Model Selection Based on Sectoral Application Scale for Increased Value of Hydroclimate-Prediction Information

Abstract

Advance predictions of seasonal precipitation may provide information to aid water resource management decisions in various sectors. Yet, a disconnect between the spatial scale upon which skillful predictions are issued and the sectoral decision-making scale renders current predictive information inadequate in many cases. This study explores season-ahead precipitation prediction skill for a local region in the Blue Nile basin, Ethiopia, to better understand how model structure may serve to provide more skillful and valuable predictive information to the end user. Statistical downscaling of global dynamic and regional empirical models and development of a high-resolution, locally tailored statistical model indicate that model structure and prediction skill are inextricably linked. Statistical approaches specific to the local region show higher prediction skill at the sectoral decision-making scale compared with dynamic approaches, offering the potential to aid local communities in many regions that are currently vulnerable to highly variable spatial precipitation patterns. Linking the local-scale precipitation predictions with a simple reservoir model in Ethiopia illustrates application of local-scale predictive information for enhanced value in planning and management decisions.