Making SENSE of the Water value chain with Copernicus Earth Observation models...
Making SENSE of the Water value chain with Copernicus Earth Observation models and in situ data
Shortages of freshwater will be one of the most pressing problems in feeding the world this century. To optimize use of available water it is important to distribute it wisely over the various competing interests, in particular ag...
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Información proyecto WaterSENSE
Duración del proyecto: 55 meses
Fecha Inicio: 2019-10-31
Fecha Fin: 2024-06-30
Líder del proyecto
ELEAF BV
No se ha especificado una descripción o un objeto social para esta compañía.
TRL
4-5
Presupuesto del proyecto
2M€
Fecha límite de participación
Sin fecha límite de participación.
Descripción del proyecto
Shortages of freshwater will be one of the most pressing problems in feeding the world this century. To optimize use of available water it is important to distribute it wisely over the various competing interests, in particular agriculture, which is responsible for 70% of all freshwater use. Irrigation is currently often unsustainable, while groundwater reserves are becoming depleted and many places in the world are suffering water shortages. Action is therefore required now to use space and in-situ monitoring systems, to create a better sense of water availability and optimise use across the planet. WaterSENSE will provide water-availability and mapping services for any place in the world at different time and space resolutions, based on integrated Copernicus data, hydrological models and local data. The results of these services will be open access so as to further develop value-adding services. WaterSENSE itself will deliver the essential value-added service of monitoring compliance of local water use against water rights and regulations (‘water auditing’). The first application will be in the multi-climate Murray-Darling Basin in Australia, followed by validation in South Africa and the Netherlands. Consortium partners already provide water-availability and water-auditing services in the latter two countries. Novel research in the project will develop scalable information services, based on advanced big-data processing algorithms, to determine variables such as evapotranspiration, irrigation water use, rainfall and soil moisture, as well as machine learning to allow automatic data processing and reduce uncertainty in the hydrological variables determined. DIAS services for data provision, as well as cloud hosting and processing of computational services, will be developed and implemented. Existing successful partnership models will be refined to ensure service providers in the water value chain achieve healthy business development.