Articles | Volume 27, issue 12
Research article
30 Jun 2023
Research article |  | 30 Jun 2023

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

Dapeng Feng, Hylke Beck, Kathryn Lawson, and Chaopeng Shen

Data sets

A large-sample watershed-scale hydrometeorological dataset for the contiguous USA A. J. Newman, K. Sampson, M. P. Clark, A. Bock, R. J. Viger, and D. Blodgett

CAMELS Extended Maurer Forcing Data F. Kratzert

CAMELS benchmark models F. Kratzert

MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500\,m SIN Grid V006 S. Running, Q. Mu, and M. Zhao

Model code and software

Differentiable hydrologic models: dPL+evolved HBV D. Feng, C. Shen, J. Liu, K. Lawson, and H. Beck

mhpi/hydroDL: MHPI-hydroDL (v2.0) K. Fang, C. Shen, and D. Feng

Short summary
Powerful hybrid models (called δ or delta models) embrace the fundamental learning capability of AI and can also explain the physical processes. Here we test their performance when applied to regions not in the training data. δ models rivaled the accuracy of state-of-the-art AI models under the data-dense scenario and even surpassed them for the data-sparse one. They generalize well due to the physical structure included. δ models could be ideal candidates for global hydrologic assessment.