Determining the effect of climate change and development on water resources management in the Sudan

The effects of development and the uncertainty of climate change in East Africa provide a myriad of challenges for water managers along the Blue Nile. The construction of the Grand renaissance dam (GRD), as well as the unknown trajectory of precipitation trends in the Ethiopian highlands may greatly affect the countries that rely on the Nile. Sudan's huge irrigation potential and dams that feed multiple current irrigation schemes as well as its location within the basin means that Sudan's water management decisions may reverberate and have social, economic and political implications within the east African sub-region. Here, we apply a suite of state-of-the-art hydrology and climate analysis tools to evaluate the sensitivity of Sudan's optimal hydropower and irrigation development pathways to hydrologic variability and climate change. Present day hydrologic conditions are derived from a gridded implementation of the Noah Land Surface Model (LSM) that includes representation of typical irrigation practices in the region. Noah is implemented using the NASA Land Information System (LIS), and draws forcing data from a combination of reanalysis and satellite meteorological products. Additional satellite inputs are used to provide a constraint on Noah evapotranspiration estimates and to acquire parameters such as crop water requirements that are crucial in determining yield and agricultural production. Future climate conditions are projected using statistical downscaling techniques trained to historical meteorological records and projected forward using inputs from the 5th Coupled Model Intercomparison Project (CMIP5) simulation database. These climatic and hydrologic inputs are combined with agronomic and economic inputs to drive an optimization model developed within the General Algebraic Modeling System (GAMS). By using output and results from climate, hydrologic and optimization models this research aims to show how these models can be integrated to aid decision makers in the quest to better manage their water resources.