Ensemble forecasting of short‐term system scale irrigation demands using real‐time flow data and numerical weather predictions. Issue 6 (25th June 2016)

• Record Type: Journal Article
• Authors: Perera, Kushan C.; Western, Andrew W.; Robertson, David E.; George, Biju; Nawarathna, Bandara
• Is Part Of: Water resources research. Volume 52:Issue 6(2016:Jun.)
• Page Start: 4801
• Page End: 4822
• Publication Date: 2016-06-25
• Subjects: ensemble forecast -- irrigation demand -- numerical weather prediction -- time series model; Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2015WR018532
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: 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. Key Points: This study forecasts probabilistic short‐term system scale irrigation demand for lead time up to 5 days Measurement/estimation/forecast errors for flows, NWP forecasts and observed weather are integrated Rank histograms and other indices indicated that a reliable ensemble spread is achieved.