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At least 433 records · Page 24Linked to original sources
USGS2005

Climate science and famine early warning

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Philosophical Transactions of the Royal Society B:↗
USGS2021

Across borders: External factors and prior behaviour influence North Pacific albatross associations with fishing vessels

Understanding encounters between marine predators and fisheries across national borders and outside national jurisdictions offers new perspectives on unwanted interactions to inform ocean management and predator conservation. Although seabird–fisheries overlap has been documented at many scales, remote identification of vessel encounters has lagged because vessel movement data often are lacking. Here, we reveal albatrosses–fisheries associations throughout the North Pacific Ocean. We identified commercial fishing operations using Global Fishing Watch data and algorithms to detect fishing vessels. We compiled GPS tracks of adult black-footed Phoebastria nigripes and Laysan Phoebastria immutabilis albatrosses, and juvenile short-tailed albatrosses Phoebastria albatrus . We quantified albatrosses-vessel encounters based on the assumed distance that birds perceive a vessel (≤30 km), and associations when birds approached vessels (≤3 km). For each event we quantified bird behaviour, environmental conditions and vessel characteristics and then applied Boosted Regression Tree models to identify drivers and the duration of these associations. In regions of greater fishing effort short-tailed and Laysan albatrosses associated with fishing vessels more frequently. However, fishing method (e.g. longline, trawl) and flag nation did not influence association prevalence nor the duration short-tailed albatrosses attended fishing vessels. Laysan albatrosses were more likely to approach longer vessels. Black-footed albatrosses were the most likely to approach vessels (61.9%), but limited vessel encounters ( n = 21) prevented evaluation of meaningful explanatory models for this species of high bycatch concern. Temporal variables (time of day and month) and bird behavioural state helped explain when short-tailed albatrosses were in close proximity to a vessel, but environmental conditions were more important for explaining interaction duration. Laysan albatrosses were more likely to associate with vessels while searching and during the last 60% (by time) of their trips. Our results provide specific species–fisheries insight regarding contributing factors of high-risk associations that could lead to bycatch of albatrosses within national waters and on the high seas. Policy implications . Given the availability of Global Fishing Watch data, our analysis can be applied to other marine predators—if tracking data are available—to identify spatio-temporal patterns, vessel specific attributes and predator behaviours associated with fishing vessel associations, thus enabling predictive modelling and targeted mitigation measures.

Journal of Applied Ecology↗
USGS2011

Analysis of the deconstruction of Dyke Marsh, George Washington Memorial Parkway, Virginia-Progression, geologic and manmade causes, and effective restoration scenarios

This report is a synthesis of the latest findings from an ongoing study of Dyke Marsh, an eroding freshwater tidal wetland that is scheduled for federal restoration. Its purpose is to provide an accurate and up-to-date temporal and geological framework for the marsh, of which most is new information (plus a compilation of historical and recent information), that is directly relevant to the restoration effort and also is relevant to short-term and long-term land management decisions regarding this natural resource. Analysis of field evidence, aerial photography, and published maps has revealed an accelerating rate of erosion and marsh loss at Dyke Marsh, which now appears to put at risk the short term survivability of this marsh. The destabilization of Dyke Marsh is outlined here, spanning an approximately 70-year time interval (1940-2010). This freshwater tidal marsh has shifted from a semi-stable net depositional environment (1864-1937) into a strongly erosional one, during a time when it currently is in early-phase planning for comprehensive restoration. The marsh has been deconstructed over the past 70 years by a combination of manmade and natural causes. The marsh initially experienced a strong destabilizing period between 1940 and 1972 by direct dredge mining of the marsh surface. By 1976 the marsh had entered a net destructive phase, where it remains at present. Photoanalysis of time-sequence aerial photographs of Dyke Marsh enabled us to calculate shoreline erosion estimates for this marsh over 19 years (1987-2006), as well as to quantify overall marsh acreage for 6 calendar years spanning an ~70 year interval (1937-2006). Photo overlay of a historic map enabled us to extend our whole-marsh acreage calculations back to 1883. Both sets of analyses were part of a geologic framework study in support of current efforts by the National Park Service (NPS) to restore this urban wetland. Two time intervals were selected for our shoreline erosion analyses, based on image quality and availability: 1987 to 2002, and 2002 to 2006. The more recent time interval shows a marked increase in erosion in the southern part of Dyke Marsh, following a wave-induced breach of a small peninsula that had protected its southern shoreline. Field observations and analyses of annual aerial imagery between 1987 and 2006 revealed a progressive increase in wave-induced erosion that presently is deconstructing Hog Island Gut, the last significant tidal creek network within the Dyke Marsh. These photo analyses documented an overall average westward shoreline loss of 6.0 to 7.8 linear feet per year along the Potomac River during this 19-year time interval. Additionally, photographic evidence documented that lateral erosion now is capturing existing higher order tributaries in the Hog Island Gut. Wave-driven stream piracy is fragmenting the remaining marsh habitat, and therefore its connectivity, relatively rapidly, causing the effective mouth of the Hog Island Gut tidal network to retreat headward visibly over the past several decades. Based on our estimates of total marsh area in the Dyke Marsh derived from 1987 aerial imagery, as much as 12 percent of the central part of the marsh has eroded in the 19 year period we studied (or ~7.5 percent of the original ~78.8 acres of 1987 marshland). Shoreline loss estimates for marsh parcels north and south of our study area have not yet been analyzed, although annual aerial photos from 1987 to 2002 confirm visible progressive shoreline loss in those areas over this same time interval.

Open-File Report↗
USGS2025

Prevalence of pelagic diatoms and harmful algae in tellinid bivalve diets during record low sea ice in the Pacific Arctic determined by DNA metabarcoding

Understanding changes at the base of the marine food web in the rapidly transforming Arctic is essential for predicting and evaluating ecosystem dynamics. The northern Bering Sea experienced record low sea ice in 2018, followed by the second lowest in 2019, highlighting the urgency of the issue for this region. In this study, we investigated the diet of the clam Macoma calcarea in the Pacific Arctic using DNA metabarcoding, employing 18S and rbcL markers to identify dietary components. Our findings revealed a strong dependence on pelagic diatoms, particularly Chaetoceros sp., with a near absence of ice algae in the clam diet. This pattern reflects the lack of lipid-rich ice algal production during these low sea ice events. Additionally, our analysis detected algae capable of producing harmful toxins, notably Alexandrium dinoflagellates, in the clam diet, underscoring the need for increased monitoring due to potential ecosystem and human health risks. This study demonstrates the utility of DNA metabarcoding in unraveling the complex dynamics of Arctic marine food webs and pelagic-benthic coupling, providing a glimpse of future conditions in a rapidly changing environment.

Bering Sea, Chukchi Sea↗
USGS2020

Capture of environmental DNA (eDNA) from water samples by flocculation

The analysis of environmental DNA (eDNA) has become a widely used approach to problem solving in species management. The detection of cryptic species including invasive and (or) species at risk is the goal, typically accomplished by testing water and sediment for the presence of characteristic DNA signatures. Reliable and efficient procedures for the capture of eDNA are required, especially those that can be performed easily in the field by personnel with limited training and citizen scientists. The capture of eDNA using membrane filtration is widely used currently. This approach has inherent issues that include the choice of filter material and porosity, filter fouling, and time required on site for the process to be performed. Flocculation offers an alternative that can be easily implemented and applied to sampling regimes that strive to cover broad territories in limited time.

JOVE Journal Of Visualized Experiments↗
USGS2012

Time-to-event analysis as a framework for quantifying fish passage performance

Fish passage is the result of a sequence of processes, whereby fish must approach, enter, and pass a structure. Each of these processes takes time, and fishway performance is best quantified in terms of the rates at which each process is completed. Optimal performance is achieved by maximizing the rates of approach, entry, and passage through safe and desirable routes. Sometimes, however, it is necessary to reduce rates of passage through less desirable routes in order to increase proportions passing through the preferred route. Effectiveness of operational or structural modifications for achieving either of these goals is best quantified by applying time-to-event analysis, commonly known as survival analysis methods, to telemetry data. This set of techniques allows for accurate estimation of passage rates and covariate effects on those rates. Importantly, it allows researchers to quantify rates that vary over time, as well as the effects of covariates that also vary over time. Finally, these methods are able to control for competing risks, i.e., the presence of alternate passage routes, failure to pass, or other fates that remove fish from the pool of candidates available to pass through a particular route. In this chapter, we present a model simulation of telemetered fish passing a hydroelectric dam, and provide step-by-step guidance and rationales for performing time-to-event analysis on the resulting data. We demonstrate how this approach removes bias from performance estimates that can result from using methods that focus only on proportions passing each route. Time-to-event analysis, coupled with multinomial models for measuring survival, provides a comprehensive set of techniques for quantifying fish passage, and a framework from which performance among different sites can be better understood.

Book chapter↗
USGS2008

Spatial elements of mortality risk in old-growth forests

For many species of long-lived organisms, such as trees, survival appears to be the most critical vital rate affecting population persistence. However, methods commonly used to quantify tree death, such as relating tree mortality risk solely to diameter growth, almost certainly do not account for important spatial processes. Our goal in this study was to detect and, if present, to quantify the relevance of such processes. For this purpose, we examined purely spatial aspects of mortality for four species, Abies concolor , Abies magnifica , Calocedrus decurrens , and Pinus lambertiana , in an old-growth conifer forest in the Sierra Nevada of California, USA. The analysis was performed using data from nine fully mapped long-term monitoring plots. In three cases, the results unequivocally supported the inclusion of spatial information in models used to predict mortality. For Abies concolor , our results suggested that growth rate may not always adequately capture increased mortality risk due to competition. We also found evidence of a facilitative effect for this species, with mortality risk decreasing with proximity to conspecific neighbors. For Pinus lambertiana , mortality risk increased with density of conspecific neighbors, in keeping with a mechanism of increased pathogen or insect pressure (i.e., a Janzen-Connell type effect). Finally, we found that models estimating risk of being crushed were strongly improved by the inclusion of a simple index of spatial proximity. Not only did spatial indices improve models, those improvements were relevant for mortality prediction. For P. lambertiana , spatial factors were important for estimation of mortality risk regardless of growth rate. For A. concolor , although most of the population fell within spatial conditions in which mortality risk was well described by growth, trees that died occurred outside those conditions in a disproportionate fashion. Furthermore, as stands of A. concolor become increasingly dense, such spatial factors are likely to become increasingly important. In general, models that fail to account for spatial pattern are at risk of failure as conditions change.

Ecology↗
USGS2011

Using avian radar to examine relationships among avian activity, bird strikes, and meteorological factors

Radar systems designed to detect avian activity at airfields are useful in understanding factors that influence the risk of bird and aircraft collisions (bird strikes). We used an avian radar system to measure avian activity at Beale Air Force Base, California, USA, during 2008 and 2009. We conducted a 2-part analysis to examine relationships among avian activity, bird strikes, and meteorological and time-dependent factors. We found that avian activity around the airfield was greater at times when bird strikes occurred than on average using a permutation resampling technique. Second, we developed generalized linear mixed models of an avian activity index (AAI). Variation in AAI was first explained by seasons that were based on average migration dates of birds at the study area. We then modeled AAI by those seasons to further explain variation by meteorological factors and daily light levels within a 24- hour period. In general, avian activity increased with decreased temperature, wind, visibility, precipitation, and increased humidity and cloud cover. These effects differed by season. For example, during the spring bird migration period, most avian activity occurred before sunrise at twilight hours on clear days with low winds, whereas during fall migration, substantial activity occurred after sunrise, and birds generally were more active at lower temperatures. We report parameter estimates (i.e., constants and coefficients) averaged across models and a relatively simple calculation for safety officers and wildlife managers to predict AAI and the relative risk of bird strike based on time, date, and meteorological values. We validated model predictability and assessed model fi t. These analyses will be useful for general inference of avian activity and risk assessment efforts. Further investigation and ongoing data collection will refine these inference models and improve our understanding of factors that influence avian activity, which is necessary to inform management decisions aimed at reducing risk of bird strikes.

California↗
USGS2017

Using state-and-transition modeling to account for imperfect detection in invasive species management

Buffelgrass, a highly competitive and flammable African bunchgrass, is spreading rapidly across both urban and natural areas in the Sonoran Desert of southern and central Arizona. Damages include increased fire risk, losses in biodiversity, and diminished revenues and quality of life. Feasibility of sustained and successful mitigation will depend heavily on rates of spread, treatment capacity, and cost–benefit analysis. We created a decision support model for the wildland–urban interface north of Tucson, AZ, using a spatial state-and-transition simulation modeling framework, the Tool for Exploratory Landscape Scenario Analyses. We addressed the issues of undetected invasions, identifying potentially suitable habitat and calibrating spread rates, while answering questions about how to allocate resources among inventory, treatment, and maintenance. Inputs to the model include a state-and-transition simulation model to describe the succession and control of buffelgrass, a habitat suitability model, management planning zones, spread vectors, estimated dispersal kernels for buffelgrass, and maps of current distribution. Our spatial simulations showed that without treatment, buffelgrass infestations that started with as little as 80 ha (198 ac) could grow to more than 6,000 ha by the year 2060. In contrast, applying unlimited management resources could limit 2060 infestation levels to approximately 50 ha. The application of sufficient resources toward inventory is important because undetected patches of buffelgrass will tend to grow exponentially. In our simulations, areas affected by buffelgrass may increase substantially over the next 50 yr, but a large, upfront investment in buffelgrass control could reduce the infested area and overall management costs.

Arizona↗
USGS2013

Resilience thinking and a decision-analytic approach to conservation: strange bedfellows or essential partners?

There has been some tendency to view decision science and resilience theory as opposing approaches, or at least as contending perspectives, for natural resource management. Resilience proponents have been especially critical of optimization in decision science, at least for those cases where it is focused on the aggressive pursuit of efficiency. In general, optimization of resource systems is held to reduce spatial, temporal, or organizational heterogeneity that would otherwise limit efficiency, leading to homogenization of a system and making it less able to cope with unexpected changes or disturbances. For their part, decision analysts have been critical of resilience proponents for not providing much practical advice to decision makers. We believe a key source of tension between resilience thinking and application of decision science is the pursuit of efficiency in the latter (i.e., choosing the “best” management action or strategy option to maximize productivity of one or few resource components), vs. a desire in the former to keep options open (i.e., maintaining and enhancing diversity). It seems obvious, however, that with managed natural systems, there must be a principle by which to guide decision making, which at a minimumallows for a comparison of projected outcomes associated with decision alternatives. This is true even if the primary concern of decision making is the preservation of system resilience. We describe how a careful framing of conservation problems, especially in terms of management objectives and predictive models, can help reduce the purported tension between resiliencethinking and decision analysis. In particular, objective setting in conservation problems needs to be more attuned to the dynamics of ecological systems and to the possibility of deep uncertainties that underlie the risk of unintended, if not irreversible, outcomes. Resilience thinking also leads to the suggestion that model development should focus more on process rather than pattern, on multiple scales of influence, and on phenomena that can create alternative stability regimes. Although we acknowledge the inherent difficulties in modeling ecological processes, we stress that formulation of useful models need not depend on a thorough mechanistic understanding or precise parameterization, assuming that uncertainty is acknowledged and treated in a systematic manner.

Ecology and Society↗
USGS2017

Grand challenges for integrated USGS science — A workshop report

Executive Summary The U.S. Geological Survey (USGS) has a long history of advancing the traditional Earth science disciplines and identifying opportunities to integrate USGS science across disciplines to address complex societal problems. The USGS science strategy for 2007–2017 laid out key challenges in disciplinary and interdisciplinary arenas, culminating in a call for increased focus on a number of crosscutting science directions. Ten years on, to further the goal of integrated science and at the request of the Executive Leadership Team (ELT), a workshop with three dozen invited scientists spanning different disciplines and career stages in the Bureau convened on February 7–10, 2017, at the USGS John Wesley Powell Center for Analysis and Synthesis in Fort Collins, Colorado. The workshop focused on identifying “grand challenges” for integrated USGS science. Individual participants identified nearly 70 potential grand challenges before the workshop and through workshop discussions. After discussion, four overarching grand challenges emerged: Natural resource security, Societal risk from existing and emerging threats, Smart infrastructure development, and Anticipatory science for changing landscapes. Participants also identified a “comprehensive science challenge” that highlights the development of integrative science, data, models, and tools—all interacting in a modular framework—that can be used to address these and other future grand challenges: Earth Monitoring, Analyses, and Projections (EarthMAP) EarthMAP is our long-term vision for an integrated scientific framework that spans traditional scientific boundaries and disciplines, and integrates the full portfolio of USGS science: research, monitoring, assessment, analysis, and information delivery. The Department of Interior, and the Nation in general, have a vast array of information needs. The USGS meets these needs by having a broadly trained and agile scientific workforce. Encouraging and supporting cross-discipline engagement would position the USGS to tackle complex and multifaceted scientific and societal challenges in the 21st Century.

Open-File Report↗
USGS2010

Methods for development of planning-level estimates of stormflow at unmonitored stream sites in the conterminous United States

This report documents methods for data compilation and analysis of statistics for stormflows that meet data-quality objectives for order-of-magnitude planning-level water-quality estimates at unmonitored sites in the conterminous United States. Statistics for prestorm streamflow, precipitation, and runoff coefficients are used to model stormflows for use with the Stochastic Empirical Loading and Dilution Model (SELDM), which is a highway-runoff model. SELDM is designed to better quantify the risk of exceeding water-quality criteria as precipitation, discharge, ambient water quality, and highway-runoff quality vary from storm to storm. Summary statistics also may be used to help estimate annual-average water-quality loads. Streamflow statistics are used to estimate prestorm flows. Streamflow statistics are estimated by analysis of data from 2,873 U.S. Geological Survey streamgages in the conterminous United States with drainage areas ranging from 10 to 500 square miles and at least 24 years of record during the period 1960−2004. Streamflow statistics are regionalized using U.S. Environmental Protection Agency Level III nutrient ecoregions. Storm-event precipitation statistics are estimated by analysis of data from 2,610 National Oceanic and Atmospheric Administration hourly-precipitation data stations in the conterminous United States with at least 25 years of data during the 1965−2006 period. Storm-event precipitation statistics are regionalized using U.S. Environmental Protection Agency rain zones. Statistics to characterize volumetric runoff coefficients are estimated using data from 6,142 storm events at 306 study sites. Runoff coefficient statistics are not regionalized, but are organized by total impervious area. All of the geographic information system files, computer programs, data files, and regression results developed for this study are included on the CD−ROM accompanying this report.

Report↗
USGS2011

A probabilistic seismic risk assessment procedure for nuclear power plants: (I) Methodology

A new procedure for probabilistic seismic risk assessment of nuclear power plants (NPPs) is proposed. This procedure modifies the current procedures using tools developed recently for performance-based earthquake engineering of buildings. The proposed procedure uses (a) response-based fragility curves to represent the capacity of structural and nonstructural components of NPPs, (b) nonlinear response-history analysis to characterize the demands on those components, and (c) Monte Carlo simulations to determine the damage state of the components. The use of response-rather than ground-motion-based fragility curves enables the curves to be independent of seismic hazard and closely related to component capacity. The use of Monte Carlo procedure enables the correlation in the responses of components to be directly included in the risk assessment. An example of the methodology is presented in a companion paper to demonstrate its use and provide the technical basis for aspects of the methodology.

Nuclear Engineering and Design↗
USGS2019

Incorporating uncertainty and risk into decision making to reduce nitrogen inputs to impaired waters

This article aims to understand decision making under uncertainty and risk, with a case study on Cape Cod, Massachusetts. Decision makers need to consider imperfect information on the cost and effectiveness of advanced nitrogen-removing on-site wastewater treatment systems as options to mitigate water quality degradation. Research included modeling nitrogen load reduction to impaired coastal waters from seven treatment system technologies and eliciting expert knowledge on their costs. Predictions of nitrogen load removal and cost for each technology incorporated variation in effectiveness and uncertainty in household water use, costs, and expert confidence in costs. The predictions were evaluated using the Pareto efficiency concept to reveal tradeoffs between cost and effectiveness. The stochastic dominance index was used to identify preferred technologies for risk-averse decision making, assuming no further learning is possible. Lastly, the predictions were combined into a cost-effectiveness metric to estimate the expected payoff of implementing the best treatment system in the face of uncertainty and the expected payoff of learning which treatment systems are most cost-effective over time. The expected value of perfect information was calculated as the difference between the expected payoffs. Three technologies revealed Pareto efficient tradeoffs between cost and effectiveness, whereas one technology was the preferred risk-averse option in the absence of future learning. There was a high expected value of perfect information, which could motivate adaptive management on Cape Cod. This research demonstrated decision analysis methods to guide future research and decision making toward meeting water quality objectives and reducing uncertainty.

Massachusetts↗
USGS2010

Flood risks and the willingness to purchase flood insurance

Computer simulation experiments were conducted to determine the effects of alternative sources of uncertainty on the willingness to pay for flood insurance. Two alternative insurance protection schemes were investigated: coinsurance and fixed coverage. The question investigated here is to what extent does the insurance scheme influence how purchasers respond to flood risks? Floods were assumed to be log normally distributed and the effects on the purchase of insurance of uncertainties in the parameters of the distribution were explored using response surface analysis. Results indicate that fixed coverage insurance provisions shift most of the uncertainty in the physical parameters governing natural disaster occurrences away from the insuree and onto the insurer. The results also show that the form of the damage function has little effect on the demand for flood insurance.

Water Resources Research↗
USGS2024

Framework for mapping liquefaction hazard–Targeted design ground motions

Liquefaction-induced ground failure poses substantial challenges to geotechnical earthquake engineering design. Current approaches for designing against liquefaction hazards, as specified in most seismic provisions, focus on estimating a liquefaction factor of safety ( 𝐹⁢𝑆𝐿 ) and typically characterize earthquake loading using design parameters based on probabilistic or deterministic ground motion levels. Because 𝐹⁢𝑆𝐿 is estimated deterministically, this basis of design neglects considerable uncertainties for estimating liquefaction triggering and its consequences and results in a lack of liquefaction-specific design criteria, particularly as structural design has advanced toward risk-targeted performance objectives. This study presents a framework for developing liquefaction-targeted design criteria based on a minimum acceptable return period of liquefaction, informed by probabilistic liquefaction hazard analysis (PLHA). PLHA quantifies annualized rates of liquefaction by considering contributions from (1) the full ground-motion probability space, and (2) uncertainties in liquefaction triggering using probabilistic models. PLHA is used in this study to characterize the current, effective return periods of 𝐹⁢𝑆𝐿 ( 𝑇𝑅,𝐹⁢𝑆 ) obtained from conventional liquefaction hazard analysis (CLHA) using uniform-hazard ground motions. 𝑇𝑅,𝐹⁢𝑆 is evaluated in a parametric study of nearly 100 sites throughout the conterminous United States. The results indicate large geographic variations in acceptable liquefaction hazard levels, with implied 𝑇𝑅,𝐹⁢𝑆 ranging between approximately 1,000 to 3,000 years. To address these inconsistencies without the computational demands of full PLHA, a framework is proposed for developing a liquefaction-targeted design peak ground acceleration, 𝑃⁢𝐺⁢𝐴𝐿 , for use in liquefaction models that result in consistent liquefaction design levels across all geographic locations. The mapped 𝑃⁢𝐺⁢𝐴𝐿 is shown to be somewhat sensitive to site-specific properties, and adjustment factors are developed and presented. The proposed 𝑃⁢𝐺⁢𝐴𝐿 mapping procedure produces 𝐹⁢𝑆𝐿 estimates that are consistent with those obtained from full PLHA at a target 𝑇𝑅,𝐹⁢𝑆 , providing a promising roadmap to incorporating PLHA concepts into current liquefaction design methods.

Journal of Geotechnical and Geoenvironmental Engin↗
USGS1996

Effects of receiving-water quality and wastewater treatment on injury, survival, and regrowth of fecal-indicator bacteria and implications for assessment of recreational water quality

Bacterial injury, survival, and regrowth were investigated by use of replicate flow-through incubation chambers placed in the Cuyahoga River or Lake Erie in the greater Cleveland metropolitan area during seven 4-day field studies. The chambers contained wastewater or combined-sewer-overflow (CSO) effluents treated three ways-unchlorinated, chlorinated, and dechlorinated. At timestep intervals, the chamber contents were analyzed for concentrations of injured and healthy fecal coliforms by use of standard selective and enhanced-recovery membrane-filtration methods. Mean percent injuries and survivals were calculated from the fecal-coliform concentration data for each field study. The results of analysis of variance (ANOVA) indicated that treatment affected mean percent injury and survival, whereas site did not. In the warm-weather Lake Erie field study, but not in the warm-weather Cuyahoga River studies, the results of ANOVA indicated that dechlorination enhanced the repair of injuries and regrowth of chlorine-injured fecal coliforms on culture media over chlorination alone. The results of ANOVA on the percent injury from CSO effluent field studies indicated that dechlorination reduced the ability of organisms to recover and regrow on culture media over chlorination alone. However, because of atypical patterns of concentration increases and decreases in some CSO effluent samples, more work needs to be done before the effect of dechlorination and chlorination on reducing fecal-coliform concentrations in CSO effluents can be confirmed. The results of ANOVA on percent survivals found statistically significant differences among the three treatment methods for all but one study. Dechlorination was found to be less effective than chlorination alone in reducing the survival of fecal coliforms in wastewater effluent, but not in CSO effluent. If the concentration of fecal coliforms determined by use of the enhanced-recovery method can be predicted accurately from the concentration found by use of the standard method, then increased monitoring and expense to detect chlorine-injured organisms would be unnecessary. The results of linear regression analysis, however, indicated that the relation between enhanced-recovery and standard-method concentrations was best represented when the data were grouped by treatment. The model generated from linear regression of the unchlorinated data set provided an accurate estimate of enhanced-recovery concentrations from standard-method concentrations, whereas the models generated from the chlorinated and dechlorinated data sets did not. In addition, evaluation of fecal-coliform concentrations found in field studies in terms of Ohio recreational water-quality standards showed that concentrations obtained by standard and enhanced-recovery methods were not comparable. Sample treatment and analysis methods were found to affect the percentage of samples meeting and exceeding Ohio's bathing-water, primary-contact, and secondary-contact standards. Therefore, determining the health risk of swimming in receiving waters was often difficult without information on enhanced-recovery method concentrations and was especially difficult in waters receiving high proportions of chlorinated or dechlorinated effluents.

Water-Resources Investigations Report↗
USGS2026

Variability and consistency in wildfire susceptibility: Insights from a national compilation

Background Wildfire risk in the United States is rising and remains a land management priority. The quantitative wildfire risk assessment (QWRA) framework integrates fuels, topography, weather and values at risk to estimate the potential change in value from wildfire. Within this, response functions (RFs) represent how values respond to fire intensity. These are often based on expert judgment, but variation across assessments is unclear. Aims This study uses data from the US Geological Survey (USGS) Wildfire Hazard and Risk Assessment Clearinghouse to characterize consistency and variation across categories and contexts. Methods We applied descriptive statistics to summarize RFs, using tables, box-and-whisker plots and heat maps stratified by highly valued resource or asset (HVRA) category and spatial scale. Key results RFs and value definitions vary, especially for ecosystem-related resources. Some functions, such as for buildings in the wildland–urban interface (WUI), translate well across contexts, while others require more input. Conclusions Some functions are broadly transferable, while others need customization. This analysis provides references and starting points for improvement to RFs in QWRAs. Interpretations Expanding the clearinghouse and dataset and building more transparency in expert elicitation can build trust among communities, agencies and end-users, and can support efficient use of limited resources to mitigate wildfire risk.

International Journal of Wildland Fire↗