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15 results for “water footprint”
Dataset associated with Schyns & Vanham (2019) "The water footprint of wood for energy consumed in the European Union"
<p>Input and output datasets related to the paper Schyns & Vanham (2019) The water footprint of wood for energy consumed in the European Union. <em>Water</em>, 11(2): 206.</p>
CWFETB-China: Gridded dataset of consumptive water footprints, evaporation, transpiration, and associate benchmarks of crop production in China (2000-2018)
<p>The CWFETB-China is a 5-arcmin gridded dataset of monthly green and blue water footprint of crop production (WFCP), evaporation (E), transpiration (Tr), and associated unit WFCP benchmarks for 21 crops grown in China during 2000-2018. As compared to the existing gridded WFCP datasets, the CWFETB-China has four improvements: (i) It evaluated the effects of different water supply modes (irrigated or rain-fed) and irrigation practices (furrow, sprinkler, and micro-irrigation) on water consumption throughout the crop growth period. (ii) It distinguished between monthly blue and green water consumption via soil evaporation and crop transpiration. (iii) The dataset encompassed both the WFCP in m<sup>3 </sup>yr<sup>-1</sup> and the uWFCP in m<sup>3 </sup>ton<sup>-1</sup>. (iv) It identified uWFCP benchmarks that differentiated between various climatic zones and irrigation practices. The dataset is able to support for precise crop water productivity assessments, agricultural water-saving evaluations, the development of sustainable irrigation techniques, cropping structure optimisation, and crop-related interregional virtual water trade analysis.</p> <p> </p> <p>Format: NetCDF-4 (5 arcmin) or .xlsx files (benchmark data).</p> <p>Projected coordinate system: WGS 84</p>
Figure 3 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 3. Flow diagram for calculation of WF in Van province.
Figure 2 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 2. Monthly average air temperature, total and effective precipitation values in Van.
Figure 8 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 8. Distribution of WF , and WF by years in Van province.
Figure 1 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 1. The study area.
Figure 4 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 4. Distribution of WF in Van province.
Figure 9 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 9. Distribution of WF in Van province when feed crops total are included in WF .
Water scarcity and water footprint estimates
<p>Intra-state water conflicts have increased substantially over the past few decades particularly in developing regions, and partly attributed to water scarcity. However, empirical studies linking water scarcity and violent conflicts are sparse, while existing quantitative studies have used mostly climate variables (precipitation and temperature) to understand this link. Most studies that have used climate variables concluded that they were not strong predictors of water conflicts. The aim of this study was to identify water scarcity hotspots and to understand the links between water scarcity and violent conflicts across the Sahel and Lake Chad Basin (LCB) over the period 2000-2021. To achieve this, we combine outputs from a global hydrological model and demographic data to develop six water scarcity metrics. The developed metrics show varying levels of water scarcity across the study region. The Falkenmark index across all capital cities (Ouagadougou-Burkina Faso, Ndjamena-Chad, Bamako-Mali, and Niamey-Niger), and Maradi-Niger and Jigawa & Kano states in Nigeria was less than 100 m<sup>3</sup>/capital/year, indicating acute water scarcity in those areas. Findings further indicated that green water scarcity (GWS) and the Falkenmark index were closely linked with water conflicts compared to the other metrics. Our findings suggest that water conflicts cannot be explained by hydroclimatic factors alone without incorporating other socioeconomic variables like demographic information. Results from this study may be used by stakeholders to tackle endemic water scarcity and to predict and mitigate water conflicts in the region.</p>
Global Energy Virtual Water Trade Network and Country Electricity Water Footprints
<p>Global energy trade accounts for between 22 and 32% of total energy consumption, approximately 2.0 x 10<sup>11</sup> GJ of energy. Based on concepts of the energy-water nexus and water footprinting, we determine the water intensity of the energy trade for 11 different energy commodities, including primary (i.e., fossil fuels) and secondary (i.e., electricity) energy. In this data description, we present a database on the water footprint of country-to-country energy trade for 2010--2018 and water intensity values of electricity for each country. The database includes the country of origin, trade partner, type of energy commodity, quantity traded, value of the energy trade, a mean water footprint value in m<sup>3</sup>, and an estimated range of uncertainty water footprint. These data provide the basis for assessing international virtual water trade, water scarcity concerns, and the environmental implications for a changing global energy system. </p>
The role of farm subsidies in changing India's water footprint (replication files)
<p>These are the replication files (codes + data) for "The role of farm subsidies in changing India's water footprint".</p> <p>All codes were written executed in STATA MP ver 18.5. Please copy the exact folder structure to your computer. The do files will replicate all tables and figures in the paper. All the data necessary is provided within the data folder.</p> <p>1. all_india.do replicates the results in Tables 1, S1 and Figures 1, S1, S2, S3, and S4.</p> <p>2. punjab_graphs.do replicates figures Figs 2a, 2b, 2c, 2d, 3, and S5.</p> <p>3. punjab regressions.do replicates the results in table S4</p> <p>4. mp_graphs.do replicates figures 4a, 4b, and 4c</p> <p>5. mp_regressions.do replicates the results in tables 2, S5, and S6.</p> <p> </p> <p>Please email Shoumitro Chatterjee (shoumitroc@jhu.edu) or Esha D. Zaveri (esha.d.zaveri@gmail.com) for any further clarification/information.</p>
Figure 7 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 7. Comparison WF and WF green of WF crop of the Van province with the Upper Tigris River Basin (Muratoglu, 2019), the worldwide blue average (Mekonnen and Hoekstra, 2011b) and the Turkish average (Mekonnen and Hoekstra, 2011a).
Figure 5. Per capita water footprint between 2004-2019 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)
Figure 5. Per capita water footprint between 2004-2019 in Van province.
RELATIONSHIP BETWEEN NUTRIENT PROFILES, CARBON, AND WATER FOOTPRINT OF HOSPITAL MENUS
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Dataset of global gridded monthly crop coefficient, yearly and monthly blue-to-total water footprint ratio, and national unit blue and green water footprints of maize (2000-2021)
<p>The data includes monthly <span><span>crop coefficient</span></span>, yearly and monthly blue-to-total water footprint ratio at a 5 arcminute spatial scale, and the unit water footprint at an annual national (regional) scale of global maize.</p>
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