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At least 73 records · Page 4Linked to original sources
USGS2022

Analysis of per capita contributions from a spatial model provides strategies for controlling spread of invasive carp

Metapopulation models may be applied to inform natural resource management to guide actions targeted at location-specific subpopulations. Model insights frequently help to understand which subpopulations to target and highlight the importance of connections among subpopulations. For example, managers often treat aquatic invasive species populations as discrete populations due to hydrological (e.g., lakes, pools formed by dams) or jurisdictional boundaries (e.g., river segments by country or jurisdictional units such as states or provinces). However, aquatic invasive species often have high rates of dispersion and migration among heterogenous locations, which complicates traditional metapopulation models and may not conform to management boundaries. Controlling invasive species requires consideration of spatial dynamics because local management activities (e.g., harvest, movement deterrents) may have important impacts on connected subpopulations. We expand upon previous work to create a spatial linear matrix model for an aquatic invasive species, Bighead Carp, in the Illinois River, USA, to examine the per capita contributions of specific subpopulations and impacts of different management scenarios on these subpopulations. Managers currently seek to prevent Bighead Carp from invading the Great Lakes via a connection between the Illinois Waterway and Lake Michigan by allocating management actions across a series of river pools. We applied the model to highlight how spatial variation in movement rates and recruitment can affect decisions about where management activities might occur. We found that where the model suggested management actions should occur depend crucially on the specific management goal (i.e., limiting the growth rate of the metapopulation vs. limiting the growth rate of the invasion front) and the per capita recruitment rate in downstream pools. Our findings illustrate the importance of linking metapopulation dynamics to management goals for invasive species control.

Illinois↗
USGS1996

Comparison of estimated and observed stormwater runoff for fifteen watersheds in west-central Florida, using five common design techniques

Hydrologists use several traditional techniques for estimating peak discharges and runoff volumes from ungaged watersheds. However, applying these techniques to watersheds in west-central Florida requires that empirical relationships be extrapolated beyond tested ranges. As a result there is some uncertainty as to their accuracy. Sixty-six storms in 15 west-central Florida watersheds were modeled using (1) the rational method, (2) the U.S. Geological Survey regional regression equations, (3) the Natural Resources Conservation Service (formerly the Soil Conservation Service) TR-20 model, (4) the Army Corps of Engineers HEC-1 model, and (5) the Environmental Protection Agency SWMM model. The watersheds ranged between fully developed urban and undeveloped natural watersheds. Peak discharges and runoff volumes were estimated using standard or recommended methods for determining input parameters. All model runs were uncalibrated and the selection of input parameters was not influenced by observed data. The rational method, only used to calculate peak discharges, overestimated 45 storms, underestimated 20 storms and estimated the same discharge for 1 storm. The mean estimation error for all storms indicates the method overestimates the peak discharges. Estimation errors were generally smaller in the urban watersheds and larger in the natural watersheds. The U.S. Geological Survey regression equations provide peak discharges for storms of specific recurrence intervals. Therefore, direct comparison with observed data was limited to sixteen observed storms that had precipitation equivalent to specific recurrence intervals. The mean estimation error for all storms indicates the method over estimates both peak discharges and runoff volumes. Estimation errors were smallest for the larger natural watersheds in Sarasota County, and largest for the small watersheds located in the eastern part of the study area. The Natural Resources Conservation Service TR-20 model, overestimated peak discharges for 45 storms and underestimated 21 storms, and overestimated runoff volumes for 44 storms and underestimated 22 storms. The mean estimation error for all storms modeled indicates that the model overestimates peak discharges and runoff volumes. The smaller estimation errors in both peak discharges and runoff volumes were for storms occurring in the urban watersheds, and the larger errors were for storms occurring in the natural watersheds. The HEC-1 model overestimated peak discharge rates for 55 storms and underestimated 11 storms. Runoff volumes were overestimated for 44 storms and underestimated for 22 storms using the Army Corps of Engineers HEC-1 model. The mean estimation error for all the storms modeled indicates that the model overestimates peak discharge rates and runoff volumes. Generally, the smaller estimation errors in peak discharges were for storms occurring in the urban watersheds, and the larger errors were for storms occurring in the natural watersheds. Estimation errors in runoff volumes; however, were smallest for the 3 natural watersheds located in the southernmost part of Sarasota County. The Environmental Protection Agency Storm Water Management model produced similar peak discharges and runoff volumes when using both the Green-Ampt and Horton infiltration methods. Estimated peak discharge and runoff volume data calculated with the Horton method was only slightly higher than those calculated with the Green-Ampt method. The mean estimation error for all the storms modeled indicates the model using the Green-Ampt infiltration method overestimates peak discharges and slightly underestimates runoff volumes. Using the Horton infiltration method, the model overestimates both peak discharges and runoff volumes. The smaller estimation errors in both peak discharges and runoff volumes were for storms occurring in the five natural watersheds in Sarasota County with the least amount of impervious cover and the lowest slopes. The largest errors were for storms occurring in the three small natural watersheds in the eastern part of the study area. The mean estimation errors for peak discharge ranged from an underestimation of 63 percent to an overestimation of 224 percent. For runoff volume, the mean estimation errors range from an underestimation of 63.3 percent to an overestimation of 267 percent.

Florida↗
USGS2019

Successful molecular detection studies require clear communication among diverse research partners

Molecular detection techniques are powerful tools used in ecological applications ranging from diet analyses to pathogen surveillance. Research partnerships that use these tools often involve collaboration among professionals with expertise in field biology, laboratory techniques, quantitative modeling, wildlife disease, and natural resource management. However, in many cases, each of these collaborators lacks specific knowledge about the approaches, decisions, methods, and terminology used by their research partners, which can impede effective communication and act as a barrier to the efficient use of molecular data for ecological inferences and subsequent conservation decision making. We outline a collaborative framework to assist colleagues with diverse types of expertise to effectively translate their scientific and management needs to research partners from other specialties. The molecular techniques used to detect organisms will continue to advance both in sophistication and in the breadth of ecological applications. Our objective is to enable ecologists to harness the full utility of these methods by developing effective collaborative partnerships.

Frontiers in Ecology and the Environment↗
USGS2020

InFish: A professional network to promote global conservation and responsible use of inland fish

Inland fishes and fisheries make substantial contributions to individuals, society, and the environment in a changing global landscape that includes climate, water allocations, and societal changes. However, current limitations to valuing the services provided by inland fish and their fisheries often leaves them out of key decision‐making discussions. InFish is a voluntary professional network with over 120 members from over 50 organizations in over 20 countries that seeks to address challenges facing inland fish through novel approaches and international collaborations. InFish fosters opportunities to share knowledge, pursue proposals, publications, and conference‐related events focused on inland fisheries. InFish has become a source of inland fisheries expertise, working collectively towards global conservation and sustainable use of inland fish through informing scientifically sound management practices. As such, InFish may serve as a model network for other natural resource challenges now and into the future.

Fisheries Magazine↗
USGS2001

Habitat measurement and modeling in the Green and Yampa Rivers: Project Report to Natural Resource Preservation Program December 2001

Populations of the endangered razorback sucker ( Xyrauchen texanus ) in the middle Green River have declined since closer of Flaming Gorge Dam in 1962. The apparent cause for the decline is a lack of successful recruitment. Recruitment failure has been attributed to habitat alteration and competition and predation by exotic fishes on early life stages of razorback sucker. This study was conducted to evaluate two of the potential reproductive bottlenecks that might limit recruitment of razorback sucker in the Green River Drainage; (1) reduced larvae production due to sediment deposition on spawning areas, and (2) reduced survival of larvae or juveniles due to lack of timely access to rood-rich backwater and floodplain habitats.

Report↗
USGS2012

The Modular Modeling System (MMS): A modeling framework for water- and environmental-resources management

The interdisciplinary nature and increasing complexity of water- and environmental-resource problems require the use of modeling approaches that can incorporate knowledge from a broad range of scientific disciplines. The large number of distributed hydrological and ecosystem models currently available are composed of a variety of different conceptualizations of the associated processes they simulate. Assessment of the capabilities of these distributed models requires evaluation of the conceptualizations of the individual processes, and the identification of which conceptualizations are most appropriate for various combinations of criteria, such as problem objectives, data constraints, and spatial and temporal scales of application. With this knowledge, "optimal" models for specific sets of criteria can be created and applied. The U.S. Geological Survey (USGS) Modular Modeling System (MMS) is an integrated system of computer software that has been developed to provide these model development and application capabilities. MMS supports the integration of models and tools at a variety of levels of modular design. These include individual process models, tightly coupled models, loosely coupled models, and fully-integrated decision support systems. A variety of visualization and statistical tools are also provided. MMS has been coupled with the Bureau of Reclamation (BOR) object-oriented reservoir and river-system modeling framework, RiverWare, under a joint USGS-BOR program called the Watershed and River System Management Program. MMS and RiverWare are linked using a shared relational database. The resulting database-centered decision support system provides tools for evaluating and applying optimal resource-allocation and management strategies to complex, operational decisions on multipurpose reservoir systems and watersheds. Management issues being addressed include efficiency of water-resources management, environmental concerns such as meeting flow needs for endangered species, and optimizing operations within the constraints of multiple objectives such as power generation, irrigation, and water conservation. This decision support system approach is being developed, tested, and implemented in the Gunni-son, Yakima, San Juan, Rio Grande, and Truckee River basins of the western United States. Copyright ASCE 2004.

Conference Paper↗
USGS2011

Hydrostratigraphic interpretation of test-hole and geophysical data, Upper Loup River Basin, Nebraska, 2008-10

Nebraska's Upper Loup Natural Resources District is currently (2011) participating in the Elkhorn-Loup Model to understand the effect of various groundwater-management scenarios on surface-water resources. During Phase 1 of the Elkhorn-Loup Model, a lack of subsurface geological information in the Upper Loup Natural Resources District, hereafter referred to as the upper Loup study area, was identified as a gap in current knowledge that needed to be addressed. To improve the understanding of the hydrogeology of the upper Loup study area, the U.S. Geological Survey, in cooperation with the Upper Loup Natural Resources District and the University of Nebraska Conservation and Survey Division, collected and described the lithology of drill cuttings from nine test holes, and concurrently collected borehole geophysical data to identify the base of the High Plains aquifer. Surface geophysical data also were collected using time-domain electromagnetic (TDEM) and audio-magnetotelluric (AMT) methods at test-hole locations and between test holes, as a quick, non-invasive means of identifying the base of the High Plains aquifer. Test-hole drilling has indicated greater variation in the base-of-aquifer elevation in the western part of the upper Loup study area than in the eastern part reflecting a number of deep paleovalleys incised into the Brule Formation of the White River Group. TDEM measurements within the upper Loup study area were shown to be effective as virtual boreholes in mapping out the base of the aquifer. TDEM estimates of the base of aquifer were in good accordance with existing test-hole data and were able to improve the interpreted elevation and topology of the base of the aquifer. In 2010, AMT data were collected along a profile, approximately 12 miles (19 kilometers) in length, along Whitman Road, in Grant and Cherry Counties. The AMT results along Whitman Road indicated substantial variability in the elevation of the base of the High Plains aquifer and in the distribution of highly permeable zones within the aquifer.

Open-File Report↗
USGS2020

Water balance as an indicator of natural resource condition: Case studies from Great Sand Dunes National Park and Preserve

Managing climate impacts to natural resources in protected areas can be hampered by lack of monitoring data, poor understanding of natural resource responses to climate, or lack of timely condition assessments that can inform management actions. Here we demonstrate the utility of water balance as a tool for understanding natural resource responses to climate by developing case studies focused on stream flow, vegetation production, and wildfire ignition at Great Sand Dunes National Park and Preserve (GSDNP), U.S.A. The efficacy of water balance to predict these responses stems from the explicit integration of climate with site conditions that modify the effects of climate. This in turn results in estimates of water availability, water use, and water need that are proximal drivers of aquatic and terrestrial natural resource conditions. The water balance model successfully forecasted stream flow (r 2 = 0.69, P < 0.001); determined the critical water needs for maintaining annual vegetation production in different vegetation types spanning a large environmental gradient (r 2 = 0.18–0.71); and predicted proportion of historic wildfire ignitions in forest (r 2 = 0.96–0.99) and non-forest (r 2 = 0.96–0.97) vegetation types. Collectively, these case studies demonstrate practical approaches to translate climate data into assessments of natural resource condition that inform long-term planning and near-term strategic actions needed for conservation of protected areas.

Great Sand Dunes National Park and Preserve↗
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↗
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↗