ABSTRACT
The area to be cropped in irrigation districts needs to be planned according to the allocated water, which in turn is a function of the available water resource. However, conservative estimates of the available resource in rivers and reservoirs may lead to unnecessary curtailments in allocated water due to conservative estimates of future (in) flows. Water allocations may be revised as the season progresses, though inconsistency in allocation is undesirable to farmers as they may then not be able to use that water, thus leading to an opportunity cost in agricultural production. In this study we assess the benefit of using reservoir inflow estimates derived from seasonal forecast datasets to improve water allocation decisions. A feedback loop between simulated reservoir storage and emulated water allocations to General Security (water allocated to irrigating) was developed to evaluate two seasonal reservoir inflow forecast datasets (POAMA and ESP), derived from the Forecast Guided Stochastic Scenarios (FoGSS), a 12 month seasonal ensemble forecast in Australia. We evaluate the approach in the Murrumbidgee basin, comparing water allocations obtained with an expected reservoir inflow from FoGSS against the allocations obtained with an expected reservoir inflow from a conservative estimate based on climatology (as currently used by the basin authority), as well as against those obtained using observed inflows (perfect information). The inconsistency in allocated water is evaluated by determining the total changes in allocated water made every 15 days from the initial allocation at the start of the water year to the end of the irrigation season, including both downward and upward revisions of allocations. Results show that the inconsistency due to upward revisions in allocated water is lower when using the forecast datasets (POAMA and ESP) compared to the conservative inflow estimates (reference) which is beneficial to the planning of cropping areas by farmers. Over confidence can, however, lead to an increase in undesirable downward revisions. This is more evident for dry years than for wet years. Even though biases are found in inflow predictions, the accuracy of the available water estimates using the forecast ensemble improves progressively during the water year, especially one and a half months before the start of the cropping season in November, providing additional time for farmers to make key decision on planting.
