10 datasets found

Applications of AI for Water Management

Since the impacts of climate change will be felt most directly through changes in water availability and water security, adequate tools are required to support water resources management decisions for the medium and long term. The Climate Risk Informed Decision Analysis (CRIDA) provides a pathway for more localized vulnerability assessments of water resources under climate change, while encompassing the uncertainty of current global climate projections. Within this framework, this paper demonstrates a climate stress test to identify changes in key performance indicators defined through local stakeholder consultations using a bottom-up approach. When applied to a case study in the Limari River basin in Chile, the stress test highlights the vulnerability of the reservoir system to climate variability and change, through a comprehensive analysis of 43 Global Circulation Models, of which 34 were retained. The Level of Concern (LoC) was defined as high, due to a high plausibility for entering into adverse water security conditions, that are expected to provoke a significant impact in the highly productive areas of the watershed. As demonstrated by the case study, the climate stress test provides a relevant assessment tool for potential climate change impacts on critical components of the water system in vulnerable catchments.

This dataset contains citizen science water quality measurements collected under the Be-Resilient initiative of the UNESCO Intergovernmental Hydrological Programme (IHP). Data were gathered across multiple Biosphere Reserves in South Africa as part of ongoing efforts to enhance community-based monitoring and local resilience to hydroclimatic risks.

The dataset includes in-situ observations of key water quality parameters, such as pH, electrical conductivity, turbidity, dissolved oxygen, and temperature, each accompanied by localized timestamps and contextual metadata. Measurements were obtained by trained citizen scientists and local volunteers, following standardized protocols designed to ensure comparability across sites and over time.

Individual identifiers have been removed to protect the privacy of participants. The dataset serves as an open, quality-controlled source for hydrological and environmental analyses, education, and early-warning applications. It contributes to the broader Be-Resilient programme objective of promoting citizen and open science approaches for climate adaptation, data democratization, and the co-production of water knowledge in vulnerable and data-scarce regions.

The Global Sandwatch Dataset consolidates participatory observations collected through UNESCO’s Sandwatch Programme, a global citizen science and education initiative promoting climate change adaptation and sustainable coastal management. The dataset contains standardized monitoring data from 145 beaches in more than 50 countries, contributed by over 300 community observers, schools, and environmental groups.

Covering over a decade of observations, the dataset documents beach morphology, erosion and accretion patterns, composition, debris, human activities, water quality, wave and current dynamics, vegetation, and fauna. Each record reflects local community engagement in data collection and environmental stewardship, fostering awareness and resilience within coastal ecosystems-especially in Small Island Developing States (SIDS) and low-lying coastal regions.

The data structure follows the Sandwatch methodology of Monitoring, Analysing, Sharing, and Taking Action (MAST) and aligns with UNESCO’s Education for Sustainable Development (ESD) and climate adaptation frameworks. It provides a unique interdisciplinary resource integrating environmental, social, and educational dimensions of coastal change, valuable for research, education, and policy design in coastal resilience and participatory monitoring.

This dataset contains a collection of presentations delivered by UNESCO’s Intergovernmental Hydrological Programme (IHP) to a variety of audiences—including IHP National Committees, project teams, Member States, and regional stakeholders—on the IHP-WINS (Water Information Network System) platform. The presentations aim to raise awareness of IHP-WINS as a central hub for hydrological, climatic, and contextual data, supporting evidence-based decision-making, digital transformation, and open science. Each presentation is tailored to the specific audience, highlighting relevant use cases, data management workflows, visualization tools, governance structures, and opportunities for collaboration. The dataset provides an overview of how IHP-WINS is positioned within UNESCO’s water programmes to enhance interoperability, promote data sharing, and support the implementation of IHP-IX priorities at national, regional, and project levels.

Composed of 669 biosphere reserves in 120 countries, including 16 transboundary sites, the World Network of Biosphere Reserves (WNBR) of the UNESCO Man And Biosphere (MAB) Programme consists of a dynamic and interactive network of sites of excellence. It works to foster the harmonious integration of people and nature for sustainable development through participatory dialogue, knowledge sharing, poverty reduction, human well-being improvements, respect for cultural values and by improving society’s ability to cope with climate change.For more information, visit: http://www.unesco.org/new/en/natural-sciences/environment/ecological-sciences/man-and-biosphere-programme/

The Reference Observatory of Basins for INternational hydrological climate change detection (ROBIN) project established a new long-term collaboration of international experts to establish and sustain a global reference hydrological network (RHN), through common standards, protocols, indicators, and data infrastructure. ‘Reference Hydrometric Networks’ (RHNs), consist of gauging stations whose catchments are relatively undisturbed and record high quality data and little missing data. The concept of RHNs, their history and evolution are described in (Whitfield et al., 2012) previously and many countries have already established RHNs, however this is the first initiative to bring them together at a global level. The ROBIN Full Dataset consists of 3,060 stations in 30 countries, however the dataset described here is the ROBIN Public Dataset which contains metadata records for all 3,060 stations and daily streamflow data for a total of 2,386 stations. This tiered approached was due to data sharing restrictions in some countries. More information about the ROBIN Network and dataset can be found on the project website: https://www.ceh.ac.uk/our-science/projects/robin

These maps, developed by Deltares, creat the flood impact of flood hazards expected in the Chimanimani and Chipinge Districts, Zimbabwe and are evaluated at 30m resolution.

Outcome of the 'Visual Inspection for Defining the Safety Upgrading Strategies’ (VISUS) approach to assess the school safety in the Chimanimani District after the Cyclone Idai. A VISUS survey across 15 schools in the Chimanimani district was conducted to gauge rehabilitation needs and identify key areas to build resilience.

This dataset contains the official materials from the launch event of the IHP-WINS (International Hydrological Programme – Water Information Network System) platform, held on 28 April 2023. It includes the launch brochure, the presentation delivered during the event, and the full recording of the webinar. These resources provide an overview of the platform’s objectives, functionalities, and relevance in supporting data sharing, open science, and collaborative water resources management. The dataset serves as a reference for stakeholders, partners, and contributors interested in learning about the vision and practical applications of IHP-WINS.