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The global water gap, water demand minus water supply, is expected to increase through 2100, negatively affecting agriculture, industry and households. Adaptation measures are necessary, but projections on their effectiveness and costs are currently unavailable. Here, we present an adaptation evaluation framework aimed at closing the water gap, which is applied offline at the global scale, and made available for regional decision making as a web service. It includes climate change and socioeconomic scenarios over the 21st century as drivers for global projections of water supply and demand. The transient water gap was calculated for 1604 water provinces globally and we determined the water gap reduction that could be achieved by three increasingly involved sets of adaptation measures. The median annual adaptation costs amount to 1.4–1.6% of the GDP per affected water province. The interactive web-based simulation allows users to include information that is not available at the global scale.

[SPA] Se utiliza un modelo hidrológico distribuido para evaluar cómo diferentes métodos influyen en la estimación de la evapotranspiración (ETc) y el balance de agua a escala de cuenca. La zona de estudio se ubica en la cuenca alta del Segura (~ 2.500 km2) en el Sureste español, zona caracterizada por una elevada heterogeneidad de condiciones del terreno y usos del suelo. El modelo hidrológico SPHY fue desarrollado y calibrado para un período de simulación de 15 años. Se emplearon cinco métodos para parametrizar el coeficiente de cultivo y se compararon los patrones espaciales y las dinámicas temporales simuladas para la evapotranspiración, la humedad del suelo y los caudales. Tres de los cinco métodos utilizan información de satélite, otro los valores del coeficiente de cultivo establecidos por FAO, y el último asume un valor constante para toda la cuenca y periodo de simulación. El análisis muestra que la generación de caudales apenas se ve afectada por la selección del método de parametrización, aunque sí es importante a la hora de calcular la evapotranspiración real, especialmente durante épocas húmedas y para los valores tabulados de FAO. [ENG] A distributed hydrologic model is used to evaluate how different methods to estimate evapotranspiration (ETc) influence the water balance and hydrologic response of basins. The study site, the upper Segura basin (~2500 km2) in Spain, is characterized by a wide range of terrain, soil, and ecosystem conditions. Input and calibration data for the hydrological model SPHY are obtained from best available data sources. The model was setup for a period of 15 year. Five crop coefficient parameterization methods are compared to explore the impact of spatial and temporal variations in these input datasets on actual evapotranspiration, streamflow and soil moisture. Methods include three that are based on remote sensing information; one based on FAO literature, and another that takes the crop coefficient equal to unity for the entire basin. The analysis shows that basin-level streamflow is hardly influenced by the choice in parameterization, but actual evapotranspiration and soil moisture are quite different, especially in the wet season and for the FAO-based method.

Remotely sensed (RS) data is a major source to obtain spatial data required for hydrological models. The challenge for the future is to obtain besides the more direct observable data (landcover, leaf area index, digital elevation model and evapotranspiration), non-visible data such as soil characteristics, groundwater depth and irrigation practices.In this study we have explore the option of using inverse modeling to obtain these non-RS-visible data. For a command area in Haryana, India, we applied for the 2000–2001 rabi season a RS-GIS-combined inverse modeling approach to derive non-RS-visible data required in the regional application of hydrological models. A Genetic Algorithm loaded stochastic physically based soil-water-atmosphere-plant model (SWAP) was developed for the inverse problem and used in the study. The results showed good agreement with the inventoried data such as soil hydraulic properties, sowing dates, ground water depths, irrigation practices and water quality. The derived data could be used to predict the state of the system at anytime in the cropping season, which can be used to evaluate operational management strategies.

The practical application of simulation models in the field is sometimes hindered by the difficulty of deriving the soil hydraulic properties of the study area. The procedure so-called inverse modelling has been investigated in many studies to address the problem where most of the studies were limited to hypothetical soil profile and soil core samples in the laboratory. Often, the numerical approach called forward-backward simulation is employed to generate synthetic data then added with random errors to mimic the real-world condition. Inverse modelling is used to backtrack the expected values of the parameters. This study explored the potential of a Genetic Algorithm (GA) to estimate inversely the soil hydraulic functions in the unsaturated zone. Lysimeter data from a wheat experiment in India were used in the analysis. Two cases were considered: (1) a numerical case where the forward-backward approach was employed and (2) the experimental case where the real data from the lysimeter experiment were used. Concurrently, the use of soil water, evapotranspiration (ET) and the combination of both were investigated as criteria in the inverse modelling. Results showed that using soil water as a criterion provides more accurate parameter estimates than using ET. However, from a practical point of view, ET is more attractive as it can be obtained with reasonable accuracy on a regional scale from remote sensing observations. The experimental study proved that the forward-backward approach does not take into account the effects of model errors. The formulation of the problem is found to be critical for a successful parameter estimation. The sensitivity of parameters to the objective function and their zone of influence in the soil column are major determinants in the solution. Generally, their effects sometimes lead to non-uniqueness in the solution but to some extent are partly handled by GA. Overall, it was concluded that the GA approach is promising to the inverse problem in the unsaturated zone.