Geology Reports⌕ Search

SEARCH · Geology Reports

Results for “Statistical Methods & Applications”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11Linked to original sources
USGS2018

Implementation of MOVE.1, censored MOVE.1, and piecewise MOVE.1 low-flow regressions with applications at partial-record streamgaging stations in New Jersey

The U.S. Geological Survey (USGS) uses Maintenance of Variance Extension Type 1 (MOVE.1) regression to transfer streamflows measured at long-term continuous-record streamgaging stations to partial-record (PR) streamgaging stations where intermittent base-flow measurements are available. MOVE.1 regression is used widely throughout the hydrologic community to extend historic low flows and low-flow statistics at continuous-record streamgaging stations to streamgaging stations that have access to only a partial record of low flows. The method correlates base-flow measurements at PR streamgaging stations with daily mean streamflows measured at index stations that exhibit similar streamflow characteristics. Following changes in the computing platform for storing, processing, retrieving, and publishing National Water Information System (NWIS) hydrologic data, legacy Statistical Analysis System (SAS) code developed by the USGS to implement the MOVE.1 regression was no longer suitable for reading and processing NWIS streamflow data. To migrate the MOVE.1 program so that it could continue to read streamflow data using the new hydrologic data platform, the SAS code was re-written in R, an open source programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The work described in this report was performed in a study conducted by USGS in cooperation with the New Jersey Department of Environmental Protection. During migration from SAS to R, graphical and tabular output generated by the R script was compared to output produced by the legacy SAS code to ensure that equations used to perform the MOVE.1 regression remained the same. An option to perform censored MOVE.1 regression was added to extend the MOVE.1 methodology to cases where one or more measured continuous-record or PR streamgaging station flows are zero valued. In addition to permitting censored regression, the new R script includes an option to perform piecewise MOVE.1 regression when the relation between PR station and index station low flows varies significantly across the range of index station streamflows. Together with traditional MOVE.1 regression, censored, and piecewise MOVE.1 regression methods implemented by the R script offer less biased estimates than ordinary least squares regression for the annual 7-day 10-year and other low-flow statistics at PR stations for a range of base-flow conditions. The R script is used to implement the MOVE.1 regression methods across a variety of computing platforms.

New Jersey↗
USGS2010

Method for Estimating Annual Atrazine Use for Counties in the Conterminous United States, 1992-2007

A method was developed to estimate annual atrazine use during 1992 to 2007 on sixteen crops and four agricultural land uses. For each year, atrazine use was estimated for all counties in the conterminous United States (except California) by combining (1) proprietary data from the Doane Marketing Research-Kynetec (DMRK) AgroTrak database on the mass of atrazine applied to agricultural crops, (2) county harvested crop acreage, by county, from the 1992, 1997, 2002, and 2007 Censuses of Agriculture, and (3) annual harvested crop acreage from National Agriculture Statistics Service (NASS) for non-Census years. DMRK estimates of pesticide use on individual crops were derived from surveys of major field crops and selected specialty crops in multicounty areas referred to as Crop Reporting Districts (CRD). The CRD-level atrazine-use estimates were disaggregated to obtain county-level application rates by dividing the mass (pounds) of pesticides applied to a crop by the acreage of that crop in the CRD to yield a rate per harvested acre. When atrazine-use estimates were not available for a CRD, crop, or year, an estimated rate was developed following a hierarchy of decision rules that checked first for the availability of a crop application rate from surveyed atrazine application rate(s) for adjacent CRDs for a specific year, and second, the rates from surveyed CRDs within for U.S. Department of Agriculture Farm Production Regions for a specific year or multiple years. The estimation method applied linear interpolation to estimate crop acreage for years when harvested acres for a crop and county were not reported in either the Census of Agriculture or the NASS database, but were reported by these data sources for other years for that crop and county. Data for atrazine use for the counties in California was obtained from farmers' reports of pesticide use collected and published by the California Department of Pesticide Regulation-Pesticide Use Reporting (DPR-PUR) because these data are more complete than DMRK survey data. National and state annual atrazine-use totals derived by this method were compared with other published pesticide-use estimates and were highly correlated. The method developed is designed to be applicable to other pesticides for which there are similar data; however, for some pesticides that are applied to specialty crops, fewer surveys are usually available to estimate application rates and there are a greater number of years with unreported crop acreage, potentially resulting in greater uncertainty in use

Scientific Investigations Report↗
USGS2002

Estimates of recharge to unconfined aquifers and leakage to confined aquifers in the seven-county metropolitan area of Minneapolis-St. Paul, Minnesota

Recharge to unconfined aquifers in the seven-county Twin Cities metropolitan area of Minnesota was estimated by five methods. Mean recharge estimated as a percentage (27 percent) of precipitation ranged from 7.7 to 8.3 in./yr across the study area. The median recharge estimated from automated analyses of streamflow-recession displacements for seven basins ranged from 1.2 to 12.2 in./yr. Median recharge estimated from graphical analyses of groundwater level fluctuations for 11 wells ranged from 4.5 to 13.6 in./yr. Recharge estimated by age dating of shallow ground water at a nested well site was 8.8 in./yr. Minimal ground-water recharge estimated by statistical analyses of watershed characteristics (geologic data and streamflow records) ranged from less than 0.1 to 5.6 in./yr. Leakage, considered recharge to confined aquifers, in the seven-county Twin Cities metropolitan area was estimated by two methods. Leakage estimated by analyses of ground-water level fluctuations for 11 wells ranged from 3.2x10 -3 to 1.1x10 -2 in./yr. Leakage estimated by analyses of vertical-hydraulic gradients based on application of the Darcy flow equation for seven nested well sites ranged from 4.6x10 -5 to 1.1x10 -1 in./yr. Recharge estimates, which generally varied within 10 in./yr for each of the methods, generally were largest based on the precipitation, ground-water level fluctuation, and age dating of shallow ground water methods, slightly smaller based on the streamflow-recession displacement method, and smallest based on the watershed characteristics method. Leakage, which was less than 1 in./yr, varied within 1 order of magnitude based on the ground-water level fluctuation method and as much as 4 orders of magnitude based on analyses of vertical-hydraulic gradients.

Minnesota↗
USGS2023

Comprehensive water-quality trend analysis for selected sites and constituents in the International Souris River Basin, Saskatchewan and Manitoba, Canada, and North Dakota, United States, 1970–2020

The Souris River Basin is an international basin in southeast Saskatchewan, north-central North Dakota, and southwest Manitoba. Sustained exceedances of water-quality objectives for total phosphorus, sodium, sulfate, total dissolved solids, and total iron have been reported since the late 1990s at the two binational sites on the Souris River (Souris River near Sherwood, North Dakota [U.S. Geological Survey station 05114000] and Souris River near Westhope, N. Dak. [U.S. Geological Survey station 05124000]). To understand conditions at the binational sites, it is important to understand water-quality changes on a basin-wide scale. Because streamflow is highly variable in the basin and changes in streamflow affect water-quality conditions, it is particularly important to use a trend-analysis method that accounts for changes in streamflow. Trends in water-quality concentrations can be affected by human-induced changes on the landscape or natural changes in land-runoff interactions that are driven by climate patterns and reflected by changes in streamflow (commonly referred to as “hydroclimatic variability”). In the primarily agricultural Souris River Basin, human-induced changes that are likely to affect trends are widespread changes in agricultural management such as fertilizer application, tilling practices, and crop types, as well as dam emplacement and artificial drainage. Around 1970, there was a long-term natural (hydroclimatic) change in the basin in which a significant transition from a dry climate state to a wet climate state resulted in higher streamflow in the basin. To assist the International Souris River Board in assessing current water-quality conditions in the Souris River Basin and exceedances of water-quality objectives at the binational sites, the U.S. Geological Survey, in cooperation with the International Joint Commission, completed a comprehensive analysis for selected ions, nutrients, and trace metals for many sites in the basin that included descriptive water-quality statistics, trend analysis using a trend method that considers interannual hydroclimatic variability, and an assessment of exceedances of the water-quality objectives for the binational sites. Water-quality and streamflow or reservoir inflow or outflow data were compiled for 34 sites (30 stream sites and four reservoir sites) and 23 constituents with established water-quality objectives from 1970 to 2020 in the Souris River Basin and were used for descriptive statistics and water-quality trend analysis. Median total dissolved solids, sulfate, and sodium concentrations were low in the headwaters of the Souris River and some of the highest median concentrations were measured in the upper basin. At main-stem Souris River sites, all median sodium concentrations were greater than the binational water-quality objective. Median total phosphorus concentrations in the Souris River Basin were highest in the headwaters of the Souris River and all sites had median concentrations greater than the water-quality objective. Median total iron concentrations were highly variable across the basin, and for most main-stem sites, median concentrations were greater than or equal to the water-quality objective. During the recent period (2009–19), the annual flow-averaged concentrations of total dissolved solids and sulfate increased for nearly all stream sites with most sites having mildly significant or significant increases. One-half of the sites had an annual flow-averaged geometric mean concentration greater than the total dissolved solids water-quality objective, and four sites had sulfate increases greater than 100 milligrams per liter. Trends in annual flow-averaged concentrations of sodium and chloride generally were small and nonsignificant. Most sites had concentrations greater than the sodium water-quality objective, whereas all sites had concentrations much less than the chloride water-quality objective. Annual flow-averaged geometric mean concentration of total phosphorus decreased for nearly all sites across the Souris River Basin, but all sites had concentrations greater than the total phosphorus water-quality objective for the entire period. Small and nonsignificant changes in annual flow-averaged geometric mean concentration of total iron were detected at all sites but the binational site at Sherwood, N. Dak., and by 2019 all sites had concentrations greater than the total iron water-quality objective. For the reservoir sites, during 2000–15, mostly significant increases for total dissolved solids, sulfate, and sodium were detected, whereas changes in total phosphorus and total iron were mixed. During the historical period (1976–2019), large and consistent increases in total dissolved solids and sulfate have occurred since the late 1980s, with the largest increases and the most sites with mildly significant or significant increases generally occurring during the middle period (1988–2005). Large and significant or mildly significant increases in sodium concentrations occurred at eight of 10 sites in the middle period (1988–2005), and by the late period (2005–19) changes were small and nonsignificant. Similar to other basins in the region, such as the Red River of the North and Heart River, large and overall consistent increases since the late 1980s in total dissolved solids and sulfate in the Souris River Basin suggest that long-term natural (hydroclimatic) processes are large contributors to increases in the concentration of salts in streams and reservoirs associated with the onset of wetter conditions. The concurrent increases in sulfate and sodium concentrations at all sites during the middle period (1988–2005) suggest that sodium-sulfate evaporite dissolution may be a factor contributing to increases. Total phosphorus concentrations oscillated between increasing and decreasing during the historical period, with concentrations increasing during the first trend period (1976–88) and decreasing in the fourth trend period (2009–19) to the lowest flow-averaged geometric mean concentration by 2019 for most sites. During the historical period, changes in total iron concentrations were mostly nonsignificant and generally small, and variability in total iron concentrations likely affected the ability to detect statistically significant changes in concentration. The probability of exceeding the water-quality objective for total dissolved solids, sulfate, and sodium increased between 1976 and 2019 for the binational sites, especially for sulfate, which more than doubled for Souris River near Sherwood, N. Dak. and increased more than seven times for Souris River near Westhope, N. Dak. Total phosphorus and total iron concentrations for the binational sites were likely to exceed the water-quality objective for most of the year, but seasonal patterns of total phosphorus and total iron concentrations were different between the sites, suggesting that different factors may affect concentrations at different times of the year. For sodium, total phosphorus, and total iron, exceedance of the water-quality objective most of the time is not unexpected given that the flow-averaged geometric mean concentration for these three constituents for most sites across the basin are greater than the water-quality objective for most of the period. If natural processes are affecting total dissolved solids and sulfate concentrations, concentrations would be expected to vary with time, and as a result, extended periods of concentrations greater or less than the water-quality objective are likely to occur depending upon climatic conditions. A better understanding of the state of water quality across the Souris River Basin is beneficial to understanding and interpreting water-quality conditions at the two Souris River binational sites. The most consistent spatial and temporal change observed for this study was large and consistent increases in sulfate and total dissolved solids among tributary and main-stem sites since the late 1980s. For sulfate and total dissolved solids, wetter climatic conditions combined with naturally occurring and abundant sources of sulfate likely contributed to sustained exceedances of water-quality objectives in recent decades, and extended periods of concentrations greater than or less than the water-quality objective are likely to occur depending on climatic conditions. For sodium, total iron, and total phosphorus, sustained exceedances of the current water-quality objective likely will continue because most sites across the basin had flow-averaged geometric mean concentrations greater than the water-quality objective; and during the 43-year period of analysis, regardless of climatic conditions, exceedances were consistently greater than the water-quality objective. Further investigation into the factors causing increasing sulfate concentrations and a better understanding of reservoir dynamics would enhance the understanding of changes in water-quality conditions in the Souris River Basin. The basin-wide approach of this report provided an improved understanding of water-quality conditions in the Souris River Basin, and results can be used to inform the current water-quality objectives, inform potential changes to water management in the basin, and serve as a starting point for tracking future progress. Gaps in understanding of water-quality conditions can be closed through continued monitoring and further investigation into causes behind changes in water-quality conditions identified in this report.

Manitoba, North Dakota, Saskatchewan↗
USGS2024

Structure and morphology of chars and activated carbons obtained from thermal treatment of coal and biomass origin materials, including their wastes: Results from the ICCP Microscopy of Carbon Materials Working Group

This paper describes the evaluation of petrographic textures in char and activated carbon derived from coal, coal by-products and biomass, formed during carbonization and activation processes. This work represents the results of interlaboratory exercises from 2016 to 2022 of the Microscopy of Carbon Materials Working Group in Commission III of the International Committee for Coal and Organic Petrology. The interlaboratory exercises were run on photomicrograph samples. For textural characterization of carbon materials, the existing American Society for Testing and Materials (ASTM) classification system for metallurgical coke was applied. Morphological differences were evaluated in 29 carbon material types, including 22 char samples, and 7 activated carbon (AC) samples obtained experimentally using conventional direct/indirect and microwave heating technologies. This approach gives an extended view on the identification of microporous carbons, and how a certain heat treatment develops a certain optical texture and structure in a raw material. The requested evaluation of carbon materials was related to their porosity, origin, extent, and characteristics, which are particular to each carbon material type. Because carbon matrices can form a wide range of optical textures during heat treatment it is important to demonstrate which carbon occurrences will have a crucial role in industrial applications dominated by adsorption phenomena. The interlaboratory exercises included 17 participants from 14 laboratories. Four sets of digital black and white and colour photomicrographs were distributed, which in total comprised 184 fields of different types of carbon material. The results were evaluated based on four levels: (i) optical texture (isotropic/anisotropic), (ii) optical type and size (punctiform, mosaic, fiber, ribbon, domain), iii) morphology (porous, non-porous/massive), and (iv) particle origin (precursor type). The statistical method applied to evaluate the results was based on “raw agreement indices”. Comparative analyses of the average values of the level of overall agreement showed homogeneity in the results, the mean value was 89%, with a minimum value of 87% and a maximum value of 91% for those who participated in at least three out of four exercises.

International Journal of Coal Geology↗
USGS1981

Quality assurance practices for the chemical and biological analyses of water and fluvial sediments

This chapter contains practices used by the U.S. Geological Survey to assure the quality of analytical data for water, fluvial sediment, and aquatic organisms. These practices are directed primarily toward personnel making water-quality measurements. Some detail specific quality control techniques, others document quality assurance procedures being used by the Central Laboratories System of the U.S. Geological Survey, and still others describe various statistical techniques and give examples of their use in evaluating and assuring the quality of analytical data. The practices are arranged into eight sections: Analytical Methods Development Procedures Standard Quantitative Analysis Techniques Instrumental Techniques Reference Material Laboratory Quality Control Quality Assurance Monitoring Documentation, Summary, and Evaluation of Data Materials Evaluation Each section is preceded by a brief description of the material covered. Similarly within each section, each practice is preceded by a description of its application or scope.

Open-File Report↗
USGS1982

Quality assurance practices for the chemical and biological analyses of water and fluvial sediments

This chapter contains practices used by the U.S. Geological Survey to assure the quality of analytical data for water, fluvial sediment, and aquatic organisms. These practices are directed primarily toward personnel making water quality measurements. Some detail specific quality control techniques, others document quality assurance procedures being used by the Central Laboratories System of the U.S. Geological Survey, and still others describe various statistical techniques and give examples of their use in evaluating and assuring the quality of analytical data. The practices are arranged into eight sections: Analytical Methods Development Procedures, Standard Quantitative Analysis Techniques, Instrumental Techniques, Reference Material, Laboratory Quality Control; Quality Assurance Monitoring; Documentation, Summary, and Evaluation of Data, Materials Evaluation. Each section is preceded by a brief description of the material covered. Similarly within each section, each practice is preceded by a description of its application or scope.

Techniques of Water-Resources Investigations↗
USGS2019

Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models

Gully erosion susceptibility mapping is a fundamental tool for land-use planning aimed at mitigating land degradation. However, the capabilities of some state-of-the-art data-mining models for developing accurate maps of gully erosion susceptibility have not yet been fully investigated. This study assessed and compared the performance of two different types of data-mining models for accurately mapping gully erosion susceptibility at a regional scale in Chavar, Ilam, Iran. The two methods evaluated were: Certainty Factor (CF), a bivariate statistical model; and Maximum Entropy (ME), an advanced machine learning model. Several geographic and environmental factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 63 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. Accuracy assessments completed with the receiver operating characteristic curve method showed that the ME-based regional gully susceptibility map has an area under the curve (AUC) value of 88.6% whereas the CF-based map has an AUC of 81.8%. According to jackknife tests that were used to investigate the relative importance of predictor variables, aspect, distance to river, lithology and land use are the most influential factors for the spatial distribution of gully erosion susceptibility in this region of Iran. The gully erosion susceptibility maps produced in this study could be useful tools for land managers and engineers tasked with road development, urbanization and other future development.

Science of the Total Environment↗
USGS2020

A random forest approach for bounded outcome variables

Random forests have become an established tool for classication and regres- sion, in particular in high-dimensional settings and in the presence of non-additive predictor-response relationships. For bounded outcome variables restricted to the unit interval, however, classical modeling approaches based on mean squared error loss may severely suer as they do not account for heteroscedasticity in the data. To address this issue, we propose a random forest approach for relating a beta dis- tributed outcome to a set of explanatory variables. Our approach explicitly makes use of the likelihood function of the beta distribution for the selection of splits dur- ing the tree-building procedure. In each iteration of the tree-building algorithm it chooses one explanatory variable in combination with a split point that maximizes the log-likelihood function of the beta distribution with the parameter estimates de- rived from the nodes of the currently built tree. Results of several simulation studies and an application using data from the U.S.A. National Lakes Assessment Survey demonstrate the properties and usefulness of the method, in particular when com- pared to random forest approaches based on mean squared error loss and parametric regression models.

Journal of Computational and Graphical Statistics↗
USGS2018

Use of landscape simulation modeling to quantify resilience for ecological applications

Goals of fostering ecological resilience are increasingly used to guide U.S. public land management in the context of anthropogenic climate change and increasing landscape disturbances. There are, however, few operational means of assessing the resilience of a landscape or ecosystem. We present a method to evaluate resilience using simulation modeling. In this method, we use historical conditions (e.g., in North America, prior to European settlement), quantified using simulation modeling, to provide a comparative reference for contemporary conditions, where substantial departures indicate loss of resilience. Contemporary ecological conditions are compared statistically to the historical time series to create a resilience index, which can be used to prioritize landscapes for treatment and inform possible treatments. However, managing for resilience based on historical conditions is tenuous in the Anthropocene, which is characterized by rapid climate change, extensive human land use, altered disturbance regimes, and exotic species introductions. To account for the future variability of ecosystems resulting from climate and disturbance regime shifts, we augment historical simulations with simulations of ecosystem dynamics under projected climate and land use changes to assess the degree of departure from benchmark historical conditions. We use a mechanistic landscape model (FireBGCv2) applied to a large landscape in western Montana, USA, to illustrate the methods presented in this paper. Spatially explicit ecosystem modeling provides the vehicle to generate the historical and future time series needed to quantify potential resilience conditions associated with past and potential future conditions. Our methods show that given selection of a useful set of metrics, managers could use simulations like ours to evaluate potential future management directions.

Ecosphere↗
USGS2009

An improved procedure for detection and enumeration of walrus signatures in airborne thermal imagery

In recent years, application of remote sensing to marine mammal surveys has been a promising area of investigation for wildlife managers and researchers. In April 2006, the United States and Russia conducted an aerial survey of Pacific walrus (Odobenus rosmarus divergens) using thermal infrared sensors to detect groups of animals resting on pack ice in the Bering Sea. The goal of this survey was to estimate the size of the Pacific walrus population. An initial analysis of the U.S. data using previously-established methods resulted in lower detectability of walrus groups in the imagery and higher variability in calibration models than was expected based on pilot studies. This paper describes an improved procedure for detection and enumeration of walrus groups in airborne thermal imagery. Thermal images were first subdivided into smaller 200 x 200 pixel "tiles." We calculated three statistics to represent characteristics of walrus signatures from the temperature histogram for each the. Tiles that exhibited one or more of these characteristics were examined further to determine if walrus signatures were present. We used cluster analysis on tiles that contained walrus signatures to determine which pixels belonged to each group. We then calculated a thermal index value for each walrus group in the imagery and used generalized linear models to estimate detection functions (the probability of a group having a positive index value) and calibration functions (the size of a group as a function of its index value) based on counts from matched digital aerial photographs. The new method described here improved our ability to detect walrus groups at both 2 m and 4 m spatial resolution. In addition, the resulting calibration models have lower variance than the original method. We anticipate that the use of this new procedure will greatly improve the quality of the population estimate derived from these data. This procedure may also have broader applicability to thermal infrared surveys of other wildlife species. Published by Elsevier B.V.

International Journal of Applied Earth Observation↗
USGS2017

Advancing mangrove macroecology

Mangrove forests provide a wide range of ecosystem services to society, yet they are among the most anthropogenically impacted coastal ecosystems in the world. In this chapter, we discuss and provide examples for how macroecology can advance our understanding of mangrove ecosystems. Macroecology is broadly defined as a discipline that uses statistical analyses to investigate large-scale, universal patterns in the distribution, abundance, diversity, and organization of species and ecosystems, including the scaling of ecological processes and structural and functional relationships. Macroecological methods can be used to advance our understanding of how non-linear responses in natural systems can be triggered by human impacts at local, regional, and global scales. Although macroecology has the potential to gain knowledge on universal patterns and processes that govern mangrove ecosystems, the application of macroecological methods to mangroves has historically been limited by constraints in data quality and availability. Here we provide examples that include evaluations of the variation in mangrove forest ecosystem structure and function in relation to macroclimatic drivers (e.g., temperature and rainfall regimes) and climate change. Additional examples include work focused upon the continental distribution of aboveground net primary productivity and carbon storage, which are rapidly advancing research areas. These examples demonstrate the value of a macroecological perspective for the understanding of global- and regional-scale effects of both changing environmental conditions and management actions on ecosystem structure, function, and the supply of goods and services. We also present current trends in mangrove modeling approaches and their potential utility to test hypotheses about mangrove structural and functional properties. Given the gap in relevant experimental work at the regional scale, we also discuss the potential use of mangrove restoration and rehabilitation projects as macroecological studies that advance the critical selection and conservation of ecosystem services when managing mangrove resources. Future work to further incorporate macroecology into mangrove research will require a concerted effort by research groups and institutions to launch research initiatives and synthesize data collected across broad biogeographic regions.

Book chapter↗
USGS2015

Methods for estimating the magnitude and frequency of peak streamflows at ungaged sites in and near the Oklahoma Panhandle

This report presents the results of a cooperative study by the U.S. Geological Survey and the Oklahoma Department of Transportation to estimate the magnitude and frequency of peak streamflows from regional regression equations for ungaged stream sites in and near the Oklahoma Panhandle. These methods relate basin characteristics (physiographic and climatic attributes) to selected peak streamflow frequency statistics with the 50-, 20-, 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probabilities. These relations were developed based on data from 32 selected streamflow-gaging stations in the Oklahoma Panhandle and in neighboring parts of Colorado, Kansas, New Mexico, and Texas. The basin characteristics for the selected streamflow-gaging stations were determined by using a geographic information system and the Oklahoma StreamStats application. Peak-streamflow frequency statistics were computed from annual peak-streamflow records from the irrigated period of record from water year 1978 through water year 2014. Generalized-least-squares multiple-linear regression analysis was used to formulate regression relations between peak-streamflow frequency statistics and basin characteristics. Contributing drainage area was the only basin characteristic determined to be statistically significant for all percentage of annual exceedance probabilities and was the only basin characteristic used in regional regression equations for estimating peak-streamflow frequency statistics on unregulated streams in and near the Oklahoma Panhandle. The regression model pseudo-coefficient of determination, converted to percent, for the Oklahoma Panhandle regional regression equations ranged from about 38 to 63 percent. The standard errors of prediction and the standard model errors for the Oklahoma Panhandle regional regression equations ranged from about 84 to 148 percent and from about 76 to 138 percent, respectively. These errors were comparable to those reported for regional peak-streamflow frequency regression equations for the High Plains areas of Texas and Colorado. The root mean square errors for the Oklahoma Panhandle regional regression equations (ranging from 3,170 to 92,000 cubic feet per second) were less than the root mean square errors for the Oklahoma statewide regression equations (ranging from 18,900 to 412,000 cubic feet per second); therefore, the Oklahoma Panhandle regional regression equations produce more accurate peak-streamflow statistic estimates for the irrigated period of record in the Oklahoma Panhandle than do the Oklahoma statewide regression equations. The regression equations developed in this report are applicable to streams that are not substantially affected by regulation, impoundment, or surface-water withdrawals. These regression equations are intended for use for stream sites with contributing drainage areas less than or equal to about 2,060 square miles, the maximum value for the independent variable used in the regression analysis.

Colorado, Kansas, New Mexico, Oklahoma, Texas↗
USGS2017

The Bayesian group lasso for confounded spatial data

Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.

Journal of Agricultural, Biological, and Environme↗
USGS2018

Incorporating spatially heterogeneous infiltration capacity into hydrologic models with applications for simulating post‐wildfire debris flow initiation

Soils in post‐wildfire environments are often characterized by a low infiltration capacity with a high degree of spatial heterogeneity relative to unburned areas. Debris flows are frequently initiated by run‐off in recently burned steeplands, making it critical to develop and test methods for incorporating spatial variability in infiltration capacity into hydrologic models. We use Monte Carlo simulations of run‐off generation over a soil with a spatially heterogenous saturated hydraulic conductivity ( K s ) to derive an expression for an aerially averaged saturated hydraulic conductivity ( ) that depends on the rainfall rate, the statistical properties of K s , and the spatial correlation length scale associated with K s . The proposed method for determining is tested by simulating run‐off on synthetic topography over a wide range of spatial scales. Results provide a simplified expression for an effective saturated hydraulic conductivity that can be used to relate a distribution of small‐scale K s measurements to infiltration and run‐off generation over larger spatial scales. Finally, we use a hydrologic model based on to simulate run‐off and debris flow initiation at a recently burned catchment in the Santa Ana Mountains, CA, USA, and compare results to those obtained using an infiltration model based on the Soil Conservation Service Curve Number.

California↗
USGS2016

Detecting sea-level hazards: Simple regression-based methods for calculating the acceleration of sea level

This report documents the development of statistical tools used to quantify the hazard presented by the response of sea-level elevation to natural or anthropogenic changes in climate and ocean circulation. A hazard is a physical process (or processes) that, when combined with vulnerability (or susceptibility to the hazard), results in risk. This study presents the development and comparison of new and existing sea-level analysis methods, exploration of the strengths and weaknesses of the methods using synthetic time series, and when appropriate, synthesis of the application of the method to observed sea-level time series. These reports are intended to enhance material presented in peer-reviewed journal articles where it is not always possible to provide the level of detail that might be necessary to fully support or recreate published results. The purpose of this report is to document and compare three simple methodologies that have previously been used to provide estimates with associated errors of the acceleration of sea-level elevation. These techniques have been used by coastal scientists and planners in assessing coastal risk over a wide range of spatial and temporal scales. Because relative sea-level (SL) elevation time series contain energetic fluctuations at many time scales, extracting what can be relatively small rate and acceleration signals (along with estimates of the error) from much larger “noise” has proven to be both difficult and controversial. Acceleration is a preferred measure of SL response to recent changes in the Earth’s climate because over time scales of 100 years or less slow vertical land motions (such as glacial isostatic adjustment) contribute only to the linear signal and not to acceleration, thus reducing the complexity of the analysis. Hence acceleration is useful if the goal of a study is to characterize and quantify the hazard associated with the changing relative elevation of water with respect to land on decadal time scales. Although in some cases it may be necessary to determine the cause of relative sea level rise, as a first step, it is important to accurately estimate the magnitude of the threat. Most researchers agree that global sea level (GSL) rose persistently through much of the 20th century at about 1.5–2.0 millimeters per year (mm/yr). There is far less agreement about whether the rate of sea-level rise (SLR) is increasing (that is, an acceleration in SL). Recent studies, and most of their predecessors, use tide gage data to quantify SL acceleration, A SL (t). In the current study, three techniques were used to calculate acceleration from tide gage data, and of those examined, it was determined that the two techniques based on sliding a regression window through the time series are more robust compared to the technique that fits a single quadratic form to the entire time series, particularly if there is temporal variation in the magnitude of the acceleration. The single-fit quadratic regression method has been the most commonly used technique in determining acceleration in tide gage data. The inability of the single-fit method to account for time-varying acceleration may explain some of the inconsistent findings between investigators. Properly quantifying A SL (t) from field measurements is of particular importance in evaluating numerical models of past, present, and future SLR resulting from anticipated climate change.

Open-File Report↗
USGS2010

Methods for estimating the magnitude and frequency of peak streamflows for unregulated streams in Oklahoma

Peak-streamflow regression equations were determined for estimating flows with exceedance probabilities from 50 to 0.2 percent for the state of Oklahoma. These regression equations incorporate basin characteristics to estimate peak-streamflow magnitude and frequency throughout the state by use of a generalized least squares regression analysis. The most statistically significant independent variables required to estimate peak-streamflow magnitude and frequency for unregulated streams in Oklahoma are contributing drainage area, mean-annual precipitation, and main-channel slope. The regression equations are applicable for watershed basins with drainage areas less than 2,510 square miles that are not affected by regulation. The resulting regression equations had a standard model error ranging from 31 to 46 percent. Annual-maximum peak flows observed at 231 streamflow-gaging stations through water year 2008 were used for the regression analysis. Gage peak-streamflow estimates were used from previous work unless 2008 gaging-station data were available, in which new peak-streamflow estimates were calculated. The U.S. Geological Survey StreamStats web application was used to obtain the independent variables required for the peak-streamflow regression equations. Limitations on the use of the regression equations and the reliability of regression estimates for natural unregulated streams are described. Log-Pearson Type III analysis information, basin and climate characteristics, and the peak-streamflow frequency estimates for the 231 gaging stations in and near Oklahoma are listed. Methodologies are presented to estimate peak streamflows at ungaged sites by using estimates from gaging stations on unregulated streams. For ungaged sites on urban streams and streams regulated by small floodwater retarding structures, an adjustment of the statewide regression equations for natural unregulated streams can be used to estimate peak-streamflow magnitude and frequency.

Scientific Investigations Report↗
USGS2024

Assessment and characterization of ephemeral stream channel stability and mechanisms affecting erosion in Grand Valley, western Colorado, 2018–21

The Grand Valley in western Colorado is in the semiarid Southwest United States. The north side of the Grand Valley has many ungaged ephemeral streams, which are of particular interest because (1) the underlying bedrock geology, Late Cretaceous Mancos Shale, is a sedimentary rock deposit identified as a major salinity contributor to the Colorado River and (2) despite infrequent streamflows of short duration, monsoon-derived floods in these ephemeral streams can carry substantial amounts of sediment downstream, affecting upstream and downstream banks and channel cross sections. The study area is of interest, because salinity, or the total dissolved solids concentration, in the Colorado River causes an estimated $300 million to $400 million per year in economic damages in the United States, and it is estimated 62 percent of the Upper Colorado River Basin’s total dissolved solid loads originate from geologic sources. In an effort to minimize salt contributions to the Colorado River from public lands administered by the Bureau of Land Management, a comprehensive salinity control approach is typically used to reduce nonpoint sources of salinity through land management techniques and practices. In 2018, the U.S. Geological Survey, in cooperation with the Bureau of Land Management, began an assessment of ephemeral streams located on the north side of the Grand Valley, western Colorado, to characterize stream channel stability and identify mechanisms affecting erosion. The U.S. Geological Survey developed a method for automatically extracting channel cross-section geometry from existing remotely sensed terrain models. Based on estimated flood stage and surrogate streamflows, hydraulic characteristics were calculated. Furthermore, the channel geometries and hydraulic characteristics were used to estimate channel stability using a statistical model. Cross-section stabilities were determined from a stream channel stability assessment for a subset of 1,406 visited (field observed) locations out of 13,415 cross sections, which were delineated from remotely sensed terrain models. The application of Manning’s resistance equation in combination with multiple logistic regression models demonstrated channel stability can be estimated with a 0.845 goodness of fit for a validation dataset when using a combination of drainage area, width-to-depth ratio, sinuosity, and shear stress as the explanatory variables. Stream channel stability was extrapolated for 13,415 unvisited (not field observed) cross sections using the multiple logistic regression model and defined explanatory variables. Mapping of the ephemeral streams and their associated stabilities may be used by the Bureau of Land Management to prioritize areas for remediation or changes in management strategies to reduce sediment and salinity loading to the Colorado River. The study found channel stability within the ephemeral streams to be spatially variable, longitudinally discontinuous, and dictated by changes in channel bed slope. The stable ephemeral streams were relatively wide and shallow and often had smaller drainage areas with less potential for producing shear stresses capable of overcoming channel adhesion. A change in channel bed slope can provide the means necessary to generate shear stresses appropriate to initiate erosion and a subsequent stability transition to incising channels. Channel widening happens when either or both banks of an incising channel reach a critical height for mass wasting, or when channel curvature causes higher sidewall stress. Regardless, widening channels can promote increases in sinuosity and subsequently reduce steep channel bed slopes. Consequently, stable and widening channels can have comparable bed slopes, making channel bed slope a poor explanatory variable to predict channel stability overall, despite its function to initiate channel instability. The results were based on a surrogate 0.10 annual exceedance probability (AEP; return period equal to the 10-year flood) interval streamflow, although it was recognized fluctuations in streamflow would also affect channel stability. Past and current changes within the study area affect streamflow; therefore, mechanisms affecting erosion include land use disturbances, soil compaction, loss of vegetation cover, drought, less frequent and more extreme precipitation, and fires—which all intensify the potential runoff and erosion within the study area.

Colorado↗