This study (https://doi.org/10.1007/s12517-017-3298-0) assesses the impacts of untreated wastewater discharge from Sulaimani City, Iraq, into the Tanjero River and proposes a framework for wastewater treatment planning. Nine sewer outlets representing residential, commercial, industrial, tourism, and hospital effluents were sampled over a year, and physicochemical analyses revealed elevated levels of turbidity, total dissolved solids, hardness, nitrates, and heavy metals—particularly in industrial zones. Questionnaire surveys conducted in 31 surrounding villages indicated significant health burdens, including chronic diseases, diarrhea, typhoid, skin disorders, and cancer, alongside impacts on livestock, fisheries, and agricultural productivity. Rice cultivation has nearly disappeared, and vegetable farming has declined due to water contamination. The findings highlight severe environmental and public health risks from current practices, where raw wastewater is still used for irrigation and animal watering. To mitigate these impacts, the study recommends constructing two wastewater treatment plants with stormwater retention structures, enforcing pretreatment of industrial effluents, and introducing appropriate on-site sanitation for unconnected households. Adoption of these measures would safeguard water resources, improve public health, and enable the safe reuse of treated wastewater for agriculture and industry.

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.

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.

This dataset is part of the mWater data for Kenya. It includes wells, boreholes, tube wells, and springs. Altitude data have been corrected by removing negative values and turning 0 values to NULL where applicable.

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.

The African Drought Atlas (ADA), a project aimed at developing a comprehensive understanding of hydro-climatic extreme events in the Africa region. The ADA is inspired by the successful Drought Atlas for Latin America and Caribbean released by UNESCO-IHP and CAZALAC.

This dataset present the Drought Atlas for Argentina, developed as part of the Latin American and Caribbean Drought Atlas. The maps show the minimum (and maximum) precipitation for different return periods (in years) and the frequency of drought occurrences (precipitation deficits with respect to the normal annual precipitation) for Argentina.

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.

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.

About the data:

This dataset consists of soil moisture and temperature measurements collected from TOMST (https://tomst.com/web/en/systems/tms/tms-4/) data loggers in several locations in Africa but also in Cuba. The dataset consists of three near-surface temperature measurements (12 cm ground surface (Temp: +12 cm), on the ground surface (Temp: 0 cm), and just below the surface (Temp: -6 cm). Measurements of soil moisture are collected at a depth of 15 cm below the ground using the Time Domain Transmittometry technique. The TOMST loggers record soil moisture measurements as raw electric signals, which have to be converted to volumetric soil moisture content by a calibration approach. At the moment, we have used a global calibration curve (independent of soil texture) as we calibrate the loggers for different textures. The dataset herein includes the raw sensor readings, which can be calibrated using the TMS calibration guide https://tomst.com/web/wp-content/uploads/2023/05/TMS-calibration-handbook.pdf

Utilization:

The dataset is intended for applications in hydrology to monitor long-term soil moisture conditions, agricultural droughts (vegetation water deficit), validate soil moisture and evapotranspiration observations from remote sensing, and soil water balance models. In some cases, the data is also being used to assess the suitability of using this type of sensor for irrigation scheduling and water conservation. We have deployed these loggers to evaluate whether the fine resolution (250m) data from FAO’s Water Productivity through Open access of Remotely sensed derived data (WaPOR) can be used to contribute to relevant and timely drought monitoring at micro-scale, and how drought indices computed from WaPOR-data correspond to soil moisture trends at field scale.

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.

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.

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).

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.

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.