Geological Survey Streamgages Research Articles

HyG: A hydraulic geometry dataset derived from historical stream gage measurements across the conterminous US

Regional- and continental-scale hydrologic models are increasingly important forecasting tools, yet they rely on highly variable channel parameters (e.g. width, depth, hydraulic resistance) that remain unquantified for millions of stream reaches across the country. Existing hydrologic models utilize over-simplified channel geometries that directly impact the accuracy of streamflow estimates, with cascading effects for water resource and hazard prediction. Here, we present a hydraulic geometry dataset, termed ‘HyG’, derived from discharge field measurements at U.S. Geological Survey (USGS) stream gages across the conterminous United States (CONUS). The HyG dataset includes (1) at-a-station hydraulic geometry parameters, (2) at-a-station hydraulic resistance (Manning’s n) calculated from the Manning equation, (3) daily discharge percentiles, and (4) regionalized downstream hydraulic geometry parameters based on HUC4 (Hydrologic Unit Code 4), derived from a total of 4,051,682 individual measurements from 66,841 total gages. The regionalized HyG dataset can be used directly to improve channel representations in models over CONUS. The original HyG relationships can also be regionalized for finer scales if required.

How has the latest IMERG V07 improved the precipitation estimates and hydrologic utility over CONUS against IMERG V06?

Precipitation, a crucial element of the water cycle, significantly impacts surface streamflow and flooding dynamics. The latest version of Integrated Multi-satellitE Retrievals for GPM (IMERG V07) has garnered global attention for its advancements over its predecessor, IMERG V06. However, the improvement in precipitation rates has not yet been fully quantified, especially when translated into improvements in hydrologic predictions. In this study, we aim to quantify the improvements of IMERG V07 over V06 in the contiguous United States (CONUS) in the aspects of (1) Evaluating the accuracy of precipitation data against Multi-Radar Multi-Sensor (MRMS); and (2) Comparing their hydrologic performance using a hydrologic model, the Coupled Routing and Excess Storage (CREST), against United States Geological Survey (USGS) streamgages. This study mainly finds that: (1) Metrics for both precipitation and streamflow from CREST show that IMERG V07 significantly outperforms IMERG V06. Specifically, the CC improved from 0.391 to 0.443 for precipitation and from 0.487 to 0.515 for streamflow; (2) The improvements in IMERG V07 are region-dependent. Significant improvements are found in basins with small areas (< 1000 km2), in mid-latitudes (41° N to 43° N), at low average elevations (< 800 m), and those located in the northeastern CONUS; (3) In certain cases, IMERG V07 demonstrates a better capability in estimating extreme precipitation, whereas IMERG V06 tends to underestimate it. This is also reflected in the streamflow data, where IMERG V07 better captures flood peaks compared to IMERG V06. This research enhances our understanding of flood dynamics by analyzing IMERG V07′s advancements and their effects on hydrologic predictions. It offers valuable insights into improved precipitation data’s role in hydrological modeling, giving potential benefits for simulating better flood prediction and helping in water management.

Advancing freshwater science with sensor data collected by community scientists

Autonomous sensor networks providing real‐time data are growing in popularity with community scientists due to instant availability of high‐frequency data. What role does this monitoring play in watershed assessment alongside agency‐run monitoring programs? How accessible, interoperable, and reusable are the data for other researchers? We compared a community science‐led stream monitoring network—EnviroDIY—in the Delaware River Basin, in which more than 50 watershed organizations have deployed more than 100 stations monitoring temperature, electric conductivity, depth, and sometimes turbidity, with the Basin's US Geological Survey (USGS) stream gauge network. The EnviroDIY network (n = 124) complemented the USGS network (n = 102) by monitoring sites with different watershed sizes and land‐use distributions. Although data were accessible and interoperable using a web data portal, community scientists had difficulty sharing metadata that would enable data reuse outside this project and they required support analyzing these large datasets to understand threats to watershed conditions. We address those needs here with a conceptual framework for interpreting data and communicating results.

Impacts of land use changes on discharge and water quality in rivers and streams: Case study of the continental United States

AbstractWater quality trends in streams and rivers are impacted by several factors including land use of the watershed; however, it is unclear what influence changes in the land use of a watershed subsequently have on changes in discharge and water quality in streams and rivers. This study seeks to fill this gap by evaluating the relationship between changes in land use and changes in discharge and water quality at United States Geological Survey (USGS) stream gages over the period of 2008–2016. Using land cover data and discharge and water quality data from 60 USGS gages, regression methods were applied to determine the strength of relationship between land use changes and changes in water quality and quantity. Trends in discharge and water quality were mixed, with a majority of watersheds demonstrating a decrease in dissolved oxygen and turbidity, no overall trend for discharge, and increases in specific conductance. A regression analysis revealed that discharge, turbidity, and specific conductance were correlated with changes in individual land use types with an R2 between 0.12 and 0.25. Combining the influences of multiple land uses in multivariate regression improved the predictions for discharge (R2 0.58) and specific conductance (R2 0.47), highlighting the magnitude for which land cover changes influence trends in water quality. Overall, this study demonstrates the impact that large‐scale land use changes have on surface water quality.

Case study of continental‐scale hydrologic modeling's ability to predict daily streamflow percentiles for regulatory application

AbstractRegulatory practitioners use hydroclimatic data to provide context to observations typically collected through field site visits and aerial imagery analysis. In the absence of site‐specific data, regulatory practitioners must use proxy hydroclimatic data and models to assess a stream's hydroclimatology. One intent of current‐generation continental‐scale hydrologic models is to provide such hydrologic context to ungaged watersheds. In this study, the ability of two state‐of‐the‐art, operational, continental‐scale hydrologic modeling frameworks, the National Water Model and the Group on Earth Observation Global Water Sustainability (GEOGloWS) European Centre for Medium‐Range Weather Forecasts (ECMWF) Streamflow Model, to produce daily streamflow percentiles and categorical estimates of the streamflow normalcy was examined. The modeled streamflow percentiles were compared to observed daily streamflow percentiles at four United States Geological Survey stream gages. The model's performance was then compared to a baseline assessment methodology, the Antecedent Precipitation Tool. Results indicated that, when compared to baseline assessment techniques, the accuracy of the National Water Model (NWM) or GEOGloWS ECMWF Streamflow Model was greater than the accuracy of the baseline assessment methodology at four stream gage locations. The NWM performed best at three of the four gages. This work highlighted a novel application of current‐generation continental‐scale hydrologic models.

Toward Improved Regional Hydrological Model Performance Using State‐Of‐The‐Science Data‐Informed Soil Parameters

AbstractAccurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF‐Hydro) over a central California domain. Our results show WRF‐Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling‐Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well‐established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS‐adjusted WRF‐Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF‐Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO‐informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre‐calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.

Projecting Flood Frequency Curves Under Near‐Term Climate Change

AbstractFlood‐frequency curves, critical for water infrastructure design, are typically developed based on a stationary climate assumption. However, climate changes are expected to violate this assumption. Here, we propose a new, climate‐informed methodology for estimating flood‐frequency curves under non‐stationary future climate conditions. The methodology develops an asynchronous, semiparametric local‐likelihood regression (ASLLR) model that relates moments of annual maximum flood to climate variables using the generalized linear model. We estimate the first two marginal moments (MM) – the mean and variance – of the underlying log‐Pearson Type‐3 distribution from the ASLLR with the monthly rainfall and temperature as predictors. The proposed methodology, ASLLR‐MM, is applied to 40 U.S. Geological Survey streamgages covering 18 water resources regions across the conterminous United States. A correction based on the aridity index was applied on the estimated variance, after which the ASLLR‐MM approach was evaluated with both historical (1951–2005) and projected (2006–2035, under RCP4.5 and RCP8.5) monthly precipitation and temperature from eight Global Circulation Models (GCMs) consisting of 39 ensemble members. The estimated flood‐frequency quantiles resulting from the ASLLR‐MM and GCM members compare well with the flood‐frequency quantiles estimated using the historical period of observed climate and flood information for humid basins, whereas the uncertainty in model estimates is higher in arid basins. Considering additional atmospheric and land‐surface conditions and a multi‐level model structure that includes other basins in a region could further improve the model performance in arid basins.

Multiscale and multi event evaluation of short-range real-time flood forecasting in large metropolitan areas

Many large metropolitan areas are especially susceptible to floods generated by heavy, short-duration rainfall. The high density of people, buildings and infrastructure in these areas underline the importance of developing flood resilient cities and communities. An accurate real-time flood forecasting system can support decision making for launching preparedness and response actions in short-range (hours to days), and assist mitigating the disturbances caused by floods. This study explores the short-range predictive capability of a real-time flood forecast system by coupling the High-Resolution Rapid Refresh (HRRR) meteorological forecasted variables with a fully distributed hydrological model (WRF-Hydro). We provide a comprehensive analysis of short-range (36 h) forecasts for 19flood events generated by heavy rainfall in small urban and suburban watersheds (<200 km2) in the National Capital Region of the United States. Flood forecast performance are then assessed using different metrics based on observed data from U.S. Geological Survey stream gauges and reanalysis simulations using the StageIV quantitative precipitation estimates. Results show that despite the high variability of the HRRR cycle-to-cycle precipitation forecasts, the real-time flood forecast system can produce skillful forecasts with similar overall performance as the reanalysis simulations, achieving 65 % of flood detection rate. This variability had more impact on forecast skill in smaller subbasins, and flood forecasts were more consistent for longer duration and larger spatial extent rainfall. Hourly flood forecasts performed well even in smaller watersheds, correctly detecting flood occurrence with up to 34 h lead time and resulting in low peak flow magnitude and timing errors.

Forest cover lessens the impact of drought on streamflow in Puerto Rico

AbstractTropical regions are experiencing high rates of forest cover loss coupled with changes in the volume and timing of rainfall. These shifts can compromise streamflow and water provision, highlighting the need to identify how forest cover influences streamflow generation under variable rainfall conditions. Although rainfall is the key driver of streamflow regimes, the role of forests is less clear, particularly in tropical regions where forest loss is an ongoing risk. Forest cover loss alters evapotranspiration, rainfall infiltration and storage, and may increase stream ecosystem vulnerability to rainfall extremes. Puerto Rico, an island with spatially heterogenous forest cover and a marked geographic rainfall gradient, is projected to experience more frequent droughts and flash flooding. Using 15‐min streamflow data collected between 2005 and 2016 from 20 US Geological Survey stream gages and 3‐hourly Multi‐Source Weighted‐Ensemble Precipitation rainfall estimates, we utilized flow‐duration curves and linear mixed regression models to examine the role of forest cover in regulating the timing and volume of streamflow. The mixed model approach helps to account for differences in watershed characteristics. We determined the effects of rainfall and forest cover on low and peak flows in Puerto Rican streams, then evaluated changes in these relationships under dry and wet antecedent rainfall conditions. Watersheds with high forest cover had consistently greater low and peak streamflow than deforested ones under all rainfall conditions, although the effect was more marked during wet antecedent conditions, suggesting that peak flow is largely the result of saturation excess overland flow. During dry antecedent rainfall conditions, highly forested watersheds had higher streamflow than deforested ones, suggesting greater hillslope storage and release may also be at play. Our results demonstrate that forest cover generated a net increase in hillslope infiltration and storage and may lessen drought impacts on streamflow in Puerto Rico. Resilience to prolonged drought may be limited by finite water storage potential in this steep, mountainous setting, highlighting maintenance of forest cover as an important water management strategy to increase infiltration.

Tropical Cyclone Flooding in the Carolinas

Abstract The climatology of tropical cyclone flooding in the Carolinas is analyzed through annual flood peak observations from 411 U.S. Geological Survey (USGS) stream gauging stations. Tropical cyclones (TCs) account for 28% of the top 10 annual flood peaks, 55% of record floods, and 91% of floods with peak magnitudes at least 5 times greater than the 10-yr floods, highlighting the prominent role of TCs for flood extremes in the Carolinas. Of all TC-related flood events, the top 10 storms account for nearly 1/3 of annual flood peaks and more than 2/3 of record floods, reflecting the dominant role of a small number of storms in determining the upper tail of flood peak distributions. Analyses of the 10 storms highlight both common elements and diversity in storm properties that are responsible for flood peaks. Extratropical transition and orographic enhancement are important elements of extreme TC flooding in the Carolinas. Analyses of the Great Flood of 1916 highlight the flood peak of 3115 m3 s−1 in French Broad River at Asheville, 2.6 times greater than the second-largest peak from a record of 124 years. We also examine the hydroclimatology, hydrometeorology, and hydrology of flooding from Hurricanes Matthew (2016) and Florence (2018). Results point to contrasting storm properties for the two events, including tracks as well as rainfall distribution and associated physical mechanisms. Climatological analyses of vertically integrated water vapor transport (IVT) highlight the critical role of anomalous moisture transport from the Atlantic Ocean in producing extreme rainfall and flooding over the Carolinas.

The effect of flow and mussel species traits on the occurrence of rare mussels: A case study within selected rivers of the West Gulf Coastal Plain

Abstract Mussel–flow relationships are not well defined and studies linking behavioural, physiological and life‐history traits to hydrological conditions remain scant. This lack of information hinders not only understanding of how flow shapes population and community processes but the development of evidence‐based flow recommendations to mitigate mussel declines. Mussel–flow relationships were evaluated for Potamilus amphichaenus (Texas heelsplitter), Pleurobema riddellii (Louisiana pigtoe) and Truncilla macrodon (Texas fawnsfoot) from the Sabine, Trinity, Neches and Cypress River basins of East Texas using Indicators of Hydraulic Alteration and random forest models. These species were chosen because all three are currently being evaluated for listing under the US Endangered Species Act. Analyses were performed on US Geological Survey stream gauge stations and mussel records within 20 km of each other. Only gauges with at least 15 years of continuous data and ≥70% overlap of flow records from 1968 to 2018 were included. For selected 28 gauges, Indicators of Hydraulic Alteration were used to calculate 47 hydrological parameters that describe the magnitude, frequency and duration of high and low flow events. Random forest classification models across species were significant and had low error rates (3.57–21.43%), moderate to high sensitivity (0.81–0.93) and moderate to high specificity (0.71–1.00). Life‐history theory was useful for explaining how mussel species cope with hydrological variation. Potamilus amphichaenus and T. macrodon/Truncilla donaciformis, considered r‐selected or opportunistic strategists, were more likely to occur in basins characterized by significant changes in flow and land use compared with P. riddellii, considered a K‐selected or equilibrium strategist. The analytical framework presented in this study should be helpful to managers and conservationists interested in determining how the flow regime is shaping aquatic communities in their region and developing reference points that could serve for ecological research, conservation and management.

Multi‐Scale Hydrologic Evaluation of the National Water Model Streamflow Data Assimilation

AbstractStreamflow predictions derived from a hydrologic model are subjected to many sources of errors, including uncertainties in meteorological inputs, representation of physical processes, and model parameters. To reduce the effects of these uncertainties and thus improve the accuracy of model prediction, the United States (U.S.) National Water Model (NWM) incorporates streamflow observations in the modeling framework and updates model‐simulated values using the observed ones. This updating procedure is called streamflow data assimilation (DA). This study evaluates the prediction performance of streamflow DA realized in the NWM. We implemented the model using WRF‐Hydro® with the NWM modeling elements and assimilated 15‐min streamflow data into the model, observed during 2016–2018 at 140 U.S. Geological Survey stream gauge stations in Iowa. In its current DA scheme, known as “nudging,” the assimilation effect is propagated downstream only, which allows us to assess the performance of streamflow predictions generated at 70 downstream stations in the study domain. These 70 locations cover basins of a range of scales, thus enabling a multi‐scale hydrologic evaluation by inspecting annual total volume, peak discharge magnitude and timing, and an overall performance indicator represented by the Kling–Gupta efficiency. The evaluation results show that DA improves the prediction skill significantly, compared to open‐loop simulation, and the improvements increase with areal coverage of upstream assimilation points.

Impacts of a regional multiyear insect defoliation event on growing‐season runoff ratios and instantaneous streamflow characteristics

AbstractRepeated moderate severity forest disturbances can cause short‐ and long‐term shifts in ecosystem processes. Prior work has found that stand‐replacing disturbances (e.g., clear‐cutting) increase streamflow in temperate forests, but streamflow responses to repeated moderate severity disturbances are more equivocal. This study examined a moderate disturbance caused by an unexpected population irruption of the invasive insect Lymantria dispar (common name: gypsy moth) in 2015–2017. This irruption resulted in defoliation that was locally severe in some areas but spatially heterogeneous at a regional scale. L. dispar larvae consume leaves during the summer growing season, which effectively reduces tree leaf area and associated evapotranspiration. Our regional approach in Southern New England, USA, used data from 83 US Geological Survey (USGS) stream gages to assess whether changes in growing‐season watershed runoff ratios and instantaneous streamflow characteristics during the 2015–2017 L. dispar irruption were associated with satellite‐derived metrics of changes in forest condition (i.e., defoliation), compared to a 20‐year baseline streamflow period. We found a small, linear increase in growing‐season runoff ratio anomalies that was associated with defoliation intensity. The association between defoliation intensity and runoff ratio anomalies was magnified in less anthropogenically impacted reference watersheds. We also found that defoliation intensity was associated with larger volumes of high and medium instantaneous streamflows compared to baseline mean flow conditions. This study provided important insights into the impacts of moderate disturbance on ecohydrology in mesic temperate forests with a unique methodological approach that assessed the impacts of spatially heterogeneous and repeated moderate disturbance at a regional scale.

Integrating Environmental DNA Results With Diverse Data Sets to Improve Biosurveillance of River Health

Autonomous, robotic environmental (e)DNA samplers now make it possible for biological observations to match the scale and quality of abiotic measurements collected by automated sensor networks. Merging these automated data streams may allow for improved insight into biotic responses to environmental change and stressors. Here, we merged eDNA data collected by robotic samplers installed at three U.S. Geological Survey (USGS) streamgages with gridded daily weather data, and daily water quality and quantity data into a cloud-hosted database. The eDNA targets were a rare fish parasite and a more common salmonid fish. We then used computationally expedient Bayesian hierarchical occupancy models to evaluate associations between abiotic conditions and eDNA detections and to simulate how uncertainty in result interpretation changes with the frequency of autonomous robotic eDNA sample collection. We developed scripts to automate data merging, cleaning and analysis steps into a chained-step, workflow. We found that inclusion of abiotic covariates only provided improved insight for the more common salmonid fish since its DNA was more frequently detected. Rare fish parasite DNA was infrequently detected, which caused occupancy parameter estimates and covariate associations to have high uncertainty. Our simulations found that collecting samples at least once per day resulted in more detections and less parameter uncertainty than less frequent sampling. Our occupancy and simulation results together demonstrate the advantages of robotic eDNA samplers and how these samples can be combined with easy to acquire, publicly available data to foster real-time biosurveillance and forecasting.

Robotic environmental DNA bio-surveillance of freshwater health

Autonomous water sampling technologies may help to overcome the human resource challenges of monitoring biological threats to rivers over long time periods and across large geographic areas. The Monterey Bay Aquarium Research Institute has pioneered a robotic Environmental Sample Processor (ESP) that overcomes some of the constraints associated with traditional sampling since it can automate water sample filtration and preservation of the captured material. The ESP was originally developed for marine environment applications. Here we evaluated whether the ESP can provide reliable, timely information on environmental (e)DNA detections of human and fish pathogens and introduced fishes at U.S. Geological Survey streamgage sites in freshwater rivers. We compared eDNA collected via ESP at high frequency (e.g., every 3 h) with manual eDNA collections collected at lower frequency (e.g., weekly). We found that water samples filtered and preserved by ESPs successfully detected the DNA of human pathogens, fish pathogens and introduced fishes. Both ESP and manually collected samples provided similar information about target DNA presence. We suggest that the greatest current benefit of the ESP is the cost savings of high frequency, bio-surveillance at remote or hard to access sites. The full potential of robotic technologies like the ESP will be realized when they can more easily execute in situ analyses of water samples and rapidly transmit results to decision-makers.

Hydrologic Signals and Surprises in U.S. Streamflow Records During Urbanization

AbstractUrban development has been observed to lead to variable magnitudes of change for stormflow volume and directions of baseflow change across cities. This work examines temporal streamflow trends across the flow duration curve in 53 watersheds during periods of peak urban development, which ranged from 1939 to 2016. We used U.S. Geological Survey streamgage records combined with pre‐development and urbanization characteristics to identify 20 years for analysis in each urbanizing watershed. Each urbanizing gage was paired with a nearby reference gage representing climatic trends over the same time period. Results indicated that urbanization, as measured by housing density, did not homogeneously alter the flow duration curve. Urbanization led to widely variable trends in low flow, where half of the urbanizing gages had increasing flow at the 10th non‐exceedance percentile, and the other half had declining low flow. High flows generally increased in streams as the area urbanized. The largest increases in high flows were in streams in semi‐arid and arid areas. The largest urban flow changes had transformations in wastewater infrastructure, water supply infrastructure, and flood control facilities. Isolating flow changes due to urbanization from those of reference sites will serve to better identify and manage synergistic effects of urban development and climate change on flooding and water availability.

Multivariate remotely sensed and in-situ data assimilation for enhancing community WRF-Hydro model forecasting

Flood is one of the most catastrophic natural disasters in the United States, particularly in the Southeast states where hurricanes and tropical storms are most prevalent, causing billions of dollars in damage annually and significant losses of life and property. The Weather Research and Forecasting Hydrological model (WRF-Hydro) is a community-based hydrologic model designed to improve the skill of hydrometeorological forecasts, such as river discharge, through simulating hydrologic prognostic (e.g., soil moisture) and diagnostic (e.g., energy fluxes) variables. These quantities are potentially biased or erroneous due to the uncertainties involved in all layers of hydrologic predictions. In this study, we use an ensemble based Data Assimilation (DA) approach to explore the benefit of independently and jointly assimilating remotely sensed SMAP (Soil Moisture Active Passive) soil moisture (at different spatial resolutions) and USGS streamflow observations to improve the accuracy and reliability of WRF-Hydro model predictions while accounting for uncertainties. This study is conducted over a large region near to Houston, Texas where heavy rainfall from hurricane Harvey caused flooding in 2017. Before implementing DA, we first calibrated the WRF-Hydro model parameters using four United States Geological Survey (USGS) stream gauges installed within the watershed. In this step, we identified the most dominant model parameters, which were used later in the development of joint state-parameter DA. The findings of this study showed that the multivariate assimilation of soil moisture and streamflow observations results in improved prediction of streamflow as compared to univariate assimilation configurations and regardless of the watershed's streamflow regime. The results also revealed that, during the normal streamflow condition, assimilation of downscaled SMAP soil moisture at 1 km spatial resolution, would improve the accuracy of streamflow simulation more than the assimilation of coarse resolution products (i.e., the native SMAP at 36 km spatial resolution and its interpolated version at 9 km spatial resolution). However, during the period of hurricane Harvey, the soil moisture observations at different resolutions showed a similar impact on improving the streamflow prediction.

Near-Field Remote Sensing of Surface Velocity and River Discharge Using Radars and the Probability Concept at 10 U.S. Geological Survey Streamgages

Near-field remote sensing of surface velocity and river discharge (discharge) were measured using coherent, continuous wave Doppler and pulsed radars. Traditional streamgaging requires sensors be deployed in the water column; however, near-field remote sensing has the potential to transform streamgaging operations through non-contact methods in the U.S. Geological Survey (USGS) and other agencies around the world. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from fixed platforms such as bridges and cable stays. Radar gages were collocated with 10 USGS streamgages in river reaches of varying hydrologic and hydraulic characteristics, where basin size ranged from 381 to 66,200 square kilometers. Radar-derived mean-channel (mean) velocity and discharge were computed using the probability concept and were compared to conventional instantaneous measurements and time series. To test the efficacy of near-field methods, radars were deployed for extended periods of time to capture a range of hydraulic conditions and environmental factors. During the operational phase, continuous time series of surface velocity, radar-derived discharge, and stage-discharge were recorded, computed, and transmitted contemporaneously and continuously in real time every 5 to 15 min. Minimum and maximum surface velocities ranged from 0.30 to 3.84 m per second (m/s); minimum and maximum radar-derived discharges ranged from 0.17 to 4890 cubic meters per second (m3/s); and minimum and maximum stage-discharge ranged from 0.12 to 4950 m3/s. Comparisons between radar and stage-discharge time series were evaluated using goodness-of-fit statistics, which provided a measure of the utility of the probability concept to compute discharge from a singular surface velocity and cross-sectional area relative to conventional methods. Mean velocity and discharge data indicate that velocity radars are highly correlated with conventional methods and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and discharge.

Metastatistical Extreme Value Distribution applied to floods across the continental United States

This study analyzes daily mean streamflow records from 5,311 U.S. Geological Survey stream gages in the continental United States and develops a Metastatistical Extreme Value Distribution (MEVD) tailored for flood frequency analysis. We compare the new tool with the Generalized Extreme Value (GEV) and Log-Pearson Type III (LP3) distributions and investigate the role of El Niño Southern Oscillation (ENSO) in the generation of floods. Hence, we formulate the MEVD in terms of mixture of distributions to describe the occurrence of flood peaks generated under different ENSO phases. We find that the MEVD outperforms GEV and LP3 distributions respectively in about 76% and 86% of the stations, with a significant improvement in the accuracy of quantiles corresponding to return periods much larger than the calibration sample size. The ENSO signature detected in the distributions of the daily peak flows does not necessarily improve the estimation of high return period flow values.

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.