scholarly journals The Value of Using Hydrological Datasets for Water Allocation Decisions: Earth Observations, Hydrological Models, and Seasonal Forecasts

2019 ◽  
Author(s):  
Alexander José Kaune Schmidt
Keyword(s):  
Water Allocation ◽  
Hydrological Models ◽  
Seasonal Forecasts ◽  
Earth Observations ◽  
Allocation Decisions

Water for agriculture and the environment: the ultimate trade-off

Water Policy ◽  
2012 ◽  
Vol 14 (S1) ◽  
pp. 136-146 ◽  
Author(s):  
Henry Vaux
Keyword(s):  
Water Allocation ◽  
Virtual Water ◽  
Environmental Services ◽  
Environmental Amenities ◽  
Global Commons ◽  
Year 2000 ◽  
Sufficient Food ◽  
Global Population ◽  
Allocation Decisions ◽  
Growing Population

Global population is projected to increase over year 2000 levels by 30% in 2025 and by 50% in 2050. Producing sufficient food to feed a more populous Earth will be a challenge requiring additional developed water supplies. Existing supplies are unevenly distributed around the planet. Some developing countries lack sufficient water to grow the food necessary to feed the growing population. With time, more countries will join that group. The strategies available to produce more food depend upon which sources are available. Two options open to all countries are improving the productivity of water in agriculture and importing virtual water in food. For some, the additional options of bringing more land into production or harvesting rainwater may also be available. All these measures reallocate water to agricultural uses from environmental uses. Such reallocations may impose potentially large losses in the form of environmental services and environmental amenities. Difficult water allocation decisions with enormous values at stake confront humanity. These decisions are confounded because they entail the protection of the global commons for which there is no successful experience to draw on.

scholarly journals Improving Meteorological Seasonal Forecasts for Hydrological Modeling in European Winter

2020 ◽  
Vol 59 (2) ◽  
pp. 317-332
Author(s):  
Nicky Stringer ◽  
Jeff Knight ◽  
Hazel Thornton
Keyword(s):  
North Atlantic ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Strongly Correlated ◽  
Hydrological Models ◽  
Seasonal Forecasts ◽  
The North ◽  
Ensemble Mean ◽  
The North Atlantic Oscillation ◽  
Correlation Skill

AbstractRecent advances in the skill of seasonal forecasts in the extratropics during winter mean they could offer improvements to seasonal hydrological forecasts. However, the signal-to-noise paradox, whereby the variability in the ensemble mean signal is lower than would be expected given its correlation skill, prevents their use to force hydrological models directly. We describe a postprocessing method to adjust for this problem, increasing the size of the predicted signal in the large-scale circulation. This reduces the ratio of predictable components in the North Atlantic Oscillation (NAO) from 3 to 1. We then derive a large ensemble of daily sequences of spatially gridded rainfall that are consistent with the seasonal mean NAO prediction by selecting historical observations conditioned on the adjusted NAO forecasts. Over northern and southwestern Europe, where the NAO is strongly correlated with winter mean rainfall, the variability of the predicted signal in the adjusted rainfall forecasts is consistent with the correlation skill (they have a ratio of predictable components of ~1) and are as skillful as the unadjusted forecasts. The adjusted forecasts show larger predicted deviations from climatology and can be used to better assess the risk of extreme seasonal mean precipitation as well as to force hydrological models.

scholarly journals The benefit of using an ensemble of seasonal streamflow forecasts in water allocation decisions

2020 ◽  
Author(s):  
Alexander Kaune ◽  
Faysal Chowdhury ◽  
Micha Werner ◽  
James Bennett
Keyword(s):  
Water Allocation ◽  
Irrigation Season ◽  
Streamflow Forecasting ◽  
Additional Time ◽  
Initial Allocation ◽  
Available Water ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Reservoir Inflow ◽  
Allocation Decisions

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.

Assessing the Water-Energy-Food nexus on the Jucar river system using hydrometeorological forecasting and stochastic hydro-economic programming

2021 ◽  
Author(s):  
Hector Macian-Sorribes ◽  
Patricia Marcos-Garcia ◽  
Ilias Pechlivanidis ◽  
Louise Crochemore ◽  
Manuel Pulido-Velazquez
Keyword(s):  
Water Allocation ◽  
River System ◽  
Economic Modelling ◽  
Seasonal Forecasts ◽  
Allocation Rules ◽  
Operating Rules ◽  
Trade Offs ◽  
Forecasting System ◽  
The Impact ◽  
Dual Dynamic Programming

<p>Multipurpose water systems are subject to complex trade-offs among competing water uses, which could eventually have a significant potential for conflict. Hence these interlinkages should be properly identified to estimate the impact of changing allocation rules and avoid the trigger of undesirable outcomes. Concretely, forecast-based water allocation requires to assess the outputs of hydrometeorological forecasting within a sectoral context (e.g. urban, agriculture, energy) and contrast it with the current statu-quo. In this regard, stochastic hydro-economic modelling is an efficient approach to compare multipurpose water allocation rules using a common monetary unit, explicitly considering inflow uncertainty and exploiting the potential of hydrometeorological forecasting systems.</p><p>Here, we analyse the economic impacts caused by the implementation of forecast-based allocation rules on the Jucar river system in Spain. The economic revenues are calculated by combining Stochastic Dual Dynamic Programming (SDDP) with Model Predictive Control (MPC) forced with hydrometeorological forecasts. The following forecasting systems have been considered: (1) the current system operating rules forced by historical observations, (2) SMHI’s pan-European E-HYPE hydrological forecasting system forced with bias-adjusted ECMWF System 4 seasonal meteorological forecasts and post-processed using fuzzy logic to adjust forecasts to the local hydrological conditions, (3) five seasonal meteorological forecasting systems from the Copernicus Climate Change Service (ECMWF SEAS5, UKMO GloSEA5, MétéoFrance System 6, DWD GCFS and CMCC SPS3), bias-adjusted using linear scaling and further combined with locally-adjusted hydrological models, and (4) an ensemble system based on local observations of past river discharge.</p><p>Results show that the forecast-based allocation rules derived from SDDP and MPC improve the revenues obtained by the current policies forced by historical observations (which is the best scenario achievable without modifying the current operation). This indicates that combining stochastic modelling with seasonal forecasts improves water allocation performance without requiring a particular forecasting system. Although the agricultural benefits depend on the forecasting system considered, hydropower’s increases of economic returns are almost the same regardless of the forecast product. This means that hydropower revenues are mainly driven by the fact that forecast-based policies are adopted instead of using a particular forecasting service. Our results show that both uses (i.e. agriculture and hydropower) can simultaneously benefit from forecast-based operating rules, offering opportunities for collaboration to increase the regional water use efficiency.</p><p><em>Acknowledgements:</em></p><p>This study has been supported by the ADAPTAMED project (RTI2018-101483-B-I00), funded by the Ministerio de Economia y Competitividad (MINECO) of Spain and with EU FEDER funds, and co-funded by the postdoctoral program of Universitat Politècnica de València (UPV)</p>

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