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At least 811 records · Page 45Linked to original sources
USGS2009

Variability of the Southern California wave climate and implications for sediment transport

We analyzed wave and wind data from 18 buoys in the Southern California Bight to characterize the spatial and temporal variability of the regional wave climate. Point Conception shelters most of the Bight from being directly impacted by North Pacific weather. The wave height inside the sheltered zone and to the east of the Channel Islands is less than half the wave height in the open ocean to the west. Within the sheltered Bight, storm waves (by proxy of being greater than the 95th percentile wave height for more than 6 hours) are mainly from the west, but long period swells (T p >15 seconds) are mainly from the south-southwest. There are on average two to four storms during each winter month (November-March) and fewer than two storms per month for the rest of the year. The Channel Islands selectively block the westerly swells and make the wave climate in the Santa Barbara Channel different from the rest of the sheltered Bight. A statistically significant wave-height minimum exists in the area offshore Dana Point and Oceanside. The multiyear (2-23 years) wave-data records from all 18 buoys show negligible temporal trend, positive or negative. Like the wave climate, the long-term probability of sediment transport on the continental shelves of the Bight displays large difference between the sheltered and open-ocean (near Point Conception) sites. The return period of incipient sediment motion on the sheltered shelf breaks (one to five months) is at least two orders of magnitude longer than that on the Point Conception shelf break (0.6 day). Similar to the spatial distribution of wave heights, there is a systematic return-period maximum on the shelf off Dana Point and Oceanside. ?? 2009 The Geological Society of America.

Special Paper of the Geological Society of America↗
USGS2006

Relationships between reef fish communities and remotely sensed rugosity measurements in Biscayne National Park, Florida, USA

The realization that coral reef ecosystem management must occur across multiple spatial scales and habitat types has led scientists and resource managers to seek variables that are easily measured over large areas and correlate well with reef resources. Here we investigate the utility of new technology in airborne laser surveying (NASA Experimental Advanced Airborne Research Lidar (EAARL)) in assessing topographical complexity (rugosity) to predict reef fish community structure on shallow (<10 m deep) patch reefs. Marine portions of Biscayne National Park, Florida, USA, were surveyed remotely using the EAARL, and reef fish populations were visually surveyed on 10 patch reefs at independent, randomly selected stations ( n = 10–13 per reef). Rugosity at each station was assessed in situ by divers using the traditional chain-transect method (10-m scale), and remotely using the EAARL submarine topography data at multiple spatial scales (2, 5, and 10 m). The rugosity and biological datasets were analyzed together to elucidate the predictive power of EAARL rugosity in describing the variance in reef fish community variables and to assess the correlation between chain-transect and EAARL rugosity. EAARL rugosity was not well correlated with chain-transect rugosity, or with species richness of fishes (although statistically significant, the amount of variance explained by the model was very low). Variance in reef fish community attributes was better explained in reef-by-reef variability than by physical variables. However, once the reef-by-reef variability was taken into account in a two-way analysis of variance, the importance of rugosity could be seen on individual reefs. Fish species richness and abundance were statistically higher at high rugosity stations compared to medium and low rugosity stations, as predicted by prior ecological research. The EAARL shows promise as an important mapping tool for reef resource managers as they strive to inventory and protect coral reef resources.

Florida↗
USGS2009

Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium

A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3-band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time-series for 1997-1999, (b) Syste me pour l'Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50km monthly time series for 1961-2000, (d) Global 30 Arc-Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite-1 Synthetic Aperture Radar (JERS-1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992-1993. A single mega-file data-cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re-sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments. Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space-time spiral curve (ST-SC) plots, (b) brightness-greenness-wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very-high-resolution imagery (VHRI) 'zoom-in views' in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high-resolution Landsat-ETM+ Geocover 150m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re-classify the MDFC, and the class identification and labelling protocol repeated. The sub-pixel area (SPA) calculations were performed by multiplying full-pixel areas (FPAs) with irrigated area fractions (IAFs) for every class. A 28 class GIAM was produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year-round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but 'equipped for irrigation' at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467 million hectares (Mha), which is sum of the non-overlapping areas of: (a) 252 Mha from season one, (b) 174 Mha from season two and (c) 41 Mha from continuous year-round crops. The TAAI at the end of the last millennium was 399 Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (370 Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79-91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org). ?? 2009 Taylor & Francis.

International Journal of Remote Sensing↗
USGS2024

Intercomparison of same-day remote sensing data for measuring winter cover crop biophysical traits

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R 2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha−1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R 2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.

Maryland↗
USGS1991

Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic

The merging of multisensor image data is becoming a widely used procedure because of the complementary nature of various data sets. Ideally, the method used to merge data sets with high-spatial and high-spectral resolution should not distort the spectral characteristics of the high-spectral resolution data. This paper compares the results of three different methods used to merge the information contents of the Landsat Thematic Mapper (TM) and Satellite Pour l'Observation de la Terre (SPOT) panchromatic data. The comparison is based on spectral characteristics and is made using statistical, visual, and graphical analyses of the results. The three methods used to merge the information contents of the Landsat TM and SPOT panchromatic data were the Hue-Intensity-Saturation (HIS), Principal Component Analysis (PCA), and High-Pass Filter (HPF) procedures. The HIS method distorted the spectral characteristics of the data the most. The HPF method distorted the spectral characteristics the least; the distortions were minimal and difficult to detect. -Authors

Photogrammetric Engineering and Remote Sensing↗
USGS2020

Balancing transferability and complexity of species distribution models for rare species conservation

Aim Species distribution models (SDMs) are valuable for rare species conservation and are commonly used to extrapolate predictions of habitat suitability geographically to regions where species occurrence is unknown (i.e., transferability). Spatially structured cross-validation can be used to infer transferability, yet, few studies have evaluated how delineation of cross-validation folds affects model complexity and predictions. We developed SDMs using multiple cross-validation approaches to understand the implications for predicting habitat suitability for northern Idaho ground squirrels, a rare, federally threatened species that has been extensively surveyed in regions where known populations occur, resulting in >8000 presence locations. Location Idaho, USA. Methods We delineated cross-validation folds by mimicking the manner in which predictions would be geographically extrapolated or by using existing dispersal barriers. We varied the distance between, number, and directionality of folds. We conducted a grid search on statistical regularization parameters to optimize model complexity, covering a range of values exceeding that typically implemented. For each cross-validation approach, we selected optimal regularization and model complexity based on out-of-sample predictive ability. Results Delineation of cross-validation folds substantially affected resulting model complexity and extrapolated predictions. All cross-validation approaches resulted in models with apparently high out-of-sample predictive ability, yet optimal model complexity varied substantially among the approaches. Regularization demonstrated a noisy relationship between model complexity and prediction, where local optima in predictive performance were common at small values. Main conclusion Subtle modelling decisions can have large consequences for predictions of habitat suitability and transferability of SDMs. When transferability is the goal, cross-validation approaches should be considered carefully and mimic the manner in which spatial extrapolation will occur, else overly complex models with inflated assessments of predictive accuracy may result. Further, spatially structured cross-validation may not guard against over-parameterization, and assessing a broader range of regularization parameters may be necessary to optimize model complexity for transferability.

Idaho↗
USGS2011

Low-flow characteristics of Virginia streams

Low-flow annual non-exceedance probabilities (ANEP), called probability-percent chance (P-percent chance) flow estimates, regional regression equations, and transfer methods are provided describing the low-flow characteristics of Virginia streams. Statistical methods are used to evaluate streamflow data. Analysis of Virginia streamflow data collected from 1895 through 2007 is summarized. Methods are provided for estimating low-flow characteristics of gaged and ungaged streams. The 1-, 4-, 7-, and 30-day average streamgaging station low-flow characteristics for 290 long-term, continuous-record, streamgaging stations are determined, adjusted for instances of zero flow using a conditional probability adjustment method, and presented for non-exceedance probabilities of 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.05, 0.02, 0.01, and 0.005. Stream basin characteristics computed using spatial data and a geographic information system are used as explanatory variables in regional regression equations to estimate annual non-exceedance probabilities at gaged and ungaged sites and are summarized for 290 long-term, continuous-record streamgaging stations, 136 short-term, continuous-record streamgaging stations, and 613 partial-record streamgaging stations. Regional regression equations for six physiographic regions use basin characteristics to estimate 1-, 4-, 7-, and 30-day average low-flow annual non-exceedance probabilities at gaged and ungaged sites. Weighted low-flow values that combine computed streamgaging station low-flow characteristics and annual non-exceedance probabilities from regional regression equations provide improved low-flow estimates. Regression equations developed using the Maintenance of Variance with Extension (MOVE.1) method describe the line of organic correlation (LOC) with an appropriate index site for low-flow characteristics at 136 short-term, continuous-record streamgaging stations and 613 partial-record streamgaging stations. Monthly streamflow statistics computed on the individual daily mean streamflows of selected continuous-record streamgaging stations and curves describing flow-duration are presented. Text, figures, and lists are provided summarizing low-flow estimates, selected low-flow sites, delineated physiographic regions, basin characteristics, regression equations, error estimates, definitions, and data sources. This study supersedes previous studies of low flows in Virginia.

Virginia↗
USGS2021

Beware of spatial autocorrelation when applying machine learning algorithms to borehole geophysical logs

Although many of the algorithms now considered to be machine learning algorithms (MLAs) have existed for nearly a century (e.g., Rosenblatt 1958 ), interest in MLAs has recently increased exponentially for solving data-driven problems across a variety of fields due to the expanded availability of large, complex datasets that may be difficult to interrogate using other methods, increases in computing power, and a growing library of easily implemented machine learning tools. While MLAs are often similar to statistical methods, there are key differences in the approach to problem solving. Namely, statistical methods are more concerned with generating informative models from “long” data (i.e., many more observations than explanatory variables), whereas MLAs are typically concerned with generating accurate predictions from “wide” data (i.e., a large number of variables with relatively fewer observations, Bzdok et al. 2018 ). In hydrogeologic studies, such wide datasets may be available from boreholes, where various types of geophysical, geochemical, and lithological information may exist. Borehole datasets are therefore a tempting target for MLAs to reveal hidden relations among gathered data and parameters of interest (e.g., contaminant concentration), and as a method of parameter reduction (e.g., reduce costs by collecting fewer datasets).

Groundwater↗
USGS2020

Projecting spatiotemporally explicit effects of climate change on stream temperature: A model comparison and implications for coldwater fishes

Conservation planners and resource managers seek information about how the availability and locations of cold-water habitats will change in the future and how these predictions vary among models. We used a physical process-based model to demonstrate the implications of climate change for streamflow and water temperature in two watersheds with distinctive flow regimes: the Snoqualmie watershed (WA) and Siletz watershed (OR), USA. Our model incorporated a downscaled ensemble of global climate model outputs and was calibrated with in situ and remotely sensed water temperatures. We compared predictions from our processed-based model to those from a publicly available and widely used statistical model. The process-based model projected greater changes in summer maximum water temperatures for the mixed-rain-snow Snoqualmie watershed than for the rain-dominated Siletz watershed as a result of the near-complete loss of winter snowpack and significant reduction in summer flow in the Snoqualmie watershed expected by the 2080s. Both models projected generally similar future spatial patterns of maximum water temperature in the two rivers, with cool reaches distributed farther upstream and fewer in number. However, the process-based model projected higher spatial heterogeneity in water temperature due to our spatially explicit simulation of streamflow and because we calibrated the model with spatially continuous remotely sensed water temperature data. We used stream temperature projections to assess the vulnerability of Pacific salmon and trout to changes in the spatial distribution of cold-water habitats during August by the 2080s. Results suggest that salmonids may have fewer summertime cold-water habitats in both watersheds. Projected stream warming may further limit particular species and life stages, especially in the Snoqualmie watershed. Our comparison of models highlights the importance of considering what might be gained by using a process-based model for evaluating and prioritizing management actions that mitigate climate impacts on cold-water habitats for stream fishes.

Oregon, Washington↗
USGS2001

Fish species and community distributions as proxies for sea-floor habitat distributions: the Stellwagen Bank National Marine Sanctuary example (northwest Atlantic, Gulf Of Maine)

Defining the habitats of fishes and associated fauna on outer continental shelves is problematic given the paucity of data on the actual types and distributions of seafloor habitats. However many regions have good data on the distributions of fishes from resource surveys or catch statistics because of the economic importance of the fisheries. Fish distribution data (species or communities) have been used as a proxy for the distribution of habitats to develop precautionary conservation strategies for habitat protection (e.g., marine protected areas, fishing gear restrictions). In this study we assessed the relationships between the distributions of fish communities and species derived from trawl survey data with the spatial distribution of sediment types determined by sampling and acoustic reflectance derived from multibeam sonar surveys in Stellwagen Bank National Marine Sanctuary. Fish communities were correlated with reflectance values but all communities did not occur in unique sediment types. This suggests that use of community distributions as proxies for habitats should include the caveat that a greater number of communities within an area could indicate a greater range of habitat types. Single species distributions showed relationships between abundance and reflectance values. Trawl catches with low abundances had wide variations in reflectance values while those with high abundances had narrower ranges indicating habitat affinities. Significant non-random frequency-dependent relationships were observed for 17 of 20 species although only 12 of 20 species had significant relationships based on rank correlation. These results suggest that species distributions based on trawl survey data can be used as proxies for the distribution of seafloor habitats. Species with known habitat associations can be used to infer habitat requirements of co-occurring species and can be used to identify a range of habitat types.

Massachusetts↗
USGS2024

Leveraging natural capital accounting to support businesses with nature-related risk assessments and disclosures

Nature loss threatens businesses, the global economy and financial stability. Understanding and addressing these risks for business will require credible measurement approaches and data. This paper explores how natural capital accounting (NCA) can support business data and information needs related to nature, including disclosures aligned with the Taskforce on Nature-related Financial Disclosures recommendations. As businesses seek to measure, manage and disclose their nature-related risks and opportunities, they will need well-organized, consistent and high-quality information regarding their dependencies and impacts on nature, which few businesses currently collect or track in-house. NCA may be useful for these purposes but has not been widely used or applied by businesses. National NCA guided by the U.N. System of Environmental-Economic Accounting may provide: (i) a useful framework for businesses in conceptualizing, organizing and managing nature-related data and statistics; and (ii) data and information that can directly support business disclosures, corporate NCA and other business applications. This paper explores these opportunities as well as synergies between national and corporate natural capital accounts. In addition, the paper discusses key barriers to advancing the wider use and benefits of NCA for business, including: awareness of NCA, data access, business capabilities related to NCA, spatial and temporal scales of data, audit and assurance considerations, potential risks, and costs and incentives.

Philosophical Transactions of the Royal Society B:↗
USGS2001

Statistical self-similarity of hotspot seamount volumes modeled as self-similar criticality

The processes responsible for hotspot seamount formation are complex, yet the cumulative frequency-volume distribution of hotspot seamounts in the Easter Island/Salas y Gomez Chain (ESC) is found to be well-described by an upper-truncated power law. We develop a model for hotspot seamount formation where uniform energy input produces events initiated on a self-similar distribution of critical cells. We call this model Self-Similar Criticality (SSC). By allowing the spatial distribution of magma migration to be self-similar, the SSC model recreates the observed ESC seamount volume distribution. The SSC model may have broad applicability to other natural systems.

Geophysical Research Letters↗
USGS2025

Development of a genotyping-in-thousands by sequencing (GT-seq) panel for identifying individuals and estimating relatedness among Alaska black bears (Ursus americanus)

The management and conservation of large mammals, such as black bears ( Ursus americanus ), have long been informed by genetic estimates of population size and individual dispersal. Amplicon sequencing methods, also known as ‘genotyping-in-thousands-by sequencing’ (GT-seq), now enable the efficient and cost-effective genotyping of hundreds of loci and individuals in the same sequencing run. Here, we develop a GT-seq panel for individual identification and kinship inference in Alaska black bears. Using genomic data from restriction site-associated DNA sequencing of hunter-harvested bears from Southcentral Alaska ( n = 85), we identified 170 microhaplotype and single nucleotide polymorphism (SNP) loci that were highly heterozygous in local populations. To enable sexing of individuals, we also included a previously published sex-linked locus in the GT-seq panel. We empirically validated the GT-seq panel using samples collected at different spatial scales. These samples included tissues ( n = 82) obtained from bears within a small geographic area in Anchorage, Alaska, which were likely to be relatives as well as the hunter-harvested samples collected from geographically widespread locations throughout Southcentral Alaska. Empirical validation indicated high genotyping success and genotype reproducibility across replicate subsamples. Computer simulations demonstrated that the GT-seq panel had ample statistical power for distinguishing distinct individuals and first-order relatives (parent-offspring and full-sibling pairs) from unrelated individuals. As a final proof of concept, the panel was used to identify individual bears and close kin sampled from urban and wild habitats in Anchorage, Alaska. We anticipate that the GT-seq panel will be a useful genomic resource for the monitoring and management of Alaska black bear populations. ons.

Alaska↗
USGS2006

Landscape conditions predisposing grizzly bears to conflicts on private agricultural lands in the western USA

We used multiple logistic regression to model how different landscape conditions contributed to the probability of human-grizzly bear conflicts on private agricultural ranch lands. We used locations of livestock pastures, traditional livestock carcass disposal areas (boneyards), beehives, and wetland-riparian associated vegetation to model the locations of 178 reported human-grizzly bear conflicts along the Rocky Mountain East Front, Montana, USA during 1986-2001. We surveyed 61 livestock producers in the upper Teton watershed of north-central Montana, to collect spatial and temporal data on livestock pastures, boneyards, and beehives for the same period, accounting for changes in livestock and boneyard management and beehive location and protection, for each season. We used 2032 random points to represent the null hypothesis of random location relative to potential explanatory landscape features, and used Akaike's Information Criteria (AIC/AICC) and Hosmer-Lemeshow goodness-of-fit statistics for model selection. We used a resulting "best" model to map contours of predicted probabilities of conflict, and used this map for verification with an independent dataset of conflicts to provide additional insights regarding the nature of conflicts. The presence of riparian vegetation and distances to spring, summer, and fall sheep or cattle pastures, calving and sheep lambing areas, unmanaged boneyards, and fenced and unfenced beehives were all associated with the likelihood of human-grizzly bear conflicts. Our model suggests that collections of attractants concentrated in high quality bear habitat largely explain broad patterns of human-grizzly bear conflicts on private agricultural land in our study area. ?? 2005 Elsevier Ltd. All rights reserved.

Biological Conservation↗
USGS2022

Aftershocks preferentially occur in previously active areas

The clearest statistical signal in aftershock locations is that most aftershocks occur close to their mainshocks. More precisely, aftershocks are triggered at distances following a power‐law decay in distance ( Felzer and Brodsky, 2006 ). This distance decay kernel is used in epidemic‐type aftershock sequence (ETAS) modeling and is typically assumed to be isotropic, even though individual sequences show more clustered aftershock occurrence. The assumption of spatially isotropic triggering kernels can impact the estimation of ETAS parameters themselves, such as biasing the magnitude‐productivity term, alpha, and assigning too much weight to secondary rather than primary (direct) triggering. Here we show that aftershock locations in southern California, at all mainshock–aftershock distances, preferentially occur in the areas of previous seismicity. For a given sequence, the scaling between aftershock rates and the previous seismicity rate is approximately linear. However, the total number of aftershocks observed for a given sequence is independent of background rate. We explain both of these observations within the framework of rate‐and‐state friction ( Dieterich, 1994 ).

California↗
USGS2004

Metadata for ReVA logistic regression dataset

The U.S. Geological Survey in cooperation with the U.S. Environmental Protection Agency's Regional Vulnerability Assessment Program, has developed a set of statistical tools to support regional-scale, ground-water quality and vulnerability assessments. The Regional Vulnerability Assessment Program goals are to develop and demonstrate approaches to comprehensive, regional-scale assessments that effectively inform water-resources managers and decision-makers as to the magnitude, extent, distribution, and uncertainty of current and anticipated environmental risks. The U.S. Geological Survey is developing and exploring the use of statistical probability models to characterize the relation between ground-water quality and geographic factors in the Mid-Atlantic Region. Available water-quality data obtained from U.S. Geological Survey National Water-Quality Assessment Program studies conducted in the Mid-Atlantic Region were used in association with geographic data (land cover, geology, soils, and others) to develop logistic-regression equations that use explanatory variables to predict the presence of a selected water-quality parameter exceeding specified management concentration thresholds. The resulting logistic-regression equations were transformed to determine the probability, P(X), of a water-quality parameter exceeding a specified management threshold. Additional statistical procedures modified by the U.S. Geological Survey were used to compare the observed values to model-predicted values at each sample point. In addition, procedures to evaluate the confidence of the model predictions and estimate the uncertainty of the probability value were developed and applied. The resulting logistic-regression models were applied to the Mid-Atlantic Region to predict the spatial probability of nitrate concentrations exceeding specified management thresholds. These thresholds are usually set or established by regulators or managers at national or local levels. At management thresholds of 1 milligram per liter, and 3 milligrams per liter, the probability of nitrate concentrations exceeding these levels is greater than 50 percent (.50) throughout much of the Mid-Atlantic Region. This includes extensive areas throughout central Maryland, southeastern Pennsylvania, northwestern Pennsylvania and the Delmarva Peninsula. In addition, extensive areas in North Carolina and Virginia also have high probabilities of nitrate concentrations in ground water exceeding management thresholds of 1 milligram per liter and 3 milligrams per liter. The mapped areas showing a high predicted probability of nitrate concentrations in ground water exceeding 1 milligram per liter and 3 milligrams per liter correspond to areas that are mapped as cultivated land cover overlying carbonate rocks. At a management threshold of 10 milligrams per liter (corresponding to the U.S. Environmental Protection Agency standard for nitrate in drinking water of 10 milligrams per liter), the predicted probability of nitrate concentrations in ground water exceeding this level are low for most of the Mid-Atlantic Region except for the Delmarva Peninsula, southeastern Pennsylvania, and areas mapped as carbonate rocks in Virginia, Maryland, and Pennsylvania.

Open-File Report↗
USGS2004

Nutrient concentrations in Upper and Lower Echo, Fallen Leaf, Spooner, and Marlette Lakes and associated outlet streams, California and Nevada, 2002-03

Five lakes and their outlet streams in the Lake Tahoe Basin were sampled for nutrients during 2002-03. The lakes and streams sampled included Upper Echo, Lower Echo, Fallen Leaf, Spooner, and Marlette Lakes and Echo, Taylor, and Marlette Creeks. Water samples were collected to determine seasonal and spatial concentrations of dissolved nitrite plus nitrate, dissolved ammonia, total Kjeldahl nitrogen, dissolved orthophosphate, total phosphorus, and total bioreactive iron. These data will be used by Tahoe Regional Planning Agency in revising threshold values for waters within the Lake Tahoe Basin. Standard U.S. Geological Survey methods of sample collection and analysis were used and are detailed herein. Data collected during this study and summary statistics are presented in graphical and tabular form.

Open-File Report↗
USGS2010

Incorporation of water-use summaries into the StreamStats web application for Maryland

Approximately 25,000 new households and thousands of new jobs will be established in an area that extends from southwest to northeast of Baltimore, Maryland, as a result of the Federal Base Realignment and Closure (BRAC) process, with consequent new demands on the water resources of the area. The U.S. Geological Survey, in cooperation with the Maryland Department of the Environment, has extended the area of implementation and added functionality to an existing map-based Web application named StreamStats to provide an improved tool for planning and managing the water resources in the BRAC-affected areas. StreamStats previously was implemented for only a small area surrounding Baltimore, Maryland, and it was extended to cover all BRAC-affected areas. StreamStats could provide previously published streamflow statistics, such as the 1-percent probability flood and the 7-day, 10-year low flow, for U.S. Geological Survey data-collection stations and estimates of streamflow statistics for any user-selected point on a stream within the implemented area. The application was modified for this study to also provide summaries of water withdrawals and discharges upstream from any user-selected point on a stream. This new functionality was made possible by creating a Web service that accepts a drainage-basin delineation from StreamStats, overlays it on a spatial layer of water withdrawal and discharge points, extracts the water-use data for the identified points, and sends it back to StreamStats, where it is summarized for the user. The underlying water-use data were extracted from the U.S. Geological Survey's Site-Specific Water-Use Database System (SWUDS) and placed into a Microsoft Access database that was created for this study for easy linkage to the Web service and StreamStats. This linkage of StreamStats with water-use information from SWUDS should enable Maryland regulators and planners to make more informed decisions on the use of water resources in the BRAC area, and the technology should be transferrable to other geographic areas.

Scientific Investigations Report↗