Résultat de la recherche
Sustainable Water Management in Arid and Semi-arid Regions
"Arid and semi-arid regions face major challenges in the management of scarce freshwater resources under pressures of population, economic development, climate change, pollution and over-abstraction. Groundwater is commonly the most important water resource in these areas. Groundwater models are widely used globally to understand groundwater systems and to guide decisions on management. However, the hydrology of arid and semi-arid areas is very different from that of humid regions, and there is little guidance on the special challenges of groundwater modelling for these areas. This book brings together the experience of internationally-leading experts to fill a gap in the scientific and technical literature. It introduces state-of-the-art methods for modelling groundwater resources, illustrated with a wide-ranging set of illustrative examples from around the world. The book is valuable for researchers, practitioners in developed and developing countries, and graduate students in hydrology, hydrogeology, water resources management, environmental engineering and geography"-- "This book brings together the worldwide experience of internationally leading experts to fill this gap in the scientific and technical literature. It introduces state-of-the-art methods for the modelling of groundwater resources and their protection from pollution"--
How can remote sensing contribute in groundwater modeling?
Groundwater resources assessment, modeling and management are hampered considerably by a lack of data, especially in semi-arid and arid environments with a weak observation infrastructure. Usually, only a limited number of point measurements are available, while groundwater models need spatial and temporal distributions of input and calibration data. If such data are not available, models cannot play their proper role in decision support as they are notoriously underdetermined and uncertain. Recent developments in remote sensing have opened new sources for distributed spatial data. As the relevant entities such as water fluxes, heads or transmissivities cannot be observed directly by remote sensing, ways have to be found to link the observable quantities to input data required by the model. An overview of the possibilities for employing remote-sensing observations in groundwater modeling is given, supported by examples in Botswana and China. The main possibilities are: use of remote-sensing data to create some of the spatially distributed input parameter sets for a model, and constraining of models during calibration by spatially distributed data derived from remote sensing. In both, models can be improved conceptually and quantitatively.