FORWARD -Operational monitoring and FOrecasting system for Resilience of agriculture and forestry under intensification of the WAteR cycle: a big Data approach

Project Interactive Website



Alberto Fernández Villán

 Executive Coordinator 

Projects  Partner and Institution:

TSK (Spain)

Technical University of Denmark (Denmark)

Sumaqua (Belgium)

Key words

Resilience, Ecohydrological, Big Data, Process-based Model, Data-driven Model, Machine Learning, Agriculture, Forestry.


The main objective of FORWARD is to understand and increase the resilience of water resources of agricultural systems and forestry through data mining and modelling in a Big Data setting.
Hereto, the project has the following three specific objectives: 1. To develop and implement an extensible and tailorable Big Data approach and framework able to manage and process multivariable information sources (see the Methodology section for a short and preliminary list of sources and variables), data-mining techniques and models at several scales (local, regional, nation and continental). 2. To provide improved model forecasting (daily-monthly-seasonal-long term; and at different spatial scales) and monitoring capabilities of eco-hydrological variables and indicators (see the Methodology section for a preliminary list of 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 3. To understand the resilience of forest and agricultural ecosystems in water-limited regions to extreme events, in particular drought, and in the context of climate change. Provide maps of most vulnerable sites to those through combining all available data sources and advanced data mining techniques.
To achieve these objectives, six specific challenges need to be tackled: (1) development of efficient data gathering protocols, including synchronization with (highly variable) data sets. Eco-hydrological data, in particular, is characterized by different spatial and temporal resolutions, gaps and unreliable data samples, necessitating proper processing protocols; (2) implementation of a scalable database warehouse; (3) configuration of industry standard APIs to ensure proper interfacing with other systems and models; (4) development and application of efficient and generalized data mining techniques (e.g. for anomaly detection); (5) development of (locally) enhanced process-based models through data-assimilation and integration with data-driven modelling; (6) integration of the development into the Big Data architecture and its application on the selected cases of study.

Project structure


The FORWARD project is divided into 5 WPs:
WP1. Big Data framework: data management and processing.
This WP will focus on data gathering, pre-processing and integration. Novel integration approaches based on Big Data technologies and not used by the traditional scientific community will be created according to the volume, velocity and variability of the sources of information.
WP2. Data-mining and data-driven modelling.
Data mining and data-driven modelling techniques will be explored to quantify variability, extremes and resilience in space and time.
WP3. Historical Process-based models using EO data.
Historical process-based modelling using EO time series. Satellite and other spatial climatic databases compiled in WP1 will be used as input into process-based model to estimate evapotranspiration and water use efficiency (WUE).
WP4. Integration and analysis of results.
A final integrated analysis will be performed studying and validating the sensitives across regions, regimes, and vegetation types. After completing all WPs, an integrated framework will emerge indicating how extreme and variable climate conditions affect the eco-hydrological behaviour of ecosystems, highlighting spatial differences across regions and response changes due to local factors.
WP5. Communication and dissemination.
To ensure that the objectives and approach of FORWARD address the stakeholders’ needs, to maximize dissemination of the outcomes of FORWARD and to create the potential of making the tools of FORWARD industry standard in resilience management and Big Data.


M1.1: Data sources defined and integrated (month 5).

M1.2: Indicators defined (month 12).
M1.3: Architecture deployed (month 12).

M2.1: All tools/techniques/data inventorized/collected (month 2)

M2.2: Semi-finalized set of data mining techniques developed (month 8)
M2.3: Preliminary set of data-driven modelling techniques implemented (month 18).

M4.1: Implementation of WP2 and WP3 techniques in the Big Data framework (month 18).

M4.2: Assembled data and configured framework for scenario simulations (month 20).

M5.1: Defined the questionnaire and identified list of contacts (month 3).

M5.2: Published at least 5 international papers and attended 5 conferences (month 24).

M5.3: Secured at least 1 additional valorisation opportunity in which all partners can participate (month 24).


D1.1: Data sources and indicators finalized (month 12).

D1.2: Finalized architecture deployment (month 24).

D2.1: Generalized data mining techniques and anomaly algorithm & indicator (month 12)

D2.2: Maps indicating hotspots, vulnerability and identified “hot time periods” (month 14)

D2.3: Generalized data-driven modelling techniques (month 20)

D4.1: Derived climate change scenarios for drivers/predictors at relevant scales (month 18).

D4.2: Deployed Big Data framework (month 20).

D4.3: Simulated defined seasonal and long term scenarios and analysed the impacts (month 22).

D4.4: Derived key indicators (month 24).
D5.1: Set-up website (month 1).
D5.2: Extracted list of most urgent needs and collected data from stakeholders (month 6).

References coordinator and  leaders of  each WP:

WP1. Leader: Alberto Fernández Villán.

WP2. Leader: Vincent Wolfs.

WP3. Leader: Mónica García.

WP4. Leader: Alberto Fernández Villán.

WP5. Leader: Mónica García.

Contact Point for  Communication/Dissemination activities:

Mónica García.

Contact Point for Open Data/Open Access activities: 

Alberto Fernández Villán.

Picture of the research team: