8 conjuntos de datos encontrados

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

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.

Este conjunto de datos contiene los materiales oficiales del evento de lanzamiento de la plataforma IHP-WINS (Sistema de Información sobre el Agua del Programa Hidrológico Internacional), celebrado el 28 de abril de 2023. Incluye el folleto de lanzamiento, la presentación utilizada durante el evento y la grabación completa del seminario web. Estos recursos ofrecen una visión general de los objetivos, funcionalidades y relevancia de la plataforma en el apoyo al intercambio de datos, la ciencia abierta y la gestión colaborativa de los recursos hídricos. El conjunto de datos sirve como referencia para partes interesadas, socios y colaboradores que deseen conocer la visión y las aplicaciones prácticas de IHP-WINS.

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.

El proyecto FRIEND/Nile, implementado en dos fases (2001-2006 y 2007-2013), tuvo como objetivo mejorar la gestión de los recursos hídricos en la cuenca del Nilo a través de la cooperación regional, el desarrollo de capacidades y la investigación hidrológica aplicada. Iniciado bajo el Programa Hidrológico Internacional (PHI) de la UNESCO y financiado por el Gobierno de Flandes en Bélgica, el proyecto involucró a instituciones clave de cinco países de la cuenca del Nilo: Egipto, Sudán, Etiopía, Kenia y Tanzania. El proyecto se centró en mejorar la comprensión del régimen hidrológico del río mediante la cooperación en la investigación y el intercambio de datos.

La Fase I (2001-2006) estableció la cooperación técnica e institucional, enfatizando cuatro componentes de investigación clave: modelado lluvia-escorrentía, transporte de sedimentos y gestión de cuencas, análisis de frecuencia de inundaciones y análisis de sequías y caudales bajos. Se llevaron a cabo más de 20 talleres de capacitación y reuniones técnicas, mejorando la capacidad de los investigadores y las instituciones de la región. El proyecto facilitó la adquisición de datos, el desarrollo de modelos y la publicación de documentos técnicos, sentando las bases para una mejor gobernanza del agua transfronteriza.

La Fase II (2007-2013) amplió estos esfuerzos abordando nuevos desafíos como la ecohidrología, el modelado estocástico y la erosión y transporte de sedimentos. Se introdujeron modelos hidrológicos avanzados, se mejoró el monitoreo del rendimiento y se evaluaron los impactos del cambio climático en la disponibilidad de agua en la cuenca del Nilo. El proyecto contribuyó a una mayor cooperación científica, fortaleció los marcos institucionales y proporcionó información relevante para la formulación de políticas de gestión sostenible del agua.