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: (1) use of remote-sensing data to create some of the spatially distributed input parameter sets for a model, and (2) constraining of models during calibration by spatially distributed data derived from remote sensing. In both, models can be improved conceptually and quantitatively.