  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.
