Global Maps of Temporal Streamflow Characteristics Based on Observations from Many Small Catchments

Abstract:

Streamflow (Q) estimation in ungauged catchments is one of the greatest challenges facing hydrologists. We used observed Q from approximately 7500 small catchments (<10,000 km²) around the globe to train neural network ensembles to estimate temporal Q distribution characteristics from climate and physiographic characteristics of the catchments. In total 17 Q characteristics were selected, including mean annual Q, baseflow index, and a number of flow percentiles. Training coefficients of determination for the estimation of the Q characteristics ranged from 0.56 for the baseflow recession constant to 0.93 for the Q timing. Overall, climate indices dominated among the predictors. Predictors related to soils and geology were the least important, perhaps due to data quality. The trained neural network ensembles were subsequently applied spatially over the ice-free land surface including ungauged regions, resulting in global maps of the Q characteristics (0.125° spatial resolution). These maps possess several unique features: 1) they represent purely observation-driven estimates; 2) are based on an unprecedentedly large set of catchments; and 3) have associated uncertainty estimates. The maps can be used for various hydrological applications, including the diagnosis of macro-scale hydrological models. To demonstrate this, the produced maps were compared to equivalent maps derived from the simulated daily Q of five macro-scale hydrological models, highlighting various opportunities for improvement in model Q behavior. The produced dataset is available for download.