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At least 955 records · Page 53Linked to original sources
USGS2024

Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better

Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub-meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management-relevant aspects of the hydrograph using non-parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer-resolution simulations are useful.

Colorado↗
USGS2024

A larval “recruitment kernel” to predict hatching locations and quantify recruitment patterns

Larval recruitment, a critical component of population connectivity, has been under investigated compared to larval dispersal. We developed a backward-in-time Lagrangian particle tracking model to predict larval hatching locations and proposed a larval recruitment kernel, to quantify recruitment patterns. Combining field data and a hydrodynamic model, our backtracking model predicted Lake Whitefish ( Coregonus clupeaformis ) hatching locations in Lake Erie. We found a strong linear correlation ( r = 0.95–0.98) between travel distance (i.e., distance along a trajectory) and pelagic larval duration (PLD), and a moderate correlation ( r = 0.66–0.68) between linear distance (i.e., displacement) and PLD. This questions the wide use of PLD as a proxy for dispersal distance. We defined the recruitment kernel using the probability density function of the linear recruitment distance. Characteristics of the recruitment kernel, such as theoretical self-recruitment, median-recruitment distance, long-distance recruitment, and openness convey significant information about population connectivity that are distinct from those derived using the well-known dispersal kernel (e.g., theoretical local retention).

Water Resources Research↗
USGS2024

A high-resolution, daily hindcast (1990-2021) of Alaskan river discharge and temperature from coupled and optimized physical models

Water quality and freshwater ecosystems are affected by river discharge and temperature. Models are frequently used to estimate river temperature on large spatial and temporal scales due to limited observations of discharge and temperature. In this study, we use physically based river routing and temperature models to simulate daily discharge and river temperature for rivers in 138 basins in Alaska, including the entire Yukon River basin, from 1990–2021. The river temperature model was optimized for ice free months using a surrogate-based model optimization method, improving model performance at uncalibrated river gages. A common statistical model relating local air and water temperature was used as a benchmark. The physically based river temperature model exhibited superior performance compared to the benchmark statistical model after optimization, suggesting river temperature model optimization could become more routine. The river temperature model demonstrated high sensitivity to air temperature and model parameterization, and lower sensitivity to discharge. Validation of the models showed a Kling-Gupta Efficiency of 0.46 for daily river discharge and a root mean square error of 2.04°C for daily river temperature, improving on the non-optimized physical model and the benchmark statistical model, which had root mean square errors of 3.24 and 2.97°C, respectively. The simulation shows that rivers in northern Alaska have higher maximum summer temperatures and more variability than rivers in the Central and Southern regions. Furthermore, this framework can be readily adapted for use across models and regions.

Alaska↗
USGS2024

A conceptual framework to assess post-wildfire water quality: State of the science and knowledge gaps

Wildfire substantially alters aquatic ecosystems by inducing moderate to catastrophic physical and chemical changes. However, the relations of environmental and watershed variables that drive those effects are complex. We present a Driver-Factor-Stressor-Effect (DFSE) conceptual framework to assess the current state of the science related to post-wildfire water-quality. We reviewed 64 peer-reviewed papers using the DFSE framework to identify drivers, factors, stressors, and effects associated with each study. A total of five drivers were identified and ranked according to their frequency of occurrence in the literature: atmospheric processes > fire characteristics > ecologic processes and characteristics > land surface characteristics > soil characteristics. Commonly reported stressors include increased nutrients, runoff, and sediment transport. Furthermore, although several different factors have been used at least once to explain water-quality effects, relatively few factors outside of precipitation and fire characteristics are frequently studied. We identified several gaps indicating the need for long-term monitoring, multi-factor studies, consideration of organic contaminants, consideration of groundwater, and inclusion of soil characteristics. This assessment expands on other reviews and meta-analyses by exploring causal linkages between influential variables and overall effects in post-wildfire watersheds. Information gathered from our assessment and the framework itself can be used to inform future monitoring plans and as a guide for modeling efforts focused on better understanding specific processes or to mitigate potential risks of post-wildfire water quality.

Water Resources Research↗
USGS2024

Social vulnerability and water insecurity in the western US: A systematic review of framings, indicators, and uncertainty

Water insecurity poses a complex challenge for the western United States. Large populations are exposed and susceptible to physical and social factors that can leave them with precarious access to sufficient water supplies. Consideration of social issues by water managers can help ensure equitable supply. However, how social factors affect water insecurity conditions remains unclear. This paper reviews literature on how social vulnerability influences water insecurity in the western United States. Through a meta-analysis, indicators measuring how dimensions of social vulnerability influence water insecurity were classified and hierarchical clustering was used to characterize the relationships among these vulnerability dimensions for the largest water-users—the agricultural and municipal sectors. The study then assessed uncertainty associated with social vulnerability dimensions and their indicators. There is greatest evidence for the influence of demographic characteristics, socioeconomic status, and exposure. Indicators of these determinants were mainly significant and exacerbated conditions of water insecurity. Evidence for indicators of social dependence and special needs populations was limited, although studies assessing these factors showed significant agreement on their influence on water insecurity. Conceptual framings of social vulnerability and water security determined which indicators were measured, whereas studies of the water-use sectors focused on differing associations of social vulnerability. These findings indicate the importance of recognizing the different contexts posed by water-use sectors and diverse conceptual framings. Further, some determinants such as living conditions remain important but underexplored drivers of a community's experience of water insecurity. Understanding the uncertainty associated with these measures has implications to equitable decision making.

Water Resources Research↗
USGS2024

Quantifying compound and nonlinear effects of hurricane-induced flooding using a dynamically coupled hydrological-ocean model

We recently developed a dynamically coupled hydrological-ocean modeling system that provides seamless coverage across the land-ocean continuum during hurricane-induced compound flooding. This study introduced a local inertial equation and a diagonal flow algorithm to the overland routing of the coupled system’s hydrology model (WRF-Hydro). Using Hurricane Florence (2018) as a test case, the performance of the coupled model was significantly improved, evidenced by its enhanced capability of capturing backwater and increased water level simulation accuracy and stability. With four model experiments, we present a framework to detangle, define, and quantify compound and nonlinear effects. The results revealed that the flood peaks in the lower Cape Fear River Basin and the coastal waters were contributed by inland flooding and storm surge, respectively. These two processes had comparable contributions to the flooding in the Cape Fear River Estuary. The compound effect was identified when the flood levels resulting from the combination of land and ocean processes surpassed those caused by an individual process alone. The compound effect during Hurricane Florence exhibited limited impact on flood peaks, primarily due to the time lag between the peaks of the storm surge and the inland flooding. In the period between the two peaks, the compound effect was salient and significantly impacted the magnitude and variation of the flood level. The nonlinear effect, defined as the difference between the compound flood level and the superposition of storm surge and inland flooding water levels, reduced flood levels in the river channels while increasing flood levels on the floodplain.

Water Resources Research↗
USGS2024

Stream nitrate dynamics driven primarily by discharge and watershed physical and soil characteristics at intensively monitored sites: Insights from deep learning

We developed a suite of models using deep learning to make hindcast predictions of the 7‐day average backward‐looking nitrate concentration at 46 predominantly agricultural sites across the midwestern and eastern United States. The models used daily observations of discharge and meteorological variables and watershed attributes describing anthropogenic modification to hydrology, nitrogen application, climate, groundwater, land use, watershed physiographic attributes, and soils. Across all sites, discharge and watershed soil and physiographic attributes showed a strong influence on model performance. Analysis of drivers across sites revealed considerable regional differences related to controlling processes such as groundwater contributions. We tested several ways to pool data across sites to develop accurate models and make the most effective use of available data. Single‐site models, in which models are trained and tested at a single location, showed generally strong predictive performance (median Kling‐Gupta Efficiency = 0.66), and accuracy at poorly performing sites could be improved by grouping sites with similar characteristics. Developing a single model for all sites reduced performance at several locations with distinct characteristics, suggesting that there is a threshold of dissimilarity beyond which more data does not improve the model. While many deep learning studies have shown that national or even global models can outperform local models, it is not clear that this is true for water quality constituents. This study demonstrates how data can be combined effectively, using deep learning to develop accurate and interpretable models of instream nitrate at sites where varying processes are responsible for changes in nitrate concentration.

Water Resources Research↗
USGS2024

Next generation public supply water withdrawal estimation for the conterminous United States using machine learning and operational frameworks

Estimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation, and thermoelectric water withdrawal. Stewards of water resources in the USA require estimates of water withdrawals to manage and plan for future demands and sustainable water supplies. This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. The modeling approach provides service area resolution estimates to allow for annual and monthly water withdrawal estimation while incorporating a complex array of driving factors that include hydroclimatic, demographic, socioeconomic, geographic, and land use factors. Model results reveal highly variable and lognormally distributed per-capita water withdrawal, spanning from 30 to 650 gallons per capita per day (GPCD), across community, regional, and national scales, with pronounced seasonal variations. Analysis of estimated withdrawal trends indicates that the national annual average withdrawal experienced a decline at a rate of 0.58 GPCD/year during the period from 2000 to 2020. Model interpretation reveals a complex interplay between public supply withdrawal and key predictors, including population size, warm-season precipitation, counts of large buildings and houses, and areas of urban and commercial land use. The developed models can forecast future public supply driven by various climate, demographic, and socioeconomic scenarios.

conterminous United States↗
USGS2024

Self-potential tomography preconditioned by particle swarm optimization— Application to monitoring hyporheic exchange in a bedrock river

A self-potential (SP) data-inversion algorithm was developed and tested on an analytical model of electrical-potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP-monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock-lined riverbed. The algorithm combined stochastic source-localization by particle-swarm-optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least-squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical-model data to evaluate the algorithm performance. Results indicated that true parameters of single-source models were inverted and approximated with small residual error, whereas inversion of analytical-model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non-uniqueness of the inverse-model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point-scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP-monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.

Tennessee↗
USGS2025

Stream discharge determinations using slug additions and specific conductance

Stream discharge is often determined by wading the stream and measuring the velocity at fixed widths and depths. However, there are conditions when wading measurements are not safe or the measurements are poor because of high turbulence, rocky streambeds, shallow or sheet flow, aquatic plants, or inaccessibility due to ice. Under these conditions, it is often preferable to determine discharge using salt slug addition and downstream measurement of salt concentration with time. A new method for determining stream discharge using specific conductance as a surrogate for salt concentrations is presented. The method adapts an approach that accurately calculates the specific conductance by utilizing ionic molal conductivities to determine the concentration of salt. The method was applied at four mountainous stream sites where a total of twenty-nine slug-additions were performed. The discharge determined from the new method was compared to four alternative methods including discharge from continuous injection, slug addition with discrete sample calibration, wading measurements with velocity measurement, and a stream gage. The discharge ranged from 21.5 to 778 L/s and the median difference between the new method and the traditional methods was -0.01%. Additionally, the p-value (0.75) determined from a paired t-test indicates that there is no significant difference between the discharge determined from the new and alternative discharge methods. The primary advantage of the new method is that it obviates the need to collect and analyze discrete samples to accurately quantify the specific conductance-salt surrogate relationship allowing for rapid, low-cost determination of discharge.

California↗
USGS2025

Streamflow response to glacier mass loss varies with basin precipitation across Alaska

Diminishing glaciers affect streamflow, and given the extent of glaciers in Alaska and adjacent Canada, continued glacier mass loss is likely to have profound effects on ecosystems sensitive to runoff. The effects of glacier mass loss on streamflow are likely to vary across the wide ranges of basin size, glacier cover, and precipitation in this region. In this study, we use U.S. Geological Survey (USGS) streamflow data with satellite-based glacier volume change estimates to quantify how glacier mass loss subsidized streamflow over the 2000–2019 period for 116 glacierized basins. We examine interannual variability in that subsidy at three USGS-monitored glaciers to explore the ability of the subsidy to buffer streamflow derived solely from precipitation. We found the relative importance of percent glacier cover on streamflow magnitude increases in drier basins. In the driest basins, glaciers produced 40 times greater percent glacier mass loss subsidies to streamflow for the percent glacier cover compared to the wettest basins. While the subsidy from glacier mass loss buffers interannual variability in streamflow to varying degrees, it can also increase streamflow variability. Smaller amounts of percent glacier cover are needed to produce summer-melt-dominated seasonal flow regimes in drier basins than in wetter basins. Decreasing glacier cover will eventually decrease summer streamflow, increasing spring streamflow in drier basins, and attenuating seasonality with increasing spring and autumnal streamflow in wetter basins. Quantifying the downstream effects of continued glacier mass loss without the computational expense of a hydrological model is broadly applicable in this changing climate.

Alaska↗
USGS2025

Evaluating the potential to quantify salmon habitat via UAS-based particle image velocimetry

Continuous, high-resolution data for characterizing freshwater habitat conditions can support successful management of endangered salmonids. Uncrewed aircraft systems (UAS) make acquiring such fine-scale data along river channels more feasible, but workflows for quantifying reach-scale salmon habitats are lacking. We evaluated the potential for UAS-based mapping of hydraulic habitats using spectrally based depth retrieval and particle image velocimetry (PIV) by comparing these methods to a more well-established flow modeling approach. Our results indicated that estimates of water depth, depth-averaged velocity, and flow direction derived via remote sensing and modeling techniques were comparable and in good agreement with field measurements. Predictions of spring-run Chinook salmon ( Oncorhynchus tshawytscha ) juvenile rearing habitat produced from PIV and model output were similar, with small errors relative to direct field observations. Estimates of hydraulic heterogeneity based on kinetic energy gradients in the flow field were generally consistent between PIV and flow modeling, but errors relative to field measurements were larger. PIV results were sensitive to the velocity index ( α ) used to convert surface velocities to depth-averaged velocities. Sun glint precluded PIV analysis along the margins of some images and a large degree of overlap between frames was thus required to obtain continuous coverage of the reach. Similarly, shadows cast by riparian vegetation caused gaps in spectrally based bathymetric maps. Despite these limitations, our results suggest that for sites with sufficient water surface texture, UAS-based PIV can provide detailed hydraulic habitat information at the reach scale, with accuracies comparable to traditional field methods and multidimensional flow modeling.

Oregon↗
USGS2024

Ensemble methods for parameter estimation of WRF-Hydro

The WRF-Hydro hydrological model has been used in many applications in the past with some level of history matching in the majority of these studies. In this study, we use the iterative Ensemble Smoother (iES), a powerful parameter estimation methodology implemented in the open-source PEST++ software. The iES provides an ensemble solution with an uncertainty bound instead of a single best estimate which has been the common approach in the previous WRF-Hydro studies. We discuss the importance of accounting for observation noise which results in a wider spread in the model solution. We investigate the impact of constructing objective functions by differentially weighting the observations to tune the model response toward model outputs appropriate for a specific application. Results confirm the necessity of differentially weighting the observations before calculation of the objective function as the optimization algorithm struggles with calculating parameter updates with uniform weighting. We also show that we achieve better model performance in terms of verification metrics with higher emphasis on the high flow events, when the objective function is tuned toward an application where the extreme events are of importance. We then investigate the impact of estimating more parameters, in particular we estimate a larger number of snow parameters. Results show a large improvement in the model performance. In summary, our study demonstrates the efficacy of employing iES alongside differential weighting of observations, highlighting its potential to enhance hydrological model parameter estimation.

Water Resources Research↗
USGS2025

Reservoir operational strategies for sustainable sand management in the Colorado River

Climate change and increasing societal demands for water pose challenges for the management of dam-regulated rivers. Management decisions impact the environment of these rivers, creating the need to balance societal needs with environmental conservation. Here we present a modeling framework that optimizes resource benefits within imposed water use goals for the Colorado River in Grand Canyon, where sandbars are a valued natural feature. The current sand-management paradigm utilizes controlled dam-release floods to build and maintain sandbars without exhausting the limited sand supplied by tributaries downstream from Glen Canyon Dam, which blocks all sand supplied from upriver. High monthly releases outside of controlled floods erode sandbars and cause net sand export from Grand Canyon, reducing the sand available to build sandbars. Releases are high in some months owing to the need to adjust flows to meet annual delivery targets, which can be updated throughout the year. Here, we present alternative strategies for operations that avoid high releases, while meeting water storage and delivery goals. We test these strategies using a simplified reservoir model which accounts for forecast uncertainty. We show how these strategies affect sand mass balance and sandbar size using previously developed models. Strategies optimal for sustainable sandbar building maintained sufficient reservoir elevations for implementing controlled floods, avoided high monthly releases by relaxing annual release constraints, and implemented controlled floods in fall immediately following tributary sand inputs. Coordinated modeling of reservoir operations and environmental resources is valuable for managers seeking to balance societal and environmental needs in regulated rivers worldwide.

Arizona↗
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

Over, under, and through: Hydrologic connectivity and the future of coastal landscape salinization

Seawater intrusion (SWI) affects coastal landscapes worldwide. Here we describe the hydrologic pathways through which SWI occurs - over land via storm surge or tidal flooding, under land via groundwater transport, and through watersheds via natural and artificial surface water channels—and how human modifications to those pathways alter patterns of SWI. We present an approach to advance understanding of spatiotemporal patterns of salinization that integrates these hydrologic pathways, their interactions, and how humans modify them. We use examples across the East Coast of the United States that exemplify mechanisms of salinization that have been reported around the planet to illustrate how hydrologic connectivity and human modifications alter patterns of SWI. Finally, we suggest a path for advancing SWI science that includes (a) deploying standardized and well-distributed sensor networks at local to global scales that intentionally track SWI fronts, (b) employing remote sensing and geospatial imaging techniques targeted at integrating above and belowground patterns of SWI, and (c) continuing to develop data analysis and model-data fusion techniques to measure the extent, understand the effects, and predict the future of coastal salinization.

Water Resources Research↗
USGS2026

Hydrologic dynamics of ephemerally flooded playas in a dryland environment

Ephemerally flooded playas are common in the southwestern United States and globally in drylands. Often formed in closed basins, playas are depressions which inundate infrequently from local precipitation and streamflow produced near the playa or from upland areas. Few studies have quantified the hydrologic connectivity between upland catchments and playas using observations. Here, we used rain gauge-corrected precipitation from weather radar and water level measurements in 18 playas of the Chihuahuan Desert to identify precipitation thresholds leading to playa inundation over a 6.4-year period. Geospatial data sets on topography, soil properties, and vegetation cover were employed to determine the controls on inundation. Only 9.4% of all precipitation events above 1 mm led to inundation, with 69.8% of all inundations occurring during the North American monsoon (NAM, July-September). Mean and standard deviations (Std) of runoff ratios at all playas were 2.74 ± 4.08% and 3.29 ± 5.19% for annual and NAM periods. At the annual scale, playa inundation occurred when mean precipitation thresholds of 18.3 ± 7.5 mm (event total) and 12.0 ± 4.5 mm/hr (60-min intensity) were exceeded. Across all playas, inundation occurrence and volume were related most strongly to precipitation metrics and catchment area, with secondary controls of soil and terrain properties. The explanatory power of the derived regressions describing the inundation response across the playas were significantly improved when considering their geological origin. As a result, the inundation response classification system could be applied to ephemeral playas in other arid and semiarid landscapes.

New Mexico↗
USGS2025

Performance mapping and weighting for the evapotranspiration models of the OpenET ensemble

Evapotranspiration (ET) accounts for the majority of water available from precipitation in the terrestrial water cycle, and improvements to the accuracy, resolution, and coverage of ET data can enhance hydrologic models and assessments. The OpenET collaboration of six remotely sensed ET modeling teams has demonstrated that an ensemble approach to ET estimation generally provides improved accuracy relative to individual ensemble members. The performance of individual models has been shown to vary by land cover type and climate zone, but a thorough study of the variables that influence model performance differences has not yet been conducted. In this paper, we model the performance of OpenET models relative to flux tower data as a function of variables such as land cover type and precipitation. These performance models are used to map estimated OpenET model performance across the conterminous United States. We develop relative weights based on these modeled performance metrics and show that a performance-weighted ensemble improves accuracy relative to the current OpenET ensemble method to varying degrees. The monthly mean absolute error of the weighted ensemble is reduced relative to the current method by 8% in agricultural settings, by 23% in shrublands and mixed forests, and by 5% in grasslands and evergreen forests. We produce weight maps that can be used to generate performance-weighted ensemble values for OpenET data. The results can be used to inform model selection and provide insight about the controls on model performance that could lead to model refinement.

Water Resources Research↗