Ocean Based Statistical Forecasts for Seasonal Irrigation Allocations

Shahbaz Khan, Aftab Ahmad, Zahid Saeed

Research output: Book chapter/Published conference paperConference paperpeer-review

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The initial general security water allocation announcements for water users in the New South Wales (NSW) part of the Murray Valley are made during July. The initial water allocation announcement is very conservative as the allocations are based on storage levels and historical minimum inflows statistics to dams during the irrigation seasons. There is a good chance that the water allocations will be increased as the season proceeds. Water availability during the cropping season is a major factor influencing planting decisions made by irrigators and can have a major bearing on the financial viability and irrigation efficiency of irrigation areas. Therefore, increased knowledge on the likely end-of-season allocation by advance predictions can assist in minimising cropping risk and can help optimise farm returns and achieve better irrigation efficiencies. Seasonal river flow forecasts are used for determining anticipated water allocations; however, this paper presents a more direct approach that forecasts water allocation instead of river flow. The study is based on the hypothesis that the sea-surface temperature (SST) and ocean based climate variability indices (CVIs) are statistically related to water allocation forecasts in a river catchment. Over 100-years data on a global two degree grid SST, CVIs, and water allocations in the Murray Irrigation Area (MIA) were analysed. Statistical techniques including probability analysis and multiple linear regression (MLR) were used to determine the underlying relationships among predictor variables and the end-of-irrigation-season water allocation (February allocation) in the MIA. The SST at three locations around the continent; one lying in the equatorial Pacific, second in the Indian Ocean and third in the Tasmanian Sea, were found highly correlated (Pearson Correlation Coefficient up to -0.83) with February allocation levels in the MIA based on analysis using the CSIRO’s SSTman software.The significant variable identified by the MLR analyses include; SST, SOI, mean sea level pressure, start-of-season (August) and mid-ofseason (October) announced allocations and the risk factor. The risk factor can be varied from 0 to 100% and relates the probability of February allocation to announced August allocation and translates degree of risk farmer may take based on known August allocation. The value of the risk factor must be chosen with care because if user/farmer decides a higher value of risk factor, the model will forecast higher allocation suggesting farmer to grow more crops but at the same time involve a higher degree of risk of actually not getting that level of allocation. The model was validated against actual announced allocations for the month of February for ten years (1996/97 to 2005/06). The model underestimatedallocations for the years 2001 and 2002. This may be due to borrowing water from future years despite exceptionally low rainfalls during the season and not taking into account then emerging drought conditions. A simple software tool developed based on the findings of this study will provide farmers with risk based crop management options.
Original languageEnglish
Title of host publicationLand, Water & Environmental Management
Subtitle of host publicationIntegrated Systems for Sustainability
EditorsLes Oxley, Don Kulasiri
Place of PublicationChristchurch, New Zealand
PublisherModelling and Simulation Society of Australia and New Zealand
Number of pages7
ISBN (Electronic)9780975840047
Publication statusPublished - 2007
EventInternational Congress on Modelling and Simulation (MODSIM) - Christchurch, New Zealand
Duration: 10 Dec 200713 Dec 2007


ConferenceInternational Congress on Modelling and Simulation (MODSIM)
CountryNew Zealand

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