Accurate Hydrologic Modeling Using Less Information

Joint models are a common and important tool in the intersect ion of machine learning and the physical sciences, particularly in contex ts where real-world measurements are scarce. Recent developments in rainfall-run off modeling, one of the prime challenges in hydrology, show the value of a joint m odel with shared representation in this important context. However, curren t state-of-the-art models depend on detailed and reliable attributes characteriz ing each site to help the model differentiate correctly between the behavior of diff erent sites. This dependency can present a challenge in data-poor regions. In this p aper, we show that we can replace the need for such location-specific attributes w ith a completely data-driven learned embedding, and match previous state-of-the -art results with less information.