Statistical and Dynamical Climate Predictions to Guide Water Resources in Ethiopia

Abstract

Climate predictions with lead times of one season or more often provide prospects for exploiting climate-related risks and opportunities. This motivates the evaluation of precipitation prediction techniques from statistical and dynamical models, and their combination, to potentially augment prediction skill over the Blue Nile Basin in Ethiopia. This work considers to what degree greater skill or reliability in a particular prediction technique translates through hydropower management models given their nonlinear response. One hundred precipitation series from 1981–2000 are generated to compare prediction techniques. The linked multimodel ensemble climate forecast/hydropower system proves superior to the statistical and dynamical prediction technique linked systems across a range of metrics. This includes an expected increase in annual benefits by $4–5–million on average. The climate forecast/hydropower system is sufficiently flexible to allow water managers to attain an optimal balance between benefits and the dependability of energy delivery by varying exceedance probability and target energy thresholds, with the added benefit of forecast guidance. Ideally this provides decision makers with incentives to integrate improved prediction techniques into sectoral management models, and further justifies expanding efforts into climate forecast improvement.