Paper No. JAWRA-10-0203-P of the Journal of the American Water Resources Association (JAWRA).Discussions are open until six months from print publication.
Error Autocorrelation and Linear Regression for Temperature-Based Evapotranspiration Estimates Improvement1
Article first published online: 2 DEC 2011
© 2011 American Water Resources Association
JAWRA Journal of the American Water Resources Association
Volume 48, Issue 2, pages 297–305, April 2012
How to Cite
Medeiros, P. V., Marcuzzo, F. F. N., Youlton, C. and Wendland, E. (2012), Error Autocorrelation and Linear Regression for Temperature-Based Evapotranspiration Estimates Improvement. JAWRA Journal of the American Water Resources Association, 48: 297–305. doi: 10.1111/j.1752-1688.2011.00614.x
- Issue published online: 4 APR 2012
- Article first published online: 2 DEC 2011
- Received November 20, 2010; accepted September 12, 2011.
Medeiros, Patrick Valverde, Francisco Fernando Noronha Marcuzzo, Cristián Youlton, and Edson Wendland, 2012. Error Autocorrelation and Linear Regression for Temperature-Based Evapotranspiration Estimates Improvement. Journal of the American Water Resources Association (JAWRA) 48(2): 297-305. DOI: 10.1111/j.1752-1688.2011.00614.x
Abstract: Estimates of evapotranspiration on a local scale is important information for agricultural and hydrological practices. However, equations to estimate potential evapotranspiration based only on temperature data, which are simple to use, are usually less trustworthy than the Food and Agriculture Organization (FAO)-Penman-Monteith standard method. The present work describes two correction procedures for potential evapotranspiration estimates by temperature, making the results more reliable. Initially, the standard FAO-Penman-Monteith method was evaluated with a complete climatologic data set for the period between 2002 and 2006. Then temperature-based estimates by Camargo and Jensen-Haise methods have been adjusted by error autocorrelation evaluated in biweekly and monthly periods. In a second adjustment, simple linear regression was applied. The adjusted equations have been validated with climatic data available for the Year 2001. Both proposed methodologies showed good agreement with the standard method indicating that the methodology can be used for local potential evapotranspiration estimates.