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dc.contributorWestern, Andrew W.en_US
dc.contributorRobertson, Daviden_US
dc.contributorGeorge, Biju Alummoottilen_US
dc.contributorNawarathna, Bandaraen_US
dc.creatorPerera, Kushan C.en_US
dc.identifier.citationKushan C. Perera, Andrew W. Western, David Robertson, Biju Alummoottil George, Bandara Nawarathna. (24/6/2016). Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions. Water Resource Research, 52 (6), pp. 4801-4822.en_US
dc.description.abstractIrrigation 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.en_US
dc.publisherAmerican Geophysical Union (AGU)en_US
dc.sourceWater Resource Research;52,(2016) Pagination 4801,4822en_US
dc.titleEnsemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictionsen_US
dc.typeJournal Articleen_US
cg.creator.idGeorge, Biju Alummoottil: 0000-0002-8427-3350en_US
cg.contributor.centerThe University of Melbourne, Department of Infrastructure Engineeringen_US
cg.contributor.centerCommonwealth Science and Industrial Research Organisation - CSIROen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerThe Bureau of Meteorology, Australia Environment and Research Divisionen_US
cg.contributor.funderInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.projectCommunication and Documentation Information Services (CODIS)en_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.regionAustralia and New Zealanden_US
dc.identifier.statusLimited accessen_US

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