 based on the land cover and hydrological soil group, the CNs are determined for all pixels in the lake basin. Next to land cover and soil type, the CN is also dependent on the hydrological condition of a 10 certain pixel. This is taken into account by the Antecedent Moisture Condition (AMC), ranging from AMC I (dry) to AMC III (wet) (Ponce and Hawkins, 1996). The AMC of a certain pixel is determined based on the cumulative precipitation of the 5 past days, as defined in table A4 (Descheemaeker et al., 2008). In case of dry or wet conditions (AMC I and III), the CN is converted using equations 2 and 3, where CNII presents the first (default) calculated CN (Ponce and Hawkins, 1996).
CNI = 0 .427 + 0CN. 00573 II CN
II
15 CNIII = 2. 281 − CN 0. 01281 II CNII (3)
By implementing the AMC, the CN becomes time dependent. In the next step, the CN is used to calculate the maximum soil water retention parameter (S), the maximum amount of water that a soil can retain when runoff is started (equation 4) (NEH4, 2004c). Finally, the runoff (Rd) is calculated based on the daily precipitation (Pd) and maximum soil water retention parameter (S) (equation 5).
20 S = CN^25400 − 254 (4)
Rd = 0 if Pd > 0. 2 S Rd = (P Pdd−+0^0 ..^28 SS) 2 if Pd ≤ 0. 2 S
Total daily inflow is obtained by summing the calculated runoff of all basin pixels. As the pixels are large sized (about 7 by 7 km), it can be assumed that pixel runoff leaves the pixel by flowing in water channels. This assumption makes that runoff routing over the basin pixels is not necessary, as is the case with smaller pixel sizes (e.g. for pixels of 200 by 200 m; Moglen, 25 2001).
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Figure 4. Climatology from 1993 to 2014: annual precipitation derived from PERSIANN-CDR observations (a), evaporation from COSMO- CLM^2 model output (b) and runoff calculated with the CN method based on PERSIANN-CDR precipitation observations (c).
Figure 5. Climatology from 1993 to 2014: annual cycle of precipitation derived from PERSIANN-CDR observations (a), evaporation from COSMO-CLM^2 model output (b) and inflow calculated with the CN method based on PERSIANN-CDR precipitation observations (c). Note the different y-axis scales.
4.1 Terms of the water balance
4.1.1 Lake precipitation
The spatial distribution of the mean annual precipitation reveals that the northeastern part of the catchment receives most precipitation, while the western part receives the least (Fig. 4a). The seasonal cycle of precipitation over the lake surface shows 5 a bimodal distribution (Fig. 5a) with two distinct peaks from March-May and October-December corresponding to the two rainy seasons (Yang et al., 2015). Difference in precipitation between the dry and rainy seasons is high and can amount up to 8 mm day-1. Over the lake, a mean annual precipitation of 1525 mm year-1^ is recorded. This value lies in the broad range of values reported in the literature (table 1). Yin and Nicholson (1998) calculated a value of 1791 mm year-1^ for the period 1956-1978, while Swenson and Wahr (2009) obtained a value of 1166 mm year-1^ using satellite data for the period 2003-2007. 10 In this period, there was a drought over Lake Victoria and its basin, with falling lake levels as a consequence (Fig. 2). This
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can partly explain the lower precipitation value of Swenson and Wahr (2009). However, even if we calculate the mean annual precipitation over the lake from the PERSIANN-CDR data for the same period as Swenson and Wahr (2009), we still obtain a value which is substantially higher (1506 mm year-1^ from 2003 to 2007). The reason for these systematic differences could be attributed to the fact that Swenson and Wahr (2009) used TRMM observations with a monthly resolution. Compared to the 5 daily PERSIANN-CDR data set, TRMM may possibly underestimate precipitation.
Table 1. Calculated mean annual water balance terms and values found in literature in mm/year (inflow and outflow are given in mm/year over the lake surface area).
Precipitation Evaporation Inflow Outflow Period This study 1525 1539 480 462 1993- Yin and 1791 1551 (Energy balance) 338 524 1956- Nicholson (1998) 1743 (Pennman) Swenson and 1166 1784 804 558 2003- Wahr (2009)
4.1.2 Lake evaporation
Evaporation is not uniform over the lake, but ranges from higher values in the southeast to lower values in the northwest (Fig. 4b). This spatial pattern does not follow the pattern of observed lake temperature (Thiery et al., 2015), which is consistent with the idea that the spatial pattern of evaporation is primarily driven by the relative humidity rather than by temperature 10 (Thiery et al., 2014a, b). The annual cycle of lake evaporation demonstrates a small drop in May and a minimum from October to December (Fig. 5b). Compared to the other WB terms, lake evaporation has a relatively low interannual variability. This justifies the decision to use the ten-year climatology for each year in the WB calculations. The mean annual evaporation over the lake is 1521 mm year-1, which is consistent with the value of 1595 mm year-1^ calculated by Piper et al. (1986) and used in the studies of Sene and Plinston (1994) and Tate et al. (2004). Yin and Nicholson (1998) calculated evaporation values both with 15 an energy balance approach (1551 mm year-1) and with the Pennman method (1743 mm year-1). By comparing the evaporation estimates with the evaporation calculated as residual from the water balance, they concluded that the first estimation of 1551 mm year-1^ is the most reliable. Swenson and Wahr (2009) estimated an annual evaporation of 1784 mm year-1^ and stated that their estimate is closer to the second estimation (1743 mm year-1) of Yin and Nicholson (1998). However, the value calculated in this study (1521 mm year-1) lies closest to the first estimation (1551 mm year-1) of Yin and Nicholson (1998) which is in 20 line with the previous studies (table 1). Nevertheless, a complete comparison is not possible, as different study periods are considered and the calculation of evaporation is associated with large uncertainties. Furthermore, the spread in precipitation estimates appears to be larger (∼ 600 mm year-1) than the spread in evaporation estimates (∼ 200 mm year-1), which indicates that the uncertainty and temporal variability in lake precipitation estimates is higher than in lake evaporation estimates.
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variations around the zero line. Accumulated over the 1993-2014 period, lake precipitation represents 76 % and lake inflow represents 24 % of the total input. This is more or less in line with Awange et al. (2007a). who stated that inflow accounts for 20 % of the lake refill. The total output accumulated over 1993 to 2014 consists for 77 % of lake evaporation and 23 % of lake outflow. 5 The modelled lake level follows the observed lake level very well, notably representing the fluctuations up in 1998 and the severe drop in 2006-2007 (Fig. 9). From 2006, the modelled lake level slightly underestimates the lake level. This is likely due to the outflow values, which are not known from 2007 on, and which are set to the last known outflow measurement. Consider- ing the magnitude of the net input and output terms of the water balance (Fig. 8), a small bias in one of these terms could lead 10 to large variations in the lake level. Moreover, no tuning is performed to match the WBM outcome to the observed lake levels. Taking these elements into account, the close correspondence of the observed and modelled lake levels is remarkable.
A similar correspondence is found in the seasonal cycle of the lake level (Fig. 10), with on average the highest accumulation rates observed around the end of May and the beginning of June and the highest water loss around end of September and Oc- 15 tober. This behaviour is consistent with the seasonal cycle of the over-lake precipitation and inflow: the lake level rises during the two rainy seasons and falls again during the dry seasons. Likewise, the rainfall seasonality provides each year two seasonal recharges. The difference between the two accumulation peaks, the first around the end of January and the second around the end of May, can be attributed to the larger decrease during the main dry season in June, July and August than during the other dry season in February. Compared to the observed seasonal cycle, the modelled lake level change has a similar amplitude 20 and phase, but lags the observed cycle by about half a month from mid May until the beginning of February. This suggests that there is a certain retention period of the water in the basin during this part of the year, which is not taken into account in the WBM. Overall the amplitude of the seasonal cycle is small (less than 0.4 m) compared to the inter-annual variation of lake level fluctuations over the observational period (maximum difference up to 2.5 m). Inter-annual lake level fluctuations are mostly due to inter-annual differences in precipitation and outflow amounts, as evaporation is nearly constant each year of the 25 observed period.
The severe decline in lake levels from 2004 until the end of 2005, can be attributed to a drought combined with an enhanced dam outflow. During 2004 and 2005, the annual precipitation amount decreases with 13% compared to the mean precipitation during the whole study period. This decrease was part of a drought occurring in the entire region, leading to a decline in both 30 lake levels as well as total water storage measured with GRACE in three of the African Great Lakes (Lake Victoria, Tanganyika and Malawi) (Hassan and Jin, 2014). When the outflow follows the Agreed Curve (Eq. 1), dam releases are adjusted based on the current climatological conditions. If the Agreed Curve scenario would have been followed from 2004 until 2005, the outflow would have been 59 km^3 , instead of the observed 78 km^3. Consequently, the lake level would have declined by only 0.35 m rather than the recorded 0.69 m. Accordingly, 52 % of the decline can be attributed to a drought over Lake Victoria 35 and its basin. The remaining 48 % of lake level decline can be attributed to an enhanced dam outflow compared to the Agreed
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Curve protocol, as it is the only WB term that can be altered by human management. Kull (2006) did a similar analysis and found an average contribution of 55% of increased dam outflow to the lake level changes in 2004 and 2005. Also Sutcliffe and Petersen (2007) concluded that the measured lake level fall during the years 2000 to 2006 is about half due to over abstraction of lake outflow. Hence, these results support the findings in this study. 5
Figure 7. Seasonal cycle of the water balance terms for the period 1993-
Figure 8. Time series of the cumulative water balance terms and resulting lake level
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fully justify the use of the CN method in this study, the skill of the CN method could be assessed by evaluating the resulting runoff using spatially runoff data from another source, like observation-based gridded runoff estimates with global coverage, which may become available in the future (Gudmundsson and Seneviratne, 2016).
5 Despite the high skill of the observational WBM, the way of constructing input terms has still several limitations. First, the use of the COSMO-CLM^2 model data makes that the water balance model is not entirely based on observations, but uses also model data as input. As evaporation observations over the lake surface are lacking, COSMO-CLM^2 currently represents the best option for obtaining reliable evaporation estimates. By using the interactive lake model FLake and a boundary-layer scheme based on Monin-Obukov similarity theory, COSMO-CLM^2 provides a calculation of the LHF over the lake which 10 is more advanced than the methods used to calculate evaporation until now. Second, by using a static land cover map for the year 2000 to calculate the CN, potential effects of land cover changes are not included in the inflow calculation. Land cover changes can have a significant impact on the curve numbers and associated inflow (Melesse and Shih, 2002; Deshmukh et al., 2013). Finally, the WBM does not account for baseflow contributing to the inflow. By relating baseflow to groundwater storage based on data from GRACE, Swenson and Wahr (2009) concluded that subsurface flow may be as important as surface 15 runoff, contributing even slightly more to the inflow during their study period (2003-2007). They calculated a larger annual value for evaporation, which lies more closely to the second estimate of Yin and Nicholson (1998) (table 1), and recorded a lower precipitation compared to the estimates of this study and Yin and Nicholson (1998), which makes that their WB is also approximately closed. Owor et al. (2011) on the other hand, estimated the groundwater flow to account for less then 1 % of the total input (lake precipitation and inflow) based on in situ hydrogeological groundwater observations. Moreover, considering 20 that the WB is almost perfectly closed in this study, the assumption of a negligible baseflow appears to be justified.
6 Conclusions
State-of-the-art satellite observations, a high-resolution reanalysis downscaling and outflow values recorded at the dam permit- ted the construction of a Water Balance Model providing the lake level fluctuations of Lake Victoria. Lake levels are calculated on a daily basis based on over-lake precipitation from the PERSIANN-CDR remote sensing product, lake evaporation from 25 COSMO-CLM^2 reanalysis model output, inflow calculated with the Curve Number method based on PERSIANN-CDR basin precipitation and outflow recorded at the dam.
By quantifying the four water balance terms, we found values that lie in the broad range of values reported in literature. Our results indicate that precipitation and evaporation are the most important terms with 76 % of the input and 77 % of the out- 30 put, respectively. Inflow accounts for 24 % of the input and outflow for 23 % of the output. Lake level fluctuations are mainly caused by precipitation over Lake Victoria and its basin and by dam outflow. The annual cycle of precipitation with two distinct rainy seasons is reflected in lake levels variations on monthly time-scales, by causing the lake level to rise from May to July and to fall from August to December. Dam outflow, controlled by human operations, plays an important role on inter-annual
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time-scales. This could be illustrated by the lake level drop in 2004 and 2005, of which 52 % is due to a drought and 48 % due to an enhanced dam outflow compared to the Agreed Curve imposed by human operations. Finally, the comparison between the modelled and remotely sensed lake levels revealed that the constructed water balance model is able to closely represent the observed lake level fluctuations and seasonal cycle of Lake Victoria. 5 Here, the WB of Lake Victoria is modelled based on spatio-temporal data and independently from lake level observations. A major advantage of this approach is that it is now possible to force the WBM with climate simulations for the future, which show a decrease in annual precipitation amounts over Lake Victoria (Souverijns et al., 2016). In a next step, future projections of the evolution of Lake Victoria’s lake level will be made, which has never been done before. This is especially relevant given 10 the high societal importance of the future behaviour of the water levels. Changes in the water levels can have far reaching consequences for the people living in the basin, water availability downstream in the Nile Basin and for estimating the future potential for hydropower generation.
Code and data availability. The Water Balance Model code is publicly available at https://www.github.com/Ivanderkelen/WBM_LakeVictoria. PERSIANN-CDR data is freely available from NOAA (https://www.ncdc.noaa.gov/cdr/atmospheric/precipitation-persiann-cdr), the soil map 15 from the Soil Atlas of Africa (https://ec.europa.eu/jrc/en/publication/books/soil-atlas-africa), GLC2000 data from the JRC of the European Commission (http://forobs.jrc.ec.europa.eu/products/glc2000/glc2000.php), DAHITI lake level data from the Technical University of Mu- nich (http://dahiti.dgfi.tum.de/en/). Lake evaporation data from the COSMO-CLM^2 model output and the HYDROMET lake level series are available upon request.
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Table A2. Hydrological Soil Groups (HSG)
HSG Runoff potential Texture A Low Sand, loamy sand or sandy loam B Moderately low Silt loam or loam C Moderately high Sandy clay loam D High Clay loam, silty clay loam, sandy clay, silty clay or clay
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Table A3. CN for CN land cover classes and Hydrologic Soil Groups
CN A B C D Woods 36 60 73 79 Brush-brush-forbs-grass mixture with brush the major element 35 56 70 77 Pasture, grassland or range-continuous forage for grazing 49 69 79 84 Crops 64 74 81 85 Mosaic forest/cropland 50 67 77 82 Fallow 77 86 91 94 Cities 59 74 82 86 Water bodies 100 100 100 100
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