32 datasets found

This dataset illustrates satellite-detected mudflow extent in Sao Vicente, Cabo Verde as observed from Pleiades very high-resolution satellite image. About 12 km² of land appears to be affected by the flood / mudflow extent. UNOSAT identified around 4200 affected buildings with around 12600 people potentially affected. In addition, approximately 80 km of roads with 5 bridges were affected.

This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

This dataset illustrates satellite-detected mudflow extent in Santo Antao, Cabo Verde as observed from Pleiades very high-resolution satellite image. About 4 km² of land appears to be affected by the flood / mudflow extent. UNOSAT identified less than 460 affected buildings with less than 1000 people potentially affected. In addition, approximately 5 km of roads with 2 bridges were affected.

This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

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 Global Lakes and Wetlands Database (GLWD) version 2 provides a comprehensive and seamless global map of inland surface waters distinguished into 33 waterbody and wetland types. GLWD v2 was developed by harmonizing the best available ground- and satellite-based data sources and has been designed to represent the maximum non-overlapping extents of aquatic ecosystems over the broad contemporary period of 1990-2020.

GLWD v2 represents a total of 18.2 million km2 of wetlands at a grid cell resolution of 15 arc-seconds (approximately 500 m at the equator). The data consist of a map of the dominant waterbody or wetland type in each grid cell, as well as 33 individual class layers which represent the sub-cell fraction of each specific class within each grid cell.

Version 2 of GLWD (Lehner et al., 2025) is the successor of the widely-used GLWD version 1 (Lehner & Döll, 2004). The quality, resolution, and format of GLWD v2 significantly improves upon GLWD v1 and supersedes the older version.

The Randolph Glacier Inventory (RGI) is a globally complete inventory of glacier outlines (excluding the ice sheets in Greenland and Antarctica). It is a subset of the database compiled by the Global Land Ice Measurements from Space (GLIMS) initiative. While GLIMS is a multi-temporal database with an extensive set of attributes, the RGI is intended to be a snapshot of the world’s glaciers at a specific target date, which in RGI 7.0 and all previous versions has been set as close as possible to the year 2000 (although in fact its range of dates can still be substantial in some regions). The RGI includes outlines of all glaciers larger than 0.01 km², which is the recommended minimum of the World Glacier Inventory.

The RGI was not designed for the measurement of glacier-by-glacier rates of area change, for which the greatest possible accuracy in dating, delineation and georeferencing is essential. While many RGI outlines meet these requirements, the primary focus of the RGI is on achieving global coverage, consistency, and proximity in a specific year. The strength of the RGI lies in its ability to handle large numbers of glaciers simultaneously. This allows, for example, for the estimation of glacier volumes and rates of elevation change at regional and global scales, as well as the simulation of cryospheric responses to climatic forcing.

Who develops and hosts the RGI? The RGI has been developed in an international community-driven effort of glaciologists starting in 2010. The inventory was named after “Randolph”, a town in New Hampshire, USA, where the team met for one of their meetings [Pfeffer et al., 2014]. In 2014 development of the RGI became the responsibility of the Working Group on the Randolph Glacier Inventory and Infrastructure for Glacier Monitoring, which operated under the International Association of Cryospheric Sciences (IACS). In 2019, a new Working Group was established to build upon the previous achievements and further expand its objectives: the IACS Working Group on the Randolph Glacier Inventory (RGI) and its role in future glacier monitoring and GLIMS.

The RGI datasets are listed on glims.org, and the RGI files can be downloaded through the data portal at the National Snow and Ice Data Center (NSIDC), which is the host for GLIMS.

Glacier product: includes outlines, attributes and auxiliary data for each individual glacier.

The World Heritage List includes 1248 properties forming part of the cultural and natural heritage which the World Heritage Committee considers as having outstanding universal value.

These include 972 cultural, 235 natural and 41 mixed properties in 170 States Parties. As of October 2024, 196 States Parties have ratified the World Heritage Convention.

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

Caravan is an open community dataset of meteorological forcing data, catchment attributes, and discharge data for catchments around the world. Additionally, Caravan provides code to derive meteorological forcing data and catchment attributes in the cloud, making it easy for anyone to extend Caravan to new catchments. The vision of Caravan is to provide the foundation for a truly global open source community resource that will grow over time.

The Caravan dataset that was released together with the paper. Since Version 1.6, the dataset is published in two different Zenodo repositories, depending on the filetype of the timeseries data.

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.

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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.