Paper No. JAWRA-11-0134-P of the Journal of the American Water Resources Association (JAWRA). This article is a U.S. Government work and is in the public domain in the USA. Discussions are open until six months from print publication.
Estimation of Evapotranspiration Across the Conterminous United States Using a Regression With Climate and Land-Cover Data1
Version of Record online: 26 DEC 2012
© 2012 American Water Resources Association
JAWRA Journal of the American Water Resources Association
Volume 49, Issue 1, pages 217–230, February 2013
How to Cite
Sanford, W. E. and Selnick, D. L. (2013), Estimation of Evapotranspiration Across the Conterminous United States Using a Regression With Climate and Land-Cover Data. JAWRA Journal of the American Water Resources Association, 49: 217–230. doi: 10.1111/jawr.12010
- Issue online: 4 FEB 2013
- Version of Record online: 26 DEC 2012
- Received December 16, 2011; accepted September 24, 2012.
Vol. 49, Issue 2, 479, Version of Record online: 13 MAR 2013
- hydrologic cycle;
Sanford, Ward E. and David L. Selnick, 2012. Estimation of Evapotranspiration Across the Conterminous United States Using a Regression with Climate and Land-Cover Data. Journal of the American Water Resources Association (JAWRA) 1-14. DOI: 10.1111/jawr.12010
Abstract: Evapotranspiration (ET) is an important quantity for water resource managers to know because it often represents the largest sink for precipitation (P) arriving at the land surface. In order to estimate actual ET across the conterminous United States (U.S.) in this study, a water-balance method was combined with a climate and land-cover regression equation. Precipitation and streamflow records were compiled for 838 watersheds for 1971-2000 across the U.S. to obtain long-term estimates of actual ET. A regression equation was developed that related the ratio ET/P to climate and land-cover variables within those watersheds. Precipitation and temperatures were used from the PRISM climate dataset, and land-cover data were used from the USGS National Land Cover Dataset. Results indicate that ET can be predicted relatively well at a watershed or county scale with readily available climate variables alone, and that land-cover data can also improve those predictions. Using the climate and land-cover data at an 800-m scale and then averaging to the county scale, maps were produced showing estimates of ET and ET/P for the entire conterminous U.S. Using the regression equation, such maps could also be made for more detailed state coverages, or for other areas of the world where climate and land-cover data are plentiful.