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USGS2018

Modeling resource selection of bobcats (Lynx rufus) and vertebrate species distributions in Orange County, southern California

For nature reserves in urban settings, wildlife and wildlife habitats may be affected by recreational activities and intensive, adjacent development. Sustaining biodiversity in such reserves is a challenge for land and natural resource managers, but identification of core areas and key resources for wildlife species may help in planning for current and emerging threats. To help identify core areas and resources, we conducted spatial analyses and predictive modeling of vertebrate distributions for a network of nature reserves in densely populated Orange County, California. We primarily focused on bobcats ( Lynx rufus ), a species with a strong association with natural habitat. Bobcat space use has been correlated with broad, simple land-use categories, but relatively little is known about the influence of greater landscape complexity on habitat suitability for bobcats. To examine habitat selection by bobcats, we developed spatial data layers representing environmental factors that might influence this species, and we used previously collected Global Positioning System tracking data for 30 male and 21 female bobcats to indicate bobcat response to complex landscape factors. We examined these inputs using Resource Selection Function (RSF) modeling and developed spatially explicit models of the probability of bobcat use (selection or avoidance) of landscape characteristics. RSF models highlighted the general importance of reserve habitat for bobcats, but suggested that female bobcats were more dependent that male bobcats on habitat within designated reserves. Male bobcats, which range more widely than female bobcats, were associated with undeveloped areas both within and outside reserves. Small areas were present outside reserves that seemed to provide additional suitable habitat or movement areas for bobcats, potentially through restoration, connectivity, or reduced edge effects. Although bobcat RSFs suggested areas of high value to this species and potentially other species, taxa can differ greatly in their resource-selection and spatial requirements. Thus, for several species of reptiles, amphibians, and birds, we adapted species distribution models based on occurrence data to examine the response of other vertebrates to the landscape. To identify potential High-Value Areas (HVAs) for single or multiple species, we then developed a step-wise filtering process that can be applied to a series of spatial data layers. We provide examples of alternative decision models for HVAs that capture different elements of biodiversity and a range of management considerations. As landscape and management challenges change, these spatial layers and decision rules can be adjusted based on new information. Our approach thus establishes a general framework for identifying high-value habitat that can be used for current management decisions and refined in the future, depending on management interests and goals and the availability of suitable quality data or adequate surrogate information.

California↗

USGS1998

Methods for estimating tributary streamflow in the Chattahoochee River basin between Buford Dam and Franklin, Georgia

Simple and reliable methods for estimating hourly streamflow are needed for the calibration and verification of a Chattahoochee River basin model between Buford Dam and Franklin, Ga. The river basin model is being developed by Georgia Department of Natural Resources, Environmental Protection Division, as part of their Chattahoochee River Modeling Project. Concurrent streamflow data collected at 19 continuous-record, and 31 partial-record streamflow stations, were used in ordinary least-squares linear regression analyses to define estimating equations, and in verifying drainage-area prorations. The resulting regression or drainage-area ratio estimating equations were used to compute hourly streamflow at the partial-record stations. The coefficients of determination (r-squared values) for the regression estimating equations ranged from 0.90 to 0.99. Observed and estimated hourly and daily streamflow data were computed for May 1, 1995, through October 31, 1995. Comparisons of observed and estimated daily streamflow data for 12 continuous-record tributary stations, that had available streamflow data for all or part of the period from May 1, 1995, to October 31, 1995, indicate that the mean error of estimate for the daily streamflow was about 25 percent.

Georgia↗

USGS2024

Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 1—Spawning area identification using bidirectional drift modeling

Control of invasive grass carp ( Ctenopharyngodon idella ) populations in the Western Lake Erie Basin merits adaptive management guided by the best available science. Presently (2024), capture of mature grass carp in rivers during spawning season is most efficient, so knowing when and where grass carp are spawning is essential information for natural resource agencies. Using bidirectional drift modeling and grass carp ichthyoplankton samples captured in the Maumee River during the 2017–2019 spawning seasons, this study identified 12 probable grass carp spawning areas in the lower 96.5-kilometers of the Maumee River. These spawning areas were located both above and below the Grand Rapids/Providence low-head dams. Three areas showed evidence of multiyear use, while nine had multi-event use. Spawning activity had no definitive diel variation and occurred at an average photoperiod of 15.15 h. The maturation metric ADD15, or annual degree days above 15 degrees Celsius, generally exceeded the 655 threshold for spawning; however, some spawning occurred when ADD15 ≤235, indicating spawners likely matured in a warmwater discharge. The probable spawning areas were generally characterized by mean velocities between 0.4 and 2.1 m per second (with locally higher velocities possible), areas of high turbulence produced by dam spillways or bedrock outcroppings, channel constrictions, confluences, islands, and bridges with piers in the water. Spawning suitability indices (SSI), based on velocity, varied considerably between spawning areas and SSI models. These results could be used to inform control efforts and predict potential grass carp spawning locations in other rivers under threat of invasion.

Ohio↗

USGS2007

Comparison of peak-flow estimation methods for small drainage basins in Maine

Understanding the accuracy of commonly used methods for estimating peak streamflows is important because the designs of bridges, culverts, and other river structures are based on these flows. Different methods for estimating peak streamflows were analyzed for small drainage basins in Maine. For the smallest basins, with drainage areas of 0.2 to 1.0 square mile, nine peak streamflows from actual rainfall events at four crest-stage gaging stations were modeled by the Rational Method and the Natural Resource Conservation Service TR-20 method and compared to observed peak flows. The Rational Method had a root mean square error (RMSE) of -69.7 to 230 percent (which means that approximately two thirds of the modeled flows were within -69.7 to 230 percent of the observed flows). The TR-20 method had an RMSE of -98.0 to 5,010 percent. Both the Rational Method and TR-20 underestimated the observed flows in most cases. For small basins, with drainage areas of 1.0 to 10 square miles, modeled peak flows were compared to observed statistical peak flows with return periods of 2, 50, and 100 years for 17 streams in Maine and adjoining parts of New Hampshire. Peak flows were modeled by the Rational Method, the Natural Resources Conservation Service TR-20 method, U.S. Geological Survey regression equations, and the Probabilistic Rational Method. The regression equations were the most accurate method of computing peak flows in Maine for streams with drainage areas of 1.0 to 10 square miles with an RMSE of -34.3 to 52.2 percent for 50-year peak flows. The Probabilistic Rational Method was the next most accurate method (-38.5 to 62.6 percent). The Rational Method (-56.1 to 128 percent) and particularly the TR-20 method (-76.4 to 323 percent) had much larger errors. Both the TR-20 and regression methods had similar numbers of underpredictions and overpredictions. The Rational Method overpredicted most peak flows and the Probabilistic Rational Method tended to overpredict peak flows from the smaller (less than 5 square miles) drainage basins and underpredict peak flows from larger drainage basins. The results of this study are consistent with the most comprehensive analysis of observed and modeled peak streamflows in the United States, which analyzed statistical peak flows from 70 drainage basins in the Midwest and the Northwest.

Maine↗

USGS2018

A spatially discrete, integral projection model and its application to invasive carp

Natural resource managers and ecologists often desire an understanding of spatial dynamics such as migration, dispersion, and meta-population dynamics. Network-node models can capture these salient features. Additionally, the state-variable used with many species may be appropriately modeled as a continuous variable (e.g., length) and management activities sometimes can only target individuals of certain sizes. Integral projection models (IPMs) can capture this life history characteristic and allow for the examination of size-specific management. We combined an IPM with a network-node model to capture both of these salient features. We then demonstrated how this model could be used to understand and manage populations of invasive species focusing on grass carp as an example. Grass carp disrupt ecosystems outside of their native range and have spread around much of the world, including North America. The impacts of grass carp include adversely changing aquatic plant communities, which in turn affect a wide range of endpoints ranging from water quality to waterfowl recruitment. We specifically examined two theoretical systems using parameters from the literature. First, we modeled a lake with two tributaries and examined how modified sterile males could be used as a control tool. We found that modified sterile males may be a feasible control tool to limit population growth. Second, we modeled a series of river pools and examined how harvest and deterrents could be used to decrease the risk of expanding grass carp's range within a river system. Within this system, we also compared the impacts of size specific harvest and uniform harvest across all sizes. We found that targeting the largest, spawning populations may be more important than targeting the populations close to the invasion front for reducing the risk of spreading grass carp. We also demonstrate that size of harvested fish was important for controlling populations.

Ecological Modelling↗

USGS2014

BatTool: an R package with GUI for assessing the effect of White-nose syndrome and other take events on Myotis spp. of bats

Background: Myotis species of bats such as the Indiana Bat and Little Brown Bat are facing population declines because of White-nose syndrome (WNS). These species also face threats from anthropogenic activities such as wind energy development. Population models may be used to provide insights into threats facing these species. We developed a population model, BatTool, as an R package to help decision makers and natural resource managers examine factors influencing the dynamics of these species. The R package includes two components: 1) a deterministic and stochastic model that are accessible from the command line and 2) a graphical user interface (GUI). Results: BatTool is an R package allowing natural resource managers and decision makers to understand Myotis spp. population dynamics. Through the use of a GUI, the model allows users to understand how WNS and other take events may affect the population. The results are saved both graphically and as data files. Additionally, R-savvy users may access the population functions through the command line and reuse the code as part of future research. This R package could also be used as part of a population dynamics or wildlife management course. Conclusions: BatTool provides access to a Myotis spp. population model. This tool can help natural resource managers and decision makers with the Endangered Species Act deliberations for these species and with issuing take permits as part of regulatory decision making. The tool is available online as part of this publication.

Source Code for Biology and Medicine↗

USGS1985

The need for accuracy in modelling: An example

The need for accurate information in modelling depends on the objectives of the effort. but trustworthy data are essential for a model intended for use in the management of natural resources. This point is illustrated by referring to a recently published model of canvasback ducks. We demonstrate that several key assumptions are not supported by biological evidence, and that inferences drawn from the model could be dangerously misleading if they were used to guide the management of canvasback ducks.

Ecological Modelling↗

USGS2014

Predicting occupancy for pygmy rabbits in Wyoming: an independent evaluation of two species distribution models

Species distribution models are an important component of natural-resource conservation planning efforts. Independent, external evaluation of their accuracy is important before they are used in management contexts. We evaluated the classification accuracy of two species distribution models designed to predict the distribution of pygmy rabbit Brachylagus idahoensis habitat in southwestern Wyoming, USA. The Nature Conservancy model was deductive and based on published information and expert opinion, whereas the Wyoming Natural Diversity Database model was statistically derived using historical observation data. We randomly selected 187 evaluation survey points throughout southwestern Wyoming in areas predicted to be habitat and areas predicted to be nonhabitat for each model. The Nature Conservancy model correctly classified 39 of 77 (50.6%) unoccupied evaluation plots and 65 of 88 (73.9%) occupied plots for an overall classification success of 63.3%. The Wyoming Natural Diversity Database model correctly classified 53 of 95 (55.8%) unoccupied plots and 59 of 88 (67.0%) occupied plots for an overall classification success of 61.2%. Based on 95% asymptotic confidence intervals, classification success of the two models did not differ. The models jointly classified 10.8% of the area as habitat and 47.4% of the area as nonhabitat, but were discordant in classifying the remaining 41.9% of the area. To evaluate how anthropogenic development affected model predictive success, we surveyed 120 additional plots among three density levels of gas-field road networks. Classification success declined sharply for both models as road-density level increased beyond 5 km of roads per km-squared area. Both models were more effective at predicting habitat than nonhabitat in relatively undeveloped areas, and neither was effective at accounting for the effects of gas-energy-development road networks. Resource managers who wish to know the amount of pygmy rabbit habitat present in an area or wanting to direct gas-drilling efforts away from pygmy rabbit habitat may want to consider both models in an ensemble manner, where more confidence is placed in mapped areas (i.e., pixels) for which both models agree than for areas where there is model disagreement.

Wyoming↗

USGS2014

Water quality and algal community dynamics of three deepwater lakes in Minnesota utilizing CE-QUAL-W2 models

Water quality, habitat, and fish in Minnesota lakes will potentially be facing substantial levels of stress in the coming decades primarily because of two stressors: (1) land-use change (urban and agricultural) and (2) climate change. Several regional and statewide lake modeling studies have identified the potential linkages between land-use and climate change on reductions in the volume of suitable lake habitat for coldwater fish populations. In recent years, water-resource scientists have been making the case for focused assessments and monitoring of sentinel systems to address how these stress agents change lakes over the long term. Currently in Minnesota, a large-scale effort called “Sustaining Lakes in a Changing Environment” is underway that includes a focus on monitoring basic watershed, water quality, habitat, and fish indicators of 24 Minnesota sentinel lakes across a gradient of ecoregions, depths, and nutrient levels. As part of this effort, the U.S. Geological Survey, in cooperation with the Minnesota Department of Natural Resources, developed predictive water quality models to assess water quality and habitat dynamics of three select deepwater lakes in Minnesota. The three lakes (Lake Carlos in Douglas County, Elk Lake in Clearwater County, and Trout Lake in Cook County) were assessed under recent (2010–11) meteorological conditions. The three selected lakes contain deep, coldwater habitats that remain viable during the summer months for coldwater fish species. Hydrodynamics and water-quality characteristics for each of the three lakes were simulated using the CE-QUAL-W2 model, which is a carbon-based, laterally averaged, two-dimensional water-quality model. The CE-QUAL-W2 models address the interaction between nutrient cycling, primary production, and trophic dynamics to predict responses in the distribution of temperature and oxygen in lakes. The CE-QUAL-W2 models for all three lakes successfully predicted water temperature, on the basis of the two metrics of absolute mean error and root mean square error, using measured inputs of water temperature and nutrients. One of the main calibration tools for CE-QUAL-W2 model development was the vertical profile temperature data, available for all three lakes. For all three lakes, the absolute mean error and root mean square error were less than 1.0 degree Celsius and 1.2 degrees Celsius, respectively, for the different depth ranges used for vertical profile comparisons. In Lake Carlos, simulated water temperatures compared better to measured water temperatures in the epilimnion than in the hypolimnion. The reverse was true for the other two lakes, Elk Lake and Trout Lake, where the simulated results were slightly better for the hypolimnion than the epilimnion. The model also was used to approximate the location of the thermocline throughout the simulation periods, approximately April to November, in all three lake models. Deviations between the simulated and measured water temperatures in the vertical lake profile commonly were because of an offset in the timing of thermocline shifts rather than the simulated results missing thermocline shifts altogether.

Minnesota↗

USGS2003

Graphic comparison of reserve-growth models for conventional oil and accumulation

The U.S. Geological Survey (USGS) periodically assesses crude oil, natural gas, and natural gas liquids resources of the world. The assessment procedure requires estimated recover-able oil and natural gas volumes (field size, cumulative production plus remaining reserves) in discovered fields. Because initial reserves are typically conservative, subsequent estimates increase through time as these fields are developed and produced. The USGS assessment of petroleum resources makes estimates, or forecasts, of the potential additions to reserves in discovered oil and gas fields resulting from field development, and it also estimates the potential fully developed sizes of undiscovered fields. The term ?reserve growth? refers to the commonly observed upward adjustment of reserve estimates. Because such additions are related to increases in the total size of a field, the USGS uses field sizes to model reserve growth. Future reserve growth in existing fields is a major component of remaining U.S. oil and natural gas resources and has therefore become a necessary element of U.S. petroleum resource assessments. Past and currently proposed reserve-growth models compared herein aid in the selection of a suitable set of forecast functions to provide an estimate of potential additions to reserves from reserve growth in the ongoing National Oil and Gas Assessment Project (NOGA). Reserve growth is modeled by construction of a curve that represents annual fractional changes of recoverable oil and natural gas volumes (for fields and reservoirs), which provides growth factors. Growth factors are used to calculate forecast functions, which are sets of field- or reservoir-size multipliers. Comparisons of forecast functions were made based on datasets used to construct the models, field type, modeling method, and length of forecast span. Comparisons were also made between forecast functions based on field-level and reservoir- level growth, and between forecast functions based on older and newer data. The reserve-growth model used in the 1995 USGS National Assessment and the model currently used in the NOGA project provide forecast functions that yield similar estimates of potential additions to reserves. Both models are based on the Oil and Gas Integrated Field File from the Energy Information Administration (EIA), but different vintages of data (from 1977 through 1991 and 1977 through 1996, respectively). The model based on newer data can be used in place of the previous model, providing similar estimates of potential additions to reserves. Fore-cast functions for oil fields vary little from those for gas fields in these models; therefore, a single function may be used for both oil and gas fields, like that used in the USGS World Petroleum Assessment 2000. Forecast functions based on the field-level reserve growth model derived from the NRG Associates databases (from 1982 through 1998) differ from those derived from EIA databases (from 1977 through 1996). However, the difference may not be enough to preclude the use of the forecast functions derived from NRG data in place of the forecast functions derived from EIA data. Should the model derived from NRG data be used, separate forecast functions for oil fields and gas fields must be employed. The forecast function for oil fields from the model derived from NRG data varies significantly from that for gas fields, and a single function for both oil and gas fields may not be appropriate.

Bulletin↗

USGS2017

Water-quality models to assess algal community dynamics, water quality, and fish habitat suitability for two agricultural land-use dominated lakes in Minnesota, 2014

Fish habitat can degrade in many lakes due to summer blue-green algal blooms. Predictive models are needed to better manage and mitigate loss of fish habitat due to these changes. The U.S. Geological Survey (USGS), in cooperation with the Minnesota Department of Natural Resources, developed predictive water-quality models for two agricultural land-use dominated lakes in Minnesota—Madison Lake and Pearl Lake, which are part of Minnesota’s sentinel lakes monitoring program—to assess algal community dynamics, water quality, and fish habitat suitability of these two lakes under recent (2014) meteorological conditions. The interaction of basin processes to these two lakes, through the delivery of nutrient loads, were simulated using CE-QUAL-W2, a carbon-based, laterally averaged, two-dimensional water-quality model that predicts distribution of temperature and oxygen from interactions between nutrient cycling, primary production, and trophic dynamics. The CE-QUAL-W2 models successfully predicted water temperature and dissolved oxygen on the basis of the two metrics of mean absolute error and root mean square error. For Madison Lake, the mean absolute error and root mean square error were 0.53 and 0.68 degree Celsius, respectively, for the vertical temperature profile comparisons; for Pearl Lake, the mean absolute error and root mean square error were 0.71 and 0.95 degree Celsius, respectively, for the vertical temperature profile comparisons. Temperature and dissolved oxygen were key metrics for calibration targets. These calibrated lake models also simulated algal community dynamics and water quality. The model simulations presented potential explanations for persistently large total phosphorus concentrations in Madison Lake, key differences in nutrient concentrations between these lakes, and summer blue-green algal bloom persistence. Fish habitat suitability simulations for cool-water and warm-water fish indicated that, in general, both lakes contained a large proportion of good-growth habitat and a sustained period of optimal growth habitat in the summer, without any periods of lethal oxythermal habitat. For Madison and Pearl Lakes, examples of important cool-water fish, particularly game fish, include northern pike ( Esox lucius ), walleye ( Sander vitreus ), and black crappie ( Pomoxis nigromaculatus ); examples of important warm-water fish include bluegill ( Lepomis macrochirus ), largemouth bass ( Micropterus salmoides ), and smallmouth bass ( Micropterus dolomieu ). Sensitivity analyses were completed to understand lake response effects through the use of controlled departures on certain calibrated model parameters and input nutrient loads. These sensitivity analyses also operated as land-use change scenarios because alterations in agricultural practices, for example, could potentially increase or decrease nutrient loads.

Minnesota↗

USGS2022

Physiological and nutritional constraints on zooplankton productivity due to eutrophication and climate change predicted using a resource-based modeling approach

Emerging evidence suggests that zooplankton production is affected by physiological and nutritional constraints due to climate change and eutrophication, which in turn could have broad implications for food-web dynamics and fisheries production. In this study, we developed a resource-based zooplankton production dynamics model that causally links freshwater cladoceran and copepod daily production-to-biomass (P/B) ratios with water temperature, phytoplankton biomass and community composition, and zooplankton feeding selectivity. This model was used to evaluate constraints on zooplankton growth under four hypothetical scenarios: involving natural plankton community seasonal succession; lake fertilization to enhance fisheries production; eutrophication; and climatic warming. Our novel modeling approach predicts zooplankton production is strongly dependent on seasonal variation in resource availability and quality, which results in more complex zooplankton dynamics than predicted by simpler temperature dependent models. For mesotrophic and hypereutrophic lakes, our study suggests that the ultimate control over zooplankton P/B ratios shifts from physiological control during colder periods to strong resource control during warmer periods. Our resource-based model provided important insights into the nature of biophysical control of zooplankton under a changing climate that has crucial implications for food web energy transfer and fisheries production.

Canadian Journal of Fisheries and Aquatic Sciences↗

USGS1998

A policy model to initiate environmental negotiations: Three hydropower workshops

How do I get started in natural resource negotiations? Natural resource managers often face difficult negotiations when they implement laws and policies regulating such resources as water, wildlife, wetlands, endangered species, and recreation. As a result of these negotiations, managers must establish rules, grant permits, or create management plans. The Legal‐Institutional Analysis Model (LIAM) was designed to assist managers in systematically analyzing the parties in natural resource negotiations and using that analysis to prepare for bargaining. The LIAM relies on the theory that organizations consistently employ behavioral roles. The model uses those roles to predict likely negotiation behavior. One practical use of the LIAM is when all parties to a negotiation conduct a workshop as a way to open the bargaining on a note of trust and mutual understanding. The process and results of three LIAM workshops designed to guide hydroelectric power licensing negotiations are presented. Our experience with these workshops led us to conclude that the LIAM can be an effective tool to begin a negotiation and that trust built through the workshops can help create a successful result.

Human Dimensions of Wildlife: An International Jou↗

USGS2008

Characterization of Suspended-Sediment Loading to and from John Redmond Reservoir, East-Central Kansas, 2007-2008

Storage capacity in John Redmond Reservoir is being lost to sedimentation more rapidly than in other federal impoundments in Kansas. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, initiated a study to characterize suspended-sediment loading to and from John Redmond Reservoir from February 21, 2007, through February 21, 2008. Turbidity sensors were installed at two U.S. Geological Survey stream gages upstream (Neosho River near Americus and the Cottonwood River near Plymouth) and one stream gage downstream (Neosho River at Burlington) from the reservoir to compute continuous, real-time (15-minute) measurements of suspended-sediment concentration and loading. About 1,120,000 tons of suspended-sediment were transported to, and 100,700 tons were transported from John Redmond Reservoir during the study period. Dependent on the bulk density of sediment stored in the reservoir, 5.0 to 1.4 percent of the storage in the John Redmond conservation pool was lost during the study period, with an average deposition of 3.4 to 1.0 inches. Nearly all (98-99 percent) of the incoming sediment load was transported during 9 storms which occurred 25 to 27 percent of the time. The largest storm during the study period (peak-flow recurrence interval of about 4.6-4.9 years) transported about 37 percent of the sediment load to the reservoir. Suspended-sediment yield from the unregulated drainage area upstream from the Neosho River near Americus was 530 tons per square mile, compared to 400 tons per square mile upstream from the Cottonwood River near Plymouth. Comparison of historical (1964-78) to current (2007) sediment loading estimates indicate statistically insignificant (<90 percent confidence) differences at the Neosho River near Americus and the Cottonwood River near Plymouth, but a significant (>99 percent) decrease in sediment loading at the Neosho River at Burlington. Ninety-percent confidence intervals of streamflow-derived estimates of total sediment load were 7 to 21 times larger than turbidity-derived estimates. Results from this study can be used by natural resource managers to calibrate sediment models and estimate the ability of John Redmond Reservoir to support designated uses into the future.

Scientific Investigations Report↗

USGS2022

Medium spatial resolution mapping of global land cover and land cover change across multiple decades from Landsat

Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related to land surface properties (e.g., terrestrial carbon models, water balance models, weather, and climate models) and are core inputs to models and analyses used by natural resource scientists and land managers. As the Earth’s global population has grown over the last several decades rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration, etc.). Hence, accurate information related to land cover is essential for both managing natural resources and for understanding society’s ecological, biophysical, and resource management footprint. To address the need for high-quality land cover information we are using the global record of Landsat observations to compile annual maps of global land cover from 2001 to 2020 at 30 m spatial resolution. To create these maps we use features derived from time series of Landsat imagery in combination with ancillary geospatial data and a large database of training sites to classify land cover at annual time step. The algorithm that we apply uses temporal segmentation to identify periods with stable land cover that are separated by breakpoints in the time series. Here we provide an overview of the methods and data sets we are using to create global maps of land cover. We describe the algorithms used to create these maps and the core land cover data sets that we are creating through this effort, and we summarize our approach to accuracy assessment. We also present a synthesis of early results and discuss the strengths and weaknesses of our early map products and the challenges that we have encountered in creating global land cover data sets from Landsat. Initial accuracy assessment for North America shows good overall accuracy (77.0 ± 2.0% correctly classified) and 79.8% agreement with the European Space Agency (ESA) WorldCover product. The land cover mapping results we report provide the foundation for robust, repeatable, and accurate mapping of global land cover and land cover change across multiple decades at 30 m spatial resolution from Landsat.

Frontiers in Remote Sensing↗

USGS1998

Thunder on the Yellowstone revisited: An assessment of management of native ungulates by natural regulation, 1968-1993

Natural regulation of native ungulates was initiated in 1968 in Yellowstone National Park (YNP) based on the premise that ungulates would reach an equilibrium with their plant resources. The natural-regulation management model stated: density dependence will regulate ungulates (i.e., a dynamic equilibrium will result between ungulates and their food supply, within some bounds of vegetation and soil effects); and no retrogression of soil and vegetation will occur from elk (Cervus elaphus) grazing during this process. The historical record indicated that elk were abundant in the system and elk were primarily food limited before settlement by European man (i.e., wolves [Canis lupus] and Native Americans were only an adjunct to the density dependent population regulation of ungulates). Density dependence was demonstrated in elk, but not in bison (Bison bison). No widespread evidence of overgrazing was observed through 1993 in study sites within vegetation communities that comprised about 97% of the winter range. No evidence of increased exotics, increased sediment yield, warming or drying of the soil, changes in soil nutrients, or differences in aboveground standing-crop biomass of plants was found between grazed and ungrazed plots. Ungulate herbivory apparently stimulated aboveground production of grasses, enhanced nitrogen and macronutrients in grasses, increased nutrient cycling, and enhanced measures of fitness in 6 common plants. However, exposed soil surface (bare ground and pebbles combined) was 11-18% greater on grazed than ungrazed plots, apparently due to a 71% decline in dead and standing litter on grazed plots. Percent live-plant basal cover, however, did not differ on grazed versus ungrazed plots, and there was no difference in soil microclimate or sediment yield. Differences in the abundance of 12% of the herbaceous species were found in grazed versus ungrazed sites (16 of 128 species); 10 were declines and 6 were increases. Willow (Salix spp.) and aspen (Populus tremuloides) declines predated the new management policy, but their slow declines also continued after 1968. Three uncommon plant species (aspen, willow, and Wyoming big sagebrush [Artemisia tridentata tridentata]) and 1 herbivore (moose [Alces alces]) declined under natural-regulation management. Two uncommon species of woody browse (aspen, Wyoming big sagebrush) were overutilized by ungulates (consumption of >2/3 current annual growth occurred). We conclude the natural-regulation model for YNP was flawed in its assumptions of a single, steady state for the park, based on conditions presumed to exist in 1870 prior to establishment of the national park. The period selected as a standard (1870) was an unusual period characterized by frequent large fires and floods, common wolves, few elk, and a cooler, wetter climate. We also conclude there is a high level of uncertainty surrounding what elk densities were in pre-Columbian times (element 3 of the management model) and what effects wolves and Native Americans had in regulating the elk population.

Wildlife Society Bulletin↗

USGS2008

Base of principal aquifer for the Elkhorn-Loup model area, North-Central Nebraska

In Nebraska, the water managers in the Natural Resources Districts and the Nebraska Department of Natural Resources are concerned with the effect of ground-water withdrawal on the availability of surface water and the long-term effects of ground-water withdrawal on ground- and surface-water resources. In north-central Nebraska, in the Elkhorn and Loup River Basins, ground water is used for irrigation, domestic supply, and public supply; surface water is used in this area for irrigation, recreation, and hydropower production. In recognition of these sometimes competing ground- and surface-water uses in the Elkhorn and Loup River Basins, the U.S. Geological Survey, the Lewis and Clark Natural Resources District, the Lower Elkhorn Natural Resources District, the Lower Loup Natural Resources District, the Lower Niobrara Natural Resources District, the Lower Platte North Natural Resources District, the Middle Niobrara Natural Resources District, the Upper Elkhorn Natural Resources District, and the Upper Loup Natural Resources District agreed to cooperatively study water resources in the Elkhorn and Loup River Basins. The goals of the overall study were to construct and calibrate a regional ground-water flow model of the area and to use that flow model as a tool to assess current and future effects of ground-water irrigation on stream base flow and to help develop long-term water-resource management strategies for this area, hereafter referred to as the Elkhorn-Loup model area. The Elkhorn-Loup model area covers approximately 30,800 square miles, and extends from the Niobrara River in the north to the Platte River in the south. The western boundary of the Elkhorn-Loup model area coincides with the western boundary of the Middle Niobrara, Twin Platte, and Upper Loup Natural Resources Districts; the eastern boundary coincides with the approximate location of the western extent of glacial till in eastern Nebraska. The principal aquifer in most of the Elkhorn-Loup model area is the High Plains aquifer; the principal aquifer in the remaining part of the Elkhorn-Loup model area is an unnamed alluvial aquifer. The upper surface of the geologic units that directly underlie the aquifer is called the 'base of aquifer' in this report. The geologic unit that forms the base of aquifer in the Elkhorn-Loup model area varies by location. The Tertiary-age Brule Formation generally is the base of aquifer in the west; the Cretaceous-age Pierre Shale generally is the base of aquifer in the east. The purpose of this report is to update the altitude and configuration of the base of the principal aquifer in the Elkhorn-Loup model area and a 2-mile buffer area around the Elkhorn-Loup model area, using base-of-aquifer data from test holes, registered water wells, and oil and gas wells within the Elkhorn-Loup model area and a 20-mile buffer area around the Elkhorn-Loup model area that have become available since the publication of earlier maps of the base of aquifer for this area. The base-of-aquifer map is important for the Elkhorn-Loup ground-water flow model because it defines the model's lower boundary. The accuracy of the Elkhorn-Loup ground-water flow model and the accuracy of the model's predictions about the effects of ground-water irrigation on stream base flow are directly related to the accuracy of the model's lower boundary.

Scientific Investigations Map↗

USGS2015

Water-budgets and recharge-area simulations for the Spring Creek and Nittany Creek Basins and parts of the Spruce Creek Basin, Centre and Huntingdon Counties, Pennsylvania, Water Years 2000–06

This report describes the results of a study by the U.S. Geological Survey in cooperation with ClearWater Conservancy and the Pennsylvania Department of Environmental Protection to develop a hydrologic model to simulate a water budget and identify areas of greater than average recharge for the Spring Creek Basin in central Pennsylvania. The model was developed to help policy makers, natural resource managers, and the public better understand and manage the water resources in the region. The Groundwater and Surface-water FLOW model (GSFLOW), which is an integration of the Precipitation-Runoff Modeling System (PRMS) and the Modular Groundwater Flow Model (MODFLOW-NWT), was used to simulate surface water and groundwater in the Spring Creek Basin for water years 2000–06. Because the groundwater and surface-water divides for the Spring Creek Basin do not coincide, the study area includes the Nittany Creek Basin and headwaters of the Spruce Creek Basin. The hydrologic model was developed by the use of a stepwise process: (1) develop and calibrate a PRMS model and steady-state MODFLOW-NWT model; (2) re-calibrate the steady-state MODFLOW-NWT model using potential recharge estimates simulated from the PRMS model, and (3) integrate the PRMS and MODFLOW-NWT models into GSFLOW. The individually calibrated PRMS and MODFLOW-NWT models were used as a starting point for the calibration of the fully coupled GSFLOW model. The GSFLOW model calibration was done by comparing observations and corresponding simulated values of streamflow from 11 streamgages and groundwater levels from 16 wells. The cumulative water budget and individual water budgets for water years 2000–06 were simulated by using GSFLOW. The largest source and sink terms are represented by precipitation and evapotranspiration, respectively. For the period simulated, a net surplus in the water budget was computed where inflows exceeded outflows by about 1.7 billion cubic feet (0.47 inches per year over the basin area); storage increased by about the same amount to balance the budget. The rate and distribution of recharge throughout the Spring Creek, Nittany Creek, and Spruce Creek Basins is variable as a result of the high degree of hydrogeologic heterogeneity and karst features. The greatest amount of recharge was simulated in the carbonate-bedrock valley, near the toe slopes of Nittany and Tussey Mountains, in the Scotia Barrens, and along the area coinciding with the Gatesburg Formation. Runoff extremes were observed for water years 2001 (dry year) and 2004 (wet year). Simulated average recharge rates (water reaching the saturated zone as defined in GSFLOW) for 2001 and 2004 were 5.4 in/yr and 22.0 in/yr, respectively. Areas where simulations show large variations in annual recharge between wet and dry years are the same areas where simulated recharge was large. Those areas where rates of groundwater recharge are much higher than average, and are capable of accepting substantially greater quantities of recharge during wet years, might be considered critical for maintaining the flow of springs, stream base flow, or the source of water to supply wells. The slopes of the Bald Eagle, Tussey, and Nittany Mountains are relatively insensitive to variations in recharge, primarily because of reduced infiltration rates and steep slopes.

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