Previous water budget studies over Lake Victoria basin have shown that there is near balance between rainfall and evaporation and that the variability of Lake Victoria levels is determined virtually entirely by changes in rainfall since evaporation is nearly constant. The variability of rainfall over East Africa is dominated by El Niño-Southern Oscillation (ENSO); however, the second and third most dominant rainfall climate modes also account for significant variability across the region. The relationship between ENSO and other significant modes of precipitation variability with Lake Victoria levels is nonlinear. This relationship should be studied to determine which modes need to be accurately modeled in order to accurately model Lake Victoria levels, which are important to the hydroelectric industry in East Africa. The objective of this analysis is to estimate the relative contributions of the dominant modes of annual precipitation variability to the modulation of Lake Victoria levels for the present day (1950–2012). The first mode of annual rainfall variability accounts for most of the variability in Lake Victoria levels, while the effects of the second and third modes are negligible even though these modes are also significant over the region.
Lake Victoria is approximately 67,000 km2 in area but shallow with an average depth of only 40 m. It is territorially administrated by Tanzania, Uganda, and Kenya and is a major source of income in those countries producing $3-4 billion annually. The lake basin provides fresh water and hydroelectric power for over 30 million people who live in the Lake Victoria basin, as well as supporting agriculture, fisheries, trade, and tourism. The lake is the primary source of the White Nile which flows through Lake Kyoga in Uganda and Lake Albert into Sudan, where it joins with the Blue Nile at Khartoum to form the Nile River, which supports the livelihood of over 300 million people.
The climate of the lake basin is dominated by the bimodal signature of the intertropical convergence zone (ITCZ). It is well known that El Niño-Southern Oscillation (ENSO) and the Indian Ocean Zonal Mode (IOZM) dominate the interannnual climate variability of Eastern Africa [
The spatial distribution of rainfall over Lake Victoria basin is complex, consisting of a wave-like dry-wet-dry-wet climatological rainfall pattern. This climatic pattern, its variability, and associated atmospheric and marine conditions determine the performance of the primary socioeconomic activities across the basin, including agriculture, fisheries, and hydroelectric power generation. The productivity of hydroelectric dams along the Nile River is, to a large extent, determined by the level of Lake Victoria, which is primarily dictated by the rainfall variability over the Lake Victoria basin. Because of this, it is important to determine the modes of variability in precipitation, which dominate the variability in lake levels.
Many studies [
While previous studies in this region have examined either the dominant modes of climate variability or the impact of total rainfall on Lake Victoria levels, the primary objective of this study is to estimate the relative contributions of the dominant modes of annual precipitation variability to the modulation of Lake Victoria levels. The relationship between precipitation over Lake Victoria basin and lake levels is nonlinear. This relationship should be studied to determine which modes need to be accurately modeled in order to accurately model Lake Victoria levels, which are important to the hydroelectric industry in East Africa.
The primary method of investigation is a water balance model in combination with application of EOF analysis of the regional Eastern Africa rainfall using the University of East Anglia Climate Research Unit (CRU) version 3.21 [
Empirical orthogonal function (EOF) analysis [
Reconstruction of variables utilizing a systematic inclusion of a subset of eigenmodes which account for most of the data variability has been utilized to filter out statistical noise by reconstructing the data using a reduced number of significant EOF modes. We reconstructed rainfall over the lake basin with only one significant model of variability at a time to examine the contributions of the individual modes to Lake Victoria levels. Since the water balance model used in this study takes in annual precipitation, precipitation is only reconstructed for the annual modes of variability. Our data reconstruction procedure is similar to the approach used in Weickmann and Chervin [
Tate et al. [
The change in lake level is calculated as follows:
Precipitation over the lake, the dominant source of water for Lake Victoria, is assumed to be a nonlinear function of the average rain gauge precipitation at six stations (Jinja, Entebbe, Kisumu, Musoma, Bukoba, and Mwanza) along the perimeter of the lake (Figure
Location of stations used in water balance model.
Lake Victoria levels calculated using the water balance model are shown as a black line in Figure
Comparison of Lake Victoria levels modeled using CRU 3.21 (black with asterisks) and observed lake levels (purple with circles).
The first mode of annual variability accounts for 37.7 percent of variability. The loading (Figure
EOF loadings and time series for first ((a), (b)), second ((c), (d)), and third ((e), (f)) modes of variability for annual precipitation.
Lake levels calculated using precipitation reconstructed from individual significant modes of annual precipitation variability, compared to the level obtained using the total annual precipitation, are shown in Figure
Results of using individual modes of annual precipitation variability in the water balance model for Lake Victoria. Levels modeled using the unfiltered CRU 3.21 dataset are in black (asterisks); levels modeled using precipitation accounted for by the first mode of annual variability are in blue (triangles); levels modeled using precipitation accounted for by the second mode of variability are in red (crosses), while levels modeled using precipitation accounted for by the third mode of variability are in green (×’s).
In order to understand the physical meaning behind the annual modes of variability, we examine the significant modes of variability for the short rains of October through December (OND) and the long rains of March through May (MAM). We relate these seasonal modes to the annual modes used above.
The first three modes of variability for the short rains, OND season, are significant and account for just over 71% of the variability for this season. We will examine these modes in detail.
The first mode of OND station precipitation variability has a loading which is entirely positive (Figure
Same as Figure
Pearson Correlation of the time series for the first mode of OND season precipitation variability with ERSST sea surface temperatures.
Similar to [
The third mode of OND variability is also shown to be statistically significant, accounting for 4.1 percent of variability over the region during the OND season. The loading for this mode shows an East-West dipole, with a positive loading to the west and a negative loading to the east, which is near neutral over Lake Victoria but has sharp contrasts over Kenya (Figure
Pearson Correlation of the time series for the third mode of OND season precipitation variability with ERSST sea surface temperatures.
The first four modes of variability for the long rains, MAM season, are significant and account for just over 51% of the variability for this season. We will examine these modes in detail.
Approximately 42 percent of the total East African rainfall is observed during the MAM season [
Same as Figure
Pearson Correlation of the time series for the first mode of MAM season precipitation variability with ERSST sea surface temperatures.
Difference between positive and negative composites of MAM EOF1 with SSTs and 850 mb winds.
The second mode is significant and has a −0.64 correlation with the second mode of annual variability. It accounts for 10.2% of variability in rainfall during the MAM season. The mode is split over Lake Victoria with slightly positive loadings near the northern and western shore and slightly negative loadings over the rest of the lake with stronger positive loadings to the south of the region (Figure
The third mode, accounting for 8.6% of the variability, has a 0.53 correlation with the third mode of annual variability. This mode has a slightly negative loading in the northwest corner of Lake Victoria but a slightly positive loading over the remainder of the lake. The strongest negative loadings are near the coast, while the strongest positive loadings are in northern Uganda (Figure
The fourth mode of variability for the MAM season is also significant; however, it is not correlated with any of the significant modes of annual precipitation used in the water balance model for Lake Victoria. Due to this lack of correlation, the fourth mode of MAM precipitation variability is not analyzed in this study.
We find that the first EOF mode of annual precipitation variability, ENSO, has the highest impact on the annual variability of Lake Victoria level. It has high correlation with the first modes of variability during the short rain (OND) and long rain (MAM) seasons. The first mode of variability during the OND season is found to be a combination of ENSO, while more work needs to be done to determine the physical mechanism behind the first mode of variability during the MAM season, although it is positively correlated with SSTs near Madagascar, suggesting that it is related to the moisture flux in that region.
While the second EOF mode of annual variability accounts for approximately 10 percent of the variability, its effect on the variability in lake levels is negligible. The second EOF mode of annual precipitation variability is significantly correlated with the second and third EOF modes of variability from the OND season and the second mode from the MAM seasons. Based on the EOF analysis of OND season rainfall, the dipoles in the second and third modes are split with very small values over the lake. The dipole in the MAM season also has small loadings over the lake. This suggests that the dipole may have negligible effect on Lake Victoria levels due to the fact that the loadings for the annual dipole are overall small since, in the OND and MAM seasons, there is a combination of positive and negative loadings over the lake that almost completely cancels out once the rainfall is interpolated to the six stations and used as input in the water balance model.
The third EOF mode of annual variability accounts for slightly over 7 percent of the variability over East Africa. It is correlated with the second mode of OND rainfall variability and the third mode of MAM variability. Similar to the second annual mode, it has negligible impact on the levels of Lake Victoria, most likely because the dipole modes over the lake are relatively small and may be cancelling each other out when they are combined.
We have sought to estimate the relative contributions of the dominant modes of annual precipitation variability to the modulation of Lake Victoria levels for the present day (1950–2012). The modes found in this study should be used to evaluate climate models over this region. Climate projections should be evaluated to investigate the evolution of these modes in the future. A change in ENSO, or a shift in one of the other modes described in this study, could have a great impact on the levels of Lake Victoria in the future.
Inflow,
Regressions of rainfall for 1956–1990.
Catchment | Regression | Correlation, |
---|---|---|
Nzoia |
|
0.54 |
Yala |
|
0.61 |
Sondu |
|
0.39 |
Awach Kaboun |
|
0.28 |
Kagera |
|
0.60 |
The outflow,
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors wish to thank Dr. Emma (Tate) Brown for her assistance with the water balance model. This research was supported by NSF Grant AGS-1043125 and NSF Expeditions in Computing Award no. 1029711.