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dc.contributorWestern, Andrew W.en_US
dc.contributorGeorge, Biju Alummoottilen_US
dc.contributorNawarathna, Bandaraen_US
dc.creatorPerera, Kushan C.en_US
dc.date2015-12-01en_US
dc.date.accessioned2016-05-03T10:56:47Z
dc.date.available2016-05-03T10:56:47Z
dc.identifierhttps://mel.cgiar.org/dspace/limiteden_US
dc.identifier.citationKushan C. Perera, Andrew W. Western, Biju Alummoottil George, Bandara Nawarathna. (1/12/2015). Multivariate time series modeling of short-term system scale irrigation demand. Journal of Hydrology, 531, pp. 1003-1019.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/4717
dc.description.abstractTravel time limits the ability of irrigation system operators to react to short-term irrigation demand fluctuations that result from variations in weather, including very hot periods and rainfall events, as well as the various other pressures and opportunities that farmers face. Short-term system-wide irrigation demand forecasts can assist in system operation. Here we developed a multivariate time series (ARMAX) model to forecast irrigation demands with respect to aggregated service points flows (IDCGi, ASP) and off take regulator flows (IDCGi, OTR) based across 5 command areas, which included area covered under four irrigation channels and the study area. These command area specific ARMAX models forecast 1–5 days ahead daily IDCGi, ASP and IDCGi, OTR using the real time flow data recorded at the service points and the uppermost regulators and observed meteorological data collected from automatic weather stations. The model efficiency and the predictive performance were quantified using the root mean squared error (RMSE), Nash–Sutcliffe model efficiency coefficient (NSE), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). During the evaluation period, NSE for IDCGi, ASP and IDCGi, OTR across 5 command areas were ranged 0.98–0.78. These models were capable of generating skillful forecasts (MSSS ⩾ 0.5 and ACC ⩾ 0.6) of IDCGi, ASP and IDCGi, OTR for all 5 lead days and IDCGi, ASP and IDCGi, OTR forecasts were better than using the long term monthly mean irrigation demand. Overall these predictive performance from the ARMAX time series models were higher than almost all the previous studies we are aware. Further, IDCGi, ASP and IDCGi, OTR forecasts have improved the operators’ ability to react for near future irrigation demand fluctuations as the developed ARMAX time series models were self-adaptive to reflect the short-term changes in the irrigation demand with respect to various pressures and opportunities that farmers’ face, such as changing water policy, continued development of water markets, drought and changing technology.en_US
dc.formatPDFen_US
dc.languageenen_US
dc.publisherElsevieren_US
dc.rightsCC-BY-NC-4.0en_US
dc.sourceJournal of Hydrology;531,(2015) Pagination 1003,1019en_US
dc.subjectirrigation demanden_US
dc.subjecttime seriesen_US
dc.subjectmultivariateen_US
dc.subjectarmaxen_US
dc.subjectexogenous variablesen_US
dc.titleMultivariate time series modeling of short-term system scale irrigation demanden_US
dc.typeJournal Articleen_US
cg.creator.idGeorge, Biju Alummoottil: 0000-0002-8427-3350en_US
cg.creator.ID-typeORCIDen_US
cg.subject.agrovocirrigationen_US
cg.subject.agrovocforecastingen_US
cg.contributor.centerThe University of Melbourne, Department of Infrastructure Engineeringen_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.crpCGIAR Research Program on Dryland Systems - DSen_US
cg.contributor.funderCGIAR System Organization - CGIARen_US
cg.date.embargo-end-dateTimelessen_US
cg.coverage.regionAustralia and New Zealanden_US
cg.coverage.countryAUen_US
cg.contactkushanr@student.unimelb.edu.auen_US
cg.identifier.doihttps://dx.doi.org/10.1016/j.jhydrol.2015.11.007en_US
dc.identifier.statusTimeless limited accessen_US
mel.impact-factor3.727en_US


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