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At least 181 records · Page 10Linked to original sources
USGS2022

Stakeholder engagement to guide decision-relevant water data delivery

Water resources management and policy making require access to reliable scientific data. However, water managers may need to overcome various obstacles to accessing data. For example, insufficient technological infrastructures, low data literacy, and data format complexities often inhibit data user access. Thus, it is imperative to include stakeholders in the design of data delivery systems. The United States Geological Survey's Water Resources Mission Area is currently developing Integrated Water Availability Assessments (IWAAs) — multi-extent, stakeholder driven, near real-time water availability census and prediction for human and ecological uses. To provide appropriate user accessibility to data delivery systems developed for IWAAs, a user-centered design process including stakeholder focus groups was used to determine potential water data user needs and preferences. Focus groups identified five types of potential users: Public sector water resources managers, Public sector water resources manager data analysts, Industry and private companies, Tribal Nations, and Nonprofit organizations. Different water data user types depended on diverse spatial and temporal scale data. Public sector water resources managers benefitted most from data synthesized into user-friendly platforms and Public sector water resources data analysts preferred easy access to raw data. These findings can support the development of a water data delivery platform that meets a variety of user needs.

Journal of the American Water Resources Associatio↗
USGS2022

Predicted uranium and radon concentrations in New Hampshire (USA) groundwater—Using Multi Order Hydrologic Position as predictors

Two radioactive elements, uranium (U) and radon (Rn), which are of potential concern in New Hampshire (NH) groundwater, are investigated. Exceedance probability maps are tools to highlight locations where the concentrations of undesirable substances in the groundwater may be elevated. Two forms of statistical analysis are used to create exceedance probability maps for U and Rn in NH groundwater. The first, Boosted Regression Tree (BRT), was selected for estimating U exceedance values. It computes exceedance values directly using the Bernoulli distribution function. The second method of statistical analysis used for Rn to determine exceedance probabilities is ordinary least squares (OLS) regression. In the process of determining exceedance probabilities for U and Rn, the utility of a new dataset is investigated. That new predictor dataset is the Multi-Order Hydrologic Position (MOHP) dataset. MOHP raster datasets have been produced nationally for the conterminous United States at a 30-m resolution. The concept behind MOHP is that, for any given point on the earth's surface, there is the potential for a longer groundwater flow path as one goes deeper beneath the land surface. MOHP predictors were tested in both models. Three MOHP predictors were found useful in the BRT model and two in the OLS model. MOHP data were found useful as predictors along with other site characteristics in predicting U and Rn exceedance probabilities in New Hampshire groundwater.

New Hampshire↗
USGS2022

Assessment of cropland inundation due to the operation of the Reelfoot Lake spillway in West Tennessee

Running Reelfoot Bayou (RRB) is the outlet canal of Reelfoot Lake, the largest natural lake in Tennessee. RRB is not able to contain discharge from Reelfoot Lake greater than the bankfull discharge of 28 m 3 /s (1000 ft 3 /s), which typically occurs at the beginning of the growing season (April–June). Historically, the planting of crops has been delayed until flooding subsides and cropland has drained. The objective of this study is a preliminary quantification of cropland inundation to determine its spatial distribution in the RRB floodplain. Inundated croplands in the RRB floodplain were delineated over a range of spillway discharges from 2 to 57 m 3 /s (70–2000 ft 3 /s), using one-dimensional–two-dimensional hydrodynamic modeling and multispectral satellite images (Landsat 8 and Sentinel-2). The composite maps made by combining the simulated and image-derived flood maps were overlaid on the United States Department of Agriculture CropScape layer to determine the inundation of individual summer crops during the growing season. About 25% of the inundated croplands are flooded at discharges of RRB less than 28 m 3 /s, implying wetland hydrology. The results of this analysis can be used to inform operational management of the Reelfoot Lake spillway.

Tennessee↗
USGS2022

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.

Journal of the American Water Resources Associatio↗
USGS2023

Wastewater reuse and predicted ecological risk posed by contaminant mixtures in Potomac River watershed streams

A wastewater model was applied to the Potomac River watershed to provide (i) a means to identify streams with a high likelihood of carrying elevated effluent-derived contaminants and (ii) risk assessments to aquatic life and drinking water. The model linked effluent discharges along stream networks, accumulated wastewater, and predicted contaminant loads of municipal wastewater constituents while accounting for instream dilution and attenuation. Simulations using 2016 data suggested that nearly 30% (8281 km) of streams were wastewater impacted. Low- to medium-order streams had the largest range of accumulated wastewater (ACCWW%) values. ACCWW% exceeded a 1% threshold at >39% of drinking-water intakes (varied by temporal condition). Risk assessments of municipal wastewater-contaminant mixtures indicated that 22% (1479 km) of streams impacted by municipal wastewater (5.5% of all reaches modeled) may pose high risk to aquatic organisms under mean-annual conditions, with fish more susceptible to chronic-exposure effects relative to other taxa. Risk varied temporally and by stream order, with the greatest risk occurring in the summer in small streams. These findings suggest that wastewater may be an important factor contributing to environmental degradation in the Potomac River watershed.

District of Columbia, Maryland, Pennsylvania, Virg↗
USGS2023

Water balance characterization of the early 21st century drought in the western United States

Monthly temperature and precipitation data for 923 United States Geological Survey 8-digit hydrologic units are used as inputs to a monthly water balance model to compute monthly actual evapotranspiration, soil moisture storage, and runoff across the western United States (U.S.) for the period 1900 through 2020. Time series of these water balance variables are examined to characterize and explain the dry conditions across the western U.S. since the year 2000. Results indicate that although precipitation deficits account for most of the changes in actual evapotranspiration and runoff, increases in temperature primarily explain decreases in soil moisture storage. Specifically, temperature has been particularly impactful on the magnitude of negative departures of soil moisture storage during the spring (April through June) and summer (July through September) seasons. These effects on soil moisture may be particularly detrimental to agriculture in regions already stressed by drought such as the western U.S.

Journal of the American Water Resources Associatio↗
USGS2023

The Chesapeake Bay Land Change Model (CBLCM): Simulating future land use scenarios and potential impacts on water quality

The Chesapeake Bay Land Change Model (CBLCM) is an open-source pseudo-cellular automata land change model tailored for loose coupling with watershed models. The CBLCM simulates infill development, residential and commercial development, natural land and agricultural land conversion, and growth served by sewer or septic wastewater treatment. The CBLCM is unique among land change models by simulating multiple types of development and explicitly accounting for infill development and the spatial patterns of development densities. The CBLCM was used to simulate five future land use scenarios, holding population constant, for all counties within and adjacent to the Chesapeake Bay watershed from 2013 to 2055. Results are presented here for the state of Maryland over the period 2013–2025 to illustrate model functionality and validation. The growth management (GM) scenario achieved the least development and potential impacts to natural and agricultural lands while accommodating the same amount of population growth as the other four scenarios. Scenarios focusing exclusively on natural or agricultural land protection shifted development to unprotected areas resulting in unforeseen water quality consequences. Simultaneously achieving more compact development while protecting the most valued natural and agricultural lands requires a combination of GM and land conservation policies and actions.

Maryland↗
USGS2023

Temporal trends in agricultural water use and the relationships to hydroclimatic factors in the High Plains aquifer region

The High Plains aquifer (HPA) is the primary water source for agricultural irrigation in the US Great Plains. The water levels in many locations of the aquifer have declined steadily over the past several decades because the rate of water withdrawals exceeds recharge, which has been a serious concern to the water resources management in the region. We evaluated temporal trends and variations in agricultural water use and hydroclimatic variables including precipitation, air temperature, reference evapotranspiration, runoff, groundwater level, and terrestrial water storage across the HPA region for different periods from 1985 to 2020 at the grid, county, or region scale. The results showed that water withdrawals decreased from 21.3 km 3 /year in 1985 to 18.2 km 3 /year in 2015, while irrigated croplands increased from 71,928 km 2 in 1985 to 78,464 km 2 in 2015 in the entire HPA. The hydroclimatic time-series showed wetting trends in most of the northern HPA, but drying and warming trends in the southern region from 1985 to 2020. The groundwater level time-series indicated flat trends in the north, but significant declining in the central and southern HPA. Trends in irrigation water withdrawals and irrigation area across the HPA were controlled by the advancement of irrigation systems and technologies and the management of sustainable water use, but also were affected by dynamical changes in the hydroclimatic conditions.

Journal of the American Water Resources Associatio↗
USGS2023

Integrated water resources trend assessments: State of the science, challenges, and opportunities for advancement

Water is vital to human life and healthy ecosystems. Here we outline the current state of national-scale water resources trend assessments, identify key gaps, and suggest advancements to better address critical issues related to changes in water resources that may threaten human development or the environment. Questions like, “Do we have less suitable drinking water now than we had 20 years ago?” or “Are flood events more common now than they were in the past?” prompted improvements in data, trend estimation methods, and modeling frameworks to track changes in, and better understand how land use and climate influence four water resources domains: surface and groundwater quantity and quality. However, continued advancement in trend assessments to better address issues related to changes in water availability is needed. Areas of need include more timely and efficient delivery of water resources trend results and improved capacity to estimate trends at unmonitored locations. Additional integration pieces include increased understanding of groundwater–surface water interactions, incorporation of both quantity and quality trends into water availability estimates, and the refinement of trend metrics to account for the competing needs of society and ecological integrity. Coupled with improved driver attribution studies, these components will better inform current and future water resources management.

Journal of the American Water Resource Association↗
USGS2023

Water quality impacts of climate change, land use, and population growth in the Chesapeake Bay watershed

The 2010 Chesapeake Bay Total Maximum Daily Load was established for the water quality and ecological restoration of the Chesapeake Bay. In 2017, the latest science, data, and modeling tools were used to develop revised Watershed Implementation Plans (WIPs). In this article, we examine the vulnerability of the Chesapeake Bay watershed to the combined pressures of climate change and growth in population, agricultural intensity, and economic activity for the 60-year period 1995–2055. The results will be used to revise WIPs, as needed, to account for expected increases in loads. Assessing changes relative to 1995 for the years 2025, 2035, 2045, and 2055, mean annual precipitation increases of 3.11%, 4.21%, 5.34%, and 6.91%, respectively, air temperature increases of 1.12, 1.45, 1.84, and 2.12°C, respectively, and potential evapotranspiration increases of 3.36%, 4.43%, 5.54%, and 6.35%, respectively, are projected. Population in the watershed is expected to grow by 3.5 million between 2025 and 2055. Watershed model results show incremental increases in streamflow (2.3%–6.2%), nitrogen (2.6%–10.8%), phosphorus (4.5%–26.7%), and sediment (3.8%–18.8%) loads to the tidal Bay due to climate change. Growth in population, agricultural intensity, development, and economic activity resulted in relatively smaller increases in loads compared to climate change.

Chesapeake Bay watershed↗
USGS2023

A survey of non-USGS continuous streamflow gaging networks in the Pacific Northwest

Extensive streamflow data sources exist beyond the largest streamflow data provider in the United States, the U.S. Geological Survey. We developed and distributed a survey to about 300 individuals and organizations that collect streamflow data across the Pacific Northwest (Idaho, Oregon, Washington). We received 100 responses with 56% of those sufficiently complete to include in the analysis. From these responses, there are about 2000 streamflow monitoring locations in the region beyond the USGS monitoring network. The duration of record for gages is related to the size of the streamflow gaging network, with small and large networks generally operating monitoring locations for less than 5 years and more than 10 years, respectively. Quality assurance and quality control are variable across organizations, with 41% of respondents having at least two review steps and 13% that audit their data for long-term consistency. Results of this survey begin to establish the differing capabilities of large and small stream gaging networks and highlight how supporting the overall quality streamflow data collection and management within the water resources community will improve our ability to harmonize these datasets in the future.

Idaho, Montana, Nevada, Oregon, Washington, Wyomin↗
USGS2023

Low-complexity floodplain inundation model performs well for ecological and management applications in a large river ecosystem

Flooding is a dominant physical process that drives the form and function of river-floodplain ecosystems. Efficiently characterizing flooding dynamics can be challenging, especially over geographically broad areas or at spatial and temporal scales relevant for ecosystem management activities. Here, we empirically evaluated a low-complexity geospatial model of floodplain inundation in six study segments of the Upper Mississippi River System (UMRS) by pairing spatially extensive, temporally limited and spatially limited, temporally extensive sampling designs. We found little evidence of systematic bias in model performance although discrepancies between model predictions and empirical data did occur locally. Assessments of model predictions revealed low segment-wide discrepancies of wetted extent under contrasting flow conditions and agreement for inundation event detection and duration. Model performance for predicting event frequency and duration was similar among sites expected to exhibit contrasting patterns of hydrologic connectivity with the main channel. Our results suggest that low-complexity models can efficiently characterize a critical physical process across geographically broad, complex river-floodplain ecosystems. Such tools have the potential for advancing scientific understanding of landscape-scale ecological patterns and for prioritizing management actions in large, complex river-floodplain ecosystems like the UMRS.

Illinois, Iowa, Minnesota, Missouri, Wisconsin↗
USGS2023

The usability gap in water resources open data and actionable science initiatives

The open data movement represents a major advancement for informed water management. Data that are findable, accessible, interoperable, and reusable—or FAIR —are now prerequisite to responsible data stewardship. In contrast to FAIR, accessibility and usability case studies and guidelines designed around human access and understanding are lacking in the literature, especially for water resources. Such decision support guidelines are critical because (i) inherent visual design trade-offs are not best made using intuition or feedback (perceived preference), and (ii) choosing designs requires a nuanced understanding of why and how the design works (revealed effectiveness). Thus, the goal of this commentary is to highlight knowledge gaps and discuss a general usability testing method which can be applied to any water resources decision support product. The user-testing approach includes (i) interviews about visualization goals, audiences, and the uses and decisions made with the data products, (ii) diagnosis of usability challenges, and (iii) redesign of decision support products given best practices and control versus treatment with intended end-user audiences. We illustrate the method using high-profile U.S. Geological Survey water science products. In sum, optimizing and testing for usability and understandability are as central to stakeholder use as FAIR standards are, and warrant being part of the development of data products and geovisualizations.

Journal of the American Water Resources Associatio↗
USGS2023

The patchwork governance of ecologically available water: A case study in the Upper Missouri Headwaters, Montana, United States

Institutional authority and responsibility for allocating water to ecosystems (“ecologically available water” [EAW]) is spread across local, state, and federal agencies, which operate under a range of statutes, mandates, and planning processes. We use a case study of the Upper Missouri Headwaters Basin in southwestern Montana, United States, to illustrate this fragmented institutional landscape. Our goals are to (a) describe the patchwork of agencies and institutional actors whose intersecting authorities and actions influence the EAW in the study basin; (b) describe the range of governance mechanisms these agencies use, including laws, policies, administrative programs, and planning processes; and (c) assess the extent to which the collective governance regime creates gaps in responsibility. We find the water governance regime includes a range of nested mechanisms that in various ways facilitate or hinder the governance of EAW. We conclude the current multilevel governance regime leaves certain aspects of EAW unaddressed and does not adequately account for the interconnections between water in different parts of the ecosystem, creating integrative gaps. We suggest that more intentional and robust coordination could provide a means to address these gaps.

Montana↗
USGS2024

Development and evaluation of public-supply community water service area boundaries for the conterminous United States

The water service area dataset, derived from the National Boundary Dataset for public-supply water systems in the United States, offers a detailed resolution surpassing county-level assessments, emphasizing water-centric land use. Crucial for linking populations and infrastructure to system withdrawals, it supports the creation of a national public-supply water-use model, enhancing accuracy in estimating water use and distinguishing between publicly supplied and self-supplied domestic water use. Integrating tabular water system data strengthens the national water-use model by enabling tracking of withdrawal locations, source water, and water quality. Evaluated against U.S. Census-derived population datasets, 16 state-provided water service area datasets, and two national land use datasets, the study covers 22,849 community water systems, excluding most small systems serving fewer than 1000 people. Robust correlations between water service areas (WSAs) and satellite-sourced urban and exurban land use types facilitate tracking changes over time. A comparison of state and national datasets for population and WSAs reveals discrepancies ranging from 5% to 73% in state-level populations and 0% to 167% in state-level WSAs. Significant differences can be attributed to the exclusion of sizable incorporated and unincorporated areas in the state-based datasets. Additional comparisons of major metropolitan areas exhibit differences ranging from 2% to 56%.

conterminous United States↗
USGS2025

Hydroclimate projections and effects on runoff at National Wildlife Refuges in the semi-arid western U.S.

This study evaluated hydroclimate projections and effects on runoff at National Wildlife Refuges in a semiarid region of the western United States (U.S. Fish and Wildlife Service Region 6) using mean air temperature (TAVE) and precipitation (PPT) inputs and runoff (RO) output from a national application of a Monthly Water Balance Model (MWBM). An ensemble of statistically downscaled global circulation models for two future emissions scenarios from Coupled Model Intercomparison Project 3 and 5 (CMIP3 and 5) were assessed at the refuges for the years 1950–2099. TAVE, PPT, and RO and departures from mean baseline conditions were analyzed from MWBM hydrologic response units within refuge boundaries. Seasonal results were evaluated across four periods: historical (1951–1969), baseline (1981–1999), 2050 (2041–2059), and 2080 (2071–2089). Projected TAVE increases for all refuges and time periods, whereas PPT and RO are much more variable across ecoregions. Using the high emission scenario, summer mean monthly TAVE increases range from 4.8°C to 5.5°C by 2080. Summer mean monthly PPT departures vary from −5.7 to 3.9 mm (up to 14% decrease), with decreases at 41% of refuges. Summer RO departures range from −16.7 to 0.2 mm (up to 60% decrease), with decreases at 71% of refuges. Under the same emission scenario, winter PPT and RO increase at most refuges by 2080. These variable departures will create substantial challenges for future conservation management in the region.

JAWRA Journal of the American Water Resources Asso↗
USGS2025

The effects of unpaved roads on instream sediment: Patterns and challenges for monitoring

Despite > 700,000 km of unpaved roads in the western United States, our knowledge of how roads impact instream sediment is unclear. We combined two studies, including (1) a regional analysis linking stream habitat data from a large-scale monitoring program with road density data to identify generalizable relationships between roads and streambed sediment distributions and (2) a targeted field study to evaluate the responses of streambed and suspended sediment collected at locations above and below road–stream connection points to better understand the consistency of responses. Regional analyses indicated a significant positive relationship between road density and fine sediment in pool tails and a significant negative relationship between road density and median particle size. We also found significant relationships between landscape, climate, and local covariates and streambed sediment metrics, where most of the parameter estimates of the covariates were equal to or stronger than those for road density. Field studies suggested higher suspended sediment levels across the seasonal hydrologic regime where roads were open to travel year-round. However, sediment responses to road–stream connection points varied by metric and site. Together, our results indicated negative relationships between increasing road densities and sediment size distributions, but detecting road effects at site scales will be challenging given the effects of covariates that can overwhelm sediment signals.

Montana↗
USGS2025

Leveraging high-frequency sensor data and U.S. National Water Model output to forecast turbidity in a drinking water supply basin

As high-frequency sensor networks increasingly enhance data-driven models of water quality, process-based models like the U.S. National Water Model (NWM) are generating accessible forecasts of streamflow at increasingly dense scales. There is now an opportunity to combine these products to construct actionable water quality forecasts. To that end, we couple streamflow forecasts from the NWM to a gradient-boosted decision tree algorithm (LightGBM) trained on 5+ years of high-frequency monitoring data to forecast in-stream turbidity levels in the Catskill Mountains, NY, USA. Results indicate LightGBM models are capable of relatively skillful predictions, which enable robust forecasts for 1–3 days lead times. LightGBM models offer improvements over a simplified linear model across the entire forecast horizon, and more spatially complex models are more resilient to error at shorter lead times (1–3 days). Moreover, interpretation of model features emphasizes high flows as a driver of turbidity in the region. Results suggest that interpretable, flexible, and efficient machine learning algorithms can produce capable water quality forecasts from streamflow forecasts and expand understanding of process dynamics. The use case illustrated here—to our knowledge the first NWM-based water quality forecast—underscores the potential to employ the NWM to expand national water quality forecasting capacity and can overall serve as a guide for similar efforts in basins across the country.

New York↗