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At least 181 records · Page 10Linked to original sources
USGS2017

A guide to multi-objective optimization for ecological problems with an application to cackling goose management

Choices in ecological research and management are the result of balancing multiple, often competing, objectives. Multi-objective optimization (MOO) is a formal decision-theoretic framework for solving multiple objective problems. MOO is used extensively in other fields including engineering, economics, and operations research. However, its application for solving ecological problems has been sparse, perhaps due to a lack of widespread understanding. Thus, our objective was to provide an accessible primer on MOO, including a review of methods common in other fields, a review of their application in ecology, and a demonstration to an applied resource management problem. A large class of methods for solving MOO problems can be separated into two strategies: modelling preferences pre-optimization (the a priori strategy), or modelling preferences post-optimization (the a posteriori strategy). The a priori strategy requires describing preferences among objectives without knowledge of how preferences affect the resulting decision. In the a posteriori strategy, the decision maker simultaneously considers a set of solutions (the Pareto optimal set) and makes a choice based on the trade-offs observed in the set. We describe several methods for modelling preferences pre-optimization, including: the bounded objective function method, the lexicographic method, and the weighted-sum method. We discuss modelling preferences post-optimization through examination of the Pareto optimal set. We applied each MOO strategy to the natural resource management problem of selecting a population target for cackling goose ( Branta hutchinsii minima ) abundance. Cackling geese provide food security to Native Alaskan subsistence hunters in the goose's nesting area, but depredate crops on private agricultural fields in wintering areas. We developed objective functions to represent the competing objectives related to the cackling goose population target and identified an optimal solution first using the a priori strategy, and then by examining trade-offs in the Pareto set using the a posteriori strategy. We used four approaches for selecting a final solution within the a posteriori strategy; the most common optimal solution, the most robust optimal solution, and two solutions based on maximizing a restricted portion of the Pareto set. We discuss MOO with respect to natural resource management, but MOO is sufficiently general to cover any ecological problem that contains multiple competing objectives that can be quantified using objective functions.

Ecological Modelling↗
USGS2009

Carbonatites of the world, explored deposits of Nb and REE— Database and grade and tonnage models

This report is based on published tonnage and grade data on 58 Nb- and rare-earth-element (REE)-bearing carbonatite deposits that are mostly well explored and are partially mined or contain resources of these elements. The deposits represent only a part of the known 527 carbonatites around the world, but they are characterized by reliable quantitative data on ore tonnages and grades of niobium and REE. Grade and tonnage models are an important component of mineral resource assessments. Carbonatites present one of the main natural sources of niobium and rare-earth elements, the economic importance of which grows consistently. A purpose of this report is to update earlier publications. New information about known deposits, as well as data on new deposits published during the last decade, are incorporated in the present paper. The compiled database (appendix 1; linked to right) contains 60 explored Nb- and REE-bearing carbonatite deposits - resources of 55 of these deposits are taken from publications. In the present updated grade-tonnage model we have added 24 deposits comparing with the previous model of Singer (1998). Resources of most deposits are residuum ores in the upper part of carbonatite bodies. Mineral-deposit models are important in exploration planning and quantitative resource assessments for two reasons: (1) grades and tonnages among deposit types vary significantly, and (2) deposits of different types are present in distinct geologic settings that can be identified from geologic maps. Mineral-deposit models combine the diverse geoscience information on geology, mineral occurrences, geophysics, and geochemistry used in resource assessments and mineral exploration. Globally based deposit models allow recognition of important features and demonstrate how common different features are. Well-designed deposit models allow geologists to deduce possible mineral-deposit types in a given geologic environment, and the grade and tonnage models allow economists to estimate the possible economic viability of these resources. Thus, mineral-deposit models play a central role in presenting geoscience information in a useful form to policy makers. The foundation of mineral-deposit models is information about known deposits. This publication presents the latest geologic information and newly developed grade and tonnage models for Nb- and REE-carbonatite deposits in digital form. The publication contains computer files with information on deposits from around the world. It also contains a text file allowing locations of all deposits to be plotted in geographic information system (GIS) programs. The data are presented in FileMaker Pro as well as in .xls and text files to make the information available to a broadly based audience. The value of this information and any derived analyses depends critically on the consistent manner of data gathering. For this reason, we first discuss the rules used in this compilation. Next, the fields of the database are explained. Finally, we provide new grade and tonnage models and analysis of the information in the file.

Open-File Report↗
USGS2017

U.S. Geological Survey Cooperative Fish and Wildlife Research Units Program—2016–2017 Research Abstracts

The U.S. Geological Survey (USGS) has several strategic goals that focus its efforts on serving the American people. The USGS Ecosystems Mission Area has responsibility for the following objectives under the strategic goal of “Science to Manage and Sustain Resources for Thriving Economies and Healthy Ecosystems”: Understand, model, and predict change in natural systems Conserve and protect wildlife and fish species and their habitats Reduce or eliminate the threat of invasive species and wildlife disease This report provides abstracts of the majority of ongoing research investigations of the USGS Cooperative Fish and Wildlife Research Units program and is intended to complement the 2016 Cooperative Research Units Program Year in Review Circular 1424 ( https://doi.org/10.3133/cir1424 ). The report is organized by the following major science themes that contribute to the objectives of the USGS: Advanced Technologies Climate Science Decision Science Ecological Flows Ecosystem Services Endangered Species Conservation, Recovery, and Proactive Strategies Energy Human Dimensions Invasive Species Landscape Ecology Species of Greatest Conservation Need Species Population, Habitat, and Harvest Management Wildlife Health and Disease

Circular↗
USGS2015

Assessment of aquifer properties, evapotranspiration, and the effects of ditching in the Stoney Brook watershed, Fond du Lac Reservation, Minnesota, 2006-9

The U.S. Geological Survey, in cooperation with the Fond du Lac Band of Lake Superior Chippewa, assessed hydraulic properties of geologic material, recharge, and evapotranspiration, and the effects of ditching on the groundwater resources in the Stoney Brook watershed in the Fond du Lac Reservation. Geologic, groundwater, and surface-water data were collected during 2006–9 to estimate hydrologic properties in the watershed. Streamflow and groundwater levels in the shallow glacial deposits in the Stoney Brook watershed were analyzed to estimate groundwater-flow directions, groundwater recharge, and evapotranspiration within the watershed and to assess the effect of ditches on surrounding groundwater resources. Groundwater, streamflow, and precipitation data collected during the study (2006–9) can be used to update the U.S. Department of Agriculture’s Natural Resource Conservation Service and Fond du Lac Resource Management Division surface-water models, which are used to evaluate the effect of proposed adjustments to the ditching system on streamflow on wild rice production and aquatic habitats. Specific yields calculated from the well water levels ranged from 0.11 to 0.40, and hydraulic conductivities determined from water levels measured during well slug tests ranged from 1 to 7 feet per day. The values for specific yields were similar to values obtained in other studies done in glacial materials of similar composition in Minnesota. The higher hydraulic conductivity estimate (7 feet per day) was similar to lower hydraulic conductivities estimated in another hydrologic study conducted in Carlton County, Minnesota. The installation of drainage ditches in the Stoney Brook watershed has reduced water levels in lakes connected to the ditch system, and has locally reduced groundwater levels in shallow groundwater adjacent to the ditches and lakes. Differences in near-ditch groundwater hydrographs relative to far-ditch groundwater hydrographs indicate that the effect of the ditches on groundwater is only localized to near-ditch areas. These hydrograph differences resulted in large differences between recharge estimated at wells near and far from ditches. In this study, recharge estimated at wells within 50 feet of a ditch was influenced by ditch-water levels. Annual groundwater recharge estimates from water levels and streamflows during 2006–9 ranged from 0.36 to 34.8 inches, and varied with climate, geology, and well location relative to ditches. The higher recharge estimates were determined from analysis of groundwater levels in wells near the ditches because the shallow groundwater in these wells received both infiltration from ditches and areal groundwater recharge from precipitation. The water-table fluctuation method using a manual groundwater recession approach for wells far from ditches provided the best estimates of areal groundwater recharge to the shallow glacial aquifer because water levels in these wells were not affected by water infiltrating from ditches (bank storage). For wells more than 400 feet from ditches, mean annual areal groundwater recharge estimates using the manual groundwater recession approach for wells screened mostly in outwash sands during 2007, 2008, and 2009 ranged from 4.47 to 18.6 inches (wells 5, 7, 13, 14 and 15), and ranged from 0.43 to 2.85 inches for wells screened mostly in clayey sand or sandy clay (wells 9 and 16). Recharge estimates at wells far from ditches were similar to basinwide recharge estimates from streamflow. Daily fluctuations in water levels in two wells indicated that the evapotranspiration extinction depth in the Stoney Brook watershed is approximately 4.6 to 6 feet below the land surface. A polynomial regression fit of the daily evapotranspiration rates during 2006–9 for well 1 produced a total evapotranspiration estimate of 16.1 inches from June 26 to October 6 for every year. Evapotranspiration estimated from daily water-level fluctuations in wells near ditches is relatively high. The ditch-water surface allowed for relatively high evaporation compared to the land surface, which, with a good hydraulic connection to surrounding groundwater, resulted in relatively high fluctuations in daily groundwater levels near ditches, resulting in high evapotranspiration estimates.

Minnesota↗
USGS1991

USFWS selected geographic analyses

The geographic information system (GIS) used by the National Wetlands Research Center (NWRC) of the U.S. Fish and Wildlife Service has been valuable in assisting natural resource managers in planning and managing coastal fish and wildlife resources. In the past 5 years, NWRC has conducted about 60 studies employing this technology. Applications have ranged from simple natural resource inventories to documentation of trends in wetland changes and complex predictive modeling of impacts of harbor expansion on biological resources. Maps, tables, and data bases from these studies have been presented before local, State, and Congressional committees as well as numerous scientific and public organizations. This paper describes and summarizes selected GIS projects related to resource inventories, analyses of wetland change, and cartographic modeling of coastal areas of the United States.

Conference Paper↗
USGS2013

Vegetation projections for Wind Cave National Park with three future climate scenarios: Final report in completion of Task Agreement J8W07100052

Introduction The effects of climate change on the natural resources protected by Parks will likely be substantial, but geographically variable, due to local variation in climate trajectories and differences among ecosystems in their vulnerability to climate change. The projections of general circulation models (GCMs) indicate the possible magnitude and direction of future climate change for a region, but the utility of these projections for more local scales, those of individual National Park Service (NPS) units, are more uncertain because the coarse-scale GCMs lack much of the topographic detail that alters local climates. In addition, complex, interacting effects of temperature, precipitation, atmospheric CO 2 concentrations, fire, and herbivores on the vegetation that is the foundational natural resource of many NPS units present challenges in assessing the effects of projected future climates on plant and animal assemblages managed by the NPS. In spring 2009, Wind Cave National Park (WICA) served as a case study in a workshop assessing the use of scenario planning as a tool for park management planning in the face of rapidly changing climate. One outcome of the workshop was the recognized need for quantitative models to better understand the range of possible vegetation changes under different future climates and management decisions. This report addresses this need; it describes our adaptation of a dynamic global vegetation model (DGVM) to WICA vegetation and the resulting projections of future vegetation under three future climate scenarios and 11 management scenarios determined by Park natural resource managers. Wind Cave National Park lies along a narrow transition zone between the ponderosa pine (Pinus ponderosa) forests of the Black Hills and the mixed grass prairie that once extended with few interruptions over the lower, gentler terrain, subject to warmer, drier climate to the east and south of the Park. The location and character of this transition is strongly influenced by fire frequency and intensity (Brown and Sieg 1999). Furthermore, the mixed grass prairie occupies a broader transition zone between eastern tallgrass prairie and the shortgrass prairie of the western Great Plains. The dominance of species characteristic of these two prairie types varies with soil moisture availability, evaporative demand, and recent grazing history (Cogan et al. 1999). In addition, Wind Cave lies near the midpoint of a long gradient of C 3 (cool season) grass dominance to the north and C 4 (warm season) grass dominance to the south. The ecotonal position of WICA may make it particularly sensitive to climate change. For example, small changes in fire frequency and/or intensity and the vigor of trees vs. grass could dramatically shift the proportions of these two lifeforms. The Park hydrology is also sensitive to changes in the balance between infiltration of precipitation and evapotranspiration, as on average, only a small fraction of annual precipitation reaches the deeper soil layers that feed permanent streamflow. The resources at risk at Wind Cave NP include the Cave itself, as well as small backcountry caves, a genetically important bison herd, and other prairie species including the black-tailed prairie dog and endangered black-footed ferrets. All of these resources will be directly affected by climate change impacts on vegetation and hydrology. Natural resource management challenges at WICA are substantial, diverse, and intertwined. Aboveground, the park has been recognized as exemplary for its high quality vegetation (Marriot et al. 1999), though the park is relatively small for the diversity of vegetation types and species that it supports. Even without a changing climate, maintaining the integrity of the plant communities is complicated by the park’s legislated responsibility to maintain viable populations of bison, elk and pronghorn. In addition, the federally endangered black-footed ferret was recently re-introduced to the park. This species requires large extents of prairie dog towns for prey and habitat. Prairie dogs impact vegetation by constant clipping, grazing and soil disturbance, thereby affecting plant composition and productivity. Moreover, naturally high interannual climate variability and the strong influence of precipitation on grass productivity in this region combine to yield high interannual variability in the amount of forage available for the wildlife that the park is tasked to maintain. Finally, fire, which is now primarily controlled by WICA and NPS Northern Great Plains fire management programs, is intertwined with all other natural resource issues at WICA, as it can impact prairie dog colony and forest expansion, ungulate foraging behavior, invasive plant species, and hydrological processes. Although not capable of capturing all of these complexities, dynamic vegetation models do provide a means for quantitatively projecting vegetation futures in future climates under plausible fire and grazing regimes. Our work uses the DGVM MC1 to simulate the effects of future climate projections and management practices on the vegetation of WICA. MC1 is designed to project potential vegetation as influenced by natural processes and hence is appropriate for national parks, where conservation of native biota and ecosystems is of great importance. Since the initial application of MC1 to a small portion of WICA (Bachelet et al. 2000), the model has been altered to improve model performance with the inclusion of dynamic fire. Applying this improved version to WICA required substantial recalibration, during which we have made a number of improvements to MC1 that will be incorporated as permanent changes. In this report we document these changes and our calibration procedure following a brief overview of the model. We compare the projections of current vegetation to the current state of the park and present projections of vegetation dynamics under future climates downscaled from three GCMs selected to represent the existing range in available GCM projections. In doing so, we examine the consequences of different management options regarding fire and grazing, major aspects of biotic management at Wind Cave.

South Dakota↗
USGS2018

Assessing the impact of site-specific BMPs using a spatially explicit, field-scale SWAT model with edge-of-field and tile hydrology and water-quality data in the Eagle Creek watershed, Ohio

The Eagle Creek watershed, a small subbasin (125 km 2 ) within the Maumee River Basin, Ohio, was selected as a part of the Great Lakes Restoration Initiative (GLRI) “Priority Watersheds” program to evaluate the effectiveness of agricultural Best Management Practices (BMPs) funded through GLRI at the field and watershed scales. The location and quantity of BMPs were obtained from the U.S. Department of Agriculture-Natural Resources Conservation Service National Conservation Planning (NCP) database. A Soil and Water Assessment Tool (SWAT) model was built and calibrated for this predominantly agricultural Eagle Creek watershed, incorporating NCP BMPs and monitoring data at the watershed outlet, an edge-of-field (EOF), and tile monitoring sites. Input air temperature modifications were required to induce simulated tile flow to match monitoring data. Calibration heavily incorporated tile monitoring data to correctly proportion surface and subsurface flow, but calibration statistics were unsatisfactory at the EOF and tile monitoring sites. At the watershed outlet, satisfactory to very good calibration statistics were achieved over a 2-year calibration period, and satisfactory statistics were found in the 2-year validation period. SWAT fixes parameters controlling nutrients primarily at the watershed level; a refinement of these parameters at a smaller-scale could improve field-level calibration. Field-scale modeling results indicate that filter strips (FS) are the most effective single BMPs at reducing dissolved reactive phosphorus, and FS typically decreased sediment and nutrient yields when added to any other BMP or BMP combination. Cover crops were the most effective single, in-field practice by reducing nutrient loads over winter months. Watershed-scale results indicate BMPs can reduce sediment and nutrients, but reductions due to NCP BMPs in the Eagle Creek watershed for all water-quality constituents were less than 10%. Hypothetical scenarios simulated with increased BMP acreages indicate larger investments of the appropriate BMP or BMP combination can decrease watershed level loads.

Ohio↗
USGS2013

U.S. Geological Survey core science systems strategy: Characterizing, synthesizing, and understanding the critical zone through a modular science framework

Executive Summary Core Science Systems is a new mission of the U.S. Geological Survey (USGS) that resulted from the 2007 Science Strategy, “Facing Tomorrow’s Challenges: U.S. Geological Survey Science in the Decade 2007–2017.” This report describes the Core Science Systems vision and outlines a strategy to facilitate integrated characterization and understanding of the complex Earth system. The vision and suggested actions are bold and far-reaching, describing a conceptual model and framework to enhance the ability of the USGS to bring its core strengths to bear on pressing societal problems through data integration and scientific synthesis across the breadth of science. The context of this report is inspired by a direction set forth in the 2007 Science Strategy. Specifically, ecosystem-based approaches provide the underpinnings for essentially all science themes that define the USGS. Every point on Earth falls within a specific ecosystem where data, other information assets, and the expertise of USGS and its many partners can be employed to quantitatively understand how that ecosystem functions and how it responds to natural and anthropogenic disturbances. Every benefit society obtains from the planet— food, water, raw materials to build infrastructure, homes and automobiles, fuel to heat homes and cities, and many others— are derived from or affect ecosystems. The vision for Core Science Systems builds on core strengths of the USGS in characterizing and understanding complex Earth and biological systems through research, modeling, mapping, and the production of high quality data on the Nation’s natural resource infrastructure. Together, these research activities provide a foundation for ecosystem-based approaches through geologic mapping, topographic mapping, and biodiversity mapping. The vision describes a framework founded on these core mapping strengths that makes it easier for USGS scientists to discover critical information, share and publish results, and identify potential collaborations that transcend all USGS missions. The framework is designed to improve the efficiency of scientific work within USGS by establishing a means to preserve and recall data for future applications, organizing existing scientific knowledge and data to facilitate new use of older information, and establishing a future workflow that naturally integrates new data, applications, and other science products to make interdisciplinary research easier and more efficient. Given the increasing need for integrated data and interdisciplinary approaches to solve modern problems, leadership by the Core Science Systems mission will facilitate problem solving by all USGS missions in ways not formerly possible. The report lays out a strategy to achieve this vision through three goals with accompanying objectives and actions. The first goal builds on and enhances the strengths of the Core Science Systems mission in characterizing and understanding the Earth system from the geologic framework to the topographic characteristics of the land surface and biodiversity across the Nation. The second goal enhances and develops new strengths in computer and information science to make it easier for USGS scientists to discover data and models, share and publish results, and discover connections between scientific information and knowledge. The third goal brings additional focus to research and development methods to address complex issues affecting society that require integration of knowledge and new methods for synthesizing scientific information. Collectively, the report lays out a strategy to create a seamless connection between all USGS activities to accelerate and make USGS science more efficient by fully integrating disciplinary expertise within a new and evolving science paradigm for a changing world in the 21st century.

Circular↗
USGS2012

Science strategy for Core Science Systems in the U.S. Geological Survey, 2013-2023

Core Science Systems is a new mission of the U.S. Geological Survey (USGS) that grew out of the 2007 Science Strategy, “Facing Tomorrow’s Challenges: U.S. Geological Survey Science in the Decade 2007–2017.” This report describes the vision for this USGS mission and outlines a strategy for Core Science Systems to facilitate integrated characterization and understanding of the complex earth system. The vision and suggested actions are bold and far-reaching, describing a conceptual model and framework to enhance the ability of USGS to bring its core strengths to bear on pressing societal problems through data integration and scientific synthesis across the breadth of science. The context of this report is inspired by a direction set forth in the 2007 Science Strategy. Specifically, ecosystem-based approaches provide the underpinnings for essentially all science themes that define the USGS. Every point on earth falls within a specific ecosystem where data, other information assets, and the expertise of USGS and its many partners can be employed to quantitatively understand how that ecosystem functions and how it responds to natural and anthropogenic disturbances. Every benefit society obtains from the planet—food, water, raw materials to build infrastructure, homes and automobiles, fuel to heat homes and cities, and many others, are derived from or effect ecosystems. The vision for Core Science Systems builds on core strengths of the USGS in characterizing and understanding complex earth and biological systems through research, modeling, mapping, and the production of high quality data on the nation’s natural resource infrastructure. Together, these research activities provide a foundation for ecosystem-based approaches through geologic mapping, topographic mapping, and biodiversity mapping. The vision describes a framework founded on these core mapping strengths that makes it easier for USGS scientists to discover critical information, share and publish results, and identify potential collaborations that transcend all USGS missions. The framework is designed to improve the efficiency of scientific work within USGS by establishing a means to preserve and recall data for future applications, organizing existing scientific knowledge and data to facilitate new use of older information, and establishing a future workflow that naturally integrates new data, applications, and other science products to make it easier and more efficient to conduct interdisciplinary research over time. Given the increasing need for integrated data and interdisciplinary approaches to solve modern problems, leadership by the Core Science Systems mission will facilitate problem solving by all USGS missions in ways not formerly possible. The report lays out a strategy to achieve this vision through three goals with accompanying objectives and actions. The first goal builds on and enhances the strengths of the Core Science Systems mission in characterizing and understanding the earth system from the geologic framework to the topographic characteristics of the land surface and biodiversity across the nation. The second goal enhances and develops new strengths in computer and information science to make it easier for USGS scientists to discover data and models, share and publish results, and discover connections between scientific information and knowledge. The third goal brings additional focus to research and development methods to address complex issues affecting society that require integration of knowledge and new methods for synthesizing scientific information. Collectively, the report lays out a strategy to create a seamless connection between all USGS activities to accelerate and make USGS science more efficient by fully integrating disciplinary expertise within a new and evolving science paradigm for a changing world in the 21st century.

Open-File Report↗
USGS2018

Revised groundwater-flow model of the glacial aquifer system north of Aberdeen, South Dakota, through water year 2015

The city of Aberdeen, in northeastern South Dakota, requires an expanded and sustainable supply of water to meet current and future demands. Conceptual and numerical models of the glacial aquifer system in the area north of Aberdeen were developed by the U.S. Geological Survey in cooperation with the City of Aberdeen in 2012. The U.S. Geological Survey, in cooperation with the City of Aberdeen, completed a study to revise the original numerical groundwater-flow model using data through water year (WY) 2015 to aid the City of Aberdeen in their development of plans and strategies for a sustainable water supply and to increase understanding of the glacial aquifer system and groundwater-flow system near Aberdeen. The original model was revised to improve the fit between model-simulated values and observed (measured or estimated) data, provide greater insight into surface-water interactions, and improve the usefulness of the model for water-supply planning. The revised groundwater-flow model (hereafter referred to as the “revised model”) presented in this report supersedes the original model. The purpose of this report is to describe a revised groundwater-flow model including data collection, model calibration, and model results for the glacial aquifer system including the Elm, Middle James, and Deep James aquifers north of Aberdeen, South Dakota, using updated hydrologic data through WY 2015. The original numerical model was revised in several ways. The model was modified by adding four new layers, which included a surficial layer, two intervening confining layers, and a shale bedrock layer. The revised model provides an improved understanding of the groundwater-flow system in comparison to the original model. The principal aquifers of the model area include portions of the Elm, Middle James, and Deep James aquifers. The lithologic information used to define and describe the aquifers in the model area was unaltered; however, aquifer properties and boundary conditions were reviewed and updated using geological information reported by the South Dakota Department of Environmental and Natural Resources and information obtained from geophysical investigations for this study. The horizontal extent of the Elm, Middle James, and Deep James aquifers was unaltered from the original model. The thickness of the Deep James aquifer was modified based on interpretations from the geophysical investigations. In general, groundwater in the Elm aquifer flowed from northwest to southeast and locally towards rivers and streams. Similarly, in the Middle James and Deep James aquifers, groundwater also typically flowed southeast. The revisions made to the original model include use of the following MODFLOW stress packages: Recharge, Evapotranspiration, Time-Variant Specified Head, Wells, Drains, and Stream Flow Routing, all of which were updated from the original model except for the Stream Flow Routing Package, which replaced the River Package used in the original model. Model calibration is the process of estimating model parameters to minimize the differences, or residuals, between observed data and simulated values; therefore, Parameter ESTimation (PEST) software was used to optimize model input parameters by matching model-simulated values to observed data. Calibration parameters included horizontal hydraulic conductivity, vertical hydraulic conductivity, specific yield, specific storage, and vertical streambed conductance for stream and drain cells. Multipliers were used to calibrate the recharge and evapotranspiration stresses. Evapotranspiration extinction depth also was adjusted during model calibration. Comparisons to the original model are described to highlight the changes made in the revised model. In general, the revised model adequately simulates the natural system and compares favorably with observed hydrologic data. Simulated water levels were evaluated by comparing them to single water-level observations at selected well locations. The selected wells were the same wells used in the original model. The coefficient of determination value between simulated and observed water levels for the revised model was 0.89 and included simulated and observed values from October 1, 1974 (WY 1975), through September 30, 2015 (WY 2015). The coefficient of determination value for the original model was 0.94 and included simulated and observed values from October 1, 1974, through September 30, 2009. The difference may indicate that the original model could have been overfit to hydraulic head observations because base flow was not simulated. The additional data used in the revised model included some climatically wetter, more extreme periods, such as 2011, in which annual precipitation was 30.9 inches. Average annual precipitation for the original model timeframe, which included data from WYs 1975–2009, was 20.26 inches. Additional precipitation data for WYs 2010–15, included in the revised model timeframe, resulted in an average annual precipitation for WYs 1975–2015 in the model area of 20.6 inches. The larger variability in climate data coupled with the additional water-level data could explain the lower coefficient of determination for water levels in the revised model. The revised model was used to calculate various groundwater-budget components for steady-state and transient conditions for WYs 1975–2015. The time-variant specified-head cells in the revised model had the largest change when compared to the original steady-state model for inflows and outflows. Comparing the transient budget components between the original and the revised models indicated that inflow from recharge and time-variant specified-head cells had the greatest effect on groundwater inflows, and outflow from storage had the greatest effect on groundwater outflows. The simulated potentiometric contours from the revised model were compared with (1) the observed (interpreted) potentiometric surface (layer 2) and the hydraulic head values (layers 4 and 6) and (2) the simulated contours from the original model. The simulated hydraulic gradients and general direction of groundwater flow in the Elm aquifer in the revised model generally matched the observed potentiometric contours, the simulated potentiometric contours from the original model, and general flow directions interpreted to be perpendicular to the contours. Minor discrepancies between simulated potentiometric contours from the revised model and the observed potentiometric contours may be due to the lack of observed data in the model area. The revised model was designed to reduce the limitations of the original model. The revisions were validated by comparing the results of the original model with the revised model. A primary benefit of the revised model is the inclusion of the surficial deposits and the confining units as explicit layers in the model. The addition of the surficial layer was beneficial for three primary reasons: (1) more accurate representation of recharge from precipitation, (2) more accurate representation of groundwater evapotranspiration, and (3) more accurate representation of groundwater and surface-water interactions. The groundwater model is a numeric approximation of a complex physical hydrologic system, and the revised model data were interpolated in regions with sparse data. Additionally, model discretization included averaged and interpolated values for water use, withdrawal rates, and hydraulic conductivity. The revised model provides a useful estimate for hydraulic gradients, groundwater-flow directions, and aquifer response to groundwater withdrawals.

South Dakota↗
USGS2019

Groundwater flow model for Western Chippewa County–Including analysis of water resources related to industrial sand mining and irrigated agriculture

A groundwater flow model for western Chippewa County, Wisconsin, was developed by the Wisconsin Geological and Natural History Survey (WGNHS) and the U.S. Geological Survey (USGS) using the computer program MODFLOW. The model is the result of a five-year groundwater study commissioned by Chippewa County in 2012 to evaluate the effects of industrial sand mining and irrigated agriculture on the county’s water resources. The study incorporates existing data and newly acquired data from fieldwork conducted within the study area. The groundwater model may be useful for future investigations, such as evaluation of proposed high-capacity well sites, development of municipal wellhead protection plans, and studies that seek to further quantify surface water-groundwater relationships. The model conceptualizes the hydrostratigraphy of western Chippewa County as six stacked layers. Each layer is distinct, beginning with unlithified glacial material at the surface, and alternating between sandstones (that act as aquifers) and shale units (that serve as aquitards). The model is bounded below by Precambrian crystalline bedrock and its perimeter was derived from a regional-scale groundwater flow model. The MODFLOW model represented average conditions during 2011–2013 with “steady-state” assumptions, meaning that simulated water levels do not fluctuate seasonally or from year to year. Steady-state models simplify natural variability, making results of scenario simulations easier to interpret and compare while also maximizing effects of stressors because the simulated stress is always applied (not halted after a few months or years). Model calibration used the parameter estimation code (PEST), and calibration targets included heads (groundwater levels) and streamflows. Calibration focused on 2011–2013 because a large amount of head and streamflow data were available for that period. The MODFLOW model explicitly simulates all sources and sinks of water, including groundwater/surface-water interaction with streamflow routing. Model input included estimates of aquifer hydraulic conductivity and a spatial groundwater recharge distribution developed using a GIS-based soil-water-balance (SWB) model applied to the model area. Groundwater withdrawals were simulated for 269 high-capacity wells across the entire model domain, which includes western Chippewa County and adjacent portions of Dunn, Barron, and Rusk Counties. Collectively, these wells withdrew about 1.14 million gallons per year between 2011 and 2013. Once the model was calibrated, it was applied to two distinct scenarios of increased groundwater withdrawals: one evaluating hydrologic effects of more intensive industrial sand mining and the second evaluating the hydrologic effects of more intensive agricultural irrigation practices. Each scenario was developed with input from Chippewa County and a stakeholder group established expressly for this study. The scenarios were designed to represent reasonable future buildout conditions for both mining and irrigated agriculture. The mining scenario underscores the potential hydrologic effects related to changing land-use practices (i.e., hilltops and farmland becoming sand mines), while the irrigated agriculture scenario illustrates the potential hydrologic effects of intensifying existing land-use practices (i.e., installing new wells to irrigate farm fields). While each scenario evaluated distinctly different conditions, modeling results demonstrated the potential of both scenarios to lower the water table and reduce baseflows in headwater streams within the modeled area. In the case of irrigated agriculture, hydrologic effects were associated directly with groundwater withdrawals. By assuming that irrigation did not decrease, this steady-state simulation represented a sustained future effect. By contrast, hydrologic effects of industrial sand mining were the result of both groundwater withdrawals at mines and land-use changes that effectively reduced recharge to groundwater over distinct phases of active mining. This scenario included a post-mining phase, during which groundwater withdrawals stopped and mined areas were reclaimed to undeveloped prairie grass cover. If reclamation to undeveloped prairie indeed occurs as simulated, long-term increases in the water table and stream baseflows are possible. In this sense, the scenario representing build out of irrigated agriculture led to long-term baseflow declines while the future buildout of industrial sand mining led to declines that dissipated following mine reclamation to undisturbed prairie. Future investigations in similar hydrogeologic settings may find the following insights gleaned from this study useful: ❚❚ The characterization of hydrogeologic properties, delineation of hydrogeologic units, and calibration of groundwater flow models benefited from incorporation of accurate well construction reports, high-quality borehole geophysical logs, and streamflow gaging data. ❚❚ Infiltration testing performed in active mining areas provided evidence that reducing the degree and extent of compaction and enhancing areas designed to retain and infiltrate stormwater runoff could potentially reduce runoff and increase groundwater recharge. ❚❚ Similarly, reclaiming mined areas to prairie grasses would be expected to reduce runoff and increase groundwater recharge by reducing compaction and improving soil structure and vegetation that can slow runoff and enhance infiltration.

Wisconsin↗
USGS2010

Land Disturbance Associated with Oil and Gas Development and Effects of Development-Related Land Disturbance on Dissolved-Solids Loads in Streams in the Upper Colorado River Basin, 1991, 2007, and 2025

Oil and gas resource development in the Upper Colorado River Basin (UCRB) has increased substantially since the year 2000. The UCRB encompasses several significant oil and gas producing areas that have the potential for continued oil and gas resource development. Land disturbance associated with oil and gas resource development is caused by activities related to constructing drill pads to contain drilling and well maintenance equipment and roads to access the drill pad. Land disturbed by oil and gas development has the potential to cause increased erosion, stream degradation, habitat fragmentation and alteration, and increase public use of areas that may be environmentally sensitive. Land disturbance resulting from oil and gas resource development has not been monitored and mapped on a regional scale in the UCRB. However, information on the location and age of oil and gas wells in the UCRB is available. These data combined with geographic data analysis and modeling techniques were used to estimate the total area of disturbed land associated with oil and gas resource development in 1991 and in 2007 in the UCRB. Additional information about anticipated oil and gas development in the UCRB was used to project land disturbance to the year 2025. Results of the analysis indicate that approximately 117,500 acres (183 mi2) of total land disturbance was associated with drill pads and related roads in the UCRB in 1991. The estimated area of disturbed land associated with oil and gas development increased 53 percent to 179,400 acres (280 mi2) in 2007. Projecting oil and gas development through 2025 results in a potential near doubling of the land surface disturbance to approximately 319,300 acres (500 mi2). Estimated land disturbance for 1991 and 2007 were input to a contaminant transport model developed for the UCRB to assess the statistical significance of energy-related land disturbance to contributing dissolved solids to basin streams. The statistical assessment was an observational study based on an existing model and available water-quality monitoring data for the basin. No new data were collected for the analysis. The source coefficient calibrated for the disturbed lands associated with oil and gas development in 2007 was zero, which indicated that estimated land disturbance from oil and gas development is not statistically significant in explaining dissolved solids in UCRB streams. The lack of significance in the contaminant transport modeling framework may be due to the amount of available monitoring data, the spatial distribution of monitoring sites with respect to land disturbance, or the overall quantity of land disturbance associated with oil and gas development basin wide. Finally, dissolved-solids loads derived from natural landscapes may be similar to loads derived from lands disturbed by oil and gas resource development. The model recalibration done for this study confirms calibration results from Kenney and others (2009): the most significant contributor to dissolved solids in the UCRB is irrigated agricultural land, which covers an area substantially larger than the estimated area disturbed by oil and gas development and is subjected to artificially applied water.

Scientific Investigations Report↗
USGS2023

A big data–model integration approach for predicting epizootics and population recovery in a keystone species

Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus ) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally >0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host–pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data–model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making.

Arizona, Colorado, Kansas, Montana, Nebraska, New ↗
USGS1996

Use of a deterministic fire growth model to test fuel treatments

Fuel treatments are necessary in many vegetated areas of the Sierra Nevada to mitigate the effects of decades of fire suppression and land-management activities on fuel accumulations and understory canopies. Treating fuels will reduce the severity of wildfires and, as a result, the threat to human lives, the destruction of property and valuable resources, and the alteration of natural fire regimes. This chapter describes the use of a deterministic fire-modeling approach to obtain information about the relative effectiveness of fuel treatments, including fuel breaks, prescribed burning, biomassing, piling and burning, and cutting and scattering. Wildfire spread was simulated under idealized conditions to see how specific fuel and stand treatments affect fire behavior. It was obvious from the simulations that fuel breaks alone do not halt the spread of wildfire. Prescribed burning appears to be the most effective treatment for reducing a fire’s rate of spread, fireline intensity, flame length, and heat per unit of area. A management scheme that includes a combination of fuel treatments in conjunction with other land-management scenarios should be successful in reducing the size and intensity of wildfires.

Book chapter↗
USGS2017

Prediction and visualization of redox conditions in the groundwater of Central Valley, California

Regional-scale, three-dimensional continuous probability models, were constructed for aspects of redox conditions in the groundwater system of the Central Valley, California. These models yield grids depicting the probability that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions, or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL). The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 m. Probability distribution grids can be extracted from the 3-D models at any desired depth, and are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions. Models were constructed using a Boosted Regression Trees (BRT) machine learning technique that produces many trees as part of an additive model and has the ability to handle many variables, automatically incorporate interactions, and is resistant to collinearity. Machine learning methods for statistical prediction are becoming increasing popular in that they do not require assumptions associated with traditional hypothesis testing. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2767 wells within the alluvial boundary of the Central Valley, and over 60 explanatory variables representing regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrologic properties. Models were trained on a USGS dataset of 932 wells, and evaluated on an independent hold-out dataset of 1835 wells from the California Division of Drinking Water. We used cross-validation to assess the predictive performance of models of varying complexity, as a basis for selecting final models. Trained models were applied to cross-validation testing data and a separate hold-out dataset to evaluate model predictive performance by emphasizing three model metrics of fit: Kappa; accuracy; and the area under the receiver operator characteristic curve (ROC). The final trained models were used for mapping predictions at discrete depths to a depth of 304.8 m. Trained DO and Mn models had accuracies of 86–100%, Kappa values of 0.69–0.99, and ROC values of 0.92–1.0. Model accuracies for cross-validation testing datasets were 82–95% and ROC values were 0.87–0.91, indicating good predictive performance. Kappas for the cross-validation testing dataset were 0.30–0.69, indicating fair to substantial agreement between testing observations and model predictions. Hold-out data were available for the manganese model only and indicated accuracies of 89–97%, ROC values of 0.73–0.75, and Kappa values of 0.06–0.30. The predictive performance of both the DO and Mn models was reasonable, considering all three of these fit metrics and the low percentages of low-DO and high-Mn events in the data.

California↗
USGS2018

Maps showing predicted probabilities for selected dissolved oxygen and dissolved manganese threshold events in depth zones used by the domestic and public drinking water supply wells, Central Valley, California

The purpose of the prediction grids for selected redox constituents—dissolved oxygen and dissolved manganese—are intended to provide an understanding of groundwater-quality conditions at the domestic and public-supply drinking water depths. The chemical quality of groundwater and the fate of many contaminants is influenced by redox processes in all aquifers, and understanding the redox conditions horizontally and vertically is critical in evaluating groundwater quality. The redox condition of groundwater—whether oxic (oxygen present) or anoxic (oxygen absent)—strongly influences the oxidation state of a chemical in groundwater. The anoxic dissolved oxygen thresholds of <0.5 milligram per liter (mg/L), <1.0 mg/L, and <2.0 mg/L were selected to apply broadly to regional groundwater-quality investigations. Although the presence of dissolved manganese in groundwater indicates strongly reducing (anoxic) groundwater conditions, it is also considered a “nuisance” constituent in drinking water, making drinking water undesirable with respect to taste, staining, or scaling. Three dissolved manganese thresholds, <50 micrograms per liter (µg/L), <150 µg/L, and <300 µg/L, were selected to create predicted probabilities of exceedances in depth zones used by domestic and public-supply water wells. The 50 µg/L event threshold represents the secondary maximum contaminant level (SMCL) benchmark for manganese (U.S. Environmental Protection Agency, 2017; California Division of Drinking Water, 2014), whereas the 300 µg/L event threshold represents the U.S. Geological Survey (USGS) health-based screening level (HBSL) benchmark, used to put measured concentrations of drinking-water contaminants into a human-health context (Toccalino and others, 2014). The 150 µg/L event threshold represents one-half the USGS HBSL. The resultant dissolved oxygen and dissolved manganese prediction grids may be of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions. Prediction grids for selected redox constituents and thresholds were created by the USGS National Water-Quality Assessment (NAWQA) modeling and mapping team.

California↗
USGS2019

Updates to the Madison Lake (Minnesota) CE–QUAL–W2 water-quality model for assessing algal community dynamics

A previously developed CE–QUAL–W2 model for Madison Lake, Minnesota, simulated the algal community dynamics, water quality, and fish habitat suitability of Madison Lake under recent (2014) meteorological conditions. Additionally, this previously developed model simulated the complex interplay between external nutrient loading, internal nutrient loading from sediment release of phosphorus, and the organic matter decomposition of the algal biomass. However, the partitioning of Cyanophyta within the modeling framework was simplified to one group and did not account for how different Cyanophyta populations are affected by light conditions, use of nitrogen, temperature growth ranges, and differences in settling rates. Properly capturing Cyanophyta dynamics is important given the potential risks posed by potential large algal blooms. For example, when Cyanophyta form large blooms, recreational activities can become restricted in certain areas because of thick algal scums or algal mats, in addition to the possible production of a class of toxins, known as cyanotoxins, capable of threatening human health, domestic animals, and wildlife. Therefore, we updated the model to partition the Cyanophyta into a group that fixed nitrogen and a second, more buoyant Cyanophyta group that did not independently fix nitrogen. The U.S. Geological Survey, in cooperation with the St. Croix Watershed Research Station (Science Museum of Minnesota) with support from the Environmental and Natural Resources Trust Fund of Minnesota (Legislative-Citizen Commission on Minnesota Resources), updated the Madison Lake CE–QUAL–W2 model to address the shortcomings of simulating Cyanophyta in the previously developed model and better characterize Cyanophyta into two groups. In addition to updating the Cyanophyta group differentiation, the part of the model that handles the simulation of algal community dynamics was updated while preserving model predictive capabilities for nutrients, water temperature, and dissolved oxygen. The calibration and validation of the model was done under recent meteorological conditions with large and persistent Cyanophyta blooms (2014 and 2016). Overall, the model simulations predicted the persistently large total phosphorus concentrations in the hypolimnion of Madison Lake and key differences in nutrient concentrations between 2014 and 2016. The Cyanophyta bloom persistence throughout the summer was also simulated by the model in 2014 and 2016, a critical goal of the model update. Finally, monthly total phosphorus budgets were calculated for the updated Madison Lake model for 2014 and 2016.

Minnesota↗
USGS2007

Facing tomorrow’s challenges—U.S. Geological Survey science in the decade 2007–2017

Executive Summary In order for the U.S. Geological Survey (USGS) to respond to evolving national and global priorities, it must periodically reflect on, and optimize, its strategic directions. This report is the first comprehensive science strategy since the early 1990s to examine critically major USGS science goals and priorities. The development of this science strategy comes at a time of global trends and rapidly evolving societal needs that pose important natural-science challenges. The emergence of a global economy affects the demand for all resources. The last decade has witnessed the emergence of a new model for managing Federal lands—ecosystem-based management. The U.S. Climate Change Science Program predicts that the next few decades will see rapid changes in the Nation’s and the Earth’s environment. Finally, the natural environment continues to pose risks to society in the form of volcanoes, earthquakes, wildland fires, floods, droughts, invasive species, variable and changing climate, and natural and anthropogenic toxins, as well as animal-borne diseases that affect humans. The use of, and competition for, natural resources on the global scale, and natural threats to those resources, has the potential to impact the Nation’s ability to sustain its economy, national security, quality of life, and natural environment. Responding to these national priorities and global trends requires a science strategy that not only builds on existing USGS strengths and partnerships but also demands the innovation made possible by integrating the full breadth and depth of USGS capabilities. The USGS chooses to go forward in the science directions proposed here because the societal issues addressed by these science directions represent major challenges for the Nation’s future and for the stewards of Federal lands, both onshore and offshore. The six science directions proposed in this science strategy are summarized in the following paragraphs. The ecosystems strategy is listed first because it has a dual nature. It is itself an essential direction for the USGS to pursue to meet a pressing national and global need, but ecosystem-based approaches are also an underpinning of the other five directions, which all require ecosystem perspectives and tools for their execution. The remaining strategic directions are listed in alphabetical order.

Circular↗