KEY FACTORS FOR IMPROVING LARGE-SCALE HYDROLOGICAL MODEL PERFORMANCE

This study focusses on identifying key factors that influence large-scale hydrological model performance. It draws on experiences from modelling in the Arctic, West Africa, and Europe with the HYPE model (HYdrological Prediction of the Environment). We use multiple evaluation criteria to analyse the influence of catchment delineation, climate input data, model parameterisation, and water management. For each factor, we compare the model performance of reference models and refined models using time-series of observed river discharge from ten to a thousand stations (depending on application). The results show that all investigated factors influence model performance to varying extents. An automated method that managed to successfully represent the catchment size of discharge stations in Europe improved model performance (NSE:+10%, aRE:-12%) relative to the reference (where the method was unsuccessful). Refining the climate input data substantially increased model performance in the Niger River Basin (NSE:+40%, aRE:-26% on average). In Europe, a refined precipitation dataset resulted in a similar performance enhancement (NSE:+2%, aRE:-8%) as a refined temperature dataset (NSE:+2%, aRE:-4%), on average. However, the temperature refinement was more consistent spatially. Linking lake parameters to spatially varying hydrological characteristics improved model performance across the Arctic domain (NSE:+11%, aRE:-8%). Refining infiltration capacities in the Niger basin improved both flow dynamics (NSE:+60%) and cumulative volumes (aRE:-40%) through modified flow paths and enhanced evaporation. Irrigation water management in the Arctic only affected model performance locally. Model performance was generally better in large and wet catchments with high runoff coefficients compared with relatively small, dry catchments with low runoff. These factors are also likely to affect model performance in other areas of the world.