60 jeux de données trouvés

Ce jeu de données, compilé par l’initiative Mengalir.co de Solar Chapter, fournit des informations essentielles sur les infrastructures et l’accès à l’eau dans 22 régences de la province de Nusa Tenggara Est, en Indonésie. Les données rassemblent des indicateurs clés d’accès à l’eau, l’état des infrastructures et des informations démographiques afin de soutenir la gestion des ressources en eau, la planification du développement et les efforts humanitaires dans cette région semi-aride. Le jeu de données inclut des coordonnées géospatiales, des divisions administratives, des données démographiques, le fonctionnement des installations d’eau, des indicateurs d’accès, des types d’infrastructures, des sources d’eau et les entités de gestion pour chaque régence.

Les données synthétisent des informations provenant des registres des gouvernements nationaux et locaux, des rapports participatifs communautaires et des bases de données d’organisations partenaires, dans un format CSV accessible aux parties prenantes confrontées aux défis régionaux liés à l’eau. Cet effort de collecte vise à appuyer une meilleure planification et mise en œuvre de solutions hydriques à Nusa Tenggara Est, où de nombreuses communautés connaissent des pénuries d’eau saisonnières.

This map illustrates the satellite-detected water extent in Sindh, Balochistan, and Punjab Provinces, Pakistan, as observed from Sentinel-2 satellite images acquired on 31 July 2025 at 13:02 local time (08:02 UTC). Within the analyzed area of approximately 83,000 km², about 6,300 km² of land appears to be affected by floodwaters. The floodwater extent appears to have increased by approximately 1,300 km² since 11 July 2025. Based on WorldPop population data and the flood extent, approximately 2.3 million people are potentially exposed or living close to the flooded areas.

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).

OpenLISEM is an open-source hydrological model suited for the simulation of floods, flash floods and erosion events. The following sections provide an overview of the results from the OpenLISEM model used in the exposure mapping A 30x30m flood map (maximum flood height) for the BuPuSa region was developed for several points on the intensity-frequency-duration curve. This curve represents the extreme value analysis (EVA) for the rainfall across the BuPuSa area. Based on 50 years of historic rainfall data from TAMSAT the EVA is developed for a 1000 year period. From this different rainfall intensities area taken which are referred to at the return period. The statistical possibility of a certain rainfall intensity to happen once in X many years. Flood maps were developed for the following return periods: 1/2, 1/10, 1/50, 1/100 and 1/1000. In addition to 5 different return periods, two different scenarios were modeled. A short high intensity rainfall event that typically causes flash floods, and a longer term lower intensity rainfall event that typically causes fluvial (river) floods. These events were represented by respectively a 6h rainfall event and a 14 day rainfall event. As a result 10 different flood maps were developed.

The Randolph Glacier Inventory (RGI) is a global set of glacier outlines intended as a snapshot of the world’s glaciers outside of ice sheets. It provides a single outline for each glacier from approximately the year 2000, as well as a set of attributes and other relevant auxiliary information. Glacier outlines are distributed as Shapefiles. Hypsometric data and attributes (CSV files) and metadata (json) are also available. All RGI data are packaged both globally and by region (as defined by the Global Terrestrial Network for Glaciers (GTN-G) Glacier Regions). The RGI is not suitable for measuring glacier-by-glacier rates of area change. However, it can be used to estimate glacier volumes; rates of elevation change at regional and global scales; and glacier responses to climatic forcing. RGI version 7.0 was developed by the “Working Group on the Randolph Glacier Inventory (RGI) and its role in future glacier monitoring” of the International Association of Cryospheric Sciences (IACS). The glaciological community contributes glacier mapping data to the Global Land Ice Measurements from Space (GLIMS) database. A subset of the glacier outlines in GLIMS are then extracted and reprocessed to produce the RGI. See the RGI documentation under "User Guide" (below) for more information.

Le visualiseur des PEID fournit des informations sur les eaux souterraines dans les Petits États Insulaires en Développement. Actuellement, le système contient principalement des données issues du Programme d'Évaluation des Eaux Transfrontalières (TWAP) sur 43 PEID. Ces données comprennent des indicateurs décrivant les dimensions hydrogéologiques, environnementales, socio-économiques et de gouvernance des systèmes d’eaux souterraines des PEID.

Les données ont été recueillies à partir d'enquêtes par questionnaire et d'une étude approfondie réalisée par l'Université Simon Fraser (Canada) et coordonnée par l'UNESCO-IHP. Les informations du système peuvent être explorées et analysées grâce à un visualiseur cartographique, particulièrement utile pour effectuer des analyses comparatives entre plusieurs PEID. De plus, des fiches d'information sur les PEID sont également disponibles, offrant des aperçus clairs pour chaque PEID. Des données supplémentaires seront collectées et intégrées au visualiseur des PEID au fur et à mesure de leur disponibilité.

Pour toute question ou commentaire sur les données et informations des PEID, veuillez consulter notre page de l’Espace Focal PEID (https://www.un-igrac.org/areas-expertise/small-island-developing-states-sids)

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.

Ces cartes, développées par Deltares, illustrent l’impact des aléas d’inondation attendus dans les districts de Chimanimani et de Chipinge, au Zimbabwe, et ont été évaluées à une résolution de 30 mètres.

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.

Natural disasters frequently damage or destroy school infrastructure, jeopardizing educational opportunities and putting school children's lives in danger. This was experienced by children and staff members in Zimbabwe, Chimanimani and Chipinge districts in particular during cyclone Idai which hit eastern Zimbabwe in 2019 and the cyclones that followed. More than 140 schools were affected by the floods and the land slides. The situation at St. Charles Lwanga High School, where 200 children, teachers and support staff were stranded for two days and had to face the cyclone, shows the importance of safe school infrastructure. To better prepare for such eventualities, UNESCO through the Zimbabwe Idai Recovery Project funded by World Bank and managed by UNOPS collaborated with the University of Udine and the University of Zimbabwe to implement the VISUS (Visual Inspection for Defining the Safety Upgrading Strategies), a multi-hazard school safety assessment methodology that help policymakers decide where to focus risk reduction efforts based on available resources and scientific evidence. The VISUS methodology helps assess schools using a holistic, multi-hazard approach that considers five aspects: site conditions, structural performance, local structural criticalities, non-structural components, and functional aspects. The methodology has also been improved to consider outbreak of disease such as COVID-19. The VISUS methodology was conceived as an effective decision making tool for planning risk mitigation actions. The project helped mainstream school safety components into the UNOPS’ School Rehabilitation Program and could contribute to the Civil Protection Unit’s School Disaster Education Programme. The team’s efforts also assisted in making investments decisions to strengthen the safety of schools efficiently and economically.

Ce jeu de données comprend des mesures d'humidité du sol et de température collectées à l'aide des enregistreurs de données TOMST (https://tomst.com/web/en/systems/tms/tms-4/) dans plusieurs endroits en Afrique ainsi qu'à Cuba. Le jeu de données contient trois mesures de température proches de la surface : à 12 cm au-dessus de la surface du sol (Temp: +12 cm), à la surface du sol (Temp: 0 cm) et juste sous la surface (Temp: -6 cm). Les mesures d'humidité du sol sont recueillies à une profondeur de 15 cm sous le sol en utilisant la technique de Transmittance par Domaine Temporel. Les enregistreurs TOMST enregistrent les mesures d'humidité du sol sous forme de signaux électriques bruts, qui doivent être convertis en contenu d'humidité volumétrique du sol grâce à une approche de calibration. Actuellement, nous utilisons une courbe de calibration globale (indépendante de la texture du sol) pendant que nous calibrons les enregistreurs pour différentes textures. Le jeu de données inclus ici contient les lectures brutes des capteurs, qui peuvent être calibrées en utilisant le guide de calibration TMS https://tomst.com/web/wp-content/uploads/2023/05/TMS-calibration-handbook.pdf.

Utilisation : Ce jeu de données est destiné à des applications en hydrologie pour surveiller les conditions d'humidité du sol à long terme, les sécheresses agricoles (déficit en eau des végétaux), valider les observations d'humidité du sol et d'évapotranspiration issues de la télédétection, et des modèles de bilan hydrique du sol. Dans certains cas, les données sont également utilisées pour évaluer l'adéquation de ce type de capteur pour la planification de l'irrigation et la conservation de l'eau. Nous avons déployé ces enregistreurs pour évaluer si les données de haute résolution (250 m) de la Productivité de l'Eau via l'accès ouvert de données dérivées de télédétection de la FAO (WaPOR) peuvent contribuer à un suivi pertinent et opportun des sécheresses à micro-échelle, et comment les indices de sécheresse calculés à partir des données de WaPOR correspondent aux tendances de l'humidité du sol à l'échelle des champs.

Description des champs de données : Le jeu de données est fourni sous forme de série temporelle contenant les champs de données suivants :

Temp: -6 cm Température du sol mesurée par l'enregistreur à 6 cm sous la surface du sol.

Temp: 0 cm Température de l'air/sol mesurée par l'enregistreur à la surface du sol.

Temp: +12 cm Température de l'air mesurée par l'enregistreur à 12 cm au-dessus de la surface du sol.

Raw sensor reading Ceci est le signal électrique de l'humidité du sol mesuré par le capteur, qui doit être converti en une valeur de contenu volumétrique d'humidité du sol.

Calibrated vmc Il s'agit du contenu volumétrique d'humidité obtenu en calibrant la lecture brute du capteur. Les valeurs sont obtenues en utilisant l'équation de Kopecky et al., (2021) https://doi.org/10.1016/j.scitotenv.2020.143785.

Logger_id Le numéro de série de l'enregistreur de données.

Latitude, Longitude Les coordonnées où se trouve le capteur.

Date installed in the field: La date à laquelle le capteur a été installé dans le champ.

Ce jeu de données comprend des mesures d'humidité du sol et de température collectées à l'aide des enregistreurs de données TOMST (https://tomst.com/web/en/systems/tms/tms-4/) dans plusieurs endroits en Afrique ainsi qu'à Cuba. Le jeu de données contient trois mesures de température proches de la surface : à 12 cm au-dessus de la surface du sol (Temp: +12 cm), à la surface du sol (Temp: 0 cm) et juste sous la surface (Temp: -6 cm). Les mesures d'humidité du sol sont recueillies à une profondeur de 15 cm sous le sol en utilisant la technique de Transmittance par Domaine Temporel. Les enregistreurs TOMST enregistrent les mesures d'humidité du sol sous forme de signaux électriques bruts, qui doivent être convertis en contenu d'humidité volumétrique du sol grâce à une approche de calibration. Actuellement, nous utilisons une courbe de calibration globale (indépendante de la texture du sol) pendant que nous calibrons les enregistreurs pour différentes textures. Le jeu de données inclus ici contient les lectures brutes des capteurs, qui peuvent être calibrées en utilisant le guide de calibration TMS https://tomst.com/web/wp-content/uploads/2023/05/TMS-calibration-handbook.pdf.

Utilisation : Ce jeu de données est destiné à des applications en hydrologie pour surveiller les conditions d'humidité du sol à long terme, les sécheresses agricoles (déficit en eau des végétaux), valider les observations d'humidité du sol et d'évapotranspiration issues de la télédétection, et des modèles de bilan hydrique du sol. Dans certains cas, les données sont également utilisées pour évaluer l'adéquation de ce type de capteur pour la planification de l'irrigation et la conservation de l'eau. Nous avons déployé ces enregistreurs pour évaluer si les données de haute résolution (250 m) de la Productivité de l'Eau via l'accès ouvert de données dérivées de télédétection de la FAO (WaPOR) peuvent contribuer à un suivi pertinent et opportun des sécheresses à micro-échelle, et comment les indices de sécheresse calculés à partir des données de WaPOR correspondent aux tendances de l'humidité du sol à l'échelle des champs.

Description des champs de données : Le jeu de données est fourni sous forme de série temporelle contenant les champs de données suivants :

Temp: -6 cm Température du sol mesurée par l'enregistreur à 6 cm sous la surface du sol.

Temp: 0 cm Température de l'air/sol mesurée par l'enregistreur à la surface du sol.

Temp: +12 cm Température de l'air mesurée par l'enregistreur à 12 cm au-dessus de la surface du sol.

Raw sensor reading Ceci est le signal électrique de l'humidité du sol mesuré par le capteur, qui doit être converti en une valeur de contenu volumétrique d'humidité du sol.

Calibrated vmc Il s'agit du contenu volumétrique d'humidité obtenu en calibrant la lecture brute du capteur. Les valeurs sont obtenues en utilisant l'équation de Kopecky et al., (2021) https://doi.org/10.1016/j.scitotenv.2020.143785.

Logger_id Le numéro de série de l'enregistreur de données.

Latitude, Longitude Les coordonnées où se trouve le capteur.

Date installed in the field: La date à laquelle le capteur a été installé dans le champ.

Reports and datasets generated as part of the Climate Risk Informed Decision Analysis (CRIDA) implemented in the Chimanimani Districts, in response to Cyclone Idai and to build resilience of local communities to climate change impacts.

Zimbabwe is exposed to multiple weather-related hazards, suffering from frequent periodic cyclones, droughts, floods, and related epidemics and landslides. On 15 March 2019, tropical Cyclone Idai hit eastern Zimbabwe, and at least 172 deaths were reported, more than 186 people were injured and 327 were missing, while over 270,000 people were affected across nine districts, particularly in Chimanimani and Chipinge. Of those affected, 20,002 households (61.5%) or 100,106 people (74.2% of the 2012 population) were in Chimanimani. Meanwhile, ecosystem damage also occurred where boulders and mud were dumped downhill, affecting wildlife habitats, water quality, tourism activities and usability of land resources. The cyclone’s aftermath has therefore increased environmental risks, which will in turn affect local adaptation. Loss of vegetation cover means the natural defense against future flood waters and landslides is no longer available. Similar events in future are therefore likely to cause even more destruction. The overall objective of the initiative is therefore to reduce the vulnerability of communities in the Chimanimani and Chipinge Districts to natural disasters, such as floods, droughts and landslides; and to enhance water resource management as well as ecosystem services in response to the uncertainty of future climate change. The project is designed to approach the water-related risk and vulnerability through an integrated strategy that targets several aspects of disaster risk reduction, and provides scalable implementation of the project through a modular pathway and the development of case studies in target flood and landslide prone areas.

The Global Gravity-based Groundwater Product (G3P) provides groundwater storage anomalies (GWSA) from a cross-cutting combination of GRACE/GRACE-FO-based terrestrial water storage (TWS) and storage compartments of the water cycle (WSCs) that are part of the Copernicus portfolio. The data set comprises gridded anomalies of groundwater, TWS, and the WSCs glacier, snow, soil moisture and surface water bodies plus layers containing uncertainty information for the individual data products. All WSCs are spatially filtered with a Gaussian filter to be compatible with TWS. Spatial coverage is global, except Greenland and Antarctica, with 0.5-degree resolution. Temporal coverage is from April 2002 to September 2023 with monthly temporal resolution. Gridded data sets are available as NetCDF files containing variables for the parameter value as anomaly in mm equivalent water height and the parameter’s uncertainty as mm equivalent water height.

The latest version of the data is visualized at the GravIS portal: https://gravis.gfz-potsdam.de/gws. From GravIS, the data is also available as area averages for several large river basins and aquifers, as well as for climatically similar regions.

G3P was funded by the EU Horizon 2020 programme in response to the call LC-SPACE-04-EO-2019-2020 “Copernicus evolution – Research activities in support of cross-cutting applications between Copernicus services” under grant agreement No. 870353.