Geology topics

William Keenan

Publications and source records attributed to William Keenan.

3 recordsLinked to original sources

USGS2026

Remotely sensed surface water storage shows distinct patterns from SWAT-simulated data

Quantifying and projecting the downstream benefits of water stored in lakes and wetlands (SW storage ) requires watershed hydrologic models, which often parameterize surface water storage in topographic depressions using static digital elevation model (DEM) data. Calibration and validation of modeled SW storage dynamics using external data sets is uncommon, particularly across major river basins, with model calibration typically focused on observed discharge. Here, we develop and assess a novel remote sensing-based (RS) SW storage data set (Sentinel-1 and Sentinel-2) for verifying simulated SW storage estimates from a Soil and Water Assessment Tool (SWAT) model of the Upper Mississippi River Basin (UMRB; ∼440,000 km 2 ). Our results suggest that static DEM-based parameterization as well as model calibration based solely on discharge do not adequately capture spatial and temporal SW storage dynamics in the UMRB. Mean SW storage as estimated by SWAT was 74% ± 122% (mean ± standard deviation) higher than RS SW storage , where SW storage in SWAT was underestimated in wetland-rich subbasins and overestimated in agricultural, tile-drained subbasins. Time series of SWAT SW storage and RS SW storage were positively correlated in only 38.8% of subbasins. As RS SW storage is also vulnerable to error, storage estimates were compared to bathymetric data in select small wetlands. While uncertainty remains in the conversion from extent to storage for RS SW storage , the method and data set presented here are a promising option for improved parameterization and calibration of SW storage processes in SWAT and other process-based hydrologic models. Further consideration of these storage processes can potentially improve the accuracy of simulated streamflow in wetland-rich model domains.

Illinois, Indiana, Iowa, Minnesota, Missouri, Sout↗

USGS2026

Characterizing the influence of remotely sensed wetland and lake water storage on discharge using LSTM models

Globally, many wetlands and lakes are at risk for further loss, which can amplify downstream consequences of flood and drought events. We derived remotely sensed based time series of surface water storage (SW storage ) to determine when and where accounting for SW storage dynamics improves predictions of river discharge. We trained four long short-term memory (LSTM) models, that differed in their inclusion of storage data and catchment characteristics, to simulate daily river discharge (2016–2023) for select watersheds across the conterminous United States. Adding SW storage to a meteorology-only or meteorology-and-catchment characteristics model improved upon model Nash-Sutcliffe efficiency (NSE) in 80.6% of the watersheds. Residuals during low-flow (Q70) events decreased by 47.6% when adding storage to meteorological data. Improvements were most consistent in ecoregions with a greater abundance of non-floodplain lakes and wetlands. This effort represents the first exploration to train a multi-watershed LSTM on landscape-scale remotely sensed time series of SW storage .

conterminous United States↗

USGS2025

Relating surface water dynamics in wetlands and lakes to spatial variability in hydrologic signatures

The retention of surface water in wetlands and lakes can modify the timing, duration, and magnitude of river discharge. However, efforts to characterize the influence of surface water on discharge regimes have been generally limited to small, wetland-dense watersheds. We developed random forest models to explain spatial variability in six hydrologic signatures, reflecting flashiness, high, and low flow conditions, at 72 gaged watersheds with variable water storage capacity across the conterminous United States. In addition to variables representing meteorology and landscape characteristics, we also tested the inclusion of surface water dynamics, derived from Sentinel-1 and Sentinel-2. Models for all six signatures improved with the addition of catchment characteristics, including surface water dynamics, relative to models with only climate variables. Percent improvement in model adjusted R 2 , mean square error, and Akaike information criterion ranged from 4.00 to 14.33%, 5.00 to 20.30%, and 2.75–8.14, respectively. Automated variable selection can be indicative of the relative importance of certain variables over others. Using a forward selection process, five of the six signature models selected remotely sensed inundation or wetland variables ( p < 0.05). For example, the variable semi-permanent and permanent (SP + P) floodplain inundation (i.e., lakes along rivers) was associated with lower annual flashiness. Further, SP + P non-floodplain waters and geographically isolated wetlands significantly contributed to explaining variability in the low flow signatures. Our findings underscore the capacity of wetlands to stabilize and maintain flows during dry periods. Improved understanding of how surface water dynamics influence hydrologic signatures can inform wetland restoration efforts and facilitate improved resilience to extreme flow conditions.

Wetland Ecology & Management↗