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At least 235 records · Page 13Linked to original sources
USGS1978

Hydrologic data for urban storm runoff from three localities in the Denver metropolitan area, Colorado

Urban storm-runoff data, collected from 1975 to 1977, on three catchment areas in the Denver, Colo., metropolitan area are presented. The catchment are predominantly a single-family residential catchment area in Littleton, a multifamily residential and commercial catchment area in Lakewood, and a high-density residential and commercial catchment area in Denver. Precipitation, rainfall-runoff, snowmelt-runoff, water-quality (common constituents, nutrients, biochemical oxygen demand, coliform bacteria, and solids, trace elements, and pesticides), and catchment-area data are necessary to use the U.S. Environmental Protection Agency 's Storm Water Management Model II. The urban storm-runoff data may be used by planning, water-management, and environmental-protection agencies to assess the impact of urban storm runoff on the hydrologic system. (Woodard-USGS)

Colorado↗
USGS2014

Response to heavy, non-floating oil spilled in a Great Lakes river environment: a multiple-lines-of-evidence approach for submerged oil assessment and recovery

The Enbridge Line 6B pipeline release of diluted bitumen into the Kalamazoo River downstream of Marshall, MI in July 2010 is one of the largest freshwater oil spills in North American history. The unprecedented scale of impact and massive quantity of oil released required the development and implementation of new approaches for detection and recovery. At the onset of cleanup, conventional recovery techniques were employed for the initially floating oil and were successful. However, volatilization of the lighter diluent, along with mixing of the oil with sediment during flooded, turbulent river conditions caused the oil to sink and collect in natural deposition areas in the river. For more than three years after the spill, recovery of submerged oil has remained the predominant operational focus of the response. The recovery complexities for submerged oil mixed with sediment in depositional areas and long-term oil sheening along approximately 38 miles of the Kalamazoo River led to the development of a multiple-lines-of-evidence approach comprising six major components: geomorphic mapping, field assessments of submerged oil (poling), systematic tracking and mapping of oil sheen, hydrodynamic and sediment transport modeling, forensic oil chemistry, and net environmental benefit analysis. The Federal On-Scene Coordinator (FOSC) considered this information in determining the appropriate course of action for each impacted segment of the river. New sources of heavy crude oils like diluted bitumen and increasing transportation of those oils require changes in the way emergency personnel respond to oil spills in the Great Lakes and other freshwater ecosystems. Strategies to recover heavy oils must consider that the oils may suspend or sink in the water column, mix with fine-grained sediment, and accumulate in depositional areas. Early understanding of the potential fate and behavior of diluted bitumen spills when combined with timely, strong conventional recovery methods can significantly influence response success.

Michigan↗
USGS2017

Hydrokinetic tidal energy resource assessments using numerical models

Hyrdokinetic tidal energy is the conversion of tidal current kinetic energy to another more useful form, frequently electricity. As with any other form of renewable energy, resource assessments are essential for the tidal energy project planning and design process. While tidal currents have significant spatial and temporal variability, the predictability of tidal flows makes deterministic modeling a suitable methodology for hydrokinetic tidal energy resource assessments. The scope (theoretical, technical, or practical resource) and scale (turbine, region, or project) of the assessment determine the basic concepts and methodology to be utilized and are described in this chapter. At the turbine scale, the technical resource is frequently quantified as the annual energy production (AEP) computed based on the velocity probability distribution for the specific location as well as the turbine properties. The uncertainty associated with the estimates of the AEP is highly dependent on the accuracy of the tidal constituent amplitudes and phases. Regional resource assessments are frequently used to determine the feasibility of tidal power at the scale of an estuary, using numerical models to predict the spatial distribution of the power density. In addition, simplified models or even analytical analysis can be done to produce an upper bound on the regional theoretical power, although with a high level of uncertainty due to the simplifications and assumptions. Resource assessments at the project scale provide both the theoretical and the technical energy as well as the practical energy accounting for many additional constraints, including social, economic, and environmental restrictions. The International Electrotechnical Commission technical specification for tidal energy resource assessments (IEC 2015 ) provides the essential guidelines for performing project-scale resource assessments. These guidelines include minimum grid resolution requirements as well as model calibration and validation procedures. In addition, larger projects will need to include the effect of energy extraction on the flow field to produce more accurate estimates of velocity probability distributions for computing the technical resource. An example case study demonstrating a regional feasibility and project-scale resource assessment is presented in this chapter.

Book chapter↗
USGS2022

Interagency Flood Risk Management (InFRM) watershed hydrology assessment for the Neches River basin. Appendix D: RiverWare analyses

RiverWare is a river system modeling tool developed by CADSWES (Center of Advanced Decision Support for Water and Environmental Systems) that allows the user to simulate complex reservoir operations and perform period-of-record analyses for different scenarios. For the InFRM hydrology studies, RiverWare is used to generate a homogeneous regulated POR by simulating the basin as if the reservoirs and their current rule sets had been present in the basin for the entire time period. Statistical analyses can then be performed on the extended records at the gages. This report summarizes the RiverWare portion of the hydrologic analysis being completed for the InFRM Hydrology study of the Neches River Basin. The RiverWare model described in this chapter presents development of the Neches River Basin hydrology, which mimics current operational conditions. The use of the RiverWare program allows for data extension to periods prior to dam construction. The utilization of longer streamgage record improves discharge frequency results and increases the confidence of the analysis being performed. The modeling evaluation criteria are: (1) evaluate output based on validating policies and functions, and (2) prioritize operation based on surcharge and flood control. A detailed explanation of the Neches River Basin POR hydrology will be in a later section. Calibration results will also be shown that illustrate model performance since the Salt Water Barrier (SWB) construction was completed in 2005. The time window simulation run is for water year (WY) 2005 – WY 2018. This time window also captures the time when Hurricane Harvey occurred (late August of 2017). Each simulated water year was inspected individually to better validate the results. After calibration, a general run for January 01, 1929 through WY 2018 was made. Historical pool elevations along with observed inflows and outflows were compared against the model simulated results. More emphasis was put on B.A. Steinhagen’s operations because the dam captures two major rivers (i.e. the Angelina and the Neches Rivers). Results were inspected closely for B.A. Steinhagen’s pool and releases, the simulated discharges at the Neches at Evadale gage, and the simulated discharges at the SWB at Beaumont, Texas.

Texas↗
USGS2018

Flow-ecology relationships are spatially structured and differ among flow regimes

In streams, hydrology is a predominant driver of ecological structure and function. Providing adequate flows to support aquatic life, or environmental flows, is therefore a top management priority in stream systems. Flow regime classification is a widely accepted approach for establishing environmental flow guidelines. However, it is surprisingly difficult to quantify relationships between hydrology and ecology (flow–ecology relationships) while describing how these relationships vary across classified flow regimes. Developing such relationships is complicated by several sources of spatial bias, such as autocorrelation due to spatial design, flow regime classification and other environmental or ecological sources of spatial bias. We used mixed moving‐average spatial stream network models to develop flow–ecology relationships across classified flow regimes and to assess spatial patterns of these relationships. We compared relationships between fish traits and life‐history strategies with hydrologic metrics across flow regimes and assessed whether spatial autocorrelation influenced these relationships. Trait–hydrology relationships varied between flow regimes and across all streams combined. Some relationships between traits and hydrologic metrics fit predictions based on life‐history theory, while others exhibited unexpected relationships with hydrology. Spatial factors described a large proportion of variability in fish traits and different patterns of spatial autocorrelation were observed in different flow regimes. Synthesis and applications. Further work is needed to understand why flow–ecology relationships vary across classified flow regimes and why these relationships may not fit predictions based on life‐history theories. Managers determining environmental flow standards need to be aware that different hydrologic metrics are often important drivers of fish trait diversity in different flow regimes. Flow–ecology relationships may therefore be confounded by spatial structure inherent in flow regime classification and much existing biological data. Complex patterns of spatial bias should be considered when managing stream systems within an environmental flows framework.

Journal of Applied Ecology↗
USGS1998

Regional land cover characterization using Landsat thematic mapper data and ancillary data sources

As part of the activities of the Multi-Resolution Land Characteristics (MRLC) Interagency Consortium, an intermediate-scale land cover data set is being generated for the conterminous United States. This effort is being conducted on a region-by-region basis using U.S. Standard Federal Regions. To date, land cover data sets have been generated for Federal Regions 3 (Pennsylvania, West Virginia, Virginia, Maryland, and Delaware) and 2 (New York and New Jersey). Classification work is currently under way in Federal Region 4 (the southeastern United States), and land cover mapping activities have been started in Federal Regions 5 (the Great Lakes region) and 1 (New England). It is anticipated that a land cover data set for the conterminous United States will be completed by the end of 1999. A standard land cover classification legend is used, which is analogous to and compatible with other classification schemes. The primary MRLC regional classification scheme contains 23 land cover classes. The primary source of data for the project is the Landsat thematic mapper (TM) sensor. For each region, TM scenes representing both leaf-on and leaf-off conditions are acquired, preprocessed, and georeferenced to MRLC specifications. Mosaicked data are clustered using unsupervised classification, and individual clusters are labeled using aerial photographs. Individual clusters that represent more than one land cover unit are split using spatial modeling with multiple ancillary spatial data layers (most notably, digital elevation model, population, land use and land cover, and wetlands information). This approach yields regional land cover information suitable for a wide array of applications, including landscape metric analyses, land management, land cover change studies, and nutrient and pesticide runoff modeling.

Environmental Monitoring and Assessment↗
USGS2023

Long-term demographic analysis of the Cape Sable seaside sparrow (1992–2021)

The Cape Sable seaside sparrow (Ammospiza maritima mirabilis) is an endangered species that has experienced a population decline of more than 60% since 1981. Despite its critical population status, a statistically robust analysis of the species’ demographic rates utilizing all data has yet to be completed (Benscoter et al. 2021). Furthermore, long-term population processes in response to hydrologic and environmental conditions have not been evaluated for this species. To address these substantial gaps in knowledge, 30 years of demographic data were synthesized to assess population dynamics of this imperiled species using an integrated population model (IPM). Three demographic data types (range-wide counts, capture-mark-recapture, nest monitoring) were incorporated into a unified IPM to evaluate demographic processes and predict population trajectories. The following were calculated: (1) annual estimates of annual population size, survival, and fecundity; (2) coefficient estimates of hydrologic and environmental variables on survival and fecundity; (3) estimates of annual population growth and their correlation with demographic rates; and (4) a Bayesian population viability analysis (BPVA) that includes predicted population size, demographic rates, and extinction risks ten years into the future.

Report↗
USGS2019

Effects of climate change on habitat and connectivity for populations of a vulnerable, endemic salamander in Iran

Habitat loss and fragmentation are among the biggest threats to amphibian populations and anthropogenic climate change may exacerbate these. The response of Iran's amphibians to climate change is uncertain and yet making an accurate prediction of how the species will respond is critical for conservation. We assessed how expected future climate scenarios before the years 2050 and 2070 might influence the geographic distribution and habitat connectivity of the Lorestan Mountain Newt (Neurergus kaiseri). We examined presence data (2010–2018) of the species according to environmental and anthropogenic factors, and created an ensemble model of habitat suitability based on eight species distribution models (SDMs). Then, we used the concept of circuit theory to estimate potential linkages between the habitat patches. We applied the ensemble calibrated models and quantified spatial connectivity to assess the influence of climate change on the species range for the years 2050 and 2070 under four representative concentration pathways (RCPs) of three general circulation models (GCMs). Models using current climate predicted that 6.8% of the 267,609 km2 study area has suitable conditions for the species, but only about 7% of these climatically suitable landscapes are covered by conservation areas. Temperature and precipitation-related climatic variables made the largest contribution to the distribution model. Under projected climate conditions, we found a decline of 56–98% of the suitable habitat and predicted a potential for distributional shifts towards higher elevations by 2050 and 2070. Although there is relatively good connectivity between many habitat patches today, models predict that suitable areas available to the newt will become increasingly fragmented under projected climate change scenarios. Our findings support the hypothesis that projected climatic shifts will negatively influence suitable habitats of amphibians and likely cause upward shifts in elevation in range of some species. Identifying potentially suitable habitats and important linkages between habitat patches under different climate scenarios are crucial steps in conservation planning for the Lorestan Mountain Newt.

Global Ecology and Conservation↗
USGS2016

Developing population models with data from marked individuals

Population viability analysis (PVA) is a powerful tool for biodiversity assessments, but its use has been limited because of the requirements for fully specified population models such as demographic structure, density-dependence, environmental stochasticity, and specification of uncertainties. Developing a fully specified population model from commonly available data sources – notably, mark–recapture studies – remains complicated due to lack of practical methods for estimating fecundity, true survival (as opposed to apparent survival), natural temporal variability in both survival and fecundity, density-dependence in the demographic parameters, and uncertainty in model parameters. We present a general method that estimates all the key parameters required to specify a stochastic, matrix-based population model, constructed using a long-term mark–recapture dataset. Unlike standard mark–recapture analyses, our approach provides estimates of true survival rates and fecundities, their respective natural temporal variabilities, and density-dependence functions, making it possible to construct a population model for long-term projection of population dynamics. Furthermore, our method includes a formal quantification of parameter uncertainty for global (multivariate) sensitivity analysis. We apply this approach to 9 bird species and demonstrate the feasibility of using data from the Monitoring Avian Productivity and Survivorship (MAPS) program. Bias-correction factors for raw estimates of survival and fecundity derived from mark–recapture data (apparent survival and juvenile:adult ratio, respectively) were non-negligible, and corrected parameters were generally more biologically reasonable than their uncorrected counterparts. Our method allows the development of fully specified stochastic population models using a single, widely available data source, substantially reducing the barriers that have until now limited the widespread application of PVA. This method is expected to greatly enhance our understanding of the processes underlying population dynamics and our ability to analyze viability and project trends for species of conservation concern.

Biological Conservation↗
USGS2021

Reconstructing population dynamics of a threatened marine mammal using multiple data sets

Models of marine mammal population dynamics have been used extensively to predict abundance. A less common application of these models is to reconstruct historical population dynamics, filling in gaps in observation data by integrating information from multiple sources. We developed an integrated population model for the Florida manatee ( Trichechus manatus latirostris ) to reconstruct its population dynamics in the southwest region of the state over the past 20 years. Our model improved precision of key parameter estimates and permitted inference on poorly known parameters. Population growth was slow (averaging 1.02; 95% credible interval 1.01–1.03) but not steady, and an unusual mortality event in 2013 led to an estimated net loss of 332 (217–466) manatees. Our analyses showed that precise estimates of abundance could be derived from estimates of vital rates and a few input estimates of abundance, which may mean costly surveys to estimate abundance don’t need to be conducted as frequently. Our study also shows that retrospective analyses can be useful to: (1) model the transient dynamics of age distribution; (2) assess and communicate the conservation status of wild populations; and (3) improve our understanding of environmental effects on population dynamics and thus enhance our ability to forecast.

Florida↗
USGS2023

Integrated analysis shows how the effects of extreme flooding events propagate through fish communities to impact amphibians

Research Highlight: Davis, C. L., Walls, S. C., Barichivich, W. J., Brown, M. E., & Miller, D. A. (2022). Disentangling direct and indirect effects of extreme events on coastal wetland communities. Journal of Animal Ecology , https://doi.org/10.1111/1365-2656.13874 . Catastrophic events such as floods, hurricanes, winter storms, droughts and wildfires increasingly touch our lives either directly or indirectly. These events draw our attention to the seriousness of changes in climate not only to human well-being but also to the integrity of ecological systems upon which we depend. Understanding the impacts of extreme events on ecological systems requires the ability to characterize the cascading effects of environmental changes on the environments in which organisms live and the altered biological interactions produced. This scientific ambition represents no small challenge for the study of animal communities, which are typically difficult to census as well as dynamic in time and space. Davis et al. (2022) in a recent study in the Journal of Animal Ecology examined the amphibian and fish communities found in depressional coastal wetlands to better understand how they respond to major rainfall and flooding events. Data from the U.S. Geological Survey's Amphibian Research and Monitoring Initiative provided an 8-year record of observations as well as environmental measurements. For this study, the authors integrated techniques for assessing the dynamics of animal populations with a Bayesian implementation of structural equation modelling. Using their integrated methodological approach permitted the authors to reveal the direct and indirect effects of extreme weather events on co-occurring amphibian and fish communities while accounting for observational uncertainty and temporal variation in population-level processes. Their findings indicate that the most prominent effects of flooding on the amphibian community were caused by changes in the fish community that led to increased predation and resource competition. In their conclusions, the authors emphasize the importance of understanding networks of abiotic and biotic effects if we are to predict and mitigate the influence of extreme weather events.

Journal of Animal Ecology↗
USGS2020

Wetlands in agricultural landscapes—Significant findings and recent advances from CEAP-Wetlands

The Wetlands Component of the USDA's Conservation Effects Assessment Project (CEAP-Wetlands) is a multi-agency effort advancing science related to quantifying and interpreting effects and effectiveness of conservation practices and programs on ecosystem services provided by wetlands in agricultural landscapes. This special section originated from a symposium held at the 73rd Soil and Water Conservation Society's International Annual Conference in Albuquerque New Mexico, July 29 to August 1, 2018. The symposium was jointly organized by the USDA Natural Resources Conservation Service and the US Geological Survey. To facilitate CEAP-Wetlands efforts, several regional assessments were conducted across the United States. These regional assessments were designed to address science gaps hindering wetland conservation and to develop tools facilitating conservation assessments. Conservation decisions affect not just agricultural wetlands, but also the services that these complex ecosystems provide to society. Papers in this special section of the Journal of Soil and Water Conservation present key findings and recent advances from several CEAP-Wetlands regional assessments and discuss the significant contributions of each assessment to an ever-increasing understanding of wetland ecosystems and their provisioning of ecosystem services. Modeling efforts using the Agricultural Policy and Environmental eXtender (APEX) and other process-based models are an integral component of CEAP-Wetlands. Results of these modeling efforts are also presented, and conservation implications are discussed.

Journal of Soil and Water Conservation↗
USGS2018

Influence of river discharge on grass carp occupancy dynamics in south-eastern Iowa rivers

Despite the longstanding presence of grass carp Ctenopharyngodon idella in the Upper Mississippi River (UMR) watershed, information regarding their populations remains largely unknown, in part because capture is difficult. Occupancy models are a popular wildlife assessment tool to account for imperfect detections but have been slow to be adopted in fisheries. Herein, we used occupancy modelling to evaluate the influence of two environmental covariates (river discharge and water temperature) on grass carp occupancy, extinction, colonization, and detection at nine sites within south-eastern Iowa rivers from April to October 2014 and 2015. Grass carp were detected at least once at all but one site. The most parsimonious model indicated that grass carp colonization probability increased from 0.15 to 0.67 with increases in river discharge. In contrast, occupancy (0.20), extinction (0.29), and detection (0.50) probabilities were temporally constant. Models indicated that water temperatures did not influence grass carp extinction or colonization probabilities relative to river discharge. Cumulative grass carp detection probability approached 1.0, whereas conditional occupancy estimates were less than 0.1 when using five or more sampling transects. The use of a robust design occupancy model allowed us to estimate site occupancy rates of grass carp corrected for imperfect detections, while demonstrating the importance of river discharge for site colonization. These results can be used to assess the distribution of a cryptic fish while helping to guide grass carp sampling and removal efforts.

Iowa↗
USGS2024

Watershed hydrology assessment for the Lower Colorado River Basin. Appendix D: RiverWare analyses

RiverWare is a river system modeling tool developed by CADSWES (Center of Advanced Decision Support for Water and Environmental Systems) that allows the user to simulate complex reservoir operations and perform period-of-record analyses for different scenarios. For the InFRM hydrology studies, RiverWare is used to generate a homogeneous regulated POR by simulating the basin as if the reservoirs and their current rule sets had been present in the basin for the entire time period. Statistical analyses can then be performed on the extended records at the gages. This report summarizes the RiverWare portion of the hydrologic analysis being completed for the InFRM Hydrology study of the Colorado River Basin. The RiverWare model described in this chapter presents development of the Colorado River Basin hydrology, which mimics current operational conditions. The use of the RiverWare program allows for data extension to periods prior to dam construction. The utilization of longer gage record improves discharge frequency results and increases the confidence of the analysis being performed. The modeling evaluation criteria are: (1) evaluate output based on validating policies and functions, and (2) prioritize operation based on surcharge and flood control. A detailed explanation of the Colorado River Basin POR hydrology will be in a later section. Calibration results will also be shown that illustrate the overall model performance for the POR. The time window simulation run is for January 01, 1930 – September 30, 2019. This time window captures all big events occurred over the Colorado River basin. Each simulated water year was inspected individually to better validate the results. Historical pool elevations along with observed inflows and outflows were compared against the model simulated results.

Texas↗
USGS2014

Improvement of the R-SWAT-FME framework to support multiple variables and multi-objective functions

Application of numerical models is a common practice in the environmental field for investigation and prediction of natural and anthropogenic processes. However, process knowledge, parameter identifiability, sensitivity, and uncertainty analyses are still a challenge for large and complex mathematical models such as the hydrological/water quality model, Soil and Water Assessment Tool (SWAT). In this study, the previously developed R program language-SWAT-Flexible Modeling Environment (R-SWAT-FME) was improved to support multiple model variables and objectives at multiple time steps (i.e., daily, monthly, and annually). This expansion is significant because there is usually more than one variable (e.g., water, nutrients, and pesticides) of interest for environmental models like SWAT. To further facilitate its easy use, we also simplified its application requirements without compromising its merits, such as the user-friendly interface. To evaluate the performance of the improved framework, we used a case study focusing on both streamflow and nitrate nitrogen in the Upper Iowa River Basin (above Marengo) in the United States. Results indicated that the R-SWAT-FME performs well and is comparable to the built-in auto-calibration tool in multi-objective model calibration. Overall, the enhanced R-SWAT-FME can be useful for the SWAT community, and the methods we used can also be valuable for wrapping potential R packages with other environmental models.

Science of the Total Environment↗
USGS2015

Linking degradation status with ecosystem vulnerability to environmental change

Environmental change can cause regime shifts in ecosystems, potentially threatening ecosystem services. It is unclear if the degradation status of ecosystems correlates with their vulnerability to environmental change, and thus the risk of future regime shifts. We assessed resilience in acidified (degraded) and circumneutral (undegraded) lakes with long-term data (1988–2012), using time series modeling. We identified temporal frequencies in invertebrate assemblages, which identifies groups of species whose population dynamics vary at particular temporal scales. We also assessed species with stochastic dynamics, those whose population dynamics vary irregularly and unpredictably over time. We determined the distribution of functional feeding groups of invertebrates within and across the temporal scales identified, and in those species with stochastic dynamics, and assessed attributes hypothesized to contribute to resilience. Three patterns of temporal dynamics, consistent across study lakes, were identified in the invertebrates. The first pattern was one of monotonic change associated with changing abiotic lake conditions. The second and third patterns appeared unrelated to the environmental changes we monitored. Acidified and the circumneutral lakes shared similar levels and patterns of functional richness, evenness, diversity, and redundancy for species within and across the observed temporal scales and for stochastic species groups. These similar resilience characteristics suggest that both lake types did not differ in vulnerability to the environmental changes observed here. Although both lake types appeared equally vulnerable in this study, our approach demonstrates how assessing systemic vulnerability by quantifying ecological resilience can help address uncertainty in predicting ecosystem responses to environmental change across ecosystems.

Oecologia↗
USGS2018

Landscape-scale variation in canopy water content of giant sequoias during drought

Recent drought (2012–2016) caused unprecedented foliage dieback in giant sequoias ( Sequoiadendron giganteum ), a species endemic to the western slope of the southern Sierra Nevada in central California. As part of an effort to understand and map sequoia response to droughts, we studied the patterns of remotely sensed canopy water content (CWC), both within and among sequoia groves in two successive years during the drought period (2015 and 2016). Our aims were: (1) to quantify giant sequoia responses to severe drought stress at a landscape scale using CWC as an indicator of crown foliage status, and (2) to estimate the effect of environmental correlates that mediate CWC change within and among giant sequoia groves. We utilized airborne high fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) data from the Carnegie Airborne Observatory to assess giant sequoia foliage status during 2015 and 2016 of the 2012–2016 droughts. A series of statistical models were generated to classify giant sequoias and to map their location in Sequoia and Kings Canyon National Parks (SEKI) and vicinity. We explored the environmental correlates and the spatial patterns of CWC change at the landscape scale. The mapped CWC was highly variable throughout the landscape during the two observation years, and proved to be most closely related to geological substrates, topography, and site-specific water balance. While there was an overall net gain in sequoia CWC between 2015 and 2016, certain locations (lower elevations, steeper slopes, areas more distant from surface water sources, and areas with greater climate water deficit) showed CWC losses. In addition, we found greater CWC loss in shorter sequoias and those growing in areas with lower sequoia stem densities. Our results suggest that CWC change indicates sequoia response to droughts across landscapes. Long-term monitoring of giant sequoia CWC will likely be useful for modeling and predicting their population-level response to future climate change.

California↗
USGS2003

Estimating the susceptibility of surface water in Texas to nonpoint-source contamination by use of logistic regression modeling

In the State of Texas, surface water (streams, canals, and reservoirs) and ground water are used as sources of public water supply. Surface-water sources of public water supply are susceptible to contamination from point and nonpoint sources. To help protect sources of drinking water and to aid water managers in designing protective yet cost-effective and risk-mitigated monitoring strategies, the Texas Commission on Environmental Quality and the U.S. Geological Survey developed procedures to assess the susceptibility of public water-supply source waters in Texas to the occurrence of 227 contaminants. One component of the assessments is the determination of susceptibility of surface-water sources to nonpoint-source contamination. To accomplish this, water-quality data at 323 monitoring sites were matched with geographic information system-derived watershed- characteristic data for the watersheds upstream from the sites. Logistic regression models then were developed to estimate the probability that a particular contaminant will exceed a threshold concentration specified by the Texas Commission on Environmental Quality. Logistic regression models were developed for 63 of the 227 contaminants. Of the remaining contaminants, 106 were not modeled because monitoring data were available at less than 10 percent of the monitoring sites; 29 were not modeled because there were less than 15 percent detections of the contaminant in the monitoring data; 27 were not modeled because of the lack of any monitoring data; and 2 were not modeled because threshold values were not specified.

Texas↗