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 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.

The Global Sandwatch Dataset consolidates participatory observations collected through UNESCO’s Sandwatch Programme, a global citizen science and education initiative promoting climate change adaptation and sustainable coastal management. The dataset contains standardized monitoring data from 145 beaches in more than 50 countries, contributed by over 300 community observers, schools, and environmental groups.

Covering over a decade of observations, the dataset documents beach morphology, erosion and accretion patterns, composition, debris, human activities, water quality, wave and current dynamics, vegetation, and fauna. Each record reflects local community engagement in data collection and environmental stewardship, fostering awareness and resilience within coastal ecosystems-especially in Small Island Developing States (SIDS) and low-lying coastal regions.

The data structure follows the Sandwatch methodology of Monitoring, Analysing, Sharing, and Taking Action (MAST) and aligns with UNESCO’s Education for Sustainable Development (ESD) and climate adaptation frameworks. It provides a unique interdisciplinary resource integrating environmental, social, and educational dimensions of coastal change, valuable for research, education, and policy design in coastal resilience and participatory monitoring.

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.

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.

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.

A compilation of secondary surface- and groundwater quality measurements assembled to characterize hydrochemistry, contaminants, isotopes, and bacteriological parameters across the Ethiopian portion of the Lake Turkana basin. This dataset underpins statistical and geospatial analyses to inform sustainable water-resource management.

Large-sample datasets are essential in hydrological science to support modelling studies and global assessments. The present dataset compiles all freshwater quality data that is available under open data policy (CC BY 4.0 or equivalent) at the GEMStat database for global water quality (www.gemstat.org). It includes over 20,000,000 measurements on 608 water quality parameters, covering 13,660 stations in 37 countries over the time period from 1906 to 2023.

GEMStat is operated by the GEMS/Water programme of the United Nations Environment Programme (UNEP) and hosted at the International Centre for Water Resources and Global Change (ICWRGC) and the German Federal Institute of Hydrology (BfG). The data in GEMStat is provided by National Hydrological Services of UN member states.

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.

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

This dataset is part of the Somalia Water sources Information Management System (SWIMS). From the various sources listed in SWIMS boreholes and wells were selected. The dataset gives information about the positioning and in few cases about water level and yield but it is not clarified if the inspection date in the data is referring to the measurements.

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.