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48 results for “water risk”
Water risks to hydropower projects in the face of climate change
<p>This repository hosts the main outputs from an analysis using the <a href="https://waterriskfilter.org/">WWF Water Risk Filter</a> to demonstrate how one such tool can be used to screen for a variety of risks at a global scale, including risks to riverine ecosystems from both climate change and hydropower as well as risks to hydropower projects — and operators, owners, and investors — from climate change and potential regulatory or reputational risk arising from negative impacts to ecosystems. The study <a href="https://www.mdpi.com/2073-4441/14/5/721">Using the WWF Water Risk Filter to Screen Existing and Projected Hydropower Projects for Climate and Biodiversity Risks (DOI 10.3390/w14050721) </a>was published in the special issue of the MDPI journal Water: <a href="https://www.mdpi.com/journal/water/special_issues/hydrometeorological_hazards">"Hydro-Meteorological Hazards under Climate Change"</a>.</p> <p>This product incorporates data from the GRanD v1.3 database which is © Global Water System Project (2011), and from the FHReD database beta version, both datasets available at <a href="http://globaldamwatch.org/">globaldamwatch.org</a> . The source code used in this study is available at <a href="https://github.com/rafaexx/hydropowerClimateChange">https://github.com/rafaexx/hydropowerClimateChange</a></p> <p>See the interactive maps using this data at <a href="https://rcamargo.shinyapps.io/HydropowerClimateChange">https://rcamargo.shinyapps.io/HydropowerClimateChange</a></p>
Streamflow drought hazard indicators for monitoring drought risk for human water supply and river ecosystems at the global scale
<p><strong>1) Indicators of streamflow drought hazard (SDHI) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong></p> <p><strong>Indicators of drought magnitude: </strong>SPI12, SPEI12, SSI1, SSI12, EP1, RDQI1</p> <p><strong>Indicators of drought severity: </strong>CDQI1-Q50, CDQI1-Q80, CDQI1-Q80-HS, CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-Q50_f, CDQI1-Q80_f, CDQI1-WUs_f, CDQI1-WUs-EFR_f, CEP1(20%)_f, CRDQI1(-50%)_f</p> <p><strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude </strong>(WaterGAP_ArcID_lon_lat.txt)</p> <p><strong>3) Streamflow observations and SDHI (based on observations) for two GRDC gauging stations: </strong>Input_Figure_2_Little_Colorado_River.txt (station near Cameron) and Input_Figure_2_Danube_River.txt (station at Hofkirchen)</p> <p><strong>4) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt</p> <p><strong>5) Input data for computing Pearson correlation between SSI1 based on observations and each of the five indicators SSI1 (simulated), SPI3, SPI6, SPI9, and SPI12.</strong> Indicators computed for 218 out of 220 GRDC gauging stations with continuous streamflow observations between 1986 and 2015. The folder also contains a list of the 220 GRDC station numbers and the related WaterGAP grid cell ID ("arcid").</p> <p> </p>
Data for Li et al., Waterborne virus transport and the associated risks in a large lake, Water Research; DOI: 10.1016/j.watres.2022.119437
<p>Experimental data (virus concentrations measured in sewage, in units of genome copies per mL sewage) </p> <p>Code (written in R) used to model virus concentration distributions</p> <p>Code (Delft3D and Matlab) used for hydrodynamic, water quality and QMRA simulations. Input files are for the "winter scenario" shown in the publication.</p>
A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis
<p>The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file '<a href="https://zenodo.org/api/files/31b59cce-8eb2-4ee7-93aa-61474c6f6359/dst_2019_SJRW_TP_TDS.zip?versionId=2af2b54d-d5fb-4720-919d-de2e827595e2">dst_2019_SJRW_TP_TDS.zip'</a> for updated files..</p>
Algae-Bacteria Community Analysis for Drinking Water Taste and Odour Risk Management
<p>The datasets and accompanying R script included in this upload are provided to complement the manuscript titled <em>"Algae-Bacteria Community Analysis for Drinking Water Taste and Odour Risk Management."</em> These resources are intended to facilitate the replication and verification of the analyses presented in the paper.</p> <p> </p> <p> </p>
Figure 5 in A simplistic water body-specific risk assessment model for zebra mussel (Dreissena polymorpha) establishment based on physicochemical characteristics
Figure 5. Overall zebra mussel establishment risk categorization of 133 Texas water bodies based on calcium, pH, salinity, and temperature. Major water bodies not included in this study due to lack of TCEQ water quality data are shown for context of the study extent. The Whittier et. al. low calcium/low risk zone delineation is shown to demonstrate level of agreement with that study, which is relatively high with some noteworthy exceptions. The Cypress, Sabine, and Neches River basins referenced in the text are the three East Texas basins with predominantly minimal risk water body categorizations.
Figure 2 in A simplistic water body-specific risk assessment model for zebra mussel (Dreissena polymorpha) establishment based on physicochemical characteristics
Figure 2. pH-based zebra mussel establishment risk categorization of 133 Texas water bodies. Major water bodies not included in this study due to lack of TCEQ water quality data are shown for context of the study extent.
Figure 4 in A simplistic water body-specific risk assessment model for zebra mussel (Dreissena polymorpha) establishment based on physicochemical characteristics
Figure 4. Temperature-based zebra mussel establishment risk categorization of 126 Texas water bodies. Major water bodies not included in this study due to lack of TCEQ water quality data are shown for context of the study extent.
Figure 3 in A simplistic water body-specific risk assessment model for zebra mussel (Dreissena polymorpha) establishment based on physicochemical characteristics
Figure 3. Salinity-based zebra mussel establishment risk categorization of 133 Texas water bodies. Major water bodies not included in this study due to lack of TCEQ water quality data are shown for context of the study extent.
Figure 1 in A simplistic water body-specific risk assessment model for zebra mussel (Dreissena polymorpha) establishment based on physicochemical characteristics
Figure 1. Calcium-based zebra mussel establishment risk categorization of 85 Texas water bodies. Areas to the east of the Whittier et al. (2008) calcium risk delineation were predicted by that study to have ≤ 12 mg/l calcium (i.e., minimal establishment risk); this delineation is shown to demonstrate level of agreement with that study. Major water bodies not included in this study due to lack of TCEQ water quality data are shown for context of the study extent. The Cypress, Sabine, and Neches River basins referenced in the text are the three East Texas basins with predominantly minimal risk water body categorizations.
Everyday risks and access to water and sanitation in Lilongwe urban and peri-urban areas
<p>The household survey INHAbIT Cities - UNHIDE (Investigating Natural, Historical and Institutional Transformations in Cities and Uncovering Hidden Dynamics in Slum Environments) focuses on urban risks and sanitation in Lilongwe. The aim was to assess access to basic services and risks perception of urban dwellers living in areas characterised by different conditions of access to water and sanitation and other basic services. Lilongwe was a small town of less than 20,000 inhabitants in 1966 and only started growing after it became the capital in 1975. Its population has reached approximately 1 million inhabitants, living in 58 administrative units, called areas. Infrastructures and service provision is concentrated in the central areas – where parliament, ministries, government offices, embassies, hotels and the commercial area were located - while low income areas suffer the most from infrastructure and basic services deficits. To illustrate, while some areas access water through in-house connections, others are served through water kiosks, characterised (in some areas) by high rates of discontinuity. Similarly, everyday risks are unevenly distributed across urban spaces: as shown in the survey perception of risks varies drastically from neighbourhood to neighbourhood and depending on the quality and availability of services provided. Data for this survey were collected between February and April 2015 by a team of local researchers, who administered the questionnaire in local language.</p> <p>Publications linked to this survey are:</p> <p>Rusca M., Alda Vidal C., Hordijk M., Kral N., (2017) Bathing without water, and other stories of everyday hygiene practices and risk perception in urban low-income areas: the case of Lilongwe, Malawi, Environment and Urbanisation Vol 29, Issue 2, pp. 533 – 550. </p> <p>Tiwale S., Rusca M<strong>.</strong>, Zwarteveen M., The power of pipes: mapping urban water inequities through the material properties of networked water infrastructures. The case of Lilongwe, Malawi, Water Alternatives, Water Alternatives 11(2): 314-335.</p> <p>Rusca M. (2018): Visualising urban inequalities: the ethics of videography and documentary filmmaking in water research, <em>Wires Water</em>, <a href="https://doi.org/10.1002/wat2.1292">https://doi.org/10.1002/wat2.1292</a></p>
philip928lin/Flood-Risks-of-Cyber-physical-Attacks-in-a-Smart-Storm-Water-System: Flood Risks of Cyber-physical Attacks in a Smart Storm Water System
<p>This is the code archive for the publication "Flood Risks of Cyber-physical Attacks in a Smart Storm Water System" in Water Resources Research.</p>
Range-wide climate risk and adaptive potential in an iconic cold-water species (Part 2/2)
Open the record for dataset details and reuse information.
Range-wide climate risk and adaptive potential in a cold-water fish species (Part 1/2)
Open the record for dataset details and reuse information.
Annexes to the Update of the risk assessment of nickel in food and drinking water
<p><strong>Annex A – Benchmark dose analysis </strong></p> <p>The Annex is provided as a separate pdf file containing the detailed results of the benchmark dose analyses from which no reference point was selected. </p> <p><strong>Annex B – Dietary surveys per country and age group available in the EFSA Comprehensive Database, considered in the exposure assessment </strong></p> <p>The Annex is provided as a separate Excel file containing the dietary surveys per country and age group.</p> <p><strong>Annex C – Occurrence data on nickel in food and drinking water</strong></p> <p>The Annex is provided as a separate Excel file containing summary statistics on occurrence data on nickel.</p> <p><strong>Annex D – Chronic and acute dietary exposure to nickel and the contribution of different food groups to the dietary exposure</strong></p> <p>The Annex is provided as a separate Excel file containing the chronic and acute dietary exposure to nickel per survey and the contribution of different food groups to the dietary exposure. </p>
Pathogen group-specific risk factors for intra-mammary infection in water buffalo
<p>A cross-sectional study was conducted to estimate the prevalence of intra-mammary infection (IMI) associated bacteria and to identify risk factors for pathogen group-specific IMI in water buffalo in Bangladesh. A California Mastitis Test (CMT) and bacteriological cultures were performed on 1,374 quarter milk samples collected from 763 water buffalo from 244 buffalo farms in nine districts in Bangladesh. Quarter, buffalo, and farm-related data were obtained through questionnaires and visual observations. A total of 618 quarter samples were found to be culture-positive. Non-<em>aureus</em> staphylococci were the predominant IMI-associated bacterial species, and <em>Staphylococcus </em>(<em>S.</em>)<em> chromogenes</em>, <em>S. hyicus</em>, and <em>S. epidermidis </em>were the most common bacteria found. The proportion of non-aureus staphylococci or <em>Mammaliicoccus sciuri</em> (NASM), <em>S. aureus</em>, and other bacterial species identified in the buffalo quarter samples varied between buffalo farms. Therefore, different management practices, buffalo breeding factors, and nutrition were considered and further analyzed when estimating the IMI odds ratio (OR). The odds of IMI by any pathogen (OR: 1.8) or by NASM (OR: 2.2) were high in buffalo herds with poor milking hygiene. Poor cleanliness of the hind quarters had a high odds of IMI caused by any pathogen (OR: 2.0) or NASM (OR: 1.9). Twice daily milking (OR: 3.1) and farms with buffalo purchased from another herd (OR: 2.0) were associated with IMI by any pathogen. Asymmetrical udders were associated with IMI-caused by any bacteria (OR: 1.7). A poor body condition score showed higher odds of IMI by any pathogen (OR: 1.4) or by NASM (OR: 1.7). This study shows that the prevalence of IMI in water buffalo was high and varied between farms. In accordance with the literature, our data highlight that IMI can be partly controlled through better farm management, primarily by improving hygiene, milking management, breeding, and nutrition.</p>
Mangroves as nature-based mitigation for ENSO-driven compound flood risks in a large river delta: supporting data - high water levels
<p>This dataset contains modelled high water levels in the Guayas delta supporting the paper 'Mangroves as nature-based mitigation for ENSO-driven compound flood risks in a large river delta' published in HESS 2024.</p> <p>The dataset is organised through two folders:</p> <ul> <li>mangroves: all data from the scenarios with mangroves included in the domain</li> <li>no_mangroves: all data from the scenarios without mangroves included in the domain</li> </ul> <p>Each folder is further divided along 6 subfolders:</p> <ul> <li>I: El Niño Ocean & river</li> <li>II: El Niño Ocean</li> <li>III: El Niño river</li> <li>IV: Neutral</li> <li>I_50per: 50 % increase in seaward El Niño anomalies</li> <li>I_150per: 150 % increase in seaward El Niño anomalies</li> </ul> <p>Each folder contains two files:</p> <ul> <li>vars.csv - each row represents a mesh node and there are three columns: <ul> <li>X: x value of the model mesh node [m]</li> <li>Y: y value of the model mesh node [m]</li> <li>HIGH_WATER: high water level [m]</li> </ul> </li> </ul>
Data and Software for "Probabilistic Trade-offs Analysis for Sustainable and Equitable Management of Climate-Induced Water Risks"
<p><span>Research data supporting the study "Probabilistic trade-offs analysis for sustainable and equitable management of climate-induced water risks"</span></p> <p><span>This repository provides data of the Stochastic Dual Dynamic Programming (SDDP) model, and the output results of the simulations of the various policies and climate scenarios considered in this study, as well as the code used for postprocessing and visualizing the results.</span></p> <p><strong><span>Contents</span></strong></p> <ol> <li><strong><span>Data: Model Inputs</span></strong><span><br>This folder contains the physical river network, reservoir and water demand, and economic data derived from the observed database.<br>The key files are:</span></li> <ul> <li><span>Input_HydrologicalData</span></li> <li><span>Input_SystemData</span></li> </ul> <li><strong><span>Results: Model Output Analysis</span></strong><span><br>This folder includes outputs from the Stochastic Dual Dynamic Programming (SDDP) model under various policies and climate scenarios. The results showcase optimized sectoral water use, including irrigated areas, hydropower generation, and allocations for agriculture, energy, and urban demands across spatial locations (upstream and downstream).<br>Key files include:</span></li> <ul> <li><strong><span>SDDP Model Outputs</span></strong><span> (MATLAB format): </span></li> <ul> <li><span>EnergyPriority_Baseline.mat</span></li> <li><span>EnergyPriority_2070.mat</span></li> <li><span>EnergyPriority_2100.mat</span></li> <li><span>AgriculturePriority_Baseline.mat</span></li> <li><span>AgriculturePriority_2070.mat</span></li> <li><span>AgriculturePriority_2100.mat</span></li> </ul> <li><strong><span>Extracted Model Results</span></strong><span> (Excel format): </span></li> <ul> <li><span>Organized for each policy and climate scenario to facilitate analysis.</span></li> </ul> </ul> <li><strong><span>Software: Data Analysis and Visualization</span></strong><span><br>Python scripts designed for outputs data analysis and visualization are included to reproduce the primary figures from the study.<br>Scripts provided:</span></li> <ul> <li><span>CDF_outflow.py</span><span>: Analyzes cumulative distribution functions for river discharge.</span></li> <li><span>CDF_sectors.py</span><span>: Examines sectoral water use distributions.</span></li> <li><span>PCP_SI.py</span><span>: Generates Parallel Coordinate Plots for trade-offs analysis.</span></li> </ul> <li><strong><span>Instructions: README File</span></strong><span><br>A comprehensive README file explains:</span></li> <ul> <li><span>Details of model input data.</span></li> <li><span>Instructions to interpret the SDDP model outputs.</span></li> </ul> </ol> <p><strong><span>Instructions:</span></strong><span><br></span><span>The Python scripts process Excel files from the model output results folder to generate and visualize the figures for the paper. Each step is clearly documented within the scripts.</span></p>
Large-scale reforestation can increase water yield and reduce drought risk for water-insecure regions in the Asia-Pacific
<p>Large-scale reforestation can potentially bring both benefits and risks to the water cycle, which needs to be better quantified under future climates to inform reforestation decisions. We identified 477 water-insecure basins worldwide accounting for 44.6% (380.2 Mha) of the global reforestation potential. As many of these basins are in the Asia-Pacific, we used regional coupled land-climate modelling for the period 2041–2070 to reveal that reforestation increases evapotranspiration and precipitation for most water-insecure regions over the Asia-Pacific. This resulted in a statistically significant increase in water yield (p < 0.05) for the Loess Plateau-North China Plain, Yangtze Plain, Southeast China and Irrawaddy regions. Precipitation feedback was influenced by the degree of initial moisture limitation affecting soil moisture response and thus evapotranspiration, as well as precipitation advection from other reforested regions and moisture transport away from the local region. Reforestation also reduces the probability of extremely dry months in most of the water-insecure regions. However, some regions experience non-significant declines in net water yield due to heightened evapotranspiration outstripping increases in precipitation, or declines in soil moisture and advected precipitation.</p>
Global water security risk assessment
<p>This dataset presents the results of the risk assessment for water security in 317 basins around the world. The shapefile GRDC_379_basins_from_mouth contains the results of this assessment from a upstream perspective, with information presented for the basin polygons. Meanwhile, the shapefile GRDC_379_Precipitationsheds_from_mouth shows the results from an upwind perspective, with results presented for the precipitationshed polygons of the basins. For more detail of this dataset see the paper Posada-Marín J.A., Salazar J. F., Rulli M. C., Wang-Erlandsson L., Jaramillo F. (2024). Upwind moisture supply increases risk to water security. <em>Nature Water</em>, In review.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.