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Results for “Water Resources Management”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 469 records · Page 26Linked to original sources
USGS2003

Data-based comparisons of moments estimators using historical and paleoflood data

This paper presents the first systematic comparison, using historical and paleoflood data, of moments-based flood frequency methods. Peak flow estimates were compiled from streamflow-gaging stations with historical and/or paleoflood data at 36 sites located in the United States, Argentina, United Kingdom and China, covering a diverse range of hydrologic conditions. The Expected Moments Algorithm (EMA) and the Bulletin 17B historical weighting procedure (B17H) were compared in terms of goodness of fit using 25 of the data sets. Results from this comparison indicate that EMA is a viable alternative to current B17H procedures from an operational perspective, and performed equal to or better than B17H for the data analyzed. We demonstrate satisfactory EMA performance for the remaining 11 sites with multiple thresholds and binomial censoring, which B17H cannot accommodate. It is shown that the EMA estimator readily incorporates these types of information and the LP-III distribution provided an adequate fit to the data in most cases. The results shown here are consistent with Monte Carlo simulation studies, and demonstrate that EMA is preferred overall to B17H. The Bulletin 17B document could be revised to include an option for EMA as an alternative to the existing historical weighting approach. These results are of practical relevance to hydrologists and water resources managers for applications in floodplain management, design of hydraulic structures, and risk analysis for dams. ?? 2003 Elsevier Science B.V. All rights reserved.

Journal of Hydrology↗
USGS2021

The drying regimes of non-perennial rivers and streams

The flow regime paradigm is central to the aquatic sciences, where flow drives critical functions in lotic systems. Non-perennial streams comprise the majority of global river length, thus we extended this paradigm to stream drying. Using 894 USGS gages, we isolated 25,207 drying events from 1979 to 2018, represented by a streamflow peak followed by no flow. We calculated hydrologic signatures for each drying event and using multivariate statistics, grouped events into drying regimes characterized by: (a) fast drying, (b) long no-flow duration, (c) prolonged drying following low antecedent flows, (d) drying without a distinctive hydrologic signature. 77% of gages had more than one drying regime at different times within the study period. Random forests revealed land cover/use are more important to how a river dries than climate or physiographic characteristics. Clustering stream drying behavior may allow practitioners to more systematically adapt water resource management practices to specific drying regimes or rivers.

Geophysical Research Letters↗
USGS2024

Post-fire sediment yield from a central California watershed: Field measurements and validation of the WEPP model

In a warming climate, an intensifying fire regime and higher likelihood of extreme rain are expected to increase watershed sediment yield in many regions. Understanding regional variability in landscape response to fire and post-fire rainfall is essential for managing water resources and infrastructure. We measured sediment yield resulting from sequential wildfire and extreme rain and flooding in the upper Carmel River watershed (116 km 2 ), on the central California coast, USA, using changes in sediment volume mapped in a reservoir. We determined that the sediment yield after fire and post-fire flooding was 854–1,100 t/km 2 /yr, a factor of 3.5–4.6 greater than the long-term yield from this watershed and more than an order of magnitude greater than during severe drought conditions. In this first large-scale field validation test of the WEPPcloud/ wepppy framework for the Water Erosion Prediction Project (WEPP) model on a burned landscape, WEPP predicted 81%–106% of the measured sediment yield. These findings will facilitate assessing and predicting future fire effects in steep watersheds with a Mediterranean climate and indicate that the increasingly widespread use of WEPP is appropriate for evaluating post-fire hillslope erosion even across 100-km 2 scales under conditions without debris flows.

California↗
USGS2024

Declining reservoir reliability and increasing reservoir vulnerability: Long-term observations reveal longer and more severe periods of low reservoir storage for major United States reservoirs

Hydrological drought is a pervasive and reoccurring challenge in managing water resources. Reservoirs are critical for lessening the impacts of drought on water available for many uses. We use a novel and generalized approach to identify periods of unusually low reservoir storage—via comparisons to operational rule curves and historical patterns—to investigate how droughts affect storage in 250 reservoirs across the conterminous U.S. (CONUS). We find that the maximum amount of water stored in reservoirs is decreasing, and that periods of unusually low storage are becoming longer, more severe, and more variable in (a) western and central CONUS reservoirs, and (b) reservoirs with primarily over-year storage. Results suggest that reservoir storage has become less reliable and more vulnerable to larger deviations from desired storage patterns. These changes have coincided with ongoing shifts to the hydroclimate of CONUS, and with sedimentation further reducing available reservoir storage.

Continental United States↗
USGS2025

A streamflow permanence classification model for forested streams that explicitly accounts for uncertainty and extrapolation

Accurate mapping of headwater streams and their flow status has important implications for understanding and managing water resources and land uses. However, accurate information is rare, especially in rugged, forested terrain. We developed a streamflow permanence classification model for forested lands in western Oregon using the latest light detection and ranging-derived hydrography published in the National Hydrography Dataset. Models were trained using 2,518 flow/no flow field observations collected in late summer 2019–2021 across headwaters of 129 sub-watersheds. The final model, the Western Oregon WeT DRy model, used Random Forest and 13 environmental covariates for classifying every 5-m stream sub-reach across 426 sub-watersheds. The most important covariates were annual precipitation and drainage area. Model output included probabilities of late summer surface flow presence and were subsequently categorized into three streamflow permanence classes—Wet, Dry, and Ambiguous. Ambiguous denoted model probabilities and associated prediction intervals that extended over the 50% classification threshold between wet and dry. Model accuracy was 0.83 for sub-watersheds that contained training data and decreased to 0.67 for sub-watersheds that did not have observations of late summer surface flow. The model identified where predictions extrapolated beyond the domain characterized by the training data. The combination of spatially continuous estimates of late summer streamflow status along with uncertainty and extrapolation estimates provide critical information for strategic project planning and designing additional field data collection.

Oregon↗
USGS2017

Partitioning evapotranspiration into green and blue water sources in the conterminous United States

In this study, we combined two 1 km actual evapotranspiration datasets (ET), one obtained from a root zone water balance model and another from an energy balance model, to partition annual ET into green (rainfall-based) and blue (surface water/groundwater) sources. Time series maps of green water ET (GWET) and blue water ET (BWET) are produced for the conterminous United States (CONUS) over 2001–2015. Our results indicate that average green and blue water for all land cover types in CONUS accounts for nearly 70% and 30% of the total ET, respectively. The ET in the eastern US arises mostly from GWET, and in the western US, it is mostly BWET. Analysis of the BWET in the 16 irrigated areas in CONUS revealed interesting results. While the magnitude of the BWET gradually showed a decline from west to east, the increase in coefficient of variation from west to east confirmed greater use of supplemental irrigation in the central and eastern US. We also established relationships between different hydro-climatology zones and their blue water requirements. This study provides insights on the relative contributions and the spatiotemporal dynamics of GWET and BWET, which could lead to improved water resources management.

Scientific Reports↗
USGS2016

Differences in flood hazard projections in Europe – their causes and consequences for decision making

This paper interprets differences in flood hazard projections over Europe and identifies likely sources of discrepancy. Further, it discusses potential implications of these differences for flood risk reduction and adaptation to climate change. The discrepancy in flood hazard projections raises caution, especially among decision makers in charge of water resources management, flood risk reduction, and climate change adaptation at regional to local scales. Because it is naïve to expect availability of trustworthy quantitative projections of future flood hazard, in order to reduce flood risk one should focus attention on mapping of current and future risks and vulnerability hotspots and improve the situation there. Although an intercomparison of flood hazard projections is done in this paper and differences are identified and interpreted, it does not seems possible to recommend which large-scale studies may be considered most credible in particular areas of Europe.

Hydrological Sciences Journal↗
USGS2024

Low-flow period seasonality, trends, and climate linkages across the United States

Low-flow period properties, including timing, magnitude, and duration, influence many key processes for water resource managers and ecosystems. We computed annual low-flow period duration and timing metrics from 1951 to 2020 for 1032 conterminous United States (CONUS) streamgages and analyzed spatial patterns, trends through time, and relationships to climate. Results show northwestern and eastern CONUS streamgages had longer and more inter-annually consistent low-flow period durations, while central CONUS periods were shorter and more variable. Low-flow periods most often occurred in summer months but start and end dates occurred later in north-central and mountainous western CONUS, which have the greatest number of low flows during cold seasons. Low-flow periods are becoming longer in southeastern and northwestern CONUS but shorter in much of the rest of CONUS. Temperature was correlated with low-flow period duration in southeastern and northwestern CONUS, and precipitation was correlated with duration everywhere, but most strongly in eastern CONUS.

conterminous United States↗
USGS2009

Use of predictive models and rapid methods to nowcast bacteria levels at coastal beaches

The need for rapid assessments of recreational water quality to better protect public health is well accepted throughout the research and regulatory communities. Rapid analytical methods, such as quantitative polymerase chain reaction (qPCR) and immunomagnetic separation/adenosine triphosphate (ATP) analysis, are being tested but are not yet ready for widespread use. Another solution is the use of predictive models, wherein variable(s) that are easily and quickly measured are surrogates for concentrations of fecal-indicator bacteria. Rainfall-based alerts, the simplest type of model, have been used by several communities for a number of years. Deterministic models use mathematical representations of the processes that affect bacteria concentrations; this type of model is being used for beach-closure decisions at one location in the USA. Multivariable statistical models are being developed and tested in many areas of the USA; however, they are only used in three areas of the Great Lakes to aid in notifications of beach advisories or closings. These “operational” statistical models can result in more accurate assessments of recreational water quality than use of the previous day's Escherichia coli (E. coli) concentration as determined by traditional culture methods. The Ohio Nowcast, at Huntington Beach, Bay Village, Ohio, is described in this paper as an example of an operational statistical model. Because predictive modeling is a dynamic process, water-resource managers continue to collect additional data to improve the predictive ability of the nowcast and expand the nowcast to other Ohio beaches and a recreational river. Although predictive models have been shown to work well at some beaches and are becoming more widely accepted, implementation in many areas is limited by funding, lack of coordinated technical leadership, and lack of supporting epidemiological data.

Aquatic Ecosystem Health & Management↗
USGS2021

Effect of backwatering a streamgage weir on the passage performance of adult American Shad (Alosa sapidissima)

Streamgage designs often include a full-width artificial hydraulic control (e.g., concrete weir) to aid in the computation of streamflow. While important to water resource managers, these weirs also tend to act as full or partial barriers to fish migration, effectively hindering the health and survival of these populations. In this study, we conducted experiments to quantify the effect of head drop and submergence of a common streamgage weir on the passage performance of an important migratory fish species, the American Shad. Three treatment conditions were selected based on the tailwater surface elevation ( El TW ): unsubmerged ( El TW = 1.05 m; head drop = 0.46 m), equal to the weir crest ( El TW = 1.20 m; head drop = 0.31 m), and submerged ( El TW = 1.36 m; head drop = 0.15 m). Fish movements were recorded via passive integrated transponder telemetry techniques. Results revealed that the backwatered Columbus-type weir was not a complete barrier at any of the three treatments, but passage was shown to be significantly impaired when the weir was unsubmerged. Passage efficiency for the unsubmerged, equal, and submerged treatments was 20.2 ± 6.2, 49.2 ± 7.2, and 64.2 ± 7.4%. Backwatering a weir, rather than removal or other major alterations that would affect weir calibration, may be an acceptable retrofit to increase fish passage.

Journal of Ecohydraulics↗
USGS2023

One Ranney well can make a difference: The impacts of a radial collector well on groundwater level and quality in the Cedar River alluvial aquifer

The City of Cedar Rapids, Iowa, depends on groundwater from the Cedar River alluvial aquifer for residential and industrial use. In 2020, the city completed an additional radial collector well, or Ranney well, and was concerned that pumping from the well at high rates may lower water level elevations in the aquifer, reduce yields from nearby production wells, and change the quality of produced water. During an operational test of the well's pumps, the U.S. Geological Survey and the city collected water level and water quality data to evaluate the effects of increased pumping rates on the aquifer and nearby production wells. Results indicated that a high rate of pumping from the new well caused sustained declines in water levels near the well and other nearby production wells, and, if maintained, the aquifer water level in the vicinity would continue to decline to levels observed during the 2012 drought. Aquifer specific conductance and temperature were altered and matched trends and values of the river, and river-to-well travel time was shortened from 7-17 days to about 3 days. Results may also provide insights to other municipal water resource managers when considering wellfield design, production expectations, and long-term management strategies for radial collector well production during drought, low streamflow, and times when high concentrations of nitrate-N or organic pesticide compounds in the river may limit production options.

Iowa↗
USGS2012

Future of groundwater modeling

With an increasing need to better manage water resources, the future of groundwater modeling is bright and exciting. However, while the past can be described and the present is known, the future of groundwater modeling, just like a groundwater model result, is highly uncertain and any prediction is probably not going to be entirely representative. Thus we acknowledge this as we present our vision of where groundwater modeling may be headed.

Ground Water↗
USGS2022

Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels

In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems in earth science and remote sensing (e.g., mapping the nationwide river streams for water resource management). Although extensive efforts have been made to reduce the reliance on labeled data (e.g., semi-supervised or unsupervised learning, few-shot learning), the complex nature of geographic data such as spatial heterogeneity still requires sufficient training labels when transferring a pre-trained model from one region to another. On the other hand, it is often much easier to collect lower-quality training labels with imperfect alignment with earth imagery pixels (e.g., through interpreting coarse imagery by non-expert volunteers). However, directly training a deep neural network on imperfect labels with geometric annotation errors could significantly impact model performance. Existing research that overcomes imperfect training labels either focuses on errors in label class semantics or characterizes label location errors at the pixel level. These methods do not fully incorporate the geometric properties of label location errors in the vector representation. To fill the gap, this article proposes a weakly supervised learning framework to simultaneously update deep learning model parameters and infer hidden true vector label locations. Specifically, we model label location errors in the vector representation to partially reserve geometric properties (e.g., spatial contiguity within line segments). Evaluations on real-world datasets in the National Hydrography Dataset (NHD) refinement application illustrate that the proposed framework outperforms baseline methods in classification accuracy.

ACM Transactions on Intelligent Systems and Techno↗
USGS2002

Primary modes and predictability of year-to-year snowpack variations in the Western United States from teleconnections with Pacific Ocean climate

Snowpack, as measured on 1 April, is the primary source of warm-season streamflow for most of the western United States and thus represents an important source of water supply. An understanding of climate factors that influence the variability of this water supply and thus its predictability is important for water resource management. In this study, principal component analysis is used to identify the primary modes of 1 April snowpack variability in the western United States. Two components account for 61% of the total snowpack variability in the western United States. Relations between these modes of variability and indices of Pacific Ocean climate [e.g., the Pacific decadal oscillation (PDO) and Nin??o-3 sea surface temperatures (SSTs)] are examined. The first mode of snowpack variability is closely associated with the PDO, whereas the second mode varies in concert with both the PDO and Nin??o-3 SSTs. Because these atmospheric-oceanic conditions change slowly from season to season, the observed teleconnections between the Pacific Ocean climate and 1 April snowpack may be useful to forecast 1 April snowpack using data that describe the Pacific Ocean climate in the previous summer and autumn seasons, especially for the northwestern United States.

Journal of Hydrometeorology↗
USGS2013

The ancient blue oak woodlands of California: longevity and hydroclimatic history

Ancient blue oak trees are still widespread across the foothills of the Coast Ranges, Cascades, and Sierra Nevada in California. The most extensive tracts of intact old-growth blue oak woodland appear to survive on rugged and remote terrain in the south Coast Ranges and on the foothills west and southwest of Mt. Lassen. In our sampling of old-growth stands, most blue oak appear to have recruited to the canopy in the mid- to late-19 th century. The oldest living blue oak tree sampled was over 459-years old and several dead blue oak logs had over 500 annual rings. Precipitation sensitive tree-ring chronologies up to 700-years long have been developed from old blue oak trees and logs. Annual ring-width chronologies of blue oak are strongly correlated with cool season precipitation totals, streamflow in the major rivers of California, and the estuarine water quality of San Francisco Bay. A new network of 36 blue oak chronologies records spatial anomalies in growth that arise from latitudinal changes in the mean storm track and location of landfalling atmospheric rivers. These long, climate-sensitive blue oak chronologies have been used to reconstruct hydroclimatic history in California and will help to better understand and manage water resources. The environmental history embedded in blue oak growth chronologies may help justify efforts to conserve these authentic old-growth native woodlands.

California↗
USGS2022

Working toward a National Coordinated Soil Moisture Monitoring Network: Vision, progress, and future directions

Soil moisture is a critical land surface variable, impacting the water, energy, and carbon cycles. While in situ soil moisture monitoring networks are still developing, there is no cohesive strategy or framework to coordinate, integrate, or disseminate these diverse data sources in a synergistic way that can improve our ability to understand climate variability at the national, state, and local levels. Thus, a national strategy is needed to guide network deployment, sustainable network operation, data integration and dissemination, and user-focused product development. The National Coordinated Soil Moisture Monitoring Network (NCSMMN) is a federally led, multi-institution effort that aims to address these needs by capitalizing on existing wide-ranging soil moisture monitoring activities, increasing the utility of observational data, and supporting their strategic application to the full range of decision-making needs. The goals of the NCSMMN are to 1) establish a national “network of networks” that effectively demonstrates data integration and operational coordination of diverse in situ networks; 2) build a community of practice around soil moisture measurement, interpretation, and application—a “network of people” that links data providers, researchers, and the public; and 3) support research and development (R&D) on techniques to merge in situ soil moisture data with remotely sensed and modeled hydrologic data to create user-friendly soil moisture maps and associated tools. The overarching mission of the NCSMMN is to provide coordinated high-quality, nationwide soil moisture information for the public good by supporting applications like drought and flood monitoring, water resource management, agricultural and forestry planning, and fire danger ratings.

Bulletin of the American Meteorological Society↗
USGS2012

History of land cover mapping

The historical roots of land-cover mapping reside in the early history of aerial photography and applications spanning forestry, agriculture, urban planning, and water-resources management. Considering this long span of mapping, any attempt to provide an exhaustive treatment of the full history of land-cover mapping will necessarily be incomplete. For that reason, this chapter on the history of land-cover mapping emphasizes the “modern” era of land-cover mapping, which has been arbitrarily defined to begin in the early 1970s. This was when civil space-based remote sensing came of age, and intellectual efforts focused on strategies for using new observations in understanding the characteristics of, and the changes in, land use and land cover.

Book chapter↗