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Results for “Environmental Modeling & Assessment”

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At least 541 records · Page 30Linked to original sources
USGS2023

Using seasonal climate scenarios in the ForageAhead annual forage production model for early drought impact assessment

High interannual variability of forage production in semiarid grasslands leads to uncertainties when livestock producers make decisions, such as buying additional feed, relocating animals, or using flexible stocking. Within-season predictions of annual forage production (i.e., yearly production) can provide specific boundaries for producers to make these decisions with more information and possibly with higher confidence. In this study, we use a recently developed forage production model, ForageAhead, that uses environmental and seasonal climate variables to estimate the annual forage production as approximated by remotely sensed vegetation data. Because, among other variables, this model uses observed summer climate data, the model output cannot be produced early enough in the year (e.g., spring months) to inform within-season management decisions. To address this issue, we developed summer climate scenarios (e.g., extremely warm and dry and moderately cool and wet) that serve as an input in the model in combination with observed winter and spring climate data from a particular year. The summer climate scenarios used historical summer precipitation and temperature data (1950–2018) categorized into three, five, and seven percentile categories. These percentile values were then combined to represent summer climate scenarios, which were further used as the ForageAhead model input. We tested the optimal number of percentile categories to be used as the model input to obtain accurate prediction of forage production while also minimizing the number of possible temperature and precipitation combinations, which increases with the number of percentile categories. For the 19-year period analysis (2000–2018), we also determined the most and least common scenarios that occurred in the western United States. When using five percentile categories for summer precipitation and temperature, we were able to capture the interannual variability in the spatial extent of abnormally low and high biomass production. The ForageAhead predictions captured similar spatial patterns of forage anomalies as another similar model (Grass-Cast). This method can be made available in a user-friendly automated system that can be used by livestock producers and rangeland managers to inform within-season management decisions. This method can be especially valuable for flexible stocking as it provides a range of possible annual forage production scenarios by the end of May.

Ecosphere↗
USGS2014

Bioavailability and bioaccumulation of metal-based engineered nanomaterials in aquatic environments: Concepts and processes

Bioavailability of Me-ENMs to aquatic organisms links their release into the environment to ecological implications. Close examination shows some important differences in the conceptual models that define bioavailability for metals and Me-ENMs. Metals are delivered to aquatic animals from Me-ENMs via water, ingestion, and incidental surface exposure. Both metal released from the Me-ENM and uptake of the nanoparticle itself contribute to bioaccumulation. Some mechanisms of toxicity and some of the metrics describing exposure may differ from metals alone. Bioavailability is driven by complex interaction of particle attributes, environmental transformations, and biological traits. Characterization of Me-ENMs is an essential part of understanding bioavailability and requires novel methodologies. The relative importance of the array of processes that could affect Me-ENM bioavailability remains poorly known, but new approaches and models are developing rapidly. Enough is known, however, to conclude that traditional approaches to exposure assessment for metals would not be adequate to assess risks from Me-ENMs.

Book chapter↗
USGS2020

Drought early warning and forecasting

Drought risk management involves three pillars: drought early warning, drought vulnerability and risk assessment, and drought preparedness, mitigation, and response. This book collects in one place a description of all the key components of the first pillar, and describes strategies for fitting these pieces together. The best modern drought early warning systems incorporate and integrate a broad array of environmental information sources: weather station observations, satellite imagery, land surface and crop model simulations, and weather and climate model forecasts, and analyze this information in context-relevant ways that take into account exposure and vulnerability. Drought Early Warning and Forecasting: Theory and Practice assembles a comprehensive overview of these components, providing examples drawn from the Famine Early Warning Systems Network and the United States Drought Monitor. This book simultaneously addresses the physical, social, and information management aspects of drought early warning, and informs readers about the tools, techniques, and conceptual models required to effectively identify, predict, and communicate potential drought-related disasters. This book is a key text for postgraduate scientists and graduate and advanced undergraduate students in hydrology, geography, earth sciences, meteorology, climatology, and environmental sciences programs. Professionals dealing with disaster management and drought forecasting will also find this book beneficial to their work.

Book↗
USGS2019

Local climate determines vulnerability to camouflage mismatch in snowshoe hares

Aim Phenological mismatches, when life‐events become mistimed with optimal environmental conditions, have become increasingly common under climate change. Population‐level susceptibility to mismatches depends on how phenology and phenotypic plasticity vary across a species’ distributional range. Here, we quantify the environmental drivers of colour moult phenology, phenotypic plasticity, and the extent of phenological mismatch in seasonal camouflage to assess vulnerability to mismatch in a common North American mammal. Location North America. Time period 2010–2017. Major taxa studied Snowshoe hare ( Lepus americanus ). Methods We used > 5,500 by‐catch photographs of snowshoe hares from 448 remote camera trap sites at three independent study areas. To quantify moult phenology and phenotypic plasticity, we used multinomial logistic regression models that incorporated geospatial and high‐resolution climate data. We estimated occurrence of camouflage mismatch between hares’ coat colour and the presence and absence of snow over 7 years of monitoring. Results Spatial and temporal variation in moult phenology depended on local climate conditions more so than on latitude. First, hares in colder, snowier areas moulted earlier in the fall and later in the spring. Next, hares exhibited phenotypic plasticity in moult phenology in response to annual variation in temperature and snow duration, especially in the spring. Finally, the occurrence of camouflage mismatch varied in space and time; white hares on dark, snowless background occurred primarily during low‐snow years in regions characterized by shallow, short‐lasting snowpack. Main conclusions Long‐term climate and annual variation in snow and temperature determine coat colour moult phenology in snowshoe hares. In most areas, climate change leads to shorter snow seasons, but the occurrence of camouflage mismatch varies across the species’ range. Our results underscore the population‐specific susceptibility to climate change‐induced stressors and the necessity to understand this variation to prioritize the populations most vulnerable under global environmental change.

Global Ecology and Biogeography↗
USGS2020

Monitoring and real-time modeling of Escherichia coli bacteria for the Chattahoochee River, Chattahoochee River National Recreation Area, Georgia, 2000–2019

The Chattahoochee River National Recreation Area (CRNRA) is a National Park Service unit/park with 48 miles of urban waterway in the Atlanta metropolitan area. The Chattahoochee River within the CRNRA is a popular place for water-based recreation but is known to periodically experience elevated levels of fecal-coliform bacteria associated with warm-blooded animals that can result in a variety of pathogen-related human illnesses. In 2000, the National Park Service entered into a public-private partnership with the U.S. Geological Survey (USGS) and the Chattahoochee Riverkeeper, called the Chattahoochee River BacteriALERT program, to monitor Escherichia coli ( E. coli ), which is a fecal indicator bacteria and a proxy for human health risk from waterborne pathogens. The BacteriALERT network monitors E. coli densities at three stations on the Chattahoochee River within the CRNRA, at Norcross (USGS station 02335000), Powers Ferry (USGS station 02335880), and Atlanta (USGS station 02336000). E. coli densities determined from water samples were compared to the U.S. Environmental Protection Agency’s Beach Action Value (BAV) of 235 colony forming units per 100 milliliters to assess whether conditions were considered safe for freshwater, primary contact recreational use. Sample E. coli densities exceeded the BAV for 15.5 percent of the samples collected at Norcross (n = 1,969) and 30.3 percent of the samples at Atlanta (n = 1,938) for the study period October 23, 2000, to May 23, 2019, and 33.6 percent of the samples from Powers Ferry (n = 134) for the study period May 5, 2016, to May 23, 2019. Models to predict E. coli densities in near real-time were developed for the three BacteriALERT stations. Models were developed using forward-stepwise multiple linear regression with the Bayesian Information Criteria and were calibrated with samples collected between October 4, 2007, and May 23, 2019. Explanatory variables included season, turbidity, water temperature, streamflow, upstream tributary streamflows, and temporal trend. The most statistically significant explanatory variables in the models were turbidity, upstream tributary streamflows, and season. The Norcross model had an increasing trend in E. coli densities of 2.3 percent per year. A significant trend was not detected for the Atlanta station, while trends were not assessed for Powers Ferry models due to the short (3-year) calibration period. Model adjusted R 2 s ranged from 0.686 (Atlanta) to 0.795 (Norcross with time trend) indicating that the models explained a substantial portion of the variations in E. coli densities. Evaluation of model predictions and residuals indicated that models were well posed and exhibited little bias. The models performed well in accurately determining compliance and exceedance of the BAV with low misidentification rates ranging from 3.5 percent (Norcross) to 11.3 percent (Powers Ferry). Misidentification was most common for densities near the BAV, and misidentification rates in the study were low despite fairly low model precisions because E. coli densities were infrequently near the BAV. The precisions of the models developed herein were comparable to the more complex models developed by Lawrence (2012) that were never implemented in the BacteriALERT program due to their computational complexity. The predictive E. coli models developed herein will improve the ability to assess the health risks of water-based recreational activities in the CRNRA in near real-time.

Georgia↗
USGS2014

The effect of adjusting model inputs to achieve mass balance on time-dynamic simulations in a food-web model of Lake Huron

Ecopath with Ecosim (EwE) is a widely used modeling tool in fishery research and management. Ecopath requires a mass-balanced snapshot of a food web at a particular point in time, which Ecosim then uses to simulate changes in biomass over time. Initial inputs to Ecopath, including estimates for biomasses, production to biomass ratios, consumption to biomass ratios, and diets, rarely produce mass balance, and thus ad hoc changes to inputs are required to balance the model. There has been little previous research of whether ad hoc changes to achieve mass balance affect Ecosim simulations. We constructed an EwE model for the offshore community of Lake Huron, and balanced the model using four contrasting but realistic methods. The four balancing methods were based on two contrasting approaches; in the first approach, production of unbalanced groups was increased by increasing either biomass or the production to biomass ratio, while in the second approach, consumption of predators on unbalanced groups was decreased by decreasing either biomass or the consumption to biomass ratio. We compared six simulation scenarios based on three alternative assumptions about the extent to which mortality rates of prey can change in response to changes in predator biomass (i.e., vulnerabilities) under perturbations to either fishing mortality or environmental production. Changes in simulated biomass values over time were used in a principal components analysis to assess the comparative effect of balancing method, vulnerabilities, and perturbation types. Vulnerabilities explained the most variation in biomass, followed by the type of perturbation. Choice of balancing method explained little of the overall variation in biomass. Under scenarios where changes in predator biomass caused large changes in mortality rates of prey (i.e., high vulnerabilities), variation in biomass was greater than when changes in predator biomass caused only small changes in mortality rates of prey (i.e., low vulnerabilities), and was amplified when environmental production was increased. When standardized to mean changes in biomass within each scenario, scenarios when vulnerabilities were low and when fishing mortality was increased explained the most variation in biomass. Our findings suggested that approaches to balancing Ecopath models have relatively little effect on changes in biomass over time, especially when compared to assumptions about how mortality rates of prey change in response to changes in predator biomass. We concluded that when constructing food-web models using EwE, determining the effect of changes in predator biomass on mortality rates of prey should be prioritized over determining the best way to balance the model.

Lake Huron↗
USGS2018

An interferometric synthetic aperture radar (InSAR) habitat suitability model to identify overwinter conditions for coregonine whitefishes in Arctic lagoons

Lagoons provide critical habitats for many fishes, including coregonine whitefishes, which are a mainstay in many subsistence fisheries of rural communities in Arctic Alaska. Despite their importance, little is known about the overwintering habits of whitefishes in Arctic Alaska due to the challenges associated with sampling during winter. We developed a habitat suitability (HS) model to understand the potential range of physical conditions that whitefishes experience during the Arctic winter, using three indicator lagoons that represent a range of environmental characteristics. The HS model was built using a three-step approach. First, remote sensing that uses interferometric synthetic aperture radar (InSAR) identified areas of floating and bottomfast ice. Second, through in-field ground-truthing, we confirmed the presence and quality of liquid water (water depth, temperature, and dissolved oxygen) beneath the ice cover. Third, we assessed the suitability of that liquid water as habitat for whitefishes based on published literature and expert interpretation of water quality parameters. InSAR determined that 0, 65.4, and 88.2% of the three lagoons were composed of floating ice corresponding with areas of liquid water beneath a layer of ice. The HS model indicated that all three lagoons had reduced suitability as whitefish habitat in winter than in summer due to the loss of habitat because of the presence of bottomfast ice and a reduction in the quality of liquid water due to cold temperatures, high salinities, and low dissolved oxygen levels. However, only the shallowest lagoon had lethal conditions and zero suitability as whitefish habitat. The methods outlined here provide a simple, cost-effective method to identify habitats that consistently provide critical winter habitat and integrate remote sensing in a HS model framework.

Alaska↗
USGS2025

Spring weather influences breeding propensity, the most important productivity component for Arctic-nesting lesser snow geese

Animal reproduction is composed of several stages, which collectively determine overall productivity. Yet, it is not fully understood how different productivity components contribute to population change. To bridge this gap, we leveraged integrated population modelling and transient life-table response experiments, together with population-level data on lesser snow geese ( Anser caerulescens caerulescens ) breeding on Wrangel Island, Russia, from 1970 to 2022. We assessed contributions of breeding propensity, clutch size, nest success, egg survival, hatching success and pre-fledging survival to population change, and tested hypotheses about the effects of environmental drivers and density dependence on different components. Breeding propensity contributed the most to variation in population growth, followed by nest success. These two components were negatively affected by the timing of snow melt. We found no overall deleterious effects of climate change on productivity. Density dependence had a positive effect on multiple productivity components, likely through predator swamping. Our results show the importance of breeding propensity to the population dynamics of this long-lived animal, which is notable because this productivity component is often overlooked. Our results also demonstrate that the effects of environmental conditions and density dependence can differ among animal populations of different sizes, locations and life histories.

Wrangel Island↗
USGS2018

Modelling sound attenuation in heterogeneous environments for improved bioacoustic sampling of wildlife populations

Acoustic sampling methods are becoming increasingly important in biological monitoring. Sound attenuation is one of the most important dynamics affecting the utility of acoustic data as it directly affects the probability of detection of individuals by acoustic sensor arrays and especially the localization of acoustic signals necessary in telemetry studies. Therefore, models of sound attenuation are necessary to make efficient use of acoustic data in ecological monitoring and assessment applications. Models of attenuation in widespread use are based on Euclidean distance between source and sensor, which is justified under spherical attenuation of sound waves in homogeneous environments. In this paper, I develop a model of sound attenuation based on a non‐Euclidean cost‐weighted distance metric which contains attenuation coefficients that characterize the attenuation of sound due to environmental heterogeneity in the vicinity of an acoustic sensor array. I show that parameters of the proposed attenuation model can be estimated by maximum likelihood using experimental data from an array of fixed sources, thus allowing investigators who use bioacoustic methods to devise explicit models of sound attenuation in situ and apply them to localization of sources and density estimation. In addition, drawing on analogy with spatial capture–recapture models, I argue that parameters of the non‐Euclidean model of attenuation can be estimated when source locations are unknown . Thus, the models can be applied to real field studies which require estimation of attenuation parameters or localization of signals. Models of heterogeneous sound attenuation allow more accurate descriptions of acoustic monitoring data, and therefore should produce more accurate estimates of ecological parameters of interest, including source locations, density, and movement trajectories. Moreover, the ability to test specific hypotheses about the effects of habitat and landscape structure on sound attenuation can improve the design of acoustic monitoring arrays and lead to more efficient deployment of acoustic sensing technology.

Methods in Ecology and Evolution↗
USGS2020

Factors influencing Cinnamon Teal nest attendance patterns

Patterns of nest attendance in birds result from complex behaviours and influence the success of reproductive events. Incubation behaviours vary based on individual body condition, energy requirements and environmental factors. We assessed nest attendance patterns in Cinnamon Teal Spatula cyanoptera breeding in the San Luis Valley of Colorado in 2016–2017 using trail and video cameras to observe behaviours throughout incubation. We evaluated the effect of temporal, life-history and environmental covariates on the frequency and duration of incubation recesses as well as the incubation constancy. There was considerable model uncertainty among the models used to evaluate recess frequency. Recess duration varied according to the interaction between nest age and a quadratic effect of time of day, with hens on older nests taking longer recesses in the afternoon and hens on nests earlier in incubation taking longer recesses in the morning and evening. Incubation constancy decreased with higher ambient temperatures in the study area. This study provides evidence that Cinnamon Teal modify their behaviour during incubation according to the age of the nest and the time of day. These results improve our knowledge of Cinnamon Teal breeding ecology and shed light on the behaviours that fast-lived species may use to cope with environmental factors during nesting.

Colorado↗
USGS2017

Application of an unstructured 3D finite volume numerical model to flows and salinity dynamics in the San Francisco Bay-Delta

A linked modeling approach has been undertaken to understand the impacts of climate and infrastructure on aquatic ecology and water quality in the San Francisco Bay-Delta region. The Delft3D Flexible Mesh modeling suite is used in this effort for its 3D hydrodynamics, salinity, temperature and sediment dynamics, phytoplankton and water-quality coupling infrastructure, and linkage to a habitat suitability model. The hydrodynamic model component of the suite is D-Flow FM, a new 3D unstructured finite-volume model based on the Delft3D model. In this paper, D-Flow FM is applied to the San Francisco Bay-Delta to investigate tidal, seasonal and annual dynamics of water levels, river flows and salinity under historical environmental and infrastructural conditions. The model is driven by historical winds, tides, ocean salinity, and river flows, and includes federal, state, and local freshwater withdrawals, and regional gate and barrier operations. The model is calibrated over a 9-month period, and subsequently validated for water levels, flows, and 3D salinity dynamics over a 2 year period. Model performance was quantified using several model assessment metrics and visualized through target diagrams. These metrics indicate that the model accurately estimated water levels, flows, and salinity over wide-ranging tidal and fluvial conditions, and the model can be used to investigate detailed circulation and salinity patterns throughout the Bay-Delta. The hydrodynamics produced through this effort will be used to drive affiliated sediment, phytoplankton, and contaminant hindcast efforts and habitat suitability assessments for fish and bivalves. The modeling framework applied here will serve as a baseline to ultimately shed light on potential ecosystem change over the current century.

California↗
USGS2013

Next generation of global land cover characterization, mapping, and monitoring

Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC).

International Journal of Applied Earth Observation↗
USGS2016

Reproduction in moose at their southern range limit

Reproduction is a critical fitness component in large herbivores. Biogeographic models predict that populations occurring at the edges of the range may have compromised reproductive rates because of inferior habitat at range peripheries. When reproductive rates are chronically low, ungulate populations may lack the resiliency to rebound quickly after periods of environmental stress, and this effect may be greatest for heat-sensitive organisms at their southern range limit. To assess the demographic vulnerability of moose ( Alces alces ), we studied relationships between reproductive rates, maternal age, and rump fat in the southernmost naturally occurring moose population in North America. For prime-aged moose in our study, pregnancy rates were high (92%), but moose aged < 3 or > 9 years had low pregnancy rates (32% and 38%, respectively). The relationship between rump fat and pregnancy was nonlinear such that a threshold of at least 2mm of rump fat yielded a high probability of being pregnant midwinter. In contrast, among pregnant moose, the probability of both producing a calf and recruiting it until spring increased linearly with rump fat. We also conducted a meta-analysis of pregnancy and twinning rates for adult (≥ 2 years) moose across a latitudinal gradient to compare reproductive rates from our study to other populations in North America. Moose living at southern latitudes tended to have lower reproductive rates than those living in the core of moose range, implying that southern moose populations may be demographically more vulnerable than northern moose populations.

Utah↗
USGS2018

Use of remote sensing to detect and predict aquatic nuisance vegetation growth in coastal Louisiana: Summary of findings

On an annual basis, federal and state agencies are responsible for mapping and removing large expanses of aquatic nuisance vegetation from navigable waterways. This study set out to achieve four primary objectives: (1) utilize recent advancements in remote sensing techniques to classify the extent and distribution of aquatic vegetation in coastal ecosystems using satellite imagery, (2) assess primary aquatic vegetation growth and management efforts in coastal Louisiana, (3) statistically identify the ecological drivers that promote growth and infestation of aquatic nuisance vegetation, and (4) develop numerical models and a spatial tool to predict the probability of occurrence and growth of aquatic vegetation given ecological drivers. Moderate spatial resolution multispectral satellite imagery were used in conjunction with environmental variables from available data streams to generate regression models that predict aquatic vegetation occurrence in the eastern coastal region of south Louisiana. Geospatial tools were developed to execute the model logic using recent environmental conditions, thereby predicting aquatic vegetation occurrence and producing classified maps for end users. These products provide more efficient and enhanced capabilities for management of aquatic nuisance vegetation.

Louisiana↗
USGS2010

Spatial and stage-structured population model of the American crocodile for comparison of comprehensive Everglades Restoration Plan (CERP) alternatives

As part of the U.S. Geological Survey Priority Ecosystems Science (PES) initiative to provide the ecological science required during Everglades restoration, we have integrated current regional hydrologic models with American crocodile (Crocodylus acutus) research and monitoring data to create a model that assesses the potential impact of Comprehensive Everglades Restoration Plan (CERP) efforts on the American crocodile. A list of indicators was created by the Restoration Coordination and Verification (RECOVER) component of CERP to help determine the success of interim restoration goals. The American crocodile was established as an indicator of the ecological condition of mangrove estuaries due to its reliance upon estuarine environments characterized by low salinity and adequate freshwater inflow. To gain a better understanding of the potential impact of CERP restoration efforts on the American crocodile, a spatially explicit crocodile population model has been created that has the ability to simulate the response of crocodiles to various management strategies for the South Florida ecosystem. The crocodile model uses output from the Tides and Inflows in the Mangroves of the Everglades (TIME) model, an application of the Flow and Transport in a Linked Overland/Aquifer Density Dependent System (FTLOADDS) simulator. TIME has the capability to link to the South Florida Water Management Model (SFWMM), which is the primary regional tool used to assess CERP restoration scenarios. A crocodile habitat suitability index and spatial parameter maps that reflect salinity, water depth, habitat, and nesting locations are used as driving functions to construct crocodile finite rate of increase maps under different management scenarios. Local stage-structured models are integrated with a spatial landscape grid to display crocodile movement behavior in response to changing environmental conditions. Restoration efforts are expected to affect salinity levels throughout the habitat of the American crocodile. This modeling effort examines how CERP restoration alternatives will affect growth and survival rates of hatchling and juvenile crocodiles, hatchling dispersal to suitable nursery habitat, and relative abundance and distribution in response to changing salinity and water depth for all stage classes of crocodiles. The response of the American crocodile to restoration efforts will provide a quantifiable measure of restoration success. By applying the crocodile model to proposed restoration alternatives and predicting population responses, we can choose alternatives that approximate historical conditions, enhance habitat for multiple species, and identify future research needs.

Open-File Report↗
USGS2023

Assessment of salinity retention or mobilization by sediment-retention ponds near Delta, Colorado, 2019

Salinity control efforts in the Colorado River Basin have focused on mobilization of salts from irrigated land, but nonirrigated rangelands are also a source of salinity. In particular, lands where soils have formed from the Late Cretaceous Mancos Shale under arid and semiarid climates contain considerable quantities of salt, mainly in the subsurface. Hundreds of thousands of contour furrows and check dams (gully plugs) were constructed by the Bureau of Land Management (BLM) and Bureau of Reclamation in the late 1950s and 1960s to reduce runoff, sedimentation, and salt mobilization from ephemeral stream channels on rangelands. Sediment-retention ponds associated with check dams are dry most of the year, except immediately following substantial rain events. Generally, no maintenance has been performed on these structures, some have degraded over time, and their current and past influence on salinity is poorly understood. To assess the influence of check dams and their associated ponds on salt retention and mobilization, the U.S. Geological Survey, in cooperation with the BLM, conducted a study of such ponds within the Gunnison Gorge National Conservation Area (GGNCA) near Delta, Colorado. This report includes conceptual models of how sediment-retention ponds function relative to salinity, and a collection of environmental data to evaluate the conceptual models. An inventory of 69 ponds indicated that 38 percent no longer had water holding capacity, and another 20 percent could hold 1 foot or less of water. Check-dam degradation was the main cause, but sediment infill of ponds contributed as well. Water content of soil profiles collected beneath ponds and immediately downstream from check dams indicated little penetration of water below 60 centimeters for most ponds and little evidence for lateral movement of water beneath check dams. Patterns of salt content in the soil profiles indicated no accumulation of salts at the pond surface from evaporating waters and little evidence for salt redistribution in the form of salt bulges or salt depletion curves at intermediate depths. Based on the conceptual models presented and interpretations of data collected by this study, it appears that the sediment-retention ponds in the GGNCA have neither mobilized nor retained substantial quantities of salt during their lifetimes.

Colorado↗
USGS2021

Microbial and viral indicators of pathogens and human health risks from recreational exposure to waters impaired by fecal contamination

Fecal indicator bacteria (FIB) (e.g., fecal coliforms, Escherichia coli , and enterococci) have been used for decades to monitor for and protect the public from waterborne pathogens from fecal contamination. However, FIB may not perform well at predicting the presence of waterborne pathogens or human health outcomes from recreational exposure to fecal-contaminated surface waters. Numerous factors can influence the relationship between FIB and pathogens or human health outcomes, including the source(s) of contamination, the type of pathogen(s) present, differences in the survival and behavior of FIB and pathogens in the wastewater conveyance and treatment process, and varying environmental conditions. As a result, different indicators, such as source-specific microbial source tracking (MST) markers and viral fecal indicators, have been used as possible surrogates to better approximate pathogen abundance and human health risks in recreational waters. The performance of these alternative indicators has been mixed, with some promise of viral indicators better approximating viral pathogens than bacterial fecal indicators, and FIB generally more closely associated with bacterial and protozoal pathogen presence than human MST markers. Many of the assays to detect and quantify fecal indicators and pathogens are polymerase chain reaction-based assays, which detect and quantify nucleic acid [deoxyribonucleic acid (DNA) and ribonucleic acid (RNA)] sequences specific to a target of interest. Recent advances in DNA and RNA sequencing technologies may push the field toward metabarcoding approaches, where multiple targets can be detected and quantified simultaneously. Metabarcoding is currently more applicable to bacterial and protozoal assessments than viral assessments based on a lack of universal metabarcoding markers for viruses. Innovative technologies, such as biosensors and nanotechnologies, may provide more sensitive and accurate tools to detect and quantify pathogens. When a specific pathogen is of concern for a recreational water body, a practical approach in estimating the likelihood of human health outcomes is the application of quantitative microbial risk assessments (QMRAs). Quantitative microbial risk assessments can be used to model the likelihood of pathogen-specific human health outcomes from recreational exposure as a function of a surrogate indicator. Inputs for QMRAs include the ratio between the indicator to be monitored and the pathogen of interest, the concentration of the indicator, the amount of water ingested, and the likelihood of the health outcome based on the estimated amount of pathogen consumed. There are numerous unknowns about the behavior and survival of fecal indicators and pathogens in environmental waters. Developing accurate models to predict pathogen concentrations from fecal indicators in recreational waters will require a better understanding of these unknowns. Current methods and technologies for detecting and quantifying fecal indicators and pathogens are limited due to the rare and patchy nature of pathogens. Technological advances may help improve sensitivity for detecting and quantifying pathogens.

Journal of Sustainable Water in the Built Environm↗
USGS2022

An initial assessment of plankton tow detection probabilities for dreissenid mussels in the western United States

Early detection of dreissenid mussels ( Dreissena polymorpha and D. rostriformis bugensis ) is crucial to mitigating the economic and environmental impacts of an infestation. Plankton tow sampling is a common method used for early detection of dreissenid mussels, but little is known about the sampling intensity required for a high probability of early detection using the method. We used implicit dynamic occupancy models to estimate plankton tow detection probabilities of dreissenid mussels from a long-term data set containing plankton tow samples collected across central and western United States. We fit models using a) the entire data set, including water bodies with unknown occupancy status in addition to heavily infested water bodies, b) a data subset that included water bodies with paired water temperature data, and c) a data subset that included water bodies with lower dreissenid densities. For the entire data set, we found that estimated detection probabilities varied by water body size and ranged from approximately 0.10 to 0.86. For the water temperature subset, we observed the same pattern between detection probability and water body size as we did for the full data but additionally found that the estimated detection probabilities were much higher when water temperatures were above 12 °C. For the lower dreissenid density subset, we found that the estimated probability of detecting dreissenid mussels with a single aggregated plankton tow sample was near zero. Given these estimates, we conclude that the number of aggregated plankton tow samples taken per water body in the data is far fewer than the number needed to ensure a high probability of detecting dreissenid mussels, especially if they are at low densities. We summarize the analyses with a discussion of plankton tow sampling protocol changes needed to improve estimates of dreissenid detection probabilities.

western United States↗