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Results for “Journal of American Water Resources Association”

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At least 109 records · Page 6Linked to original sources
USGS2017

Decreased runoff response to precipitation, Little Missouri River Basin, northern Great Plains, USA

High variability in precipitation and streamflow in the semiarid northern Great Plains causes large uncertainty in water availability. This uncertainty is compounded by potential effects of future climate change. We examined historical variability in annual and growing season precipitation, temperature, and streamflow within the Little Missouri River Basin and identified differences in the runoff response to precipitation for the period 1976-2012 compared to 1939-1975 ( n = 37 years in both cases). Computed mean values for the second half of the record showed little change (<5%) in annual or growing season precipitation, but average annual runoff at the basin outlet decreased by 22%, with 66% of the reduction in flow occurring during the growing season. Our results show a statistically significant ( p < 0.10) 27% decrease in the annual runoff response to precipitation (runoff ratio). Surface-water withdrawals for various uses appear to account for <12% of the reduction in average annual flow volume, and we found no published or reported evidence of substantial flow reduction caused by groundwater pumping in this basin. Results of our analysis suggest that increases in monthly average maximum and minimum temperatures, including >1°C increases in January through March, are the dominant driver of the observed decrease in runoff response to precipitation in the Little Missouri River Basin.

Montana, North Dakota, South Dakota, Wyoming↗
USGS2017

Spatiotemporal variability of snow depletion curves derived from SNODAS for the conterminous United States, 2004-2013

Assessment of water resources at a national scale is critical for understanding their vulnerability to future change in policy and climate. Representation of the spatiotemporal variability in snowmelt processes in continental-scale hydrologic models is critical for assessment of water resource response to continued climate change. Continental-extent hydrologic models such as the U.S. Geological Survey National Hydrologic Model (NHM) represent snowmelt processes through the application of snow depletion curves (SDCs). SDCs relate normalized snow water equivalent (SWE) to normalized snow covered area (SCA) over a snowmelt season for a given modeling unit. SDCs were derived using output from the operational Snow Data Assimilation System (SNODAS) snow model as daily 1-km gridded SWE over the conterminous United States. Daily SNODAS output were aggregated to a predefined watershed-scale geospatial fabric and used to also calculate SCA from October 1, 2004 to September 30, 2013. The spatiotemporal variability in SNODAS output at the watershed scale was evaluated through the spatial distribution of the median and standard deviation for the time period. Representative SDCs for each watershed-scale modeling unit over the conterminous United States ( n = 54,104) were selected using a consistent methodology and used to create categories of snowmelt based on SDC shape. The relation of SDC categories to the topographic and climatic variables allow for national-scale categorization of snowmelt processes.

Journal of the American Water Resources Associatio↗
USGS2017

Annual estimates of recharge, quick-flow runoff, and ET for the contiguous U.S. using empirical regression equations

This study presents new data-driven, annual estimates of the division of precipitation into the recharge, quick-flow runoff, and evapotranspiration (ET) water budget components for 2000-2013 for the contiguous United States (CONUS). The algorithms used to produce these maps ensure water budget consistency over this broad spatial scale, with contributions from precipitation influx attributed to each component at 800 m resolution. The quick-flow runoff estimates for the contribution to the rapidly varying portion of the hydrograph are produced using data from 1,434 gaged watersheds, and depend on precipitation, soil saturated hydraulic conductivity, and surficial geology type. Evapotranspiration estimates are produced from a regression using water balance data from 679 gaged watersheds and depend on land cover, temperature, and precipitation. The quick-flow and ET estimates are combined to calculate recharge as the remainder of precipitation. The ET and recharge estimates are checked against independent field data, and the results show good agreement. Comparisons of recharge estimates with groundwater extraction data show that in 15% of the country, groundwater is being extracted at rates higher than the local recharge. These maps of the internally consistent water budget components of recharge, quick-flow runoff, and ET, being derived from and tested against data, are expected to provide reliable first-order estimates of these quantities across the CONUS, even where field measurements are sparse.

Journal of the American Water Resources Associatio↗
USGS2017

A comparison of three federal datasets for thermoelectric water withdrawals in the United States for 2010

Historically, thermoelectric water withdrawal has been estimated by the Energy Information Administration (EIA) and the U.S. Geological Survey's (USGS) water-use compilations. Recently, the USGS developed models for estimating withdrawal at thermoelectric plants to provide estimates independent from plant operator-reported withdrawal data. This article compares three federal datasets of thermoelectric withdrawals for the United States in 2010: one based on the USGS water-use compilation, another based on EIA data, and the third based on USGS model-estimated data. The withdrawal data varied widely. Many plants had three different withdrawal values, and for approximately 54% of the plants the largest withdrawal value was twice the smallest, or larger. The causes of discrepancies among withdrawal estimates included definitional differences, definitional noise, and various nondefinitional causes. The uncertainty in national totals can be characterized by the range among the three datasets, from 5,640 m 3 /s (129 billion gallons per day [bgd]) to 6,954 m 3 /s (158 bgd), or by the aggregate difference between the smallest and largest values at each plant, from 4,014 m 3 /s (92 bgd) to 8,590 m 3 /s (196 bgd). When used to assess the accuracy of reported values, the USGS model estimates identify plants that need to be reviewed.

Journal of the American Water Resources Associatio↗
USGS2017

Modeling summer month hydrological drought probabilities in the United States using antecedent flow conditions

Climate change raises concern that risks of hydrological drought may be increasing. We estimate hydrological drought probabilities for rivers and streams in the United States (U.S.) using maximum likelihood logistic regression (MLLR). Streamflow data from winter months are used to estimate the chance of hydrological drought during summer months. Daily streamflow data collected from 9,144 stream gages from January 1, 1884 through January 9, 2014 provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February, estimating outcomes 5-11 months ahead of their occurrence. Few drought prediction methods exploit temporal links among streamflows. We find MLLR modeling of drought streamflow probabilities exploits the explanatory power of temporally linked water flows. MLLR models with strong correct classification rates were produced for streams throughout the U.S. One ad hoc test of correct prediction rates of September 2013 hydrological droughts exceeded 90% correct classification. Some of the best-performing models coincide with areas of high concern including the West, the Midwest, Texas, the Southeast, and the Mid-Atlantic. Using hydrological drought MLLR probability estimates in a water management context can inform understanding of drought streamflow conditions, provide warning of future drought conditions, and aid water management decision making.

Journal of the American Water Resources Associatio↗
USGS2017

Forecasting the probability of future groundwater levels declining below specified low thresholds in the conterminous U.S.

We present a logistic regression approach for forecasting the probability of future groundwater levels declining or maintaining below specific groundwater-level thresholds. We tested our approach on 102 groundwater wells in different climatic regions and aquifers of the United States that are part of the U.S. Geological Survey Groundwater Climate Response Network. We evaluated the importance of current groundwater levels, precipitation, streamflow, seasonal variability, Palmer Drought Severity Index, and atmosphere/ocean indices for developing the logistic regression equations. Several diagnostics of model fit were used to evaluate the regression equations, including testing of autocorrelation of residuals, goodness-of-fit metrics, and bootstrap validation testing. The probabilistic predictions were most successful at wells with high persistence (low month-to-month variability) in their groundwater records and at wells where the groundwater level remained below the defined low threshold for sustained periods (generally three months or longer). The model fit was weakest at wells with strong seasonal variability in levels and with shorter duration low-threshold events. We identified challenges in deriving probabilistic-forecasting models and possible approaches for addressing those challenges.

Journal of the American Water Resources Associatio↗
USGS2017

Characterizing uncertainty in daily streamflow estimates at ungauged locations for the Massachusetts sustainable yield estimator

Hydrologic characterization at ungauged locations is one of the quintessential challenges of hydrology. Beyond simulation of historical streamflows, it is similarly important to characterize the level of uncertainty in hydrologic estimates. In tandem with updates to Massachusetts Sustainable Yield Estimator, this work explores the application of global uncertainty estimates to daily streamflow simulations. Expanding on a method developed for deterministic modeling, this approach produces confidence intervals on daily streamflow developed through nonlinear spatial interpolation of daily streamflow using flow duration curves; the 95% confidence is examined. Archived cross‐validations of daily streamflows from 66 watersheds in and around Massachusetts are used to evaluate an approach to uncertainty characterization. Neighboring sites are treated as ungauged, producing relative errors that can be resampled and applied to target sites. The method, with some modification, is found to provide appropriately narrow confidence intervals that contain 95% of the observed streamflows in cross‐validation. Further characterizing uncertainty, multiday means of daily streamflow are evaluated. Working through cross‐validation in Massachusetts, two‐ to three‐month averages of daily streamflow show the best performance. These two approaches to uncertainty characterization inform how streamflow simulation produced for prediction in ungauged basins can be used for water resources management.

Massachusetts↗
USGS2017

Estimating floodwater depths from flood inundation maps and topography

Information on flood inundation extent is important for understanding societal exposure, water storage volumes, flood wave attenuation, future flood hazard, and other variables. A number of organizations now provide flood inundation maps based on satellite remote sensing. These data products can efficiently and accurately provide the areal extent of a flood event, but do not provide floodwater depth, an important attribute for first responders and damage assessment. Here we present a new methodology and a GIS-based tool, the Floodwater Depth Estimation Tool (FwDET), for estimating floodwater depth based solely on an inundation map and a digital elevation model (DEM). We compare the FwDET results against water depth maps derived from hydraulic simulation of two flood events, a large-scale event for which we use medium resolution input layer (10 m) and a small-scale event for which we use a high-resolution (LiDAR; 1 m) input. Further testing is performed for two inundation maps with a number of challenging features that include a narrow valley, a large reservoir, and an urban setting. The results show FwDET can accurately calculate floodwater depth for diverse flooding scenarios but also leads to considerable bias in locations where the inundation extent does not align well with the DEM. In these locations, manual adjustment or higher spatial resolution input is required.

Journal of the American Water Resources Associatio↗
USGS2018

Floodplain trapping and cycling compared to streambank erosion of sediment and nutrients in an agricultural watershed

Floodplains and streambanks can positively and negatively influence downstream water quality through interacting geomorphic and biogeochemical processes. Few studies have measured those processes in agricultural watersheds. We measured inputs (floodplain sedimentation and dissolved inorganic loading), cycling (floodplain soil nitrogen [N] and phosphorus [P] mineralization), and losses (bank erosion) of sediment, N, and P longitudinally in stream reaches of Smith Creek, an agricultural watershed in the Valley and Ridge physiographic province. All study reaches were net depositional (floodplain deposition > bank erosion), had high N and P sedimentation and loading rates to the floodplain, high soil concentrations of N and P, and high rates of floodplain soil N and P mineralization. High sediment, N, and P inputs to floodplains are attributed to agricultural activity in the region. Rates of P mineralization were much greater than those measured in other studies of nontidal floodplains that used the same method. Floodplain connectivity and sediment deposition decreased longitudinally, contrary to patterns in most watersheds. The net trapping function of Smith Creek floodplains indicates a benefit to water quality. Further research is needed to determine if future decreases in floodplain deposition, continued bank erosion, and the potential for nitrate leaching from nutrient-enriched floodplain soils could pose a long-term source of sediment and nutrients to downstream rivers.

Virginia↗
USGS2018

Featured collection introduction: Connectivity of streams and wetlands to downstream waters

Connectivity is a fundamental but highly dynamic property of watersheds. Variability in the types and degrees of aquatic ecosystem connectivity presents challenges for researchers and managers seeking to accurately quantify its effects on critical hydrologic, biogeochemical, and biological processes. However, protecting natural gradients of connectivity is key to protecting the range of ecosystem services that aquatic ecosystems provide. In this featured collection, we review the available evidence on connections and functions by which streams and wetlands affect the integrity of downstream waters such as large rivers, lakes, reservoirs, and estuaries. The reviews in this collection focus on the types of waters whose protections under the U.S. Clean Water Act have been called into question by U.S. Supreme Court cases. We synthesize 40+ years of research on longitudinal, lateral, and vertical fluxes of energy, material, and biota between aquatic ecosystems included within the Act's frame of reference. Many questions about the roles of streams and wetlands in sustaining downstream water integrity can be answered from currently available literature, and emerging research is rapidly closing data gaps with exciting new insights into aquatic connectivity and function at local, watershed, and regional scales. Synthesis of foundational and emerging research is needed to support science‐based efforts to provide safe, reliable sources of fresh water for present and future generations.

Journal of the American Water Resources Associatio↗
USGS2018

Connectivity of streams and wetlands to downstream waters: An integrated systems framework

Interest in connectivity has increased in the aquatic sciences, partly because of its relevance to the Clean Water Act. This paper has two objectives: (1) provide a framework to understand hydrological, chemical, and biological connectivity, focusing on how headwater streams and wetlands connect to and contribute to rivers; and (2) briefly review methods to quantify hydrological and chemical connectivity. Streams and wetlands affect river structure and function by altering material and biological fluxes to the river; this depends on two factors: (1) functions within streams and wetlands that affect material fluxes; and (2) connectivity (or isolation) from streams and wetlands to rivers that allows (or prevents) material transport between systems. Connectivity can be described in terms of frequency, magnitude, duration, timing, and rate of change. It results from physical characteristics of a system, e.g., climate, soils, geology, topography, and the spatial distribution of aquatic components. Biological connectivity is also affected by traits and behavior of the biota. Connectivity can be altered by human impacts, often in complex ways. Because of variability in these factors, connectivity is not constant but varies over time and space. Connectivity can be quantified with field‐based methods, modeling, and remote sensing. Further studies using these methods are needed to classify and quantify connectivity of aquatic ecosystems and to understand how impacts affect connectivity.

Journal of the American Water Resources Associatio↗
USGS2018

Biota connect aquatic habitats throughout freshwater ecosystem mosaics

Freshwater ecosystems are linked at various spatial and temporal scales by movements of biota adapted to life in water. We review the literature on movements of aquatic organisms that connect different types of freshwater habitats, focusing on linkages from streams and wetlands to downstream waters. Here, streams, wetlands, rivers, lakes, ponds, and other freshwater habitats are viewed as dynamic freshwater ecosystem mosaics (FEMs) that collectively provide the resources needed to sustain aquatic life. Based on existing evidence, it is clear that biotic linkages throughout FEMs have important consequences for biological integrity and biodiversity. All aquatic organisms move within and among FEM components, but differ in the mode, frequency, distance, and timing of their movements. These movements allow biota to recolonize habitats, avoid inbreeding, escape stressors, locate mates, and acquire resources. Cumulatively, these individual movements connect populations within and among FEMs and contribute to local and regional diversity, resilience to disturbance, and persistence of aquatic species in the face of environmental change. Thus, the biological connections established by movement of biota among streams, wetlands, and downstream waters are critical to the ecological integrity of these systems. Future research will help advance our understanding of the movements that link FEMs and their cumulative effects on downstream waters.

Journal of the American Water Resources Associatio↗
USGS2018

Effects of turbidity, sediment, and polyacrylamide on native freshwater mussels

Turbidity is a ubiquitous pollutant adversely affecting water quality and aquatic life in waterways globally. Anionic polyacrylamide (PAM) is widely used as an effective chemical flocculent to reduce suspended sediment (SS) and turbidity. However, no information exists on the toxicity of PAM‐flocculated sediments to imperiled, but ecologically important, freshwater mussels (Unionidae). Thus, we conducted acute (96 h) and chronic (24 day) laboratory tests with juvenile fatmucket ( Lampsilis siliquoidea ) and three exposure conditions (nonflocculated settled sediment, SS, and PAM‐flocculated settled sediment) over a range of turbidity levels (50, 250, 1,250, and 3,500 nephelometric turbidity units). Survival and sublethal endpoints of protein oxidation, adenosine triphosphate (ATP) production, and protein concentration were used as measures of toxicity. We found no effect of turbidity levels or exposure condition on mussel survival in acute or chronic tests. However, we found significant reductions in protein concentration, ATP production, and oxidized proteins in mussels acutely exposed to the SS condition, which required water movement to maintain sediment in suspension, indicating responses that are symptoms of physiological stress. Our results suggest anionic PAM applied to reduce SS may minimize adverse effects of short‐term turbidity exposure on juvenile freshwater mussels without eliciting additional lethal or sublethal toxicity.

Journal of the American Water Resources Associatio↗
USGS2018

Relative importance of water-quality stressors in predicting fish community responses in midwestern streams

Fish, habitat, and water chemistry data were collected from 98 streams in the midwestern United States, an area dominated by intense cultivation of row crops, in order to identify important water‐quality stressors to fish communities. We focused on 10 stressors including riparian disturbance, riparian vegetative cover, instream fish cover, streambed sedimentation, streamflow variability, total nitrogen, total phosphorus, minimum dissolved oxygen, pesticides, and bed sediment contaminants. Fish community response variables included a measure of observed/expected taxonomic completeness; species‐specific tolerances to nitrogen, phosphorus, dissolved oxygen, and water temperature; the percent of species classified as macrohabitat generalists; and an index of pesticide toxicity to fish. Multivariate analysis indicated that total nitrogen was the most important stressor, signifying that fish communities were responding to total nitrogen despite relatively high levels common to an agricultural setting. Individually, fish taxonomic completeness decreased with increasing streambed sedimentation, whereas fish community tolerance to total phosphorus increased with increasing streambed sedimentation, riparian disturbance, and total nitrogen. These findings underscore the importance of multiple biological response metrics to better understand the effects of water‐quality stressors on fish communities and highlight the complex relations between total phosphorus and fish communities.

Midwest↗
USGS2018

Spatial relationships of levees and wetland systems within floodplains of the Wabash Basin, USA

Given the unique biogeochemical, physical, and hydrologic services provided by floodplain wetlands, proper management of river systems should include an understanding of how floodplain modifications influence wetland ecosystems. The construction of levees can reduce river–floodplain connectivity, yet it is unclear how levees affect wetlands within floodplains, let alone the cumulative impacts within an entire watershed. This paper explores spatial relationships between levee and floodplain wetland systems in the Wabash Basin, United States. We used a hydrogeomorphic floodplain delineation technique to map floodplain extents and identify wetlands that may be hydrologically connected to river networks. We then spatially examined the relationship between levee presence, wetland area, and other river network attributes within discrete subbasins. Our results show that cumulative wetland area is relatively constant in subbasins that contain levees, regardless of maximum stream order within the subbasin. In subbasins that do not contain levees, cumulative wetland area increases with maximum stream order. However, we found that wetland distributions around levees can be complex, and further studies on the influence of levees on wetland habitat may need to consider finer resolution spatial scales.

Wabash Basin↗
USGS2018

Estimating lag to peak between rainfall and peak streamflow with a mixed-effects model

We test the use of a mixed-effects model for estimating lag to peak for small basins in Maine (drainage areas from 0.8 to 78 km2). Lag to peak is defined as the time between the center of volume of the excess rainfall during a storm event and the resulting peak streamflow. A mixed-effects model allows for multiple observations at sites without violating model assumptions inherent in traditional ordinary least squares models, which assume each observation is independent. The mixed model includes basin drainage area and maximum 15-min rainfall depth for individual storms as explanatory features. Based on a remove-one-site cross-validation analysis, the prediction errors of this model ranged from 42% to +73%. The mixed model substantially outperformed three published models for lag to peak and one published model for centroid lag for estimating lag to peak for small basins in Maine. Lag to peak estimates are a key input to rainfallrunoff models used to design hydraulic infrastructure. The improved accuracy and consistency with model assumptions indicates that mixed models may provide increased data utilization that could enhance models and estimates of lag to peak in other regions.

Maine↗
USGS2018

Sediment fingerprinting to delineate sources of sediment in the agricultural and forested Smith Creek Watershed, Virginia, USA

The sediment fingerprinting approach was used to apportion fine‐grained sediment to cropland, pasture, forests, and streambanks in the agricultural and forested Smith Creek, watershed, Virginia. Smith Creek is a showcase study area in the Chesapeake Bay watershed, where management actions to reduce nutrients and sediment are being monitored. Analyses of suspended sediment at the downstream and upstream sampling sites indicated streambanks were the major source of sediment (76% downstream and 70% upstream). Current management strategies proposed to reduce sediment loadings for Smith Creek do not target streambanks as a source of sediment, whereas the results of this study indicate that management strategies to reduce sediment loads in Smith Creek may be effective if directed toward managing streambank erosion. The results of this study also highlight the utility of sediment fingerprinting as a management tool to identify sediment sources.

Virginia↗
USGS2018

Biological connectivity of seasonally ponded wetlands across spatial and temporal scales

Many species that inhabit seasonally ponded wetlands also rely on surrounding upland habitats and nearby aquatic ecosystems for resources to support life stages and to maintain viable populations. Understanding biological connectivity among these habitats is critical to ensure that landscapes are protected at appropriate scales to conserve species and ecosystem function. Biological connectivity occurs across a range of spatial and temporal scales. For example, at annual time scales many organisms move between seasonal wetlands and adjacent terrestrial habitats as they undergo life‐stage transitions; at generational time scales, individuals may disperse among nearby wetlands; and at multigenerational scales, there can be gene flow across large portions of a species’ range. The scale of biological connectivity may also vary among species. Larger bodied or more vagile species can connect a matrix of seasonally ponded wetlands, streams, lakes, and surrounding terrestrial habitats on a seasonal or annual basis. Measuring biological connectivity at different spatial and temporal scales remains a challenge. Here we review environmental and biological factors that drive biological connectivity, discuss implications of biological connectivity for animal populations and ecosystem processes, and provide examples illustrating the range of spatial and temporal scales across which biological connectivity occurs in seasonal wetlands.

Journal of the American Water Resources Associatio↗