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[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.

En este estudio se presentan datos de la distribución espacial de las precipitaciones y las tendencias en la provincia de Tungurahua, Ecuador, a partir de un refinamiento de métodos científicamente probados. Se combinaron datos de las estaciones meteorológicas con datos de teledetección para conocer mejor la distribución espacial de la precipitación en las zonas donde se carece de observaciones directas y donde se quiere promover y modernizar la agricultura. La cantidad total de la precipitación en la cuenca varía desde alrededor de 500 mm/año en torno a Ambato hasta 4000 mm en el sureste de la provincia y se concluye que tanto la variabilidad temporal como la espacial son muy significativas. La media anual para toda la provincia oscila entre 1400 y 1700 mm. La zona más seca es también la zona con mayor variabilidad en el tiempo, coincidiendo con la zona principal agrícola. El régimen pluvial permite aquí cultivos de ciclo corto, lo que refleja la situación actual. Los cultivos plurianuales se producen en las zonas con una mayor precipitación y con menor variabilidad temporal. La comparación entre las observaciones y los valores obtenidos mediante el método indican una buena correlación. Se ha llevado a cabo una validación cruzada de las salidas del algoritmo para determinar en qué zonas la predicción es mejor o peor. En general se concluye que el método utilizado es muy útil, los resultados son satisfactorios y aplicables en otros lugares.

The broad objective of AIACC project 47 was to develop the capacity to estimate and compare the benefits and costs of projects in natural resource sectors that reduce the expected damages from climate change in Southern and West Africa. There are two parts to this project. The first consists of using well-established principles from economic benefit-cost analysis to develop a framework to estimate the economic benefits and costs associated with the expected climate change damages avoided by a development project that does not take climate change into account. Then, these benefits and costs can be compared to the case where planners incorporate expected climate change into the project assessment. The second part consists of demonstrating this methodology in two project case studies, one in The Gambia and the other in South Africa. The South African case study examines the benefits and costs of avoiding climate change damages through structural and institutional options for increasing water supply in the Berg River Basin in the Western Cape Province. The Gambian study, on the other hand, focuses on the agricultural sector, particularly on millet, the predominant crop in the country. To facilitate analysis, the Gambian study uses a detailed water–crop model, defines and explores adaptation strategies with the model and uses the results to carry out an economic analysis. The South African project develops and applies a Berg River Dynamic Spatial Equilibrium Model as a water planning and policy evaluation tool to compare benefits and costs and economic impacts of alternatives for coping with longterm water shortages due to climatic change. Results from the study will contribute to the development of international climate change policies and programs, particularly in regard to adaptation activities in developing countries under the United Nations Framework Convention on Climate Change (UNFCCC).

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.

A clear understanding of the current water balance is required to explore options for water saving measures. However, measurement of all the terms in the water balance is infeasible in terms of spatial and temporal scale, but hydrological simulation models can fill the gap between measured and required data. For a basin in Western Turkey, simulation modeling at three different scales, field, irrigation scheme and basin scale, was performed to obtain all terms of the water balance. These water balance numbers were used to calculate the Productivity of Water at the three spatial levels distinguished to assess the performance of the systems.

This working paper gives an overview of the available global datasets on irrigated areas and an evaluation of their strengths and weaknesses. From these analyses an outline on how to develop a global irrigated area map, based on a generic methodology, is be presented. Some examples are given for the area covering India, Pakistan and Sri Lanka. These countries offer a broad range of irrigated areas in different environmental settings, ranging from deserts to humid tropics.

A global estimate of the potential for rain-fed agriculture could provide an answer to the question "How much irrigation is required?" Global studies done to date have relied on course resolution climate data (0.5-1 degree arc). In this study a high-resolution climate dataset (10-minute arc) was combined with a soil water storage capacity map and a dynamic water and crop model to estimate the potential for rain-fed agriculture. The methodology applied here, based on a high-resolution climate dataset, allows analyses on a global scale without losing the smaller regional-scale issues

Water scarcity and salinization are major threats to sustainable irrigation in Iran as well as other parts of the world. Irrigation schemes are part of a basin and as such, irrigation research must be conducted in a basin context. For the Zayandeh Rud basin in central Iran, a simplified Water and Salinity Basin Model (WSBM) was developed for a quick analysis of river basin processes. First the model was calibrated and used for current and past water resources analyses. Despite the simplicity of the model, observed and simulated stream flows were similar, proving that the model could be used for scenario analyses. The first scenario was setup to analyze the effect of more efficient irrigation techniques on the basin water resources. As a consequence of these efficient irrigation practices, return flows will decrease, resulting in less water available for downstream users. It was concluded that the effect on the downstream irrigation schemes was dramatic, with a 22% decrease in yield. A second scenario was defined where the effect of an increase of water extraction for Esfahan was evaluated. In terms of basin scale water quantity aspects this increased extraction is negligible as extractions are relatively low and return flows are high. The last scenario was developed to study the additional releases required from the reservoir to provide sufficient water for expansion of the tail-end Rudasht irrigation scheme. If no restriction is imposed on water quality, additional releases from the reservoir are limited. However, if salinity levels are not to exceed 2 dS m-1, mean annual water release requirements from the reservoir will increase from 52 m3 s-1 to 64 m3 s-1, and peak requirements during the irrigation season will increase from 85 to 112 m3 s-1. Finally, it was concluded that the methodology and the model developed were useful for a swift and transparent analysis of past, current and future water and salt resources, and to perform scenario analyses.