EVAPOTRANSPIRATION TRANSFER-FUNCTION-NOISE MODELING

Water-resource projects require a correct estimation of evapotranspira tion for their successful planning, design, and operation. The accurac y of evapotranspiration is particularly important in, for example, wat er-quality models. Evapotranspiration estimation is very sensitive to the degree of forecasting error and even small errors can produce high ly misleading results. Unfortunately, the time-dependent behavior of e vapotranspiration makes it very hard to be represented by simpler expr essions and forecasting based on such relations will naturally be inac curate. In this study, the inherently stochastic process of reference evapotranspiration is modeled in a dynamic regression environment, als o known as transfer-function-noise (TFN) modeling. TFN models have the capability to relate different time series and the capacity to allow delays between system inputs and responses, which make them quite attr active. In this study, various single input-single output TFN models a re developed for reference evapotranspiration. It is shown that evapot ranspiration can be adequately represented and forecasted by TFN model s.