A Regional Semi-Distributed Streamflow Model Using Deep Learning

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Authors

Zhongrun Xiang , Ibrahim Demir, Ricardo Mantilla , Witold F. Krajewski 

Abstract

Recent studies have shown that deep learning models in hydrological applications significantly improved streamflow predictions at multiple stations compared to traditional machine learning approaches. However, most studies lack generalization; i.e. researchers are training separate models for each location. The spatial and temporal generalization ability of deep learning models in hydrology that can be gained by training a single model for multiple stations is evaluated in this study. We developed a generalized model with a multi-site structure for hourly streamflow hindcasts on 125 USGS gauges. Considering watershed-scale features including drainage area, time of concentration, slope, and soil types, the proposed models have acceptable performance and slightly higher median NSE value than training individual models for each USGS station. Furthermore, we showed that the trained generalized model can be applied to any new gauge in the state of Iowa that was not used in the training set with acceptable accuracy. This study demonstrates the potential of deep learning studies in hydrology where more domain knowledge and physical features can support further generalization.

DOI

https://doi.org/10.31223/X5GW3V

Subjects

Civil and Environmental Engineering, Engineering, Environmental Studies, Hydrology

Keywords

data integration modeling, general semi-distributed model, streamflow hindcasting

Dates

Published: 2021-03-08 05:37

Last Updated: 2021-03-08 10:37

License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
None

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