Interacting Effects of Precipitation and Potential Evapotranspiration Biases on Hydrological Modeling

The quality of precipitation (P) and potential evapotranspiration (PET) data greatly affects the hydrological modeling performance. Considerable attention has been paid to identifying influence biased P or PET inputs independently. However, few studies have explored joint interaction biases in on simulations. Here, we investigate mutual compensation performance two widely used conceptual models, Xinanjiang model Probability Distributed Model. from HYREX (HYdrological Radar EXperiment) CAMELS-GB (Catchment Attributes Meteorology for Large-sample Studies Great Britain) sets are collected over five catchments with varying characteristics Britain. Different added these original time series generate 6560 input scenarios. results suggest that there is a certain compensational relationship between reproduce desirable streamflow A new proxy named Compensational Interaction Angle (CIA) identified found be stationary various periods, as well stable different models despite equifinality. Further, CIA highly relates long-term climate aridity ratio. greater larger CIAs. This study offers fresh perspective analyze errors modeling. can help better understand interactions modeling, guide selection/evaluation/bias-correction applications.