Can discharge be used to inversely correct precipitation?

This study explores the feasibility of using the information contained in observed streamflow discharge measurements to inversely correct catchment-average precipitation time series provided by reanalysis products. We explore this possibility by training LSTM models to predict precipitation. The first model uses discharge as an input feature along with other meteorological factors, while the second model uses only the meteorological factors. Although the model provided with discharge information showed better mean performance, a detailed analysis of various time series measures across the continental scale revealed underestimation biases when compared with the original reanalysis product used for training. However, an out-of-sample test showed that the inversely estimated precipitation is better able to reproduce small-scale, high-impact events that are poorly represented in the original reanalysis product. Further, using the inversely generated precipitation time series for classical hydrological “forward” modeling resulted in improved estimates for streamflow and soil moisture. Given the notable disconnect between reanalysis products and extreme events, particularly in data-scarce regions worldwide, our findings have implications for achieving better estimates of precipitation associated with high-impact events.