Data release for integrating remotely sensed surface water dynamics in hydrologic signature modeling
Variability in river discharge, particularly very high flow and low flow conditions, has far-reaching environmental and economic consequences. The retention of water in surface storage, concentrated in lakes, ponds, wetlands, floodplains, and temporary water in flood prone areas, can potentially contribute to flow generation and flood regulation. However, the impact of surface water storage on river discharge can be challenging to isolate and quantify. A suite of hydrologic signatures were generated for 72 gages across the conterminous United States. The hydrologic signatures were selected to characterize all flows as well as isolating high and low flows, and machine learning models were developed to explain watershed variability in signature values. Wetland related variables, including multi-sensor-based surface water extent and hydroperiod, were compared with other drivers, including climate, topography, and land cover. An improved understanding of how surface water dynamics influence river discharge can be used to improve the resilience of river systems to climate extremes.
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
"010:12"
]
|
| contactPoint |
{
"fn": "Melanie Vanderhoof",
"@type": "vcard:Contact",
"hasEmail": "mailto:mvanderhoof@usgs.gov"
}
|
| description | Variability in river discharge, particularly very high flow and low flow conditions, has far-reaching environmental and economic consequences. The retention of water in surface storage, concentrated in lakes, ponds, wetlands, floodplains, and temporary water in flood prone areas, can potentially contribute to flow generation and flood regulation. However, the impact of surface water storage on river discharge can be challenging to isolate and quantify. A suite of hydrologic signatures were generated for 72 gages across the conterminous United States. The hydrologic signatures were selected to characterize all flows as well as isolating high and low flows, and machine learning models were developed to explain watershed variability in signature values. Wetland related variables, including multi-sensor-based surface water extent and hydroperiod, were compared with other drivers, including climate, topography, and land cover. An improved understanding of how surface water dynamics influence river discharge can be used to improve the resilience of river systems to climate extremes. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Digital Data",
"format": "XML",
"accessURL": "https://doi.org/10.5066/P9RLFMEQ",
"mediaType": "application/http",
"description": "Landing page for access to the data"
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{
"@type": "dcat:Distribution",
"title": "Original Metadata",
"format": "XML",
"mediaType": "text/xml",
"description": "The metadata original format",
"downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.652027f4d34e44db0e2e43b4.xml"
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|
| identifier | http://datainventory.doi.gov/id/dataset/USGS_652027f4d34e44db0e2e43b4 |
| keyword |
[
"Arizona",
"Arkansas",
"California",
"Delaware",
"Georgia",
"Idaho",
"Illinois",
"Iowa",
"Kansas",
"Maryland",
"Minnesota",
"Mississippi",
"Missouri",
"Montana",
"Nebraska",
"New Mexico",
"North Carolina",
"North Dakota",
"Oregon",
"Pennsylvania",
"South Carolina",
"South Dakota",
"Texas",
"USGS:652027f4d34e44db0e2e43b4",
"United States",
"Virginia",
"Wisconsin",
"drought",
"floodplain",
"floods",
"geographically isolated wetlands",
"hydrologic signatures",
"inundation",
"lakes",
"non-floodplain wetlands",
"stream discharge metrics",
"wetlands"
]
|
| modified | 2025-07-08T00:00:00Z |
| publisher |
{
"name": "U.S. Geological Survey",
"@type": "org:Organization"
}
|
| spatial | -123.3585, 28.9579, -75.5841, 48.9995 |
| theme |
[
"Geospatial"
]
|
| title | Data release for integrating remotely sensed surface water dynamics in hydrologic signature modeling |
