Ciência dos dados na gestão dos recursos hídricos

Data science


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People in charge of the innovative practice :

Enric Vazquez Sune –

IDAEA is an environmental science institute dedicated to future challenges linked to climate change and water scarcity.

The Department of Geosciences investigates the hydraulic, chemical, thermal and mechanical processes associated with hydrogeology. Participates in the development of numerical and mathematical models and modelling techniques for complex processes. In this sense, it has developed multiple innovative applications for efficient data management.

These include an application to create geological cuts from sounding data; the use of artificial intelligence to predict the levels of rivers, aquifers, and similar physical environments; a program for the calculation of hydraulic parameters from hydrogeological data combined with the integration of neural networks; in addition to other geospatial applications based on the use of artificial intelligence, big data and the advanced processing and processing of big data.

These methodologies arise from the need to properly manage the amount of data intake implied by the current digitalization of the sector, a growing trend in recent years. To this problem must be added the need for an efficient and immediate management that can be achieved with these open-access applications.

Responsible entity

The Institute for Environmental Assessment and Water Research (IDAEA) is an institute of environmental sciences of the Spanish National Research Council (CSIC) founded in 2008 in Barcelona and dedicated to the study of the human footprint in the biosphere.

IDAEA-CSIC’s work focuses on two of the environmental challenges of our time: the preservation of water quality and availability and air quality, guided by the principle that our scientific understanding of current threats to global ecosystems is best addressed from a holistic perspective.

The Institute stands out in the analysis of organic and inorganic pollutants and their impact on ecosystems, the study, modelling and management of water resources, the development of algorithms in different scientific fields and the study of inhalable particles and toxic gases.

Detailed explanation

A responsible and efficient management of water resources requires knowledge to face the possible conditions and problems related to water.

It is important to integrate conceptual knowledge of water bodies. For this it is needed; i) know the system (conceptual model), usually with fieldwork and georeferencing using geographic information systems (GIS); ii) a monitoring network that provides information on the status of the resource and validates the conceptual model.

Large volumes of data must be collected and stored efficiently in order to be consulted.

In order to relate the conceptual models with the treatment of the data generated by the monitoring, a series of platforms and applications appear to transfer data, integrate them into database systems in order to perform a correct analysis and interpretation and make a more efficient management.

Creation of geological sections: “Geopropy Tool”

It allows the creation of three-dimensional geological cuts using an open source in Python. Profiles are generated from polling data.

Water Level Prediction Tool

This tool using machine learning is able to show the relationships between variables in a certain time series within a hydrological model. In some hydrological models it is achieved from real-time monitoring, after an upstream condition (rainfall episodes, excess extractions …) predict the short-term behavior downstream.

Hydraulic parameter calculation tools

With granulometry, the permeability of the terrain can be determined from a series of analytical formulas. Meddiante artificial intelligence (neural networks) it is possible to identify which formula describes the reality in each case in a more detailed way. Hydraulic parameters are predicted from “cheap” data efficiently with the complementation of empirical formulas and neural networks

Hydrochemical indicators

The use of machine learning is used to find the chemical components that indicate a certain group of very specific compounds or high analytical cost.

Geospatial models

Geospatial maps are generated not based on physical processes. An example is its application in the Andes, a determination of the isotopic composition of rainwater throughout the territory was determined continuously in order to concisely evaluate the recharge of the aquifer without direct measures of it, in a complex management situation.

Meteorological data provide data on volumes and rainfall. The isotopic data are collected from different analytics and campaigns carried out in the territory that are usually not obtained systematically and / or continuously. Two models are generated that are integrated into one. The first represents the meteorological distribution that overlaps the timing and value of the analytics. The integrated model allows calculations of the isotopy of the waters that are recharging the aquifers.

Managing large volumes of data

To deal with a large volume of data, special database management platforms and data calculation and processing hardware are needed.

IDAEA has applied this methodology in radar interferometry to measure ground deformations. The images that are generated contain millions of pixels, generating hundreds of images. To deal with this volume of data, the data is filtered and then quantified and interpreted. To be able to perform this task requires a more capable system than Excel or a database and hardware with greater computing capacity.

Institutional setting

IDAEA was conceived as a new multidisciplinary research institute within the CSIC that brings together a wide range of knowledge in environmental sciences and is organized into two broad Departments: Geosciences, to which they correspond to the contributions reflected in this document, and Environmental Chemistry.

Geographical setting

The methodologies provided by IDAEA are not linked to a location. You can consult the projects and publications and openly on the IDAEA website. 

Historical overview

Water management has evolved at different stages depending on the needs and concerns of each moment.

Water 1.0: Theobjective was to increase the supply by dominating the resource, making large dams and transfers. It was necessary to bring water to cities and crops. The price was not decisive and the environmental repercussions of the implementations were not evaluated. 3

Water 2.0: During this period users and managers become aware of the importance of the resource. The economy generated by water and its price is valued, hydraulic works require greater permits and end users are asked to assume the costs. Importance is given to the uses, differentiating the quantity and quality of water they require. Policies that involve users appear and social impacts are valued. At this stage of water management, he begins to appreciate the ecological value of water and the environmental impact of its exploitation.

Water 3.0: The term sustainable management is introduced, based on respect for the environment, citizen participation and nature-based technologies. Works and actions that increase resilience, the restoration of ecosystems and the preservation of environmental values are financed.

Water 4.0 or Smart Water: It is the digital transformation of water with the aim of making the sector more efficient. It seeks to modernize conventional systems that act on water resources through infrastructures and management systems, taking advantage of the opportunities of ICTs that have appeared massively in the twenty-first century (Bufler et al. 2017; BCX, 2016).

The methodologies developed by IDEA allow to advance in the management of the resource from stage 3.0 to 4.0 and are part of the tools that will allow an intelligent use of water based on intensive monitoring and massive data processing.

Evidence of benefits from implementation

These tools are useful for professionals in the sector and research teams, improve efficiency in the interpretation of data and allow us to better adapt to new forms of information and decision making. With its application, a more efficient exploration of existing data is possible. They also allow conceptual knowledge to be integrated with the aim of acting more quickly, effectively and adaptablely in an area characterized by the complexity and high variability of its different components.


El uso de estas metodologías no está asociado a una acción concreta: son herramientas informáticas avanzadas que permiten afrontar problemas actuales y futuros, basados en tratamiento correcto de datos masivos, la extracción de conocimiento y la toma de decisiones.

Future outlook

A growing volume of data requires software and hardware with the ability to integrate various types of data in order to acquire valuable information.

Key points of the innovative method

  • IDAEA develops computer tools framed in the generation of complex software suitable for the treatment of large volumes of data.
  • The treatment of data provides valuable knowledge about water systems that, with traditional systems, would not be possible
    These diverse software libraries are aimed at the interpretation of massive data such as those generated by digitalization in the water sector.


Innovative practice was suggestedby Dr. Enric Vázquez-Suñé, invedstigador de IDAEA (CSIC).


  1. Prieto, F. (2015, marzo 12). Agua 3.0: gestión inteligente del agua. iAgua.
  2. Bufler, R., Clausnitzer, V., Vestner, R., Werner, U. and Ziemer, C. (2017). Water 4.0 – An Important Element for the Germany Water Industry. Germany Water Partnership. p_materialien/GWP_Brochure_Water_4.0.pdf.
  3. BCX. (2016). Manufacturing: Leveraging Industry 4.0 to Create Smart Factories.


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