Water resources such as snow or groundwater can be estimated using satellite remote sensing observations and numerical models. Both models and observations have inherent uncertainties and limitations related to observation errors, model parameterization, and input uncertainties. A promising method to alleviate shortcomings in models and observations is data assimilation because it combines existing and emerging observations with model estimates, thus bridging scale and limitation gaps between observations and models.
Using these tools, we can address the following science questions: How much water is stored as seasonal snow? How much is in the groundwater aquifers? Can we quantify hydrological changes due to human induced processes (e.g., irrigation)? This presentation will focus on the estimation of snow seasonal amounts in mountainous regions, the water towers of the world. They supply a substantial part of both natural and anthropogenic water demands and they are also highly sensitive and prone to climate change. Slides of the talk can be found by clicking on the above figure.
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