Analysis of continuous streamflow regionalization methods within a virtual setting

This paper presents an analysis of three common hydrological regionalization methods (multiple linear regression, spatial proximity and physical similarity) in a virtual-world setting, using a 15km resolution regional climate model to eliminate uncertainty due to measurement errors and missing data. It was found that in many cases the best donor is neither the most similar nor the closest watershed to the ungauged site, indicating a need for better hydrologically relevant catchment descriptors. Results show that using the closest donors yields satisfactory results only if they share similar characteristics with the ungauged basin, confirming that the proximity method is a good proxy only if there is reason to believe that the basins are physically similar. It was also shown that the ability to predict whether a method will succeed or fail is limited by the quality of catchment descriptors and the inherent probabilistic nature of the problem. A method to determine whether a regionalization method will fail or succeed based on the ungauged catchment's characteristics failed to recognize a successful candidate 20% of the time, whereas it incorrectly classified a poor candidate in 30% of cases. The results indicate that there are unknown properties or processes that contribute to the hydrological behaviour of ungauged basins.