Climate change is altering hydrological processes with varying degrees in various regions of the world. This research work investigates the possible impacts of climate change on water resource and Hydropower production potential in central and southern Africa. The Congo, Zambezi and Kwanza, Shire, Kafue and Kabompo basins that lie in central and southern Africa are used as case studies. The review of climate change impact studies shows that there are few studies on impacts of climate change on hydropower production. Most of these studies were carried out in Europe and north America and very few in Asia, south America and Africa. The few studies indicate that southern Africa would experience reduction in precipitation and runoff, consequently reductions in hydropower production. There are no standard methods of assessing the resulting impacts. Two approaches were used to assess the impacts of climate change on water resources and hydropower. One approach is lumping changes on country or regional level and use the mean climate changes on mean annual flows as the basis for regional changes in hydropower production. This is done to get an overall picture of the changes on global and regional level. The second approach is a detailed assessment process in which downscaling, hydrological modelling and hydropower simulations are carried out. The possible future climate scenarios for the region of central and southern Africa depicted that some areas where precipitation are likely to have increases while other, precipitation will reduce. The region northern Zambia and southern Congo showed increases while the northern Congo basin showed reductions. Further south in southern African region, there is a tendency of decreases in precipitation. To the west, in Angola, inland showed increases while towards the coast highlighted some decreases in precipitation. On a global scale, hydropower is likely to experience slight changes (0.08%) due to climate change by 2050. Africa is projected for a slight decrease (0.05%), Asia with an increase of 0.27%, Europe a reduction up to 0.16% while America is projected to have an increase of 0.05%. In the eastern African region, it was shown that hydropower production is likely to increase by 0.59%, the central with 0.22% and the western with a 0.03%. The southern, and northern African regions were projected to have reductions of 0.83% and 0.48% respectively. The basins with increases in flow projections have a slight increase on hydropower production but not proportional to the increase in precipitation. The basins with decreases had even high change as the reduction was further increased by evaporation losses. The hydropower production potential of most of southern African basins is likely to decrease in the future due to the impact of climate change while the central African region shows an increasing trend. The hydropower system in these regions will be affected consequently. The hydropower production changes will vary from basin to basin in these regions. The Zambezi, Kafue and Shire river basins have negative changes while the Congo, Kwanza and Kabompo river basins have positive changes. The hydropower production potential in the Zambezi basin decreases by 9 - 34%. The hydropower production potential in the Kafue basin decreases by 8 - 34% and the Shire basin decreases by 7 - 14 %. The southern region will become drier with shorter rainy seasons. The central region will become wetter with increased runoff. The hydropower production potential in the Congo basin reduces slightly and then increases by 4% by the end of the century. The hydropower production potential in the Kwanza basin decreases by 3% and then increases by 10% towards the end of the century and the Kabompo basin production increases by 6 - 18%. It can be concluded that in the central African region hydropower production will, in general, increase while the southern African region, hydropower production will decrease. In summary, the analysis has shown that the southern African region is expected to experience decreases in rainfall and increases in temperature. This will result in reduced runoff. However the northern part of southern Africa is expected to remain relatively the same with slight increase, moving northwards towards the central African region where mainly increases have been registered. The southern African region is likely to experience reductions up to 5 - 20% while the central African region is likely to experience an increase in runoff in the range of 1 - 5%. Lack of data was observed as a critical limiting factor in modelling in the central and southern Africa region. The designs, plans and operations based on poor hydrological data severely compromise performance and decrease efficiency of systems. Climate change is expected to change these risks. The normal extrapolations of historical data will be less reliable as the past will become an increasingly poor predictor of the future. Better (observed) data is recommended in future assessments and if not better tools and methods for data collection/ should be used. Future designs, plans and operations should include and aspect of climate change, if the region is to benefit from the climate change impacts.

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General Circulation Models (GCMs), which are mathematical models based on principles of fluid dynamics, thermodynamics and radiative transfer, are the most reliable tools available for projecting climate change. However, the spatial scale on which typical GCMs operate is very coarse as compared to that of a hydrologic process and hence, the output from a GCM cannot be directly used in hydrologic models. Statistical Downscaling (SD) derives a statistical or empirical relationship between the variables simulated by the GCM (predictors) and a point-scale meteorological series (predictand). In this work, a new downscaling model called CRF-downscaling model, is developed where the conditional distribution of the hydrologic predictand sequence, given atmospheric predictor variables, is represented as a conditional random field (CRF) to downscale the predictand in a probabilistic framework. Features defined in the downscaling model capture information about various factors influencing precipitation such as circulation patterns, temperature and pressure gradients and specific humidity levels. Uncertainty in prediction is addressed by projecting future cumulative distribution functions (CDFs) for a number of most likely precipitation sequences. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework, and changes in the non-parametric distribution of precipitation and dry and wet spell lengths are projected. Application of the method is demonstrated with the case study of downscaling to daily precipitation in the Mahanadi basin in Orissa, with the A1B scenario of the MIROC3.2 GCM from the Center for Climate System Research (CCSR), Japan. An uncertainty modeling framework is presented in this work, which combines GCM, scenario and downscaling uncertainty using the Dempster-Shafer (D-S) evidence theory for representing and combining uncertainty. The methodology for combining uncertainties is applied to projections of hydrologic drought in terms of monsoon standardized streamflow index (SSFI-4) from streamflow projections for the Mahanadi river at Hirakud. The results from the work indicate an increasing probability of extreme, severe and moderate drought and decreasing probability of normal to wet conditions, as a result of a decrease in monsoon streamflow in the Mahanadi river due to climate change. In most studies to date, the nature of the downscaling relationship is assumed stationary, or remaining unchanged in a future climate. In this work, an uncertainty modeling framework is presented in which, in addition to GCM and scenario uncertainty, uncertainty in the downscaling relationship itself is explored by linking downscaling with changes in frequencies of modes of natural variability. Downscaling relationships are derived for each natural variability cluster and used for projections of hydrologic drought. Each projection is weighted with the future projected frequency of occurrence of that cluster, called ‘cluster-linking’, and scaled by the GCM performance with respect to the associated cluster for the present period, called ‘frequency scaling’. The uncertainty modeling framework is applied to a case study of projections of hydrologic drought or SSFI-4 classifications, using projected streamflows for the Mahanadi river at Hirakud. It is shown that a stationary downscaling relationship will either over- or under-predict downscaled hydrologic variable values and associated uncertainty. Results from the work show improved agreement between GCM predictions at the regional scale, which are validated for the 20th century, implying that frequency scaling and cluster-linking may indeed be a valid method for constraining uncertainty. To assess the impact of climate change on reservoir performance, in this study, a range of integrated hydrologic scenarios are projected for the future. The hydrologic scenarios incorporate increased irrigation demands; rule curves dictated by increased need for flood storage and downscaled projections of streamflow from an ensemble of GCMs and emission scenarios. The impact of climate change on multipurpose reservoir performance is quantified, using annual hydropower and RRV criteria, under GCM and scenario uncertainty. The ‘business-as-usual’ case using Standard Operating Policy (SOP) is studied initially for quantifying impacts. Adaptive Stochastic Dynamic Programming (SDP) policies are subsequently derived for the range of future hydrologic scenarios, with the objective of maximizing reliabilities with respect to multiple reservoir purposes of hydropower, irrigation and flood control. It is shown that the hydrologic impact of climate change is likely to result in decreases in performance criteria and annual hydropower generation for Hirakud reservoir. Adaptive policies show that a marginal reduction in irrigation and flood control reliability can achieve increased hydropower reliability in future. Hence, reservoir rules for flood control may have to be revised in the future.

General Circulation Models (GCMs) are tools designed to simulate time series of climate variables globally, accounting for effects of greenhouse gases in the atmosphere. They attempt to represent the physical processes in the atmosphere, ocean, cryosphere and land surface. They are currently the most credible tools available for simulating the response of the global climate system to increasing greenhouse gas concentrations, and to provide estimates of climate variables (e.g. air temperature, precipitation, wind speed, pressure etc.) on a global scale. GCMs demonstrate a significant skill at the continental and hemispheric spatial scales and incorporate a large proportion of the complexity of the global system; they are, however, inherently unable to represent local subgrid-scale features and dynamics. The spatial scale on which a GCM can operate (e.g., 3.75° longitude x 3.75° latitude for Coupled Global Climate Model, CGCM2) is very coarse compared to that of a hydrologic process (e.g., precipitation in a region, streamflow in a river etc.) of interest in the climate change impact assessment studies. Moreover, accuracy of GCMs, in general, decreases from climate related variables, such as wind, temperature, humidity and air pressure to hydrologic variables such as precipitation, evapotranspiration, runoff and soil moisture, which are also simulated by GCMs. These limitations of the GCMs restrict the direct use of their output in hydrology. This thesis deals with developing statistical downscaling models to assess climate change impacts and methodologies to address GCM and scenario uncertainties in assessing climate change impacts on hydrology. Downscaling, in the context of hydrology, is a method to project the hydrologic variables (e.g., rainfall and streamflow) at a smaller scale based on large scale climatological variables (e.g., mean sea level pressure) simulated by a GCM. A statistical downscaling model is first developed in the thesis to predict the rainfall over Orissa meteorological subdivision from GCM output of large scale Mean Sea Level Pressure (MSLP). Gridded monthly MSLP data for the period 1948 to 2002, are obtained from the National Center for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) reanalysis project for a region spanning 150 N -250 N in latitude and 800 E -900 E in longitude that encapsulates the study region. The downscaling model comprises of Principal Component Analysis (PCA), Fuzzy Clustering and Linear Regression. PCA is carried out to reduce the dimensionality of the larger scale MSLP and also to convert the correlated variables to uncorrelated variables. Fuzzy clustering is performed to derive the membership of the principal components in each of the clusters and the memberships obtained are used in regression to statistically relate MSLP and rainfall. The statistical relationship thus obtained is used to predict the rainfall from GCM output. The rainfall predicted with the GCM developed by CCSR/NIES with B2 scenario presents a decreasing trend for non-monsoon period, for the case study. Climate change impact assessment models developed based on downscaled GCM output are subjected to a range of uncertainties due to both ‘incomplete knowledge’ and ‘unknowable future scenario’ (New and Hulme, 2000). ‘Incomplete knowledge’ mainly arises from inadequate information and understanding about the underlying geophysical process of global change, leading to limitations in the accuracy of GCMs. This is also termed as GCM uncertainty. Uncertainty due to ‘unknowable future scenario’ is associated with the unpredictability in the forecast of socio-economic and human behavior resulting in future Green House Gas (GHG) emission scenarios, and can also be termed as scenario uncertainty. Downscaled outputs of a single GCM with a single climate change scenario represent a single trajectory among a number of realizations derived using various GCMs and scenarios. Such a single trajectory alone can not represent a future hydrologic scenario, and will not be useful in assessing hydrologic impacts due to climate change. Nonparametric methods are developed in the thesis to model GCM and scenario uncertainty for prediction of drought scenario with Orissa meteorological subdivision as a case study. Using the downscaling technique described in the previous paragraph, future rainfall scenarios are obtained for all available GCMs and scenarios. After correcting for bias, equiprobability transformation is used to convert the precipitation into Standardized Precipitation Index-12 (SPI-12), an annual drought indicator, based on which a drought may be classified as a severe drought, mild drought etc. Disagreements are observed between different predictions of SPI-12, resulting from different GCMs and scenarios. Assuming SPI-12 to be a random variable at every time step, nonparametric methods based on kernel density estimation and orthonormal series are used to determine the nonparametric probability density function (pdf) of SPI-12. Probabilities for different categories of drought are computed from the estimated pdf. It is observed that there is an increasing trend in the probability of extreme drought and a decreasing trend in the probability of near normal conditions, in the Orissa meteorological subdivision. The single valued Cumulative Distribution Functions (CDFs) obtained from nonparametric methods suffer from limitations due to the following: (a) simulations for all scenarios are not available for all the GCMs, thus leading to a possibility that incorporation of these missing climate experiments may result in a different CDF, (b) the method may simply overfit to a multimodal distribution from a relatively small sample of GCMs with a limited number of scenarios, and (c) the set of all scenarios may not fully compose the universal sample space, and thus, the precise single valued probability distribution may not be representative enough for applications. To overcome these limitations, an interval regression is performed to fit an imprecise normal distribution to the SPI-12 to provide a band of CDFs instead of a single valued CDF. Such a band of CDFs represents the incomplete nature of knowledge, thus reflecting the extent of what is ignored in the climate change impact assessment. From imprecise CDFs, the imprecise probabilities of different categories of drought are computed. These results also show an increasing trend of the bounds of the probability of extreme drought and decreasing trend of the bounds of the probability of near normal conditions, in the Orissa meteorological subdivision. Water resources planning requires the information about future streamflow scenarios in a river basin to combat hydrologic extremes resulting from climate change. It is therefore necessary to downscale GCM projections for streamflow prediction at river basin scales. A statistical downscaling model based on PCA, fuzzy clustering and Relevance Vector Machine (RVM) is developed to predict the monsoon streamflow of Mahanadi river at Hirakud reservoir, from GCM projections of large scale climatological data. Surface air temperature at 2m, Mean Sea Level Pressure (MSLP), geopotential height at a pressure level of 500 hecto Pascal (hPa) and surface specific humidity are considered as the predictors for modeling Mahanadi streamflow in monsoon season. PCA is used to reduce the dimensionality of the predictor dataset and also to convert the correlated variables to uncorrelated variables. Fuzzy clustering is carried out to derive the membership of the principal components in each of the clusters and the memberships thus obtained are used in RVM regression model. RVM involves fewer number of relevant vectors and the chance of overfitting is less than that of Support Vector Machine (SVM). Different kernel functions are used for comparison purpose and it is concluded that heavy tailed Radial Basis Function (RBF) performs best for streamflow prediction with GCM output for the case considered. The GCM CCSR/NIES with B2 scenario projects a decreasing trend in future monsoon streamflow of Mahanadi which is likely to be due to high surface warming. A possibilistic approach is developed next, for modeling GCM and scenario uncertainty in projection of monsoon streamflow of Mahanadi river. Three GCMs, Center for Climate System Research/ National Institute for Environmental Studies (CCSR/NIES), Hadley Climate Model 3 (HadCM3) and Coupled Global Climate Model 2 (CGCM2) with two scenarios A2 and B2 are used for the purpose. Possibilities are assigned to GCMs and scenarios based on their system performance measure in predicting the streamflow during years 1991-2005, when signals of climate forcing are visible. The possibilities are used as weights for deriving the possibilistic mean CDF for the three standard time slices, 2020s, 2050s and 2080s. It is observed that the value of streamflow at which the possibilistic mean CDF reaches the value of 1 reduces with time, which shows reduction in probability of occurrence of extreme high flow events in future and therefore there is likely to be a decreasing trend in the monthly peak flow. One possible reason for such a decreasing trend may be the significant increase in temperature due to climate warming. Simultaneous occurrence of reduction in Mahandai streamflow and increase in extreme drought in Orissa meteorological subdivision is likely to pose a challenge for water resources engineers in meeting water demands in future.

Understanding climate change and variability at urban scale is essential for water resource management, land use planning, and development of adaption plans. However, there are serious challenges to meet these goals due to unavailability of observed and / or simulated high resolution spatial and temporal climate data. Recent efforts made possible the availability of high resolution climate data from non-hydrostatic regional climate model (RCM) and statistically downscaled General Circulation Models (GCMs). This study investigates trends in climate and urbanization and their impact on surface water supply for the city of Addis Ababa, Ethiopia. The methodology presented in this study focused on the observed and projected NIMRHadGEM2- AO model and Special Report on Emissions Scenarios (SRES) of B2 and A2 of HadCM3 model are also employed for rainfall, maximum temperature and minimum temperature data using for climate analysis. Water Evaluation and Planning (WEAP) modeling system was used for determination of climate and urbanization impacts on water. Land-Sat images were analyzed using Normalized Differencing Vegetation Index (NDVI). Statistical downscaling model (SDSM) was employed to investigate the major changes and intensity of the urban heat island (UHI). The result indicates monthly rainfall anomalies with respect to the baseline mean showing wet anomaly in summer (kiremt) during 2030s and 2050s, and a dry anomaly in the 2080s under A2 and B2 scenarios with exception of a wet anomaly in September over the city. The maximum temperature anomalies under Representative Concentration Pathways (RCPs) also show warming during near, mid and end terms. The mean monthly minimum temperature anomalies under A2 and B2 scenarios are warm but the anomalies are much lower than RCPs. The climate under the RCP 8.5 and high population growth (3.3 %) scenario will lead to the unmet demand of 462.77 million m3 by 2039. Future projection of urban heat island under emission pathway of A2 and B2 scenario shows that, the nocturnal UHI will be intense in winter or dry season episodes in the city. Under A2 scenario the highest urban warming will occur during October to December (2.5 ºC to 3.2 ºC). Under RCP 8.5 scenario the highest urban warming will occur during October to December (0.5 ºC to 1.0 °C) in the 2050s and 2080s. Future management and adaptation strategies are to expand water supply to meet future demand and to implement demand side water management systems of the city and UHI / Environmental Sciences / Ph. D. (Environmental Management)

Understanding climate change and variability at urban scale is essential for water resource management, land use planning, and development of adaption plans. However, there are serious challenges to meet these goals due to unavailability of observed and / or simulated high resolution spatial and temporal climate data. Recent efforts made possible the availability of high resolution climate data from non-hydrostatic regional climate model (RCM) and statistically downscaled General Circulation Models (GCMs). This study investigates trends in climate and urbanization and their impact on surface water supply for the city of Addis Ababa, Ethiopia. The methodology presented in this study focused on the observed and projected NIMRHadGEM2- AO model and Special Report on Emissions Scenarios (SRES) of B2 and A2 of HadCM3 model are also employed for rainfall, maximum temperature and minimum temperature data using for climate analysis. Water Evaluation and Planning (WEAP) modeling system was used for determination of climate and urbanization impacts on water. Land-Sat images were analyzed using Normalized Differencing Vegetation Index (NDVI). Statistical downscaling model (SDSM) was employed to investigate the major changes and intensity of the urban heat island (UHI). The result indicates monthly rainfall anomalies with respect to the baseline mean showing wet anomaly in summer (kiremt) during 2030s and 2050s, and a dry anomaly in the 2080s under A2 and B2 scenarios with exception of a wet anomaly in September over the city. The maximum temperature anomalies under Representative Concentration Pathways (RCPs) also show warming during near, mid and end terms. The mean monthly minimum temperature anomalies under A2 and B2 scenarios are warm but the anomalies are much lower than RCPs. The climate under the RCP 8.5 and high population growth (3.3 %) scenario will lead to the unmet demand of 462.77 million m3 by 2039. Future projection of urban heat island under emission pathway of A2 and B2 scenario shows that, the nocturnal UHI will be intense in winter or dry season episodes in the city. Under A2 scenario the highest urban warming will occur during October to December (2.5 ºC to 3.2 ºC). Under RCP 8.5 scenario the highest urban warming will occur during October to December (0.5 ºC to 1.0 °C) in the 2050s and 2080s. Future management and adaptation strategies are to expand water supply to meet future demand and to implement demand side water management systems of the city and UHI / College of Agriculture and Environmental Sciences / Ph. D. (Environmental Management)