20 jeux de données trouvés

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

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.

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.

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.

Ce jeu de données contient les documents officiels liés au lancement de la plateforme IHP-WINS (Système d’Information sur l’Eau du Programme Hydrologique International), qui a eu lieu le 28 avril 2023. Il comprend la brochure de lancement, la présentation utilisée lors de l'événement, ainsi que l'enregistrement complet du webinaire. Ces ressources offrent un aperçu des objectifs, des fonctionnalités et de la pertinence de la plateforme pour soutenir le partage de données, la science ouverte et la gestion collaborative des ressources en eau. Ce jeu de données constitue une référence pour les parties prenantes, partenaires et contributeurs souhaitant mieux comprendre la vision et les applications pratiques de l’IHP-WINS.

This publication aims to bridge the gap between climate and disasters, in the face of the uncertainties that climate change poses to water managers and policymakers. Composed of a compilation of worldwide case studies, it provides examples of innovative water management and climate risk assessment approaches. The publication also highlights the National Determined Contributions (NDCs) and National Adaptation Plans (NAPs) with the aim of identifying links between these high-level frameworks, DRR and water issues, and describing how the policy-practice linkages can be turned into action.

This publication reviews the current state-of-the-art of AI and Machine Learning (ML) applications within water management, introducing some of the main concepts and providing the reader with a general understanding of different technologies and concepts. Further, it features examples of the most influential applications of AI within water management and highlights the ethical challenges when streamlining AI for water resources management.

Le projet FRIEND/Nile, mis en œuvre en deux phases (2001-2006 et 2007-2013), visait à améliorer la gestion des ressources en eau du bassin du Nil grâce à la coopération régionale, au renforcement des capacités et à la recherche hydrologique appliquée. Lancé sous le Programme Hydrologique International (PHI) de l'UNESCO et financé par le gouvernement flamand de Belgique, le projet a impliqué des institutions clés de cinq pays du bassin du Nil : l'Égypte, le Soudan, l'Éthiopie, le Kenya et la Tanzanie. Le projet s'est concentré sur l'amélioration de la compréhension du régime hydrologique du fleuve par le biais de la coopération scientifique et de l'échange de données.

La Phase I (2001-2006) a établi la coopération technique et institutionnelle, mettant l'accent sur quatre composantes de recherche clés : la modélisation pluie-débit, le transport des sédiments et la gestion des bassins versants, l'analyse de la fréquence des crues et l'analyse des sécheresses et des faibles débits. Plus de 20 ateliers de formation et réunions techniques ont été organisés, renforçant les capacités des chercheurs et des institutions de la région. Le projet a facilité l'acquisition de données, le développement de modèles et la publication de rapports techniques, posant ainsi les bases d'une meilleure gouvernance transfrontalière de l'eau.

La Phase II (2007-2013) a élargi ces efforts en abordant de nouveaux défis tels que l'écohydrologie, la modélisation stochastique et l'érosion et le transport des sédiments. Des modèles hydrologiques avancés ont été introduits, le suivi des performances a été amélioré et les impacts du changement climatique sur la disponibilité de l'eau dans le bassin du Nil ont été évalués. Le projet a contribué à renforcer la coopération scientifique, à consolider les cadres institutionnels et à fournir des informations pertinentes pour soutenir la gestion durable des ressources en eau.

These datasets and maps present the landslides observed and landslide susceptibility of the Chimanimani and Chipinge Districts of Zimbabwe. The maps provide a qualitative assessment of the likelihood of a landslide occurrence in an area depend on the terrain condition, and knowledge of prior landslides in the region. These maps have been prepared with the recording of 20 000 landslides, that have been identified in the region, particularly those as a result of the Cyclone Idai that occurred in 2019.

This dataset includes pdf versions of presentations delivered during all sessions of the Water Family Symposium: Opening Session / Session 1: Scientific contribution of the Water Family / Session 2: Contribution of the Water Family to Capacity Development / Session 3: Contribution of the Water Family to Capacity Development.

These datasets and maps present the exposure to landslides (return period in years) for the Chimanimani and Chipinge Districts of Zimbabwe. The maps provide a qualitative assessment of the likelihood of a landslide impact to to the terrain conditions, knowledge of prior landslides in the region and landslide susceptibility. These maps have been prepared with the recording of 20 000 landslides, that have been identified in the region, particularly those as a result of the Cyclone Idai that occurred in 2019.