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Proposing Institution

Institut für Meteorologie und Klimaforschung, Zugspitze KIT
Project Manager

Patrick Laux
Kreuzeckbahnstr. 19
82467 Garmisch-Partenkirchen
It is expected that until 2025 approximately 1.8 billion people will suffer from water scarcity. In order to deal with this projected development, scientists and decision makers rely their planning more and more on global hydrometeorological remote-sensing- or modelsystems as the number of in situ gauges is significantly decreasing. The major task of SaWaM is therefore the development of methods and tools for the practice transfer of regionalized global data for water resources management. The performance of the developed products will be evaluated over selected semi-arid regions. Special focus is on the seasonal prediction of water availability, the state of the eco-system, and the modeling of sediment flow. This will be achieved through an integrative approach between climate-, hydrological-, and ecosystem sciences together with remote-sensing based methods and data. The practice application of the developed products will be analyzed within a consortium of seven universities and research institutes, two internationally active German business partners as well as several regional decision makers. The full model- and information chain will be analyzed for 3 dedicated development regions (Sudan, Northeast-Brazil and Iran) while selected features willbe further evaluated over two perspective regions (Ecuador, West Africa). In the end, it is expected that the superordinate product from SaWaM will be a scienti c-based online tool, which allows to examine and apply all developed products from the methods and data to the seasonal predictions for water resources management.In this proposal, only the work tasks related to the downscaling of seasonal climate forecasting will be addressed. Dynamical downscaling of global seasonal forecasts is a new emerging research topic, but very CPU demanding. Assessing the skill of downscaling of global seasonal forecasts as function of lead time, initial conditions and target resolution for different regions worldwide can be considered as highly innovative research, but is not feasible without access to a HPC environment.

Impressum, Conny Wendler