Bias-correcting input variables enhances forecasting of reference crop evapotranspiration

Abstract

Abstract. Reference crop evapotranspiration (ETo) is calculated using a standard formula with temperature, vapor pressure, solar radiation, and wind speed as input variables. ETo forecasts can be produced when forecasts of these input variables from numerical weather prediction (NWP) models are available. As raw ETo forecasts are often subject to systematic errors, statistical calibration is needed for improving forecast quality. The most straightforward and widely used approach is to directly calibrate raw ETo forecasts constructed with the raw forecasts of input variables. However, the predictable signal in ETo forecasts may not be fully implemented by this approach, which does not deal with error propagation from input variables to ETo forecasts. We hypothesize that correcting errors in input variables as a precursor to forecast calibration will lead to more skillful ETo forecasts. To test this hypothesis, we evaluate two calibration strategies that construct raw ETo forecasts with the raw (strategy i) or bias-corrected (strategy ii) input variables in ETo forecast calibration across Australia. Calibrated ETo forecasts based on bias-corrected input variables (strategy ii) demonstrate lower biases, higher correlation coefficients, and higher skills than forecasts produced by the calibration using raw input variables (strategy i). This investigation indicates that improving raw forecasts of input variables could effectively reduce error propagation and enhance ETo forecast calibration. We anticipate that future NWP-based ETo forecasting will benefit from adopting the calibration strategy developed in this study to produce more skillful ETo forecasts.

Validation and Bias Correction of Forecast Reference Evapotranspiration for Agricultural Applications in Nevada

“…Application of bias-correction ratios to ET 0 generally reduced RMSE daily values by 5%-30%. Testing other bias-correction methods (e.g., Durai and Bhradwaj 2014) or correcting the ET 0 input variables first (e.g., Yang et al 2021) is recommended in future studies to determine if other methods may provide better forecast skill than the ratio method used in this study. It is yet to be seen whether the level of skill from these bias-corrected forecasts is sufficient to aid farmers in irrigation scheduling and waterconservation efforts.…”

Future climate or land use? Attribution of changes in surface runoff in a typical Sahelian landscape

“…Future daily PET is further calculated with the calibrated HS equation (presented in Section 2.3). This strategy is adopted here since correcting biases in dependent variables (here, temperature) has the potential of significantly reduce biases in PET forecasts [Yang et al, 2021].…”