Digital innovation in water

Water accounting integrates hydrological processes with land use, managed water flows, and the services that result from water consumption in river basins. Its objective is to strive to achieve equitable and transparent water governance for all users and a  sustainable water balance  Users can provide value assessments of certain process, and more accurate data sets, that replaces the default data collected from open access sources that represent best estimates.

The Water Accounting Plus project focuses on increasing opportunities to inform decision making for in river basin management by further improving the tools and synthesising the training materials for a wider audience.

The WaPOR project assists partner countries in developing their capacity to monitor and improve water and land productivity in agriculture, both rainfed and irrigated. It responds to the challenges that are posed by the dwindling of freshwater resources and the need to sustain agricultural production to ensure food security in the face of a changing climate. In other words, producing more while consuming less water.

The the WaPOR portal gives open access to near real time, pixel-based information that can be used by farmers, irrigation managers, river basin organisations and governmental agencies to improve water productivity and allocation on the ground.

The goal of the WaterPIP project is to increase water productivity in the agricultural sector using WaPOR, with an initial focus on Africa and the Near East.

H2020 project EIFFEL is a game changer in the domain of climate change adaptation and mitigation by harvesting the benefits of the GEOSS data. The project will offer to the Earth Observation community the ground-breaking capacity of exploiting existing GEOSS datasets and will also build upon prior knowledge, with minimal new data collection activities. Added-value services interoperable with GEOSS will be designed, using cognitive search and metadata augmentation tools based on Artificial Intelligence, including Natural Language Processing. These tools will leverage advanced cognitive features to extract meaningful information from and enrich GEOSS metadata.

IHE Delft will primarily contribute to EIFFEL project in designing, developing and testing modelling and decision support applications for water and land management, with specific focus on a drought-prone area downstream of the Aa or Weerijs catchments in the Netherlands. This pilot will be developed in partnership with two other Dutch partners (Open University - OU and the Province of North Brabant - PNB), with the primary aim of testing and demonstrating the value of using GEOSS data (in combination with locally available data) for modelling and implementation of climate adaptation measures based on natural solutions.

Additionally, IHE Delft will participate to the development of components for improved access and usage of GEOSS data, based on AI techniques and models, as well as to tasks related to upscaling / downscaling of water-related GEOSS data. Finally, given its experience in education and training IHE Delft, will be involved in dissemination, training and impact demonstration of the EIFFEL project.

In the H2020 NAIADES project, water modelling techniques are used to generate big data to feed an Artificial Intelligence platform to support efficient water management.

NAIADES supports the modernization and digitization of the water sector by providing a holistic solution for the control and management of water ecosystems. The project aims to address the increased need for sustainable and eco-friendly water methodologies by defining a new ICT framework, with a wider scope than a sole technical proposition. Driven by the need to yield an end-to-end, uniform approach, NAIADES redefines water management by taking into consideration issues pertaining to cost, safety, complexity, vulnerability, societal acceptance, user behaviour and ethics.

IHE Delft is in charge of the Urban Water Module, conceived as a complementary way to produce large amounts of data related to events that have never been recorded, or that are unlikely to happen, with the purpose of feeding an Artificial Intelligent (AI) Platform. The general idea is to make the AI aware of such events, so that decision-makers can be informed about the possible causes of a particular critical event and possible operational options to cope with it.

The Water, Peace and Security partership is pioneering development of innovative information tools and practical approaches that can support evidence-based actions to reduce water-related security risks.