Physically Informed Machine Learning for Hydrological Modeling Under Climate Nonstationarity
| dc.contributor.author | Nearing, Grey S. | |
| dc.contributor.author | Pelissier, Craig S. | |
| dc.contributor.author | Kratzert, Frederik | |
| dc.contributor.author | Klotz, Daniel | |
| dc.contributor.author | Gupta, Hoshin V. | |
| dc.contributor.author | Frame, Jonathan M. | |
| dc.contributor.author | Sampson, Alden K. | |
| dc.date.accessioned | 2020-08-26T17:10:29Z | |
| dc.date.available | 2020-08-26T17:10:29Z | |
| dc.date.issued | 2019-10 | |
| dc.description.abstract | There is an understanding in the hydrological sciences community that physical realism is necessary for providing hydrological forecasts under changing conditions (Blöschl et al., 2019; Clark et al., 2016; Milly et al., 2008). At present, however, machine learning (ML) generally provides the best estimates of most hydrological states and fluxes, even in extrapolation (e.g., Best et al., 2015; Kratzert et al., 2019a,b; Nearing et al., 2018). A notable example of this was provided by Kratzert et al. (2019a), who showed that Long Short Term Memory networks (LSTMs) produce, on average, better predictions in basins that did not supply training data (effectively ungaged basins) than a conceptual model well-calibrated to gauge data in individual basins (gaged basins). This is significant in that the 2003-2012 decadal problem of the International Association of Hydrological Sciences (IAHS) was `Prediction in Ungauged Basins' (PUB) (Hrachowitz et al., 2013). Prior to Kratzert et al. (2019a), best practices for PUB required extensive catchment-specific investment (Blöschl, 2016), which is infeasible at large scales (e.g., regional, continental, global). | en_US |
| dc.description.sponsorship | Grey Nearing was supported in part by a UCAR COMET Cooperative Project between the University of Alabama and the National Water Center and in part by a grant from the NASA Terrestrial Hydrology Program. Frederik Kratzert was supported by a Google Faculty Research Award. | en_US |
| dc.description.uri | https://www.nws.noaa.gov/ost/climate/STIP/44CDPW/44cdpw-GNearing.pdf | en_US |
| dc.format.extent | 5 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.identifier | doi:10.13016/m2ccvs-iggq | |
| dc.identifier.citation | Grey S. Nearing et al., Physically Informed Machine Learning for Hydrological Modeling Under Climate Nonstationarity, Science and Technology Infusion Climate Bulletin (2019), https://www.nws.noaa.gov/ost/climate/STIP/44CDPW/44cdpw-GNearing.pdf | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/19521 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | Public Domain Mark 1.0 | * |
| dc.rights | This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law | |
| dc.rights.uri | http://creativecommons.org/publicdomain/mark/1.0/ | * |
| dc.title | Physically Informed Machine Learning for Hydrological Modeling Under Climate Nonstationarity | en_US |
| dc.type | Text | en_US |