Climate change enhances interannual variability of the Nile river flow

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1 To fully utilize the water resources of the basin, several dams were built in the previous century to control the seasonal and interannual variability of the Nile flow. The recent conflict over the Nile water has received significant attention in the past few years after the decision by Ethiopia to build a large dam on the Blue Nile (the Grand Ethiopian Renaissance Dam, or GERD) to produce electricity, mostly for export to neighbouring countries. The dam, currently under is relatively compared to previous designs In the construction, format provided by thelarge authors and unedited. for the same location, which raised serious concerns regarding its effect on water shares of downstream countries (that is, Egypt and Sudan). If variability of the Nile flow changes in the future, then water storage capacity in the basin will need to be re-evaluated. Until recently, attempts to project the future of the Nile flow yielded inconsistent results. Although several studies examined the impacts of climate change on the Nile basin using different approaches10 18, the uncertainty surrounding conclusions from these studies was high for several reasons. First, none of the previous studies presented observational evidence to support their hypotheses, as they estimateds. thesiam impactsand of climate change the flow of Nile Mohamed Elfatih A. B.onEltahir 5 N ,000 1,000 1,500 1,500 2,000 2,000 2,500 SUPPLEMENTARY INFORMATION LETTERS 0 DOI: /NCLIMATE3273 2,500 3,000 PUBLISHED ONLINE: 24 APRIL 2017 DOI: /NCLIMATE3273 3,000 3,500 3,500 4, Kilometres 5 S Climate change enhances interannual variability of the Nile river flow * 25 E 30 E 35 E Figure 1 Topographic map of Eastern Africa and the Nile sub-basins. Topographic map of Eastern Africa showing the main tributaries of Nile basins (Upper Blue Nile, Sobat, Atbara and Bahr el-jebel) and different dams in these basins. The rainfall and runoff data analysed in this paper are averaged over the Upper Blue Nile, Sobat and Atbara basins. d Re The human population living in the Nile basin countries is Mediterranean Sea projected to double by Department 2050, approaching one billion1.engineering, The Ralph M. Parsons Laboratory, of Civil and Environmental Massachusetts Institute of Technology, Cambridge, increase in water demand associated with this burgeoning msiam@mit.edu Massachusetts 02139, USA. * N population will put significant stress on the available water resources. Potential changes in the flow of the Nile River as a 30 N NATURE CLIMATE CHANGE ADVANCE ONLINE PUBLICATION 1 result of climate change may further strain this critical situa2, Macmillan Publishers Limited, part of Springer Nature. All rights reserved. tion. Here, we present empirical evidence from observations and consistent projections from climate model simulations suggesting that the standard deviation describing interannual variability of total Nile flow could increase by 50% (±35%) 25 N (multi-model ensemble mean ±1 standard deviation) in the twenty-first century compared to the twentieth century. We Aswan Dam attribute the relatively large change in interannual variability of the Nile flow to projected increases in future occurrences of Dongola El Niño and La Niña events4,5 and to observed teleconnection Merowe Dam 20 N between the El Niño Southern Oscillation and Nile River Atbara flow6,7. Adequacy of current water storage capacity and plans for additional storage capacity in the basin will need to be Lower Blue Nile re-evaluated given the projected enhancement of interannual Khashm El Girba Dam variability in the future flow of the Nile river. Sennar Dam 15 N The Nile river basin is an ecosystem under severe stress. The Tekeze Dam basin is shared by about 400 million people in eleven countries Roseiras Dam with economies that depend heavily on agriculture, which employs 1 GERD Dam the vast majority of the labour force in most of these countries. Furthermore, almost half of the Nile basin countries are projected to Upper Blue Nile 10 N live below the water scarcity level, 1,000 m3 /person/year, by 20308,9. Bahr el-jebel Thus, any future changes in the magnitude of the flow volume of the Nile river can lead to significant impacts on the lives of people Sobat Fincha Dam living within the basin and may increase the already high level of water stress. To fully utilize the water resources of the basin, several dams were 5 N built in the previous century to control the seasonal and interannual variability of the Nile flow. The recent conflict over the Nile water 500 1,000 has received significant attention in the past few years after the 1,000 1,500 decision by Ethiopia to build a large dam on the Blue Nile (the Grand 1,500 2,000 Ethiopian Renaissance Dam, or GERD) to produce electricity, 0 2,000 2,500 mostly for export to neighbouring countries. The dam, currently 2,500 3,000 under construction, is relatively large compared to previous designs 3,000 3,500 for the same location, which raised serious concerns regarding its 3,500 4,000 effect on water shares of downstream countries (that is, Egypt and Sudan). If variability of the Nile flow changes in the future, then S Kilometres water storage capacity in the basin will need to be re-evaluated. Until recently, attempts to project the future of the Nile flow 25 E 30 E 35 E yielded inconsistent results. Although several studies examined the impacts of climate change on the Nile basin using different ap- Figure 1 Topographic map of Eastern Africa and the Nile sub-basins. proaches10 18, the uncertainty surrounding conclusions from these Topographic map of Eastern Africa showing the main tributaries of Nile studies was high for several reasons. First, none of the previous stud- basins (Upper Blue Nile, Sobat, Atbara and Bahr el-jebel) and different ies presented observational evidence to support their hypotheses, as dams in these basins. The rainfall and runoff data analysed in this paper are they estimated the impacts of climate change on the flow of Nile averaged over the Upper Blue Nile, Sobat and Atbara basins. a Se Ralph M. Parsons Laboratory, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, NATURE CLIMATE02139, CHANGE Massachusetts USA. * msiam@mit.edu 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved. 1

2 25 26 The Supplementary Information document includes the following sections: Description of observed flow and rainfall data. 2- Selection method of climate models. 3- Description of the teleconnection between El Niño Southern Oscillation and the variability in the flow of Nile river. 4- Sensitivity of the projected future changes in the flow of the Nile river to the choice of GCMs

3 45 1- Observed flow and rainfall data In this paper, we focus on the data of the Eastern Nile sub-basins (Upper Blue Nile, Sobat and Atbara) as they contribute by 80% of the total Nile flow at Dongola, hence, responsible of the seasonality and inter-annual variability in the total flow of the main Nile. Furthermore, an analysis (Supplementary Fig. 12) of the correlation between the flow at Dongola and Blue Nile showed that the Blue Nile explains almost 80% of the variability of the flow at Dongola. The observed flow data for Dongola was available through personal communications with the ministries of water in the countries of the Nile basin and Global River Discharge Database (GRDD) 1. The data from the ministries were verified against the flow from the GRDD at Aswan from 1870 till 1960 (before the construction of High Aswan Dam) and at Dongola from 1912 till The data were in good agreement between the different sources. It is also important to note that the flow from 1870 till 1900 was relatively higher compared to the well-documented long-term mean of the Nile flow ~84 km 3 /year. Thus, we focus in our analysis using the bias correction and sampling approaches on the flow in Dongola starting The observed flow data for the Upper Blue Nile and Atbara basins was available through personal communications with the ministries of water in the countries of the Nile basin. The authors could not access similar data for the flow of the Sobat basin. Several rainfall stations are also used to estimate the changes in trend of the rainfall over the Eastern Nile basin. However, only a few stations were available over the Upper Blue Nile basin with sufficient data that overlaps with the flow data. These stations are (Addis Ababa, Debre Markos, Dessie, Gondar and Nekemet). The rainfall data were available using the Global Historical Climatology Network (GHCN-V2). The data of the different rainfall stations was averaged by using a multiple linear regression of the 3

4 different rainfall data for each station as the predictors and the observed flow of the Upper Blue Nile basin as predictand. The analysis was repeated using Thiessen polygons approach and the same changes in mean and variability were found

5 Selection method of climate models. In this study, 18 GCMs that participated in the Coupled Model Inter-comparison Project Phase 5 (CMIP5) with the Representative Concentration Pathway (RCP 8.5) for future greenhouse gases projected emissions were selected 2 (Supplementary Table 1). The selected models were identified in previous studies by other research groups to be the best in simulating the frequency of El Niño and La Niña events as they have a skewness of rainfall over the Nino3 region greater than 1 and were able to simulate at least one event of extreme El Niño 3,4. ENSO is one of the most important phenomena shaping tropical climate. Hence, models used to predict the future climate in any tropical region should have credibility in simulating this phenomenon and also the observed teleconnections with ENSO 5. Moreover, we tested the sensitivity of our results using different combinations of these GCMs as presented in Section 4 of this document Supplementary Figure 1 shows the changes in the seasonal cycle of the precipitation and runoff using the 18 GCMs and compared to observed precipitation from TRMM V7-3B43 and CRU TS 3.1, and stream flow for the Upper Blue Nile, Sobat and Atbara basins. Supplementary Table 4 summarizes the changes in the mean, standard deviation and coefficient of variation of the runoff from the 18 GCMs

6 Supplementary Table 1 Summary of Global Climate Models (GCMs) used in this study Models AGCM (Lon. x Lat.) Agency BCC-CSM1-1M 1.12 o 1.12 o Beijing Climate Center, China Meteorological Administration BCC-CSM o 2.8 o Beijing Climate Center, China Meteorological Administration CCSM o 0.94 o National Center of Atmospheric Research, USA CMCC-CMs 1.88 o 1.87 o Centro Euro-Mediterraneo per I Cambiamenti Climatici CMCC-CM 0.75 o 0.75 o Centro Euro-Mediterraneo per I Cambiamenti Climatici CNRM-CM5 1.4 o 1.4 o National Centre of Meteorological Research, France GFDL-CM3 2.5 o 2.0 o NOAA Geophysical Fluid Dynamics Laboratory, USA GFDL-ESM2G 2.5 o 2.0 o NOAA Geophysical Fluid Dynamics Laboratory, USA GFDL-ESM2M 2.5 o 2.0 o NOAA Geophysical Fluid Dynamics Laboratory, USA MPI-ESM-MR 1.88 o 1.87 o Max Planck Institute for Meteorology, Germany MRI-CGCM3 1.1 o 1.1 o Meteorological Research Institute, Japan NORESM1-ME 2.5 o 1.9 o Norwegian Climate Center, Norway NORESM1-M 2.5 o 1.9 o Norwegian Climate Center, Norway CMCC-CESM 3.75 o 3.71 o Centro Euro-Mediterraneo per I Cambiamenti Climatici CESM1-BGC o 0.94 o Community Earth System Model Contributors CESM1-CAM o 0.94 o Community Earth System Model Contributors ACCESS o x1.87 o Commonwealth Scientific and Industrial Research Organization/ Bureau of Meteorology (CSIRO-BOM) FGOALS 2.8 o 2.8 o LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences

7 a Supplementary Figure 1 Changes in the seasonal cycles of precipitation and runoff. a, b, Changes in the seasonal cycle of precipitation and runoff over the Eastern Nile basin, where the blue and red lines are the averages of the periods ( ) and ( ), respectively, while in (a) the green and brown lines are the average observed precipitation seasonal cycle from TRMM V7-3B43 for the period ( ) and CRU TS3.1 for the period ( ), respectively. The green line in (b) is the observed seasonal cycle of the streamflow of the Upper Blue Nile, Sobat and Atbara basins. In a,b, the error bars are ±1 standard deviation of the rainfall and runoff for each month. In a,b, the numbers in blue and red are the annual averages for the periods ( ) and ( ), respectively, and the green and brown numbers are the annual average of precipitation for the observation from TRMM V7-3B43 and CRU TS 3.1, respectively in (a). The green number in (b) is the annual average observed streamflow for the Upper Blue Nile, Sobat and Atbara basins. Supplementary Figure 2 Changes in the mean, standard deviation and coefficient of variation for the runoff simulated by 18 CMIP5 GCMs over the Eastern Nile basin. Percent changes in the mean, standard deviation and coefficient of variation for the period ( ) relative to the period ( ) showing the number of models that fall in each group of these percent changes b 7

8 El Niño Southern Oscillation and the variability in the flow of Nile river. During El Niño events, sea surface temperature (SST) in the tropical Indian Ocean increases in response to the warming in the tropical Eastern Pacific Ocean through oceanic connection and forces a Gill-type circulation, i.e., enhanced westerly, low-level air flow over East Africa and the Indian Ocean. This anomalous low-level flow enhances the flux of air and moisture away from the Nile basin, resulting in a reduction of rainfall and river flow 6. This mechanism is somewhat similar to mechanism discussed in previous studies about the westward extension of the warm pool over the Indian ocean leading to extension of the Walker circulation and drying over Eastern Africa 7. In comparing the Nile basin to other tropical regions, we note that the correlation between the Nile flow and ENSO indices is among the highest observed correlations between ENSO indices and rainfall/flow over other regions in the world as shown in Supplementary Table The longest observational record on the Nile describes the flow at Aswan for the period is presented in Supplementary Figure 3. However, the potential for using this record to study long term trends in the mean flow is limited due to three factors: (a) The rating curve calibration used for generating the flow before 1902 was not accurate resulting in discharges that were about 8% higher 8,9 ; (b) The construction of the Low Aswan Dam in 1902, and the High Aswan Dam in 1960s impacted the location of the station resulting in non-homogeneity of the record, which eventually made it necessary to substitute the flow in Dongola for the flow at Aswan after 1960; and (c) The increasing rate of water abstraction upstream due to expansion of irrigation in Sudan. The current estimates of this abstraction rate range from 12 to 14 cubic kilometer per year 10. All these factors taken together limit the potential for using this record to detect long term changes in the mean Nile flow. Instead, we focus mainly on the shorter but homogeneous record presented in Figure 2. We 8

9 assume that the Aswan record can be used to describe approximately the variability of the flow around the slowly varying mean, as we did in Figure 3c, since we use a different mean for assessing variability within a moving window of 30 years The time series of the total annual flow at Dongola (green line) is characterized by two periods (i.e. around 1920 and after 1980) of high inter-annual variability as illustrated by the 30 years moving average of the coefficient of variation (blue line) (Supplementary Figure 3). These periods also coincide with periods of high ENSO activity (shaded in gray) (i.e. high number of El Nino and La Nina events). During these two periods, the number of El Nino and La Nina was more than 14 events per 30 years period and the coefficient of variation was around 25%. On the other hand, the period (1940 to 1970) had a low number of El Nino and La Nina events (i.e. around 11 events per 30 years period) and low inter-annual variability as illustrated by a low coefficient of variation less than 15%. The comparison between periods of low and high ENSO activity shows that a slight increase in the number of El Nino and La Nina events can enhance the inter-annual variability of the total Nile flow. Furthermore, the presented analysis based on observations of SST and Nile flow shows that the coefficient of variation of the total Nile flow can increase by approximately 67% (from 15% to 25%) with a slight increase of the number of El Nino and La Nina events from around 11 events to 14 events, which has implications of the future inter-annual variability of the flow in the Nile river if the number of future El Nino and La Nina events changes The GCMs simulate the frequency of El Niño, La Niña and neutral events for the twentieth century with some differences but are generally in reasonable agreement with observations (Supplementary Fig. 4a). There is an agreement between the GCM results presented in 9

10 Supplementary Fig. 4a that the frequency of extreme and moderate El Niño and extreme La Niña events may increase in the 21 st century. These increases are associated with a decrease in the frequency of neutral years (Supplementary Fig. 4a), which are characterized by average conditions of SST, convective activity and rainfall over the Pacific Ocean 3, 4. These changes in the frequencies of El Niño and La Niña events would change the Nile flow patterns through the observed teleconnection between ENSO and the Nile, in which El Niño and La Niña events induce low and high flows in the river, respectively. The teleconnection is evident through direct observations of both SST in the Pacific Ocean and the Nile flow (Supplementary Fig. 4b). The analysis of the GCMs also shows that most of the GCMs can reasonably simulate the teleconnection between the Nile flow and ENSO (Supplementary Table 2 and 9). It is also important to note that some of the GCMs fail to simulate the observed teleconnection between ENSO and the Nile flow. For example, 7 out of 18 GCMs have average rainfall during moderate El Nino events that is higher than during neutral years and 5 out of 18 have average rainfall during La Nina events that is lower than neutral years. Thus, it is important to interpret the GCMs results while recognizing their limited accuracy in simulating the Nile-ENSO teleconnection as discussed in Section Here, we project future changes in the Nile river flow using a sampling approach (described in Sampling analysis in Methods) that combines GCM projections of changes in the frequency El Niño and La Niña events with empirical relationships describing the teleconnection between ENSO and the Nile flow. The resulting distribution of the projected future flow in the Nile river confirms that the future average flow and inter-annual variability may increase (Supplementary Fig. 4c), which is consistent with the simulated runoff by the GCMs. We also use a standard bias correction approach (Supplementary Fig. 4d) (described in Bias correction using probability 10

11 matching method in Methods) to project the future Nile flows. The main difference between the two approaches is that the sampling approach relies on the frequencies of different El Niño and La Niña events to predict the Nile flows, regardless of the simulated flow by the models. On the other hand, the bias correction approach relies completely on the simulated runoff by the models. The two approaches yield the same conclusions and show similar distributions of the past and future Nile flows (Supplementary Fig. 4c and 4d). Hence, we can conclude that the changes in the variability of the simulated runoff in the Nile by the GCMs is due to the changes in frequencies of El Niño and La Niña events. Furthermore, the results of each approach (i.e. sampling and bias correction) agree on the sign of change in the inter-annual variability in the Nile flow. All the 18 GCMs show simulate an increase of the standard deviation using the two approaches. On the other hand, it is also important to note that 5 (4) GCMs out of the 18 GCMs show a decrease in the longterm mean of the Nile flow using the sampling (bias correction) approaches. In addition, two GCMs (i.e. GFDL-ESM2M and ACCESS1) show different direction of the change in the longterm mean (in the two approaches). This discrepancy can be primarily due to the small change in the long-term mean in one of the approaches. For example, the increase in the long-term mean of the GFDL-ESM2M using the bias correction is only +2.3% (from 79.2 km 3 to 81.1 km 3 ) and similarly the decline in the long-term mean of the ACCESS1-0.4% (from 77.4 km 3 to 77.1 km 3 ).The distribution of the flow in the twentieth century is closely clustered around the mean with few extreme events (i.e., below 70 km3/year and higher than 100 km3/year), while the future flows have fewer normal events (i.e., between 70 km3/year and 100 km3/year) and more highflow events (i.e., greater than 100 km3/year) (Supplementary Fig. 4c and 4d). These changes in the distribution of the flow are evident in the changes of the 10 th and 90 th percentiles of the future flow ( ) compared to the past flow ( ) as shown in Supplementary Table 3. In 11

12 addition, the changes in the distribution are tested using Kolmogorov-Smirnov test and 14 out of the 18 GCMs show significant change in the distribution of future Nile flow ( ) compared to the past ( ) as shown in Supplementary Table 5. Similarly, most the GCMs show positive significant trends as shown in Supplementary Table 6 using Mann-Kendall test. Furthermore, the changes in the flow pattern are consistent among the models regardless of which combination of models are chosen to estimate the trends in the mean or variance as discussed in Section 4 of this document (Supplementary Fig. 8, 9 and 10 and Supplementary table 8). It is important to note that the ensemble average runoff during extreme El Nino event for the period ( ) is 0.58 mm/day, while for the future period ( ) is 0.65 mm/day and similarly for the other events as shown in supplementary table 9. Thus, in the future the mean flow may increase because of the increased values of flow during El Nino and La Nina events, and the variability may increase because of the increased frequency of El Nino and La Nina events Finally, Supplementary Table 7 summarizes the water storage analysis using the Hurst equation for the 18 GCMs and using the bias correction and sampling approaches. All the GCMs show an increase of the future water storage required to accommodate the enhanced inter annual variability in the future flow of the Nile

13 Supplementary Figure 3 Time series of total annual Nile flow (green line), and the coefficient of variation of the total annual Nile flow computed for a 30-years moving window (blue line), for the period 1870 to The shaded rectangles in gray are the corresponding periods from Figure 3c with high number of El Nino and La Nina events (i.e. more than 14 events per 30 years)

14 a b c d Supplementary Figure 4 Changes in the future Nile flows and their association with changes in the frequencies of El Nino and La Nina through the teleconnection between the Nile and ENSO. a, Multi-model average changes in the future frequencies of El Nino and La Nina events using 18 CMIP5 GCMs and their comparisons to the observed number of events. b, Relation between annual Nile flow averaged between June and May and the sea surface temperature over Nino 3 (5 N-5 S, 150 W-90 W), Nino 4 (5 N-5 S, 160 E-150 W) and ENSO index (6N 2 N, W; 2 N 6 S, 180 o W 90 W; and 6 o S 10 S, 150 W 110 W). c, d, Changes in the distribution in future Nile flow based on sampling and bias correction approaches respectively. The change in the long-term average of annual Nile flow for the future period ( ; red dashed lines) is statically significant above the 95% confidence level using Student t-test compared the past period ( ; blue solid lines). 14

15 Supplementary Figure 5 Changes in the mean, standard deviation and coefficient of variation for the flow estimated using sampling (a) and bias correction (b) approaches based on 18 CMIP5 GCMs over the Eastern Nile basin. Percent changes in the mean, standard deviation and coefficient of variation for the period ( ) relative to the period ( ) showing the number of models that fall in each group of these percent changes. a b

16 a b b Supplementary Figure 6 Ranges of the simulated runoff by the GCMs for each type of ENSO events for (a) period, and (b) The red line represents the median, the boundaries of the blue box are for the 25% and 75% quantiles of the simulated runoff by the GCMs. The black lines in (b) are the median of the period ( ) for each type of events. Detailed data used in these plots are available in Supplementary Table

17 a b Supplementary Figure 7 Ranges of the long-term mean (a) and standard deviation (b) of the GCMs runoff using sampling and bias correction approaches for the period ( ) The red line represents the median, the boundaries of the blue box are for the 25% and 75% quantiles of the simulated runoff by the GCMs. The straight black lines are the median of the longterm mean and standard deviation corresponding to the period ( ). Detailed data used in these plots are available in Supplementary Table 4. 17

18 BCC- CSM1-1M BCC- CSM1-1 CCSM4 CMCC- CMs Supplementary Table 2 Number of different El Nino and La Nina events simulated by the GCMs and the corresponding runoff simulated over the Eastern Nile basin (ENB). The classification of the different types of El Nino and La Nina events are as described in the sampling approach in Methods Section. GCMs highlighted in green are the models that simulate higher runoff during extreme La Nina events in the past and future compared to extreme El Nino events in the past and future. CMCC-CM CNRM- CM5 GFDL- CM3 GFDL- ESM2G GFDL- ESM2M MPI-ESM- MR MRI- CGCM3 Moderate El Nino Extreme El Nino Moderate La Nina Extreme La Nina Neutral Years Events ENB Runoff Events ENB Runoff Events ENB Runoff Events ENB Runoff Events ENB Runoff Events ENB Runoff Events ENB Runoff Events ENB Runoff Events ENB Runoff Events ENB Runoff Events

19 ENB Runoff NORESM1 -ME Events ENB Runoff NORESM1 -M Events ENB Runoff CMCC- CESM Events ENB Runoff CESM1- BGC Events ENB Runoff CESM1- CAM5 Events ENB Runoff ACCES1 Events ENB Runoff FGOALS Events ENB Runoff Equivalent observed Nile flow ( ) All the runoff values are in (mm/day)

20 Supplementary Table 3 Statistics of the change in the Nile flow for the different GCMs estimated using the bias correction and sampling approaches. The statistics include changes in the 10 th and 90 th percentiles of the flows for the past period ( ) and future period ( ) th Percentile th Percentile Sampling 90th Percentile th Percentile th Percentile th Percentile Bias 90th Percentile th Percentile BCC- CSM1-1M BCC- CSM CCSM CMCC- CMs CMCC-CM CNRM- CM GFDL-CM GFDL- ESM2G GFDL- ESM2M MPI-ESM- MR MRI- CGCM NORESM1- ME NORESM1- M CMCC- CESM CESM1- BGC CESM1- CAM ACCESS FGOALS Avgerage All GCMs

21 Supplementary Table 4 Statistics of the change in the Nile flow for the different GCMs estimated using the bias correction and sampling approaches. The statistics include changes in the mean, standard deviation of the flows for the past period ( ) and future period ( ). Mean Mean Sampling Std. deviation Std. deviation Mean Mean Bias Std. deviation Std. deviation BCC- CSM1-1M BCC- CSM CCSM CMCC- CMS CMCC-CM CNRM- CM GFDL-CM GFDL- ESM2G GFDL- ESM2M MPI-ESM- MR MRI- CGCM NORESM1- ME NORESM1- M CMCC- CESM CESM1- BGC CESM1- CAM ACCESS FGOALS Avgerage (All GCMs) Average future percent change (All GCMs) 14.4% 48.4% 13.4% 49.4% Std. deviation of future percent change (All GCMs) 19.1% 32.1% 14.9% 38.2%

22 Supplementary Table 5 The test statistic of the Kolmogorov Smirnov test for the change in the distribution of the future Nile flow ( ) relative the past ( ) calculated for the runoff simulated by the GCMs (raw data) and using the flows estimated with the bias correction and sampling approaches. At least 14 out 18 GCMs show significant change in the distribution of the future Nile flow compared to the past using three approaches Bias Correction Sampling Raw Data CNRM-CM CESM1-BGC 0.47* 0.38* 0.45* BCC-CSM1-1M 0.62* 0.72* 0.62* CCSM4 0.51* 0.45* 0.49* MRI-CGCM3 0.28* 0.41* 0.27* FGOALS 0.46* 0.68* 0.44* CMC-CMS 0.33* 0.31* 0.33* BCC-CSM * * CMC-CM * 0.09 GFDL-CM3 0.42* 0.37* 0.41* GFDL-ESM2G 0.21* 0.33* 0.28* GFDL-ESM2M * 0.16 MPI-ESM-MR 0.31* 0.33* 0.34* NORESM1-ME 0.37* 0.42* 0.36* NORESM1-M 0.28* * CMCC-CESM 0.25* * CESM1-CAM5 0.41* 0.38* 0.42* ACCESS * 0.16 *Indicates values that are significant at 5% significance level 22

23 Supplementary Table 6 Mann-Kendall coefficients (tau) for the trends in the time series of the Nile flow ( ) for the different GCMs, calculated for the simulated runoff (raw data), and using the flows estimated with the bias correction, and sampling approaches. The number of GCMs with statistically significant and positive trends are 12, 10, and 12 for the bias correction, sampling and raw data respectively. The corresponding number of GCMs with statistically significant and negative trends are 2, 3, and 3 for the bias correction, sampling and raw data respectively Bias Correction Sampling Raw Data CNRM-CM CESM1-BGC 0.42* 0.15* 0.41* BCC-CSM1-1M 0.48* 0.44* 0.49* CCSM4 0.42* 0.27* 0.42* MRI-CGCM3 0.26* 0.2* 0.23* FGOALS 0.36* 0.34* 0.36* CMC-CMS -0.14* -0.18* -0.17* BCC-CSM * * CMC-CM GFDL-CM3 0.35* 0.19* 0.35* GFDL-ESM2G * -0.13* GFDL-ESM2M MPI-ESM-MR 0.21* 0.19* 0.24* NORESM1-ME 0.26* 0.27* 0.27* NORESM1-M 0.25* 0.16* 0.19* CMCC-CESM -0.21* * CESM1-CAM5 0.4* 0.14* 0.41* ACCESS * *Indicates values that are significant at 5% significance level

24 Supplementary Table 7 Changes in the Hurst coefficients and corresponding changes in the 100-year storage (R100) return period required to accommodate the change in the future Nile flows using the bias correction and sampling approaches. Hurst coefficient R100 Model Bias Correction Sampling Bias Correction Sampling BCC-CM1-M BCC-CSM CCSM CMCC-CMS CMCC-CM CNRM-CM GFDL-CM GFDL- ESM2G GFDL- ESM2M MPI-ESM MRI-CGCM NORESM1- ME NORESM1-M CMCC- CESM CESM-BGC CESM-CAM ACCESS FGOALS Average All GCMs Standard deviation All GCMs

25 Sensitivity of the projected future changes in the flow of the Nile river to the choice of GCMs In the following analysis, we test the robustness of the projected changes in the future flow of the Nile river using different combination of GCMs to show that these changes are independent of the choice of GCMs. The 18 GCMs are divided to 5 groups as following: Group 1 includes the GCMs that simulate correctly the teleconnection between Nile flow and ENSO. Each GCM is evaluated based on its ability to simulate the teleconnection between Nile flow and ENSO for four different criteria (i.e., the Nile flow simulated during extreme El Nino years is less than that of extreme La Nina years; the Nile flow simulated during moderate El Nino years is less than that of moderate La Nina years; the Nile flow simulated during moderate El Nino years is less than that of neutral years; and the Nile flow simulated during moderate La Nina years is greater than that of neutral years). Group 2 includes the GCMs that are able to simulate at least three out of four teleconnection between ENSO and the Nile flow as previously described, and the coefficient of determination between the average annual seasonal cycle of the simulated rainfall over the ENB compared to observation from TRMM-V73B43 is at least 50% and the simulated rainfall is within ±20% of the observations. Group 3 combines the GCMs that meet all four teleconnection criteria, and the coefficient of determination between the average annual seasonal cycle of the simulated rainfall over the ENB compared to observations from TRMM-V73B43 is at least 50% and the simulated rainfall is within ±20% of the observations. Group 4 includes the GCMs that are the best in simulating the different ENSO feedbacks depicted by Bjerknes index

26 Group 5 includes the GCMs that have the highest spatial resolution as the models with the highest resolution tend to better represent the rainfall seasonal cycle over the basin 12 (i.e., higher than 1.5 o x 1.5 o ), and the coefficient of determination between the average annual seasonal cycle of the simulated rainfall over the ENB compared to observations from TRMM-V73B43 is at least 50% and the simulated rainfall is within ±20% of the observations. The results of this sensitivity analysis are presented in Supplementary Figures 8, 9 and 10, and Supplementary Tables 8 and 9. The results are consistent among the different combination of GCMs. Supplementary Figure 8 shows that the 30-year moving average of the runoff mean and standard deviation is increasing with time for the 5 groups. Supplementary Figures 9 and 10 show the changes in the distribution of the flows for the period ( ) compared to ( ) for the different combinations and show the increase of extreme wet events in the future and reduction of occurrence of average flow conditions (i.e. between 70 km3/year and 100 km3/year). Supplementary Table 8 summarizes the changes in the mean, standard deviation, 10 th and 90 th percentiles of the flow for the different groups of GCMs. The results show that the mean and standard deviation may increase by 15% and 50% respectively in the future. The 10 th and 90 th percentiles may increase from 60 km 3 /year to 65 km 3 /year and from 100 km 3 /year to 120 km 3 /year respectively

27 a b Supplementary Figure 8 30-year moving averages of the mean (a) and standard deviation (b) of the runoff simulated by the models from 1900 to 2100 for the different combination of GCMs. 27

28 a b c d e Supplementary Figure 9 Changes in the future Nile flows using different combinations of GCMs based on bias correction approach. a, b, c, d, e are the flow distributions for the different combinations of GCMs, which represent Group 1, 2, 3, 4 and 5, respectively. The future flows for the period ( ) are in red and the past flows for period ( ) are in blue. 28

29 a b c d e Supplementary Figure 10 Changes in the future Nile flows using different combinations of GCMs based on a sampling approach. a, b, c, d, e are the flow distributions for the different combinations of GCMs, which represent Group 1, 2, 3, 4 and 5, respectively. The future flows for the period ( ) are in red and the past flows for period ( ) are in blue. 29

30 Model Group 1 Group 2 Group 3 Group 4 Group 5 All GMCs Observation Supplementary Table 8 Statistics of the change in the Nile flow for the different combinations of GCMs estimated using the bias correction and sampling approaches. The statistics include changes in the mean, standard deviation and 10 th and 90 th percentile of the flows. Period Bias Correction Mean Standard deviation 10th Percentile 90th Percentile Flow Sampling Mean Standard deviation 10th Percentile 90th Percentile

31 Runoff (mm/day) Supplementary Table 9 Summary of the runoff simulated over the Eastern Nile basin during different El Nino and La Nina types for different combinations of GCMs. Extreme El Nino Moderate El Nino Neutral Extreme La Nina Std. Std. Std. Std. Mean deviation Mean deviation Mean deviation Mean deviation Moderate La Nina All GCMs ( ) Group 1 ( ): BCC-CSM1-1M, CCSM4, CMCC-CMS, MRI- CGCM3, FGOALS Group 2 ( ): BCC-CSM1-1M, CCSM4, CNRM-CM5, CESM BGC Group 3 ( ): BCC-CSM1-1M, CCSM Group 4 ( ): CCSM4, GFDL-CM3, GFDL-ESM2M, NOREMS1-ME, NORESM1-1M, ACCESS Group 5 ( ): BCC-CSM1-1M, CCSM4, CMCC-CMS, CNRM- CM5, MPI-ESM-MR, CESM1-BGC, CESM1- CAM5, ACCESS All GCMs ( ) Group 1 ( ): BCC-CSM1-1M, CCSM4, CMCC-CMS, MRI- CGCM3, FGOALS Group 2 ( ): BCC-CSM1-1M, CCSM4, CNRM-CM5, CESM BGC Group 3 ( ): BCC-CSM1-1M, CCSM NA Group 4 ( ): CCSM4, GFDL-CM3, GFDL-ESM2M, NOREMS1-ME, NORESM1-1M, ACCESS Group 5 ( ): BCC-CSM1-1M, CCSM4, CMCC-CMS, CNRM- CM5, MPI-ESM-MR, CESM1-BGC, CESM1- CAM5, ACCESS Observed corresponding Nile Flow (km 3 /year) Mean Std. deviation 31

32 Additional Supplementary Figures and Tables Supplementary Figure 11 Increase in the population with time. Increase in the population (solid brown line) versus the decrease of the water share per person assuming no changes in the flow (i.e., 80 km 3 /year, which is the long-term average between 1900 and 2000, blue solid line), and the water share per person using the multi-model average time series of the Nile flow projected using the bias correction (dashed red line). Climate change is likely to increase the mean flow in the basin. This increase in the flow is a welcome change, and can slightly enhance the water share per person and thus reduce the water stress in the basin, but this positive impact will persist for only a few years before being overwhelmed by large decrease in water share per capita due to projected population growth. Population growth poses a serious challenge to availability of water in the Nile. 32

33 Supplementary Figure 12 Relation between annual flows at Dongola and Upper Blue Nile basin from Supplementary Table 10 Correlation between ENSO indices and Rainfall/flow over different regions in the world. Basin/Region Coefficient of Correlation (R)/ Coefficient of Determination (R 2 ) Amazon basin flow /9.6% Congo basin flow /9.5% Parana basin flow /19.3% Nile basin flow /25% Rainfall over India /32% Rainfall over Southern California /16.8% 33

34 References 1. Vorosmarty, C.J., Fekete, B.M. and Tucker, B.A. Global River Discharge, , Version. 1.1 (RivDIS). Data set. Available on-line [ daac. ornl. gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA Taylor, KE., Stouffer, RJ., Meehl, GA. An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93: Cai, W., et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nature Climate Change 4, Cai, W. et al., Increased frequency of extreme La Nina events under greenhouse warming." Nature Climate Change 5, Rowell, D.P. Simulating SST teleconnections to Africa: What is the state of the art?. Journal of Climate, 26(15), pp Siam, M. S., Wang, G., Demory, M. E., Eltahir, E. A. B. Role of the Indian Ocean sea surface temperature in shaping the natural variability in the flow of Nile River."Climate dynamics 43, no. 3-4: Williams, A.P. and Funk, C. A westward extension of the warm pool leads to a westward extension of the Walker circulation, drying eastern Africa. Climate Dynamics, 37(11-12), pp Sutcliffe, J.V. and Parks, Y.P. The hydrology of the Nile (No. 5). Wallingford, Oxfordshire, UK: International Association of Hydrological Sciences Hurst, H.E. and Phillips, P. The Nile Basin, Volume IV. Ten-day mean and monthly mean discharges of the Nile and its tributaries. Cairo: Ministry of Public Works, Physical Department Slaman, S. Sudan Continues Relinquishing a Growing Portion of Nile Water Share. Sudan Vision, Issue: 3414, December 10 th, Kim, S. T., et al. An., Response of El Niño sea surface temperature variability to greenhouse warming, Nat. Clim. Change, 4, Siam, M. S., Demory, M. E., Eltahir, E. A. B. Hydrological cycles over the Congo and upper Blue Nile basins: evaluation of general circulation models simulations and reanalysis data products. J Clim. doi: /jcli-d

35 Amarasekera, K.N., Lee, R.F., Williams, E.R. and Eltahir, E.A.B. ENSO and the natural variability in the flow of tropical rivers. Journal of Hydrology, 200(1), pp Ihara, C., Kushnir, Y., Cane, M.A. and De La Peña, V.H. Indian summer monsoon rainfall and its link with ENSO and Indian Ocean climate indices. International Journal of Climatology, 27(2), pp Jong, B.T., Ting, M. and Seager, R. El Niño's impact on California precipitation: seasonality, regionality, and El Niño intensity. Environmental Research Letters, 11(5), p

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