Comparison between observed and remotely sensed attributes to include in the region-of-influence approach of extreme precipitation estimation: a case study in the Yangtze River basin, China

Abstract

ABSTRACT The application of remotely sensed (RS) data at ungauged locations is well recognized in hydrological studies; however, its suitability for use as a descriptor in the region-of-influence (ROI) approach is hardly assessed. This study compares two types of at-site attributes, observed and RS, to include in the ROI approach for the estimation of extreme precipitation, in particular at ungauged locations in China. The performance of the method against the fixed-group-based regional approach is also examined. The results, which are based on data for the Yangtze River basin, showed that the ROI scheme that used physical proximity and elevation combined produced the lowest error and performed better than that containing RS data. The scheme also outperformed the fixed regional models in terms of error. Overall, although the application of RS data is intuitively attractive, its inclusion is unable to outperform the observed site descriptors for the study region.