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At least 613 records · Page 34Linked to original sources
USGS2015

Quantitative attribution of major driving forces on soil organic carbon dynamics

Soil organic carbon (SOC) storage plays a major role in the global carbon cycle and is affected by many factors including land use/management changes (e.g., biofuel production-oriented changes). However, the contributions of various factors to SOC changes are not well understood and quantified. This study was designed to investigate the impacts of changing farming practices, initial SOC levels, and biological enhancement of grain production on SOC dynamics and to attribute the relative contributions of major driving forces (CO 2 enrichment and farming practices) using a fractional factorial modeling design. The case study at a crop site in Iowa in the United States demonstrated that the traditional corn-soybean (CS) rotation could still accumulate SOC over this century (from 4.2 to 6.8 kg C/m 2 ) under the current condition; whereas the continuous-corn (CC) system might have a higher SOC sequestration potential than CS. In either case, however, residue removal could reduce the sink potential substantially. Long-term simulation results also suggested that the equilibrium SOC level may vary greatly (∼5.7 to ∼11 kg C/m 2 ) depending on cropping systems and management practices, and projected growth enhancement could make the magnitudes higher (∼7.8 to ∼13 kg C/m 2 ). Importantly, the factorial design analysis indicated that residue management had the most significant impact (contributing 49.4%) on SOC changes, followed by CO 2 Enrichment (37%), Tillage (6.2%), the combination of CO 2 Enrichment-Residue removal (5.8%), and Fertilization (1.6%). In brief, this study is valuable for understanding the major forces driving SOC dynamics of agroecosystems and informative for decision-makers when seeking the enhancement of SOC sequestration potential and sustainability of biofuel production, especially in the Corn Belt region of the United States.

Journal of Advances in Modeling Earth Systems↗
USGS2022

Decision support for aquatic restoration based on species-specific responses to disturbance

Disturbances to aquatic habitats are not uniformly distributed within the Great Lakes and acute effects can be strongest in nearshore areas where both landscape and within lake effects can have strong influence. Furthermore, different fish species respond to disturbances in different ways. A means to identify and evaluate locations and extent of disturbances that affect fish is needed throughout the Great Lakes. We used partial Canonical Correspondence Analysis to separate “natural” effects on nearshore assemblages from disturbance effects. Species-specific quadratic models of fish abundance as functions of in-lake disturbance or watershed-derived disturbance were developed separately for each of 35 species and lakewide predictions mapped for Lake Erie. Most responses were unimodal and more species decreased in abundance with increasing watershed disturbance than increased. However, eight species increased in abundance with current in-lake disturbance conditions. Optimum Yellow Perch ( Perca flavescens ) abundance occurred at in-lake disturbance values less than the gradient mean, but decreased continuously from minimum watershed disturbance to higher values. Bands of optimum in-lake conditions occurred throughout the eastern and western portions of the Lake Erie nearshore zone; some areas were less disturbed than desirable. However, watershed-derived disturbance conditions were generally poor for Yellow Perch throughout the lake. In contrast, optimum Smallmouth Bass ( Micropterus dolomieu ) abundance occurred at in-lake disturbance values greater than the gradient mean and continuously increased with increasing watershed disturbance. Smallmouth Bass responses to disturbance indicated that most of the nearshore zone was less disturbed than is desirable and were most abundant in areas that the Yellow Perch response indicated were highly disturbed. Mapping counts of species response models that agreed on the disturbance level in each spatial unit of the nearshore zone showed a fine-scale mosaic of areas in which habitat restoration may benefit many or few species. This tool may assist managers in prioritizing conservation and restoration efforts and evaluating environmental conditions that may be improved.

Great Lakes↗
USGS2003

Understanding climatic impacts, vulnerabilities, and adaptation in the United States: Building a capacity for assessment

Based on the experience of the U.S. National Assessment, we propose a program of research and analysis to advance capability for assessment of climate impacts, vulnerabilities, and adaptation options. We identify specific priorities for scientific research on the responses of ecological and socioeconomic systems to climate and other stresses; for improvement in the climatic inputs to impact assessments; and for further development of assessment methods to improve their practical utility to decision-makers. Finally, we propose a new institutional model for assessment, based principally on regional efforts that integrate observations, research, data, applications, and assessment on climate and linked environmental-change issues. The proposed program will require effective collaboration between scientists, resource managers, and other stakeholders, all of whose expertise is needed to define and prioritize key regional issues, characterize relevant uncertainties, and assess potential responses. While both scientifically and organizationally challenging, such an integrated program holds the best promise of advancing our capacity to manage resources and the economy adaptively under a changing climate.

Climatic Change↗
USGS2024

Molecular sexing of birds using quantitative PCR (qPCR) of sex-linked genes and logistic regression models

The ability to sex individuals is an important component of many behavioural and ecological investigations and provides information for demographic models used in conservation and species management. However, many birds are difficult to sex using morphological characters or traditional molecular sexing methods. In this study, we developed probabilistic models for sexing birds using quantitative PCR (qPCR) data. First, we quantified distributions of gene copy numbers at a set of six sex-linked genes, including the sex-determining gene DMRT1 , for individuals across 17 species and seven orders of birds ( n = 150). Using these data, we built predictive logistic models for sex identification and tested their performance with independent samples from 51 species and 13 orders ( n = 209). Models using the two loci most highly correlated with sex had greater accuracy than models using the full set of sex-linked loci, across all taxonomic levels of analysis. Sex identification was highly accurate when individuals to be assigned were of species used in model building. Our analytical approach was widely applicable across diverse neognath bird lineages spanning millions of years of evolutionary divergence. Unlike previous methods, our probabilistic framework incorporates uncertainty around qPCR measurements as well as biological variation within species into decision-making rules. We anticipate that this method will be useful for sexing birds, including those of high conservation concern and/or subsistence value, that have proven difficult to sex using traditional approaches. Additionally, the general analytical framework presented in this paper may also be applicable to other organisms with sex chromosomes.

Molecular Ecology Resources↗
USGS2009

Natural offshore oil seepage and related tarball accumulation on the California coastline — Santa Barbara Channel and the southern Santa Maria Basin; source identification and inventory

Oil spillage from natural sources is very common in the waters of southern California. Active oil extraction and shipping is occurring concurrently within the region and it is of great interest to resource managers to be able to distinguish between natural seepage and anthropogenic oil spillage. The major goal of this study was to establish the geologic setting, sources, and ultimate dispersal of natural oil seeps in the offshore southern Santa Maria Basin and Santa Barbara Basins. Our surveys focused on likely areas of hydrocarbon seepage that are known to occur between Point Arguello and Ventura, California. Our approach was to 1) document the locations and geochemically fingerprint natural seep oils or tar; 2) geochemically fingerprint coastal tar residues and potential tar sources in this region, both onshore and offshore; 3) establish chemical correlations between offshore active seeps and coastal residues thus linking seep sources to oil residues; 4) measure the rate of natural seepage of individual seeps and attempt to assess regional natural oil and gas seepage rates; and 5) interpret the petroleum system history for the natural seeps. To document the location of sub-sea oil seeps, we first looked into previous studies within and near our survey area. We measured the concentration of methane gas in the water column in areas of reported seepage and found numerous gas plumes and measured high concentrations of methane in the water column. The result of this work showed that the seeps were widely distributed between Point Conception east to the vicinity of Coal Oil Point, and that they by in large occur within the 3-mile limit of California State waters. Subsequent cruises used sidescan and high resolution seismic to map the seafloor, from just south of Point Arguello, east to near Gaviota, California. The results of the methane survey guided the exploration of the area west of Point Conception east to Gaviota using a combination of seismic instruments. The seafloor was mapped by sidescan sonar, and numerous lines of high -resolution seismic surveys were conducted over areas of interest. Biomarker and stable carbon isotope ratios were used to infer the age, lithology, organic matter input, and depositional environment of the source rocks for 388 samples of produced crude oil, seep oil, and tarballs mainly from coastal California. These samples were used to construct a chemometric fingerprint (multivariate statistics) decision tree to classify 288 additional samples, including tarballs of unknown origin collected from Monterey and San Mateo County beaches after a storm in early 2007. A subset of 9 of 23 active offshore platform oils and one inactive platform oil representing a few oil reservoirs from the western Santa Barbara Channel were used in this analysis, and thus this model is not comprehensive and the findings are not conclusive. The platform oils included in this study are from west to east: Irene, Hildago, Harvest, Hermosa, Heritage, Harmony, Hondo, Holly, Platform A, and Hilda (now removed). The results identify three “tribes” of 13 C-rich oil samples inferred to originate from thermally mature equivalents of the clayey-siliceous, carbonaceous marl, and lower calcareous-siliceous members of the Monterey Formation. Tribe 1 contains four oil families having geochemical traits of clay-rich marine shale source rock deposited under suboxic conditions with substantial higher-plant input. Tribe 2 contains four oil families with intermediate traits, except for abundant 28,30-bisnorhopane, indicating suboxic to anoxic marine marl source rock with hemipelagic input. Tribe 3 contains five oil families with traits of distal marine carbonate source rock deposited under anoxic conditions with pelagic but little or no higher-plant input. Tribes 1 and 2 occur mainly south of Point Conception in paleogeographic settings where deep burial of the Monterey Formation source rock favored generation from all three members or their equivalents. In this area, oil from the clayey-siliceous and carbonaceous marl members (Tribes 1 and 2) may overwhelm that from the lower calcareous-siliceous member (Tribe 3) because the latter is thinner and less oil-prone than the overlying members. Tribe 3 occurs mainly north of Point Conception, where shallow burial caused preferential generation from the underlying lower calcareous-siliceous member or another unit with similar characteristics. It is very desirable to be able to clearly distinguish the naturally occurring seep oils from the anthropogenically derived platform oils. Within the “training set” of oils and tars (388 samples), the biomarker parameters are sometimes sufficient to allow unique discrimination of individual platform oils. More often however, platform samples and seep samples with sources geographically close to each other are too similar to each other, with respect to the biomarker parameters, to definitively differentiate them on that basis alone. In some cases other parameters can be helpful. These other parameters are related to the degree of biogeochemical degradation or weathering that the oils or tars have experienced. These components include the typical oil distribution of n-alkane hydrocarbons and isoprenoids pristane and phytane. All of the platform oils in our sample set contain these components. On the other hand, the seep oils or tars have been exposed to significant biodegradation while in the near subsurface. The majority, but not all of seep oils or tars have been biodegraded up to or beyond the loss of n-alkanes and isoprenoids. Seep oils found in the vicinity of Coal Oil Point or Arroyo Burro are apparently the least weathered and are particularly likely to retain significant n-alkanes and isoprenoids. Therefore the combination of chemometric fingerprinting and the presence or absence of n-alkanes and isoprenoids help to differentiate anthropogenic production oils versus natural seeps oils and tars. The differentiation is not always definitive because of the close chemical similarity of some samples and the variability in the biodegradation progression. This is the case near Coal Oil Point, and near Platform A (Dos Cuadros Field) where seep oils and Platform Holly and Platform A oils are genetically very similar and cannot be definitively distinguished after a period of a few days of weathering. In contrast, oils from the Point Conception platforms can be distinguished on the basis of chemometric fingerprinting alone. In the middle of this spectrum are oils from Platforms Harmony, Heritage, and Hondo, where it is expected that oil weathering would take on the order of two weeks to a month to produce tarballs similar to those seen near Point Conception. In this case there is a much greater degree of weathering needed to proceed from produced oil to the biodegraded tar characteristic of tarball stranded on the beach. Tar deposition on beaches was monitored as part of cooperative with the County of Santa Barbara Energy Division and the U.S. Geological Survey during 2001-2003. We found tar deposition varies on a seasonal basis. In general, tarballs accumulate at a faster rate or remain longer on all beaches during the summer and fall months. The reasons for this are unclear based on our limited observations, however we speculate that factors such as prevailing winds and currents combined with more quiescent wave conditions favors the accumulation and preservation of tarballs on the beach during the summer and fall months. In contrast, winter storms, with much greater wave action remove beach sand and other materials, and stormy seas tend to break up oil that might weather into tarballs. Natural seepage is affected by the spring/neap tidal cycle; however, the link to tar deposition is unclear. Longer periods of monitoring are needed to address the variability in the data and provide a more robust statistical analysis.

California↗
USGS2019

The ‘Ike Wai Hawai‘i groundwater recharge tool

This paper discusses the design and implementation of the ‘Ike Wai Hawai‘i Groundwater Recharge Tool, an application for providing data and analyses of the impacts of land-cover and climate modifications on groundwater-recharge rates for the island of O‘ahu. This application uses simulation data based on a set of 29 land-cover types and two rainfall scenarios to provide users with real-time recharge calculations for interactively defined land-cover modifications. Two visualizations, representing the land cover for the island and the resultant groundwater-recharge rates, and a set of metrics indicating the changes to groundwater recharge for relevant areas of the map are provided to present a set of easily interpreted outcomes based on the user-defined simulations. Tools are provided to give users varying degrees of control over the granularity of data input and output, allowing for the quick production of a roughly defined simulation, or more precise land-cover models that can be exported for further analysis. Heuristics are used to provide a responsive user interface and performant integration with the database containing the full set of simulation data. This tool is designed to provide user-friendly access to the information on the impacts of land-cover and climate changes on groundwater recharge rates needed to make data-driven decisions.

Hawaii↗
USGS2026

Creating usable science: A Southwest Climate Adaptation Science Center study

The southwestern United States consists of diverse ecosystems that are experiencing increasing pressures from rising temperatures, increasing aridity, and sea level rise. To prepare this region for future uncertainty, there is a need for strong partnerships among researchers and societal partners. The Southwest Climate Adaptation Science Center (SW CASC) acts to foster engaged scholarship from diverse perspectives to produce science relevant for management and policy decisions. Here, we synthesized a subset of SW CASC-funded projects and published manuscripts to illustrate how the combination of new scientific knowledge and the research and practice of engaged scholarship produce actionable science with direct societal impacts. In the SW CASC Contributions to Regional Science section, we touch on new research produced from funded projects on the most common topics: (1) Forest Ecosystems, (2) Coastal Ecosystems, (3) Hydrometeorology, and (4) Research on Engaged Scholarship. In the SW CASC Contributions to Regional Impacts section, we outline engaged scholarship activities to demonstrate how they created societal impacts: (1) the Keepers of the Flame course and workshops, (2) the Southwest Fire Climate Adaptation Partnership and the Southern California Montane Forests Project, (3) the Coastal Managers Workshop, (4) the Colorado River Basin Wiki, and (5) the Natural Resource Workforce Development Program. In the SW CASC Publications Cited in Policy section, we used a bibliometric analysis to assess the impact of the SW CASC-funded research on policy. We analyzed how often publications were used in policy documents, finding a citation rate of 27.6%. This is high compared to similar analyses that found peer-reviewed research articles cited at a rate of 3.9% in policy documents. We conclude that coproduced science makes unique and important contributions to the resilience of the Southwest, not only by generating new knowledge but by building capacity, creating lasting relationships, and informing policy and conservation and management decisions.

Arizona, California, Nevada, Utah↗
USGS2013

Modeling rain-fed maize vulnerability to droughts using the standardized precipitation index from satellite estimated rainfall—Southern Malawi case study

During 1990s, disaster risk reduction emerged as a novel, proactive approach to managing risks from natural hazards. The World Bank, USAID, and other international donor agencies began making efforts to mainstream disaster risk reduction in countries whose population and economies were heavily dependent on rain-fed agriculture. This approach has more significance in light of the increasing climatic hazard patterns and the climate scenarios projected for different hazard prone countries in the world. The Famine Early Warning System Network (FEWS NET) has been monitoring the food security issues in the sub-Saharan Africa, Asia and in Haiti. FEWS NET monitors the rainfall and moisture availability conditions with the help of NOAA RFE2 data for deriving food security status in Africa. This paper highlights the efforts in using satellite estimated rainfall inputs to develop drought vulnerability models in the drought prone areas in Malawi. The satellite RFE2 based SPI corresponding to the critical tasseling and silking phases (in the months of January, February, and March) were statistically regressed with drought-induced yield losses at the district level. The analysis has shown that the drought conditions in February and early March lead to most damage to maize yields in this region. The district-wise vulnerabilities to drought were upscaled to obtain a regional maize vulnerability model for southern Malawi. The results would help in establishing an early monitoring mechanism for drought impact assessment, give the decision makers additional time to assess seasonal outcomes, and identify potential food-related hazards in Malawi.

International Journal of Disaster Risk Reduction↗
USGS2024

Evaluating seawater intrusion forecast uncertainty under climate change in the Pajaro Valley, California

Climate change and climate variability impacts such as rising sea levels have the potential to exacerbate seawater intrusion and the strain on coastal freshwater resources in already stressed groundwater basins such as those in the Pajaro Valley groundwater basin, California. Regional hydrologic models are often coupled with climate projections to forecast future hydrologic conditions and inform adaptive resources management strategies. However, there is high uncertainty in the future projections of water resources due to uncertainties from downscaling global general circulation models (GCMs) to local scale climate change projections, future land use changes, and the inherent uncertainty of developed hydrologic models. Future climate projections and the magnitude of their influence on modeled hydrologic drivers are highly variable. Therefore, to develop a forecast model, an ensemble of different projections can be used to capture a wider range of basin responses and the associated uncertainties in the modeled forecasts. Understanding the reliability and uncertainty of forecasts is important for developing climate adaptation strategies such as developing protective thresholds, particularly at the basin scale where the impacts are felt, and adaptation is implemented. To demonstrate this, an uncertainty analysis of groundwater level and seawater intrusion forecasts for the Pajaro Valley groundwater basin was performed using an ensemble of three future climate projections with the Pajaro Valley Integrated Hydrologic Model (PVIHM) and the first-order second moment (FOSM) method. FOSM uncertainty analysis of hydrologic forecasts across a multi-GCM climate ensemble provides an upper and lower bound of potential impacts of climate change on sustainability targets related to mitigating seawater intrusion. The groundwater level forecasts’ narrow range of variability can help policymakers in adaptation planning by constraining possible outcomes to a focused range for risk-management decisions. However, less than one-third of groundwater level forecasts met the current protection thresholds to prevent chronic lowering of groundwater. Therefore, sustainability targets may need to be reassessed. Relative to groundwater level changes, the seawater intrusion forecasts had larger uncertainty due to the GCM climate projections and the simulated hydrologic response that were compounded by the propagation of scaling and bias from the GCMs and model simplifications in simulating the coastal boundary.

California↗
USGS1999

A comparison of the weights-of-evidence method and probabilistic neural networks

The need to integrate large quantities of digital geoscience information to classify locations as mineral deposits or nondeposits has been met by the weights-of-evidence method in many situations. Widespread selection of this method may be more the result of its ease of use and interpretation rather than comparisons with alternative methods. A comparison of the weights-of-evidence method to probabilistic neural networks is performed here with data from Chisel Lake-Andeson Lake, Manitoba, Canada. Each method is designed to estimate the probability of belonging to learned classes where the estimated probabilities are used to classify the unknowns. Using these data, significantly lower classification error rates were observed for the neural network, not only when test and training data were the same (0.02 versus 23%), but also when validation data, not used in any training, were used to test the efficiency of classification (0.7 versus 17%). Despite these data containing too few deposits, these tests of this set of data demonstrate the neural network's ability at making unbiased probability estimates and lower error rates when measured by number of polygons or by the area of land misclassified. For both methods, independent validation tests are required to ensure that estimates are representative of real-world results. Results from the weights-of-evidence method demonstrate a strong bias where most errors are barren areas misclassified as deposits. The weights-of-evidence method is based on Bayes rule, which requires independent variables in order to make unbiased estimates. The chi-square test for independence indicates no significant correlations among the variables in the Chisel Lake–Andeson Lake data. However, the expected number of deposits test clearly demonstrates that these data violate the independence assumption. Other, independent simulations with three variables show that using variables with correlations of 1.0 can double the expected number of deposits as can correlations of −1.0. Studies done in the 1970s on methods that use Bayes rule show that moderate correlations among attributes seriously affect estimates and even small correlations lead to increases in misclassifications. Adverse effects have been observed with small to moderate correlations when only six to eight variables were used. Consistent evidence of upward biased probability estimates from multivariate methods founded on Bayes rule must be of considerable concern to institutions and governmental agencies where unbiased estimates are required. In addition to increasing the misclassification rate, biased probability estimates make classification into deposit and nondeposit classes an arbitrary subjective decision. The probabilistic neural network has no problem dealing with correlated variables—its performance depends strongly on having a thoroughly representative training set. Probabilistic neural networks or logistic regression should receive serious consideration where unbiased estimates are required. The weights-of-evidence method would serve to estimate thresholds between anomalies and background and for exploratory data analysis.

Natural Resources Research↗
USGS2004

The effects of macroinvertebrate taxonomic resolution in large landscape bioassessments: An example from the Mid-Atlantic Highlands, U.S.A.

1. During late spring 1993-1995, the U.S. Environmental Protection Agency's Environmental Monitoring and Assessment Program (EMAP) sampled 490 wadeable streams in the mid-Atlantic Highlands (MAH) of the U.S. for a variety of physical, chemical and biological indicators of environmental condition. We used the resulting data set to evaluate the importance of differing levels of macroinvertebrate taxonomic resolution in bioassessments by comparing the ability of family versus genus to detect differences among sites classified by type and magnitude of human impact and by stream size. We divided the MAH into two physiographic regions: the Appalachian Plateau where mine drainage (MD) and acidic deposition are major stressors, and the Ridge and Valley where nutrient enrichment is a major stressor. Stream sites were classified into three or four impact classes based on water chemistry and habitat. We used stream order (first to third Strahler order) in each region as a measure of stream size. Ordination, 2 x 2 chi-square and biotic metrics were used to compare the ability of family and genus to detect differences among both stressor and size classes. 2. With one notable exception, there were only a small number of different genera per family (interquartile range = 1-4). Family Chironomidae, however, contained 123 different genera. As a result, significant information loss occurred when this group was only classified to family. The family Chironomidae did not discriminate among the predefined classes but many chironomid genera did: by chi-square analysis, 10 and 28 chironomid genera were significant in discriminating MD and nutrient impacts, respectively. 3. Family and genus data were similar in their ability to distinguish among the coarse impacts (e.g. most severe versus least severe impact classes) for all cases. Though genus data in many cases distinguished the subtler differences (e.g. mixed/moderate impacts versus high or low impacts) better than family, differences in significance levels between family and genus analyses were relatively minor. However, genus data detected differences among stream orders in ordination analyses that were not revealed at the family level. In the ordinations, both family and genus levels of analysis responded to similar suites of environmental variables. 4. Our results suggest that identification to the family level is sufficient for many bioassessment purposes. However, identifications to genus do provide more information in genera-rich families like Chironomidae. Genus or finer levels of identification are important for investigating natural history, stream ecology, biodiversity and indicator species. Decisions about the taxonomic level of identification need to be study specific and depend on available resources (cost) and study objectives.

Freshwater Biology↗
USGS2026

Requirements, capabilities, and analysis for Earth observations—From user needs to Earth observation solutions

Introduction At the core of the Nation’s land imaging enterprise, the Requirements, Capabilities, and Analysis for Earth Observations (RCA-EO) project serves as the backbone for understanding user needs and shaping the future of Earth observation (EO) systems, products, and technology development. Understanding user needs and continuously tracking observing system capabilities is essential for EO because it ensures that the U.S. Government investments remain aligned with the real-world decisions, applications, and societal benefits they are intended to support. The RCA-EO project collects data users’ land imaging needs, focusing on what needs to be measured rather than specific technology. Collecting this data has resulted in a comprehensive understanding of civil land imaging user needs across scientific and operational applications. Over the past 10 years, the RCA-EO project has interviewed hundreds of subject matter experts, yielding more than a thousand user needs. The RCA-EO project’s analytical tools, models, and methods help inform the development of land imaging systems and products, determine appropriate data purchases, and prioritize partnerships for access to data from missions outside of the USGS. Our tools and databases support decisions with credible, needs-based information.

Fact Sheet↗
USGS2013

Satellite images of the September 2013 flood event in Lyons, Colorado

The U.S. Geological Survey (USGS) Special Applications Science Center (SASC) produced an image base map showing high-resolution remotely sensed data over Lyons, Colorado—a city that was severely affected by the flood event that occurred throughout much of the Colorado Front Range in September of 2013. The 0.5-meter WorldView-2 data products were created from imagery collected by DigitalGlobe on September 13 and September 24, 2013, during and following the flood event. The images shown on this map were created to support flood response efforts, specifically for use in determining damage assessment and mitigation decisions. The raw, unprocessed imagery were orthorectified and pan-sharpened to enhance mapping accuracy and spatial resolution, and reproduced onto a cartographic base map. These maps are intended to provide a snapshot representation of post-flood ground conditions, which may be useful to decisionmakers and the general public. The SASC also provided data processing and analysis support for other Colorado flood-affected areas by creating cartographic products, geo-corrected electro-optical and radar image mosaics, and GIS water cover files for use by the Colorado National Guard, the National Park Service, the U.S. Forest Service, and the flood response community. All products for this International Charter event were uploaded to the USGS Hazards Data Distribution System (HDDS) website (http://hdds.usgs.gov/hdds2/) for distribution.

Colorado↗
USGS2025

Tracking status and trends in seven key indicators of river and stream condition in the Chesapeake Bay watershed

Freshwater streams and rivers are recognized as vital habitats within the Chesapeake Bay watershed, which has been undergoing extensive restoration efforts for more than 30 years. Resource managers need to understand stream and river condition and how these conditions are changing over time to determine whether regional long-term restoration and conservation goals are being met. The objective of this report was to document the spatial and temporal variability of conditions for seven indicators of river and stream health across the nontidal Chesapeake Bay watershed. The framework for the U.S. Geological Survey’s Nontidal Network (NTN), a network of more than 100 nutrient and suspended sediment monitoring locations, was extended to assess conditions for six additional indicators of stream health: temperature, salinity, toxic contaminants, streamflow, hydromorphology, and biological aquatic communities. For each indicator, the latest available data from multiple sources were compiled and harmonized, and key metrics were identified to describe indicator conditions across space and time. A status condition was defined for each indicator to describe overall spatial variability in recent condition, and trend analyses were used to describe changes in each indicator metric over time. The analysis revealed clear differences in spatial and temporal data coverage across the seven indicators, so individual indicator trend analyses were not constrained to a common time interval. However, a status snapshot was conducted across all indicators for the 2015–17 period to simultaneously explore spatial variability across all indicators. The status snapshot highlighted general degraded conditions across multiple indicators in large metropolitan regions, such as the Baltimore–Washington, D.C., metropolitan area. Regression analysis between indicator status metrics and major land cover for the sites suggest urbanization as a potential driver of degraded conditions for many of the indicator metrics, including total phosphorus, salinity, temperature, high-flow frequency, and metrics of habitat and biological assemblage quality. A final analysis exploring the spatial representation of each indicator network showed that some indicator monitoring networks did not cover certain settings, such as small watersheds. These results provided an initial assessment of stream health status and trends and will continue to be leveraged to describe conditions across the Chesapeake Bay watershed to help inform local and regional management decisions. These results also highlighted the need for improved coordination among monitoring organizations to support long-term multi-indicator monitoring and assessment.

Chesapeake Bay watershed↗
USGS2022

Balancing future renewable energy infrastructure siting and associated habitat loss for migrating whooping cranes

The expansion of human infrastructure has contributed to novel risks and disturbance regimes in most ecosystems, leading to considerable uncertainty about how species will respond to altered landscapes. A recent assessment revealed that whooping cranes ( Grus americana ), an endangered migratory waterbird species, avoid wind-energy infrastructure during migration. However, uncertainties regarding collective impacts of other types of human infrastructure, such as power lines on migration, variable drought conditions, and continued construction of wind energy infrastructure may compromise ongoing recovery efforts for whooping cranes. Droughts are increasing in frequency and severity throughout the whooping crane migration corridor, and the impacts of drought on stopover habitat use are largely unknown. Moreover, decision-based analyses are increasingly advocated to guide recovery planning for endangered species, yet applications remain rare. Using GPS locations from 57 whooping cranes from 2010 through 2016 in the United States Great Plains, we assessed habitat selection and avoidance of potential disturbances during migration relative to drought conditions, and we used these results in an optimization analysis to select potential sites for new wind energy developments that minimize relative habitat loss for whooping cranes and maximize wind energy potential. Drought occurrence and severity varied spatially and temporally across the migration corridor during our study period. Whooping cranes rarely used areas <5 km from human settlements and wind energy infrastructure under both drought and non-drought conditions, and <2 km from power lines during non-drought conditions, with the lowest likelihood of use near wind energy infrastructure. Whooping cranes differed in their selection of wetland and cropland land cover types depending on drought or non-drought conditions. We identified scenarios for wind energy expansion across the migration corridor and in select states, which are robust to uncertain drought conditions, where future loss of highly selected stopover habitats could be minimized under a common strategy. Our approach was to estimate functional habitat loss while integrating current disturbances, potential future disturbances, and uncertainty in drought conditions. Therefore, dynamic models describing potential costs associated with risk-averse behaviors resulting from future developments can inform proactive conservation before population impacts occur.

Kansas, Montana, Nebraska, North Dakota, South Dak↗
USGS2014

Virtual Beach 3: user's guide

Virtual Beach version 3 (VB 3 ) is a decision support tool that constructs site-specific statistical models to predict fecal indicator bacteria (FIB) concentrations at recreational beaches. VB 3 is primarily designed for beach managers responsible for making decisions regarding beach closures or the issuance of swimming advisories due to pathogen contamination. However, researchers, scientists, engineers, and students interested in studying relationships between water quality indicators and ambient environmental conditions will find VB 3 useful. VB 3 reads input data from a text file or Excel document, assists the user in preparing the data for analysis, enables automated model selection using a wide array of possible model evaluation criteria, and provides predictions using a chosen model parameterized with new data. With an integrated mapping component to determine the geographic orientation of the beach, the software can automatically decompose wind/current/wave speed and magnitude information into along-shore and onshore/offshore components for use in subsequent analyses. Data can be examined using simple scatter plots to evaluate relationships between the response and independent variables (IVs). VB 3 can produce interaction terms between the primary IVs, and it can also test an array of transformations to maximize the linearity of the relationship The software includes search routines for finding the "best" models from an array of possible choices. Automated censoring of statistical models with highly correlated IVs occurs during the selection process. Models can be constructed either using previously collected data or forecasted environmental information. VB 3 has residual diagnostics for regression models, including automated outlier identification and removal using DFFITs or Cook's Distances.

Report↗
USGS2023

Using public litigation records to identify priority science needs for managing public lands

Relevant science is essential for effective natural resource decision making, including on public lands managed by the United States Department of the Interior (DOI) Bureau of Land Management (BLM), that cover 1/10th of the United States. Most of the BLM’s management decisions require analyses under the National Environmental Policy Act, and the use of science in these decisions is often challenged. Using coproduction, we assembled an interagency team of scientists and resource managers to develop a method for using public litigation to identify priority science needs for the BLM. We searched publicly available case documents finalized from 2015–2019 in Wyoming, Colorado, Utah, and New Mexico within federal courts and the DOI Office of Hearings and Appeals, and identified 108 case documents that involved challenges to the BLM’s use of science. We retained 48 case documents that contained at least one challenge about the BLM’s use of science for a specific resource. We categorized all challenges in each case document according to the proposed action, affected resource, type of science challenged (data about resources, science relevant to potential impacts, methods for analyzing potential impacts, and mitigation actions), and specific nature of the challenge (e.g., challenging direct effects analysis). We identified priority science needs based on the frequency of challenges, the number of states where similar challenges occurred, whether the BLM lost the challenge, and whether the case was remanded. Top needs related to oil and gas development actions and included science about effects on air quality and climate, water, and socioeconomics; data for air quality and climate; and methods for analyzing potential impacts to cultural resources and air quality and climate. The BLM can use this information to prioritize actions (e.g., funding new research or science syntheses) to strengthen its science foundation for decision-making.

Colorado, New Mexico, Utah, Wyoming↗
USGS2017

Motivations for enrollment into the Conservation Reserve Enhancement Program in the James River Basin of South Dakota

The Conservation Reserve Enhancement Program (CREP) targets high-priority conservation needs (e.g., water quality, wildlife habitat) by paying landowners an annual rental rate to remove environmentally sensitive or agriculturally unproductive lands from rowcrop production, and then implement conservation practices on these lands. This study examined motivations of South Dakota landowners for enrolling in the James River Basin CREP. All 517 newly enrolled landowners were mailed a questionnaire in 2014 measuring demographics, behaviors, opinions, and motivations (60% response rate). Cluster analysis of 10 motivations for enrolling identified three motivation groups (wildlife = 40%, financial = 35%, environmental = 25%). The financial group had the youngest mean age (62 years), followed by the wildlife (65) and environmental groups (68). Among respondents, 43% favored the public access requirement of this CREP with the environmental group most in favor. Understanding landowner enrollment motivations and decision criteria will assist in strategies (e.g., financial incentives, increasing yield via habitat restoration) for increasing future participation.

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