Farmers and irrigation system operators make real-time irrigation decisions based on a range of factors including crop water requirement and short-term weather forecasts of rainfall and air temperature. Forecasts of reference crop evapotranspiration (ET O ) can be calculated from numerical weather prediction (NWP) forecasts and ET O has the advantage of being more directly relevant to crop water requirements than air temperature. This paper aims to discuss the forecasting ability of ET O using outputs from the Bureau of Meteorology's operational NWP forecasts derived from the Australian Community Climate and Earth System Simulator -Global (ACCESS-G). The daily ET O forecasts were evaluated for the Shepparton Irrigation Area in Victoria. Forecast performance for ET O was quantified using the root mean squared error (RMSE), coefficient of determination (r 2 ), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). Lead times of daily ET O forecasts up to 9 days were compared against ET O calculated using hourly observations from the Shepparton airport automatic weather station. It was found that forecasting daily ET O was better than using the long-term monthly mean ET O for lead times up to 6 days and beyond that the longterm monthly mean was better. The average MSSS of ET O forecasts varied between 64% and 4 % for 1 to 6 day lead times, respectively. The most influential forecast weather variable for daily ET O forecasts was mean wind speed, air temperature and incoming solar radiation for 1, 2-3 and 4-9 day lead times respectively. Also, it was found that the forecast performance for incoming solar radiation and mean wind speed was relatively poor compared with the air and dew point temperatures.

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Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real‐time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short‐term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash–Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.

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Farmers and irrigation system operators make real-time irrigation decisions based on a range of factors including crop water requirement and short-term weather forecasts of rainfall and air temperature. Forecasts of reference crop evapotranspiration (ET O) can be calculated from numerical weather prediction (NWP) forecasts and ET O has the advantage of being more directly relevant to crop water requirements than air temperature. This paper aims to discuss the forecasting ability of ET O using outputs from the Bureau of Meteorology's operational NWP forecasts derived from the Australian Community Climate and Earth System Simulator-Global (ACCESS-G). The daily ET O forecasts were evaluated for the Shepparton Irrigation Area in Victoria. Forecast performance for ET O was quantified using the root mean squared error (RMSE), coefficient of determination (r 2), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). Lead times of daily ET O forecasts up to 9 days were compared against ET O calculated using hourly observations from the Shepparton airport automatic weather station. It was found that forecasting daily ET O was better than using the long-term monthly mean ET O for lead times up to 6 days and beyond that the longterm monthly mean was better. The average MSSS of ET O forecasts varied between 64% and 4 % for 1 to 6 day lead times, respectively. The most influential forecast weather variable for daily ET O forecasts was mean wind speed, air temperature and incoming solar radiation for 1, 2-3 and 4-9 day lead times respectively. Also, it was found that the forecast performance for incoming solar radiation and mean wind speed was relatively poor compared with the air and dew point temperatures.