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211 records · Page 12Linked to original sources
USGS2011

A method for estimating peak and time of peak streamflow from excess rainfall for 10- to 640-acre watersheds in the Houston, Texas, metropolitan area

Estimates of peak and time of peak streamflow for small watersheds (less than about 640 acres) in a suburban to urban, low-slope setting are needed for drainage design that is cost-effective and risk-mitigated. During 2007-10, the U.S. Geological Survey (USGS), in cooperation with the Harris County Flood Control District and the Texas Department of Transportation, developed a method to estimate peak and time of peak streamflow from excess rainfall for 10- to 640-acre watersheds in the Houston, Texas, metropolitan area. To develop the method, 24 watersheds in the study area with drainage areas less than about 3.5 square miles (2,240 acres) and with concomitant rainfall and runoff data were selected. The method is based on conjunctive analysis of rainfall and runoff data in the context of the unit hydrograph method and the rational method. For the unit hydrograph analysis, a gamma distribution model of unit hydrograph shape (a gamma unit hydrograph) was chosen and parameters estimated through matching of modeled peak and time of peak streamflow to observed values on a storm-by-storm basis. Watershed mean or watershed-specific values of peak and time to peak ("time to peak" is a parameter of the gamma unit hydrograph and is distinct from "time of peak") of the gamma unit hydrograph were computed. Two regression equations to estimate peak and time to peak of the gamma unit hydrograph that are based on watershed characteristics of drainage area and basin-development factor (BDF) were developed. For the rational method analysis, a lag time (time-R), volumetric runoff coefficient, and runoff coefficient were computed on a storm-by-storm basis. Watershed-specific values of these three metrics were computed. A regression equation to estimate time-R based on drainage area and BDF was developed. Overall arithmetic means of volumetric runoff coefficient (0.41 dimensionless) and runoff coefficient (0.25 dimensionless) for the 24 watersheds were used to express the rational method in terms of excess rainfall (the excess rational method). Both the unit hydrograph method and excess rational method are shown to provide similar estimates of peak and time of peak streamflow. The results from the two methods can be combined by using arithmetic means. A nomograph is provided that shows the respective relations between the arithmetic-mean peak and time of peak streamflow to drainage areas ranging from 10 to 640 acres. The nomograph also shows the respective relations for selected BDF ranging from undeveloped to fully developed conditions. The nomograph represents the peak streamflow for 1 inch of excess rainfall based on drainage area and BDF; the peak streamflow for design storms from the nomograph can be multiplied by the excess rainfall to estimate peak streamflow. Time of peak streamflow is readily obtained from the nomograph. Therefore, given excess rainfall values derived from watershed-loss models, which are beyond the scope of this report, the nomograph represents a method for estimating peak and time of peak streamflow for applicable watersheds in the Houston metropolitan area. Lastly, analysis of the relative influence of BDF on peak streamflow is provided, and the results indicate a 0:04log 10 cubic feet per second change of peak streamflow per positive unit of change in BDF. This relative change can be used to adjust peak streamflow from the method or other hydrologic methods for a given BDF to other BDF values; example computations are provided.

Texas↗
USGS2023

Effects of lead exposure on birds breeding in the Southeast Missouri Lead Mining District

Lead mining in the Southeast Missouri Lead Mining District began in the 1700s and continued for nearly 300 years; the waste piles associated with smelting, mining, and milling of lead ores have released metal residues that have contaminated soil and water in the region. Previous studies in the district have indicated potential harm to wildlife, including birds, because of elevated lead concentrations associated with mining. Exposure to soil-borne lead was correlated with elevated lead concentrations in tissues, inhibition of δ-aminolevulinic acid dehydratase (δALAD), and renal lesions in birds foraging on ground-dwelling invertebrates at contaminated sites (compared to reference sites) in the Southeast Missouri Lead Mining District. This study assessed reproductive outcomes for songbirds exposed to soil-borne lead in the district, examined the relation between lead concentrations in soils and in tissues of ground-feeding birds and prey species, and compared the results to literature-based toxicity thresholds for lead that are associated with negative effects in birds. Three lead-contaminated sites and three reference sites (with background concentrations of lead and no known mining inputs) were compared in two ways: individually to all other sites or by site type. Additional effects of lead exposure were evaluated by examining concentrations of biomarkers (oxidative stress, lipid peroxidation, and deoxyribonucleic acid damage) in liver tissues, δALAD inhibition, and renal and hepatic microscopic lesions in birds from lead-contaminated and reference sites. Lead concentrations in soil were site-dependent and were also generally heterogeneous within the lead-contaminated sites. Between 17 and 74 percent of all soil samples at contaminated sites had lead concentrations that exceeded a threshold (1,000 milligrams per kilogram [mg/kg] lead in soil) previously associated with adverse physiological effects in birds in the Southeast Missouri Lead Mining District. Lead concentrations in mixed invertebrates from lead-contaminated sites (282 to 2,230 mg/kg dry weight [dw]) indicated that consuming soil-dwelling prey species is a potential exposure pathway for adult birds and their broods. At lead-contaminated sites, lead concentrations in 40.5 percent of blood samples (adults and their broods) were within a subclinical effects range (0.9 to 2.3 mg/kg dw), and 18.7 percent of samples had lead concentrations that exceeded clinical effects criteria (greater than 2.3 mg/kg dw). In contrast, only 2.6 percent of blood samples from reference sites were within the subclinical effects range for lead; all other blood samples from the reference sites had lead concentrations representative of background concentrations (less than 0.9 mg/kg dw). Subclinical and clinical threshold exceedances for lead concentrations in livers and kidneys were similarly more prevalent at the contaminated sites compared to the reference sites. Lead concentrations in blood were positively correlated with lead concentrations in soil, livers, and kidneys. Lead concentrations in blood were negatively correlated with δALAD activity; greater than 50 percent of the birds collected at lead-contaminated sites exhibited injury via greater than 50 percent inhibition of δALAD in blood compared to birds at reference sites. Birds with elevated lead concentrations in tissues also exhibited enhanced oxidative stress. Microscopic lesions in the livers and kidneys of birds had similar rates of occurrence at the contaminated and reference sites, and lesion prevalence could not be directly linked to lead exposure. Reproductive success was monitored at 585 nests, and 3 out of 5 species had reduced nest success associated with elevated lead concentrations in soil; habitat measures did not help explain nest success. Reduced nest success may have resulted from greater nest predation resulting from neurological and behavioral effects of lead exposure. Ultimately, these lines of evidence indicate that bird health and reproduction have been negatively affected by exposure to lead-contaminated soils in the Southeast Missouri Lead Mining District.

Missouri↗
USGS2025

Do bighorn sheep use desert tortoise burrow spoil piles as mineral licks in southern California?

In a previous study, bighorn sheep ( Ovis canadensis ) were periodically photographed by trail cameras when they visited desert tortoise ( Gopherus agassizii ) burrows in the San Bernardino Mountains near Palm Springs, California, USA. The authors suggested that bighorn sheep may utilize the excavated spoil material from burrows as a mineral lick. To test that supposition, we collected soil samples from the spoil piles and undisturbed surface soil up to 1 m away at two desert tortoise study sites with bighorn sheep at the aforementioned site and another nearby site. We hypothesized that sodium concentrations would be greater in spoil than in nearby surface soil samples. Common major cations were quantified in soil samples using inductively coupled plasma-mass spectrometry following microwave-assisted digestion to solubilize the fraction of elements that could become biologically or environmentally available. Mean sodium concentrations were greater in burrow spoil samples than in undisturbed soil samples at both sites, likely due to aqueous leaching of sodium from surface to subsurface soil horizons. Although we did not collect evidence of geophagy by bighorn sheep, the behavior is well known. However, trail camera photographs of bighorn sheep with their head next to the entrance of tortoise burrows suggests that they may periodically utilize sodium and other elements brought to the surface by desert tortoises that are otherwise unavailable to bighorn sheep on the surface.

California↗
USGS2023

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations from other sites to inform focal sites. In this paper, we evaluate two different DL model structures, a long short-term memory neural network (LSTM) and a recurrent graph convolutional neural network (RGCN), both with and without data assimilation for forecasting daily maximum stream temperature 7 days into the future at monitored and unmonitored locations in a 70-segment stream network. All our DL models performed well when forecasting stream temperature as the root mean squared error (RMSE) across all models ranged from 2.03 to 2.11°C for 1-day lead times in the validation period, with substantially better performance at gaged locations (RMSE = 1.45–1.52°C) compared to ungaged locations (RMSE = 3.18–3.27°C). Forecast uncertainty characterization was near-perfect for gaged locations but all DL models were overconfident (i.e., uncertainty bounds too narrow) for ungaged locations. Our results show that the RGCN with data assimilation performed best for ungaged locations and especially at higher temperatures (>18°C) which is important for management decisions in our study location. This indicates that the networked model structure and data assimilation techniques may help borrow information from nearby monitored sites to improve forecasts at unmonitored locations. Results from this study can help guide DL modeling decisions when forecasting other important environmental variables.

Frontiers in Water↗
USGS2026

Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations

Forecasts of streamflow drought, when streamflow declines below typical levels, are notably less available than for floods or meteorological drought, despite widespread impacts. We apply machine learning (ML) models to forecast streamflow drought 1–13 weeks ahead at 3,219 streamgages across the conterminous United States. We applied two ML methods (Long short-term memory neural networks; Light Gradient-Boosting Machine) and two benchmark models (persistence; Autoregressive Integrated Moving Average) to predict weekly streamflow percentiles with independent models for each forecast horizon. ML models outperformed benchmarks in predicting continuous streamflow percentiles below 30%. ML models generally performed worse than persistence models for discrete classification (moderate, severe, extreme) but exceeded the benchmark models for drought onset/termination. Performance was better for less intense droughts and shorter horizons, with predictive power for 1–4 weeks for severe droughts (10% threshold). This work highlights challenges and opportunities to advance hydrological drought forecasting and supports a new experimental forecasting tool.

conterminous United States↗
USGS2018

Exposure of Alaska brown bears (Ursus arctos) to bacterial, viral, and parasitic agents varies spatiotemporally and may be influenced by age

We collected blood and serum from 155 brown bears ( Ursus arctos ) inhabiting five locations in Alaska during 2013–16 and tested samples for evidence of prior exposure to a suite of bacterial, viral, and parasitic agents. Antibody seroprevalence among Alaska brown bears was estimated to be 15% for Brucella spp., 10% for Francisella tularensis , 7% for Leptospira spp., 18% for canine adenovirus type 1 (CAV-1), 5% for canine distemper virus (CDV), 5% for canine parvovirus, 5% for influenza A virus (IAV), and 44% for Toxoplasma gondii . No samples were seropositive for antibodies to Trichinella spp. Point estimates of prior exposure to pathogens among brown bears at previously unsampled locations generally fell within the range of estimates for previously or contemporaneously sampled bears in Alaska. Statistical support was found for variation in antibody seroprevalence among bears by location or age cohort for CAV-1, CDV, IAV, and Toxoplasma gondii . There was limited concordance in comparisons between our results and previous serosurveys regarding spatial and age-related trends in antibody seroprevalence among Alaska brown bears suggestive of temporal variation. However, we found evidence that the seroprevalence of CAV-1 antibodies is consistently high in bears inhabiting SW Alaska and the cumulative probability of exposure may increase with age. We found evidence for seroconversion or seroreversion to six different infectious agents in one or more bears. Results of this study increase our collective understanding of disease risk to both Alaska brown bear populations and humans that utilize this resource.

Alaska↗
USGS2023

Survey for selected parasites in Alaska brown bears (Ursus arctos)

To assess infection with or exposure to endo- and ectoparasites in Alaska brown bears ( Ursus arctos ), blood and fecal samples were collected during 2013–17 from five locations: Gates of the Arctic National Park and Preserve; Katmai National Park; Lake Clark National Park and Preserve; Yakutat Forelands; and Kodiak Island. Standard fecal centrifugal flotation was used to screen for gastrointestinal parasites, molecular techniques were used to test blood for the presence of Bartonella and Babesia spp., and an ELISA was used to detect antibodies to Sarcoptes scabiei , a species of mite recently associated with mange in American black bears ( Ursus americanus ). From fecal flotations ( n =160), we identified the following helminth eggs: Uncinaria sp. ( n =16, 10.0%), Baylisascaris sp. ( n =5, 3.1%), Dibothriocephalus sp. ( n =2, 1.2%), and taeniid-type eggs ( n =1, 0.6%). Molecular screening for intraerythrocytic parasites ( Babesia spp.) and intracellular bacteria ( Bartonella spp.) was negative for all bears tested. We detected antibodies to S. scabiei in six of 59 (10.2%) individuals. The relatively low level of parasite detection in this study meets expectations for brown bear populations living in large, relatively undisturbed habitats near the northern edge of the range. These results provide a contemporary understanding of parasites in Alaska brown bears and establish baseline levels of parasite presence to monitor for changes over time and relative to ecologic alterations.

Alaska↗
USGS2012

Bioenergy potential of the United States constrained by satellite observations of existing productivity

Background/Question/Methods Currently, the United States (U.S.) supplies roughly half the world’s biofuel (secondary bioenergy), with the Energy Independence and Security Act of 2007 (EISA) stipulating an additional three-fold increase in annual production by 2022. Implicit in such energy targets is an associated increase in annual biomass demand (primary bioenergy) from roughly 2.9 to 7.4 exajoules (EJ; 1018 Joules). Yet, many of the factors used to estimate future bioenergy potential are relatively unresolved, bringing into question the practicality of the EISA’s ambitious bioenergy targets. Here, our objective was to constrain estimates of primary bioenergy potential (PBP) for the conterminous U.S. using satellite-derived net primary productivity (NPP) data (measured for every 1 km2 of the 7.2 million km2 of vegetated land in the conterminous U.S) as the most geographically explicit measure of terrestrial growth capacity. Results/Conclusions We show that the annual primary bioenergy potential (PBP) of the conterminous U.S. realistically ranges from approximately 5.9 (± 1.4) to 22.2 (± 4.4) EJ, depending on land use. The low end of this range represents current harvest residuals, an attractive potential energy source since no additional harvest land is required. In contrast, the high end represents an annual harvest over an additional 5.4 million km2 or 75% of vegetated land in the conterminous U.S. While we identify EISA energy targets as achievable, our results indicate that meeting such targets using current technology would require either an 80% displacement of current croplands or the conversion of 60% of total rangelands. Our results differ from previous evaluations in that we use high resolution, satellite-derived NPP as an upper-envelope constraint on bioenergy potential, which removes the need for extrapolation of plot-level observed yields over large spatial areas. Establishing realistically constrained estimates of bioenergy potential seems a critical next step for effectively incorporating bioenergy into future U.S. energy portfolios.

Conference Paper↗
USGS2013

Empirical flow parameters : a tool for hydraulic model validity

The objectives of this project were (1) To determine and present from existing data in Texas, relations between observed stream flow, topographic slope, mean section velocity, and other hydraulic factors, to produce charts such as Figure 1 and to produce empirical distributions of the various flow parameters to provide a methodology to "check if model results are way off!"; (2) To produce a statistical regional tool to estimate mean velocity or other selected parameters for storm flows or other conditional discharges at ungauged locations (most bridge crossings) in Texas to provide a secondary way to compare such values to a conventional hydraulic modeling approach. (3.) To present ancillary values such as Froude number, stream power, Rosgen channel classification, sinuosity, and other selected characteristics (readily determinable from existing data) to provide additional information to engineers concerned with the hydraulic-soil-foundation component of transportation infrastructure.

Book↗