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| Created: | Sep 30, 2022 at 4:39 p.m. | |
| Last updated: | Oct 03, 2022 at 9:51 p.m. | |
| Citation: | See how to cite this resource |
| Sharing Status: | Private (Accessible via direct link sharing) |
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Abstract
Variations in surface water extent estimates across the United States, derived from available regional- and global-scale datasets: (1) HydroLAKES polygons, (2) LAGOS polygons, (3) 90-m OSM, (4) 10-m ESA, (5) 30-m GSWO, and (6) 30-m NED. The LAGOS and NED data are currently available only for the United States.
"Surface water" in these datasets are defined as follows: HydroLAKES (lakes ≥10 ha; Messager et al., 2016), LAGOS (lakes ≥1 ha; Cheruvelil et al., 2021), OSM (large lakes and rivers; Yamazaki et al., 2019), ESA (permanent waterbodies and herbaceous wetlands; ESA, 2022), GSWO (maximum water extents; Pekel et al., 2016; JRC, 2022), NED (potential maximum depressional inundation; Rajib et al., 2020).
The surface water estimates are derived by first converting all gridded datasets into polygons, then removing the extents of the polygons belonging to rivers and streams using a global bankfull river width dataset (Lin et al., 2020) in order to consider only the non-river surface waters in the analysis. The extents of the Great Lakes are excluded to enable clearer visualization of smaller waterbodies across watersheds.
Here, OSM = OpenStreetMap, ESA = European Space Agency, GSWO = Global Surface Water Occurrence, NED = National Elevation Dataset.
Subject Keywords
Coverage
Spatial
Content
Related Resources
| The content of this resource is derived from | Messager, M.L., Lehner, B., Grill, G., Nedeva, I. and Schmitt, O., 2016. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications, 7(1). https://doi.org/10.1038/ncomms13603 |
| The content of this resource is derived from | Cheruvelil, K.S., Soranno, P.A., McCullough, I.M., Webster, K.E., Rodriguez, L.K. and Smith, N.J., 2021. LAGOS‐US LOCUS v1.0: Data module of location, identifiers, and physical characteristics of lakes and their watersheds in the conterminous US. Limnology and Oceanography Letters, 6(5). https://doi.org/10.1002/lol2.10203 |
| The content of this resource is derived from | JRC, 2022. Joint Research Centre Global Surface Water – Data Users Guide (v3). Available online at: https://global-surface-water.appspot.com/download. Last accessed on July 4, 2022. |
| The content of this resource is derived from | ESA, 2022. European Space Agency WorldCover 10 m 2020 v100. Available online at: https://esa-worldcover.org/en. Last accessed on: May 15, 2022. |
| The content of this resource is derived from | Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P.D., Allen, G.H. and Pavelsky, T.M., 2019. MERIT Hydro: A high‐resolution global hydrography map based on latest topography dataset. Water Resources Research, 55(6). https://doi.org/10.1029/2019WR024873 |
| The content of this resource references | Rajib, A., Golden, H.E., Lane, C.R. and Wu, Q., 2020. Surface depression and wetland water storage improves major river basin hydrologic predictions. Water Resources Research, 56(7). https://doi.org/10.1029/2019WR026561 |
| The content of this resource is derived from | Lin, P., Pan, M., Allen, G. H., de Frasson, R. P., Zeng, Z., Yamazaki, D., and Wood, E. F., 2020. Global estimates of reach‐level bankfull river width leveraging big data geospatial analysis. Geophysical Research Letters, 47. https://doi.org/10.1029/2019GL086405 |
| This resource is described by | Khare, A., Rajib, A., Zheng, Q., Golden, H. et al. Global Surface Water Estimates: Critical Need for Data Consistency and Integration (under peer-review) |
| The content of this resource references | Pekel, J.F., Cottam, A., Gorelick, N. and Belward, A.S., 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633). https://doi.org/10.1038/nature20584 |
Credits
Funding Agencies
This resource was created using funding from the following sources:
| Agency Name | Award Title | Award Number |
|---|---|---|
| National Science Foundation (NSF) | CyberTraining for Open Science in Climate, Water, and Environmental Sustainability | 2230093 |
| National Aeronautics and Space Administration (NASA) | Land Information System Enabling Predictions of Aquatic Health for Comprehensive Water Security Assessment | 80NSSC 22K1661 |
| U.S. Department of Defense | Evaluating Non-floodplain Wetlands for Flood-Risk Reduction and Nutrient Mediation in the Mississippi River Basin | W912HZ2020071 |
How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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