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Machine learning-enabled model-data integration for predicting subsurface water storage...

by Dan Lu, Eric M Pierce, Shih-chieh Kao, David E Womble
Publication Type
Conference Paper
Journal Name
Conference on Neural Information Processing Systems
Book Title
NeurIPS 2021 Workshop: Tackling Climate Change with Machine Learning
Publication Date
Page Numbers
1 to 5
Publisher Location
United States of America
Conference Name
Conference on Neural Information Processing Systems (NeurIPS)
Conference Location
Virtual conference, California, United States of America
Conference Sponsor
multiple institutions
Conference Date

Subsurface water storage (SWS) is a key variable of the climate system and a storage component for precipitation and radiation anomalies, inducing persistence in the climate system. It plays a critical role in climate-change projections and can mitigate the impacts of climate change on ecosystems. However, because of the difficult accessibility of the underground, hydrologic properties and dynamics of SWS are poorly known. Direct observations of SWS are limited, and accurate incorporation of SWS dynamics into Earth system land models remains challenging. We propose a machine learning-enabled model-data integration framework to improve the SWS prediction at local to conus scales in a changing climate by leveraging all the available observation and simulation resources, as well as to inform the model development and guide the observation collection. The accurate prediction will enable an optimal decision of water management and land use and improve the ecosystem's resilience to the climate change.