2022
DOI: 10.1038/s41597-022-01290-w
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U.S. national water and energy land dataset for integrated multisector dynamics research

Abstract: Understanding resource demands and tradeoffs among energy, water, and land socioeconomic sectors requires an explicit consideration of spatial scale. However, incorporation of land dynamics within the energy-water nexus has been limited due inconsistent spatial units of observation from disparate data sources. Herein we describe the development of a National Water and Energy Land Dataset (NWELD) for the conterminous United States. NWELD is a 30-m, 86-layer rasterized dataset depicting the land use of mappable … Show more

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Cited by 1 publication
(2 citation statements)
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“…, in the Eagle Ford shale play and the Marcellus shale play). Integrating our spatially explicitly mapping to the NLCD (30 meter resolution), 49 which was previously used as a proxy of large-scale mapping of natural gas production, 55 could potentially provide both a more complete and accurate mapping for natural gas production infrastructure (Fig. 2b) and a large dataset for future land conversion studies.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…, in the Eagle Ford shale play and the Marcellus shale play). Integrating our spatially explicitly mapping to the NLCD (30 meter resolution), 49 which was previously used as a proxy of large-scale mapping of natural gas production, 55 could potentially provide both a more complete and accurate mapping for natural gas production infrastructure (Fig. 2b) and a large dataset for future land conversion studies.…”
Section: Resultsmentioning
confidence: 99%
“…32 A cluster of wells can include up to 20,000 wells, requires processing ~50,000 images, and covers 19,000 square kilometers (Figure S3), which indicates that our approach is suitable for large-scale land use mapping for areas with a intense natural gas production activity (e.g., in the Eagle Ford shale play and the Marcellus shale play). Integrating our spatially explicitly mapping to the national land cover data (NLCD) (30-meter resolution), 49 which was previously used as a proxy of large-scale mapping of natural gas production, 55 could potentially provide both a more complete and accurate mapping for natural gas production infrastructure (Figure 2b) and a large dataset for future land conversion studies.…”
Section: Performance Of Deep Learning Modelmentioning
confidence: 99%