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USGS2006

Kendall-Theil Robust Line (KTRLine--version 1.0)-A Visual Basic Program for Calculating and Graphing Robust Nonparametric Estimates of Linear-Regression Coefficients Between Two Continuous Variables

The Kendall-Theil Robust Line software (KTRLine-version 1.0) is a Visual Basic program that may be used with the Microsoft Windows operating system to calculate parameters for robust, nonparametric estimates of linear-regression coefficients between two continuous variables. The KTRLine software was developed by the U.S. Geological Survey, in cooperation with the Federal Highway Administration, for use in stochastic data modeling with local, regional, and national hydrologic data sets to develop planning-level estimates of potential effects of highway runoff on the quality of receiving waters. The Kendall-Theil robust line was selected because this robust nonparametric method is resistant to the effects of outliers and nonnormality in residuals that commonly characterize hydrologic data sets. The slope of the line is calculated as the median of all possible pairwise slopes between points. The intercept is calculated so that the line will run through the median of input data. A single-line model or a multisegment model may be specified. The program was developed to provide regression equations with an error component for stochastic data generation because nonparametric multisegment regression tools are not available with the software that is commonly used to develop regression models. The Kendall-Theil robust line is a median line and, therefore, may underestimate total mass, volume, or loads unless the error component or a bias correction factor is incorporated into the estimate. Regression statistics such as the median error, the median absolute deviation, the prediction error sum of squares, the root mean square error, the confidence interval for the slope, and the bias correction factor for median estimates are calculated by use of nonparametric methods. These statistics, however, may be used to formulate estimates of mass, volume, or total loads. The program is used to read a two- or three-column tab-delimited input file with variable names in the first row and data in subsequent rows. The user may choose the columns that contain the independent (X) and dependent (Y) variable. A third column, if present, may contain metadata such as the sample-collection location and date. The program screens the input files and plots the data. The KTRLine software is a graphical tool that facilitates development of regression models by use of graphs of the regression line with data, the regression residuals (with X or Y), and percentile plots of the cumulative frequency of the X variable, Y variable, and the regression residuals. The user may individually transform the independent and dependent variables to reduce heteroscedasticity and to linearize data. The program plots the data and the regression line. The program also prints model specifications and regression statistics to the screen. The user may save and print the regression results. The program can accept data sets that contain up to about 15,000 XY data points, but because the program must sort the array of all pairwise slopes, the program may be perceptibly slow with data sets that contain more than about 1,000 points.

Techniques and Methods↗

USGS2006

User guide to the UNC process and three utility programs for computation of nonlinear confidence and prediction intervals using MODFLOW-2000

This report introduces and documents the Uncertainty (UNC) Process, a new Process in MODFLOW-2000 that calculates uncertainty measures for model parameters and for predictions produced by the model. Uncertainty measures can be computed by various methods, but when regression is applied to calibrate a model (for example when using the Parameter-Estimation Process of MODFLOW-2000) it is advantageous to also use regression-based methods to quantify uncertainty. For this reason the UNC Process computes (1) confidence intervals for parameters of the Parameter-Estimation Process and (2) confidence and prediction intervals for most types of functions that can be computed by a MODFLOW-2000 model calibrated by the Parameter-Estimation Process. The types of functions for which the Process works include hydraulic heads, hydraulic head differences, head-dependent flows computed by the head-dependent flow packages for drains (DRN6), rivers (RIV6), general-head boundaries (GHB6), streams (STR6), drain-return cells (DRT1), and constant-head boundaries (CHD), and for differences between flows computed by any of the mentioned flow packages. The UNC Process does not allow computation of intervals for the difference between flows computed by two different flow packages. The report also documents three programs, RESAN2-2k, BEALE2-2k, and CORFAC-2k, which are valuable for the evaluation of results from the Parameter-Estimation Process and for the preparation of input values for the UNC Process. RESAN2-2k and BEALE2-2k are significant updates of the residual analysis and modified Beale's measure programs first published by Cooley and Naff (1990) and later modified for use with MODFLOWP (Hill, 1994) and MODFLOW-2000 (Hill and others, 2000). CORFAC-2k is a new program that computes correction factors to be used by UNC.

Techniques and Methods↗

USGS2005

Documentation of the Streamflow-Routing (SFR2) Package to Include Unsaturated Flow Beneath Streams - A Modification to SFR1

Many streams in the United States, especially those in semiarid regions, have reaches that are hydraulically disconnected from underlying aquifers. Ground-water withdrawals have decreased water levels in valley aquifers beneath streams, increasing the occurrence of disconnected streams and aquifers. The U.S. Geological Survey modular ground-water model (MODFLOW-2000) can be used to model these interactions using the Streamflow-Routing (SFR1) Package. However, the approach does not consider unsaturated flow between streams and aquifers and may not give realistic results in areas with significantly deep unsaturated zones. This documentation describes a method for extending the capabilities of MODFLOW-2000 by incorporating the ability to simulate unsaturated flow beneath streams. A kinematic-wave approximation to Richards' equation was solved by the method of characteristics to simulate unsaturated flow beneath streams in SFR1. This new package, called SFR2, includes all the capabilities of SFR1 and is designed to be used with MODFLOW-2000. Unlike SFR1, seepage loss from the stream may be restricted by the hydraulic conductivity of the unsaturated zone. Unsaturated flow is simulated independently of saturated flow within each model cell corresponding to a stream reach whenever the water table (head in MODFLOW) is below the elevation of the streambed. The relation between unsaturated hydraulic conductivity and water content is defined by the Brooks-Corey function. Unsaturated flow variables specified in SFR2 include saturated and initial water contents; saturated vertical hydraulic conductivity; and the Brooks-Corey exponent. These variables are defined independently for each stream reach. Unsaturated flow in SFR2 was compared to the U.S. Geological Survey's Variably Saturated Two-Dimensional Flow and Transport (VS2DT) Model for two test simulations. For both test simulations, results of the two models were in good agreement with respect to the magnitude and downward progression of a wetting front through an unsaturated column. A third hypothetical simulation is presented that includes interaction between a stream and aquifer separated by an unsaturated zone. This simulation is included to demonstrate the utility of unsaturated flow in SFR2 with MODFLOW-2000. This report includes a description of the data input requirements for simulating unsaturated flow in SFR2.

Techniques and Methods↗

USGS2012

Advanced methods for modeling water-levels and estimating drawdowns with SeriesSEE, an Excel add-in

Water-level modeling is used for multiple-well aquifer tests to reliably differentiate pumping responses from natural water-level changes in wells, or “environmental fluctuations.” Synthetic water levels are created during water-level modeling and represent the summation of multiple component fluctuations, including those caused by environmental forcing and pumping. Pumping signals are modeled by transforming step-wise pumping records into water-level changes by using superimposed Theis functions. Water-levels can be modeled robustly with this Theis-transform approach because environmental fluctuations and pumping signals are simulated simultaneously. Water-level modeling with Theis transforms has been implemented in the program SeriesSEE, which is a Microsoft® Excel add-in. Moving average, Theis, pneumatic-lag, and gamma functions transform time series of measured values into water-level model components in SeriesSEE. Earth tides and step transforms are additional computed water-level model components. Water-level models are calibrated by minimizing a sum-of-squares objective function where singular value decomposition and Tikhonov regularization stabilize results. Drawdown estimates from a water-level model are the summation of all Theis transforms minus residual differences between synthetic and measured water levels. The accuracy of drawdown estimates is limited primarily by noise in the data sets, not the Theis-transform approach. Drawdowns much smaller than environmental fluctuations have been detected across major fault structures, at distances of more than 1 mile from the pumping well, and with limited pre-pumping and recovery data at sites across the United States. In addition to water-level modeling, utilities exist in SeriesSEE for viewing, cleaning, manipulating, and analyzing time-series data.

Techniques and Methods↗

USGS2010

Land-Use Portfolio Modeler, Version 1.0

Natural hazards pose significant threats to the public safety and economic health of many communities throughout the world. Community leaders and decision-makers continually face the challenges of planning and allocating limited resources to invest in protecting their communities against catastrophic losses from natural-hazard events. Public efforts to assess community vulnerability and encourage loss-reduction measures through mitigation often focused on either aggregating site-specific estimates or adopting standards based upon broad assumptions about regional risks. The site-specific method usually provided the most accurate estimates, but was prohibitively expensive, whereas regional risk assessments were often too general to be of practical use. Policy makers lacked a systematic and quantitative method for conducting a regional-scale risk assessment of natural hazards. In response, Bernknopf and others developed the portfolio model, an intermediate-scale approach to assessing natural-hazard risks and mitigation policy alternatives. The basis for the portfolio-model approach was inspired by financial portfolio theory, which prescribes a method of optimizing return on investment while reducing risk by diversifying investments in different security types. In this context, a security type represents a unique combination of features and hazard-risk level, while financial return is defined as the reduction in losses resulting from an investment in mitigation of chosen securities. Features are selected for mitigation and are modeled like investment portfolios. Earth-science and economic data for the features are combined and processed in order to analyze each of the portfolios, which are then used to evaluate the benefits of mitigating the risk in selected locations. Ultimately, the decision maker seeks to choose a portfolio representing a mitigation policy that maximizes the expected return-on-investment, while minimizing the uncertainty associated with that return-on-investment. The portfolio model, now known as the Land-Use Portfolio Model (LUPM), provided the framework for the development of the Land-Use Portfolio Modeler, Version 1.0 software (LUPM v1.0). The software provides a geographic information system (GIS)-based modeling tool for evaluating alternative risk-reduction mitigation strategies for specific natural-hazard events. The modeler uses information about a specific natural-hazard event and the features exposed to that event within the targeted study region to derive a measure of a given mitigation strategy`s effectiveness. Harnessing the spatial capabilities of a GIS enables the tool to provide a rich, interactive mapping environment in which users can create, analyze, visualize, and compare different

Techniques and Methods↗

USGS2007

Landbird Monitoring Protocol for National Parks in the North Coast and Cascades Network

This protocol narrative outlines the rationale, sampling design and methods for monitoring landbirds in the North Coast and Cascades Network (NCCN) during the breeding season. The NCCN, one of 32 networks of parks in the National Park System, comprises seven national park units in the Pacific Northwest, including three large, mountainous, natural area parks (Mount Rainier [MORA] and Olympic [OLYM] National Parks, North Cascades National Park Service Complex [NOCA]), and four small historic cultural parks (Ebey's Landing National Historical Reserve [EBLA], Lewis and Clark National Historical Park [LEWI], Fort Vancouver National Historical Park [FOVA], and San Juan Island National Historical Park [SAJH]). The protocol reflects decisions made by the NCCN avian monitoring group, which includes NPS representatives from each of the large parks in the Network as well as personnel from the U.S. Geological Survey Forest and Rangeland Ecosystem Science Center (USGS-FRESC) Olympic Field Station, and The Institute for Bird Populations, at meetings held between 2000 (Siegel and Kuntz, 2000) and 2005. The protocol narrative describes the monitoring program in relatively broad terms, and its structure and content adhere to the outline and recommendations developed by Oakley and others (2003) and adopted by NPS. Finer details of the methodology are addressed in a set of standard operating procedures (SOPs) that accompany the protocol narrative. We also provide appendixes containing additional supporting materials that do not clearly belong in either the protocol narrative or the standard operating procedures.

Techniques and Methods↗

USGS2023

Initial comparison of pollen counting methods using precipitation and ambient air samples and automated artificial intelligence to support national monitoring objectives

Given the endemic nature of pollen throughout the environment, the impact upon human health, and the need for more extensive and better measurements of pollen in the USA, a preliminary project within the National Atmospheric Deposition Program’s (NADP) National Trends Network (NTN) was developed. Pollen was measured in ambient air by several methods and in precipitation wet deposition samples at three monitoring sites in the NTN. A method for counting pollen on filters was developed and provided pollen counts for NADP atmospheric wet-deposition samples and high-volume ambient air samplers (HVAS) for comparison with co-located traditional National Allergy Bureau microscopy samples and a commercially available pollen sensor (PS) counting method during the 2021 pollen season. The goals of this project were to test the potential of available air-monitoring infrastructures to obtain improved spatial measurements of aeroallergens, compare pollen counting results from the various methods, and to determine the suitability of using wet deposition samples for pollen collection. The onset and senescence of pollen seasons for general categories of genera compared favorably for each method at each site, indicating that pollen monitoring using wet-deposition and ambient air sampling filters could provide useful information to inform scientific studies, but not likely for public health objectives. Pollen counts were log transformed for Pearson product moment correlation. Tree pollen counts were correlated at all sites for daily PS data and traditional counting data ( R = 0.69–0.84), but statistical correlations between methods for grass and weed pollen were weak (0.40 < R < 0.60) or considered not correlated ( R < 0.40). Total pollen counts in NADP precipitation samples were correlated with traditional and PS counts at only one of three sites. Pollen counts for the weekly HVAS filter samples were correlated with PS counts for trees ( R = 0.62) and with NAB counts for trees ( R = 0.68) and weeds ( R = 0.72). Correlations in the data between methods suggest that, given further methods development, a variety of techniques could be integrated to expand and enhance existing pollen monitoring networks. Improved ambient air and atmospheric deposition sampling methods specifically targeted for pollen capture and analysis could support the collection of accurate and efficient meaningful aeroallergen data from existing atmospheric monitoring networks.

Aerobiologia↗

USGS2008

The critical role of volcano monitoring in risk reduction

Data from volcano-monitoring studies constitute the only scientifically valid basis for short-term forecasts of a future eruption, or of possible changes during an ongoing eruption. Thus, in any effective hazards-mitigation program, a basic strategy in reducing volcano risk is the initiation or augmentation of volcano monitoring at historically active volcanoes and also at geologically young, but presently dormant, volcanoes with potential for reactivation. Beginning with the 1980s, substantial progress in volcano-monitoring techniques and networks - ground-based as well space-based - has been achieved. Although some geochemical monitoring techniques (e.g., remote measurement of volcanic gas emissions) are being increasingly applied and show considerable promise, seismic and geodetic methods to date remain the techniques of choice and are the most widely used. Availability of comprehensive volcano-monitoring data was a decisive factor in the successful scientific and governmental responses to the reawakening of Mount St. Helens (Washington, USA) in 1980 and, more recently, to the powerful explosive eruptions at Mount Pinatubo (Luzon, Philippines) in 1991. However, even with the ever-improving state-ofthe-art in volcano monitoring and predictive capability, the Mount St. Helens and Pinatubo case histories unfortunately still represent the exceptions, rather than the rule, in successfully forecasting the most likely outcome of volcano unrest.

Advances in Geosciences↗

USGS2013

Stochastic empirical loading and dilution model (SELDM) version 1.0.0

The Stochastic Empirical Loading and Dilution Model (SELDM) is designed to transform complex scientific data into meaningful information about the risk of adverse effects of runoff on receiving waters, the potential need for mitigation measures, and the potential effectiveness of such management measures for reducing these risks. The U.S. Geological Survey developed SELDM in cooperation with the Federal Highway Administration to help develop planning-level estimates of event mean concentrations, flows, and loads in stormwater from a site of interest and from an upstream basin. Planning-level estimates are defined as the results of analyses used to evaluate alternative management measures; planning-level estimates are recognized to include substantial uncertainties (commonly orders of magnitude). SELDM uses information about a highway site, the associated receiving-water basin, precipitation events, stormflow, water quality, and the performance of mitigation measures to produce a stochastic population of runoff-quality variables. SELDM provides input statistics for precipitation, prestorm flow, runoff coefficients, and concentrations of selected water-quality constituents from National datasets. Input statistics may be selected on the basis of the latitude, longitude, and physical characteristics of the site of interest and the upstream basin. The user also may derive and input statistics for each variable that are specific to a given site of interest or a given area. SELDM is a stochastic model because it uses Monte Carlo methods to produce the random combinations of input variable values needed to generate the stochastic population of values for each component variable. SELDM calculates the dilution of runoff in the receiving waters and the resulting downstream event mean concentrations and annual average lake concentrations. Results are ranked, and plotting positions are calculated, to indicate the level of risk of adverse effects caused by runoff concentrations, flows, and loads on receiving waters by storm and by year. Unlike deterministic hydrologic models, SELDM is not calibrated by changing values of input variables to match a historical record of values. Instead, input values for SELDM are based on site characteristics and representative statistics for each hydrologic variable. Thus, SELDM is an empirical model based on data and statistics rather than theoretical physiochemical equations. SELDM is a lumped parameter model because the highway site, the upstream basin, and the lake basin each are represented as a single homogeneous unit. Each of these source areas is represented by average basin properties, and results from SELDM are calculated as point estimates for the site of interest. Use of the lumped parameter approach facilitates rapid specification of model parameters to develop planning-level estimates with available data. The approach allows for parsimony in the required inputs to and outputs from the model and flexibility in the use of the model. For example, SELDM can be used to model runoff from various land covers or land uses by using the highway-site definition as long as representative water quality and impervious-fraction data are available.

Techniques and Methods↗

USGS2019

User’s guide for Assessment Tract Aggregation GUI (ATA GUI)—A graphical user interface for the AggtEx.fn R script

The U.S. Geological Survey three-part method for mineral resource assessments estimates numbers of undiscovered mineral deposits as probability distributions in geologically defined regions termed “permissive tracts.” This report describes a graphical user interface (GUI) script developed in open-source statistical software (R) that aggregates estimated undiscovered deposits of a given type from two or more permissive tracts using the AggtEx.fn R script. The AggtEx.fn R script aggregates undiscovered deposit estimates assuming independence, total dependence, or some degree of correlation among aggregated areas, given a user-specified correlation matrix. The script outputs three sets of aggregated estimates based on those three assumptions. The GUI script described in this report, Assessment Tract Aggregation GUI (ATA GUI), provides an easy-to-use tool that supports implementation of the AggtEx.fn R script, installation of the R packages needed to run the application, and creation of a combined input file from individual files generated by the MapMark4GUI software. Users can also use EMINERS output information by creating a file of output values following the MapMark4GUI output file format. The probabilistic estimates of aggregated undiscovered deposits produced by ATA GUI can be used as input for MapMark4GUI to estimate contained resources for the aggregated tracts. MapMark4GUI uses Monte Carlo simulation to combine undiscovered deposit estimates with tonnage and grade models to simulate undiscovered mineral resources for a region of interest. This simulation includes the amounts of commodities and rock that could be present within a permissive tract. This report includes instructions on installing and running the ATA GUI script and describes the input and output files used and created during the aggregation process.

Techniques and Methods↗

USGS2022

U.S. Geological Survey Hydrologic Toolbox — A graphical and mapping interface for analysis of hydrologic data

The Hydrologic Toolbox is a Windows-based desktop software program that provides a graphical and mapping interface for analysis of hydrologic time-series data with a set of widely used and standardized computational methods. The software combines the analytical and statistical functionality provided in the U.S. Geological Survey Groundwater and Surface-Water Toolboxes and provides several enhancements to these programs. The main analytical methods are the computation of hydrologic-frequency statistics such as the 7-day minimum flow that occurs on average only once every 10 years (7Q10); the computation of design flows, including biologically based flows; the computation of flow-duration curves and duration hydrographs; eight computer-programming methods for hydrograph separation of a streamflow time series, including the Base-Flow Index (BFI), HYSEP, PART, and SWAT Bflow methods and Eckhardt’s two-parameter digital-filtering method; and the RORA recession-curve displacement method and associated RECESS program to estimate groundwater-recharge values from streamflow data. Several of the statistical methods provided in the Hydrologic Toolbox are used primarily for computation of critical low-flow statistics. The Hydrologic Toolbox also facilitates retrieval of streamflow and groundwater-level time-series data from the U.S. Geological Survey National Water Information System and outputs text reports that describe their analyses. The Hydrologic Toolbox was developed by use of the DotSpatial geographic information system (GIS) programming library, which is part of the MapWindow project. DotSpatial is a nonproprietary, open-source program written for the .NET framework that includes a spatial data viewer and GIS capabilities. Advantages of the DotSpatial system include its pure .NET implementation for both the user interface and the GIS mapping engine, and thus the DotSpatial system simplifies software deployment and installation. In addition to combining the functionality of the separate Groundwater and Surface-Water Toolboxes, the Hydrologic Toolbox also organizes the functionality by theme (Groundwater Tools, Surface-Water Tools, and general Time-Series Tools). This report provides a description of how to build a Hydrologic Toolbox project and to download and manage hydrologic time-series data. It includes an overview of the analytical and statistical capabilities of the Hydrologic Toolbox and highlights the primary differences between the Hydrologic Toolbox and the Groundwater and Surface-Water Toolboxes. The report supplements information available in an extensive online Help manual and is intended to provide a set of instructions that will allow users to quickly develop skills to use the mapping, data-retrieval, and computational tools of the program.

Techniques and Methods↗

USGS2015

Scoops3D: software to analyze 3D slope stability throughout a digital landscape

The computer program, Scoops3D, evaluates slope stability throughout a digital landscape represented by a digital elevation model (DEM). The program uses a three-dimensional (3D) method of columns approach to assess the stability of many (typically millions) potential landslides within a user-defined size range. For each potential landslide (or failure), Scoops3D assesses the stability of a rotational, spherical slip surface encompassing many DEM cells using a 3D version of either Bishop’s simplified method or the Ordinary (Fellenius) method of limit-equilibrium analysis. Scoops3D has several options for the user to systematically and efficiently search throughout an entire DEM, thereby incorporating the effects of complex surface topography. In a thorough search, each DEM cell is included in multiple potential failures, and Scoops3D records the lowest stability (factor of safety) for each DEM cell, as well as the size (volume or area) associated with each of these potential landslides. It also determines the least-stable potential failure for the entire DEM. The user has a variety of options for building a 3D domain, including layers or full 3D distributions of strength and pore-water pressures, simplistic earthquake loading, and unsaturated suction conditions. Results from Scoops3D can be readily incorporated into a geographic information system (GIS) or other visualization software. This manual includes information on the theoretical basis for the slope-stability analysis, requirements for constructing and searching a 3D domain, a detailed operational guide (including step-by-step instructions for using the graphical user interface [GUI] software, Scoops3D-i) and input/output file specifications, practical considerations for conducting an analysis, results of verification tests, and multiple examples illustrating the capabilities of Scoops3D. Easy-to-use software installation packages are available for the Windows or Macintosh operating systems; these packages install the compiled Scoops3D program, the GUI (Scoops3D-i), and associated documentation. Several Scoops3D examples, including all input and output files, are available as well. The source code is written in the Fortran 90 language and can be compiled to run on any computer operating system with an appropriate compiler.

Techniques and Methods↗

USGS2007

MMA, A Computer Code for Multi-Model Analysis

This report documents the Multi-Model Analysis (MMA) computer code. MMA can be used to evaluate results from alternative models of a single system using the same set of observations for all models. As long as the observations, the observation weighting, and system being represented are the same, the models can differ in nearly any way imaginable. For example, they may include different processes, different simulation software, different temporal definitions (for example, steady-state and transient models could be considered), and so on. The multiple models need to be calibrated by nonlinear regression. Calibration of the individual models needs to be completed before application of MMA. MMA can be used to rank models and calculate posterior model probabilities. These can be used to (1) determine the relative importance of the characteristics embodied in the alternative models, (2) calculate model-averaged parameter estimates and predictions, and (3) quantify the uncertainty of parameter estimates and predictions in a way that integrates the variations represented by the alternative models. There is a lack of consensus on what model analysis methods are best, so MMA provides four default methods. Two are based on Kullback-Leibler information, and use the AIC (Akaike Information Criterion) or AICc (second-order-bias-corrected AIC) model discrimination criteria. The other two default methods are the BIC (Bayesian Information Criterion) and the KIC (Kashyap Information Criterion) model discrimination criteria. Use of the KIC criterion is equivalent to using the maximum-likelihood Bayesian model averaging (MLBMA) method. AIC, AICc, and BIC can be derived from Frequentist or Bayesian arguments. The default methods based on Kullback-Leibler information have a number of theoretical advantages, including that they tend to favor more complicated models as more data become available than do the other methods, which makes sense in many situations. Many applications of MMA will be well served by the default methods provided. To use the default methods, the only required input for MMA is a list of directories where the files for the alternate models are located. Evaluation and development of model-analysis methods are active areas of research. To facilitate exploration and innovation, MMA allows the user broad discretion to define alternatives to the default procedures. For example, MMA allows the user to (a) rank models based on model criteria defined using a wide range of provided and user-defined statistics in addition to the default AIC, AICc, BIC, and KIC criteria, (b) create their own criteria using model measures available from the code, and (c) define how each model criterion is used to calculate related posterior model probabilities. The default model criteria rate models are based on model fit to observations, the number of observations and estimated parameters, and, for KIC, the Fisher information matrix. In addition, MMA allows the analysis to include an evaluation of estimated parameter values. This is accomplished by allowing the user to define unreasonable estimated parameter values or relative estimated parameter values. An example of the latter is that it may be expected that one parameter value will be less than another, as might be the case if two parameters represented the hydraulic conductivity of distinct materials such as fine and coarse sand. Models with parameter values that violate the user-defined conditions are excluded from further consideration by MMA. Ground-water models are used as examples in this report, but MMA can be used to evaluate any set of models for which the required files have been produced. MMA needs to read files from a separate directory for each alternative model considered. The needed files are produced when using the Sensitivity-Analysis or Parameter-Estimation mode of UCODE_2005, or, possibly, the equivalent capability of another program. MMA is constructed using

Techniques and Methods↗

USGS2014

Automated determination of the stable carbon isotopic composition (δ 13 C) of total dissolved inorganic carbon (DIC) and total nonpurgeable dissolved organic carbon (DOC) in aqueous samples: RSIL lab codes 1851 and 1852

The purposes of the Reston Stable Isotope Laboratory (RSIL) lab codes 1851 and 1852 are to determine the total carbon mass and the ratio of the stable isotopes of carbon (δ 13 C) for total dissolved inorganic carbon (DIC, lab code 1851) and total nonpurgeable dissolved organic carbon (DOC, lab code 1852) in aqueous samples. The analysis procedure is automated according to a method that utilizes a total carbon analyzer as a peripheral sample preparation device for analysis of carbon dioxide (CO 2 ) gas by a continuous-flow isotope ratio mass spectrometer (CF-IRMS). The carbon analyzer produces CO 2 and determines the carbon mass in parts per million (ppm) of DIC and DOC in each sample separately, and the CF-IRMS determines the carbon isotope ratio of the produced CO 2 . This configuration provides a fully automated analysis of total carbon mass and δ 13 C with no operator intervention, additional sample preparation, or other manual analysis. To determine the DIC, the carbon analyzer transfers a specified sample volume to a heated (70 °C) reaction vessel with a preprogrammed volume of 10% phosphoric acid (H 3 PO 4 ), which allows the carbonate and bicarbonate species in the sample to dissociate to CO 2 . The CO 2 from the reacted sample is subsequently purged with a flow of helium gas that sweeps the CO 2 through an infrared CO 2 detector and quantifies the CO 2 . The CO 2 is then carried through a high-temperature (650 °C) scrubber reactor, a series of water traps, and ultimately to the inlet of the mass spectrometer. For the analysis of total dissolved organic carbon, the carbon analyzer performs a second step on the sample in the heated reaction vessel during which a preprogrammed volume of sodium persulfate (Na 2 S 2 O 8 ) is added, and the hydroxyl radicals oxidize the organics to CO 2 . Samples containing 2 ppm to 30,000 ppm of carbon are analyzed. The precision of the carbon isotope analysis is within 0.3 per mill for DIC, and within 0.5 per mill for DOC.

Techniques and Methods↗

USGS2017

Documentation for the “XT3D” option in the Node Property Flow (NPF) Package of MODFLOW 6

This report describes the “XT3D” option in the Node Property Flow (NPF) Package of MODFLOW 6. The XT3D option extends the capabilities of MODFLOW by enabling simulation of fully three-dimensional anisotropy on regular or irregular grids in a way that properly takes into account the full, three-dimensional conductivity tensor. It can also improve the accuracy of groundwater-flow simulations in cases in which the model grid violates certain geometric requirements. Three example problems demonstrate the use of the XT3D option to simulate groundwater flow on irregular grids and through three-dimensional porous media with anisotropic hydraulic conductivity. Conceptually, the XT3D method of estimating flow between two MODFLOW 6 model cells can be viewed in terms of three main mathematical steps: construction of head-gradient estimates by interpolation; construction of fluid-flux estimates by application of the full, three-dimensional form of Darcy’s Law, in which the conductivity tensor can be heterogeneous and anisotropic; and construction of the flow expression by enforcement of continuity of flow across the cell interface. The resulting XT3D flow expression, which relates the flow across the cell interface to the values of heads computed at neighboring nodes, is the sum of terms in which conductance-like coefficients multiply head differences, as in the conductance-based flow expression the NPF Package uses by default. However, the XT3D flow expression contains terms that involve “neighbors of neighbors” of the two cells for which the flow is being calculated. These additional terms have no analog in the conductance-based formulation. When assembled into matrix form, the XT3D formulation results in a larger stencil than the conductance-based formulation; that is, each row of the coefficient matrix generally contains more nonzero elements. The “RHS” suboption can be used to avoid expanding the stencil by placing the additional terms on the right-hand side of the matrix equation and evaluating them at the previous iteration or time step. The XT3D option can be an alternative to the Ghost-Node Correction (GNC) Package. However, the XT3D formulation is typically more computationally intensive than the conductance-based formulation the NPF Package uses by default, either with or without ghost nodes. Before deciding whether to use the GNC Package or XT3D option for production runs, the user should consider whether the conductance-based formulation alone can provide acceptable accuracy for the particular problem being solved.

Techniques and Methods↗

USGS2022

Methods for evaluating Gap Analysis Project habitat distribution maps with species occurrence data

The National Gap Analysis Project created species habitat distribution models for all terrestrial vertebrates in the United States to support conservation assessments and explore patterns of species richness. Those models link species to specific habitats throughout the range of each species. For most vertebrates, there are not enough occurrence data to drive inductive, range-wide species habitat distribution models at high spatial and thematic resolution. However, it is possible to use occurrence data for model evaluation. The combination of citizen science, formal species survey work, and digitized specimen archives are making millions of observations available to the scientific community. Our challenge is to combine the mostly unstructured data into metrics that help us characterize and understand patterns of biodiversity. In this work, we propose two model-evaluation metrics. The first, a buffer proportion assessment, is based on the proportion of habitat in the range relative to the mean proportion of habitat around each of the species’ occurrence records. The second is a measure of the sensitivity (proportion of true presence) to buffer distances around occurrence records. The buffer proportion is a modification of model prevalence versus point prevalence metric, whereby comparison to a null model allows us to determine if the model performs better or worse than random. In this report, we describe the workflow used to compile and filter the species occurrence records from online resources (for example, the Global Biodiversity Information Facility) and show results for a single species, Desmognathus quadramaculatus (black-bellied salamander). For the salamander, 222 occurrence points met our criteria for inclusion in the evaluation. We found the model performed better than random with a buffer proportion index of 1.745, indicating about 5 times as much habitat was found adjacent to known occurrence records than would be expected from randomly located sites throughout the range. Sensitivity increased with larger buffer distances and leveled off to around 0.7 between 1,000- and 2,000-meter buffer distances, indicating the model is likely best suited for scales exceeding 1,000 meters. We plan to report the buffer proportion assessment and sensitivity metrics along with the full species model reports to increase understanding of the model’s performance and to use the metrics to help prioritize revisions to the models.

Alabama, Georgia, North Carolina, Tennessee, Virgi↗

USGS2012

Determination of the δ 2 H and δ 18 O of soil water and water in plant matter; RSIL lab code 1700

The purpose of the Reston Stable Isotope Laboratory lab code 1700 is to determine the δ 2 H/ 1 H), abbreviated as δ 2 H, and the δ 18 O/ 16 O), abbreviated as δ 18 O, of soil water and water in plant matter. This method is based on the observation that water and toluene form an azeotropic mixture at 84.1 °C. This temperature is substantially lower than the boiling points of water (100 °C) and toluene (110 °C), but water and toluene are immiscible at ambient temperature. The water content of a soil or plant is determined by weighing, drying, and reweighing a small amount of sample. Sufficient sample to collect 3 to 5 milliliters of water after distillation is loaded into a distillation flask. Sufficient toluene is added so that the sample is immersed throughout the entire distillation to minimize evaporation of water, which would affect the δ 2 H and δ 18 O values. The mixture of sample and toluene is heated in a flask to its boiling point (84.1 °C) so that water from the sample and toluene can distill together into a specially designed collection funnel. The temperature of 84.1 °C is maintained until the water has been quantitatively transferred to the collection funnel, at which time the temperature is raised to the boiling point of the remaining component (toluene, 110 °C). The collection funnel is maintained at ambient temperature so that the sample water and toluene can be separated physically. After separation, the sample water is purified by addition of paraffin wax to the container with the sample water, capping the container, and heating to approximately 60 °C to melt the wax. Trace amounts of toluene will dissolve in the wax, purifying the sample water for isotopic analysis. The isotopic composition of the purified water is then determined by equilibration with gaseous hydrogen or carbon dioxide, followed by dual-inlet isotope-ratio mass spectrometry. Because laser-absorption spectrometry is sensitive to organic compounds, such as trace toluene remaining in water samples, water samples should be analyzed for isotopic composition only by mass spectrometry and not by laser-absorption spectrometry.

Techniques and Methods↗

USGS2023

White-Nose Syndrome Diagnostic Laboratory Network handbook

When responding to a wildlife disease outbreak, managers depend on consistent and clear data to make decisions. However, diagnostic methods for detecting pathogens of wildlife often lack the level of procedural and interpretational standardization that occurs in the investigation of human and domestic animal diseases. This lack of standardization can hamper diagnostic reliability in two ways. First is the inappropriate application of tests to new species or in situations that are outside of the original (in other words, validated) purpose. Second is the use of laboratory-specific modifications or analytical parameters without thorough investigation of how those changes affect result comparisons across institutions or the ability to make broader conclusions about pathogen or disease. White-nose syndrome (WNS) is a disease caused by the fungal pathogen Pseudogymnoascus destructans ( Pd ), which has spread rapidly and is causing population-level declines in some species of North American bats. During the last decade, quantitative polymerase chain reaction (qPCR) has become the most common method of testing for Pd because of qPCR’s speed, accuracy, and simplicity across a wide range of invasive and noninvasive sample types. Its widespread use by many State, Federal, Provincial, and academic institutions has inevitably led to variations in methodology and interpretation among laboratories. The progressive geographic spread of fungus and disease has also led to sampling contexts and strategies that differ from those for which the qPCR assay was originally developed and validated. These factors have resulted in inconsistencies among results tested in different laboratories and, subsequently, confusion for managers and decision makers. To address these challenges, the WNS National Response Team Diagnostic Working Group launched a project congruent with increased calls for the harmonization of wildlife disease diagnostic results, and reporting standards across disparate methodologies and laboratories. Beginning in 2019, interlaboratory testing was done to better understand how variations to Pd qPCR methodology affect diagnostic consistency and to reassess the assay’s fit for purpose in new testing contexts. This information led to expanded conversations within the Diagnostic Working Group related to best practices in Pd qPCR diagnostic testing, the development of common interpretation language for classifying test results, and the incorporation of that language into an updated WNS case definition. This handbook is the resulting product and is intended to help further harmonize Pd qPCR diagnostic testing by establishing recommendations related to voluntary participation in a WNS Diagnostic Laboratory Network, documenting the currently (2022) practiced Pd qPCR methodologies, discussing general best practices for molecular diagnostics and laboratory networks, and elaborating on the epidemiologic and diagnostic basis of the agreed-upon classification language for Pd qPCR results. Through this voluntary, consensus-based approach to diagnostic harmonization, this work aims to improve the confidence of management agencies in reported Pd qPCR results and can serve as an example of national diagnostic coordination for other unregulated wildlife diseases.

Techniques and Methods↗