Green infrastructure systems can be selected methodically considering watershed parameters, then the existing urban water network, and surrounding land uses.

Abstract:Optimal operation of reservoirs is very essential for water resource planning and management, but it is very challenging and complicated when dealing with climate change impacts. The objective of this paper was to assess existing and future hydropower operation at the Tekeze reservoir in the face of climate change. In this study, a calibrated and validated Soil and Water Assessment Tool (SWAT) was used to model runoff inflow into the Tekeze hydropower reservoir under present and future climate scenarios. Inflow to the reservoir was simulated using hydro-climatic data from an ensemble of downscaled climate data based on the Coordinated Regional climate Downscaling Experiment over African domain (CORDEX-Africa) with Coupled Intercomparison Project Phase 5 (CMIP5) simulations under Representative Concentration Pathway (RCP)4.5 and RCP8.5 climate scenarios. Observed and projected inflows to Tekeze hydropower reservoir were used as input to the US Army Corps of Engineer's Reservoir Evaluation System Perspective Reservoir Model (HEC-ResPRM), a reservoir operation model, to optimize hydropower reservoir release, storage and pool level. Results indicated that climate change has a clear impact on reservoir inflow and showed increase in annual and monthly inflow into the reservoir except in dry months from May to June under RCP4.5 and RCP8.5 climate scenarios. HEC-ResPRM optimal operation results showed an increase in Tekeze reservoir power storage potential up to 25% and 30% under RCP4.5 and RCP8.5 climate scenarios, respectively. This implies that Tekeze hydropower production will be affected by climate change. This analysis can be used by water resources planners and mangers to develop reservoir operation techniques considering climate change impact to increase power production.

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This study examines the performance of multimodel numerical simulations and multiobservational databases focusing on seasonal cycles and spatial variations of precipitation over Ethiopia. Seven regional climate models (RCMs) driven by the European Center for Medium Range Weather Forecasting (ECMWF) Interim reanalysis (ERA-Interim) and generated in the framework of COordinated Regional climate Downscaling EXperiment (CORDEX) project, and four observational databases computed using different interpolation techniques and blending strategies were evaluated against typical observational database produced by Climate Research Unit (CRU) over Ethiopia on monthly basis. All were produced at 48.8 km grid resolution for the period 1989-2008. The preliminary results showed that ensembles [multimodel ensemble (MME) + multiobservational ensemble (MOE)] were as good as CRU in reproducing the temporal variability and the geographical distribution of precipitation. Comparison of seasonal means and temporal correlation results revealed that there were good agreements between ensembles and CRU at each grid point and in close proximity to each other. Results of rotated principal components (RPCs), rotated empirical orthogonal functions (REOFs), and the associated power spectra showed that every ensemble's element was able to simulate the seasonal cycles and homogeneous precipitation zones of CRU reasonably well. Excessive and deficient rainfall periods, which were seen in every ensemble's RPCs, matched CRU historical records. BackgroundClimate models are perpetually ameliorated and upgraded to higher and higher grid resolution with the intention of reproducing climate variables at finer scale. However, as climate model resolution tends to be finer, the noise on grid cells is likely to happen. Uncertainty in climate model is becoming much of the present interest as poor performance in present climate conditions is linked with outliers in the future projection (Knutti et al., 2010;Brands et al., 2011). Despite the fact that RCMs are downscaling tools aspired to upgrade the modelling of local physical processes, they are highly sensitive to model formulation, grid resolution, numerical schemes, and other physical parameterizations and result, therefore, in differences in downscaling skills (Fowler and Ekström, 2009;Maraun et al., 2010). There is growing evidence that regions and seasons showing the greatest model biases in the simulation of climate variables are often those with the greatest intermodel differences (Frei et al., 2006;Fowler et al., 2007;Maraun et al., 2010). Before using RCMs for future studies, it is believed that * Correspondence to: D. T. Reda, Department of Physics, College of Natural Science, Addis Ababa University, Addis Ababa, Ethiopia. E-mail: daniell.tsegay@gmail.com they should pass through some evaluation mechanisms. To address this issue, several climate modelling groups from around the world have been downscaling global climate models (GCMs) to regional scale using their own model setup. The aim ...