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Robert W. Dudley

Publications and source records attributed to Robert W. Dudley.

At least 19 recordsLinked to original sources
USGS2026

Estimating baseline flow resistance parameters for remote sensing river discharge (RSQ) from river morphology characteristics

This study demonstrates that flow resistance in rivers, quantified via calibration of a modified Manning equation, can be estimated from channel hydrometric information available from globally derived data sets for a subset of river reaches in Alaska and with less certainty in a subset of river reaches in the contiguous United States. The channel morphometry data sets tested include the National Aeronautics and Space Administration (NASA) Surface Water Ocean Topography (SWOT) river database (SWORD) and U.S. Geological Survey (USGS) Scale Specific Sinuosity (S3) river channel morphometry metrics. It is found that for the subset of rivers considered, the meander length, number of active channels, maximum width, width variance, and slope of the channel are strongly correlated with the flow resistance term in the Manning equation. Analytical machine learned models were derived, and a statistical multiple regression model developed to map the flow resistance in rivers across Alaska that are contained in the SWORD data base. The study points to the possibility of global a priori estimation of flow resistance in rivers for application in remote sensing of river discharge from river water surface-width, slope and height measurements made by SWOT and other satellites.

Journal of the American Water Resources Associatio↗
USGS2026

Groundwater drought in the United States: Spatial and temporal variability

Many communities and ecosystems in the United States that are dependent on groundwater are potentially adversely affected by groundwater drought. We computed yearly groundwater-drought metrics and mean groundwater levels at well locations across the conterminous United States (CONUS), using data from wells and remotely sensed and modeled Gravity Recovery and Climate Experiment Drought Monitor Data Assimilation (GRACE-DADM). We also modeled the probability of low or high human impact at each well location. The spatial distribution of groundwater-drought duration and severity from 2001 to 2020 for 1,510 wells shows longer maximum duration and higher maximum severity events in drier regions like the Southwest than in wetter regions like the Northeast. Based on 613 wells in CONUS from 1981 to 2020, there are many significant decreases in drought duration and severity in the Northeast and many significant increases in annual-mean groundwater levels. In contrast, there are many significant increases in drought metrics and decreases in mean water levels in parts of the Southeast. There are major differences in trends from 2001 to 2020 between well-based and GRACE-DADM-based groundwater metrics in some CONUS regions and a very low correlation between trends at individual locations across CONUS. A potential reason for this disparity is the low GRACE-DADM resolution (∼12 km) and the potential for a large amount of groundwater variation at the local scale. Also, GRACE-DADM represents shallow, unconfined aquifers which may not match the screened interval of the monitoring wells we evaluated. Large spatial gaps in long-term, high frequency, and quality-assured groundwater-well monitoring data present a challenge for understanding groundwater-drought variability across CONUS. Remote sensing tools such as GRACE can help but cannot fully replace well monitoring, as highlighted by our study results. Substantially more long-term monitoring wells would more accurately represent groundwater-drought trends and spatial variability across CONUS, particularly in western regions.

conterminous United States↗
USGS2025

Launching into societal benefits from the Surface Water and Ocean Topography (SWOT) mission

The 10th Surface Water and Ocean Topography (SWOT) Applications Meeting, held one year after the satellite's launch, highlighted significant milestones in mission progress and showcased the innovative work of SWOT Early Adopters (EA) using mission data products. Over 100 participants from diverse sectors convened to discuss operational applications leveraging SWOT's unprecedented water surface measurements. The meeting emphasized applied science efforts to enhance hydrology and oceanographic models. This summary highlights the breadth of operational and private-sector uses of SWOT data, emphasizing its potential to drive new innovations and deliver societal benefits, such as improved water resource management, flood prediction, and climate resilience.

Water Resources Research↗
USGS2025

Remote sensing of river discharge based on critical flow theory

Critical flow theory provides a physical foundation for inferring discharge from measurements of wavelength and channel width made from images. In rivers with hydraulically steep local slopes greater than ∼0.01, flow velocities are high and the Froude number F r (ratio of inertial to gravitational forces) can approach 1.0 (critical flow) or greater. Under these conditions, undular hydraulic jumps (UHJ's) can form as standing wave trains at slope transitions or constrictions. The presence of UHJ's indicates that mean F r ≈ 1, implying that the velocity and depth of the flow and the spacing of the waves are uniquely related to one another. Discharges estimated from 82 Google Earth images agreed closely with discharges recorded at gaging stations ( R 2 = 0.98), with a mean bias of 1% ± 11%. This approach could provide reliable discharge information in many fluvial environments where critical flow occurs, which tend to be underrepresented in gage networks

Geophysical Research Letters↗
USGS2025

A first look at river discharge estimation from SWOT satellite observations

The Surface Water and Ocean Topography (SWOT) satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Discharge is estimated from SWOT observations of water surface elevation, width, and slope. A first assessment using just the highest quality SWOT measurements, over the first 15 months (March 2023–July 2024) of the mission evaluated at 65 gauged reaches shows results consistent with pre-launch expectations. SWOT estimates track discharge dynamics without relying on any gauge information: median correlation is 0.73, with a correlation interquartile range of 0.51–0.89. SWOT estimates capture discharge magnitude correctly in some cases but are biased (median bias is 50%) in others. There are already a total of 11,274 ungauged global locations with highest quality SWOT measurements where SWOT discharge is expected to accurately track discharge variations: this value will increase as SWOT data record length grows, algorithms are refined and SWOT measurements are reprocessed. This first look indicates that SWOT discharge is performing as expected for SWOT data that achieve performance requirements, providing observed information on discharge variations in ungauged basins globally.

Ohio River basin↗
USGS2024

Siting considerations for satellite observation of river discharge

With growing global capability for satellite measurement of river discharge (flow) comes a need to understand and reduce error in satellite-based discharge measurements. Satellite-based discharge estimates are based on measurements of water surface width, elevation, slope, and potentially velocity. Site selection is important for reducing error and uncertainty in both conventional and satellite-based discharge measurements because geomorphic river characteristics have strong control over the relationships between discharge and width, water surface elevation (or depth), slope, and velocity. A large ground-truth data set of 8,445 conventional hydraulic measurements, collected by acoustic Doppler current profilers at 503 stations in the United States, was developed and quality assured to examine correlation between river discharge and water surface width, depth, velocity, and cross-sectional area. A separate database of river surface slope and discharge time-series was developed from paired continuous monitoring stations to examine slope-discharge correlations. Results show that discharge correlates most strongly with velocity, cross-sectional area, depth, width, and slope, in that order. Uncertainty of satellite discharge estimates is affected by observed hydraulic variable and reach-specific variability in observed variable(s) characteristics including range of variability, georegistration accuracy, and stability over time of relationships between discharge and observed hydraulic variable.

Water Resources Research↗
USGS2023

The consequences of neglecting reservoir storage in national-scale hydrologic models: An appraisal of key streamflow statistics

A better understanding of modeled streamflow errors related to basin reservoir storage is needed for large regions, which normally have many ungaged basins with reservoirs. We quantified the difference between modeled and observed streamflows for one process-based and three statistical-transfer hydrologic models, none of which explicitly accounted for reservoir storage. Streamflow statistics representing low to high flows, seasonality, annual variability, and daily autocorrelation were examined at 1082 study basins across the conterminous USA. All models increasingly overpredict (or decreasingly underpredict) observed annual maximum flows with increasing storage. Correlations between absolute values of errors for low-flow statistics and storage are often larger in magnitude than those for signed errors—additional storage is associated with increases in model errors in both directions even when its overall effect in one direction is weak. The rate of increase in absolute values of model errors was nonlinear for most statistics. For low flows, model errors had a change point to larger errors at 48 days of reservoir storage (relative to long-term mean daily flow); mean and high flows had change points at 147 to 176 days. We present predicted-to-observed errors for nine streamflow statistics over a large range of reservoir storage to help modelers and users of modeled streamflow understand the amount of storage for which explicit reservoir modeling is needed.

Journal of the American Water Resources Associatio↗
USGS2023

A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission

The Surface Water and Ocean Topography (SWOT) mission will vastly expand measurements of global rivers, providing critical new data sets for both gaged and ungaged basins. SWOT discharge products (available approximately 1 year after launch) will provide discharge for all river that reaches wider than 100 m. In this paper, we describe how SWOT discharge produced and archived by the US and French space agencies will be computed from measurements of river water surface elevation, width, and slope and ancillary data, along with expected discharge accuracy. We present for the first time a complete estimate of the SWOT discharge uncertainty budget, with separate terms for random (standard error) and systematic (bias) uncertainty components in river discharge time series. We expect that discharge uncertainty will be less than 30% for two-thirds of global reaches and will be dominated by bias. Separate river discharge estimates will combine both SWOT and in situ data; these “gage-constrained” discharge estimates can be expected to have lower systematic uncertainty. Temporal variations in river discharge time series will be dominated by random error and are expected to be estimated within 15% for nearly all reaches, allowing accurate inference of event flow dynamics globally, including in ungaged basins. We believe this level of accuracy lays the groundwork for SWOT to enable breakthroughs in global hydrologic science.

Water Resources Research↗
USGS2023

Groundwater recharge in northern New England: Meteorological drivers and relations with low streamflow

Meteorological drivers of groundwater recharge for spring (February–June), fall (October–January), and recharge-year (October–June) recharge seasons were evaluated for northern New England and upstate New York from 1989 to 2018. Monthly groundwater recharge was computed at 21 observation wells by subtracting the water levels at the end of each month from the level of the previous month; only positive monthly values were used to compute seasonal recharge. Precipitation, temperature, sea-level pressure, 500-mb geopotential heights, and various teleconnection indices were tested as explanatory variables for the interannual variability of recharge using random forest machine learning models. Precipitation within recharge seasons was positively correlated with groundwater recharge for most wells in all seasons. In general, whilst groundwater recharge in the study area was generally highest during the months of March and April, October precipitation was an important month for explaining the interannual groundwater recharge variability. This is likely because the variability in recharge in October may be high or low for given years. Sea-level pressure and 500-mb heights were typically inversely correlated with groundwater recharge during the recharge-year and fall recharge seasons, as higher sea-level pressure and heights are usually associated with clearer skies and less precipitation. The North Atlantic Oscillation, Pacific-North American pattern, and Pacific Decadal Oscillation teleconnections affected groundwater recharge differently by well and season. The influence of groundwater recharge on minimum daily streamflows during the subsequent summer/fall was also analysed. Summer precipitation was the most important explanatory variable for study streams whilst groundwater recharge and summer air temperature were significant variables for a few streams.

Maine, New Hampshire, Vermont↗
USGS2023

Satellite remote sensing of river discharge: A framework for assessing the accuracy of discharge estimates made from satellite remote sensing observations

This research presents an evaluation of the accuracy and uncertainty of estimates of river discharge made using satellite observed data sources as input to a modified form of Manning’s equation. Conventional U.S. Geological Survey (USGS) streamflow gaging station data and in-situ measurements of width, depth, height, slope, discharge, and velocity from 30 USGS gage sites were used as ground-truth to assess accuracy. This study explores accuracy in relation to the amount of ground truth information available, the number of calibration points available, and the accuracy of the input data. This research indicates that remotely sensed discharge estimates associated with the modified Manning equation may be expected to have an uncertainty in range of 10% overall given a sufficient number of calibration points. The uncertainty associated with the modified Manning algorithm increased markedly for depths <3 meters (m) and for discharges <1000 cubic meters per second (m 3 / s) for many rivers after calibration. Rivers that exhibit (1) a wide range of flow conditions, (2) a significant number of dams in the watershed and along the channel, and (3) a high baseflow index are more likely to have relatively large errors overall and particularly at the low end of the streamflow range. Uncertainty in remotely sensed measurements of water-surface elevation (WSE) and width in the expected range (WSE, + / − 10 cm; Width, + / − 15 m) introduces uncertainty in the discharge estimates on the order of 10% and is greatest at the low end of discharge as rivers get shallower and narrower. As WSE and width measurement uncertainty increases, discharge uncertainty increases accordingly. In general, the observation errors are greater than the errors associated with the algorithm for a well-calibrated model (e.g., 20 calibration points).

Journal of Applied Remote Sensing↗
USGS2022

A hydrologic perspective of major U.S. droughts

Drought is a recurring natural hazard that has substantial human and environmental impacts. Given continued global warming and associated climate change, there is concern that droughts could become more severe and longer lasting. To better monitor and understand drought development and persistence, it is helpful to understand the development and climatic drivers of past droughts. In this study we use monthly runoff percentiles to identify five major drought events in the conterminous United States (CONUS) from 1901 through 2020. For each drought event we examined spatial patterns of departures of mean monthly precipitation, temperature, soil moisture storage, and runoff for 2,107 hydrologic units (HUs) across the CONUS. Results indicated that precipitation deficits have been the primary driver of past major-drought events and temperature a secondary driver, even of the most recent drought event (September 1999 through September 2015) when positive temperature anomalies occurred over most of the CONUS. Additionally, negative soil moisture storage departures were more negative than runoff departures during the five drought events we examined, which emphasizes the importance of measuring both runoff and soil moisture to monitor drought conditions. We also examined the use of statistical persistence to develop short-term (i.e., 1 month) forecasts of runoff drought conditions in the CONUS by developing autoregressive integrated moving average (ARIMA) models for each HU. Results indicated that persistence can be used to predict short-term changes in the spatial pattern of drought and the areal extent of drought, but that predictions of runoff magnitude for any particular site are often poor.

International Journal of Climatology↗
USGS2022

Going beyond low flows: Streamflow drought deficit and duration illuminate distinct spatiotemporal drought patterns and trends in the U.S. during the last century

Streamflow drought is a recurring challenge, and understanding spatiotemporal patterns of past droughts is needed to manage future water resources. We examined regional patterns in streamflow drought metrics and compared these metrics to low flow timing and magnitude using long-term daily records for 555 minimally disturbed watersheds. For each streamgage, we calculated streamflow drought duration (number of days) and deficit (flow volume below a specified threshold) for each climate year (April 1–March 31). We identified drought using five thresholds (2%–30%) and two approaches: variable thresholds with unique values for each day of the year, and a fixed threshold based on all period-of-record flows. We then analyzed drought trends using the Mann-Kendall test with persistence adjustment for 1921–2020, 1951–2020, and 1981–2020, and computed correlations between annual streamflow drought metrics and climate metrics using values from a monthly water balance model. Spatial patterns in drought metrics were consistent between variable and fixed approaches, though fixed threshold durations were typically longer and variable threshold deficits larger. High interannual variability in drought duration emerged in the central, interior west, and southwestern U.S., with high deficit variability in the interior west. Drought metrics were weakly correlated with low flow magnitude and timing, providing unique information. Drought duration and deficit increased in the southern and western U.S. for both 1951–2020 and 1981–2020, particularly using fixed thresholds, and paralleled trends in aridity. Projections of continued aridification for the southern and western U.S. may increase drought durations and deficits and intensify water availability impacts.

Water Resources Research↗
USGS2022

Methods for computing 7Q2 and 7Q20 low-streamflow statistics to account for possible trends

Low-streamflow statistics, such as the annual minimum 7-day streamflow (which is the 7-day streamflow likely to be exceeded in 9 out of 10 years on average [7Q10]), that are computed by using the full historical streamflow record may not accurately represent current conditions at sites with statistically significant trends in low streamflow over time. Recent research suggests that using a contemporary subset of the historical streamflow record (specifically, the most recent 30 years) to compute an estimate of 7Q10 more accurately represents current streamflow conditions when a statistically significant trend in the streamflow record is present. This report presents the results of a Monte Carlo simulation experiment on artificial low-streamflow records, derived from the characteristics of streamflows at 174 U.S. Geological Survey streamgages, to test whether a similar approach is appropriate for the computation of the annual minimum 7-day streamflow exceeded in 1 out of 2 years on average (7Q2) and the annual minimum 7-day streamflow exceeded in 19 out of 20 years on average (7Q20). The results indicate that using the most recent 30-year subset of the low-streamflow record also may be the best approach when computing 7Q2 and 7Q20 at sites where a statistically significant trend in low streamflows is detected.

Open-File Report↗
USGS2021

Model estimated baseflow for streams with endangered Atlantic Salmon in Maine, USA

We present a regression model for estimating mean August baseflow per square kilometer of drainage area to help resource managers assess relative amounts of baseflow in Maine streams with Atlantic Salmon habitat. The model was derived from mean August baseflows computed at 31 USGS streamflow gages in Maine. We use an ordinary least squares regression model to estimate mean August baseflow per unit drainage area from two explanatory variables: percentage of the basin underlain by sand and gravel aquifers and mean July precipitation in the basin. This model provides the ability to estimate mean August baseflow in cubic meters per second per square kilometer of basin area on user-selected, ungaged sites throughout Maine south of 46° 21′55″ N latitude. The model has an adjusted R 2 of 0.78 and a mean 95% prediction interval of plus or minus 0.002 cubic meters per second per square kilometer. A map of the Narraguagus watershed in eastern coastal Maine shows reaches color coded by relative amounts of baseflow predicted by the model as an example of how this method could be applied throughout Maine. The map can be used to identify reaches with relatively higher amounts of baseflow during summer low flows for habitat conservation and restoration work. These areas have the potential to be high-quality habitat for Atlantic salmon and other cold-water fish because baseflows are known to moderate stream temperatures in summer low-flow periods.

Maine↗
USGS2021

Exploring the factors controlling the error characteristics of the Surface Water and Ocean Topography mission discharge estimates

The Surface Water and Ocean Topography (SWOT) satellite mission will measure river width, water surface elevation, and slope for rivers wider than 50-100 m. SWOT observations will enable estimation of river discharge by using simple flow laws such as the Manning-Strickler equation, complementing in-situ streamgages. Several discharge inversion algorithms designed to compute unobserved flow law parameters ( e.g. friction coefficient, bathymetry) have been proposed, but to date, a systematic assessment of factors controlling algorithm performance has not been conducted. Here, we assess the performance of the five algorithms that are expected to be used in the construction of the SWOT product. To perform this assessment, we used synthetic SWOT observations created with hydraulic model output corrupted with SWOT-like error. Prior information provided to the algorithms was purposefully limited to an estimate of mean annual flow (MAF), designed to produce a “worst case” benchmark. Prior MAF error was an important control on algorithm performance, but discharge estimates produced by the algorithms are less biased than the MAF; thus, the discharge algorithms improve on the prior. We show for the first time that accuracy and frequency of remote sensing observations are less important than prior bias, hydraulic variability among reaches, and flow law accuracy in governing discharge algorithm performance. The discharge errors and error sensitivities reported herein are a bounding benchmark, representing worst possible expected errors and error sensitivities. This study lays the groundwork to develop predictive power of algorithm performance, and thus map the global distribution of worst-case SWOT discharge accuracy.

Water Resources Research↗
USGS2020

Comparing trends in modeled and observed streamflows at minimally altered basins in the United States

We compared modeled and observed streamflow trends from 1984–2016 using five statistical transfer models and one deterministic, distributed-parameter, process-based model, for 26 flow metrics at 502 basins in the United States that are minimally influenced by development. We also looked at a measure of overall model fit and average bias. A higher percentage of basins, for all models, had relatively low trend differences between modeled and observed mean/ median flows than for very high or low flows such as the annual 1-day high and 7-day low flows. Mean-flow metrics also had the largest percentage of basins with relatively good overall model fit and low bias. The five statistical transfer models performed better at more basins than the process-based model. The overall model fit for all models, for mean and/or high flows, was correlated with one or more measures of basin precipitation or aridity. Our study and previous studies generally observed good model performance for high flows up to 90th or 95th percentile flows. However, we found model performance was substantially worse for more extreme flows, including 99th percentile and annual 1-day high flows; this shows the importance of including more extreme high flows in analyses of model performance.

Water↗
USGS2020

Low streamflow trends at human-impacted and reference basins in the United States

We present a continent-scale exploration of trends in annual 7-day low streamflows at 2482 U.S. Geological Survey streamgages across the conterminous United States over the past 100, 75, and 50 years (1916–2015, 1941–2015 and 1966–2015). We used basin characteristics to identify subsets of study basins representative of reference basins with streamflow relatively free from human effects (n = 259), and predominantly agricultural basins (n = 78), regulated basins (n = 220), and urban basins (n = 121). Trend significance was computed using the Mann-Kendall test considering short- and long-term persistence. Lag-one autocorrelation tests of detrended 7-day low streamflows for all gage classes show that time-series independence is not an appropriate assumption for annual low streamflow data at many basins. Among all study gages, upward trends (wetter conditions) in 7-day low streamflows outnumbered downward trends (drier conditions) approximately 2–1 for the 75- and 100-year trend periods—50-year trends indicated roughly equal numbers of increases and decreases. Increases in 7-day low streamflow were consistently observed for all time periods throughout much of the northeastern quadrant of the conterminous U.S. including western New England and the Mid-Atlantic, the southeastern Great Lakes basin, northern Ohio River basin, and the Upper Mississippi River and eastern Missouri River basins. Decreases in 7-day low streamflow were consistently observed for all time periods at many gages in the southeastern U.S. and in the northwestern U.S. in much of Idaho and northwestern Washington. Overall, we observed greater percentages of statistically significant trends at gages with human-induced influences than at reference gages. Low-flow trends at agricultural gages were regionally consistent with trends at reference gages. Regulated basins had many statistically significant upward trends for all three time periods tested, which may be attributed in part to substantial increases in dam-related storage prior to 1970. Urban gages had the greatest percentage of significant decreases in 7-day low flows compared to all other gage classes even though most urban gages saw upward trends in mean annual flows. Urban gages also had the greatest percentage of significant increases in low flows second only to regulated gages, highlighting that urban development can increase or decrease low streamflows depending on the basin-specific development.

Conterminous United States↗