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USGS2012

An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data

The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary data (e.g., elevation, slope, precipitation, temperature), and in situ data. First, the process involved producing an accurate reference (or truth) cropland layer (TCL), consisting of cropland extent, areas, and irrigated vs. rainfed cropland areas, for the entire country of Tajikistan based on MFDC of year 2005 (MFDC2005). The methods involved in producing TCL included using ISOCLASS clustering, Tasseled Cap bi-spectral plots, spectro-temporal characteristics from MODIS 250 m monthly normalized difference vegetation index (NDVI) maximum value composites (MVC) time-series, and textural characteristics of higher resolution imagery. The TCL statistics accurately matched with the national statistics of Tajikistan for irrigated and rainfed croplands, where about 70% of croplands were irrigated and the rest rainfed. Second, a rule-based ACCA was developed to replicate the TCL accurately (∼80% producer’s and user’s accuracies or within 20% quantity disagreement involving about 10 million Landsat 30 m sized cropland pixels of Tajikistan). Development of ACCA was an iterative process involving series of rules that are coded, refined, tweaked, and re-coded till ACCA derived croplands (ACLs) match accurately with TCLs. Third, the ACCA derived cropland layers of Tajikistan were produced for year 2005 (ACL2005), same year as the year used for developing ACCA, using MFDC2005. Fourth, TCL for year 2010 (TCL2010), an independent year, was produced using MFDC2010 using the same methods and approaches as the one used to produce TCL2005. Fifth, the ACCA was applied on MFDC2010 to derive ACL2010. The ACLs were then compared with TCLs (ACL2005 vs. TCL2005 and ACL2010 vs. TCL2010). The resulting accuracies and errors from error matrices involving about 152 million Landsat (30 m) pixels of the country of Tajikistan (of which about 10 million Landsat size, 30 m, cropland pixels) showed an overall accuracy of 99.6% (k hat = 0.97) for ACL2005 vs. TCL2005. For the 3 classes (irrigated, rainfed, and others) mapped in ACL2005, the producer’s accuracy was >86.4% and users accuracy was >93.6%. For ACL2010 vs. TCL2010, the error matrix showed an overall accuracy on 96.2% (k hat = 0.96). For the 3 classes (irrigated, rainfed, and others) mapped in ACL2010, the producer’s and user’s accuracies for the irrigated areas were ≥82.9%. Any intermixing was overwhelmingly between irrigated and rainfed croplands, indicating that croplands (irrigated plus rainfed areas) as well as irrigated areas were mapped with high levels of accuracies (∼90% or higher) even for the independent year. The ACL2005 and ACL2010, each, were produced using ACCA algorithm in ∼30 min using a Dell Precision desktop T7400 computer for the entire country of Tajikistan once the MFDCs for the years were ready. The ACCA algorithm for Tajikistan is made available through US Geological Survey’s ScienceBase: http://www.sciencebase.gov/catalog/folder/4f79f1b7e4b0009bd827f548 or at: https://powellcenter.usgs.gov/globalcroplandwater/content/models-algorithms . The research contributes to the efforts of global food security through research on global croplands and their water use (e.g., https://powellcenter.usgs.gov/globalcroplandwater/ ). The above results clearly demonstrated the ability of a rule-based ACCA to rapidly and accurately produce cropland data layer year after year (hindcast, nowcast, forecast) for the country it was developed using MFDCs that consist of combining multiple sensor data and secondary data. It needs to be noted that the ACCA is applicable to the area (e.g., country, region) for which it is developed. In this case, ACCA is applicable for the Country of Tajikistan to hindcast, nowcast, and forecast agricultural cropland extent, areas, and irrigated vs. rainfed. The same fundamental concept of ACCA applies to other areas of the World where ACCA codes need to be modified to suite the area/region of interest. ACCA can also be expanded to compute other crop characteristics such as crop types, cropping intensities, and phenologies.

Remote Sensing↗

USGS2007

Earthquake likelihood model testing

INTRODUCTION The Regional Earthquake Likelihood Models (RELM) project aims to produce and evaluate alternate models of earthquake potential (probability per unit volume, magnitude, and time) for California. Based on differing assumptions, these models are produced to test the validity of their assumptions and to explore which models should be incorporated in seismic hazard and risk evaluation. Tests based on physical and geological criteria are useful but we focus on statistical methods using future earthquake catalog data only. We envision two evaluations: a test of consistency with observed data and a comparison of all pairs of models for relative consistency. Both tests are based on the likelihood method, and both are fully prospective ( i.e. , the models are not adjusted to fit the test data). To be tested, each model must assign a probability to any possible event within a specified region of space, time, and magnitude. For our tests the models must use a common format: earthquake rates in specified “bins” with location, magnitude, time, and focal mechanism limits. Seismology cannot yet deterministically predict individual earthquakes; however, it should seek the best possible models for forecasting earthquake occurrence. This paper describes the statistical rules of an experiment to examine and test earthquake forecasts. The primary purposes of the tests described below are to evaluate physical models for earthquakes, assure that source models used in seismic hazard and risk studies are consistent with earthquake data, and provide quantitative measures by which models can be assigned weights in a consensus model or be judged as suitable for particular regions. In this paper we develop a statistical method for testing earthquake likelihood models. A companion paper ( Schorlemmer and Gerstenberger 2007 , this issue) discusses the actual implementation of these tests in the framework of the RELM initiative. Statistical testing of hypotheses is a common task and a wide range of possible testing procedures exist. Jolliffe and Stephenson ( 2003 ) present different forecast verifications from atmospheric science, among them likelihood testing of probability forecasts and testing the occurrence of binary events. Testing binary events requires that for each forecasted event, the spatial, temporal and magnitude limits be given. Although major earthquakes can be considered binary events, the models within the RELM project express their forecasts on a spatial grid and in 0.1 magnitude units; thus the results are a distribution of rates over space and magnitude. These forecasts can be tested with likelihood tests. In general, likelihood tests assume a valid null hypothesis against which a given hypothesis is tested. The outcome is either a rejection of the null hypothesis in favor of the test hypothesis or a nonrejection, meaning the test hypothesis cannot outperform the null hypothesis at a given significance level. Within RELM, there is no accepted null hypothesis and thus the likelihood test needs to be expanded to allow comparable testing of equipollent hypotheses. To test models against one another, we require that forecasts are expressed in a standard format: the average rate of earthquake occurrence within pre-specified limits of hypocentral latitude, longitude, depth, magnitude, time period, and focal mechanisms. Focal mechanisms should either be described as the inclination of P -axis, declination of P -axis, and inclination of the T -axis, or as strike, dip, and rake angles. Schorlemmer and Gerstenberger ( 2007 , this issue) designed classes of these parameters such that similar models will be tested against each other. These classes make the forecasts comparable between models. Additionally, we are limited to testing only what is precisely defined and consistently reported in earthquake catalogs. Therefore it is currently not possible to test such information as fault rupture length or area, asperity location, etc. Also, to account for data quality issues, we allow for location and magnitude uncertainties as well as the probability that an event is dependent on another event. As we mentioned above, only models with comparable forecasts can be tested against each other. Our current tests are designed to examine grid-based models. This requires that any fault-based model be adapted to a grid before testing is possible. While this is a limitation of the testing, it is an inherent difficulty in any such comparative testing. Please refer to appendix B for a statistical evaluation of the application of the Poisson hypothesis to fault-based models. The testing suite we present consists of three different tests: L-Test, N-Test, and R-Test. These tests are defined similarily to Kagan and Jackson ( 1995 ). The first two tests examine the consistency of the hypotheses with the observations while the last test compares the spatial performances of the models.

Seismological Research Letters↗

USGS2010

Western Mineral and Environmental Resources Science Center--providing comprehensive earth science for complex societal issues

Minerals in the environment and products manufactured from mineral materials are all around us and we use and come into contact with them every day. They impact our way of life and the health of all that lives. Minerals are critical to the Nation's economy and knowing where future mineral resources will come from is important for sustaining the Nation's economy and national security. The U.S. Geological Survey (USGS) Mineral Resources Program (MRP) provides scientific information for objective resource assessments and unbiased research results on mineral resource potential, production and consumption statistics, as well as environmental consequences of mining. The MRP conducts this research to provide information needed for land planners and decisionmakers about where mineral commodities are known and suspected in the earth's crust and about the environmental consequences of extracting those commodities. As part of the MRP scientists of the Western Mineral and Environmental Resources Science Center (WMERSC or 'Center' herein) coordinate the development of national, geologic, geochemical, geophysical, and mineral-resource databases and the migration of existing databases to standard models and formats that are available to both internal and external users. The unique expertise developed by Center scientists over many decades in response to mineral-resource-related issues is now in great demand to support applications such as public health research and remediation of environmental hazards that result from mining and mining-related activities. Western Mineral and Environmental Resources Science Center Results of WMERSC research provide timely and unbiased analyses of minerals and inorganic materials to (1) improve stewardship of public lands and resources; (2) support national and international economic and security policies; (3) sustain prosperity and improve our quality of life; and (4) protect and improve public health, safety, and environmental quality. The MRP supports approximately 40 USGS research specialists who utilize cooperative agreements with universities, industry, and other governmental agencies to support their collaborative research and information exchange. Scientists of the WMERSC study how and where non-fuel mineral resources form and are concentrated in the earth's crust, where mineral resources might be found in the future, and how mineral materials interact with the environment to affect human and ecosystem health. Natural systems (ecosystems) are complex - our understanding of how ecosystems operate requires collecting and synthesizing large amounts of geologic, geochemical, biologic, hydrologic, and meteorological information. Scientists in the Center strive to understand the interplay of various processes and how they affect the structure, composition, and health of ecosystems. Such understanding, which is then summarized in publicly available reports, is used to address and solve a wide variety of issues that are important to society and the economy. WMERSC scientists have extensive national and international experience in these scientific specialties and capabilities - they have collaborated with many Federal, State, and local agencies; with various private sector organizations; as well as with foreign countries and organizations. Nearly every scientific and societal challenge requires a different combination of scientific skills and capabilities. With their breadth of scientific specialties and capabilities, the scientists of the WMERSC can provide scientifically sound approaches to a wide range of societal challenges and issues. The following sections describe examples of important issues that have been addressed by scientists in the Center, the methods employed, and the relevant conclusions. New directions are inevitable as societal needs change over time. Scientists of the WMERSC have a diverse set of skills and capabilities and are proficient in the collection and integration of

Circular↗

USGS2016

Uncertainty in biological monitoring: a framework for data collection and analysis to account for multiple sources of sampling bias

Biological monitoring programmes are increasingly relying upon large volumes of citizen-science data to improve the scope and spatial coverage of information, challenging the scientific community to develop design and model-based approaches to improve inference. Recent statistical models in ecology have been developed to accommodate false-negative errors, although current work points to false-positive errors as equally important sources of bias. This is of particular concern for the success of any monitoring programme given that rates as small as 3% could lead to the overestimation of the occurrence of rare events by as much as 50%, and even small false-positive rates can severely bias estimates of occurrence dynamics. We present an integrated, computationally efficient Bayesian hierarchical model to correct for false-positive and false-negative errors in detection/non-detection data. Our model combines independent, auxiliary data sources with field observations to improve the estimation of false-positive rates, when a subset of field observations cannot be validated a posteriori or assumed as perfect. We evaluated the performance of the model across a range of occurrence rates, false-positive and false-negative errors, and quantity of auxiliary data. The model performed well under all simulated scenarios, and we were able to identify critical auxiliary data characteristics which resulted in improved inference. We applied our false-positive model to a large-scale, citizen-science monitoring programme for anurans in the north-eastern United States, using auxiliary data from an experiment designed to estimate false-positive error rates. Not correcting for false-positive rates resulted in biased estimates of occupancy in 4 of the 10 anuran species we analysed, leading to an overestimation of the average number of occupied survey routes by as much as 70%. The framework we present for data collection and analysis is able to efficiently provide reliable inference for occurrence patterns using data from a citizen-science monitoring programme. However, our approach is applicable to data generated by any type of research and monitoring programme, independent of skill level or scale, when effort is placed on obtaining auxiliary information on false-positive rates.

Methods in Ecology and Evolution↗

USGS2022

Ignoring species availability biases occupancy estimates in single-scale occupancy models

Most applications of single-scale occupancy models do not differentiate between availability and detectability, even though species availability is rarely equal to one. Species availability can be estimated using multi-scale occupancy models; however, for the practical application of multi-scale occupancy models, it can be unclear what a robust sampling design looks like and what the statistical properties of the multi-scale and single-scale occupancy models are when availability is less than one. Using simulations, we explore the following common questions asked by ecologists during the design phase of a field study: (Q1) what is a robust sampling design for the multi-scale occupancy model when there are a priori expectations of parameter estimates? (Q2) what is a robust sampling design when we have no expectations of parameter estimates? and (Q3) can a single-scale occupancy model with a random effects term adequately absorb the extra heterogeneity produced when availability is less than one and provide reliable estimates of occupancy probability? Our results show that there is a tradeoff between the number of sites and surveys needed to achieve a specified level of acceptable error for occupancy estimates using the multi-scale occupancy model. We also document that when species availability is low (<0.40 on the probability scale), then single-scale occupancy models underestimate occupancy by as much as 0.40 on the probability scale, produce overly precise estimates, and provide poor parameter coverage. This pattern was observed when a random effects term was and was not included in the single-scale occupancy model, suggesting that adding a random-effects term does not adequately absorb the extra heterogeneity produced by the availability process. In contrast, when species availability was high (>0.60), single-scale occupancy models performed similarly to the multi-scale occupancy model. Users can further explore our results and sampling designs across a number of different scenarios using the RShiny app https://gdirenzo.shinyapps.io/multi-scale-occ/ . Our results suggest that unaccounted for availability can lead to underestimating species distributions when using single-scale occupancy models, which can have large implications on inference and prediction, especially for those working in the fields of invasion ecology, disease emergence, and species conservation.

Methods in Ecology and Evolution↗

USGS2013

Estimation of annual agricultural pesticide use for counties of the conterminous United States, 1992-2009

A method was developed to calculate annual county level pesticide use for selected herbicides, insecticides, and fungicides applied to agricultural crops grown in the conterminous United States from 1992 through 2009. Pesticide-use data compiled by proprietary surveys of farm operations located within Crop Reporting Districts were used in conjunction with annual harvested-crop acreage reported by the U.S. Department of Agriculture National Agricultural Statistics Service (NASS) to calculate use rates per harvested crop acre, or an 'estimated pesticide use' (EPest) rate, for each crop by year. Pesticide-use data were not available for all Crop Reporting Districts and years. When data were unavailable for a Crop Reporting District in a particular year, EPest extrapolated rates were calculated from adjoining or nearby Crop Reporting Districts to ensure that pesticide use was estimated for all counties that reported harvested-crop acreage. EPest rates were applied to county harvested-crop acreage differently to obtain EPest-low and EPest-high estimates of pesticide-use for counties and states, with the exception of use estimates for California, which were taken from annual Department of Pesticide Regulation Pesticide Use Reports. Annual EPest-low and EPest-high use totals were compared with other published pesticide-use reports for selected pesticides, crops, and years. EPest-low and EPest-high national totals for five of seven herbicides were in close agreement with U.S. Environmental Protection Agency and National Pesticide Use Data estimates, but greater than most NASS national totals. A second set of analyses compared EPest and NASS annual state totals and state-by-crop totals for selected crops. Overall, EPest and NASS use totals were not significantly different for the majority of crop-stateyear combinations evaluated. Furthermore, comparisons of EPest and NASS use estimates for most pesticides had rank correlation coefficients greater than 0.75 and median relative errors of less than 15 percent. Of the 48 pesticide-by-crop combinations with 10 or more state-year combinations, 12 of the EPest-low and 17 of the EPest-high totals showed significant differences (p < 0.05) from NASS use estimates. The differences between EPest and NASS estimates did not follow consistent patterns related to particular crops, years, or states, and most correlation coefficients were greater than 0.75. EPest values from this study are suitable for making national, regional, and watershed assessments of annual pesticide use from 1992 to 2009. Although estimates are provided by county to facilitate estimation of watershed pesticide use for a wide variety of watersheds, there is a greater degree of uncertainty in individual county-level estimates when compared to Crop Reporting District or state-level estimates because (1) EPest crop-use rates were developed on the basis of pesticide use on harvested acres in multi-county areas (Crop Reporting Districts) and then allocated to county harvested cropland; (2) pesticide-by-crop use rates were not available for all Crop Reporting Districts in the conterminous United States, and extrapolation methods were used to estimate pesticide use for some counties; and (3) it is possible that surveyed pesticide-by-crop use rates do not reflect all agricultural use on all crops grown. The methods developed in this study also are applicable to other agricultural pesticides and years.

Scientific Investigations Report↗

USGS2024

Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin

As part of the 2014 Chesapeake Bay Watershed Agreement, all Bay States and the District of Columbia have committed to improving the condition of the Bay, which includes a goal to achieve sustainable fisheries. One outcome under that broad goal is improved effectiveness of fish habitat conservation and preservation efforts. In support of that outcome, the U.S. Geological Survey Eastern Ecological Science Center (USGS-EESC) and the National Oceanic and Atmospheric Association’s National Centers for Coastal Ocean Science (NOAA-NCCOS) are actively developing datasets, methods, and analyses to conduct fish habitat assessments in the Chesapeake Bay watershed, guided by recommendations from a regional stakeholder workshop held by the Chesapeake Bay Program’s (CBP) Fish Habitat Action Team (FHAT) in 2018. The joint USGS and NOAA team has been collaborating on methods for conducting inland and estuarine assessments and exploring whether a seamless headwater to estuary assessment could be developed. The goals of this assessment are to benefit both State and Federal fisheries managers, help advance fisheries science, and provide beneficial information for the public. While past national and regional assessments (e.g. the National Fish Habitat Partnership National Assessment) treated inland and estuarine fish habitat conditions separately due to differences in environments, GIS data representation, and data availability, a seamless habitat assessment could be of value for a broad range of stakeholders as many fish species, several of which are invasive or under federal jurisdiction, use habitats across both inland and estuarine waters. This project developed a pilot framework, explored and tested methods necessary for a finer scale, seamless assessment across both inland and estuarine waters, and demonstrated its use. Although there was interest by the CBP FHAT for the generation of a Baywide fish habitat assessment that spanned tidal salt, tidal fresh, warm non-tidal and cold non-tidal waters, there are a myriad of implementation details and considerations around conducting a Baywide assessment across all four of these general habitat areas. Therefore, the practical need to conduct a tributary-specific pilot assessment arose. At the beginning of this pilot process, members of the FHAT were presented with a decision matrix to choose a study basin using factors such as data availability and tributary size. FHAT members chose the Patuxent River basin, which has been relatively well sampled and studied. Several spatial frameworks were considered before selection of an inclusive gridded framework for summary and analysis that represented inland drainage networks and landscape influences as well as estuarine bathymetry. A suite of landscape and in-water stressor variables were summarized into the framework and were largely generalized over time. In order to assess the viability of the framework, we chose to use species distribution modeling for each of the species to test the framework’s ability to predict habitat use of non-tidal resident, estuarine resident, and migratory species. Tessellated darter (Etheostoma olmstedi), American eel (Anguilla rostrata), and white perch (Morone americana) were chosen as illustrative fish species based on data availability, and differences in life history and habitat use. A nested modeling approach, which involved successive model runs at multiple scales (1000m, 100m, and 10m raster grids) was developed to examine differences in variable importance at different spatial scales and to enhance modeling efficiency. For white perch, a complementary modeling analysis was performed for variables available only in estuarine waters. For all testing, an ensemble modeling approach was conducted, using a suite of potential statistical techniques driven by model strength and variable predictive power. The statistical testing that we conducted was intended only to test the framework and modeling approach, and not to definitively predict all habitats where specific fish species might be present. The modeling we conducted to test the framework did have some limitations. For example, the spatial distribution of favorable habitat areas for white perch was likely influenced by the predominance of fish survey locations near the center channel of the river and the use of generalized in-water conditions. For all species, the use of juvenile and adult fish survey data limits the estimation of habitat use to those life stages. Despite such limitations of the data inputs and modeling approach, we found the framework could seamlessly predict fish habitat distribution across freshwater and tidal environments and integrate the influence of landscape stressors with local in-water factors. The developed framework presented to the Sustainable Fisheries Goal Implementation Team (GIT) and FHAT is informative and could potentially be used for other modeling applications in the Chesapeake Bay watershed and elsewhere. In particular the framework and modeling approach lend themselves to evaluating living resource distributions and underlying habitat conditions in shallow tidal waters and beyond, as recommended by the recent Comprehensive Evaluation of System Response (CESR) report from the Chesapeake Bay Program.

Maryland↗

USGS1993

National water summary 1990-91: Hydrologic events and stream water quality

National Water Summary 1990-91 Hydrologic Events and Stream Water Quality was planned to complement existing Federal-State water-quality reporting to the U.S. Congress that is required by the Clean Water Act of 1972. This act, formally known as the Federal Water Pollution Control Act Amendments of 1972 (Public Law 92-500), and its amendments in 1977,1979,1980,1981,1983, and 1987, is the principal basis for Federal-State cooperation on maintaining and reporting on water quality in the United States. Under section 305(b) of the Clean Water Act, the States must designate uses for waterbodies, biennially assess whether the waterbodies meet designated uses, and report to the U.S. Environmental Protection Agency (EPA), which in turn summarizes the findings of the State assessments in a biennial National Water Quality Inventory report to the Congress. This volume of the National Water Summary uses a nationally consistent data base and methods of statistical analysis to document stream water quality in the United States, Puerto Rico, and the Western Pacific Islands. As a basis for preparation of this report, the U.S. Geological Survey (uses) created a data base of waterquality data from about 2,900 stream water-quality monitoring stations in the United States (Lanfear, 1993). These data, which were extracted primarily from the uses National Water Information System (NWIS) and supplemented with data from the EPA national data base known as STORET, consisted mostly of water-chemistry and physical-sediment data. About 1,400 of these stations met a criterion, used in this volume for trends analysis of data, of having a period of record that began before water year 1980. (A water year is the period from October 1 through September 30 and is identified by the calendar year at the end of the period.) Inasmuch as the determination of trends requires about 10 years of data, few biological data were suitable for trends analysis because biological indicators of water quality were not being collected extensively by water year 1980. Also, because of the lack of long-term data, an assessment of the water quality of lakes and reservoirs is not included in this National Water Summary . Constituent and waterproperty data were selected from this data base and analyzed with statistical techniques that have been used extensively by the uses to summarize conditions and trends in water quality. (Throughout the remainder of this article, the term "constituent" is used also to represent water properties, such as pH and alkalinity.) Selected results from this analysis are presented graphically, both by State and nationally, in this volume. The data base created for preparation of this volume and the resulting nationally consistent statistical analysis of these data provide a unique opportunity to compare and contrast stream water-quality conditions and trends nationally and State by State. Because of the complexity of water quality and the natural seasonal and diurnal variations in the concentrations of waterquality constituents, the determination of trends in water quality requires a relatively long period of record. Trends in constituent concentrations in the State summaries in this volume were calculated for one or more of four periods water years 1970-89,1975-89,1980- 89, and 1982-89. Unfortunately, few water-quality monitoring stations have periods of record of more than 10 or 15 years, and therefore most analyses in the State summaries were done for 8- or 10-year periods. Although interest in water quality has continued to heighten since passage of the Clean Water Act, funds for support of data-collection programs are limited and data records of many of the constituents now of current concern, especially organic constituents, do not exist or are less than 10-years long; consequently only a modest number of organic constituents are presented in this volume. Discussion of conditions and trends in water quality in this National Water Summary also draw upon information about land use, population, water use, application of agricultural chemicals in rural areas, the siting of industrial facilities, and other ancillary information. Because the results of analysis of a restricted number of constituents are presented in this volume, State 305(b) reports also are cited in the discussion of water quality in each State. The following discussion is an overview of the three parts of this 1990-91 National Water Summary - "Hydrologic Conditions and Water-Related Events, Water Years 1990-91," "Hydrologic Perspectives on Water Issues," and "State Summaries of Stream Water Quality."

Water Supply Paper↗

USGS2015

Status and understanding of groundwater quality in the Cascade Range and Modoc Plateau study unit, 2010: California GAMA Priority Basin Project

Groundwater quality in the Cascade Range and Modoc Plateau study unit was investigated as part of the California State Water Resources Control Board’s Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project. The study was designed to provide a statistically unbiased assessment of untreated groundwater quality in the primary aquifer system. The depth of the primary aquifer system for the Cascade Range and Modoc Plateau study unit was delineated by the depths of the screened or open intervals of wells in the State of California’s database of public-supply wells. Two types of assessments were made: a status assessment that described the current quality of the groundwater resource, and an understanding assessment that made evaluations of relations between groundwater quality and potential explanatory factors representing characteristics of the primary aquifer system. The assessments characterize the quality of untreated groundwater, not the quality of treated drinking water delivered to consumers by water distributors. The status assessment was based on water-quality data collected in 2010 by the U.S. Geological Survey from 90 wells and springs (USGS-grid wells) and on water-quality data compiled from the State of California’s regulatory compliance database for samples collected from 240 public-supply wells between September 2007 and September 2010. To provide context, the water-quality data discussed in this report were compared to California and Federal drinking-water regulatory and non-regulatory benchmarks for treated drinking water. Groundwater quality is defined in terms of relative concentrations (RCs), which are calculated by dividing the concentration of a constituent in groundwater by the concentration of the benchmark for that constituent. The RCs for inorganic constituents (major ions, trace elements, nutrients, and radioactive constituents) were classified as “high” (the RC is greater than 1.0, indicating that the concentration is above the benchmark), “moderate” (the RC is from 1.0 to greater than 0.5), or “low” (the RC is less than or equal to 0.5). For organic constituents (volatile organic compounds and pesticides) and special-interest constituents (perchlorate), the boundary between moderate and low RCs was set at 0.1. All benchmarks used for organic constituents were health-based. For inorganic constituents, health-based and aesthetic-based benchmarks were used. Constituents without benchmarks were not considered in the status assessment . The primary metric used for quantifying regional-scale groundwater quality was the aquifer-scale proportion—the areal percentages of the primary aquifer system with high, moderate, and low RCs for a given constituent or class of constituents. The study unit was divided into six study areas on the basis of geologic differences (Eastside Sacramento Valley, Honey Lake Valley groundwater basin, Cascade Range and Modoc Plateau Low Use Basins, Quaternary Volcanic Areas, Shasta Valley and Mount Shasta Volcanic Area, and Tertiary Volcanic Areas), and each study area was divided into equal-area grid cells. Aquifer-scale proportions were calculated for individual constituents and constituent classes for each of the six study areas and for the study unit as a whole by using grid-based (one well per cell) and spatially weighted (many wells per cell) statistical methods. The status assessment showed that inorganic constituents were present at high and moderate RCs in greater proportions of the Cascade Range and Modoc Plateau study unit than were organic constituents. One or more inorganic constituents with health-based benchmarks were present at high RCs in 9.4 percent, and at moderate RCs in 14.7 percent of the primary aquifer system. Arsenic was present at high RCs in approximately 3 percent of the primary aquifer system; boron, molybdenum, uranium, and vanadium each were present at high RCs in approximately 2 percent of the primary aquifer system. One or more inorganic constituents with aesthetic-based benchmarks were present at high RCs in 15.1 percent of the primary aquifer system and at moderate RCs in 4.9 percent. Manganese, iron, and total dissolved solids were present at high RCs in approximately 12 percent, 5 percent, and 2 percent, respectively, of the primary aquifer system. Organic constituents were not detected at high or moderate RCs in the primary aquifer system, and one or more organic constituents were detected at low RCs in approximately 40 percent of the primary aquifer system. Two classes of organic constituents were detected in more than 10 percent of the primary aquifer system: trihalomethanes (chloroform only) and herbicides. The special interest constituent perchlorate was not detected at high RCs, but was detected at moderate RCs in approximately 2 percent of the primary aquifer system. The understanding assessment relied on statistical tests to evaluate relations between concentrations of constituents and values of potential explanatory factors representing geology, land use, well construction, hydrologic conditions, groundwater age, and geochemical conditions. The majority of the high and moderate RCs of arsenic, boron, molybdenum, uranium, and total dissolved solids were in samples from the Honey Lake Valley groundwater basin study area. Groundwater mixing with hydrothermal fluids present in the study area, evaporative concentration of groundwater in the Honey Lake playa, presence of uranium-bearing sediment derived from the adjacent Sierra Nevada, and release of arsenic and other trace elements from sediments under high pH and low dissolved oxygen conditions all appeared to contribute to these elevated concentrations. Thermal springs are in many parts of the Cascade Range and Modoc Plateau study unit and could account for locally elevated concentrations of arsenic, boron, molybdenum, and total dissolved solids in samples from the other study areas. Vanadium concentrations were greater in oxic samples than in anoxic samples, but were not correlated with pH, contrary to expectations from previous studies. Organic constituents were not detected at high or moderate RCs, and the occurrence of low organic constituents at low RCs ranged from 27 percent to 73 percent of the primary aquifers system in the six study areas. The Shasta Valley and Mount Shasta Volcanic study area had significantly greater occurrence of low RCs of herbicides compared to all of the other study areas, which could reflect the greater prevalence of modern groundwater in the Shasta Valley and Mount Shasta Volcanic study area and the presence of potential sources of herbicides, including applications to timberlands and roadside rights-of-way. The Eastside Sacramento Valley study area had the greatest occurrence of low concentrations of chloroform, and chloroform occurrence was most strongly associated with the combination of septic-tank density greater than two tanks per square kilometer and urban land use greater than 10 percent within a radius of 500 meters of the well. These conditions were most prevalent in the Eastside Sacramento Valley study area. The detection frequency of low concentrations of perchlorate was consistent with the probability of occurrence expected under natural conditions, except in the Eastside Sacramento Valley study area, where detection frequencies were much higher than expected and could not be explained by known anthropogenic sources of perchlorate.

California↗

USGS2016

Methods for estimating selected spring and fall low-flow frequency statistics for ungaged stream sites in Iowa, based on data through June 2014

A statewide study was led to develop regression equations for estimating three selected spring and three selected fall low-flow frequency statistics for ungaged stream sites in Iowa. The estimation equations developed for the six low-flow frequency statistics include spring (April through June) 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years and fall (October through December) 1-, 7-, and 30-day mean low flows for a recurrence interval of 10 years. Estimates of the three selected spring statistics are provided for 241 U.S. Geological Survey continuous-record streamgages, and estimates of the three selected fall statistics are provided for 238 of these streamgages, using data through June 2014. Because only 9 years of fall streamflow record were available, three streamgages included in the development of the spring regression equations were not included in the development of the fall regression equations. Because of regulation, diversion, or urbanization, 30 of the 241 streamgages were not included in the development of the regression equations. The study area includes Iowa and adjacent areas within 50 miles of the Iowa border. Because trend analyses indicated statistically significant positive trends when considering the period of record for most of the streamgages, the longest, most recent period of record without a significant trend was determined for each streamgage for use in the study. Geographic information system software was used to measure 63 selected basin characteristics for each of the 211streamgages used to develop the regional regression equations. The study area was divided into three low-flow regions that were defined in a previous study for the development of regional regression equations. Because several streamgages included in the development of regional regression equations have estimates of zero flow calculated from observed streamflow for selected spring and fall low-flow frequency statistics, the final equations for the three low-flow regions were developed using two types of regression analyses—left-censored and generalized-least-squares regression analyses. A total of 211 streamgages were included in the development of nine spring regression equations—three equations for each of the three low-flow regions. A total of 208 streamgages were included in the development of nine fall regression equations—three equations for each of the three low-flow regions. A censoring threshold was used to develop 15 left-censored regression equations to estimate the three fall low-flow frequency statistics for each of the three low-flow regions and to estimate the three spring low-flow frequency statistics for the southern and northwest regions. For the northeast region, generalized-least-squares regression was used to develop three equations to estimate the three spring low-flow frequency statistics. For the northeast region, average standard errors of prediction range from 32.4 to 48.4 percent for the spring equations and average standard errors of estimate range from 56.4 to 73.8 percent for the fall equations. For the northwest region, average standard errors of estimate range from 58.9 to 62.1 percent for the spring equations and from 83.2 to 109.4 percent for the fall equations. For the southern region, average standard errors of estimate range from 43.2 to 64.0 percent for the spring equations and from 78.1 to 78.7 percent for the fall equations. The regression equations are applicable only to stream sites in Iowa with low flows not substantially affected by regulation, diversion, or urbanization and with basin characteristics within the range of those used to develop the equations. The regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system application. StreamStats allows users to click on any ungaged stream site and compute estimates of the six selected spring and fall low-flow statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged site are provided. StreamStats also allows users to click on any Iowa streamgage to obtain computed estimates for the six selected spring and fall low-flow statistics.

Iowa↗

USGS2013

Concentration, flux, and the analysis of trends of total and dissolved phosphorus, total nitrogen, and chloride in 18 tributaries to Lake Champlain, Vermont and New York, 1990–2011

Annual concentration, flux, and yield for total phosphorus, dissolved phosphorus, total nitrogen, and chloride for 18 tributaries to Lake Champlain were estimated for 1990 through 2011 using a weighted regression method based on time, tributary streamflows (discharges), and seasonal factors. The weighted regression method generated two series of daily estimates of flux and concentration during the period of record: one based on observed discharges and a second based on a flow-normalization procedure that removes random variation due to year-to-year climate-driven effects. The flownormalized estimate for a given date is similar to an average estimate of concentration or flux that would be made if all of the observed discharges for that date were equally likely to have occurred. The flux bias statistic showed that 68 of the 72 flux regression models were minimally biased. Temporal trends in the concentrations and fluxes were determined by calculating percent changes in flow-normalized annual fluxes for the full period of analysis (1990 through 2010) and for the decades 1990–2000 and 2000–2010. Basinwide, flow-normalized total phosphorus flux decreased by 42 metric tons per year (t/yr) between 1990 and 2010. This net result reflects a basinwide decrease in flux of 21 metric tons (t) between 1990 and 2000, followed by a decrease of 20 t between 2000 and 2010; both results were largely influenced by flux patterns in the large tributaries on the eastern side of the basin. A comparison of results for total phosphorus for the two separate decades of analysis found that more tributaries had decreasing concentrations and flux rates in the second decade than the first. An overall reduction in dissolved phosphorus flux of 0.7 t/yr was seen in the Lake Champlain Basin during the full period of analysis. That very small net change in flux reflects substantial reductions between 1990 and 2000 from eastern tributaries, especially in Otter Creek and the LaPlatte and Winooski Rivers that largely were offset by increases in the Missisquoi and Saranac Rivers in the second decade (between 2000 and 2010). The number of tributaries that had increases in dissolved phosphorus concentrations stayed constant at 13 or 14 during the period of analysis. Total nitrogen concentration and flux for most of the monitored tributaries in the Lake Champlain Basin have decreased since 1990. Between 1990 and 2010, flow-normalized total nitrogen flux decreased by 386 t/yr, which reflects an increase of 440 t/yr between 1990 and 2000 and a decrease of 826 t/yr between 2000 and 2010. All individual tributaries except the Winooski River had decreases in total nitrogen concentration and flux between 2000 and 2010. The decrease in total nitrogen flux over the period of record could be related to the decrease in nitrogen from atmospheric deposition observed in Vermont or to concurrent benefits realized from the implementation of agricultural best-management practices in the Lake Champlain Basin that were designed primarily to reduce phosphorus runoff. For chloride, large increases in flow-normalized concentrations and flux between 1990 and 2000 for 17 of the 18 tributaries diminished to small increases or decreases between 2000 and 2010. Between 1990 and 2010, flow-normalized flux increased by 32,225 t/yr, 78 percent of which (25,163 t) was realized during the first decade, from 1990 through 2000. The five tributaries that had decreasing concentration and flux of chloride between 2000 and 2010 were all on the eastern side of Lake Champlain, possibly related to reductions since 1999 in winter road salt application in Vermont. Positive correlations of phosphorus flux and changes in phosphorus concentration and flux in tributaries with phosphorus inputs to basins from point sources, suggest that point sources have an effect on stream phosphorus chemistry. Several measures of changes in agricultural statistics, such as agricultural land use, acres of land in farms, acres of cropland, and acres of corn for grain or seed, are positively correlated with changes in phosphorus concentration or flux in the tributaries. Negative correlations of the amount of money spent on agricultural best-management practices with changes in phosphorus concentration or flux in the tributaries, suggest that best-management practices may be an effective tool, along with point-source reductions, in making progress towards management goals for phosphorus reductions in Lake Champlain.

New York;Vermont↗

USGS2024

Low-flow statistics computed for streamflow gages and methods for estimating selected low-flow statistics for ungaged stream locations in Ohio, water years 1975–2020

A study was conducted by the U.S. Geological Survey, in cooperation with the Ohio Water Development Authority and the Ohio Environmental Protection Agency, to compute low-flow frequency, flow-duration, and harmonic mean flow statistics for long-term streamflow gages and to develop regression equations to estimate those statistics at unregulated, ungaged stream locations in Ohio. The flow statistics were computed with data collected after the 1974 water year because upward trends and statistically significant step changes (occurring after the late 1960s but before 1975) in annual flow statistics were detected at many candidate gages in Ohio. A total of 180 continuous-record gages in Ohio and bordering States were identified as having at least 10 years of daily flow records during the analytical period (water years 1975–2020). Also identified were five low-flow partial-record gages in Ohio that had instantaneous low flows that correlated strongly with daily streamflows at one of the continuous-record gages (also referred to as index gages). For continuous-record gages, the following flow statistics were computed: annual and seasonal minimum 1-, 7-, 30-, and 90-day flows with 2-, 5-, 10-, 20-, and 50-year recurrence intervals; annual and seasonal 98-, 95-, 90-, 85-, 80-, 75-, 70-, 60-, 50-, 40-, 30-, 20-, and 10-percent duration flows; and the harmonic mean flow. For partial-record gages, estimates were made for annual and seasonal minimum 1-, 7-, 30-, and 90-day low flows with 2-, 10-, and 20-year recurrence intervals and annual and seasonal 98-, 95-, 90-, 85-, and 80-percent duration flows. The drainage basin of each gage was inspected for anthropogenic or karst features that could appreciably affect or regulate low flows. That inspection resulted in data from 53 of the 180 continuous-record gages and the 5 low-flow partial-record gages being categorized as “unregulated” and subsequently used in regression analyses to develop equations for estimating low-flow statistics. Two hundred and sixty potential explanatory variables were tested for this study. In most cases, a streamflow-variability index (SVI) was chosen as the sole explanatory variable for the regression analyses to predict the harmonic mean and annual and seasonal low-flow yields. The exceptions were for one of the September–November low-flow yield statistics and all the December–February yield statistics. Drainage area, decimal longitude, and usually SVI were chosen as the explanatory variables for those exceptions and to predict the 80-percent duration flows. The SVI values used in the model were estimated from a geospatial grid of SVI values developed for this study by using an empirical Bayesian kriging regression prediction. Observations for continuous-record gages used in the regression analyses were weighted as a function of their record length. Weights for partial-record gages were estimated based on the weights determined for their index gages. Equations for low-flow yields were developed by using censored regressions with a censoring level of 0.00001 cubic foot per second per square mile. Numerical constraints were placed on the yield equations if they could compute yields less than the yield censoring level or if the yields did not monotonically decrease with increasing SVI. Logistic-regression equations were developed, with SVI and drainage area as explanatory variables, to estimate the probability that the low-flow statistics were greater than the flow censoring level (0.01 cubic foot per second). The regression equations presented in this report were developed for implementation in the Ohio StreamStats application. The equations are applicable to unregulated streams in Ohio and are not applicable to streams with karst drainage features, diversions, regulation, or other anthropogenic activities that can appreciably affect low flow. The equations were developed by using observations with a range of SVI values from 0.41 to 1.23 log10 cubic foot per second and a range of drainage areas from 0.21 to 540 square miles. The applicability of the equations outside these ranges is not known.

Ohio↗

USGS2015

Effect of land-applied biosolids on surface-water nutrient yields and groundwater quality in Orange County, North Carolina

Land application of municipal wastewater biosolids is the most common method of biosolids management used in North Carolina and the United States. Biosolids have characteristics that may be beneficial to soil and plants. Land application can take advantage of these beneficial qualities, whereas disposal in landfills or incineration poses no beneficial use of the waste. Some independent studies and laboratory analysis, however, have shown that land-applied biosolids can pose a threat to human health and surface-water and groundwater quality. The effect of municipal biosolids applied to agriculture fields is largely unknown in relation to the delivery of nutrients, bacteria, metals, and contaminants of emerging concern to surface-water and groundwater resources. Therefore, the North Carolina Department of Environment and Natural Resources (NCDENR) collaborated with the U.S. Geological Survey (USGS) through the 319 Nonpoint Source Program to better understand the transport of nutrients and bacteria from biosolids application fields to groundwater and surface water and to provide a scientific basis for evaluating the effectiveness of the current regulations. The USGS conducted a paired agricultural watershed study in the Collins Creek and Cane Creek Reservoir watersheds in Orange County, North Carolina. Field activities were conducted from March 2011 through May 2013 at two field study sites, including biosolids field application sites owned by Orange County Water and Sewer Authority (OWASA) in the Collins Creek watershed and a background study site in the Cane Creek watershed that has no fields receiving biosolids applications. Samples of biosolids source material and soil were collected from the land-application fields for laboratory analyses. Soil samples were also collected from a background agricultural field in the Cane Creek watershed that has never received land-applied municipal biosolids. Shallow groundwater samples were collected quarterly from new monitoring wells installed by NCDENR along the edge of the biosolids land-application fields and a background agricultural field for laboratory analyses. Two surface-water monitoring sites were established on Collins Creek to compute continuous streamflow and collect discrete baseflow and stormwater runoff water-quality data upstream and downstream from the biosolids land-application fields. Surface water-quality samples were also collected for baseflow and stormwater runoff conditions at an existing USGS streamgage on Cane Creek to monitor water-quality conditions in the background study watershed. The study primarily focused on nutrients and bacteria; however, data for field properties and water-quality constituents, including metals, major ions, and contaminants of emerging concern (household-, industrial-, and agricultural-use compounds, pharmaceutical compounds, hormones, and antibiotics) also were collected and used in the analyses. There were no exceedances of the 10 elements with designated U.S. Environmental Protection Agency (EPA) ceiling concentrations for land-applied biosolids in any of the biosolids samples. Treatment processes and storage techniques used by OWASA are effective in eliminating Escherichia coli and fecal coliform bacteria from biosolids. Copper, molybdenum, total Kjeldahl nitrogen, and total phosphorus were elevated in the soil from biosolids land-application fields relative to the background field. The relative richness of these constituents in the biosolids land-application fields is consistent with biosolids being the source of the elevated concentrations given the relatively high concentrations of these constituents in the biosolids samples that were collected. Shallow groundwater in the transitional zone wells, which were located adjacent to and topographically downgradient from all the biosolids land-application fields, were found to be statistically different and had higher nitrate concentrations (medians greater than 12 milligrams per liter) than all the other wells sampled as part of the study. Surface-water nutrient concentrations and yields, primarily nitrate, were higher at the monitoring site on Collins Creek downstream from the biosolids land-application fields than the other study sites that drained watersheds without biosolids land application. The largest differences in concentrations between sites were measured at baseflow conditions, which indicate that the main cause of these differences, particularly between Cane Creek and the Collins Creek site downstream from the OWASA application fields, is related to nitrate contribution from the shallow groundwater. Contaminants of emerging concern were detected in approximately 40 percent of the laboratory analyses of the biosolids samples and more frequently in soil samples from the biosolids land-application fields (approximately 40 percent of laboratory analyses) relative to the soil samples from the background field (approximately 12 percent of laboratory analyses). However, contaminants of emerging concern detected in the laboratory analysis for this study do not appear to be good indicators of human-waste contaminants derived from land-applied biosolids in groundwater or surface-water because the number of detections and concentrations at the background wells and surface-water monitoring sites are similar to or higher than those at wells and monitoring sites adjacent to or downstream from the biosolids land-application fields. The data, analysis, and conclusions associated with this study can be used by regulatory agencies, resource managers, and wastewater-treatment operators to (1) better understand the quantity and characteristics of nutrients, bacteria, metals, and contaminants of emerging concern that are transported away from biosolids land-application fields to surface water and groundwater under current regulations for the purposes of establishing effective total maximum daily loads (TMDLs) and restoring impaired water resources, (2) assess how well existing regulations protect waters of the State and potentially recommend effective changes to regulations or land-application procedures, and (3) establish a framework for developing guidance on effective techniques for monitoring and regulatory enforcement of permitted biosolids land-application fields.

North Carolina↗

USGS2014

Methods for estimating magnitude and frequency of floods in Arizona, developed with unregulated and rural peak-flow data through water year 2010

Flooding is among the worst natural disasters responsible for loss of life and property in Arizona, underscoring the importance of accurate estimation of flood magnitude for proper structural design and floodplain mapping. Twenty-four years of additional peak-flow data have been recorded since the last comprehensive regional flood frequency analysis conducted in Arizona. Periodically, flood frequency estimates and regional regression equations must be revised to maintain the accurate estimation of flood frequency and magnitude. Annual peak-flow data collected through water year 2010 were compiled from 448 unregulated streamflow-gaging stations, hereafter referred to as streamgages, in Arizona having a minimum of 10 years of record. Flood frequency estimates were first computed with station (or at-site) skew using the Expected Moments Algorithm with a multiple Grubbs-Beck test to identify multiple potentially influential low flows to fit a Pearson Type III distribution. Next, a multiple step Bayesian least-squares-regression approach was used to determine a new statewide regional skew of −0.09. No basin characteristics analyzed were statistically significant in explaining the variation in skew and as a result, the constant model was chosen as the best regional skew model for the Arizona study area. The mean square error used in Bulletin 17B (B17B) of the Interagency Advisory Committee on Water Data is used to describe the precision of the regional skew. The constant model had a mean square error equal to 0.08, which corresponds to an effective record length of 85 years. This is a marked improvement over a previous Arizona regional skew analysis, with a reported mean square error of 0.31, for a corresponding effective record length of around 17 years. Thus the new regional model had almost five times the information content (as measured by effective record length) of that calculated in USGS Water Supply Paper 2433, published in 1997, or the value of 0.302 reported in the B17B generalized skew map. The flood frequency estimates were recalculated using a weighted skew of the station and regional skew. Station flood frequency estimates for each streamgage are presented for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities. Geographical information systems were used to compute basin characteristic information for each streamgage for the purpose of developing regional equations to estimate flood statistics at ungaged basins. Five hydrologic flood regions in Arizona were defined in a multivariate regionalization process based on mean basin elevation, mean annual precipitation, and soil permeability. A regional generalized least-squares-regression analysis was used to develop five sets of equations from 344 nonredundant streamgages, corresponding to five regions, for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities at ungaged basins in Arizona. The regression equations developed for these five regions were based on one or more of the statistically significant explanatory variables: drainage area, mean basin elevation, and mean annual precipitation. Average standard errors of prediction for the regression regions for the five regions ranged from 27 to 122 percent and the pseudo-coefficients of determination (pseudo-R 2 ), a measure of the proportion of peak-flow variation that is explained by the basin characteristics, ranged from 68 to 98 percent. Regression equations for Central Highlands (region 4) had the lowest model error and the greatest pseudo-R 2 metrics. The equations for Colorado Plateau (region 2) regression equations generally had greater model error and lower pseudo-R 2 metrics. The improvement of regional regression equation model error and pseudo-R 2 metrics was related to higher numbers of streamgages, longer period of record, and even spatial coverage within a region. The regional regression equations were integrated into the U.S. Geological Survey’s StreamStats program. The StreamStats program is a national map-based web application that allows the public to easily access published flood frequency and basin characteristic statistics. The interactive web application allows a user to select a point within a watershed (gaged or ungaged) and retrieve flood-frequency estimates derived from the current regional regression equations and geographic information system data within the selected basin. StreamStats provides users with an efficient and accurate means for retrieving the most up to date flood frequency and basin characteristic data. StreamStats is intended to provide consistent statistics, minimize user error, and reduce the need for large datasets and costly geographic information system software.

Arizona↗

USGS2016

Estimating flood magnitude and frequency at gaged and ungaged sites on streams in Alaska and conterminous basins in Canada, based on data through water year 2012

Estimates of the magnitude and frequency of floods are needed across Alaska for engineering design of transportation and water-conveyance structures, flood-insurance studies, flood-plain management, and other water-resource purposes. This report updates methods for estimating flood magnitude and frequency in Alaska and conterminous basins in Canada. Annual peak-flow data through water year 2012 were compiled from 387 streamgages on unregulated streams with at least 10 years of record. Flood-frequency estimates were computed for each streamgage using the Expected Moments Algorithm to fit a Pearson Type III distribution to the logarithms of annual peak flows. A multiple Grubbs-Beck test was used to identify potentially influential low floods in the time series of peak flows for censoring in the flood frequency analysis. For two new regional skew areas, flood-frequency estimates using station skew were computed for stations with at least 25 years of record for use in a Bayesian least-squares regression analysis to determine a regional skew value. The consideration of basin characteristics as explanatory variables for regional skew resulted in improvements in precision too small to warrant the additional model complexity, and a constant model was adopted. Regional Skew Area 1 in eastern-central Alaska had a regional skew of 0.54 and an average variance of prediction of 0.45, corresponding to an effective record length of 22 years. Regional Skew Area 2, encompassing coastal areas bordering the Gulf of Alaska, had a regional skew of 0.18 and an average variance of prediction of 0.12, corresponding to an effective record length of 59 years. Station flood-frequency estimates for study sites in regional skew areas were then recomputed using a weighted skew incorporating the station skew and regional skew. In a new regional skew exclusion area outside the regional skew areas, the density of long-record streamgages was too sparse for regional analysis and station skew was used for all estimates. Final station flood frequency estimates for all study streamgages are presented for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities. Regional multiple-regression analysis was used to produce equations for estimating flood frequency statistics from explanatory basin characteristics. Basin characteristics, including physical and climatic variables, were updated for all study streamgages using a geographical information system and geospatial source data. Screening for similar-sized nested basins eliminated hydrologically redundant sites, and screening for eligibility for analysis of explanatory variables eliminated regulated peaks, outburst peaks, and sites with indeterminate basin characteristics. An ordinary least‑squares regression used flood-frequency statistics and basin characteristics for 341 streamgages (284 in Alaska and 57 in Canada) to determine the most suitable combination of basin characteristics for a flood-frequency regression model and to explore regional grouping of streamgages for explaining variability in flood-frequency statistics across the study area. The most suitable model for explaining flood frequency used drainage area and mean annual precipitation as explanatory variables for the entire study area as a region. Final regression equations for estimating the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probability discharge in Alaska and conterminous basins in Canada were developed using a generalized least-squares regression. The average standard error of prediction for the regression equations for the various annual exceedance probabilities ranged from 69 to 82 percent, and the pseudo-coefficient of determination (pseudo-R 2 ) ranged from 85 to 91 percent. The regional regression equations from this study were incorporated into the U.S. Geological Survey StreamStats program for a limited area of the State—the Cook Inlet Basin. StreamStats is a national web-based geographic information system application that facilitates retrieval of streamflow statistics and associated information. StreamStats retrieves published data for gaged sites and, for user-selected ungaged sites, delineates drainage areas from topographic and hydrographic data, computes basin characteristics, and computes flood frequency estimates using the regional regression equations.

Alaska↗

USGS2017

Preliminary assessment of a water-quality monitoring program for total maximum daily loads in Johnson County, Kansas, January 2015 through June 2016

Municipalities in Johnson County in northeastern Kansas are required to implement stormwater management programs to reduce pollutant discharges, protect water quality, and comply with applicable water-quality regulations in accordance with National Pollutant Discharge Elimination System permits for stormwater discharge. To this end, municipalities collect grab samples at streams entering and leaving their jurisdiction to determine levels of excessive nutrients, sediment, and fecal bacteria to characterize pollutants and understand the factors affecting them. In 2014, the U.S. Geological Survey and the Johnson County Stormwater Management Program, with input from the Kansas Department of Health and Environment, initiated a 5-year monitoring program to satisfy minimum sampling requirements for each municipality as described by new stormwater permits issued to Johnson County municipalities. The purpose of this report is to provide a preliminary assessment of the monitoring program. The monitoring program is described, a preliminary assessment of the monitoring program design is provided using water-quality data collected during the first 2 years of the program, and the ability of the current monitoring network and sampling plan to provide data sufficient to quantify improvements in water quality resulting from implemented and planned best management practices is evaluated. The information in this initial report may be used to evaluate changes in data collection methods while data collection is still ongoing that may lead to improved data utility. Discrete water-quality samples were collected at 27 sites and analyzed for nutrients, Escherichia coli ( E. coli ) bacteria, total suspended solids, and suspended-sediment concentration. In addition, continuous water-quality data (water temperature, pH, dissolved oxygen, specific conductance, turbidity, and nitrate plus nitrite) were collected at one site to characterize variability and provide a basis for comparison to discrete data. Base flow samples indicated that point sources are likely affecting nutrient concentrations and E. coli bacteria densities at several sites. Concentrations of all analytes in storm runoff samples were characterized by substantial variability among sites and samples. About one-half of the sites, representing different watersheds, had storm runoff samples with nitrogen concentrations greater than 10 milligrams per liter. About one-third of the sites, representing different watersheds, had storm runoff samples with total phosphorus concentrations greater than 3 milligrams per liter. Six sites had samples with E. coli densities greater than 100,000 colonies per 100 milliliters of water. Total suspended solids concentrations of about 12,000 milligrams per liter or greater occurred in samples from three sites. Data collected for this monitoring program may be useful for some general assessment purposes but may also be limited in potential to fully inform stormwater management activities. Valuable attributes of the monitoring program design included incorporating many sites across the county for comparisons among watersheds and municipalities, using fixed-stage samplers to collect multiple samples during single events, collection of base flow samples in addition to storm samples to isolate possible point sources from stormwater sources, and use of continuous monitors to characterize variability. Limiting attributes of the monitoring program design included location of monitoring sites along municipal boundaries to satisfy permit requirements rather than using watershed-based criteria such as locations of tributaries, potential pollutant sources, and implemented management practices. Additional limiting attributes include having a large number of widespread sampling locations, which presented logistical challenges for predicting localized rainfall and collecting and analyzing samples during short timeframes associated with storms, and collecting storm samples at fixed-stage elevations only during the rising limb of storms, which does not characterize conditions over the storm hydrograph. The small number of samples collected per site resulted in a sample size too small to be representative of site conditions, including seasonal and hydrologic variability, and insufficient for meaningful statistical analysis or site-specific modeling. Several measures could be taken to improve data utility and include redesigning the monitoring network according to watershed characteristics, incorporating a nested design in which data are collected at different scales (watershed, subwatershed, and best management practices), increasing sampling frequency, and combining different methods to allow for flexibility to focus on areas and conditions of particular interest. A monitoring design that would facilitate most of these improvements would be to focus efforts on a limited number of watersheds for several years, then cycle to the next set of watersheds for several years, eventually returning to previously monitored watersheds to document changes. Redesign of the water-quality monitoring program requires considerable effort and commitment from municipalities of Johnson County. However, the long-term benefit likely is a monitoring program that results in improved stream conditions and more effective management practices and efficient expenditure of resources.

Kansas↗

USGS2012

Determination of steroid hormones and related compounds in filtered and unfiltered water by solid-phase extraction, derivatization, and gas chromatography with tandem mass spectrometry

A new analytical method has been developed and implemented at the U.S. Geological Survey National Water Quality Laboratory that determines a suite of 20 steroid hormones and related compounds in filtered water (using laboratory schedule 2434) and in unfiltered water (using laboratory schedule 4434). This report documents the procedures and initial performance data for the method and provides guidance on application of the method and considerations of data quality in relation to data interpretation. The analytical method determines 6 natural and 3 synthetic estrogen compounds, 6 natural androgens, 1 natural and 1 synthetic progestin compound, and 2 sterols: cholesterol and 3--coprostanol. These two sterols have limited biological activity but typically are abundant in wastewater effluents and serve as useful tracers. Bisphenol A, an industrial chemical used primarily to produce polycarbonate plastic and epoxy resins and that has been shown to have estrogenic activity, also is determined by the method. A technique referred to as isotope-dilution quantification is used to improve quantitative accuracy by accounting for sample-specific procedural losses in the determined analyte concentration. Briefly, deuterium- or carbon-13-labeled isotope-dilution standards (IDSs), all of which are direct or chemically similar isotopic analogs of the method analytes, are added to all environmental and quality-control and quality-assurance samples before extraction. Method analytes and IDS compounds are isolated from filtered or unfiltered water by solid-phase extraction onto an octadecylsilyl disk, overlain with a graded glass-fiber filter to facilitate extraction of unfiltered sample matrices. The disks are eluted with methanol, and the extract is evaporated to dryness, reconstituted in solvent, passed through a Florisil solid-phase extraction column to remove polar organic interferences, and again evaporated to dryness in a reaction vial. The method compounds are reacted with activated -methyl--trimethylsilyl trifluoroacetamide at 65 degrees Celsius for 1 hour to form trimethylsilyl or trimethylsilyl-enol ether derivatives that are more amenable to gas chromatographic separation than the underivatized compounds. Analysis is carried out by gas chromatography with tandem mass spectrometry using calibration standards that are derivatized concurrently with the sample extracts. Analyte concentrations are quantified relative to specific IDS compounds in the sample, which directly compensate for procedural losses (incomplete recovery) in the determined and reported analyte concentrations. Thus, reported analyte concentrations (or analyte recoveries for spiked samples) are corrected based on recovery of the corresponding IDS compound during the quantification process. Recovery for each IDS compound is reported for each sample and represents an absolute recovery in a manner comparable to surrogate recoveries for other organic methods used by the National Water Quality Laboratory. Thus, IDS recoveries provide a useful tool for evaluating sample-specific analytical performance from an absolute mass recovery standpoint. IDS absolute recovery will differ and typically be lower than the corresponding analyte’s method recovery in spiked samples. However, additional correction of reported analyte concentrations is unnecessary and inappropriate because the analyte concentration (or recovery) already is compensated for by the isotope-dilution quantification procedure. Method analytes were spiked at 10 and 100 nanograms per liter (ng/L) for most analytes (10 times greater spike levels were used for bisphenol A and 100 times greater spike levels were used for 3--coprostanol and cholesterol) into the following validation-sample matrices: reagent water, wastewater-affected surface water, a secondary-treated wastewater effluent, and a primary (no biological treatment) wastewater effluent. Overall method recovery for all analytes in these matrices averaged 100 percent, with overall relative standard deviation of 28 percent. Mean recoveries of the 20 individual analytes for spiked reagent-water samples prepared along with field samples and analyzed in 2009–2010 ranged from 84–104 percent, with relative standard deviations of 6–36 percent. Concentrations for two analytes, equilin and progesterone, are reported as estimated because these analytes had excessive bias or variability, or both. Additional database coding is applied to other reported analyte data as needed, based on sample-specific IDS recovery performance. Detection levels were derived statistically by fortifying reagent water at six different levels (0.1 to 4 ng/L) and range from about 0.4 to 4 ng/L for 16 analytes. Interim reporting levels applied to analytes in this report range from 0.8 to 8 ng/L. Bisphenol A and the sterols (cholesterol and 3-beta-coprostanol) were consistently detected in laboratory and field blanks. The minimum reporting levels were set at 100 ng/L for bisphenol A and at 200 ng/L for the two sterols to prevent any bias associated with the presence of these compounds in the blanks. A minimum reporting level of 2 ng/L was set for 11-ketotestosterone to minimize false positive risk from an interfering siloxane compound emanating as chromatographic-column bleed, from vial septum material, or from other sources at no more than 1 ng/L.

Techniques and Methods↗

USGS1980

Handbook of acute toxicity of chemicals to fish and aquatic invertebrates : summaries of toxicity tests conducted at Columbia National Fisheries Research Laboratory, 1965-78

Acute toxicity is a major subject of research at Columbia National Fisheries Research Laboratory for evaluating the impact of toxic chemicals on fishery resources. The Laboratory has played a leading role in developing research technology for toxicity testing and data interpretation. In 1965-78, more than 400 chemicals were tested against a variety of invertebrates and fish species representative of both cold- and warm-water climates. The use of acute toxicity tests for assessing the potential hazard of chemical contaminants to aquatic organisms is well documented (Boyd 1957; Henderson et al. 1960; Sanders and Cope 1966; Macek and McAllister 1970). Static acute toxicity tests provide rapid and (within limits) reproducible concentration-response curves for estimating toxic effects of chemicals on aquatic organisms. These tests provide a database for determining relative toxicity of a large number of chemicals to a variety of species and for estimating acute effects of chemical spills on natural aquatic systems; they also assist in determining priority and design of additional toxicity studies. Acute toxicity tests usually provide estimates of the exposure concentration causing 50% mortality (LC50) to test organisms during a specified period of time. For certain invertebrates, the effective concentration is based on immobilization, or some other identifiable endpoint, rather than on lethality. The application of the LC50 has gained acceptance among toxicologists and is generally the most highly rated test for assessing potential adverse effects of chemical contaminants to aquatic life (Brungs and Mount 1978; American Institute for Biological Sciences 1978 a ). The literature contains numerous papers dealing with the acute toxicity of chemicals to freshwater organisms. However, there is a tremendous need for a concise compendium of toxicity data covering a large variety of chemicals and test species. This Handbook is a compilation of a large volume of acute toxicity data from the Columbia Laboratory and its field laboratories. It presents definitive acute toxicity data on 271 chemicals tested against a variety of freshwater invertebrates and fishes. The chemicals represent all major groups of pesticides, as well as numerous industrial chemicals. This compilation should serve as a useful database for the many agencies and organizations dealing with research and management programs concerned with the impact of chemicals on aquatic resources. The Columbia Laboratory has played a major role in developing currently used standard methodology for static acute toxicity testing. The use of standardized methodology greatly reduces variation in results. The data presented here have been carefully scrutinized to eliminate tests that failed to follow acceptable procedures. Handling of test organisms and procedures for static toxicity tests followed those described by Lennon and Walker (1964) and Macek and McAllister (1970), and conform well with those recommended by Brauhn and Schoettger (1975) and the Committee on Methods for Toxicity Tests with Aquatic Organisms (1975). The species of fish and invertebrates that were tested are listed in phylogenetic order in Tables 1 and 2. Fish were obtained from Federal and State hatcheries as either eggs or fry. Original stocks of invertebrates were collected and cultured from wild populations with no known source of contamination; these populations were replenished regularly. The invertebrates were cultured in the Laboratory by methods similar to those described by Sanders and Cope (1966). Test chemicals usually consisted of technical or analytical grade samples of known purity. Formulations of the chemicals were also tested when available. When purity of test chemicals was known, all calculated concentrations were based on percent active ingredients. Stock solutions were prepared immediately before each test, with commercial grade acetone as the carrier solvent. Occasionally, ethanol or dimethyl-formamide was substituted. Solvent concentrations did not exceed 0.5 mL/L in final dilution water. Test water (dilution water) was reconstituted from deionized water of at least 10 6 ohms resistivity by the addition of appropriate reagent grade chemicals (Marking 1969). Water was buffered to maintain a pH of 7.2 to 7.5, an alkalinity of 30 to 35 mg/L, and a hardness of 40 to 50 mg/L as CaCO 3 . Test water was mixed thoroughly and aerated before transfer into test chambers. Fish were acclimated to dilution water by gradually changing the water in acclimated tanks from 100% well water to 100% reconstituted water over a 1- to 3-day period at the desired testing temperature. Invertebrates were acclimated from well water to dilution water over a 4- to 6-h period. Toxicity tests were conducted under static conditions without aeration, and the organisms were not fed during acclimation or testing. Temperature of test solutions was maintained within ± 1°C of that required for a given test. Toxicity tests with fish were conducted in 18.9-liter (5-gal) wide-mouthed jars containing 15 liters of test solution. Fingerling fish weighing 0.2 to 1.5 g were tested at each concentration. Caution was taken not to exceed 0.8 g of test organisms per liter of solution. Duplicate test chambers were used to accommodate larger fish. Test chambers varied in size for invertebrates, depending on the species used; volume of test solution ranged from 0.25 to 4 liters. At least 10 organisms were exposed to each concentration for all definitive tests. At least six concentrations were used per toxicity test. The tests began upon initial exposure to the toxicant and continued for 96 h. Immobilization tests with invertebrates were conducted for only 48 h. The number of dead or affected organisms in each test chamber were recorded and the dead organisms were removed every 24 h; general observations on the condition of test organisms were also recorded at these times. Toxicity data were analyzed by a statistical method described by Litchfield and Wilcoxon (1949) to determine LC50 (theoretical estimate of the concentration lethal to 50% of the test animals) and 95% confidence intervals. This method is recommended by the American Public Health Association (1971) and by Sprague (1969) for determining median lethal concentrations. The procedure is easily modified for computing a single LC50 when replicate tests are performed.

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