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Results for “Journal of American Water Resources Association”

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At least 127 records · Page 7Linked to original sources
USGS2018

Differing modes of biotic connectivity within freshwater ecosystem mosaics

We describe a collection of aquatic and wetland habitats in an inland landscape, and their occurrence within a terrestrial matrix, as a “freshwater ecosystem mosaic” (FEM). Aquatic and wetland habitats in any FEM can vary widely, from permanently ponded lakes, to ephemerally ponded wetlands, to groundwater‐fed springs, to flowing rivers and streams. The terrestrial matrix can also vary, including in its influence on flows of energy, materials, and organisms among ecosystems. Biota occurring in a specific region are adapted to the unique opportunities and challenges presented by spatial and temporal patterns of habitat types inherent to each FEM. To persist in any given landscape, most species move to recolonize habitats and maintain mixtures of genetic materials. Species also connect habitats through time if they possess needed morphological, physiological, or behavioral traits to persist in a habitat through periods of unfavorable environmental conditions. By examining key spatial and temporal patterns underlying FEMs, and species‐specific adaptations to these patterns, a better understanding of the structural and functional connectivity of a landscape can be obtained. Fully including aquatic, wetland, and terrestrial habitats in FEMs facilitates adoption of the next generation of individual‐based models that integrate the principles of population, community, and ecosystem ecology.

Journal of the American Water Resources Associatio↗
USGS2018

Efficient delineation of nested depression hierarchy in digital elevation models for hydrological analysis using level-set method

In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts and thus filled and removed to create a depressionless DEM. Various algorithms have been developed to identify and fill depressions in DEMs during the past decades. However, few studies have attempted to delineate and quantify the nested hierarchy of actual depressions, which can provide crucial information for characterizing surface hydrologic connectivity and simulating the fill‐merge‐spill hydrological process. In this paper, we present an innovative and efficient algorithm for delineating and quantifying nested depressions in DEMs using the level‐set method based on graph theory. The proposed level‐set method emulates water level decreasing from the spill point along the depression boundary to the lowest point at the bottom of a depression. By tracing the dynamic topological changes (i.e., depression splitting/merging) within a compound depression, the level‐set method can construct topological graphs and derive geometric properties of the nested depressions. The experimental results of two fine‐resolution Light Detection and Ranging‐derived DEMs show that the raster‐based level‐set algorithm is much more efficient (~150 times faster) than the vector‐based contour tree method. The proposed level‐set algorithm has great potential for being applied to large‐scale ecohydrological analysis and watershed modeling.

Journal of the American Water Resources Associatio↗
USGS2018

How hydrologic connectivity regulates water quality in river corridors

Downstream flow in rivers is repeatedly delayed by hydrologic exchange with off‐channel storage zones where biogeochemical processing occurs. We present a dimensionless metric that quantifies river connectivity as the balance between downstream flow and the exchange of water with the bed, banks, and floodplains. The degree of connectivity directly influences downstream water quality — too little connectivity limits the amount of river water exchanged and leads to biogeochemically inactive water storage, while too much connectivity limits the contact time with sediments for reactions to proceed. Using a metric of reaction significance based on river connectivity, we provide evidence that intermediate levels of connectivity, rather than the highest or lowest levels, are the most efficient in removing nitrogen from Northeastern United States’ rivers. Intermediate connectivity balances the frequency, residence time, and contact volume with reactive sediments, which can maximize the reactive processing of dissolved contaminants and the protection of downstream water quality. Our simulations suggest denitrification dominantly occurs in riverbed hyporheic zones of streams and small rivers, whereas vertical turbulent mixing in contact with sediments dominates in mid‐size to large rivers. The metrics of connectivity and reaction significance presented here can facilitate scientifically based prioritizations of river management strategies to protect the values and functions of river corridors.

Journal of the American Water Resources Associatio↗
USGS2018

Effects of antecedent streamflow and sample timing on trend assessments of fish, invertebrate, and diatom communities

Detecting trends in biological attributes is central to many stream monitoring programs; however, understanding how natural variability in environmental factors affects trend results is not well understood. We evaluated the influence of antecedent streamflow and sample timing (covariates) on trend estimates for fish, invertebrate, and diatom taxa richness and biological condition from 2002 to 2012 at 51 sites distributed across the conterminous United States. A combination of linear regression and Kendall‐tau test for trends were used to evaluate covariate influence on trend estimates. Adjusting for covariates changed the magnitude of trend estimates in two‐thirds of cases on average by 21%, most often reducing the estimated magnitude of the trend. Additionally, covariates influenced the interpretation of over one‐third of trend estimates by either strengthening or weakening trends after adjustment. Our findings clearly indicate that antecedent streamflow and sample timing influences trend estimates and subsequent interpretation. Accounting for covariates during trend analysis will enhance stream monitoring programs by providing a better understanding and interpretation of estimated changes in biological endpoints at monitored sites. Failure to account for antecedent streamflow and sample timing may lead to mischaracterization of a trend and/or misunderstanding of potential causes.

Journal of the American Water Resources Associatio↗
USGS2019

Modeling connectivity of non‐floodplain wetlands: Insights, approaches, and recommendations

Representing hydrologic connectivity of non‐floodplain wetlands (NFWs) to downstream waters in process‐based models is an emerging challenge relevant to many research, regulatory, and management activities. We review four case studies that utilize process‐based models developed to simulate NFW hydrology. Models range from a simple, lumped parameter model to a highly complex, fully distributed model. Across case studies, we highlight appropriate application of each model, emphasizing spatial scale, computational demands, process representation, and model limitations. We end with a synthesis of recommended “best modeling practices” to guide model application. These recommendations include: (1) clearly articulate modeling objectives, and revisit and adjust those objectives regularly; (2) develop a conceptualization of NFW connectivity using qualitative observations, empirical data, and process‐based modeling; (3) select a model to represent NFW connectivity by balancing both modeling objectives and available resources; (4) use innovative techniques and data sources to validate and calibrate NFW connectivity simulations; and (5) clearly articulate the limits of the resulting NFW connectivity representation. Our review and synthesis of these case studies highlights modeling approaches that incorporate NFW connectivity, demonstrates tradeoffs in model selection, and ultimately provides actionable guidance for future model application and development.

Journal of the American Water Resources Associatio↗
USGS2019

A multi-scale soil moisture monitoring strategy for California: Design and validation

A multi‐scale soil moisture monitoring strategy for California was designed to inform water resource management. The proposed workflow classifies soil moisture response units (SMRUs) using publicly available datasets that represent soil, vegetation, climate, and hydrology variables, which control soil water storage. The SMRUs were classified, using principal component analysis and unsupervised K‐means clustering within a geographic information system, and validated, using summary statistics derived from measured soil moisture time series. Validation stations, located in the Sierra Nevada, include transect of sites that cross the rain‐to‐snow transition and a cluster of sites located at similar elevations in a snow‐dominated watershed. The SMRUs capture unique responses to varying climate conditions characterized by statistical measures of central tendency, dispersion, and extremes. A topographic position index and landform classification is the final step in the workflow to guide the optimal placement of soil moisture sensors at the local‐scale. The proposed workflow is highly flexible and can be implemented over a range of spatial scales and input datasets can be customized. Our approach captures a range of soil moisture responses to climate across California and can be used to design and optimize soil moisture monitoring strategies to support runoff forecasts for water supply management or to assess landscape conditions for forest and rangeland management.

California↗
USGS2019

Hydroclimatology of the Mississippi River Basin

Model estimated monthly water balance (WB) components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff [ R ]) for 848 United States (U.S.) Geological Survey 8-digit hydrologic units located in the Mississippi River Basin (MRB) are used to examine the temporal and spatial variability of the MRB WB for water years 1901 through 2014. Results indicate the MRB can be divided into nine subregions with similar temporal variability in R . The WB analyses indicated ~79% of total water-year MRB runoff is generated by four of the nine subregions and most of the R in the basin is derived from surplus ( S ) water during the months of December through May. Furthermore, the analyses showed temporal variability in S is largely controlled by the occurrence of negative atmospheric pressure anomalies over the western U.S. and positive atmospheric pressure anomalies over the eastern U.S. coast. This combination of atmospheric pressure anomalies results in an anomalous flow of moist air from the Gulf of Mexico into the MRB. In the context of paleo-climate reconstructions of the Palmer Drought Severity Index, since about 1900 the MRB has experienced wetter conditions than were experienced during the previous 500 years.

Mississippi River Basin↗
USGS2019

Toward explaining nitrogen and phosphorus trends in Chesapeake Bay tributaries, 1992-2012

Understanding trends in stream chemistry is critical to watershed management, and often complicated by multiple contaminant sources and landscape conditions changing over varying time scales. We adapted spatially-referenced regression (SPARROW) to infer causes of recent nutrient trends in Chesapeake Bay tributaries by relating observed fluxes during 1992, 2002, and 2012 to contemporary inputs and watershed conditions. The annual flow-normalized nitrogen flux to the bay from its watershed declined by 14 percent to 127,000 Mg (metric tons) between 1992 and 2012, due primarily (more than 80 percent of the decline) to reduced point sources. The remainder of the decline was due to reduced atmospheric deposition (13 percent) and urban non-point sources. Agricultural inputs, which contribute most nitrogen to the bay, changed little, although trends in the average nitrogen yield (flux per unit area) from cropland and pasture to streams in some settings suggest possible effects of evolving nutrient applications or other land management practices. Point sources of phosphorus to local streams declined by half between 1992 and 2012, while non-point inputs were relatively unchanged. Annual phosphorus delivery to the bay increased by 9 percent to 9,570 Mg between 1992 and 2012, however, due mainly to reduced retention in the Susquehanna River at Conowingo Reservoir.

Delaware, Maryland, New Jersey, Pennsylvania, Virg↗
USGS2019

Reduced soil macropores and forest cover reduce warm-season baseflow below ecological thresholds in the upper Delaware River Basin

We examined the impacts of changes in land cover and soil conditions on the flow regime of the upper Delaware River Basin using the Water Availability Tool for Environmental Resources (WATER). We simulated flows for two periods, circa 1600 and 1940, at three sites using the same temperature and precipitation conditions: the East Branch (EB), West Branch (WB), and mainstem Delaware River at Callicoon, NY. The 1600 period represented pristine forest and soils. The 1940 period included reduced forest cover, increased agriculture, and degraded soils with reduced soil macropore fractions. A model-sensitivity test examined the impact of soil macropore and land cover change separately. We assessed changes in flow regimes between the 1600 and 1940 periods using a variety of flow statistics, including established ecological limits of hydrologic alteration (ELOHA) thresholds. Reduced forest soil macropore fraction significantly reduced summer and fall base flows. The 1940 period had significantly lower Q50 flows (50% exceedance) than the 1600 period, as well as summer and fall Q90 and Q75-90 flows below the ELOHA thresholds. The 1- to 7-day minimum flows were also lower for the 1940 period, by 17% on the mainstem. 1940 flows were 6% more likely than the 1600 period to fall below the low-flow threshold for federally endangered dwarf wedgemussel (Alasmidonta heterodon) habitat. In contrast, the 1940 period had higher flows than the 1600 period from late fall to early winter.

New York, Pennsylvania↗
USGS2019

Phosphorus and nitrogen transport in the binational Great Lakes Basin estimated using SPARROW watershed models

Eutrophication problems in the Great Lakes are caused by excessive nutrient inputs (primarily phosphorus, P, and nitrogen, N) from various sources throughout its basin. In developing protection and restoration plans, it is important to know where and from what sources the nutrients originate. As part of a binational effort, Midcontinent SPARROW (SPAtially Referenced Regression On Watershed attributes) models were developed and used to estimate P and N loading from throughout the entire basin based on nutrient inputs similar to 2002; previous SPARROW models only estimated U.S. contributions. The new models have a higher resolution (~2‐km 2 catchments) enabling improved descriptions of where nutrients originate and the sources at various spatial scales. The models were developed using harmonized geospatial datasets describing the stream network, nutrient sources, and environmental characteristics affecting P and N delivery. The models were calibrated using loads from sites estimated with ratio estimator and regression techniques and additional statistical approaches to reduce spatial correlation in the residuals and have all monitoring sites equally influence model development. SPARROW results, along with interlake transfers and direct atmospheric inputs, were used to quantify the entire P and N input to each lake and describe the importance of each nutrient source. Model results can be used to compare loading and yields from various tributaries and jurisdictions.

Great Lakes↗
USGS2019

Hydrologic resilience from summertime fog and recharge: A case study for coho salmon recovery planning

Fog and low cloud cover (FLCC) and late summer recharge increase stream baseflow and decrease stream temperature during arid Mediterranean climate summers, which benefits salmon especially under climate warming conditions. The potential to discharge cool water to streams during the late summer (hydrologic capacity; HC) furnished by FLCC and recharge were mapped for the 299 subwatersheds ranked Core, Phase 1, or Phase 2 under the National Marine Fisheries Service Recovery Plan that prioritized restoration and threat abatement action for endangered Central California Coast Coho Salmon evolutionarily significant unit. Two spatially continuous gridded datasets were merged to compare HC: average hrs/day FLCC, a new dataset derived from a decade of hourly National Weather Satellite data, and annual average mm recharge from the USGS Basin Characterization Model. Two use‐case scenarios provide examples of incorporating FLCC‐driven HC indices into long‐term recovery planning. The first, a thermal analysis under future climate, projected 65% of the watershed area for 8–19 coho population units as thermally inhospitable under two global climate models and identified several units with high resilience (high HC under the range of projected warming conditions). The second use case investigated HC by subwatershed rank and coho population, and identified three population units with high HC in areas ranked Phase 1 and 2 and low HC in Core. Recovery planning for cold‐water fish species would benefit by including FLCC in vulnerability analyses.

California↗
USGS2020

Spatiotemporal variability of modeled watershed scale surface-depression storage and runoff for the conterminous United States

This study uses the explores the viability of a proxy model calibration strategy through assessment of the spatiotemporal variability of surface-depression storage and runoff generated with the U.S. Geological Survey’s National Hydrologic Model (NHM) infrastructure for hydrologic response units (HRUs; n=109,951) across the conterminous United States (CONUS). Simulated values for each HRU of daily surface-depression storage (treated as a decimal fraction of total possible volume) and monthly normalized runoff (0 to 1) values were calculated using Spearman’s rho at monthly and annual aggregations. Locations where values are correlated show where previously-developed proxy calibration strategies are likely to be effective. In addition, differences in the correlation for monthly and annual time scale aggregations show which time scale drives surface-depression storage processes in the NHM. Results show overall long-term (annual) correlation is more common than short-term (monthly) correlation over the CONUS; however, summary statistics for eighty-six ecoregions show five with higher ranges of monthly relative to annual Spearman’s rank coefficient values. This landscape-scale analysis shows simulations aggregated to an annual time scale are generally more dominant for the CONUS; however, simulations aggregated to monthly, short-term time scales are more dominant in focused areas where surface-depression storage processes are investigated.

Journal of the American Water Resources Associatio↗
USGS2020

Salt flushing, salt storage, and controls on selenium: A 31-year mass-balance analysis of an irrigated, semiarid valley

Salinity, selenium, and uranium pose water‐quality challenges for the Arkansas River in southeastern Colorado and other rivers that support irrigation in semiarid regions. This study used 31 years of continuous discharge and specific conductance (SC) monitoring data to assess interannual patterns in water quality using mass balance on a 120‐km reach of river. Discrete sampling data were used to link the SC records to salinity, selenium, and uranium. Several important patterns emerged. Consumptive use reduced discharge by a median value of 33% and drove corresponding increases in salinity and uranium concentrations. Increased water availability for irrigation from rainfall and upstream snowpack in 1995–1999 flushed additional salinity and uranium into the river in 1996–2000; average annual total dissolved solids (salinity) concentrations increased 25%, and loads increased 131%. Smaller flushing events have occurred, sometimes lagging an increase in water availability by about one year. The pattern indicates flushing of salts temporarily stored, evaporatively concentrated, or of geologic origin. Mobilization of selenium from the reach was minor compared to salinity and uranium, and net selenium removal from the river was suggested in some years. Several processes related to irrigation could be removing selenium. The results provide context for efforts to improve water quality in the Arkansas River and rivers in other semiarid regions.

Colorado↗
USGS2020

Comparing environmental flow implementation options with structured decision making: Case study from the Willamette River, Oregon

Many frameworks have been used to identify environmental flows for sustaining river ecosystems or specific taxa in the face of widespread flow alteration for human use. However, these methods mostly focus on identifying suitable flows and largely ignore the important links between management actions, resulting flows, flow variability, and ecosystem or social responses. Structured decision making (SDM) could assist the comparison and implementation of environmental flows by providing a framework to compare effects of flow management actions on objectives via environmental flow science. We describe the SDM process and illustrate its application using a case study focused on comparing environmental flow scenarios for the mainstem Willamette River, Oregon, USA. In a short timeframe, SDM was successfully applied to identify management objectives, develop empirical and expert opinion based models predicting ecological responses, and compare scenarios while accounting for uncertainty and partial controllability. We found that no flow scenario was clearly preferred based on available knowledge, largely because river flows could only be partially controlled through available dam operations. Participants agreed that the SDM process was useful and that an additional iteration focused on refining predictive models and incorporating additional objectives could help better inform dam release decisions for the entire basin. In our view, SDM can provide managers with more realistic comparisons of environmental flows by accounting for partial controllability and uncertainty, which may result in greater implementation of available flow management actions.

Oregon↗
USGS2020

The water-year water balance of the Colorado River Basin

Model‐estimated monthly water balance components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff ( R )) for 146 United States (U.S.) Geological Survey 8‐digit hydrologic units located in the Colorado River Basin (CRB) are used to examine the temporal and spatial variability of the CRB water balance for water years 1901 through 2014 (a water year is the period from October 1 of one year through September 30 of the following year). Results indicate that the CRB can be divided into six subregions with similar temporal variability in monthly R . The water balance analyses indicated that approximately 75% of total water‐year R is generated by just one CRB subregion and that most of the R in the basin is derived from surplus ( S ) water generated during the months of October through April. Furthermore, the analyses show that temporal variability in S is largely controlled by the occurrence of negative atmospheric pressure anomalies over the northwestern conterminous U.S. (CONUS) and positive atmospheric pressure anomalies over the southeastern CONUS. This combination of atmospheric pressure anomalies results in an anomalous flow of moist air from the North Pacific Ocean into the CRB, particularly the Upper CRB. Additionally, the occurrence of extreme dry and wet periods in the CRB appears to be related to variability of the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation.

Arizona, California, Colorado, Nevada, New Mexico,↗
USGS2020

Changes in climate and land cover affect seasonal streamflow forecasts in the Rio Grande headwaters

Seasonal streamflow forecast bias, changes in climate, snowpack, and land cover, and the effects of these changes on relations between basin‐wide snowpack, SNOw TELemetry (SNOTEL) station snowpack, and seasonal streamflow were evaluated in the headwaters of the Rio Grande, Colorado. Results indicate that shifts in the seasonality of precipitation and changing climatology are consistent with periods of overprediction and underprediction in streamflow forecasts. Multiple linear regression of SNOTEL data, postcedent precipitation, and land‐cover changes explained 2%–18% more variability in streamflow prediction than using SNOTEL station data alone. Simulated basin‐wide snowpack from a physically based model had significant negative trends in snow water equivalent (−4.33 mm/yr) and snow‐covered area (−0.05%/yr) during the melt period April–June. Simulated streamflow from a precipitation‐runoff model increased an average 5% when the effects of bark beetle‐induced tree mortality were compared to a baseline simulation with static vegetation. The effects of a 2013 wildfire increased simulated seasonal streamflow an average 35% for 1–4 years postfire. The combined effects of climate and land‐cover changes on snowpack‐streamflow relations highlight the difficulty in seasonal streamflow forecasting, which has important implications for water‐resource management.

Colorado↗
USGS2020

The influence of climate variability on the accuracy of NHD perennial and non-perennial stream classifications

National Hydrography Dataset (NHD) stream permanence classifications (SPC; perennial, intermittent, and ephemeral) are widely used for data visualization and applied science, and have implications for resource policy and management. NHD SPC were assigned using a combination of topographic field surveys and interviews with local residents. However, previous studies indicate that non‐NHD, in situ streamflow observations (NNO) frequently disagree with NHD SPC. We hypothesized that differences in annual climate conditions between map creation years and the years NNO were collected contributed to disagreement between NNO and NHD SPC. We compared NHD SPC to 10,055 NNO (classified as “wet” or “dry”) collected in the Pacific Northwest between 1977 and 2015. Annual climate conditions were described with the Palmer Drought Severity Index (PDSI). Stream order was added as a covariate to account for different effects along the stream network. NHD SPC agreed with 80.5% of NNO. “Dry” NNO were five times more likely to disagree with NHD than “wet” NNO ( p < 0.0001). Disagreement was greatest on first‐order streams. When NHD SPC were collected during a wetter period than NNO the probability of disagreement increased by a factor of 1.17 ( p < 0.0001) per unit difference in PDSI. The influence of climate on disagreements between NNO and NHD SPC provides support for the continued development of dynamic models representing SPC as opposed to static NHD classifications.

Journal of the American Water Resources Associatio↗
USGS2020

elfgen: A new instream flow framework for rapid generation and optimization of flow-ecology relations

Effective water resource management requires practical, data‐driven determination of instream flow needs. Newly developed, high‐resolution flow models and aquatic species databases provide enormous opportunity, but the volume of data can prove challenging to manage without automated tools. The objective of this study was to develop a framework of analytical methods and best practices to reduce costs of entry into flow–ecology analysis by integrating widely available hydrologic and ecological datasets. Ecological limit functions (ELFs) describing the relation between maximum species richness and stream size characteristics (streamflow or drainage area) were developed. Species richness is expected to increase with streamflow through a watershed up to a point where it either plateaus or transitions to a decreasing trend in larger streams. Our results show that identifying the location of this "breakpoint" is critical for producing optimal ELF model fit. We found that richness breakpoints can be estimated using automated low‐supervision methods, with high‐supervision providing negligible improvement in detection accuracy. Model fit (and predictive capability) was found to be superior in smaller hydrologic units. The ELF model ("elfgen" R package available on GitHub: https://github.com/HARPgroup/elfgen ) can be used to generate ELFs using built‐in datasets for the conterminous United States, or applied anywhere else streamflow and biodiversity data inputs are available.

Virginia↗