The benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisions
<p>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 allo...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-07-01
|
| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/24/3851/2020/hess-24-3851-2020.pdf |
| _version_ | 1818285185552089088 |
|---|---|
| author | A. Kaune A. Kaune A. Kaune F. Chowdhury M. Werner M. Werner J. Bennett J. Bennett |
| author_facet | A. Kaune A. Kaune A. Kaune F. Chowdhury M. Werner M. Werner J. Bennett J. Bennett |
| author_sort | A. Kaune |
| collection | DOAJ |
| description | <p>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.</p> |
| first_indexed | 2024-12-13T01:04:41Z |
| format | Article |
| id | doaj.art-9a4face453574a29bf892fc65aa20259 |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-13T01:04:41Z |
| publishDate | 2020-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-9a4face453574a29bf892fc65aa202592022-12-22T00:04:35ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382020-07-01243851387010.5194/hess-24-3851-2020The benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisionsA. Kaune0A. Kaune1A. Kaune2F. Chowdhury3M. Werner4M. Werner5J. Bennett6J. Bennett7Water Resources and Ecosystems Department, IHE Delft Institute for Water Education, Delft, the NetherlandsWageningen Institute for Environment and Climate Research, Wageningen University & Research, the NetherlandsFutureWater, Costerweg 1V, 6702 AA Wageningen, the NetherlandsWater Resources and Ecosystems Department, IHE Delft Institute for Water Education, Delft, the NetherlandsWater Resources and Ecosystems Department, IHE Delft Institute for Water Education, Delft, the NetherlandsDivision of Inland Water Systems, Deltares, Delft, the NetherlandsCSIRO Land & Water, Clayton, Victoria, AustraliaInstitute for Marine and Antarctic Studies, University of Tasmania, Battery Point, Tasmania, Australia<p>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.</p>https://hess.copernicus.org/articles/24/3851/2020/hess-24-3851-2020.pdf |
| spellingShingle | A. Kaune A. Kaune A. Kaune F. Chowdhury M. Werner M. Werner J. Bennett J. Bennett The benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisions Hydrology and Earth System Sciences |
| title | The benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisions |
| title_full | The benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisions |
| title_fullStr | The benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisions |
| title_full_unstemmed | The benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisions |
| title_short | The benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisions |
| title_sort | benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisions |
| url | https://hess.copernicus.org/articles/24/3851/2020/hess-24-3851-2020.pdf |
| work_keys_str_mv | AT akaune thebenefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT akaune thebenefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT akaune thebenefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT fchowdhury thebenefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT mwerner thebenefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT mwerner thebenefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT jbennett thebenefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT jbennett thebenefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT akaune benefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT akaune benefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT akaune benefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT fchowdhury benefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT mwerner benefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT mwerner benefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT jbennett benefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions AT jbennett benefitofusinganensembleofseasonalstreamflowforecastsinwaterallocationdecisions |