About

FORWARD seeks to understand and increase the resilience of water resources of agricultural systems and forestry through data mining and modelling in a Big Data setting.

The objectives of FORWARD are:

• To develop and implement an extensible and tailorable Big Data approach and framework able to manage and process multivariable information sources, data-mining techniques and models at several scales (local, regional, national and continental).
• To provide improved model forecasting (daily-monthly-seasonal-long term; and at different spatial scales) and monitoring capabilities of eco-hydrological variables and indicators relevant to forestry and agricultural applications by combining different modeling types (data-driven, process based and statistical time series analysis) in a Big Data framework.
• ­­­­To comprehend the resilience of forest and agricultural ecosystems in water-limited regions to extreme events, in particular drought, and in the context of climate change.
• To provide maps of the most vulnerable sites to those through combining all available data sources and advanced data mining techniques.

Results

• New techniques for data-mining and analytics.
• New knowledge and understanding of the processes underlying resilience of agriculture and forestry.
• Essential tools for efficient and future proof water management and decision making.
• Resilience maps for the Spanish Iberian Peninsula and Balearic Islands
• Big Data tools and techniques in environmental fields (Big Data framework deployed + API interface).
• Data-driven modelling for prediction and forecasting of crop yield and NDVI time series.
• Machine Learning models for crop yield prediction.
• Protocol to estimate actual, potential evapotranspiration and Gross Primary Productivity from satellite data and gridded climatic datasets.
• Evapotranspiration anywhere in the world on a click (Google Earth).
• Computer Fluid Dynamics (CFD) model to simulate Evapotranspiration Fluxes.