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4DHydro – Hyper-resolution Earth observations and land-surface modeling for a better understanding of the water cycle
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The project brings together the EO water cycle community developing novel high-resolution EO data products, and the land surface and hydrological modelling community engaged in advancing hyper-resolution modelling of the hydrological cycle at [...] |
HELMHOLTZ – ZENTRUM FUER UMWELTFORS (DE) |
Science |
hydrology science cluster, terrestrial hydrosphere, water cycle and hydrology |
The project brings together the EO water cycle community developing novel high-resolution EO data products, and the land surface and hydrological modelling community engaged in advancing hyper-resolution modelling of the hydrological cycle at regional and continental scales to assess the uncertainty of existing EO and LSM/HM data sets related to key terrestrial ECVs and generate improved datasets at 1 km spatial resolution in the selected study areas. Targeted science cases will demonstrate the synchronization of EO products and LSM/HMs models for improved predictability of hydrology systems at higher spatial and temporal resolutions, while use cases will develop tools to enhance the ability of end-users and decision-makers to extract and manipulate existing and future reanalysis and climate data sets.
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4DMED-Hydrology
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4DMED-Hydrology aims at developing an advanced, high-resolution, and consistent reconstruction of the Mediterranean terrestrial water cycle by using the latest developments of Earth Observation (EO) data as those derived from the ESA-Copernicus [...] |
CNR-RESEARCH INSTITUTE FOR GEO-HYDROLOGICAL PROTECTION – IRPI (IT) |
Science |
hydrology science cluster, Mediterranean, regional initiatives, science, terrestrial hydrosphere, water cycle and hydrology |
4DMED-Hydrology aims at developing an advanced, high-resolution, and consistent reconstruction of the Mediterranean terrestrial water cycle by using the latest developments of Earth Observation (EO) data as those derived from the ESA-Copernicus missions. In particular, by exploiting previous ESA initiatives, 4DMED-Hydrology intends:
to demonstrate how this EO capacity can help to describe the interactions between complex hydrological processes and anthropogenic pressure (often difficult to model) in synergy with model-based approaches;
to exploit synergies among EO data to maximize the retrieval of information of the different water cycle components (i.e., precipitation, soil moisture, evaporation, runoff, river discharge) to provide an accurate representation of our environment and advanced fit-for-purpose decision support systems in a changing climate for a more resilient society.
4DMED-Hydrology will focus on four test areas, namely the Po river basin in Italy, the Ebro River basin in Spain, the Hérault River basin in France and the Medjerda River basin in Tunisia, which are representatives of climates, topographic complexity, land use, human activities and hydrometeorological hazards of the Mediterranean Region (MR). The developed products will be then extended to the entire region. The resulting EO-based products (i.e., experimental datasets, EO products) will be made available in an Open Science catalogue hosted and operated by ESA.
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AI4SNOW-ARTIFICIAL INTELLIGENCE FOR SNOW COVER IN MOUNTAIN REGIONS
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The component of the Earth System addressed in this project is snow cover in mountain regions. The main scientific and technical objectives of AI4Snow are the development and training of AI methods to greatly improve remote sensing-based snow [...] |
DLR – GERMAN AEROSPACE CENTER (DE) |
Science |
AI4Science, hydrology science cluster, snow and ice |
The component of the Earth System addressed in this project is snow cover in mountain regions. The main scientific and technical objectives of AI4Snow are the development and training of AI methods to greatly improve remote sensing-based snow cover products for mountain regions as well as the implementation of data cubes containing all necessary datasets to apply these AI methods to the desired study regions. The aim is to produce a consistent, gapless, high resolution set of snow products suitable for highly detailed analyses even within complex terrain. The data cubes constituting the basis for these products shall be designed in a way that makes the application of the AI methods easily scalable and transferrable to any desired region of interest. The training of the AI models will be performed relying on an innovative approach which combines a physical-based snow process model with the remote sensing-based and meteorological datasets. This approach provides a very high density of available training data covering three large test domains within Switzerland, ensuring that a huge variety of topographic, climatic, and land cover characteristics will be represented. The project will include a scientific application/a scientific case, where the results produced by the developed AI-models will be used in a hydrological model. The results from this model will be compared with the outputs based on traditional input data, which shall demonstrate the value of the AI4snow-developments.
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AlpGlacier
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The Glacier Science in the Alps project is part of the Alps Regional Initiative and is aimed at maximising the scientific return of European investments in EO specifically from Sentinel-1 and Sentinel-2 specifically to provide first enhanced [...] |
UNIVERSITY OF ZURICH (CH) |
Regional Initiatives |
Alps, cryosphere, Glaciers and Ice Sheets, hydrology science cluster, polar science cluster, science, Sentinel-1, Sentinel-2, snow and ice, water resources |
The Glacier Science in the Alps project is part of the Alps Regional Initiative and is aimed at maximising the scientific return of European investments in EO specifically from Sentinel-1 and Sentinel-2 specifically to provide first enhanced observation capacity for glaciers in the Alps beyond area to glacier velocity and end of season snow cover on a weekly-annual basis and second to provide a scientifically sound assessment of hazard state as a direct function of glacier change, specifically, lake size and slope movement around glaciers. This project attempts to provide a wall-to-wall coverage of glaciers in the Alps for the full Sentinel era and will analyse changes taking place in this time period and in contrast with earlier data from the EO archives.
Discover more projects, activities and resources on the Alps regional initiative (EO4ALPS) page.
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AlpLakes
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The AlpLakes project aims at providing operational products based on a combination of remote sensing and hydrodynamic models. ALPLAKES is a continuation of the CORESIM (ESA SEOM S2-4Sci Land and Water) project based on the recommendations [...] |
EAWAG (CH) |
Applications |
Alps, hydrology science cluster, lakes, Sentinel-2, water resources |
The AlpLakes project aims at providing operational products based on a combination of remote sensing and hydrodynamic models. ALPLAKES is a continuation of the CORESIM (ESA SEOM S2-4Sci Land and Water) project based on the recommendations provided during the CORESIM roadmap. While many studies have focused on ecosystem variability over a latitudinal gradient (Woolway and Merchant, 2019), the response of the freshwater systems to climate changes over an altitudinal gradient is comparatively less understood. For this purpose, the project upscaled the web-based platform (see Meteolakes, http://meteolakes.ch/ as baseline) by integrating 11 lakes from ~60 m.a.s.l to 1800 m.a.s.l around the Alps with a 3D modelling approach.
Sentinel-2 products are used to improve the quality of the hydrodynamic model by providing, in near real-time, information about light penetration. This parameter is essential for the distribution of the incoming solar radiation as it controls, together with the atmospheric forcing, the evolution of the lake’s thermal structure. Then, there will be an evaluation of the short time evolution of identified patterns from Sentinel-2 products by applying a particle-tracking technique. The final application will be openly available for in-depth analyses of specific events in a new web-based platform.
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Alpsnow
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The AlpSnow project will develop improved products for a number of snow parameters (area extent, albedo, grain size, depth, snow water equivalent, snow melt area and wetness). A dataset covering the entire Alps for 4 years will be produced, and [...] |
ENVEO – ENVIRONMENTAL EARTH OBSERVATION GMBH (AT) |
Regional Initiatives |
Alps, hydrology science cluster, polar science cluster, science, snow and ice, water cycle and hydrology |
The AlpSnow project will develop improved products for a number of snow parameters (area extent, albedo, grain size, depth, snow water equivalent, snow melt area and wetness). A dataset covering the entire Alps for 4 years will be produced, and its usefulness will be demonstrated through three science cases and three demonstration cases related to land surface modelling, hydrology, numerical weather forecasting and water management.
Discover more projects, activities and resources on the Alps regional initiative (EO4ALPS) page.
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EO4FLOOD
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Floods rank among the most destructive natural disasters, causing significant harm to human health, the environment, cultural heritage, and economies. In Europe alone, floods have led to approximately 4,000 fatalities and $274 billion in [...] |
CNR-RESEARCH INSTITUTE FOR GEO-HYDROLOGICAL PROTECTION – IRPI (IT) |
Science |
hydrology science cluster, natural hazards and disaster risk, surface water, water cycle and hydrology |
Floods rank among the most destructive natural disasters, causing significant harm to human health, the environment, cultural heritage, and economies. In Europe alone, floods have led to approximately 4,000 fatalities and $274 billion in economic losses over the past 50 years, with even more severe impacts in developing countries. As climate change accelerates the frequency and intensity of floods, there is an urgent need for innovative flood forecasting systems that can effectively reduce societal impacts.
The EO4FLOOD Project (Water Cycle Hydrology Science cluster – Advancing Flood Forecasting) aims at demonstrating the maturity and effectiveness of cutting-edge satellite data in enhancing flood forecasting systems. The project focuses on leveraging advanced satellite technologies and algorithms to improve the accuracy and timeliness of existing hydrological and hydraulic models, resulting in more reliable and precise flood predictions.
EO4FLOOD is structured around three key pillars:
Development of an Advanced Open Earth Observation Dataset (EO4FLOOD dataset) that leverages the latest products from both ESA and non-ESA satellite missions, ensuring global coverage with high spatial and temporal resolutions. This dataset provides viable information to the global scientific community for enhanced flood forecasting by offering critical information on key variables such as precipitation, soil moisture, snow, flood extent and river discharge.
Integration of the EO4FLOOD Dataset into Flood Forecasting Models through the combination of hydrological, hydraulic, and flood models with machine learning techniques to predict floods up to 7 days in advance. This integration enables more accurate and timely predictions that can be crucial for effective disaster preparedness and response, also assessing predictive uncertainty.
Demonstration of EO Data and Models for Science and Society to show how the integration of EO data and models can improve flood forecasting and risk management. The initiative is addressed to explore the impact of human activities, such as land use changes or dam construction on flood dynamics, contributing to better disaster preparedness and policy-making.
The EO4FLOOD project is based on the use of the last frontiers in terms of advanced algorithms and satellite products to feed hydrological and hydraulic modelling to enhance flood forecasting systems and deliver a robust framework for predicting flood events and managing their impacts on society and the environment.
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Introducing physics to artificial intelligence methods to improve satellite monitoring of the water cycle
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The project aims to develop, train, and apply a hybrid neural network model to optimise EO data for a coherent, balanced water cycle at the global scale resulting in a new pixel-resolution datasets for the four water cycle components: [...] |
ESTELLUS SAS (FR) |
Science |
AI4EO, hydrology science cluster, permanently open call, water cycle and hydrology |
The project aims to develop, train, and apply a hybrid neural network model to optimise EO data for a coherent, balanced water cycle at the global scale resulting in a new pixel-resolution datasets for the four water cycle components: precipitation, evapotranspiration, change in water storage, and runoff (or river discharge). These data will cover the entire globe on quarter-degree grid cells and on a monthly time scale.
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Irrigation+
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The project aims at advancing capabilities towards a quantitative, accurate and routine estimation of irrigation information by means of multi-mission satellite EO approaches: Irrigation mapping, quantifying the irrigation amount and detecting [...] |
CNR – INSTITUTE FOR ELECTROMAGNETIC SENSING OF THE ENVIRONMENT (IREA) (IT) |
Science |
agriculture, hydrology science cluster, science, Sentinel-1 |
The project aims at advancing capabilities towards a quantitative, accurate and routine estimation of irrigation information by means of multi-mission satellite EO approaches: Irrigation mapping, quantifying the irrigation amount and detecting the seasonal timing of irrigation.
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