spatial Interpolation of Penman Evapotranspiration

ABSTRACT FOR irrigation scheduling and hydrologic studies, it is often necessary to estimate reference evapotranspiration at points located some distance from a weather station. For regions which are served by weather station networks, one may interpolate, using evapotranspiration estimates from monitored locations. The approaches which are currently used for spatial interpolation include the Thiessen polygon, simple averaging, and inverse distance weighting. The present work examines the use of Kalman filtering as an alternate approach. The Kalman filter offers the advantages of considering reference evapotranspiration as a stochastic process and of accounting for measurement error and model error explicitly. The filter was found to be an acceptable algorithm for spatial interpolation of reference evapotranspiration based on diagonostic checks, lowest sum of squared error, and minimum variance estimates.