Global Earth Observation for integrated water reso.. (eartH2Observe)
Global Earth Observation for integrated water resource assessment
Date du début: 1 janv. 2014,
Date de fin: 31 déc. 2017
The project eartH2Observe brings together the findings from European FP projects DEWFORA, GLOWASIS, WATCH, GEOWOW and others. It will integrate available global earth observations (EO), in-situ datasets and models and will construct a global water resources re-analysis dataset of significant length (several decades). The resulting data will allow for improved insights on the full extent of available water and existing pressures on global water resources in all parts of the water cycle. The project will support efficient and globally consistent water management and decision making by providing comprehensive multi-scale (regional, continental and global) water resources observations. It will test new EO data sources, extend existing processing algorithms and combine data from multiple satellite missions in order to improve the overall resolution and reliability of EO data included in the re-analysis dataset. The usability and operational value of the developed data will be verified and demonstrated in a number of case-studies across the world that aim to improve the efficiency of regional water distribution. The case-studies will be conducted together with local end-users and stakeholders. Regions of interest cover multiple continents, a variety of hydrological, climatological and governance conditions and differ in degree of data richness (e.g. the Mediterranean and Baltic region, Ethiopia, Colombia, Australia, New Zealand and Bangladesh). The data will be disseminated though an open data Water Cycle Integrator portal to ensure increased availability of global water resources information on both regional and global scale. The data portal will be the European contributor to the existing GEOSS water cycle platforms and communities. Project results will be actively disseminated using a combination of traditional methods (workshops, papers, website and conferences) and novel methods such as E-learning courses and webinars that promote the use of the developed dataset.
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