Daniel Klotz, Frederik Kratzert, Martin Gauch , Alden Keefe Sampson , Günter Klambauer , Sepp Hochreiter , Grey Nearing 

Deep Learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales.
Uncertainty estimations are critical for actionable hydrological forecasting, and while standardized community benchmarks are becoming an increasingly important part of hydrological model development and research, similar tools for benchmarking uncertainty estimation are lacking.
We establish an uncertainty estimation benchmarking procedure and present four Deep Learning baselines, out of which three are based on Mixture Density Networks and one is based on Monte Carlo dropout. Additionally, we provide a post-hoc model analysis to put forward some qualitative understanding of the resulting models.
Most importantly however, we show that accurate, precise, and reliable uncertainty estimation can be achieved with Deep Learning.