On the data synergy effect of large-sample multi-physics catchment modeling with machine learning

Watersheds in the world are often perceived as being unique from each other, requiring customized study for each basin. Models uniquely built for each watershed, in general, cannot be leveraged for other watersheds. It is also a customary practice in hydrology and related geoscientific disciplines to divide the whole domain into multiple regimes and study each region separately, in an approach sometimes called regionalization or stratification. However, in the era of big-data machine learning, models can learn across regions and identify commonalities and differences. In this presentation, we first show that machine learning can derive highly functional continental-scale models for streamflow, evapotranspiration, and water quality variables. Next, through two hydrologic examples (soil moisture and streamflow), we argue that unification can often significantly outperform stratification, and systematically examine an effect we call data synergy, where the results of the DL models improved when data were pooled together from characteristically different regions and variables. In fact, the performance of the DL models benefited from some diversity in training data even with similar data quantity. However, allowing heterogeneous training data makes eligible much larger training datasets, which is an inherent advantage of DL. We also share our recent developments in advancing hydrologic deep learning and machine learning driven parameterization.