Incorporating reanalysis-based short-term forecasts from a regional climate model in an irrigation scheduling optimization problem

A coupled simulation-optimization with reanalysis-based short-term weather forecasts from a regional climate model (RCM) is proposed to optimize an irrigation scheduling problem. Using different physical configurations of the climate extension of a weather research and forecasting model (CWRF) that is driven by national atmospheric model project reanalysis data, five ensemble outlooks of 15 consecutive daily forecasts have been generated during five different crop-growing seasons. Six daily climatic variables are forecasted, namely, rainfall, minimum temperature, maximum temperature, humidity, wind speed, and solar radiation. To correct the forecasts for any inherent bias, the quantile mapping method is applied to all six daily climatic variables. After bias correction, a skill assessment of the reanalysis-based RCM forecasts indicate that only the first three climatic variables are predicted with reliable accuracy; thus, average climatic means are used to replace the remaining three variables (humidity, wind speed, and solar radiation). The framework is applied to the Havana Lowlands region, Illinois, as a case study, and the value of forecasts is assessed against two baseline scenarios: no-rain forecast (a pessimistic case) and average climatology (a normal case). Using reanalysis-based RCM forecasts to guide farmers' irrigation decisions could yield about 1-3% in expected profit gain and 4-6% in water reduction when compared to the no-rain forecast scenario, and 1-6% in expected profit gain when compared to the average climatology scenario. This study is a first preliminary attempt to use an ensemble of weather simulations in the optimization of irrigation scheduling, and the developed framework can be used to incorporate operational forecasting once the reanalysis boundary is replaced by global weather forecasts.