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. Initially conservative estimates of future (in)flows in rivers and reservoirs may lead to unnecessary reduction of the water allocated. Though water allocations may be revised as the season progresses, inconsistency in allocation is undesirable to farmers as they may then not be able to use that water, leading to an opportunity cost in agricultural production. We assess the benefit of using reservoir inflow estimates derived from seasonal forecast datasets to improve water allocation decisions. A decision model is developed to emulate the feedback loop between simulated reservoir storage and water allocations to irrigated crops and is evaluated using inflow forecasts generated with the Forecast Guided Stochastic Scenarios (FoGSS) model, a 12-month ensemble streamflow forecasting system. Two forcings are used to generate the forecasts: ensemble streamflow prediction – ESP (historical rainfall) – and POAMA (calibrated rainfall forecasts from the POAMA climate prediction system). We evaluate the approach in the Murrumbidgee basin in Australia, comparing water allocations obtained with an expected reservoir inflow from FoGSS against the allocations obtained with the currently used conservative estimate based on climatology as well as against allocations obtained using observed inflows (perfect information). The inconsistency in allocated water is evaluated by determining the total changes in allocated water made every 15 d 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. Overconfidence can, however, lead to an increase in undesirable downward revisions. This is more evident for dry years than for wet years. Over the 28 years for which allocation decisions are evaluated, we find that the accuracy of the available water estimates using the forecast ensemble improves progressively during the water year, especially 1.5 months before the start of the cropping season in November. This is significant as it provides farmers with additional time to make key decisions on planting. Our results show that seasonal streamflow forecasts can provide benefit in informing water allocation policies, particularly by earlier establishing final water allocations to farmers in the irrigation season. This allows them to plan better and use water allocated more efficiently.
Specific storage (SS) has considerable predictive importance in the modelling of groundwater systems, yet little is known about its statistical distribution and dependency on other hydrogeological characteristics. This study provides a comprehensive overview and compiles 430 values of SS from 183 individual studies, along with complementary hydrogeological information such as estimation methods, lithology, porosity, and formation compressibility. Further evaluation of different approaches to determine and utilize SS values for numerical groundwater modelling, along with the scale and source of uncertainty of different measurement methods, was carried out. Overall, SS values range across six orders of magnitude (from 3.2 × 10–9 to 6 × 10–3 m–1) with a geometric mean of 1.1 × 10–5 m–1 and the majority (> 67%) of values are in the order of 10–5 and 10–6 m–1. High SS values of ~10–4 m–1 were reported for glacial till and sandy lithologies, particularly for shallow and thin strata where leakage may obscure the estimation of SS. A parallel assessment of 45 transient regional-scale groundwater models reveals a disconnect between findings of this study and the way SS is treated in practice, and that there is a lack of foundational SS data to conduct quantitative uncertainty analysis. This study provides the first probability density functions of SS for a variety of lithology types based on the field and laboratory tests collated from the literature. Log transformed SS values follow a Gaussian/normal distribution which can be applied to evaluate uncertainties of modelling results and therefore enhance confidence in the groundwater models that support decision making.
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. Initially conservative estimates of future (in) flows in rivers and reservoirs may lead to unnecessary reduction of the water allocated. Though water allocations may be revised as the season progresses, inconsistency in allocation is undesirable to farmers as they may then not be able to use that water, leading to an opportunity cost in agricultural production. We assess the benefit of using reservoir inflow estimates derived from seasonal forecast datasets to improve water allocation decisions. A decision model is developed to emulate the feedback loop between simulated reservoir storage and water allocations to irrigated crops, and is evaluated using inflow forecasts generated with the Forecast Guided Stochastic Scenarios (FoGSS) model, a 12-month ensemble streamflow forecasting system. Two forcings are used to generate the forecasts: ESP (historical rainfall) and POAMA (calibrated rainfall forecasts from the POAMA climate prediction system). We evaluate the approach in the Murrumbidgee basin in Australia, comparing water allocations obtained with an expected reservoir inflow from FoGSS against the allocations obtained with the currently used conservative estimate based on climatology, as well as against allocations 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. Overconfidence can, however, lead to an increase in undesirable downward revisions. This is more evident for dry years than for wet years. Over the 28 years for which allocation decisions are evaluated, we find that 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. This is significant as it provides farmers additional time to make key decision on planting.
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