FORWARD
FORWARD - Operational monitoring and FOrecasting system for Resilience of agriculture and forestry under intensification of the WAteR cycle: a big Data approach |
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Coordinator Executive Coordinator |
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Projects Partner and Institution: Sumaqua (Belgium) |
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Key words: Resilience, Ecohydrological, Big Data, Process-based Model, Data-driven Model, Machine Learning, Agriculture, Forestry. |
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Abstract:
Water stress becomes increasingly problematic due to various changes, including the increasing urbanization, land use changes, population growth and the increasing demand for (fresh) water. On the other hand, climate change induces more variability and extremes in rainfall and temperature. This intensification of the water cycle will highly affect agriculture and forestry, giving rise to the urgent need to assess the resilience of agricultural and forest ecosystems under varying hydro-climatic conditions and more specifically in water limited regions. The objectives of the project have been mainly three.
FORWARD had tackled these challenges by combining Big Data, data mining and advanced analytics, with different model types (both data-driven and process-based) and assimilation techniques. The entire course of the project has been characterized by an intensive multidisciplinary approach, as it was necessary for each member to address some fields of knowledge quite different from its own. Collaboration was then crucial to understand the work processes performed by other members with mainly scientific and academic profile, on one side, and industrial technological and software development profile, on the other side. The most remarkable result of the project has been the development and implementation of an extensible and tailorable Big Data framework able to manage and process multivariable information sources, including large volumes of geospatial datasets, data-mining techniques and models at several scales. Such powerful functionalities have been built on top of a set of data-driven algorithms and process-based models using Earth Observations (EO). These models enable for prediction capabilities, as well as real time monitoring and forecasting. The enhanced forecasting system, expected knowledge gain into concepts of resilience of forestry and agriculture, and the modular Big Data framework goes beyond the state-of-the-art in ecohydrological sciences, and exploits all available sources of information and techniques. The consortium managed to collect the stakeholders interests and most urgent needs in order to address them in the project. This way, the project succeeded to engage with very diverse stakeholders, such as international organisms, grassroots non-governmental organizations, private companies and Ministries from national government and several researchers/universities from all around the world. Their feedback was taken into account in order to decide the most relevant indicators to be produced as project outputs. The project outcomes have a considerable potential to produce impacts from the scientific and societal point of view through different mechanisms, especially on research, industry, end users and policy. The algorithms developed for data analysis of variables relevant for agriculture and forestry will be important for monitoring and warning, data mining, and gaining insight into vegetation anomalies. Moreover, with Big Data analytics, problems can be detected faster and at a greater scale with more in-depth statistics, which helps end-users to make reliable and better-informed decisions. There is impact for industry applications as well, considering the insurance industry stands to benefit from the generated data mining techniques for anomaly detection, for instance. Another important remark of FORWARD project was the engagement in public plans using open software (GEE), which was developed by DHI Gras Company in close collaboration with some members of the consortium. This way, the scientific methods and model outputs has been made readily available to different stakeholders (scientific, companies, government agencies, international agencies) by making the code available in google Earth Engine (GEE). |
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Project structure: |
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Implementation: The FORWARD project is divided into 5 WPs: |
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Outcome/deliverables: |
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Deliverables: |
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References coordinator and leaders of each WP: |
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Main outputs:
More results on the project: Data and resources |
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Contact Point for Communication/Dissemination activities: Mónica García. |
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Contact Point for Open Data/Open Access activities: Alberto Fernández Villán. |