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.

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. This dataset is associated with the following publication: Vanderhoof, M., P. Nieuwlandt, H. Golden, C. Lane, J. Christensen, W. Keenan, and W. Dolan. Relating surface water dynamics in wetlands and lakes to spatial variability in hydrologic signatures. Wetlands Ecology and Management. Springer Science and Business Media B.V;Formerly Kluwer Academic Publishers B.V., GERMANY, 33(53): 1-36, (2025).

High-frequency observations of surface water at fine spatial scales are critical to effectively manage aquatic habitat, flood risk and water quality. We developed inundation algorithms for Sentinel-1 and Sentinel-2 across 12 sites within the conterminous United States (CONUS) covering >536,000 km2 and representing diverse hydrologic and vegetation landscapes. These algorithms were trained on data from 13,412 points spread throughout the 12 sites. Each scene in the 5-year (2017-2021) time series was classified into open water, vegetated water, and non-water at 20 m resolution using variables not only from Sentinel-1 and Sentinel-2, but also variables derived from topographic and weather datasets. The Sentinel-1 model was developed distinct from the Sentinel-2 model to enable the two time series to be integrated into a single high-frequency time series, while open water and vegetated water were both mapped to retain mixed pixel inundation. Results were validated against 7,200 visually inspected points derived from WorldView and PlanetScope imagery. Classification accuracy for open water was high across the 5-year period, with an omission and commission error of only 3.1% and 0.9% for Sentinel-1 and 3.1% and 0.5% for Sentinel-2, respectively. Vegetated water accuracy was lower, as expected given that the class represents mixed pixels. Sentinel-2 showed higher accuracy (10.7% omission and 7.9% commission error) relative to Sentinel-1 (28.4% omission and 16.0% commission error). Our results demonstrated that Sentinel-1 and Sentinel-2 time series can be integrated to improve the temporal resolution when mapping open and vegetated waters, although sensor-specific differences, such as sensitivity to vegetation structure versus pixel color, complicate the data integration for subpixel, vegetated water compared with open water.

High-frequency observations of surface water at fine spatial scales are critical to effectively manage aquatic habitat, flood risk and water quality. We developed inundation algorithms for Sentinel-1 and Sentinel-2 across 12 sites within the conterminous United States (CONUS) covering >536,000 km2 and representing diverse hydrologic and vegetation landscapes. These algorithms were trained on data from 13,412 points spread throughout the 12 sites. Each scene in the 5-year (2017-2021) time series was classified into open water, vegetated water, and non-water at 20 m resolution using variables not only from Sentinel-1 and Sentinel-2, but also variables derived from topographic and weather datasets. The Sentinel-1 model was developed distinct from the Sentinel-2 model to enable the two time series to be integrated into a single high-frequency time series, while open water and vegetated water were both mapped to retain mixed pixel inundation. Results were validated against 7,200 visually inspected points derived from WorldView and PlanetScope imagery. Classification accuracy for open water was high across the 5-year period, with an omission and commission error of only 3.1% and 0.9% for Sentinel-1 and 3.1% and 0.5% for Sentinel-2, respectively. Vegetated water accuracy was lower, as expected given that the class represents mixed pixels. Sentinel-2 showed higher accuracy (10.7% omission and 7.9% commission error) relative to Sentinel-1 (28.4% omission and 16.0% commission error). Our results demonstrated that Sentinel-1 and Sentinel-2 time series can be integrated to improve the temporal resolution when mapping open and vegetated waters, although sensor-specific differences, such as sensitivity to vegetation structure versus pixel color, complicate the data integration for subpixel, vegetated water compared with open water.

This data release contains grids, in geographic tagged imaged file (.tif) format, summarizing inundation frequency of the U. S. Geological Survey (USGS) Dynamic Surface Water Extent (DSWE) Landsat Science Product at 114 National Wildlife Refuges throughout the U.S. Fish and Wildlife Service (USFWS) Mountain-Prairie Region (Colorado, Kansas, Montana, Nebraska, North Dakota, South Dakota, Utah, and Wyoming). The DSWE product provides long-term (1982 to present), high temporal resolution data (30-meter) on surface water inundation patterns that can help identify locations of past or current drought conditions. For each refuge, three files were produced using data from different periods: the baseline period (1982-2000), the evaluation period (2001–20), and the period of record (1982-2020). Inundation frequencies for each pixel were derived by dividing the total number of observations classified as any one of the DSWE water classes by the total number of observations of water extent or presence.

Understanding and projecting the downstream benefits of terrestrial surface water storage (volumetric water stored in lakes and wetlands, SWstorage) requires watershed hydrologic models. Use of external datasets to calibrate and validate modeled SWstorage dynamics remains uncommon, particularly across major river basins. Here, we: (1) develop and assess the utility of a novel remote sensing-based (RS) SWstorage approach for verifying watershed-model SWstorage estimates, (2) compare average modeled and RS SWstorage volume across the landscape, and (3) compare variability in modeled and RS SWstorage through time. We used SWstorage informed by Sentinel-1 and -2 (RS SWstorage), with Soil and Water Assessment Tool (SWAT) model simulations (SWAT SWstorage) across the ~450,000 km2 Upper Mississippi River Basin. We found that RS SWstorage was, on average, lower than SWAT SWstorage in tile-drained agricultural regions where static Digital Elevation Model (DEM)-generated depressions used in the SWAT model often did not contain RS surface water. Conversely, RS SWstorage was higher than SWAT SWstorage in wetland-rich regions where surface water was shallower than DEM vertical accuracy. In modeled subbasins where DEM-generated maximum SWstorage capacity was low relative to SWAT SWstorage volumes, SWAT SWstorage was effectively capped and unable to vary through time, whereas RS SWstorage in the same subbasins continued to vary. Thus, RS SWstorage allows for a more accurate representation of where, when, and how much water is on the landscape. This finding is useful for informing watershed model initial conditions and highlights the potential for RS to be used in SWstorage calibration or data assimilation.

Understanding and projecting the downstream benefits of terrestrial surface water storage (volumetric water stored in lakes and wetlands, SWstorage) requires watershed hydrologic models. Use of external datasets to calibrate and validate modeled SWstorage dynamics remains uncommon, particularly across major river basins. Here, we: (1) develop and assess the utility of a novel remote sensing-based (RS) SWstorage approach for verifying watershed-model SWstorage estimates, (2) compare average modeled and RS SWstorage volume across the landscape, and (3) compare variability in modeled and RS SWstorage through time. We used SWstorage informed by Sentinel-1 and -2 (RS SWstorage), with Soil and Water Assessment Tool (SWAT) model simulations (SWAT SWstorage) across the ~450,000 km2 Upper Mississippi River Basin. We found that RS SWstorage was, on average, lower than SWAT SWstorage in tile-drained agricultural regions where static Digital Elevation Model (DEM)-generated depressions used in the SWAT model often did not contain RS surface water. Conversely, RS SWstorage was higher than SWAT SWstorage in wetland-rich regions where surface water was shallower than DEM vertical accuracy. In modeled subbasins where DEM-generated maximum SWstorage capacity was low relative to SWAT SWstorage volumes, SWAT SWstorage was effectively capped and unable to vary through time, whereas RS SWstorage in the same subbasins continued to vary. Thus, RS SWstorage allows for a more accurate representation of where, when, and how much water is on the landscape. This finding is useful for informing watershed model initial conditions and highlights the potential for RS to be used in SWstorage calibration or data assimilation.

The U.S. Geological Survey (USGS) and the U.S. Army Corps of Engineers (USACE) are collaborating with the U.S. Federal Emergency Management Agency (FEMA) on improving flood-frequency analysis methods to account for mixed populations arising from different flood causal mechanisms. Precipitation data at different timescales are widely used in flood-typing studies. Various gridded precipitation datasets were validated by comparison against station observations to support flood-typing over six pilot regions in the contiguous U.S. (CONUS), where flood-typing approaches will be initially tested. The six pilot regions are (1) the Delaware River, (2) the Iowa River, (3) Puget Sound, (4) the Red River of the North, (5) the Trinity River, and (6) the Upper Colorado River. Various precipitation datasets derived from station, radar, reanalysis data, or combinations thereof, were validated in terms of their ability to capture the spatiotemporal characteristics of daily precipitation as well as multi-day (1–14 day) extreme precipitation events. The datasets were validated by comparison against gage data from the NOAA Global Historical Climatology Network daily (GHCNd) for the periods 1981-2013 and 1998-2013. Taylor diagrams and the Kling-Gupta efficiency (KGE) metric were used for validation. This data release consists of three tables in EXCEL spreadsheet format: -- Normalized Taylor diagram statistics for daily precipitation by season in each pilot region, for the two periods (All_precipitation_statistics.xlsx). -- Normalized Taylor diagram statistics for multi-day extreme precipitation in each pilot region, for the two periods (Extreme_precipitation_statistics.xlsx). -- List of GHCNd stations used for evaluation of daily precipitation by season and for evaluation of multi-day precipitation performance in each pilot region, for the two periods (GHCND_stations.xlsx).