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Climatic Change (2010) 100:433–461 DOI 10.1007/s10584-009-9693-0 Hydrologic impacts of climate change on the Nile River Basin: implications of the 2007 IPCC scenarios Tazebe Beyene · Dennis P. Lettenmaier · Pavel Kabat Received: 3 September 2007 / Accepted: 28 July 2009 / Published online: 13 October 2009 © Springer Science + Business Media B.V. 2009 Abstract We assess the potential impacts of climate change on the hydrology and water resources of the Nile River basin using a macroscale hydrology model. Model inputs are bias corrected and spatially downscaled 21st Century simulations from 11 General Circulation Models (GCMs) and two global emissions scenarios (A2 and B1) archived from the 2007 IPCC Fourth Assessment Report (AR4). While all GCMs agree with respect to the direction of 21st Century temperature changes, there is considerable variability in the magnitude, direction, and seasonality of projected precipitation changes. Our simulations show that, averaged over all 11 GCMs, the Nile River is expected to experience increase in streamflow early in the study period (2010–2039), due to generally increased precipitation. Streamflow is expected to decline during mid- (2040–2069) and late (2070–2099) century as a result of both precipitation declines and increased evaporative demand. The predicted multimodel average streamflow at High Aswan Dam (HAD) as a percentage of historical (1950– 1999) annual average are 111 (114), 92 (93) and 84 (87) for A2 (B1) global emissions scenarios. Implications of these streamflow changes on the water resources of the Nile River basin were analyzed by quantifying the annual hydropower production and irrigation water release at HAD. The long-term HAD release for irrigation increases early in the century to 106 (109)% of historical, and then decreases to 87 (89) and 86 (84)% of historical in Periods II and III, respectively, for the A2 (B1) global emissions scenarios. Egypt’s hydropower production from HAD will be above the mean annual average historical value of about 10,000 GWH for the early part of 21st century, and thereafter will generally follow the streamflow trend, however T. Beyene · D. P. Lettenmaier (B) Department of Civil and Environmental Engineering, University of Washington, Box 352700, Seattle, WA 98195, USA e-mail: [email protected] P. Kabat ALTERRA Green World Research, Wageningen University and Research Centre, P.O. Box 47, 6700 AA, Wageningen, The Netherlands 434 Climatic Change (2010) 100:433–461 with large variability among GCMs. Agricultural water supplies will be negatively impacted, especially in the second half of the century. 1 Introduction The climate of Africa is both varied and varying. Varied, because climate ranges from humid equatorial to seasonally arid and sub-tropical Mediterranean, and varying because African climate exhibits differing degrees of temporal and spatial variability (Hulme et al. 2001). Africa is vulnerable to the effects of interannual climate variations such as the El Nino-Southern Oscillation (ENSO) with respect to extreme events (drought, floods, and changes in hydrologic patterns) (Conway et al. 2007; Latif and Dommenget 1999; Hulme et al. 2001). The portion of sub-Saharan Africa that depends entirely on the Nile River for its water supply is particularly susceptible to hydrologic changes that might be associated with a warmer climate. Flooding and droughts will be pose challenges in the face of increasing pressure on water supplies due to rapid population growth and dwindling resources (IPCC 2007; El-Fadel et al. 2003). The Nile River basin is home to almost 20% of Africa’s population. It has experienced high population growth rates, with the basin’s population expected to double by 2025 (Population Action International 2001). Virtually all projections are for continuing population growth, which in turn will increase demand for natural resources among the ten Nile River riparian countries. In recent years the potential effects of climate change on the Nile River basin have been given much more attention, in part as a result of studies (e.g. Yates and Strzepek 1998a, b; Conway et al. 2007) showing that the water resources of the basin are critically sensitive to climate change. The IPCC Fourth Assessment Report (AR4) has resulted in a wealth of General Circulation Model (GCM) simulations that have been archived in a consistent manner at the Lawrence Livermore National Laboratory Program for Climate Model Diagnosis and Intercomparison (PCMDI). These simulations provide the basis for a much more coherent analysis of possible effects of climate change using multimodel ensemble techniques (e.g., Krishnamurti et al. 2000) than has previously been possible. For instance, Maurer (2007) evaluated implications of projected 21st Century climate for California’s water resources using PCMDI-archived output from 11 IPCC/AR4 GCMs. Christensen and Lettenmaier (2007) evaluated implications of IPCC AR4 climate projections for Colorado River water resources from the same 11 GCMs and two global emissions scenarios. We follow the lead of these recent studies in using multimodel ensemble methods to evaluate the implications of 21st Century climate change for the Nile River basin. Future changes and uncertainties in the allocation of Nile River water resources may have significant effects on local and regional economies, agricultural production, energy availability, and environmental quality (NBI 2001; Hulme et al. 2005; Conway and Hulme 1993; Yates and Strzepek 1998a, b). Water resources planning based on the concept of a stationary climate is increasingly considered inadequate for sustainable water resources management (Mohamed et al. 2005; Milly et al. 2008). In addition to natural variability, which is incorporated in existing water planning methods, design of new water projects will have to deal with uncertainty Climatic Change (2010) 100:433–461 435 associated with population growth and a changing climate. Therefore, understanding the uncertainty in projected climate change over the next century is essential to understanding how the economy of the Nile basin will evolve, including social and environmental impacts. This uncertainty is attributable both to uncertainty in future global emissions (related to policy decisions and public response) and to uncertainties in model projections (due to differing sensitivities of the GCMs to perturbations in atmospheric composition). Water resources planning studies, which typically are conducted for time horizons of several decades, now require consideration of ongoing and future global climate change. The near certainty of increased future water demand in the Nile basin (notwithstanding uncertainty as to magnitude of the increases) contrasts with the uncertainty of climatically-induced changes in Nile River flow (Conway and Hulme 1996; Yates and Strzepek 1998a; Strzepek et al. 1995, 2000), both as to magnitude and direction. Although this study represents the first attempt to apply multimodel ensemble techniques to the Nile River basin using the IPCC AR4 global emission scenarios, the Nile River has been the focus of several previous studies of climate change using different climate models and techniques (e.g. Conway 2005; Conway and Hulme 1996; Conway et al. 2007; Hulme 1992; Hulme et al. 2005; Strzepek and Yates 1999; Strzepek et al. 1995). Yates and Strzepek (1998a, b) found that several climate change scenarios implied that agriculture would be negatively affected by climate change despite increased water availability and only moderate yield declines, as a result of impacts associated with changes in local and regional biophysical systems and shifts in national agricultural economies. Conway and Hulme (1996) used CO2 doubling climate scenarios which provided widely diverging pictures of possible future Nile River flows, ranging from a 30% increase to a 77% decrease. Strzepek and Yates (1999) used a dry and wet scenario analysis of possible future conditions and found that under wet climate scenarios, annual surplus water beyond 75 BCM (75 × 109 m3 ) remained unused whereas for drier scenarios (below 75 BCM), water was a constraint to agricultural production into the 21st century. As a result, resources were diverted to less water demanding crops and to livestock and non-agricultural sectors. Tate et al. (2004) analyzed the sensitivity of the water balance of Lake Victoria to climate change using HadCM3 A2 and B2 emission scenarios, and found that changes in annual rainfall and evaporation derived from HadCM3 implied that declines in water levels would occur during the 2021–2050 time horizon. This contrasted with projected increases in water levels later in the century (2070–2099). Since most of these studies were based on a single GCM and/or emission scenario, the uncertainty in these predictions was not evaluated. All of these studies were limited by the coarse spatial resolution of the climate models and the small number of climate models that could be evaluated. In this paper, we assess the hydrologic implications of climate change on the water resources of the Nile River basin through a four-step process. The first step is extraction of the key hydrologic drivers (precipitation and temperature) from the PCMDI archives. In our case, we extracted output from 11 GCMs to be consistent with Christensen and Lettenmaier (2007) and Maurer (2007). All the selected GCMs included at least one 100-year run, and two SRES (Scientific Report on Emission Scenarios) global emissions scenarios (A2, corresponding roughly to unconstrained growth in emissions, and B1, corresponding to elimination of global emissions 436 Climatic Change (2010) 100:433–461 increases by 2100) from PCMDI. The second step is removal of the inherent bias in the climate model predictions and resolution of the scale mismatch between the GCMs (most of which run at a spatial resolution of 2◦ to 5◦ latitude–longitude) and the spatial scale of our hydrologic model (which was applied at a 1/2 × 1/2◦ latitude– longitude spatial resolution). The third step is forcing the hydrologic model using the downscaled and bias corrected climate model output. The final step is driving a Lake Nasser reservoir operations model using the simulated streamflows associated with the 11 GCMs and two global emissions scenarios to assess the impact of the changing climate on hydropower production and irrigation water supply extracted from the Nile River at High Aswan Dam (HAD). 2 Approach Our overall approach is to start with multiple model ensembles of projected future (21st century) climate scenarios from the 2007 IPCC Fourth Assessment Report (AR4), which we then spatially downscale and bias correct to a spatial scale appropriate to a regional hydrology model. Each of 22 100-year ensembles (11 GCMs each using two emissions scenarios) is used to force the Variable Infiltration Capacity (VIC) hydrology model, which produces corresponding ensembles of projected streamflow at inflow points to major dams and reservoirs in the Nile River basin. Implications of the changes in streamflow for water management are analyzed by using the hydrology model sequences as input to a model of the Lake Nasser reservoir, formed by High Aswan Dam (HAD). We detail the specifics of each step in this section. 2.1 Study area The Nile River flows some 6700 km through ten countries before reaching the Mediterranean Sea, making it the longest international river system in the world. Its headwaters are in Lake Victoria at about 4◦ S latitude, from which it flows mostly northward to its mouth at 32◦ N latitude (see Fig. 1a). It has a drainage area of about 3.35 million km2 , which is equivalent to about 10% of the African continent or roughly 1/2 the area of the continental United States. Egypt and the Sudan are the two major users of this river, while Ethiopia is the primary source. This imbalance in the primary producers and consumers of the basin’s water resources lead Swain (1997) to conclude that the Nile is the international river system which currently has the greatest potential to precipitate major armed conflict. The major lakes in the basin (Lakes Victoria, Nasser and Tana) have a total surface area of about 81,500 km2 and the area covered by swamps is an additional 69,700 km2 (Biswas 1994). Precipitation is to a large extent governed by the movement of the Inter-Tropical Convergence Zone (ITCZ) and its interaction with topography. In general, precipitation increases from north to south, and with elevation. Precipitation is virtually zero in the Sahara Desert, and increases southward to about 1,200–1,600 mm/year on the Ethiopian and Equatorial Lakes Plateaus (Mohamed et al. 2005). The Nile is formed by three tributaries, the Blue Nile, the White Nile, and the Atbara. The flow of the Blue Nile is strongly seasonal because its runoff is primarily Climatic Change (2010) 100:433–461 437 Fig. 1 Head waters and sub-basins of the main stem Nile (a) and locations of the two gauging stations and flow routing network derived from 1/2◦ (latitude–longitude) DEM (b) driven by monsoon precipitation. The Blue Nile contributes about 60% of the total flow of the Nile, whereas the Baro-Akobo (Sobat), and Tekezze (Atbara) contribute slightly less than 15% each. The headwaters of all the tributaries of the Blue Nile are in the highlands of Ethiopia, and the bulk of their runoff (70% on average) occurs between July and September. Among the tributaries of the Blue Nile, the Upper Blue Nile (drainage area 175,000 km2 ), which contributes about 50% of the Nile’s flow at High Aswan Dam (HAD), is the most important. The White Nile and the Blue Nile join north of Khartoum, where they are joined by the Atbara. The river then flows north through Lake Nasser, the second largest man-made lake in the world, before splitting into two major distributaries just north of Cairo. 438 Climatic Change (2010) 100:433–461 2.2 General Circulation Models (GCMs) General Circulation Models (GCMs) are mathematical representations of atmospheric, oceanic, and continental processes and interactions. These models are limited by complexity and uncertainty as well as non-linear interactions among atmospheric and oceanic processes (Hillel and Rosenzweig 1989). Table 1 summarizes the 11 GCMs for which model output was used to construct forcing data for the hydrologic model. For IPCC/AR4, six global greenhouse gas emissions scenarios were used following the IPCC 2001 scenarios. The global emissions scenarios ranked from highest to lowest in terms of global average emissions at mid-century are A1FI, A2, A1B, B2, A1T and B1. The A1 world describes a future of rapid economic growth, low population growth, and rapid introduction of new and more efficient technology. World population approaches 8.25 billion by 2080 and economic growth averages 3.3%. The A1F1 and A1B subgroups differ only in their descriptions of the world’s energy sources; both assume continued reliance on fossil fuels. The A2 world is one of high population growth (14 billion by 2080) and less rapid economic development (2.3%). B1 describes a future of rapid change in economic structures and the introduction of clean technologies, where population growth is low (8.25 billion by 2080) but economic growth is relatively rapid (2.9%). In the B2 world, both population growth and economic growth are moderate (10.1 billion by 2080 coupled with 2.7% economic growth). Global emission scenarios A2 and B1 were chosen for this study because they are the most widely simulated global emission scenarios in all models. The A2 global emission scenario projects global average CO2 concentrations will reach 850 ppm by Table 1 Description of GCMs (General Circulation Models) GCM Modeling group, country IPCC model ID Reference CNRM Centre National de Recherches Météoroliques, France CSIRO Atmospheric Research, Australia Geophysical Fluid Dynamics Laboratory, USA Goddard Institute for Space Studies, USA Hadley Center for Climate and Prediction and Research, UK Institute for Numerical Mathematics, Russia Institut Pierre Simon Laplace, France Center for Climate Systems Research, Japan Max Planck Institute for Meteorology, Germany Meteorological Research Institute, Japan National Center for Atmospheric Research, USA CNRM-CM3 Salas-Mélia et al. 2005 CSIRO-Mk3.0 Gordon et al. 2002 GFDL-CM2.0 Delworth et al. 2006 GISS-ER Russell et al. 1995, 2000 UKMO-HadCM3 Gordon et al. 2002 INM-CM3.0 Diansky and Volodin 2002 IPSL-CM4 IPSL 2005 MIROC3.2 K-1 model developers 2004 ECHAM5/MPI-OM Jungclaus et al. 2006 MRI-CGCM2.3.2 Yukimoto et al. 2001 PCM Washington et al. 2000 CSIRO GFDL GISS HADCM3 INMCM IPSL MIROC MPI MRI PCM Climatic Change (2010) 100:433–461 439 2100. Scenario B1, on the other hand, assumes an increasing dependence on clean and resource-efficient technologies. CO2 concentrations under this scenario initially increase at nearly the same rate as in the A2 scenario, but then level off around midcentury and reach 550 ppm by 2100. 2.3 VIC land surface hydrologic model The Variable Infiltration Capacity (VIC) model of Liang et al. (1994, 1996) is a semi-distributed grid-based land surface hydrological model which parameterizes the dominant hydrometeorological processes taking place at the land surface– atmosphere interface. The VIC model forcings were daily precipitation, maximum and minimum temperature, and daily average wind speed, for each 1/2◦ model grid cell (other forcing variables—specifically downward solar and longwave radiation, and dew point were derived from daily temperature and temperature range using methods described by Maurer et al. (2002). The model was implemented at 1/2◦ (∼48 km) spatial resolution for the entire Nile basin. The model consists of two major components: vertical and horizontal. The vertical component calculates the water and energy balance components for each individual grid cell. The horizontal component is a convolution integral, which routes the runoff generated at each grid cell to specific locations. A mosaic representation of land cover, and sub-grid parameterizations for infiltration and the spatial variability of precipitation and temperature, account for spatial heterogeneities in key hydrological processes. The model uses three soil layers and one vegetation layer with energy and moisture fluxes exchanged between the layers. The model was calibrated for the entire Nile basin at three stream gauge locations, the Blue Nile at Eldiem, the main stem at Dongola, and the main stem at HAD. Calibration for the Lake Victoria basin could not be performed due to lack of reliable observed streamflow records. A calibration procedure similar to that described in Nijssen et al. (1997) and Payne et al. (2004) was followed to assure a match between model-simulated and observed flows for the period in which historic streamflow observations were available. Validation results for two gauging stations are shown in Fig. 2a–b. The simulations generally reproduce the observed long term monthly mean hydrograph and also capture interannual flow variations. Since VIC was run at a finer spatial resolution than the General Circulation Models, a bias correction and spatial downscaling step (method described in the Section 2.5) was used to bridge the resolution gap between climate model and VIC for the future climate simulations. 2.4 Uncertainties in GCMs prediction of climate change variables Despite advances in GCMs and computational resources, which have resulted in increased spatial resolution, climate model outputs nonetheless have considerable biases and uncertainties in the simulation of both temperature and precipitation under current climate conditions. These biases are usually large enough that direct use of these variables as input to hydrological models is infeasible (see e.g. IPCC AR4 WG1 report, Tebaldi et al. 2004, Greene et al. 2006 and Wilby et al. 2000), and therefore a bias removal and spatial downscaling step is essential. Although spatial resolution can be improved through use of regional climate models, and biases reduced somewhat, Wood et al. (2004) show that even when regional climate models 440 Climatic Change (2010) 100:433–461 Fig. 2 Naturalized and observed Nile River flow at selected gauging stations and some measures of agreement between naturalized and observed flow. R correlation coefficient, E Nash–Sutcliffe model efficiency coefficient, BIAS bias for the period 1966–1999. Main Nile River flow at DONGOLA (a) and Upper Blue Nile at Rosiers Upstream of El diem (b), Sudan for the period 1970–1999. Validation period shown excludes the period 1950–1965 used for model calibration are used, a bias removal step is essential where the climate model output is used to force a hydrological model. We summarize in this section the approach that we have used, which is a variation of the bias correction and spatial downscaling (BCSD) approach described in more detail by Wood et al. (2002, 2004), and used in a number of previous studies (e.g. Christensen et al. 2004; Christensen and Lettenmaier 2007; Hayhoe et al. 2007; Maurer 2007). 2.5 Bias Correction and Spatial Downscaling (BCSD) The BCSD approach we use in this study is described by Wood et al. (2002, 2004). It takes monthly values of precipitation (Ptot) and average temperature (Tavg) from Climatic Change (2010) 100:433–461 441 the GCM output. These values are disaggregated in space (to the 1/2◦ latitude– longitude resolution) and in time (from monthly to daily) to provide forcings to our land surface hydrologic model. Although GCMs produce output at much shorter time steps than monthly, the archived output at PCMDI is monthly for most of the 11 models we used. The BCSD method uses empirical quantile–quantile mapping as its main element. In brief, the method downscales monthly simulated and observed temperature and precipitation at the GCM spatial scale (regridded to common 2 × 2◦ latitude by longitude spatial resolution) to 1/2◦ resolution at which the VIC hydrology model was applied. The quantile–quantile mapping procedure is “trained” to monthly empirical probability distributions of the climate model output for current climate and observed gridded climate data at the same (2◦ ) spatial resolution. The climate model output was temporally and spatially disaggregated using the resampling approach of Wood et al. (2002, 2004) to create a daily time series for the hydrology model at the 1/2◦ spatial resolution. The resampling was performed on a monthly basis by sampling with replacement from the historical observation record in such a way that the statistics of the monthly 1/2◦ values were preserved, while the sequencing of daily values was provided by the subsampling. In application of the method, the linear trend in Tavg (daily average temperature) is removed prior to bias correction and is re-imposed afterward, to avoid distortion of the tails of the probability distribution of temperature as temperatures rises. The climatology for the historical run (1950– 1999) was the global gridded precipitation data set developed and described in Adam and Lettenmaier (2003), and Adam et al. (2006). 3 Results and discussions In this section we summarize our analysis of the results from bias corrected and spatially downscaled GCM climate models and two SRES global emission scenarios A2 and B1. The bias corrected and spatially-downscaled hydrologic drivers (temperature and precipitation) for future climate (2000–2100) are compared to the 1950–1999 gridded historical global data set of Adam and Lettenmaier (2003). We further analyzed and compared derived hydrologic variables (runoff, evapotranspiration and soil moisture) simulated by VIC forced by GCM scenarios to historical simulations (1950–1999). The climate change simulations derived from both global emissions scenarios are segregated into three time horizons, period I (2010–2039), period II (2040–2069), and period III (2070–2099). Hydrologic response of the basin to changing climate is analyzed by quantifying future streamflow changes at selected gauging stations and further evaluating the implications of these streamflows for water management using a reservoir operation model that represents annual hydropower production and irrigation water releases at High Aswan Dam. 3.1 Temperature and precipitation changes Although GCM model output is archived at a monthly time step and is downscaled to a daily time step to force the VIC model, we focus here on precipitation and temperature changes in northern hemisphere winter (DJF) and summer (JJA), as these are the main rainy season of the Nile basin (DJF for Equatorial Africa and JJA 442 Climatic Change (2010) 100:433–461 for Eastern Africa), as well as annually. We summarize model results both as the multimodel mean, and a measure of Inter-Model-Spread, range of prediction, which gives an indication of the uncertainty in the future projections. The bias-corrected and downscaled temperature and precipitation time series from each climate model Fig. 3 a Downscaled and bias-corrected long-term (30 year) mean monthly temperature, precipitation, simulated runoff, evapotranspiration, and soil moisture for the entire Nile basin. b Downscaled and bias-corrected long-term (30 year) mean monthly temperature, precipitation, simulated runoff, evapotranspiration, and soil moisture for Blue Nile basin region. c Downscaled and bias-corrected long-term (30 year) mean monthly temperature, precipitation, simulated runoff, evapotranspiration, and soil moisture for the Lake Victoria region Climatic Change (2010) 100:433–461 443 (ensemble member) was used to force the VIC model for both A2 and B1 global emissions scenarios. Figure 3a–c show the bias-corrected and downscaled long-term multimodel average precipitation, temperature and simulated hydrologic parameters (evaporation, runoff, and soil moisture). The 1950–1999 (Adam and Lettenmaier 2003; Adam et al. 2006) historical precipitation and temperature data were used as a baseline reference for the purpose of model calibration, validation and evaluation of predicted changes from climate models. Because the sub-basins of the Nile basin have different physical and hydroclimatological characteristics, we evaluated results Fig. 3 (continued) 444 Climatic Change (2010) 100:433–461 over the main stem Nile (Main Nile) and the two most important sub-basins, the Blue Nile sub-basin and the Lake Victoria region (Equatorial region) separately. Annual average temperature changes across the Nile basin relative to the 1950– 1999 historical average range from 0.91 to 1.95◦ C for A2 and 0.94 to 1.54◦ C for B1 global emissions scenario for period I (2010–2039). These values are equivalent to about 0.3 to 0.6◦ C and 0.3 to 0.5◦ C warming per decade for A2 and B1 global emissions scenarios, respectively. The model projections indicate that warming will continue across the Nile region through the late 21st century to a level of (3.4, 5.9)◦ C Fig. 3 (continued) Climatic Change (2010) 100:433–461 445 and (3.0, 4.1)◦ C (Min, Max) for A2 and B1 global emissions scenarios by 2070–2099 relative to the 1950–1999 historical average. For the purpose of assessment of the impact and implications of green house gas-induced climate change scenarios of temperature and precipitation to the water resources management of the Nile basin, we used the multimodel mean (average of 11 GCMs) as well as range of prediction. As expected, Scenario A2 is generally warmer than Scenario B1. For the entire Nile basin (Fig. 3a) multimodel annual average temperature increased by 1.5 (1.3), 3.2 (2.8) and 4.4 (3.6)◦ C relative to the historical (1950–1999) average for the A2 (B1) global emissions scenarios and periods I to III respectively (Table 2). Unlike future temperature predictions, the trends in which are unidirectional across GCM models and throughout the Nile River basin, the directions of precipitation changes are mixed and highly variable both from sub-basin to sub-basin and from season to season. Averaged across GCM models for SRES A2 and B1 global emissions scenarios and for periods I to III, multimodel averages suggest that the entire Nile basin, Blue Nile and Lake Victoria regions will experience increases in DJF rainfall with the exception of the Lake Victoria region which is expected to experience decreases in the late 21st Century (Fig. 5). JJA precipitation changes have no clear signals in terms of magnitude and direction and vary substantially from subbasin to sub-basin (Fig. 4). Individual GCM model predictions show wide range of prediction by region and season for precipitation. For the entire Nile region for the A2 emissions scenario, DJF precipitation changes range from −24 to 37% and −40 to 18% for period I and III respectively. JJA precipitation for the entire Nile basin and A2 emissions scenario ranges from −21 to 34 and −42 to 15% for periods I and III respectively, showing relatively narrower range of prediction for JJA than DJF. The entire Nile basin is projected to experience multimodel average DJF precipitation increases up to 23 (12%) to 12 (3%) for A2 (B1) global emissions scenarios for periods I and III respectively (Fig. 5). JJA precipitation changes will have early increases of up to 19 (15%) and then decreases in mid century of −3 (−1%) and then increases of 7 (4%) in period III, mainly contributed by the late 2080–2090 JJA increases in the Blue Nile sub-basin for A2 (B1) global emissions scenarios. Similarly, multimodel average DJF and JJA precipitation changes for Blue Nile and Lake Victoria regions are shown in Fig. 5. Previous studies of seasonal precipitation changes over Africa are qualitatively similar as to the direction and magnitude of seasonal precipitation changes. Hulme et al. (2001) studied the impacts of African climate change by generating some Table 2 Multimodel average annual and seasonal (JJA and DJF) temperature changes as differences in degrees (◦ C) of the historical (1950–1999) for two sub-basins of the Nile basin and entire Nile basin for A2 (B1) global emission scenarios and three periods Scenario Lake Blue Entire Nile basin Victoria Nile Annual DJF JJA Historical 19.8◦ C 21.25◦ C 23.45◦ C 22.11◦ C 25.24◦ C A2 2010–39 A2 2040–69 A2 2070–99 B1 2010–39 B1 2040–69 B1 2070–99 1.06 2.51 3.45 1.0 1.9 2.91 1.25 3.1 4.18 1.22 2.55 3.37 1.48 3.22 4.36 1.31 2.76 3.65 1.38 2.89 3.90 1.02 2.78 3.54 1.16 2.45 3.60 1.15 2.50 3.58 446 Climatic Change (2010) 100:433–461 Fig. 4 Seasonal changes in precipitation and runoff for wet season (JJA). Panel (a) shows A2 emissions scenario JJA precipitation changes relative to mean historical (1950–1999), upper left panel shows observed JJA 1950–99 precipitation, upper right panel shows changes in period 1, lower left shows changes in period 2, and lower right shows changes in period 3. Panel (b): same as panel (a), but for runoff sensible guesses of climate change magnitudes. They sampled a plausible range of scenarios at a global scale by making choices about future greenhouse gas forcings and climate sensitivity following Hulme and Carter (1999b) and Carter et al. (1999). Their analysis for the A2 emissions scenario combined with what they termed a high sensitivity output showed that large areas of equatorial Africa will experience ‘significant’ increases in DJF rainfall of up to 50 or 100%, which is roughly comparable to our 7 to 51% increases in the Blue Nile sub-basin. They also found for the same scenario that precipitation would decrease ‘significantly’ in JJA over parts of the Horn of Africa and northwest Africa. In contrast, the direction and magnitude of Climatic Change (2010) 100:433–461 447 Fig. 5 Seasonal changes in precipitation and for wet season (JJA) and dry season (DJF). Upper panel shows A2 and B1 emissions scenarios for DJF precipitation change and lower panel shows JJA precipitation changes relative to mean historical (1950–1999) DJF and JJA precipitation our JJA changes varies by time period, and do not show a clear signal, especially for Blue Nile sub-basin. In general, there is much less consistency among climate model projections of precipitation than temperature (see Fig. 3a–c). In the multimodel ensemble, mean annual precipitation increases over the entire basin for period I, however, by period III, there are substantial basin-wide precipitation decreases. The IPCC TAR (IPCC 2001) concluded that the Nile basin will experience decreases in precipitation ranging from zero and 40% by the end of the 21st century, a result which was based on nine GCMs. Although less specific to the Nile basin, similar conclusions are also inferred from the IPCC AR4. The report on African climate change (IPCC 2007) found that sometime between 2080 and the end of the century, average annual precipitation is very likely to decrease by a fifth along Africa’s Mediterranean coast, with declines also expected in the northern Sahara and the northern West African coast. Declines were also predicted for much of southern Africa with the extreme west of the region likely to experience decreases of as much as 40% through June and August. In contrast, tropical and eastern Africa may experience increased rainfall of 7% (IPCC 2007). As we will show below, the range of precipitation predictions by the IPCC/AR4 climate models is generally consistent with this earlier expectation as summarized in IPCC TAR. 448 Climatic Change (2010) 100:433–461 Despite the variations in individual climate model predictions, over the entire Nile basin eight (nine) and three (six) of the 11 GCMs for the A2 (B1) global emission scenario predicted increases in precipitation for period I and III, respectively (Tables 3, 4, 5, 6, 7 and 8). In the multimodel ensemble average, Nile basin changes in precipitation, expressed as percentages of historical (1950–1999), are 115 (117), 98 (104) and 93 (96) for SRES A2 (B1) global emissions scenarios with range of prediction for the A2 emission scenario of (−2, 26), (−12, 12) and (−20, 3), The range of prediction values for scenario B1 are (5, 30), (−5, 15) and (−10, 6). A range of prediction gives a rough indication of the ranges of uncertainty of climate change predictions among GCMs; wider range indicates more uncertainty and greater divergence among climate change predictions. The general trend is that range of prediction becomes smaller towards the late 21st century, although the reason is not clear. The multimodel ensemble average annual precipitation changes for the Blue Nile sub-basin expressed as a percentage of 1950–99 annual average precipitation are 115 (117), 98 (104) and 106 (96) for the A2 (B1) global emission scenario and periods I to III, respectively. Changes for the Lake Victoria sub-basin are 117 (123), 98 (105) and 90 (97) relative to the historical average (1950–1999). Tables 3, 4, 5, 6, 7, and 8 summarize the annual average changes in precipitation and temperature for the entire Nile basin and the two sub-basins. We evaluated the end points in precipitation projections, which correspond to the GISS (Goddard Institute for Space Studies) and GFDL (Geophysical Fluid Dynamics Laboratory) climate models. Averaged over the entire Nile basin and considering A2 global emissions scenario, GISS is the wettest of the models, with precipitation increases of 40, 20, 16% relative to the 1950–1999 historical average for periods I to III, respectively. GFDL is the driest model with corresponding predicted decreases in precipitation of 16, 38, and 36% relative to historical for the three future periods. The corresponding projected annual average temperature increases range from 1.0 to 6.0◦ C for GISS and 1.1 to 3.7◦ C for GFDL for the entire Nile basin for periods I and III (Tables 9 and 10). 3.2 Streamflow changes Conway and Hulme (1993) and Calder et al. (1995) found that the combined effects of precipitation and temperature changes would have profound effects on the streamflow regime of the Nile River. In this section, we analyze the inferred changes in streamflow that would accompany the precipitation and temperature changes summarized in Section 3.1. Table 11 shows the projected changes in Nile River flow at HAD and the Blue Nile flow at Eldiem. Although there is a consistent decline in streamflow in the ensemble mean and for most of the GCMs by the end of the 21st century, the specific magnitudes and direction of changes in predictions differ substantially across GCMs. Changes in precipitation and temperature result in disproportional changes in the hydrologic response of a river basin. For the A2 emission scenario, Fig. 6a–d show simulated mean monthly hydrographs derived from all 11 GCMs at HAD, and Blue Nile at ElDiem for period I and III. Under the A2 emission scenario, projected changes in precipitation range between −16 to 40%, with a multimodel median of 7% early in the century (2010–2039). These changes resulted in changes in the long-term mean annual streamflow entering HAD ranging from −23 to 34%, with a multimodel Lake Victoria Blue Nile Entire Nile GFDL 1.11 (1.23) 1.42 (1.25) 1.55 (1.35) CSIRO 0.87 (0.92) 1.57 (1.44) 1.77 (1.52) CNRM 1.12 (1.09) 1.5 (1.1) 1.63 (1.23) MPI 1.02 (0.96) 1.34 (1.3) 1.27 (1.17) MRI 0.97 (0.89) 1.09 (0.78) 1.41 (0.94) MIROC 1.33 (1.16) 1.76 (1.66) 1.95 (1.37) IPSL 1.05 (1.03) 1.65 (1.49) 1.43 (1.23) INMCM 1.22 (1.15) 1.13 (0.9) 1.38 (1.01) HadCM3 1.28 (1.08) 1.35 (1.12) 1.35 (1.12) PCM 0.72 (0.65) 0.96 (0.99) 0.91 (1.09) Lake Victoria Blue Nile Entire Nile GFDL 1.89 (1.44) 3.21 (2.87) 3.67 (2.73) 2.2 (1.62) 3.15 (2.62) 3.47 (2.84) CSIRO CNRM 2.57 (2.05) 3.13 (2.33) 3.77 (2.66) MPI 2.45 (1.95) 2.71 (2.65) 3.59 (2.58) MRI 2.67 (1.94) 3.56 (2.67) 3.78 (2.69) MIROC 3.23 (2.46) 3.54 (2.79) 3.99 (3.43) IPSL 2.75 (2.56) 3.05 (2.83) 3.31 (2.97) INMCM 2.96 (1.78) 3.25 (2.76) 3.69 (3.09) HadCM3 2.76 (1.68) 3.26 (2.83) 3.75 (3.22) PCM 2.22 (1.95) 2.54 (1.99) 2.77 (2.25) Lake Victoria Blue Nile Entire Nile GFDL 3.44 (2.94) 3.21 (3.87) 3.67 (3.37) CSIRO 3.23 (3.11) 3.89 (3.42) 4.53 (3.79) CNRM 3.32 (2.81) 3.83 (3.21) 4.77 (3.64) MPI 3.88 (3.17) 4.71 (3.38) 4.87 (3.81) MRI 3.47 (3.05) 4.56 (3.41) 4.88 (3.59) MIROC 3.93 (3.38) 5.54 (3.79) 5.86 (4.13) IPSL 3.37 (2.66) 3.85 (3.13) 4.56 (3.45) INMCM 3.91 (2.18) 4.23 (3.76) 4.49 (3.58) HadCM3 4.01 (3.26) 4.66 (3.83) 4.21 (3.99) PCM 2.39 (2.25) 2.84 (2.87) 3.38 (3.01) GISS 141 (147) 153 (158) 140 (142) Region Lake Victoria Blue Nile Entire Nile 82 (92) 87 (96) 84 (90) GFDL 135 (148) 118 (121) 131 (138) CSIRO 128 (139) 109 (119) 107 (114) CNRM 124 (129) 119 (114) 127 (133) MPI 115 (110) 133 (139) 101 (110) MRI 122 (132) 128 (135) 105 (109) MIROC 131 (135) 107 (122) 125 (126) IPSL 97 (101) 93 (90) 92 (97) INMCM 96 (100) 102 (109) 95 (101) HadCM3 124 (125) 123 (118) 120 (126) PCM Table 6 2010–2039 multimodel annual average change in precipitation (%) relative to 1950 1999 historical average for sub-basins of the Nile basin and the entire basin for each GCM and the A2 (B1) global emissions scenario GISS 3.95 (3.22) 4.72 (3.40) 5.04 (3.74) Region Table 5 2070–2099 multimodel annual average change in temperature (◦ C) relative to 1950–1999 historical average for sub-basins of the Nile basin and the entire basin for each GCM and the A2 (B1) global emissions scenario GISS 2.01 (1.55) 2.72 (1.55) 3.0 (1.97) Region Table 4 2040–2069 multimodel annual average change in temperature (◦ C) relative to 1950–1999 historical average for sub-basins of the Nile basin and the entire basin for each GCM and the A2 (B1) global emissions scenario GISS 0.99 (0.85) 1.51 (1.38) 1.64 (1.28) Region Table 3 2010–2039 multimodel annual average change in temperature (◦ C) relative to 1950–1999 historical average for sub-basins of the Nile basin and the entire basin for each GCM and the A2 (B1) global emissions scenario Climatic Change (2010) 100:433–461 449 131 (137) 124 (128) 120 (124) Lake Victoria Blue Nile Entire Nile 68 (87) 66 (72) 62 (77) GFDL 104 (112) 89 (101) 113 (114) CSIRO 105 (109) 87 (111) 94 (103) CNRM 103 (106) 105 (102) 109 (117) MPI 98 (100) 100 (119) 89 (99) MRI 107 (112) 103 (121) 93 (103) MIROC 112 (105) 83 (107) 115 (112) IPSL 81 (92) 79 (83) 81 (79) INMCM 78 (81) 85 (93) 88 (91) HadCM3 100 (111) 101 (103) 110 (115) PCM Lake Victoria Blue Nile Entire Nile GFDL 56 (68) 73 (79) 64 (72) 98 (113) 99 (101) 103 (108) CSIRO CNRM 100 (104) 126 (113) 84 (93) MPI 90 (94) 89 (94) 91 (100) 82 (101) 121 (123) 77 (96) MRI 97 (96) 113 (111) 85 (88) MIROC 107 (102) 93 (103) 102 (106) IPSL 68 (77) 96 (91) 71 (72) INMCM 73 (84) 97 (116) 93 (94) HadCM3 101 (103) 114 (98) 104 (106) PCM Lake Victoria 116 mm 117% (136 mm) 98.81% (115 mm) 89.63% (104 mm) 123.45% (143 mm) 104.7% (121 mm) 96.63% (112 mm) Scenario Historical (1950–1999) A2 2010–39 A2 2040–69 A2 2070–99 B1 2010–39 B1 2040–69 B1 2070–99 116% (113 mm) 91% (89 mm) 105% (103 mm) 120% (117 mm) 104% (102 mm) 106% (104 mm) 98 mm Blue Nile 112% (78 mm) 98% (68 mm) 90% (63 mm) 117% (82 mm) 103% (72 mm) 95% (67 mm) 70 mm Annual Entire Nile basin +12% (7 mm) −7% (−4 mm) −11% (−0.12 mm) +8% (4.5 mm) −3% (−1.65 mm) −10% (5.5 mm) +23% (3.5 mm) +8% (1.2 mm) +12% (1.8 mm) +12% (1.8 mm) +10% (1.5 mm) +3% (0.15 mm) JJA 55 mm 15 mm DJF Table 9 Multimodel average annual and seasonal (JJA and DJF) precipitation changes as percentages (mm) of the historical (1950–1999) long-term mean monthly precipitation for two sub-basins of the Nile basin and entire Nile basin for the A2 (B1) global emissions scenario and three periods GISS 114 (121) 137 (142) 116 (113) Region Table 8 2070–2099 multimodel annual average change in precipitation (%) relative to 1950–1999 historical average for sub-basins of the Nile basin and the entire basin for each GCM and the A2 (B1) global emissions scenario GISS Region Table 7 2040–2069 multimodel annual average change in precipitation (%) relative to 1950–1999 historical average for sub-basins of the Nile basin and the entire basin for each GCM and the A2 (B1) global emissions scenario 450 Climatic Change (2010) 100:433–461 Climatic Change (2010) 100:433–461 Table 10 Simulated streamflow by each climate model routed to the main stem Nile gauging station at High Aswan Dam (HAD) for periods I to III and global emission scenarios A2 (B1) Numbers are expressed as percentages of the mean annual historical flow (1950–1999) 451 GCM Period 1 2010–2039 Period 2 2040–2069 Period 3 2070–2099 GFDL GISS CSIRO INMCM MPI CNRM IPSL PCM MIROC HADCM3 MRI 77 (92) 134 (136) 108 (127) 95 (99) 130 (128) 112 (123) 101 (110) 110 (125) 119 (119) 89 (95) 126 (118) 71 (85) 126 (127) 102 (105) 82 (83) 112 (105) 96 (100) 87 (90) 94 (103) 102 (98) 77 (78) 108 (98) 62 (75) 112 (115) 107 (112) 70 (73) 97 (93) 83 (85) 75 (79) 81 (91) 85 (86) 67 (68) 93 (95) median of 10%. By the late 21st century (2070–2099), projected changes in precipitation range from −24 to 26%, with multimodel median of −3% and resulting streamflow changes between −38 to 12%, with multimodel median of −17%. Projections of streamflow for the B1 scenario are similar, but by 2070–2099 the declines in streamflow are less severe than for the A2 emissions scenario. In general, the above results indicate increasing precipitation during the early 21st century in the multimodel average under both emissions scenarios which results in larger basin streamflow increases due a multiplier effect (elasticity) on streamflow changes relative to precipitation changes, and modest negative effects on streamflow resulting from temperature increases. In contrast, the temperature effects tend to dominate later in the 21st century, and are compounded by precipitation decreases. To have a sense of the seasonal inter-model variability of predicted streamflow changes, we analyzed ranges of precipitation changes and associated streamflow changes for DJF and JJA for individual GCMs and the multimodel average. For the downstream-most gauge (HAD), Fig. 7a–b show increases in DJF streamflow for the three time periods (from early to late 21st century), with a declining trend towards the end of 21st Century mainly due to reduced DJF precipitation in the Lake Victoria region (about −10% as shown in Fig. 5). The JJA seasonal streamflow distribution has a mixed signal as do the precipitation predictions, but the ranges of precipitation changes are generally larger than the streamflow ranges. Table 11 Simulated streamflow for each climate model routed to the Blue Nile gauging station at Eldiem (Sudan) for periods I to III and the global emission scenario A2 (B1) Numbers in parenthesis are percentages of the mean annual historical flow (1950–1999) GCM Period 1 2010–2039 Period 2 2040–2069 Period 3 2070–2099 GFDL GISS CSIRO INMCM MPI CNRM IPSL PCM MIROC HADCM3 MRI 83 (87) 148 (154) 126 (125) 100 (106) 113 (112) 105 (106) 131 (130) 127 (125) 130 (129) 106 (115) 131 (120) 77 (78) 133 (136) 102 (102) 81 (87) 92 (85) 86 (91) 106 (113) 102 (101) 105 (105) 86 (92) 106 (99) 71 (72) 126 (125) 112 (117) 74 (81) 83 (91) 97 (105) 96 (105) 103 (112) 95 (104) 76 (95) 96 (101) 452 Climatic Change (2010) 100:433–461 Fig. 6 Simulated mean seasonal cycle of streamflow derived from all 11 GCMs for A2 global emissions scenario at the main stem Nile gauging station, HAD (a, b) for the beginning (2010–2039) and end of the 21st century (2070–2099) and at Blue Nile gauging station (El diem—Sudan) for the same time period (c, d) The Blue Nile basin, which receives more than 70% of its annual runoff during the JJA season, experiences mixed signals among the models. JJA seasonal precipitation predictions show increases on average, with predictions ranging from −26 to 53%, and multimodel median of 18%; these changes in precipitation result in streamflow increases ranging from −17 to 48%, with multimodel median of 26% for period I Climatic Change (2010) 100:433–461 453 Fig. 7 Simulated seasonal streamflow changes derived from all 11 GCMs and multimodel average for A2 global emissions scenario at the main stem Nile gauging station, HAD (a, b) and Blue Nile at El Diem (c, d) for early (2010–2039), mid-(2040–2069) and late-(2070–2099) 21st century expressed as percentage changes from mean seasonal Historical flow (1950–1999) and A2 emissions scenario. The range of precipitation predictions becomes wider through the late 21st century as compared to streamflow. By 2070–2099 precipitation predictions for the Blue Nile basin range from (−36 to 24%, with multimodel median 24%, resulting in streamflow predictions ranging from −29 to 23% with a multimodel median of −10%. There are disparities among models as to precipitation predictions over both the Blue Nile and White Nile basins (Hulme et al. 2001). However, increasing temperature will lead to greater evapotranspiration placing additional stress on water resources regardless of changes in precipitation. The seasonal precipitation and streamflow changes summarized above suggest that the Blue Nile flow tends to be more sensitive to temperature than to precipitation, particularly late in the 21st century as annual average temperature is expected to be warmer by 3◦ C or so in the multimodel average. Similarly, in the Lake Victoria region, Conway and Hulme (1996) found that a 10% increase in Lake Victoria rainfall caused a 31% increase in runoff, whereas and a 4% increase in lake evaporation caused an 11% decrease in runoff. Previous studies of the impact of climate change on Nile River flow have been confounded by inconsistencies in global emissions scenarios and other aspects of the model simulations and partially for that reason have produced widely divergent results. There are considerable inter-model differences in precipitation and 454 Climatic Change (2010) 100:433–461 temperature, which result in relatively large ranges in predicted future Nile flow. Nonetheless, there is some general consistency of our results with other recent studies. For instance, Arnell (1999) suggested that precipitation in the Nile basin would increase by about 10% by 2050, but he inferred that this increased precipitation would be offset by increased evapotranspiration, implying that the net effect on the main stem flow might be insignificant. Conway and Hulme (1996) used hydrologic models of the Blue Nile and Lake Victoria basins similar to the water balance model of Piper et al. (1986). By combining changes in Lake Victoria outflows with changes in runoff in the other Nile sub-basins, they estimated a range of −9% to +12% changes in mean annual Nile flows at HAD for 2025. Strzepek et al. (1995) assessed the impact of climate change using three GCMs with doubled global atmospheric concentrations (2 × CO2 ) to predict Nile flow changes at HAD for 2060. In their results, the wettest model resulted in a 30% increase in annual streamflow; whereas for the intermediate model, there was a 12% decrease; and for the driest model there was a 78% decrease. Yates and Strzepek (1998a) found declines up to 9% in the annual flow at HAD by 2060 for doubled CO2 for one model, but increasing precipitation for two other models for the same period that resulted in about 40% increase in annual flow at HAD. Yates and Strzepek (1998b) reanalyzed the results of their CO2 doubling scenarios, and found that five of six GCMs showed increased streamflows at HAD for the 2060s (roughly the time of CO2 doubling) with increases as much as 137% for one model. Only one GCM showed a decline in annual flow at Aswan (−15%). In summary, the multimodel approach we used shows some agreement with previous studies to the extent that temperatures rise throughout the Nile basin in all studies, which has a negative effect on streamflow. However, considerable uncertainty and disparity in spatial and temporal predictions of changes in precipitation complicates the overall analysis of streamflow changes. To provide a sense of the characteristics of the multimodel ensemble streamflow predictions, we summarize our streamflow prediction results in Tables 11 and 12. These results show that for a change in precipitation of +15 (+17)% associated with warming of +1.5 (+1.3)◦ C for the period 2010–2039 for the A2 (B1) global emissions scenario from the historical simulation, annual Nile flow at HAD will increase by +11 (14)%. However, for a precipitation change of −7 (−4)% and warming of +4.4 (+3.6)◦ C for the period 2070–2099, mean annual Nile flow will decrease by −16 (−13)% for the A2 (B1) global emissions scenario. Although we did not undertake an analysis of elasticity of Nile River flows (the fractional change in flow per fractional change in precipitation) Table 12 Multimodel High Aswan Dam hydropower production metrics (×103 GWH/year) Scenario Minimum 1st quartile Historical 9.66 × 103 GWH/year A2 2010–39 4.7 7.5 A2 2040–69 3.7 8.2 A2 2070–99 3.1 6.5 B1 2010–39 4.8 8.1 B1 2040–69 4.3 8.8 B1 2070–99 2.3 6.8 Mean 3rd quartile Max Percentage change relative to historical annual hydropower production (×103 GWH/year) 10.7 9.9 9.0 11.4 10.5 8.8 13.2 11.0 10.4 13.2 11.7 10.1 20.5 17.3 17.5 21.6 18.8 16.6 110.3% (1) 102.6% (0.25) 93.4% (−0.65) 117.6% (1.7) 108.2% (0.8) 91.6% (−0.84) Climatic Change (2010) 100:433–461 455 (see e.g., Dooge et al. 1999), it is clear that the effect of the temperature increases is to decrease the effective elasticity with respect to increasing precipitation, and to increase it with respect to decreasing precipitation. 3.3 Implications of climate change to water resource management Because water resources management is inextricably linked with climate, the prospect of global climate change raises serious concerns as to the sustainability of water resources and regional development (Riebsame et al. 1995). Efforts to provide adequate water resources in the Nile River basin will be challenged over the next century by the pressures of increasing population and resulting land use change, and with potential hydroecological consequences. Changes in climate and climate variability (which results in droughts and flooding) will inevitably complicate water resources management in the Nile basin. According to the IPCC (1998), the Nile River experienced reductions in runoff of 20% between 1972 and 1987, which lead to significant interruptions in hydropower generation. A 1995 study by Riebsame et al. (1995) also concluded that among the several major rivers studied, the Nile was the most susceptible to climate change in terms of its potential for hydropower production. To identify the nature of interactions between climate change and the managed water resource system, we used a simple reservoir operations model which had the objective of meeting target hydropower and irrigation releases at HAD. We term this model the Lake Nasser Reservoir Model (LNRM). LNRM is a simulation model which represents the major physical water management structures, reservoir operation rule curves and water use policies of the Nile system. In constructing LNRM, we made two assumptions. First, we based operation of HAD on the provisions of the 1959 treaty between Sudan and Egypt, a treaty that was not agreed to or recognized by other riparian countries (including Ethiopia, which is the headwater source for more than 86% of the Nile’s water). The treaty awarded Sudan 25% (18.5 BCM) of the mean annual naturalized Nile flow at HAD of 84 BCM and 75% (55.5 BCM) to Egypt with any flow in excess of the mean annual flow of 84 BCM to be shared equally between Egypt and Sudan (Said 1993; Johnson and Curtis 1994). The calculation includes ten BCM which is estimated to be the annual evaporation loss from Lake Nasser. The second assumption is based on an assessment of flow and hydropower impacts of two development scenarios in the Blue Nile Basin by the Nile Decision Support System (DSS) of Georgakakos (2004) which was based on an assumption of climate stationarity. The first scenario in the DSS was the current and existing conditions of river flow and energy production whereas the second scenario was based on development of four major hydropower plants (progressive hydropower development in Ethiopia). The study concluded that this development will not negatively impact downstream hydropower production; therefore we have not included future upstream development in the Blue Nile basin in LNRM. Furthermore, Sudan’s abstractions are assumed to be similar to those required to meet historical demand even though completion of Merowe Dam in Sudan will affect evaporation losses. LNRM represents: (1) Lake Nasser reservoir and HAD characteristics, (2) simulated river flows (reservoir inflows), (3) existing installed hydropower facilities, (4) water use policies, and (5) reservoir operation rules. All the sequences of data flow are 456 Climatic Change (2010) 100:433–461 linked using flow network nodes, and represent locations of local inflow and/or water withdrawals and returns. LNRM is driven by naturalized streamflow simulated using the VIC hydrologic model. The system basically models Sudan’s complex water management system upstream of Lake Nasser as a single, lumped withdrawal upstream of the inflow to Lake Nasser. LNRM calculates evaporative losses from the lake surface based on reservoir surface area which is derived as a nonlinear function of head and reservoir storage. Hydropower production and irrigation water release are modelled as a function of inflow, minimum and maximum release constraints, reservoir elevation, tailwater elevation, turbine efficiency, and reservoir storage. The hundred-year time series of monthly inflow records associated with each GCM represents the dynamic supply to the reservoir, with the initial storage in Lake Nasser was set to its 1999 value of 162.5 BCM (reservoir elevation 182.1 m). The reservoir storage is linked to converters that describe the relationships resulting from the particular state of the storage. They produce reservoir elevation, power generation, and spill. Required release from the reservoir is linked to the set of converters and together they describe the water release requirements from the reservoir for irrigation water supply. To make the LNRM simulation realistic, the irrigation water demand was assumed to be supply driven and dynamic in nature, which allows future agricultural sector expansion, with mandated 5%, 10% and 15% annual average increases in irrigation water demand for Periods I–III, respectively. The model also allows for cutbacks in the demand by percentages which are designed to overcome water shortages. Monthly streamflows produced by forcing VIC with bias corrected and downscaled output from each GCM were translated first into time series that account for upstream monthly withdrawals. The monthly withdrawal associated with each climate model’s streamflow sequence was subtracted as a lumped single withdrawal prior to running LNRM. This approach was also used by Strzepek et al. (1995). LNRM system objectives include meeting water supply targets and avoiding water shortages (for irrigation), avoiding excessive spills and generating as much firm and average energy as possible. Figure 8 shows a wide spread in the multimodel distribution of annual hydropower production at HAD for the A2 and B1 global emission scenarios. The multimodel average hydropower production is summarized in Table 11. Much of the average mandated power requirement is satisfied in period I in the multimodel average, however predicted hydropower generation fails to meet the annual average hydropower production targets for Periods II and III in the multimodel averages, mainly due to inflow reductions to HAD. This is a significant issue, since the historical annual average hydropower generated at HAD represents 10,000 GWH, or 20% of total power generated within Egypt. The predicted multimodel annual average power production at HAD generally follows changes in streamflow, increasing early in the century to mid century with ensemble mean annual average percentage increases of 10 (18), and 3 (8) for periods I and II and percentage decreases of 7 (8) by period III for A2 (B1) global emissions scenarios (Table 12). In summary, the results indicate that in the multimodel average, Egypt will maintain its historical annual hydropower production of 10,000 GWH from HAD hydropower through Periods I and II, but by period III hydropower production will be reduced to 93 (92)% of its current climate mean annual production for A2 (B1) emissions scenario. Climatic Change (2010) 100:433–461 457 Fig. 8 Projected multi-model ensemble average hydropower production at HAD for the base (historical) period and three future periods, period I (2010–2039), period II (2040–2069) and period III (2070–2099) for A2 and B1 global emission scenarios The irrigation release results (Table 13) indicate that, averaged over the GCM ensembles, Egypt will satisfy its current climate irrigation water requirement with an additional 5% presumed irrigation water demand increase during Period I. In addition to the increased additional irrigable land, spills account for 4 BCM/yr through Toshika to the west, which might be a potential source of irrigation water if properly utilized. The predicted multimodel mean annual irrigation water releases for the three periods and two global emission scenarios A2 (B1) are 59 (60), 48 (49) and 48 (47) BCM, as compared with the historical mean annual release of 55.4 BCM (Table 13). These results suggest that irrigation water releases will be much more affected by future climate in Periods II and III than is hydropower generation. The results indicate that Egypt will experience a reduction of 7 BCM (roughly equivalent to 457,000 ha of irrigable land) by the end of 21st century. The nonlinearity in the irrigation water and hydropower production tradeoffs is mainly Table 13 Multimodel long term average monthly irrigation releases from High Aswan Dam (BCM) for historical 1950–1999 and three future periods Month Historical A2_2010– 2039 A2_2040– 2069 A2_2070– 2099 B1_2010– 2039 B1_2040– 2069 B1_2070– 2099 Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec Annual 3.50 4.00 4.20 4.00 5.30 6.50 7.00 6.30 4.30 3.70 3.60 3.00 55.40 3.71 4.24 4.45 4.24 5.62 6.89 7.42 6.68 4.56 3.92 3.82 3.18 58.72 3.06 3.49 3.67 3.49 4.63 5.67 6.11 5.50 3.75 3.23 3.14 2.62 48.36 3.01 3.44 3.61 3.44 4.56 5.59 6.02 5.42 3.70 3.18 3.10 2.58 47.64 3.82 4.36 4.58 4.36 5.78 7.09 7.63 6.87 4.69 4.03 3.92 3.27 60.39 3.12 3.56 3.74 3.56 4.72 5.79 6.23 5.61 3.83 3.29 3.20 2.67 49.31 2.94 3.36 3.53 3.36 4.45 5.46 5.88 5.29 3.61 3.11 3.02 2.52 46.54 458 Climatic Change (2010) 100:433–461 due to the fact that hydropower production and irrigation water releases are partially competing at HAD, and lower inflows are magnified in hydropower production, which is dependent on both water level and reservoir inflows. Given the potential population growth of the region, the results suggest that reduced water supply will pose a critical problem for Egypt by the late 21st century. 4 Conclusions For the entire Nile basin and its two major sub-basins (Blue Nile and Lake Victoria region), the simulated hydrologic impacts of future climate projected by 11 GCMs forced under two SRES emissions scenarios were examined, as were their implications for Nile River water management. Based on A2 and B1 emissions scenarios and from the 11 GCMs projections, temperature changes for the entire Nile and the two major two sub-basins are expected to be warmer for the entire 21st century. Warming is expected to be modest across the basin early in 21st century (2010–2039) with average annual temperature projections by A2 and B1 emission scenarios similar. By the mid and late 21st century, temperatures are warmer for A2 emission than for B1 emissions scenario. Averaged over the entire Nile basin and all GCMs, average annual temperature for the period 2070–2099 will increase by 4.4 ± 0.5◦ C for A2 and 3.6 ± 0.4◦ C for B1 global emissions scenario relative to the historical (1950–1999) average annual temperature. In general, in the multimodel averages, precipitation increases early in the century and declines late in the 21st Century over most of the basin, however inter-model variations are high. In the multimodel average, the models projects increases in precipitation in the winter (DJF) season, whereas JJA precipitation changes are mixed for the two source sub-basins. There is much less agreement among the models as to the magnitude, and even direction and seasonal changes in precipitation, and subsequently streamflow. In the multimodel average, annual average Nile River inflow to HAD is projected to increase by 11 (14)% in Period 1, but decrease by 8 (7) and 16 (13)% for A2 (B1) global emissions scenarios, relative to 1950–1999 annual average historical flows. In the ensemble means, runoff declines for periods II and III for both global emissions scenarios, with the greatest changes occurring in period III for the A2 global emissions scenario. Reservoir simulations showed that annual average energy production will remain relatively unaffected by climate change early in the 21st century (2010–2039). However, in the multimodel average, gradual reductions in hydropower production will occur by mid and late 21st Century with annual hydropower production ranging from −50 to 110% and −70 to 75% for the A2 emissions scenario, and −55 to 120% and −60 to 80% for the B1 emissions scenario for periods I and III respectively, relative to the historical simulation. The agricultural sector in general will experience increasing shortfalls in releases from Lake Nasser by mid to late century. References Adam JC, Lettenmaier DP (2003) Adjustment of global gridded precipitation for systematic bias. J Geophys Res 108:1–14 Climatic Change (2010) 100:433–461 459 Adam JC, Clark EA, Lettenmaier DP, Wood EF (2006) Correction of global precipitation products for orographic effects. J Clim 19:15–38 Arnell NW (1999) Climate change and global water resources. Glob Environ Change 9:S31–S50 Biswas AK (1994) International waters of the Middle East: from euphrates - Tigris to Nile. Oxford University Press, p 221 Calder IR, Hall RL, Bastable HG, Gunston HM, Shela O, Chirwa A, kafundu R (1995) The impact of land use change on water resources in sub-Saharan Africa: a modelling study of Lake Malawi. J Hydrol 170:123–135 Carter TR, Hulme M, Tuomenvirta H, New MG, Osborn TJ, Crossley J, Doherty RM, Jones PD (1999) Interim characterizations of regional climate and related changes up to 2100 associated with the SRES emissions scenarios. IPCC Working Group II, Technical Support Unit, Washington DC, USA, 33 pp Christensen N, Lettenmaier DP (2007) A multimodel ensemble approach to assessment of climate change impacts on the hydrology and water resources of the Colorado River basin. Hydrol Earth Syst Sci 11:1417–1434 Christensen NS, Wood AW, Lettenmaier DP, Palmer RN (2004) Effects of climate change on the hydrology and water resources of the Colorado River Basin. Clim Change 62:337–363 Conway D (2005) From headwater tributaries to international river: observing and adapting to climate variability and change in the Nile Basin. Glob Environ Change 15:99–114 Conway D, Hulme M (1993) Recent fluctuations in precipitation and runoff over the Nile sub-basins and their impact on main Nile discharge. Clim Change 25:127–151 Conway D, Hulme M (1996) The impacts of climate variability and future climate change in the Nile basin on water resources in Egypt. Water Resour Dev 12:277–296 Conway D, Hanson CE, Doherty R, Persechino A (2007) GCM simulations of the Indian Ocean dipole influence on East African rainfall: present and future. Geophys Res Lett 34:L03705 Delworth TL, Broccoli AJ, Rosati A, Stouffer RJ, Balaji V, Beesley JA, Cooke WF, Dixon KW, Dunne J, Dunne KA, Durachta JW, Findell KL, Ginoux P, Gnanadesikan A, Gordon CT, Griffies SM, Gudgel R, Harrison MJ, Held IM, Hemler RS, Horowitz LW, Klein SA, Knutson TR, Kushner PJ, Langenhorst AR, Lee HC, Lin SJ, Lu J, Malyshev SL, Milly PCD, Ramaswamy V, Russell J, Schwarzkopf MD, Shevliakova E, Sirutis JJ, Spelman MJ, Stern WF, Winton M, Wittenberg AT, Wyman B, Zeng F, Zhang R (2006) GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. J Clim 19:643–674 Diansky NA, Volodin EM (2002) Simulation of present-day climate with a coupled atmosphereocean general circulation model. Izv Atmos Ocean Phys (Engl Transl) 38:732–747 Dooge JCI, Bruen M, Parmentier B (1999) A simple model for estimating the sensitivity of runoff to long-term changes in precipitation without a change in vegetation. Adv Water Resour 23: 153–163 El-Fadel M, El-Sayegh Y, El-Fadl K, Khorbotly D (2003) The Nile River Basin: a case study in surface water conflict resolution. J Nat Resour Life Sci Educ 32:107–117 Georgakakos A (2004) Decision support systems for integrated water resources management with an application to the Nile Basin: IFAC Modelling and Control for Participatory Planning and Managing Water Systems. International Federation of Automatic Control, Venice, Italy Gordon HB, Rotstayn LD, McGregor JL, Dix MR, Kowalczyk EA, O’Farrell SP, Waterman LJ, Hirst AC, Wilson SG, Collier MA, Watterson IG, Elliott TI (2002) The CSIRO Mk3 climate system model. CSIRO Atmospheric Research Technical Paper No. 60, CSIRO. Division of Atmospheric Research, Victoria, Australia, 130 pp Greene AM, Goddard L, Lall U (2006) Performance-based multimodel climate change scenarios 1: low-frequency temperature variations. J Clim 19:4326–4343 Hayhoe K, Wake CP, Huntington TG, Luo L-F, Schwartz MD, Sheffield J, Wood EF, Anderson B, Bradbury J, DeGaetano A, Troy TJ, Wolfe D (2007) Past and future changes in climate and hydrological indicators in the U.S. Northeast. Clim Dyn 28:381–407 Hillel D, Rosenzweig C (1989) The greenhouse effect and its implication regarding global agriculture. Massachusetts Agricultural Experiment Station 724 Hulme M (1992) Recent and future precipitation changes over the Nile Basin. In: Zeid A, Biswas AK (eds) Climatic fluctuations and water management, vol 1. Butterworth Heinemann, London, pp 187–201 Hulme M, Carter TR (1999b) The changing climate of Europe. In: Parry ML (ed) An assessment of the potential effects of climate change in Europe, chap 3. The draft report of the ACACIA Concerted Action, September, 1999. UEA, Norwich, UK, 350 pp Hulme M, Doherty R, Ngara T, New M, Lister D (2001) African climate change: 1900–2100. Clim Res 17:145–168 460 Climatic Change (2010) 100:433–461 Hulme M, Doherty R, Ngara T, New M (2005) Global warming and African climate change: a reassessment. Cambridge University Press, Cambridge, 338 pp IPCC (1998) The regional impacts of climate change: an assessment of vulnerability. In: Watson RT, Zinyowera MC, Moss RH (eds) A special report of IPCC working group II. Cambridge University Press, New York, NY, USA IPCC (2001) Climate change 2001: the scientific basis contribution of working group I to the third assessment report of the Intergovernmental Panel on Climate Change (IPCC). In: Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Xiaosu D (eds). Cambridge University Press, Cambridge, 944 pp IPCC (2007) Climate change 2007: impacts, adaptation, and vulnerability: working group II contribution to the intergovernmental panel on climate change fourth assessment report, summary for policymakers. IPCC Secretariat, Geneva, 22 p IPSL (2005) The new IPSL climate system model: IPSL-CM4’. Institut Pierre Simon Laplace des Sciences de l’Environnement Global, Paris, France, 73 pp Johnson P, Curtis P (1994) Water balance of Blue Nile River Basin in Ethiopia. J Irrig Drain Eng 120:573–590 Jungclaus JH, Botzet M, Haak H, Keenlyside N, Luo J-J, Latif M, Marotzke J, Mikolajewicz U, Roeckner E (2006) Ocean circulation and tropical variability in the AOGCM ECHAM5/MPIOM. J. Climate 19:3952–3972 K-1 model developers (2004) K-1 coupled model (MIROC) description. K-1 technical report, vol 1. In: Hasumi H, Emori S (eds) Center for climate system research. University of Tokyo, 34 pp Krishnamurti TN et al (2000) Multimodel ensemble forecasts for weather and seasonal climate. J Clim 13:4196–4216 Latif M, Dommenget D (1999) The role of Indian Ocean sea surface temperature in forcing East African rainfall anomalies during December–January 1997/98. Am Meteorol Soc 12:3497–3504 Liang X, Lettenmaier DP, Wood EF, Burges SJ (1994) A simple hydrologically based model of land surface water and energy fluxes for General Circulation Models. J Geophys Res 99:415–14,428 Liang X, Wood EF, Lettenmaier DP (1996) Surface soil moisture parameterization of the VIC-2L model: evaluation and modifications. Glob Planet Change 13:195–206 Maurer EP (2007) Uncertainty in hydrologic impacts of climate change in the Sierra Nevada, California under two emissions scenarios. Clim Change 82:309–325 Maurer EP, Wood AW, Adam JC, Lettenmaier DP, Nijssen B (2002) A long-term hydrologicallybased data set of land surface fluxes and states for the conterminous United States. J Climate 15:3237–3251 Milly PCD, Betancourt J, Falkenmark M, Hirsch RM, Kundzewicz ZW, Lettenmaier DP, Stouffer RJ (2008) Stationarity is dead: whither water management? Science 319:573–574 Mohamed YA, van den Hurk BJJM, Savenijeand HHG, Bastiaanssen WGM (2005) Hydroclimatology of the Nile: results from a regional climate model. J Hydrol Earth Syst Sci 9:263–278 Nijssen B, Lettenmaier DP, Liang X, Wetzel SW, Wood EF (1997) Multimodel ensemble forecasts for weather and seasonal climate. Water Resour Res 33:711–724 Nile Basin Initiative (NBI) Shared vision program 2001: report on Nile River Basin: transboundary environmental analysis. United Nations Development Programme World Bank and Global Environment Facility Payne JT, Wood AW, Hamlet AF, Palmer RN, Lettenmaier DP (2004) Mitigating the effect of climate change and water resources of Colombia River basin. Clim Change 62:233–256 Piper BS, Plinston DT, Sutcliffe JV (1986) The water balance of Lake Victoria. Hydrol Sci J 31:25–37 Population Action International (2001) Why population matters to freshwater availability. Population Action International, Washington, DC. Available at http://www.populationaction.org/ resources/factsheets/factsheet_6.htm Riebsame WE, Strzepek KM, Wescoat Jr JL, Perrit R, Graile GL, Jacobs J, Leichenko R, Magadza C, Phien H, Urbiztondo BJ, Restrepo P, Rose WR, Saleh M, Ti LH, Tucci C, Yates D (1995) Complex river basins. In: Strzepek KM, Smith JB (eds) As climate changes, international impacts and implications. Cambridge University Press, Cambridge, pp 57–91 Russell GL, Miller JR, Rind D (1995) A coupled atmosphere ocean model for transient climate change studies. Atmos Ocean 33:683–730 Russell GL, Miller JR, Rind D, Ruedy RA, Schmidt GA, Sheth S (2000) Comparison of model and observed regional temperature changes during the past 40 years. J Geophys Res 105:14891–14898 Said R (1993) The river Nile: geology, hydrology and utilization. Pergamon, Amsterdam Climatic Change (2010) 100:433–461 461 Salas-Mélia D, Chauvin F, Déqué M, Douville H, Gueremy JF, Marquet P, Planton S, Royer JF, Tyteca S (2005) Description and validation of the CNRM-CM3 global coupled model. CNRM working note 103. Available at http://www.cnrm.meteo.fr/scenario2004/paper_cm3.pdf Strzepek KM, Yates DN (1999) Responses and thresholds of the Egyptian economy to climate change impacts on the water resources of the Nile River. Clim Change 46:339–356 Strzepek K, Onega C, Saleh M, Yates D (1995) Assessment of integrated climate change impacts on Egypt. In: Climate changes: international impacts and implications. Cambridge University Press, Cambridge, pp 57–91 Swain A (1997) The Nile River dispute: Ethiopia, the Sudan, and Egypt. J Mod Afr Stud 35:675–694 Tate E, Sutcliffe J, Declan C, Farquharson F (2004) Water balance of Lake Victoria: update to 1999 and Climate change modeling to 2100. Hydrol Earth Syst Sci 49:563–574 Tebaldi C, Mearns LO, Nychka D, Smith R (2004) Regional probabilities of precipitation change: a Bayesian analysis of multimodel simulations. Geophys Res Lett 31:L24213 Washington WM, Weatherly JW, Meehl GA, Semtner AJ, Bettge TW, Craig AP, Strand WG, Arblaster J, Wayland VB, James R, Zhang Y (2000) Parallel Climate Model (PCM) control and transient simulations. Clim Dyn 16:755–774 Wilby RL, Hay LE, Gutowski WJ, Arritt RW, Takle ES, Pan Z, Leavesley GH, Clark P (2000) Hydrological responses to dynamically and statistically downscaled climate model output. Geophys Res Lett 27:1199–1202 Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Long-range experimental hydrologic forecasting for the eastern United States. J Geophys Res-Atmos 107:4429 Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62:189–216 Yates DN, Strzepek KM (1998a) Assessment of integrated climate change impacts on the agricultural economy of Egypt. Clim Change 38:261–287 Yates DN, Strzepek KM (1998b) Modeling the Nile basin under climatic change. J Hydrol Eng 3:98–108 Yukimoto S, Noda A, Kitoh A, Sugi M, Kitamura Y, Hosaka M, Shibata K, Maeda S, Uchiyama T (2001) The new meteorological research institute coupled GCM (MRI-CGCM2), model climate and variability. Pap Meteorol Geophys 51:47–88