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.

Reservoir Storage Content (acre-feet)

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.

California Great Basin Region Water Operations Data for Spring Creek Reservoir and Debris Dam.

Reservoir Storage Content (acre-feet)

This Data Release serves as a repository for a set of time-series data used in Scientific Investigations Report 2018-5040. The data represent continuous measurements of specific conductance, water temperature, and/or water level (stage), recorded by a variety of types of data loggers during three multi-day interference tests conducted on the Virgin River at Pah Tempe Springs during November 2013, February 2014, and November 2014. The data presented are the raw data downloaded from the data loggers and are organized according to the date of the test and the type and name of the observation site. The Data Release contains 3 items: 1. An explanatory table, "PahTempe_table1.xlsx", which indicates which parameters were collected and on what instrument at each site during a given test 2. The data, "PahTempe_data.zip"; this zipped file contains the raw data logger files in comma-separated values (CSV) format, organized into folders according to the date of the interference pumping test 3. The metadata document, "PahTempe_metadata.xml" Because these data were collected during multi-day interference pumping tests, they do not represent natural hydrologic conditions in the river, springs, or shallow groundwater system. Users of this data are advised to refer to the larger work citation for proper use and interpretation of the data.

Reservoir Storage Content (acre-feet)

Reservoir Storage Content (acre-feet)

Data are from the Lower Colorado Hydrologic Database and updated once daily.