The data synergy effects of time-series deep learning models in hydrology

Kuai Fang, Daniel Kifer, Kathryn Lawson, Dapeng Feng, and Chaopeng Shen

Department of Energy, Office of Science, Earth & Environmental Systems Modeling, MultiSector Dynamics Program Acknowledged Support: No, other Non-MSD source of support

DOI: https://doi-org.proxy.library.cornell.edu/10.1029/2021WR029583

Abstract

When fitting statistical models to variables in geoscientific disciplines such as hydrology, it is a customary practice to stratify a large domain into multiple regions (or regimes) and study each region separately. Traditional wisdom suggests that models built for each region separately will have higher performance because of homogeneity within each region. However, each stratified model has access to fewer and less diverse data points. Here, through two hydrologic examples (soil moisture and streamflow), we show that conventional wisdom may no longer hold in the era of big data and deep learning (DL). We systematically examined an effect we call data synergy, where the results of the DL models improved when data were pooled together from characteristically different regions. The performance of the DL models benefited from modest diversity in the training data compared to a homogeneous training set, even with similar data quantity. Moreover, allowing heterogeneous training data makes eligible much larger training datasets, which is an inherent advantage of DL. A large, diverse data set is advantageous in terms of representing extreme events and future scenarios, which has strong implications for climate change impact assessment. The results here suggest the research community should place greater emphasis on data sharing.

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