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Climate Change, Water and Productivity in the Mediterranean: Capacity Building for Geospatial Modelling (CLIMed)
Date du début: 1 févr. 2009, Date de fin: 31 janv. 2011 PROJET  TERMINÉ 

The aim of this research is capacity building through exchanging young scientist to estimate both hydrological and rates of biogeochemical cycling in order to test hypotheses on the relationship between climate change and ecosystem productivity in the Mediterranean environment. Remotely sensed-data, GIS and process-based simulation models have provided the capability to assess ecological change at broad spatio-temporal scales. The proposed exchange programme can be considered as a chance of connecting the hydrology and the remote sensing communities from Europe and US to develop tools for understanding of climate change consequences using geospatial technologies. The requested EU exchange project pursues a clear application and enables to transfer expertise from science into practice and on the other hand the mutual exchange of knowledge between research groups from Turkey, Germany, England and US. The major focus of the project using geospatial technologies is in two folds: i) Climate change and water management and modelling; Water scarcity and management is a cross-cutting issue in the world. This is especially true in the Mediterranean region where water resources are limited and water issues are the most urgent. Modelling the water, solute and erosion dynamics will be done by means of the J2000 physically based system which will be supported by GIS-analysis and remote sensing; ii) Climate change and the ecosystem productivity; The main steps are, a) Classification, quantification and analysis of land-use, b) Collection of inputs (climate, soil and biotic data) for simulation models, c) Quantification of C-N budgets and simulation of temperature rise scenarios. These topics are interdisciplinary in nature and involve processes that are relevant to biophysical ecology, biogeography, and climate. To study these topics, the research relies heavily on remote sensing, biophysical models of land-atmosphere energy exchange, GIS and statistical methods.

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