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27 results for “Water Sustainability”
An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona
<p><em><strong>The manuscript for this dataset is accepted by AGU Advances and can be accessed here: <a href="https://doi.org/10.1029/2022AV000816">link</a>. Please cite the literature when using the datasets.</strong></em></p> <p><strong>How to cite this article: Yuanhui Zhu, Soe Myint, Xin Feng, Yubin Li. An Innovative Scheme to Confront the Trade‐Off Between Water Conservation and Heat Alleviation With Environmental Justice for Urban Sustainability: The Case of Phoenix, Arizona. AGU Advances, 4, e2022AV000816. <a href="https://doi.org/10.1029/2022AV000816">https://doi.org/10.1029/2022AV000816</a></strong></p> <p>This study aims to develop a practical and integrated framework to tackle the tradeoff between land surface temperature (LST) reduction and water conservation for heat mitigation and resilience planning in Phoenix, Arizona. We developed a multi-objective framework of spatial optimization for priority areas that considers environmental justice. We employed the priority areas (i.e., residential districts, socio-economically disadvantaged neighborhoods, hotspot regions, and opportunity areas), ECOSTRESS-based LST, actual evapotranspiration (ETa, as a proxy to water use), Landsat-based LST and ETa changes (2000–2020), and the evaporative stress index (ESI). These datasets are used to identify the priority areas in which environmental conditions need to be improved seriously and (2) spatially optimize the placement of new green space (tree %, grass %) in the priority areas to realize the most significant LST reduction and minimum OWU. We provide the results of the new green space configurations with the scenarios for the percentage of new vegetation coverage (including trees and grass) overall increased to 25%, 35%, and 45% within the entire study areas, residential districts, socio-economically disadvantaged neighborhoods, and hotspot regions.</p> <table> <caption>The dataset summarization</caption> <tbody> <tr> <td>Category</td> <td>Dataset</td> <td>Resolution</td> <td>Source/method</td> <td>Time</td> </tr> <tr> <td>Environmental database</td> <td>Summer daytime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer nighttime LST</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ETa</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental database</td> <td>Summer ESI</td> <td>70m</td> <td>ECOSTRESS</td> <td>2019</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer LST changes</td> <td>30m</td> <td>Landsat-based Statistical Mono-Window algorithm</td> <td>2000-2020</td> </tr> <tr> <td>Environmental change database</td> <td>Trends of summer ETa changes</td> <td>30m</td> <td>Landsat-based Simplified Surface Energy Balance</td> <td>2000-2020</td> </tr> <tr> <td>The results of new green space configurations</td> <td>The spatial distributions of new green space</td> <td>--</td> <td>Spatial optimization</td> <td>--</td> </tr> </tbody> </table> <p>note: LULC: Land use and land cover; LST: Land Surface Temperature; ETa: Actual Evapotranspiration; ESI: Evaporative Stress Index</p> <p>We provide the different scenarios in shapefile format for spatial distributions of new space configurations. The naming convention for attribute tables in shapefile is :</p> <p>VV_new_perNN_LSTWW</p> <p>where:</p> <ul> <li>VV = New vegetation for tree or grass</li> <li>NN = The scenarios with new vegetation increased to 25%, 35%, or 45% (unit: %)</li> <li>WW = The weight values of land surface temperature range from 0 to 1 (unit: %) when executing spatial optimization for the tradeoff between land surface temperature reduction and outdoor water use conservation with vegetation coverage. The weight of 0 represents that our spatial optimization models only focus on outdoor water use conservation, and the weight of 1 denotes that we only consider land surface temperature reduction. </li> </ul> <p>Example: grass_new_per25_LST65 means -- new vegetation for grass; the scenario is set up by new vegetation increased to 25%; the weight of land surface temperature is 0.65. </p> <p> </p>
Global expansion of sustainable irrigation limited by water storage (Schmitt, Rosa, Daily - PNAS 2022)
<p>Global input and output data for "Global expansion of sustainable irrigation limited by water storage" by Rafael J. P. Schmitt, Lorenzo Rosa and Gretchen C. Daily, PNAS, 2022. See included Metadata file for more details. </p> <p></p>
A Refined Supply-demand Framework to Quantify variability in Ecosystem Services Related to Surface Water in support of Sustainable Development Goals
<p>The file named 'Results' stores the data produced in this study. The file named 'Scripts' stores the python codes used in this study. The file named 'Software' stores the software installation package (Windows 64-bit system). The file named '3basin' stores the shapefile data of Level 3 basin in Xinjiang.</p>
Figure 6 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 6: Percentage of maturity level of female swimming crabs
Figure 5 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 5. Percentage of spawning females Phase
Figure 4 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 4: Sex ratio distribution of swimming crab
Figure 1 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 1: Maturity level composition of swimming crab based on carapace width
Figure 3 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 3: Average weight of swimming crab
Figure 2 in Analysis of Carapas Width and Weight on Maturity Level of Swimming Crab (Portunus pelagicus) as Basis for Sustainable Resource Management in the Eastern Waters of Surabaya
Figure 2: Average Carapace Width of Swimming Crab
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>
Data from: Comparing the importance of farming resource endowments and agricultural livelihood diversification for agricultural sustainability from the perspective of the food–energy–water nexus
<p>Household Survey Data for journal article 'Comparing the importance of farming resource endowments and agricultural livelihood diversification for agricultural sustainability from the perspective of the food–energy–water nexus', without protected data.</p>
Data from "Towards a sustainable use of shower water: Habits and explanatory factors in southern Spain"
<p>One of the targets of SDG 6 is the efficient use of water resources in all sectors in order to tackle water shortages. In the home, showering is one of the main water consuming activities. How can people make a more sustainable use of shower water? To answer this question, this research analyses showering habits and explanatory factors of shower use. The study is carried out with data from 945 students of the University of Granada, Spain. Significant differences are observed in shower use during the summer and winter months: the average duration is 8.8 and 11.6 minutes, respectively, and shower frequency is greater in summer. Determinants of different shower water use include gender, ideology, pro-environmental actions, inherent values and connectedness to nature, among others. Those variables relate differently to duration and frequency of showers, according to the season, thus highlighting the importance of seasonality. The results show that there is room to achieve a more sustainable use of the shower, in terms of frequency and duration, through awareness measures that are tailored to groups that make a less sustainable use of showers. The main recommendation is that awareness campaigns should be designed on the basis of the user profile as well as the season.</p>
Data from: How does sustainable water consumption in the shower relate to different dimensions of perceived well-being? Empirical evidence from university students
<p>Water scarcity is already a worrying issue and it is predicted to get worse in the future. This creates an imperative to use water efficiently and sustainably. In the domestic sphere, one of the main uses of water is showering, not only for hygiene reasons but also as a wellness activity. In order to gain insight into the implications of sustainable shower use, in this paper we analyse the relationship between subjective well-being and water consumption in the shower. We aim to answer the following questions: 1) How does shower water consumption relate to subjective well-being, 2) Does this relationship differ depending on showering habits (time spent in the shower, and number of showers per week), and 3) Does this relationship differ depending on the season (winter and summer). The dataset contains information on 937 students from different disciplines at the University of Granada, Spain. The different interpretations of subjective well-being considered are life satisfaction, affect, and vitality. Results suggest that there is a negative relationship between water consumption and subjective well-being, in line with the literature that identifies a well-being dividend from green behaviour (being pro-environmental helps the environment and increases happiness). All subjective well-being dimensions are negatively related to time spent in the shower, regardless of the season. In contrast, the frequency of showering is not significantly related to well-being. Therefore, it appears that higher water consumption does not translate into higher perceived well-being, indicating that there is no conflict between efficient shower water use and individual well-being.</p>
Data from: Like a fish takes to water: Minimum discharge requirements to sustain rheophilic fish community dominance in bypassed river reaches
Open the record for dataset details and reuse information.
A Refined Supply-demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals
<p>This database relies on the article entitled "A Refined Supply-demand Framework to Quantify Variability in Ecosystem Services Related to Surface Water in Support of Sustainable Development Goals " to be published in Earth's Future. The file named 'Results' stores the data produced in this study. The file named 'Scripts' stores the python codes used in this study. The file named 'Software' stores the software installation package (Windows 64-bit system). The file named '3basin' stores the shapefile data of Level 3 basin in Xinjiang. The file named "Supplementary Data" contains the necessary Water Bulletin and Statistical Yearbook data.</p>
Augmented Net Primary Production in Arctic Ocean Sustained by Increased Subarctic Inflow Water
<p>Developing an optical classification-based chlorophyll (Chla) retrieval algorithm for the Arctic Ocean by matching in situ and remote sensing data. The statistical relationship between mixed layer depth integrated primary productivity (NPP) and sea surface Chla was also regressed from a large field measurement data set. Through principal component analysis and correlation analysis, NPP environmental driving factors were analyzed. Data sets include remote sensing reflectance data used to invert NPP and environmental drivers of NPP changes.</p>
Data from the article "Robust Photocatalytic MICROSCAFS® with Interconnected Macropores for Sustainable Solar-Driven Water Purification" https://doi.org/10.3390/ijms25115958
<p>Version 2 - the units of the apparent kinetic rate constants in the flow reactor kinetic models were corrected.</p>
Data from: Morphological change and phenotypic plasticity in native and non–native pumpkinseed sunfish in response to sustained water velocities
Phenotypic plasticity can contribute to the proliferation and invasion success of nonindigenous species by promoting phenotypic changes that increase fitness, facilitate range expansion and improve survival. In this study, differences in phenotypic plasticity were investigated using young-of-year pumpkinseed sunfish from colonies established with lentic and lotic populations originating in Canada (native) and Spain (non-native). Individuals were subjected to static and flowing water treatments for 80 days. Inter- and intra-population differences were tested using ancova and discriminant function analysis, and differences in phenotypic plasticity were tested through a manova of discriminant function scores. Differences between Iberian and North American populations were observed in dorsal fin length, pectoral fin position and caudal peduncle length. Phenotypic plasticity had less influence on morphology than genetic factors, regardless of population origin. Contrary to predictions, Iberian pumpkinseed exhibited lower levels of phenotypic plasticity than native populations, suggesting that canalization may have occurred in the non-native populations during the processes of introduction and range expansion.
Data repository for "Navigating trade-offs and sustainable development pathways in the Andean water-energy-food-ecosystem nexus"
<p>Data and code supporting the research article: Navigating trade-offs and sustainable development pathways in the Andean water-energy-food-ecosystem nexus.</p>
Data for "Early planting adaptation makes the coupled food-water system more sustainable under climate change"
<p>Data for submission "Early planting adaptation makes the coupled food-water system more sustainable under climate change"</p><p>All data are in netcdf format and can be read in ncl, python, R code capacity.</p><p> </p><p><strong>geo_em.d01.conus.corn:</strong></p><p>Domain setup file for the Noah-MP crop model in the US corn belt. Lat/lon location specified by "XLAT_<i>M" and "XLONG_M</i>" variable and corn planting area specified by "CROPTYPE" variable.</p><p> </p><p>Three zip files are uploaded containing data from model simulations and county-level yield and irrigation record:</p><p><strong>Yield_data.zip:</strong></p><p>yield data from model simulations (denoted by three scenarios, CTRL, PGW, TAVE), with irrigation (irr), and from USDA NASS (NASS).</p><p><strong>Irrigation_data.zip:</strong></p><p>Irrigation amount data from three scenarios (CTRL, PGW, TAVE for early planting), and from USGS water use record (2005 and 2010).</p><p><strong>TempPrecPET.zip:</strong></p><p>temperature and precipitation and potential evapotranspiration data (PET) for the CTRL and PGW climate scenarios. </p>
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