New England wildlife: a model for ecosystem management -- ECORESEARCH
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General Technical Report↗
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Hydraulics of Alaska steeppass fishway model A40
No abstract available at this time
Research Information Bulletin↗
Potential impacts of the Black Canyon Bridge on bighorn sheep: A reevaluation using a GIS habitat evaluation model
No abstract available at this time
Technical Report↗
Development of grade and tonnage models for different deposit types
No abstract available.
Special Paper↗
Surface mine reclamation for wildlife: a model reclamation plan for southern Appalachia
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FWS/OBS↗
Description and User Manual for a Web-Based Interface to a Transit-Loss Accounting Program for Monument and Fountain Creeks, El Paso and Pueblo Counties, Colorado
The U.S. Geological Survey, in cooperation with Colorado Springs Utilities, the Colorado Water Conservation Board, and the El Paso County Water Authority, began a study in 2004 with the following objectives: (1) Apply a stream-aquifer model to Monument Creek, (2) use the results of the modeling to develop a transit-loss accounting program for Monument Creek, (3) revise an existing accounting program for Fountain Creek to easily incorporate ongoing and future changes in management of return flows of reusable water, and (4) integrate the two accounting programs into a single program and develop a Web-based interface to the integrated program that incorporates simple and reliable data entry that is automated to the fullest extent possible. This report describes the results of completing objectives (2), (3), and (4) of that study. The accounting program for Monument Creek was developed first by (1) using the existing accounting program for Fountain Creek as a prototype, (2) incorporating the transit-loss results from a stream-aquifer modeling analysis of Monument Creek, and (3) developing new output reports. The capabilities of the existing accounting program for Fountain Creek then were incorporated into the program for Monument Creek and the output reports were expanded to include Fountain Creek. A Web-based interface to the new transit-loss accounting program then was developed that provided automated data entry. An integrated system of 34 nodes and 33 subreaches was integrated by combining the independent node and subreach systems used in the previously completed stream-aquifer modeling studies for the Monument and Fountain Creek reaches. Important operational criteria that were implemented in the new transit-loss accounting program for Monument and Fountain Creeks included the following: (1) Retain all the reusable water-management capabilities incorporated into the existing accounting program for Fountain Creek; (2) enable daily accounting and transit-loss computations for a variable number of reusable return flows discharged into Monument Creek at selected locations; (3) enable diversion of all or a part of a reusable return flow at any selected node for purposes of storage in off-stream reservoirs or other similar types of reusable water management; (4) and provide flexibility in the accounting program to change the number of return-flow entities, the locations at which the return flows discharge into Monument or Fountain Creeks, or the locations to which the return flows are delivered. The primary component of the Web-based interface is a data-entry form that displays data stored in the accounting program input file; the data-entry form allows for entry and modification of new data, which then is rewritten to the input file. When the data-entry form is displayed, up-to-date discharge data for each station are automatically computed and entered on the data-entry form. Data for native return flows, reusable return flows, reusable return flow diversions, and native diversions also are entered automatically or manually, if needed. In computing the estimated quantities of reusable return flow and the associated transit losses, the accounting program uses two sets of computations. The first set of computations is made between any two adjacent streamflow-gaging stations (termed 'stream-segment loop'); the primary purpose of the stream-segment loop is to estimate the loss or gain in native discharge between the two adjacent streamflow-gaging stations. The second set of computations is made between any two adjacent nodes (termed 'subreach loop'); the actual transit-loss computations are made in the subreach loop, using the result from the stream-segment loop. The stream-segment loop is completed for a stream segment, and then the subreach loop is completed for each subreach within the segment. When the subreach loop is completed for all subreaches within a stream segment, the stream-segment loop is initiated for the ne
Scientific Investigations Report↗
Hydrogeology of the Valley-Fill Aquifer in the Onondaga Trough, Onondaga County, New York
Continuing efforts to improve water quality in Onondaga Lake, New York and its tributaries require an understanding of how the natural, brine-filled aquifer in the Onondaga Trough (valley) affects the freshwater in Onondaga Lake. The city of Syracuse, locally known as "The Salt City," was built around the salt springs, which issued from a valley-fill aquifer that contains a highly concentrated brine (up to six times as salty as sea water), but little is known about the source of the brine, its movement within the glacial sediments that partly fill the Onondaga Trough, and the interaction of the aquifer and the lake. This report summarizes initial data-collection and analysis efforts in the 25-mile long Onondaga Trough that extends from near Tully, N.Y., to the outlet of Onondaga Lake and presents results of some initial chemical and geographic analyses that will lead to the development of a mathematical ground-water-flow model of the valley-fill aquifer.
Scientific Investigations Report↗
Appendix A: Modeling appendix for the Northwestern and Southwestern pond turtle (Actinemys marmorata , Actinemys pallida )
To predict future status of the northwestern pond turtle ( Actinemys marmorata ) and southwestern pond turtle ( Actinemys pallida ) species, we developed a stochastic stage-based matrix population model to simulate future population conditions. We constructed a demographic population viability analysis for each species based on a post-breeding, single sex, stage-based life history diagram elicited from taxa experts and derived from relevant literature. Demographic parameters were based on estimates from published literature and data provided to the U.S. Fish and Wildlife Service (USFWS). Using the most recent observations of turtles, available habitat, local abundances, and current threat conditions, we calculated spatially explicit initial abundances to initialize our stochastic projection. In order to incorporate multiple types of uncertainty (ecological, parametric, temporal), we built three embedded simulation loops within the simulation model. Representing ecological uncertainty, species status was projected into the future using multiple plausible future scenarios based on two representative concentration pathways (RCP 4.5, 8.5) and two shared socioeconomic pathways (SSP 2, 5) to reflect plausible alternative future trajectories of relevant environmental conditions. Parametric uncertainty was included for survival estimates of all life stages due the inconsistency of estimates across the species’ range. Temporal variability or environmental stochasticity was included in the form of randomized variation from the mean demographic parameter values in each year of the approximately 80-year simulation. The model output included probability of extinction and estimated abundance through 2100 for each unique Analysis Unit (AU) and for the full geographic range of the species except populations in the state of Washington. The AUs in Washington are conservation dependent and sustained by a head-starting and reintroduction program. Thus, the population dynamics do not match our model for the rest of the range and therefore the Washington AUs were included in this projection modeling effort. There is already pre-existing, detailed PVA for these specific populations (Pramuk et al. 2012, p.41-60), and the Status assessment report can use those results for inference about future status. We discuss the results of Pramuk et al. (2012, p.41-61) alongside our own. Probability of extinction was overall higher for the southwestern pond turtle as compared to the northwestern species and population growth rates were strongly negative for both species (approximately -3% annually for all AUs for all scenarios). This appendix is organized into three primary sections: 1) a description of the life history, the core population dynamics model, and demographic parameters, 2) a description of methods for establishing initial abundances of the populations for the future viability modeling, and 3) a description of the methods for modeling effects of various threats on future demographic rates and the results of future conditions scenarios.
Report↗
A strategy for assessing potential future changes in climate, hydrology, and vegetation in the Western United States
Historical and geological data indicate that significant changes can occur in the Earth's climate on time scales ranging from years to millennia. In addition to natural climatic change, climatic changes may occur in the near future due to increased concentrations of carbon dioxide and other trace gases in the atmosphere that are the result of human activities. International research efforts using atmospheric general circulation models (AGCM's) to assess potential climatic conditions under atmospheric carbon dioxide concentrations of twice the pre-industrial level (a '2 X CO2' atmosphere) conclude that climate would warm on a global basis. However, it is difficult to assess how the projected warmer climatic conditions would be distributed on a regional scale and what the effects of such warming would be on the landscape, especially for temperate mountainous regions such as the Western United States. In this report, we present a strategy to assess the regional sensitivity to global climatic change. The strategy makes use of a hierarchy of models ranging from an AGCM, to a regional climate model, to landscape-scale process models of hydrology and vegetation. A 2 X CO2 global climate simulation conducted with the National Center for Atmospheric Research (NCAR) GENESIS AGCM on a grid of approximately 4.5o of latitude by 7.5o of longitude was used to drive the NCAR regional climate model (RegCM) over the Western United States on a grid of 60 km by 60 km. The output from the RegCM is used directly (for hydrologic models) or interpolated onto a 15-km grid (for vegetation models) to quantify possible future environmental conditions on a spatial scale relevant to policy makers and land managers.
Circular↗
Methods for Estimating Peak Discharges and Unit Hydrographs for Streams in the City of Charlotte and Mecklenburg County, North Carolina
Procedures for estimating peak discharges and unit hydrographs were developed for streams in the city of Charlotte and Mecklenburg County in response to a need for better techniques for characterizing the flow of streams. The procedures presented in this report provide the means for estimating unit hydrographs as part of the process used in watershed modeling and(or) design of stormwater-management structures. The procedures include three statistical relations for use in estimating storm peak discharge, unit-hydrograph peak discharge, and unit-hydrograph lag time. A final component of the procedures is the development of a dimensionless unit hydrograph developed from streamflow and rainfall data collected during the 1995-2000 water years at 25 streamgaging stations and up to 60 raingages in the city and county. The statistical relation to estimate the storm peak discharge is based on analyses of observed peak discharges regressed against rainfall and basin characteristics using a database of 412 observations from 61 storm events among the 25 gaging stations. The rainfall characteristics included basin-average rainfall amounts as well as estimates of the maximum and minimum storm rainfall in the basin. The basin characteristics consisted of land-use information and other physical basin characteristics, such as drainage area, channel length, channel slope, percentage of impervious area, and percentage of the basin served by detention. The analyses resulted in a relation that can be used for estimating storm peak discharge based on drainage area, basin-average rainfall, and impervious area. Average unit hydrographs were developed for 24 of 25 streamgaging stations, using from three to nine storms at each site. The average unit hydrograph for each station was converted into four classes of unit hydrographs with durations corresponding to one-fourth, one-third, one-half, and three-fourths of the station-average lag time. For 23 sites, the lag-time-duration hydrographs were then translated into dimensionless unit hydrographs by dividing time ordinates by the lag time and discharge ordinates by peak discharge. For each lag-time-duration class, the dimensionless unit hydrographs for the sites were combined to create an average dimensionless unit hydrograph. The four average dimensionless unit hydrographs were later tested (with estimates of unit-hydrograph peak discharges and lag times) for selection of an overall dimensionless unit hydrograph to be used at ungaged sites in the study area. The two sites where the procedures did not produce unit hydrographs that could be included in the development of the overall dimensionless unit hydrograph had the smallest drainage areas among the sites used in the investigation. The statistical relations for estimating unit-hydrograph peak discharge and lag time were developed by regressing the dependent variables against explanatory variables that describe the basin characteristics. The statistical analyses resulted in a relation for use in estimating a unit-hydrograph peak discharge based on the drainage area. The estimation of the unit-hydrograph lag time is based on the drainage area and percentage of land use in the basin classified as 'woods/brush.' Both relations have coefficients of determination (R2 values) of 0.9 or better. The three components for estimating a unit hydrograph are the dimensionless unit hydrograph and two statistical relations for estimating the unit-hydrograph peak discharge and lag time. These components were applied by using each of the four lag-time-duration average dimensionless unit hydrographs to determine which would be selected as the final overall dimensionless unit hydrograph for streams in the city of Charlotte and Mecklenburg County. Comparisons of the simulated and observed hydrographs were based on the following: (1) hydrograph width at 50 percent of the peak discharge, (2) hydrograph width at 75 percent of the peak discharge, (3) peak discharge
North Carolina↗
Predicted spatial distribution of the Eastern Spotted Skunk (Spilogale putorius) in Virginia using detection and non-detection records
The geographic distribution of a species is a fundamental component in understanding its ecology and is necessary for forming effective conservation plans. For rare and elusive species of conservation concern, accurate maps of predicted occurrence are particularly problematic and often highly subjective. Spilogale putorius (Eastern Spotted Skunk) populations have experienced large declines since the 1940s. Their elusive behavior and perceived rarity result in low detection probability when using conventional methods for sampling small mammals. Low detection probability often causes uncertainty as to where Eastern Spotted Skunks could be a management concern. We modeled the distribution of predicted occurrence of Eastern Spotted Skunks using verifiable occurrence and non-detection records obtained throughout Virginia from 2010 to 2020. Occurrence data consisted of trapping records reported to the Virginia Department of Wildlife Resources, incidental photo-verified reports of sightings and road-killed animals, and remote-camera detections. Non-detections were presumed at baited remote-camera locations following intense survey efforts. We fit predicted occurrence models using generalized linear modeling in an information-theoretic framework using the package ‘stats’ in Program R. Our results incidated a greater probability of presence from the Blue Ridge westward, increasing with slope steepness along northeastern- to southeastern-facing slopes and decreasing with slope steepness along southeastern- to southwestern-facing slopes. Emergent rock outcrops prominent along northeastern slopes offer ample protective rocky cover, whereas mixed Quercus spp. (oak), Kalmia latifolia (Mountain Laurel), and Rhododendron maximum (Rosebay Rhododendron) forest communities along southern-facing slopes provide suitable areas of cover, both of which are critical for spotted skunk survival and reproductive success. Our analysis provides insight into the relationships between landscape features and Eastern Spotted Skunk distributions across Virginia. Understanding these relationships is critical for the effective management and conservation of this vulnerable species.
Virginia↗
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↗
Aquifer systems in the Great Basin region of Nevada, Utah, and adjacent states; summary report
Findings of the Great Basin Regional Aquifer System Analysis (RASA) are summarized. The Great Basin RASA study encompasses an area of about 140,000 square miles. Regional hydrology and ground-water hydrology of the area are described. Five models of basin-fill aquifers, a ground-water flow model of the Fish Springs system, and a regional ground-water flow model of the carbonate-rock province (eastern Nevada and western Utah) are presented and discussed.
Professional Paper↗
PaCTS 1.0: A crowdsourced reporting standard for paleoclimate data
The progress of science is tied to the standardization of measurements, instruments, and data. This is especially true in the Big Data age, where analyzing large data volumes critically hinges on that data being standardized. Accordingly, the lack of community-sanctioned data standards in paleoclimatology has largely precluded the benefits of Big Data advances in the field. Building upon recent efforts to standardize the format and terminology of paleoclimate data, this article describes the Paleoclimate Community reporTing Standard (PaCTS), a crowdsourced reporting standard for such data. PaCTS captures which information should be included when reporting paleoclimate data, with the goal of maximizing the re-use value of paleoclimate datasets, particularly for synthesis work and comparison to climate model simulations. Initiated by the LinkedEarth project, this standard elicitation process involved an international workshop in 2016, various forms of digital community engagement over the next few years, and grassroots working groups. Participants in this process identified important properties across paleoclimate archives, in addition to the reporting of uncertainties and chronologies; they also identified archive-specific properties and distinguished reporting standards for new vs. legacy datasets. This work shows that at least 135 respondents overwhelmingly support a drastic increase in the amount of metadata accompanying paleoclimate datasets. Since such goals are at odds with present practices, we discuss a transparent path towards implementing or revising these recommendations in the near future, using both bottom-up and top-down approaches.
Paleoceanography and Paleoclimatology↗
The pyramid system for multiscale raster analysis
Geographical research requires the management and analysis of spatial data at multiple scales. As part of the U.S. Geological Survey's global change research program a software system has been developed that reads raster data (such as an image or digital elevation model) and produces a pyramid of aggregated lattices as well as various measurements of spatial complexity. For a given raster dataset the system uses the pyramid to report: (1) mean, (2) variance, (3) a spatial autocorrelation parameter based on multiscale analysis of variance, and (4) a monofractal scaling parameter based on the analysis of isoline lengths. The system is applied to 1-km digital elevation model (DEM) data for a 256-km2 region of central California, as well as to 64 partitions of the region. PYRAMID, which offers robust descriptions of data complexity, also is used to describe the behavior of topographic aspect with scale. ?? 1993.
Computers & Geosciences↗
Numerical simulation of groundwater and surface-water interactions in the Big River Management Area, central Rhode Island
The Rhode Island Water Resources Board is considering use of groundwater resources from the Big River Management Area in central Rhode Island because increasing water demands in Rhode Island may exceed the capacity of current sources. Previous water-resources investigations in this glacially derived, valley-fill aquifer system have focused primarily on the effects of potential groundwater-pumping scenarios on streamflow depletion; however, the effects of groundwater withdrawals on wetlands have not been assessed, and such assessments are a requirement of the State’s permitting process to develop a water supply in this area. A need for an assessment of the potential effects of pumping on wetlands in the Big River Management Area led to a cooperative agreement in 2008 between the Rhode Island Water Resources Board, the U.S. Geological Survey, and the University of Rhode Island. This partnership was formed with the goal of developing methods for characterizing wetland vegetation, soil type, and hydrologic conditions, and monitoring and modeling water levels for pre- and post-water-supply development to assess potential effects of groundwater withdrawals on wetlands. This report describes the hydrogeology of the area and the numerical simulations that were used to analyze the interaction between groundwater and surface water in response to simulated groundwater withdrawals. The results of this analysis suggest that, given the hydrogeologic conditions in the Big River Management Area, a standard 5-day aquifer test may not be sufficient to determine the effects of pumping on water levels in nearby wetlands. Model simulations showed water levels beneath Reynolds Swamp declined by about 0.1 foot after 5 days of continuous pumping, but continued to decline by an additional 4 to 6 feet as pumping times were increased from a 5-day simulation period to a simulation period representative of long-term average monthly conditions. This continued decline in water levels with increased pumping time is related to the shift from the primary source of water to the pumped wells being derived from aquifer storage during the early-time (5 days) simulation to being derived more from induced infiltration from the flooded portion of the Big River (southernmost extent of the Flat River Reservoir) during the months of March through October or from captured groundwater discharge to this portion of the Big River when the downstream Flat River Reservoir is drained for weed control during the months of November through February, as was the case for the long-term monthly conditions.
Rhode Island↗
Comparison of surrogate models to estimate pesticide concentrations at six U.S. Geological Survey National Water Quality Network sites during water years 2013–18
During water years 2013–18, the U.S. Geological Survey National Water-Quality Assessment Project sampled the National Water Quality Network for Rivers and Streams year-round and reported on 221 pesticides at 72 sites across the United States. Pesticides are difficult to measure, their concentrations often represent discrete snapshots in time, and capturing peak concentrations is expensive. Three types of regression models were developed to estimate concentrations for two selected pesticides at each of six National Water Quality Network for Rivers and Streams sites. The regression models used continuously measured streamflow and water-quality properties (differing combinations of pH, specific conductance, turbidity, and water temperature); discrete water-quality samples analyzed for atrazine, azoxystrobin, bentazon, bromacil, imidacloprid, simazine, and triclopyr; and time as an additional explanatory variable for seasonality. The modeling approaches included (1) a standard regression that included surrogates (differing combinations of pH, specific conductance, turbidity, and water temperature) and periodic functions (sine-cosine) of pesticide application use as predictor variables; (2) the seasonal wave with flow adjustment model that included a seasonal component and flow anomalies but excluded surrogates; and (3) the seasonal wave with flow adjustment model that included a seasonal component, flow anomalies, and surrogates. Models were evaluated using three measures of model performance: generalized coefficient of determination (generalized R 2 ), Akaike’s Information Criteria, and scale (the estimated standard deviation of the tobit regression error term). Because of low observation numbers, results from this study can be considered a pilot effort with the possibility that some models are overfit. In all cases, estimated pesticide concentrations modeled with base SEAWAVE-Q were better than the standard surrogate regression models; all 39 generalized R 2 values increased by 3–56 percent (median of 25 percent) when compared to the standard surrogate regression models, and all Akaike’s Information Criteria and scale values decreased. The addition of surrogate variables such as pH, specific conductance, turbidity, and water temperature to the base SEAWAVE-Q model to improve estimates of pesticide concentrations resulted in only modest improvements; generalized R 2 values increased by only 0–10 percent (median of 3 percent). In some instances, combinations of the surrogates produced more appreciative improvements in model results, but in those instances, we hypothesize that the surrogates correlated with some unknown measure that directly relates to pesticide transport.
Indiana, Kansas, Missouri, North Dakota, Oregon↗
Large scale snow water status monitoring: Comparison of different snow water products in the upper Colorado basins
We illustrate the ability to monitor the status of snow water content over large areas by using a spatially distributed snow accumulation and ablation model that uses data from a weather forecast model in the upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) data-sets; remaining meteorological model input data were from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a region of the western US that covers parts of the upper Colorado Basin. We also compared the SWE product estimated from the special sensor microwave imager (SSM/I) and scanning multichannel microwave radiometer (SMMR) to the SNODAS and SNOTEL SWE data-sets. Agreement between the spatial distributions of the simulated SWE with MPE data was high with both SNODAS and SNOTEL. Model-simulated SWE with TRMM precipitation and SWE estimated from the passive microwave imagery were not significantly correlated spatially with either SNODAS or the SNOTEL SWE. Average basin-wide SWE simulated with the MPE and the TRMM data were highly correlated with both SNODAS ( r = 0.94 and r = 0.64; d.f. = 14 – d.f. = degrees of freedom) and SNOTEL ( r = 0.93 and r = 0.68; d.f. = 14). The SWE estimated from the passive microwave imagery was significantly correlated with the SNODAS SWE ( r = 0.55, d.f. = 9, p = 0.05) but was not significantly correlated with the SNOTEL-reported SWE values ( r = 0.45, d.f. = 9, p = 0.05).The results indicate the applicability of the snow energy balance model for monitoring snow water content at regional scales when coupled with meteorological data of acceptable quality. The two snow water contents from the microwave imagery (SMMR and SSM/I) and the Utah Energy Balance forced with the TRMM precipitation data were found to be unreliable sources for mapping SWE in the study area; both data sets lacked discernible variability of snow water content between sites as seen in the SNOTEL and SNODAS SWE data. This study will contribute to better understanding the adequacy of data from weather forecast models, TRMM, and microwave imagery for monitoring status of the snow water content.
Colorado, Utah, Wyoming↗