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USGS2016

Deriving habitat models for northern long-eared bats from historical detection data: A case study using the Fernow Experimental Forest

The listing of the northern long-eared bat ( Myotis septentrionalis ) as federally threatened under the Endangered Species Act following severe population declines from white-nose syndrome presents considerable challenges to natural resource managers. Because the northern long-eared bat is a forest habitat generalist, development of effective conservation measures will depend on appropriate understanding of its habitat relationships at individual locations. However, severely reduced population sizes make gathering data for such models difficult. As a result, historical data may be essential in development of habitat models. To date, there has been little evaluation of how effective historical bat presence data, such as data derived from mist-net captures, acoustic detection, and day-roost locations, may be in developing habitat models, nor is it clear how models created using different data sources may differ. We explored this issue by creating presence probability models for the northern long-eared bat on the Fernow Experimental Forest in the central Appalachian Mountains of West Virginia using a historical, presence-only data set. Each presence data type produced outputs that were dissimilar but that still corresponded with known traits of the northern long-eared bat or are easily explained in the context of the particular data collection protocol. However, our results also highlight potential limitations of individual data types. For example, models from mist-net capture data only showed high probability of presence along the dendritic network of riparian areas, an obvious artifact of sampling methodology. Development of ecological niche and presence models for northern long-eared bat populations could be highly valuable for resource managers going forward with this species. We caution, however, that efforts to create such models should consider the substantial limitations of models derived from historical data, and address model assumptions.

West Virginia↗

USGS2014

The 3D Elevation Program: summary for Montana

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Montana, elevation data are critical for flood risk management, infrastructure and construction management, agriculture and precision farming, geologic resource assessment and hazard mitigation, natural resources conservation, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The new 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

Montana↗

USGS2014

The 3D Elevation Program: summary for Oregon

Elevation data are essential to a broad range of business uses, including forest resources management, wildlife and habitat management, national security, recreation, and many others. In the State of Oregon, elevation data are critical for river and stream resource management; forest resources management; water supply and quality; infrastructure and construction management; wildfire management, planning and response; natural resources conservation; and other business uses. Today, high-density light detection and ranging (lidar) data are the primary source for deriving elevation models and other datasets. The Oregon Lidar Consortium (OLC), led by the Oregon Department of Geology and Mineral Industries (DOGAMI), has developed partnerships with Federal, State, Tribal, and local agencies to acquire quality level 1 data in areas of shared interest. The goal of OLC partners is to acquire consistent, high-resolution and high-quality statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

Oregon↗

USGS2010

A methodology for the assessment of unconventional (continuous) resources with an application to the Greater Natural Buttes gas field, Utah

The Greater Natural Buttes tight natural gas field is an unconventional (continuous) accumulation in the Uinta Basin, Utah, that began production in the early 1950s from the Upper Cretaceous Mesaverde Group. Three years later, production was extended to the Eocene Wasatch Formation. With the exclusion of 1100 non-productive (“dry”) wells, we estimate that the final recovery from the 2500 producing wells existing in 2007 will be about 1.7 trillion standard cubic feet (TSCF) (48.2 billion cubic meters (BCM)). The use of estimated ultimate recovery (EUR) per well is common in assessments of unconventional resources, and it is one of the main sources of information to forecast undiscovered resources. Each calculated recovery value has an associated drainage area that generally varies from well to well and that can be mathematically subdivided into elemental subareas of constant size and shape called cells. Recovery per 5-acre cells at Greater Natural Buttes shows spatial correlation; hence, statistical approaches that ignore this correlation when inferring EUR values for untested cells do not take full advantage of all the information contained in the data. More critically, resulting models do not match the style of spatial EUR fluctuations observed in nature. This study takes a new approach by applying spatial statistics to model geographical variation of cell EUR taking into account spatial correlation and the influence of fractures. We applied sequential indicator simulation to model non-productive cells, while spatial mapping of cell EUR was obtained by applying sequential Gaussian simulation to provide multiple versions of reality (realizations) having equal chances of being the correct model. For each realization, summation of EUR in cells not drained by the existing wells allowed preparation of a stochastic prediction of undiscovered resources, which range between 2.6 and 3.4 TSCF (73.6 and 96.3 BCM) with a mean of 2.9 TSCF (82.1 BCM) for Greater Natural Buttes. A second approach illustrates the application of multiple-point simulation to assess a hypothetical frontier area for which there is no production information but which is regarded as being similar to Greater Natural Buttes.

Utah↗

USGS2014

Watershed-scale modeling of streamflow change in incised montane meadows

Land use practices have caused stream channel incision and water table decline in many montane meadows of the Western United States. Incision changes the magnitude and timing of streamflow in water supply source watersheds, a concern to resource managers and downstream water users. The hydrology of montane meadows under natural and incised conditions was investigated using watershed simulation for a range of hydrologic conditions. The results illustrate the interdependence between: watershed and meadow hydrology; bedrock and meadow aquifers; and surface and groundwater flow through the meadow for the modeled scenarios. During the wet season, stream incision resulted in less overland flow and interflow and more meadow recharge causing a net decrease in streamflow and increase in groundwater storage relative to natural meadow conditions. During the dry season, incision resulted in less meadow evapotranspiration and more groundwater discharge to the stream causing a net increase in streamflow and a decrease in groundwater storage relative to natural meadow conditions. In general, for a given meadow setting, the magnitude of change in summer streamflow and long-term change in watershed groundwater storage due to incision will depend on the combined effect of: reduced evapotranspiration in the eroded meadow; induced groundwater recharge; replenishment of dry season groundwater storage depletion in meadow and bedrock aquifers by precipitation during wet years; and groundwater storage depletion that is not replenished by precipitation during wet years.

Water Resources Research↗

USGS2014

Applying threshold concepts to conservation management of dryland ecosystems: Case studies on the Colorado Plateau

Ecosystems may occupy functionally distinct alternative states, some of which are more or less desirable from a management standpoint. Transitions from state to state are usually associated with a particular trigger or sequence of triggers, such as the addition or subtraction of a disturbance. Transitions are often not linear, rather it is common to see an abrupt transition come about even though the trigger increases only incrementally; these are examples of threshold behaviors. An ideal monitoring program, such as the National Park Service’s Inventory and Monitoring Program, would quantify triggers, and be able to inform managers when measurements of a trigger are approaching a threshold so that management action can avoid an unwanted state transition. Unfortunately, both triggers and the threshold points at which state transitions occur are generally only partially known. Using case studies, we advance a general procedure to help identify triggers and estimate where threshold dynamics may occur. Our procedure is as follows: (1) Operationally define the ecosystem type being considered; we suggest that the ecological site concept of the Natural Resource Conservation Service is a useful system, (2) Using all available a priori knowledge to develop a state-and-transition model (STM), which defines possible ecosystem states, plausible transitions among them and likely triggers, (3) Validate the STM by verifying the existence of its states to the greatest degree possible, (4) Use the STM model to identify transitions and triggers likely to be detectable by a monitoring program, and estimate to the greatest degree possible the value of a measurable indicator of a trigger at the point that a state transition is imminent (tipping point), and values that may indicate when management intervention should be considered (assessment points). We illustrate two different methods for attaining these goals using a data-rich case study in Canyonlands National Park, and a data-poor case study in Wupatki National Monument. In the data-rich case, STMs are validated and revised, and tipping and assessment points are estimated using statistical analysis of data. In the data-poor case, we develop an iterative expert opinion survey approach to validate the degree of confidence in an STM, revise the model, identify lack of confidence in specific model components, and create reasonable first approximations of tipping and assessment points, which can later be refined when more data are available. Our goal should be to develop the best set of models possible given the level of information available to support decisions, which is often not much. The approach presented here offers a flexible means of achieving this goal, and determining specific research areas in need of study.

Book chapter↗

USGS2023

Decision science as a framework for combining geomorphological and ecological modeling for the management of coastal systems

The loss of ecosystem services due to climate change and coastal development is projected to have significant impacts on local economies and conservation of natural resources. Consequently, there has been an increase in coastal management activities such as living shorelines, oyster reef restoration, marsh restoration, beach and dune nourishment, and revegetation projects. Coastal management decisions are complex and include challenging trade-offs. Decision science offers a useful framework to address such complex problems. Here, we provide a synthesis about how decision science can help to integrate research from multiple disciplines (physical and life sciences) with management of coastal and marine systems. Specifically, we discuss the importance of considering concepts and techniques from ecology, coastal geology, geomorphology, climate science, oceanography, and decision analysis when developing conservation plans for coastal restoration. We illustrate the process with several coastal restoration studies. Our capstone example is based on a recent barrier island restoration assessment project at Dauphin Island, Alabama, which included the development of geomorphological and ecological models. We show how decision science can be used as a framework to combine geomorphological and ecological modeling to help inform management decisions while considering uncertainty about system changes and risk tolerance. We also build on our examples through a review of recently developed techniques for spatial conservation planning for land acquisition decisions and the application of adaptive management for sequential decisions.

Alabama↗

USGS2012

A stakeholder project to model water temperature under future climate scenarios in the Satus and Toppenish watersheds of the Yakima River Basinin Washington, USA

The goal of this study was to support an assessment of the potential effects of climate change on select natural, social, and economic resources in the Yakima River Basin. A workshop with local stakeholders highlighted the usefulness of projecting climate change impacts on anadromous steelhead ( Oncorhynchus mykiss) , a fish species of importance to local tribes, fisherman, and conservationists. Stream temperature is an important environmental variable for the freshwater stages of steelhead. For this study, we developed water temperature models for the Satus and Toppenish watersheds, two of the key stronghold areas for steelhead in the Yakima River Basin. We constructed the models with the Stream Network Temperature Model (SNTEMP), a mechanistic approach to simulate water temperature in a stream network. The models were calibrated over the April 15, 2008 to September 30, 2008 period and validated over the April 15, 2009 to September 30, 2009 period using historic measurements of stream temperature and discharge provided by the Yakama Nation Fisheries Resource Management Program. Once validated, the models were run to simulate conditions during the spring and summer seasons over a baseline period (1981–2005) and two future climate scenarios with increased air temperature of 1°C and 2°C. The models simulated daily mean and maximum water temperatures at sites throughout the two watersheds under the baseline and future climate scenarios.

Washington↗

USGS2025

Participatory modelling to support evaluation of management actions for recreational fisheries

Participatory modelling (PM) is a collaborative problem-solving approach that involves shared learning through the development of a model or multiple models with a group of participants and a modelling team. PM has the potential to be used more frequently to address recreational fisheries problems. We focus on our experience working with small groups (usually <40 people) to address natural resource issues. This chapter focuses on building a PM team and the communications that occur before, during, and after a PM process. The participants usually include people like recreational and commercial fishers, members of environmental non-governmental organizations, and fishery managers or other decision makers. Ground rules, expectations, and end goals should be established early in the PM process. We emphasize that benefits arise from including the participants in the development and validation of the models in a process that includes feedback between the modellers and participants. Although participants are ideally involved in all stages, their inclusion during model development and testing can be critical for buy-in within the group. When the modellers listen carefully to participant concerns and respond to those concerns during model development, we have experienced favourable process outcomes such as improved participant engagement and building of trust.

Book chapter↗

USGS2015

Spatial patterns of atmospheric deposition of nitrogen and sulfur using ion-exchange resin collectors in Rocky Mountain National Park, USA

Lakes and streams in Class 1 wilderness areas in the western United States (U.S.) are at risk from atmospheric deposition of nitrogen (N) and sulfur (S), and protection of these resources is mandated under the Federal Clean Air Act and amendments. Assessment of critical loads, which are the maximum exposure to pollution an area can receive without adverse effects on sensitive ecosystems, requires accurate deposition estimates. However, deposition is difficult and expensive to measure in high-elevation wilderness, and spatial patterns in N and S deposition in these areas remain poorly quantified. In this study, ion-exchange resin (IER) collectors were used to measure dissolved inorganic N (DIN) and S deposition during June 2006–September 2007 at approximately 20 alpine/subalpine sites spanning the Continental Divide in Rocky Mountain National Park. Results indicated good agreement between deposition estimated from IER collectors and commonly used wet + dry methods during summer, but poor agreement during winter. Snowpack sampling was found to be a more accurate way of quantifying DIN and S deposition during winter. Summer DIN deposition was significantly greater on the east side of the park than on the west side (25–50%; p ≤ 0.03), consistent with transport of pollutants to the park from urban and agricultural areas to the east. Sources of atmospheric nitrate (NO 3 − ) were examined using N isotopes. The average δ 15 N of NO 3 − from IER collectors was 3.5&permil; higher during winter than during summer ( p < 0.001), indicating a seasonal shift in the relative importance of regional NO x sources, such as coal combustion and vehicular sources of atmospheric NO 3 − . There were no significant differences in δ 15 N of NO 3 − between east and west sides of the park during summer or winter ( p = 0.83), indicating that the two areas may have similar sources of atmospheric NO 3 − . Results from this study indicate that a combination of IER collectors and snowpack sampling can be used to characterize spatial variability in DIN and S deposition in high-elevation wilderness areas. These data can improve our ability to model critical loads by filling gaps in geographic coverage of deposition monitoring/modeling programs and thus may enable policy makers to better protect sensitive natural resources in Class 1 Wilderness areas.

Colorado↗

USGS2009

Evaluation of procedures for prediction of unconventional gas in the presence of geologic trends

This study extends the application of local spatial nonparametric prediction models to the estimation of recoverable gas volumes in continuous-type gas plays to regimes where there is a single geologic trend. A transformation is presented, originally proposed by Tomczak, that offsets the distortions caused by the trend. This article reports on numerical experiments that compare predictive and classification performance of the local nonparametric prediction models based on the transformation with models based on Euclidean distance. The transformation offers improvement in average root mean square error when the trend is not severely misspecified. Because of the local nature of the models, even those based on Euclidean distance in the presence of trends are reasonably robust. The tests based on other model performance metrics such as prediction error associated with the high-grade tracts and the ability of the models to identify sites with the largest gas volumes also demonstrate the robustness of both local modeling approaches. ?? International Association for Mathematical Geology 2009.

Natural Resources Research↗

USGS1984

A workshop model simulating fate and effect of drilling muds and cuttings on benthic communities

Oil and gas exploration and production at marine sites has generated concern over potential environmental impacts resulting from the discharge of spent drilling muds and cuttings. This concern has led to a broad array of publicly and privately sponsored research. This report described a cooperative modeling effort designed to focus information resulting from this research through construction of explicit equations that simulate the potential impacts of discharge drilling fluids (muds) and cuttings on marine communities. The model is the result of collaboration among more than 30 scientists. The principal cooperating organizations were the E.S. Environmental Protection Agency, the U.S. Minerals Management Service, the Offshore Operators Committee, and the Alaska Oil and Gas Association. The overall simulation model can be conceptualized as three connected submodels: Discharge and Plume Fate, Sediment Redistribution, and Benthic Community Effects. On each day of simulation, these submodels are executed in sequence, with flows of information between submodels. The Benthic Community Effects submodel can be further divided into sections that calculate mortality due to burial, mortality due to toxicity, mortality due to resuspension disturbance, and growth of the community. The model represents a series of seven discrete 1-m 2 plots at specified distances along a transect in one direction away from a discharge point. It consists of coupled difference equations for which parameter values can easily be set to evaluate different conditions or to examine the sensitivity of output to various assumptions. Sets of parameter values were developed to represent four general cases or scenarios: (1) a shallow (5 m), cold environment with ice cover during a substantial fraction of the year, such as might be encountered in the Beaufort Sea, Alaska; (2) a shallow (20 m), temperate environment, such as might be encountered in the Gulf of Mexico; (3) a deeper (80 m), temperate environment, such as might be encountered in the Gulf of Mexico; and (4) a very deep (1,000 m) environment, such as might be encountered on the Atlantic slope. The focus of the modeling effort was on the connection of a reasonable representation of physical fate to the biological responses of populations, rather than on highly detailed representations of individual processes. For example, the calculations of physical fate are not as detailed as those in the recently published model of Brandsma et al. (1983). The value of the model described herein is in the broad scope of processes that are explicitly represented and linked together. The model cannot be considered to produce reliable predictions of the quantitative impacts of discharged drilling fluids and cuttings on biological populations at a particular site. Limitations of the model in predicting integrated fate and effects can be traced to three general areas: level of refinement of the algorithms used in the model; lack of understanding of the processes determining fate and effects; and parameter and data values. Despite the limitations, several qualitative conclusions concerning both potential impacts and the importance of various remaining data gaps can be drawn from the modeling effort. These include: (1) Simple, unequivocal conclusions about fate and effects across geographical regions and drilling operations are difficult, if not misleading, due to the large amount of variability in characteristics of discharged materials (e.g., oil content and toxicity), discharge conditions (e.g., duration of drilling operations), physical environments (e.g., water depth, current direction, and sediment disturbance regimes), and biological communities (e.g., intrinsic growth rates). Different combinations of these characteristics can result in substantial differences in simulated environmental fate and biological effects. For examples, simulated recovery in some high-energy environments occurs within months after the cessation of discharge operations, even at heavily impacted sites, whereas simulated recover in some low-energy environments takes years at heavily impacted sites. <2) Considerable difficulties remain in the reliable extrapolation of results from laboratory toxicity experiments to predictions of population effects in the field. (3) The volume of material discharged and duration of operations in the production drilling operations simulated by the model are sufficient to produce substantial simulated biological impacts at some plots, both in terms of differences from a control plot during the period of discharge operations, and in terms of the recovery period following the perturbations. Evaluation of the significance of potential effects involves the following factors: • Definition of a specific spatial and temporal reference frame (e.g., What is the natural variation? Is 1 year to be considered a "long" or "short" time? Is 50 m to be considered a "large" or "trivial" distance? • Consideration of rare or unique resources and particularly sensitive biotic assemblages. • Consideration of the potential for long term, cumulative effects. Some of these aspects are clearly beyond the scope of this modeling efforts (e.g., the model does not simulate the long term fate of resuspended material). The model does, however, contain an internal "reference frame" by comparison to simulated behavior at a control plot. The model, in general, simulates substantial "natural" variation at the reference or control plots, both over time, due to sediment disturbance events in medium to high energy environments, and over space, due to geographically varying conditions, such as water depth and current regime.

Report↗

USGS2014

Hydrologic Drought Decision Support System (HyDroDSS)

The hydrologic drought decision support system (HyDroDSS) was developed by the U.S. Geological Survey (USGS) in cooperation with the Rhode Island Water Resources Board (RIWRB) for use in the analysis of hydrologic variables that may indicate the risk for streamflows to be below user-defined flow targets at a designated site of interest, which is defined herein as data-collection site on a stream that may be adversely affected by pumping. Hydrologic drought is defined for this study as a period of lower than normal streamflows caused by precipitation deficits and (or) water withdrawals. The HyDroDSS is designed to provide water managers with risk-based information for balancing water-supply needs and aquatic-habitat protection goals to mitigate potential effects of hydrologic drought. This report describes the theory and methods for retrospective streamflow-depletion analysis, rank correlation analysis, and drought-projection analysis. All three methods are designed to inform decisions made by drought steering committees and decisionmakers on the basis of quantitative risk assessment. All three methods use estimates of unaltered streamflow, which is the measured or modeled flow without major withdrawals or discharges, to approximate a natural low-flow regime. Retrospective streamflow-depletion analysis can be used by water-resource managers to evaluate relations between withdrawal plans and the potential effects of withdrawal plans on streams at one or more sites of interest in an area. Retrospective streamflow-depletion analysis indicates the historical risk of being below user-defined flow targets if different pumping plans were implemented for the period of record. Retrospective streamflow-depletion analysis also indicates the risk for creating hydrologic drought conditions caused by use of a pumping plan. Retrospective streamflow-depletion analysis is done by calculating the net streamflow depletions from withdrawals and discharges and applying these depletions to a simulated record of unaltered streamflow. Rank correlation analysis in the HyDroDSS indicates the persistence of hydrologic measurements from month to month for the prediction of developing hydrologic drought conditions and quantitatively indicates which hydrologic variables may be used to indicate the onset of hydrologic drought conditions. Rank correlation analysis also indicates the potential use of each variable for estimating the monthly minimum unaltered flow at a site of interest for use in the drought-projection analysis. Rank correlation analysis in the HyDroDSS is done by calculating Spearman’s rho for paired samples and the 95-percent confidence limits of this rho value. Rank correlation analysis can be done by using precipitation, groundwater levels, measured streamflows, and estimated unaltered streamflows. Serial correlation analysis, which indicates relations between current and future values, can be done for a single site. Cross correlation analysis, which indicates relations among current values at one site and current and future values at a second site, also can be done. Drought-projection analysis in the HyDroDSS indicates the risk for being in a hydrologic drought condition during the current month and the five following months with and without pumping. Drought-projection analysis also indicates the potential effectiveness of water-conservation methods for mitigating the effect of withdrawals in the coming months on the basis of the amount of depletion caused by different pumping plans and on the risk of unaltered flows being below streamflow targets. Drought-projection analysis in the HyDroDSS is done with Monte Carlo methods by using the position analysis method. In this method the initial value of estimated unaltered streamflows is calculated by correlation to a measured hydrologic variable (monthly precipitation, groundwater levels, or streamflows from an index station identified with the rank correlation analysis). Then a pseudorandom number generator is used to create 251 six-month-long flow traces by using a bootstrap method. Serial correlation of the estimated unaltered monthly minimum streamflows determined from the rank correlation analysis is preserved within each flow trace. The sample of unaltered streamflows indicates the risk of being below flow targets in the coming months under simulated natural conditions (without historic withdrawals). The streamflow-depletion algorithms are then used to estimate risks of flow being below targets if selected pumping plans are used. This report also describes the implementation of the HyDroDSS. The HyDroDSS was developed as a Microsoft Access® database application to facilitate storage, handling, and use of hydrologic datasets with a simple graphical user interface. The program is implemented in the database by using the Visual Basic for Applications® (VBA) programming language. Program source code for the analytical techniques is provided in the HyDroDSS and in electronic text files accompanying this report. Program source code for the graphical user interface and for data-handling code, which is specific to Microsoft Access® and the HyDroDSS, is provided in the database. An installation package with a run-time version of the software is available with this report for potential users who do not have a compatible copy of Microsoft Access®. Administrative rights are needed to install this version of the HyDroDSS. A case study, to demonstrate the use of HyDroDSS and interpretation of results for a site of interest, is detailed for the USGS streamgage on the Hunt River (station 01117000) near East Greenwich in central Rhode Island. The Hunt River streamgage was used because it has a long record of streamflow and is in a well-studied basin with a substantial amount of hydrologic and water-use data including groundwater pumping for municipal water supply.

Rhode Island↗

USGS2011

Modeling hydrodynamics, water temperature, and water quality in the Klamath River upstream of Keno Dam, Oregon, 2006-09

A hydrodynamic, water temperature, and water-quality model was constructed for a 20-mile reach of the Klamath River downstream of Upper Klamath Lake, from Link River to Keno Dam, for calendar years 2006-09. The two-dimensional, laterally averaged model CE-QUAL-W2 was used to simulate water velocity, ice cover, water temperature, specific conductance, dissolved and suspended solids, dissolved oxygen, total nitrogen, ammonia, nitrate, total phosphorus, orthophosphate, dissolved and particulate organic matter, and three algal groups. The Link-Keno model successfully simulated the most important spatial and temporal patterns in the measured data for this 4-year time period. The model calibration process provided critical insights into water-quality processes and the nature of those inputs and processes that drive water quality in this reach. The model was used not only to reproduce and better understand water-quality conditions that occurred in 2006-09, but also to test several load-reduction scenarios that have implications for future water-resources management in the river basin. The model construction and calibration process provided results concerning water quality and transport in the Link-Keno reach of the Klamath River, ranging from interesting circulation patterns in the Lake Ewauna area to the nature and importance of organic matter and algae. These insights and results include: * Modeled segment-average water velocities ranged from near 0.0 to 3.0 ft/s in 2006 through 2009. Travel time through the model reach was about 4 days at 2,000 ft 3 /s and 12 days at 700 ft3/s flow. Flow direction was aligned with the upstream-downstream channel axis for most of the Link-Keno reach, except for Lake Ewauna. Wind effects were pronounced at Lake Ewauna during low-flow conditions, often with circulation in the form of a gyre that rotated in a clockwise direction when winds were towards the southeast and in a counterclockwise direction when winds were towards the northwest. * Water temperatures ranged from near freezing in winter to near 30 degrees C at some locations and periods in summer; seasonal water temperature patterns were similar at the inflow and outflow. Although vertical temperature stratification was not present at most times and locations, weak stratification could persist for periods up to 1-2 weeks, especially in the downstream parts of the reach. Thermal stratification was important in controlling vertical variations in water quality. * The specific conductance, and thus density, of tributaries within the reach usually was higher than that of the river itself, so that inflows tended to sink below the river surface. This was especially notable for inflows from the Klamath Straits Drain, which tended to sink to the bottom of the Klamath River at its confluence and not mix vertically for several miles downstream. * The model was able to capture most of the seasonal changes in the algal population by modeling that population with three algal groups: blue-green algae, diatoms, and other algae. The blooms of blue-green algae, consisting mostly of Aphanizomenon flos aquae that entered from Upper Klamath Lake, were dominant, dwarfing the populations of the other two algae groups in summer. A large part of the blue-green algae population that entered this reach from upstream tended to settle out, die, and decompose, especially in the upper part of the Link-Keno reach. Diatoms reached a maximum in spring and other algae in midsummer. * Organic matter, occurring in both dissolved and particulate forms, was critical to the water quality of this reach of the Klamath River, and was strongly tied to nutrient and dissolved-oxygen dynamics. Dissolved and particulate organic matter were subdivided into labile (quickly decaying) and refractory (slowing decaying) groups for modeling purposes. The particulate matter in summer, consisting largely of dead blue-green algae, decayed quickly. Consequently, this particulate matt

Oregon↗

USGS2010

Land-Use Portfolio Modeler, Version 1.0

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

Techniques and Methods↗

USGS2016

Effects of water-supply reservoirs on streamflow in Massachusetts

State and local water-resource managers need modeling tools to help them manage and protect water-supply resources for both human consumption and ecological needs. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, has developed a decision-support tool to estimate the effects of reservoirs on natural streamflow. The Massachusetts Reservoir Simulation Tool is a model that simulates the daily water balance of a reservoir. The reservoir simulation tool provides estimates of daily outflows from reservoirs and compares the frequency, duration, and magnitude of the volume of outflows from reservoirs with estimates of the unaltered streamflow that would occur if no dam were present. This tool will help environmental managers understand the complex interactions and tradeoffs between water withdrawals, reservoir operational practices, and reservoir outflows needed for aquatic habitats. A sensitivity analysis of the daily water balance equation was performed to identify physical and operational features of reservoirs that could have the greatest effect on reservoir outflows. For the purpose of this report, uncontrolled releases of water (spills or spillage) over the reservoir spillway were considered to be a proxy for reservoir outflows directly below the dam. The ratio of average withdrawals to the average inflows had the largest effect on spillage patterns, with the highest withdrawals leading to the lowest spillage. The size of the surface area relative to the drainage area of the reservoir also had an effect on spillage; reservoirs with large surface areas have high evaporation rates during the summer, which can contribute to frequent and long periods without spillage, even in the absence of water withdrawals. Other reservoir characteristics, such as variability of inflows, groundwater interactions, and seasonal demand patterns, had low to moderate effects on the frequency, duration, and magnitude of spillage. The reservoir simulation tool was used to simulate 35 single- and multiple-reservoir systems in Massachusetts over a 44-year period (water years 1961 to 2004) under two water-use scenarios. The no-pumping scenario assumes no water withdrawal pumping, and the pumping scenario incorporates average annual pumping rates from 2000 to 2004. By comparing the results of the two scenarios, the total streamflow alteration can be parsed into the portion of streamflow alteration caused by the presence of a reservoir and the additional streamflow alteration caused by the level of water use of the system. For each reservoir system, the following metrics were computed to characterize the frequency, duration, and magnitude of reservoir outflow volumes compared with unaltered streamflow conditions: (1) the median number of days per year in which the reservoir did not spill, (2) the median duration of the longest consecutive period of no-spill days per year, and (3) the lowest annual flow duration exceedance probability at which the outflows are significantly different from estimated unaltered streamflow at the 95-percent confidence level. Most reservoirs in the study do not spill during the summer months even under no-pumping conditions. The median number of days during which there was no spillage was less than 365 for all reservoirs in the study, indicating that, even under reported pumping conditions, the reservoirs refill to full volume and spill at least once during nondrought years, typically in the spring. Thirteen multiple-reservoir systems consisting of two or three hydrologically connected reservoirs were included in the study. Because operating rules used to manage multiple-reservoir systems are not available, these systems were simulated under two pumping scenarios, one in which water transfers between reservoirs are minimal and one in which reservoirs continually transferred water to intermediate or terminal reservoirs. These two scenarios provided upper and lower estimates of spillage under average pumping conditions from 2000 to 2004. For sites with insufficient data to simulate daily water balances, a proxy method to estimate the three spillage metrics was developed. A series of 4,000 Monte Carlo simulations of the reservoir water balance were run. In each simulation, streamflow, physical reservoir characteristics, and daily climate inputs were randomly varied. Tobit regression equations that quantify the relation between streamflow alteration and physical and operational characteristics of reservoirs were developed from the results of the Monte Carlo simulations and can be used to estimate each of the three spillage metrics using only the withdrawal ratio and the ratio of the surface area to the drainage area, which are available statewide for all reservoirs. A graphical user-interface for the Massachusetts Reservoir Simulation Tool was developed in a Microsoft Access environment. The simulation tool contains information for 70 reservoirs in Massachusetts and allows for simulation of additional scenarios than the ones considered in this report, including controlled releases, dam seepage and leakage, demand management plans, and alternative water withdrawal and transfer rules.

Massachusetts↗

USGS2024

Enhancing assessments of coastal wetland migration potential with sea-level rise: Accounting for uncertainty in elevation data, tidal data, and future water levels

Sea-level rise rates are predicted to surpass rates of wetland vertical adjustment in the coming decades in many areas, increasing the potential for wetland submergence. Information on where wetland migration is possible can help natural resource managers for planning land acquisition or enhancing habitat connectivity to bolster adaptation of coastal wetlands to rising seas. Elevation-based models of wetland migration are often hampered with uncertainty associated with ground surface elevation, current water levels (i.e., tides and extreme water levels), and future water levels from sea-level rise. Here, we developed an approach that involved digital elevation model error reduction and the use of Monte Carlo simulations that utilize uncertainty assumptions regarding elevation error, contemporary water levels, and future sea levels to identify potential wetland migration areas. Our analyses were developed for Duvall and Nassau Counties in northeastern Florida (USA). We focus on the migration of regularly oceanic-flooded wetlands (i.e., flooded by oceanic water daily) and irregularly oceanic-flooded wetlands (i.e., flooded by oceanic water less frequently than daily). For two relative sea-level rise scenarios based on the 0.5 m and the 1.5 m global mean sea-level rise scenarios, we quantified migration by wetland flooding frequency class and identified land cover and land use types that are vulnerable to future exposure to oceanic waters. The variability in total coverage and relative coverage of wetland migration from our results highlights how topography and accelerated sea-level rise interact. Our wetland migration results communicate uncertainty by showing flooding frequency class as probabilistic outputs.

Florida↗

USGS2010

Development of a decision support system for monitoring, reporting and forecasting ecological conditions of the Appalachian Trail

This paper introduces a collaborative multi-agency effort to develop an Appalachian Trail (A.T.) MEGA-Transect Decision Support System (DSS) for monitoring, reporting and forecasting ecological conditions of the A.T. and the surrounding lands. The project is to improve decisionmaking on management of the A.T. by providing a coherent framework for data integration, status reporting and trend analysis. The A.T. MEGA-Transect DSS is to integrate NASA multi-platform sensor data and modeling through the Terrestrial Observation and Prediction System (TOPS) and in situ measurements from A.T. MEGA-Transect partners to address identified natural resource priorities and improve resource management decisions.

Connecticut, Georgia, Maine, Maryland, Massachuset↗