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USGS1993

Managing biological diversity

Biological diversity is the variety of life and accompanying ecological processes (Off. Technol. Assess. 1987, Wilcove and Samson 1987, Keystone 1991). Conservation of biological diversity is a major environmental issue (Wilson 1988, Counc. Environ. Quality 1991). The health and future of the earth's ecological systems (Lubchenco et al. 1991), global climate change (Botkin 1990), and an ever-increasing rate in loss of species, communities, and ecological systems (Myers 1990) are among issues drawing biological diversity to the mainstream of conservation worldwide (Int. Union Conserv. Nat. and Nat. Resour. [IUCN] et al. 1991). The legal mandate for conserving biological diversity is now in place (Carlson 1988, Doremus 1991). More than 19 federal laws govern the use of biological resources in the United States (Rein 1991). The proposed National Biological Diversity Conservation and Environmental Research Act (H.R. 585 and S.58) notes the need for a national biological diversity policy, would create a national center for biological diversity research, and recommends a federal interagency strategy for ecosystem conservation. There are, however, hard choices ahead for the conservation of biological diversity, and biologists are grappling with how to set priorities in research and management (Roberts 1988). We sense disillusion among field biologists and managers relative to how to operationally approach the seemingly overwhelming charge of conserving biological diversity. Biologists also need to respond to critics like Hunt (1991) who suggest a tree farm has more biological diversity than an equal area of old-growth forest. At present, science has played only a minor role in the conservation of biological diversity (Weston 1992) with no unified approach available to evaluate strategies and programs that address the quality and quantity of biological diversity (Murphy 1990, Erwin 1992). Although actions to conserve biological diversity need to be clearly defined by viewing issues across biological, spatial, and temporal scales (Knopf and Smith 1992), natural resource managers find much conflicting information in the literature on strategies and programs for the conservation of biological diversity (Ehrlich 1992). Moreover, recommendations provided in much of the published information available for planning or decisions not only can be debated but may prove counterproductive if implemented. Current operational efforts beg for clearer focus on fundamental concepts central to daily decisions that impact native biological diversity. Recognizing that many biologists would provide different council and at the risk of oversimplification, we offer the following 4 topical issues as fundamental guidance to wise conservation action. These recommendations are based on our collective experiences working within conservation agencies since our original, collaborative essay (Samson and Knopf 1982). They are offered as initial, rather than authoritative, steps to better align research and management decisions with what we perceive as the critical issues in conserving biological diversity at the landscape and ecosystem levels of resolution.

Wildlife Society Bulletin↗
USGS2019

Managing dams for energy and fish tradeoffs: What does a win-win solution take?

Management activities to restore endangered fish species, such as dam removals, fishway installations, and periodic turbine shutdowns, usually decrease hydropower generation capacities at dams. Quantitative analysis of the tradeoffs between energy production and fish population recovery related to dam decision-making is still lacking. In this study, an integrated hydropower generation and age-structured fish population model was developed using a system dynamics modeling method to assess basin-scale energy-fish tradeoffs under eight dam management scenarios. This model ran across 150 years on a daily time step, applied to five hydroelectric dams located in the main stem of the Penobscot River, Maine. We used alewife ( Alosa pseudoharengus ) to be representative of the local diadromous fish populations to link projected hydropower production with theoretical influences on migratory fish populations on the model river system. Our results show that while the five dams can produce around 427 GWh/year of energy, without fishway installations they would contribute to a 90% reduction in the alewife spawner abundance. The effectiveness of fishway installations is largely influenced by the size of reopened habitat areas and the actual passage rate of the fishways. Homing to natal habitat has an insignificant effect on the growth of the simulated spawner abundance. Operating turbine shutdowns during alewives' peak downstream migration periods, in addition to other dam management strategies, can effectively increase the spawner abundance by 480–550% while also preserving 65% of the hydropower generation capacity. These data demonstrate that in a river system where active hydropower dams operate, a combination of dam management strategies at the basin scale can best balance the tradeoff between energy production and the potential for migratory fish population recovery.

Maine↗
USGS2011

A Bayesian network to predict coastal vulnerability to sea level rise

Sea level rise during the 21st century will have a wide range of effects on coastal environments, human development, and infrastructure in coastal areas. The broad range of complex factors influencing coastal systems contributes to large uncertainties in predicting long-term sea level rise impacts. Here we explore and demonstrate the capabilities of a Bayesian network (BN) to predict long-term shoreline change associated with sea level rise and make quantitative assessments of prediction uncertainty. A BN is used to define relationships between driving forces, geologic constraints, and coastal response for the U.S. Atlantic coast that include observations of local rates of relative sea level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline change rate. The BN is used to make probabilistic predictions of shoreline retreat in response to different future sea level rise rates. Results demonstrate that the probability of shoreline retreat increases with higher rates of sea level rise. Where more specific information is included, the probability of shoreline change increases in a number of cases, indicating more confident predictions. A hindcast evaluation of the BN indicates that the network correctly predicts 71% of the cases. Evaluation of the results using Brier skill and log likelihood ratio scores indicates that the network provides shoreline change predictions that are better than the prior probability. Shoreline change outcomes indicating stability (-1 < rate < 1 m/yr) or erosion (rate < -1 m/yr) tend to occur for two sets of input scenarios. Stable shoreline change rates occur mainly for low rates of relative sea level rise and occur in low-vulnerability geomorphic settings. Rates indicating erosion result for cases where the rate of relative sea level rise is high and moderate-to-high vulnerability geomorphic settings occur. In contrast, accretion (rate > 1 m/yr) was not well predicted. We find that BNs can assimilate important factors contributing to coastal change in response to sea level rise and can make quantitative, probabilistic predictions that can be applied to coastal management decisions. Copyright ?? 2011 by the American Geophysical Union.

Journal of Geophysical Research F: Earth Surface↗
USGS2016

Sediment load and distribution in the lower Skagit River, Skagit County, Washington

The Skagit River delivers about 40 percent of all fluvial sediment that enters Puget Sound, influencing flood hazards in the Skagit lowlands, critically important estuarine habitat in the delta, and some of the most diverse and productive agriculture in western Washington. A total of 175 measurements of suspended-sediment load, made routinely from 1974 to 1993, and sporadically from 2006 to 2009, were used to develop and evaluate regression models of sediment transport (also known as “sediment-rating curves”) for estimating suspended-sediment load as a function of river discharge. Using a flow-range model and 75 years of daily discharge record (acquired from 1941 to 2015), the mean annual suspended-sediment load for the Skagit River near Mount Vernon, Washington, was estimated to be 2.5 teragrams (Tg, where 1 Tg = 1 million metric tons). The seasonal model indicates that 74 percent of the total annual suspended‑sediment load is delivered to Puget Sound during the winter storm season (from October through March), but also indicates that discharge is a poor surrogate for suspended‑sediment concentration (SSC) during the summer low-flow season. Sediment-rating curves developed for different time periods revealed that the regression model slope of the SSC-discharge relation increased 66 percent between the periods of 1974–76 and 2006–09 when suspended-sediment samples were collected, implying that changes in sediment supply, channel hydraulics, and (or) basin hydrology occurred between the two time intervals. In the relatively wet water year 2007 (October 1, 2006, through September 30, 2007), an automated sampler was used to collect daily samples of suspended sediment from which an annual load of 4.5 Tg was calculated, dominated by a single large flood event that contributed 1.8 Tg, or 40 percent of the total. In comparison, the annual load calculated for water year 2007 using the preferred flow-range model was 4.8 Tg (+6.7 percent), in close agreement with the measured value. Particle size affects sediment transport, fate and distribution across watersheds, and therefore is important for predicting how coastal environments, particularly deltas and beaches, will respond to changes in climate and sea-level. Particle-size analysis of winter storm samples indicated that about one-half of the suspended-sediment load consisted of fines (that is, silt- and clay-sized particles smaller than 0.0625 mm in diameter), and the remainder consisted of mostly fine- to medium-sized sand (0.0625–0.5 mm), whereas bedload during winter storm flows (about 1–3 percent of total sediment load) was predominantly composed of medium to coarse sand (0.25–1 mm). A continuous turbidity record from the Anacortes Water Treatment Plant (water years 1999–2013), used as a surrogate for the concentration of fines ( R 2 = 0.93, p = 4.2E-10, n = 17), confirms that about one-half of the mean annual suspended-sediment load is composed of fines. The distribution of flow through the delta distributaries (that is, the channels into which the main stem splits as it approaches the delta) is dynamic, with twice as much flow through the North Fork of the Skagit River relative to the South Fork during low-flow conditions, and close to equal flows in the two channels during high-flow conditions. Turbidity, monitored at several locations in the lower river in spring 2009, was essentially uniform among sites, indicating that fines are well mixed in the lower Skagit River system (defined as the Skagit River and all its distributaries downstream of the Mount Vernon streamgage). A strong relation ( R 2 = 0.95, p = 3.2E-14, n = 21; linear regression) between the concentration of fines and turbidity measured at various locations in summer 2009 indicates that turbidity is an effective surrogate for the concentration of fines, independent of location in the river, under naturally well-mixed fluvial conditions. This relation is especially useful for monitoring suspended sediment in western Washington rivers that are seasonally dominated by glacier meltwater because glacial melting typically produces suspended-sediment concentrations that are not well correlated with discharge. These results provide a comprehensive set of tools to estimate sediment delivery and delta responses of interest to scientists and resource managers including decision-makers examining options for flood hazard mitigation, estuary restoration, and climate change adaptation.

Washington↗
USGS2021

Determination of burn severity models ranging from regional to continental scales for the conterminous United States

Identifying meaningful measures of ecological change over large areas is dependent on the quantification of robust relationships between ecological metrics and remote sensing products. Over the past several decades, ground observations of wildfire and prescribed fire severity have been acquired across hundreds of wildland fires in the United States, primarily utilizing the Composite Burn Index (CBI) plot protocol. These observations have been coupled to spaceborne passive spectral reflectance indices (e.g. Landsat-derived variations of the Normalized Burn Ratio [NBR]) to produce regression models describing their relationship. Here we develop regression models by vegetation type for multiple vegetation classification systems representing a range of spatial scales, and a decision tree framework for evaluating these regression models. Our overall goals were to determine which scale of ecological classifications provided the best estimate of burn severity from Landsat data and how to choose the best regression model. We aggregated a total of 6280 CBI plots for 234 wildland fires that burned between 1994 and 2017 and produced Landsat-derived NBR and differenced NBR (dNBR) values for each plot. We then calculated best fit linear or higher order regression equations between CBI and NBR/dNBR for each landcover classification system from smallest to largest scale: LANDFIRE Biophysical Settings (BPS), National Vegetation Classification macrogroup (NVC) landcover classifications, Omernick III, II, and I ecoregions, LANDFIRE Fire Regime Groups (FRG), and the entire conterminous United States (CONUS) dataset. The CONUS regression model goodness of fit was moderate (R 2 = 0.55, P < 0.001) for dNBR and poor (R 2 = 0.30, P < 0.001) for NBR. Within landcover classifications, CBI was better fit by dNBR than NBR. Finer scale regional regression models including BPS (dNBR R 2 &#xAF; "> R2¯ = 0.56 and 0.00–0.83 R 2 range; NBR R 2 &#xAF; "> R(bar) 2 = 0.43 and 0.00–0.82 R 2 range) and NVC (dNBR R 2 &#xAF; "> R(bar) 2 = 0.55 and 0.15–0.78 R 2 range; NBR R 2 &#xAF; "> R(bar) 2 = 0.41 and 0.00–0.79 R 2 range) were on average the same or better than the CONUS models for dNBR and NBR, with the strongest fit models exhibiting R 2 ≥ 0.70, whereas larger scale regional models R 2 &#xAF; "> R(bar) 2 ranged from 0.28 to 0.5. However, variation in accuracy among landcover types indicate that dNBR and NBR regression models could be used to effectively estimate CBI for future fires in certain regions, while for other regions models may require additional field observations or alternative spectral transformations. Our decision tree schema can be used to help users determine which scale is likely to produce the most accurate results using our models. The CBI regression models developed here, paired with the decision tree, provide users with a simple method to estimate burn severity in units of CBI for any fire within CONUS with moderate to high levels of confidence and provide a template for further development of models with new data going forward.

Conterminous United States↗
USGS2012

USGS Environmental health science strategy: providing environmental health science for a changing world: Public review release

America has an abundance of natural resources. We have bountiful clean water, fertile soil, and unrivaled national parks, wildlife refuges, and public lands. These resources enrich our lives and preserve our health and wellbeing. These resources have been maintained because of our history of respect for their value and an enduring commitment to their vigilant protection. Awareness of the social, economic, and personal value of the health of our environment is increasing. The emergence of environmentally driven diseases caused by environmental exposure to contaminants and pathogens is a growing concern worldwide. New health threats and patterns of established threats are affected by both natural and anthropogenic changes to the environment. Human activities are key drivers of emerging (new and re-emerging) health threats. Societal demands for land and natural resources, a better quality of life, improved economic prosperity, and the environmental impacts associated with these demands will continue to increase. Natural earth processes, climate trends, and related climatic events will add to the environmental impact of human activities. These environmental drivers will influence exposure to disease agents, including viral, bacterial, prion, and fungal pathogens, parasites, natural earth materials, toxins and other biogenic compounds, and synthetic chemicals and substances. The U.S. Geological Survey (USGS) defines environmental health science broadly as the interdisciplinary study of relations among the quality of the physical environment, the health of the living environment, and human health. The interactions among these three spheres are driven by human activities, ecological processes, and natural earth processes; the interactions affect exposure to contaminants and pathogens and the severity of environmentally driven diseases in animals and people. This definition provides USGS with a framework for synthesizing natural science information from across the Bureau and providing it to environmental, natural resource, agricultural, and public-health managers. The USGS is a Federal science agency with a broad range of natural science expertise relevant to environmental health. USGS provides scientific information and tools as a scientific basis for management and policy decision making. USGS specializes in science at the environment-health interface, by characterizing the processes that affect the interaction among the physical environment, the living environment, and people, and the resulting factors that affect ecological and human exposure to disease agents. This report describes a 10-year strategy that encompasses the portfolio of USGS environmental health science. It summarizes national environmental health priorities that USGS is best suited to address, and will serve as a strategic framework for USGS environmental health science goals, actions, and outcomes for the next decade. Implementation of this strategy is intended to aid coordination of USGS environmental health activities and to provide a focal point for disseminating information to stakeholders. The "One Health" paradigm advocated by the World Health Organization (WHO, 2011), and the American Veterinary Medicine Association (AVMA, 2008), among others, is based on a general recognition that the health of humans, animals, and the environment are inextricably linked. Thus, successful efforts to protect that health will require increased interdisciplinary research and increased communication and collaboration among the broader scientific and health community. This strategy is built upon that paradigm. The vision, mission, and five cornerstone goals of the USGS Environmental Health Science Strategy were developed with significant input from a wide range of stakeholders. Vision - The USGS is a premier source of the environmental health science needed to safeguard the health of the environment, fish, wildlife, and people. Mission - The mission of USGS in environmental health science is to contribute scientific information to environmental, natural resource, agricultural, and public-health managers, who use that science to support sound decision making. USGS provides the science to: - Goal 1: Identify, prioritize, and detect contaminants and pathogens of emerging environmental concern. - Goal 2: Reduce the impact of contaminants on the environment, fish, wildlife, and people. - Goal 3: Reduce the impact of pathogens on the environment, fish, wildlife, and people. - Goal 4: Discover the complex interactions and combined effects of exposure to contaminants and pathogens. - Goal 5: Prepare for and respond to environmental impacts and related health threats of natural and anthropogenic disasters. Goals 1 through 4 are intended to provide science to address environmental health threats in a logical order, from informing prevention and preparedness, to supporting systematic management response to environmental health issues. Goal 4 addresses the interaction among contaminants and pathogens, an issue of emerging concern in environmental health science. Goal 5 acknowledges the fact that natural and anthropogenic disasters can cause immediate and prolonged adverse environmental health threats. This strategy proposes that USGS take the following strategic science actions to achieve each of the five goals of this strategy: Goal 1: Identify, prioritize, and detect contaminants and pathogens of emerging environmental concern. - Strategic Science Action 1. - Prioritize contaminants and pathogens of emerging concern to guide research, detection, and management activities. - Strategic Science Action 2. - Conduct surveillance and monitoring to provide early warning of emerging health threats. - Strategic Science Action 3. - Develop approaches and tools that identify vulnerable environmental settings, ecosystems, and species. Goal 2: Reduce the impact of contaminants on the environment, fish, wildlife, and people. - Strategic Science Action 1. - Systematically characterize the sources, occurrence, transport and fate of environmental contaminants to guide efforts to manage and mitigate contamination. - Strategic Science Action 2. - Evaluate the threats of contamination on the health of the environment, fish, wildlife, and people, and inform the associated management and protection efforts. - Strategic Science Action 3. - Characterize potential human exposure to support establishment of health-based standards or guidelines and contamination-reduction efforts. Goal 3: Reduce the impact of pathogens on the environment, fish, wildlife, and people. - Strategic Science Action 1. - Determine the biotic and abiotic factors that control the ecology of infectious diseases affecting natural populations of aquatic and terrestrial species and potential transmission to other animals and humans. - Strategic Science Action 2. - Establish how natural and anthropogenic environmental changes affect the distribution and severity of infectious diseases in natural populations of aquatic and terrestrial species and potential transmission to other animals and humans. - Strategic Science Action 3. - Develop surveillance systems to identify changing patterns of disease activity in priority geographic areas. Goal 4: Discover the complex interactions and combined effects of exposure to contaminants and pathogens. - Strategic Science Action 1. - Identify how exposure to one class of disease agents (contaminants or pathogens) can make an organism more susceptible to effects from exposure to the other class of disease agents. - Strategic Science Action 2. - Implement interdisciplinary studies that characterize the effects of combined exposure to pathogens and contaminants. Goal 5: Prepare for and respond to the environmental impacts and related health threats of natural and anthropogenic disasters. - Strategic Science Action 1. - Establish a formal interdisciplinary science capability to rapidly assess the environmental health risks associated with disasters. - Strategic Science Action 2. - Enhance methods to anticipate, prepare for, and identify environmental, ecological, and related health impacts of future disasters. This strategy is one of seven USGS science strategies developed concurrently: - Climate and Land Use Change - Core Science Systems - Ecosystems - Energy and Mineral Resources - Environmental Health - Natural Hazards - Water. This strategy describes how USGS will address the highest priority environmental health issues facing the Nation. The ultimate intended outcome of this science strategy is prevention and reduction of adverse impacts to the quality of the environment, the health of our living resources, and human health. Communication with, and receiving input from, partners and stakeholders regarding their science needs is essential for successful implementation of this strategy. It is incumbent on USGS to reach out to all stakeholders to ensure that USGS efforts are focused on the highest priority environmental health issues and that products are provided in the most timely and usable form to all those who can use them. USGS must reach out to the scientific community, internally and externally, to ensure that our efforts are integrated with and take full advantage of the activities of others.

Open-File Report↗
USGS2007

Potential for shoreline changes due to sea-level rise along the U.S. mid-Atlantic region

Sea-level rise over the next century is expected to contribute significantly to physical changes along open-ocean shorelines. Predicting the form and magnitude of coastal changes is important for understanding the impacts to humans and the environment. Presently, the ability to predict coastal changes is limited by the scientific understanding of the many variables and processes involved in coastal change, and the lack of consensus regarding the validity of existing conceptual, analytical, or numerical models. In order to assess potential future coastal changes in the mid-Atlantic U.S. for the U.S. Climate Change Science Program (CCSP), a workshop was convened by the U.S. Geological Survey. Assessments of future coastal change were made by a committee of coastal scientists with extensive professional experience in the mid-Atlantic region. Thirteen scientists convened for a two-day meeting to exchange information and develop a consensus opinion on potential future coastal changes for the mid-Atlantic coast in response to sea-level rise. Using criteria defined in past work, the mid-Atlantic coast was divided into four geomorphic compartments: spits, headlands, wave-dominated barriers, and mixed-energy barriers. A range of potential coastal responses was identified for each compartment based on four sea-level rise scenarios. The four scenarios were based on the assumptions that: a) the long-term sea-level rise rate observed over the 20th century would persist over the 21st century, b) the 20th century rate would increase by 2 mm/yr, c) the 20th century rate would increase by 7 mm/yr, or d) sea-level would rise by 2 m over the next few hundred years. Potential responses to these sea-level rise scenarios depend on the landforms that occur within a region and include increased likelihood for erosion and shoreline retreat for all coastal types, increased likelihood for erosion, overwash and inlet breaching for barrier islands, as well as the possibility of a threshold state (e.g., dramatic change in barrier evolution, such as segmentation or disintegration) for some barrier island systems. The likelihood of the potential coastal responses is expressed using standard terminology employed in climate change assessments (e.g., as used by the Intergovernmental Panel on Climate Change and CCSP). This assessment was based on the coastal geomorphology in its present condition and does not consider any coastal protection that might be undertaken in the future. The committee recognized that a variety of erosion mitigation measures have been implemented along developed portions of the coast and these are very likely to be applied in the future. It was also acknowledged that economics, political will, and other factors can drive decisions to implement these measures, and that such decisions cannot be predicted with confidence. The results of this assessment are depicted graphically on maps of the study area.

Mid-Atlantic Coast↗
USGS2024

Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin

As part of the 2014 Chesapeake Bay Watershed Agreement, all Bay States and the District of Columbia have committed to improving the condition of the Bay, which includes a goal to achieve sustainable fisheries. One outcome under that broad goal is improved effectiveness of fish habitat conservation and preservation efforts. In support of that outcome, the U.S. Geological Survey Eastern Ecological Science Center (USGS-EESC) and the National Oceanic and Atmospheric Association’s National Centers for Coastal Ocean Science (NOAA-NCCOS) are actively developing datasets, methods, and analyses to conduct fish habitat assessments in the Chesapeake Bay watershed, guided by recommendations from a regional stakeholder workshop held by the Chesapeake Bay Program’s (CBP) Fish Habitat Action Team (FHAT) in 2018. The joint USGS and NOAA team has been collaborating on methods for conducting inland and estuarine assessments and exploring whether a seamless headwater to estuary assessment could be developed. The goals of this assessment are to benefit both State and Federal fisheries managers, help advance fisheries science, and provide beneficial information for the public. While past national and regional assessments (e.g. the National Fish Habitat Partnership National Assessment) treated inland and estuarine fish habitat conditions separately due to differences in environments, GIS data representation, and data availability, a seamless habitat assessment could be of value for a broad range of stakeholders as many fish species, several of which are invasive or under federal jurisdiction, use habitats across both inland and estuarine waters. This project developed a pilot framework, explored and tested methods necessary for a finer scale, seamless assessment across both inland and estuarine waters, and demonstrated its use. Although there was interest by the CBP FHAT for the generation of a Baywide fish habitat assessment that spanned tidal salt, tidal fresh, warm non-tidal and cold non-tidal waters, there are a myriad of implementation details and considerations around conducting a Baywide assessment across all four of these general habitat areas. Therefore, the practical need to conduct a tributary-specific pilot assessment arose. At the beginning of this pilot process, members of the FHAT were presented with a decision matrix to choose a study basin using factors such as data availability and tributary size. FHAT members chose the Patuxent River basin, which has been relatively well sampled and studied. Several spatial frameworks were considered before selection of an inclusive gridded framework for summary and analysis that represented inland drainage networks and landscape influences as well as estuarine bathymetry. A suite of landscape and in-water stressor variables were summarized into the framework and were largely generalized over time. In order to assess the viability of the framework, we chose to use species distribution modeling for each of the species to test the framework’s ability to predict habitat use of non-tidal resident, estuarine resident, and migratory species. Tessellated darter (Etheostoma olmstedi), American eel (Anguilla rostrata), and white perch (Morone americana) were chosen as illustrative fish species based on data availability, and differences in life history and habitat use. A nested modeling approach, which involved successive model runs at multiple scales (1000m, 100m, and 10m raster grids) was developed to examine differences in variable importance at different spatial scales and to enhance modeling efficiency. For white perch, a complementary modeling analysis was performed for variables available only in estuarine waters. For all testing, an ensemble modeling approach was conducted, using a suite of potential statistical techniques driven by model strength and variable predictive power. The statistical testing that we conducted was intended only to test the framework and modeling approach, and not to definitively predict all habitats where specific fish species might be present. The modeling we conducted to test the framework did have some limitations. For example, the spatial distribution of favorable habitat areas for white perch was likely influenced by the predominance of fish survey locations near the center channel of the river and the use of generalized in-water conditions. For all species, the use of juvenile and adult fish survey data limits the estimation of habitat use to those life stages. Despite such limitations of the data inputs and modeling approach, we found the framework could seamlessly predict fish habitat distribution across freshwater and tidal environments and integrate the influence of landscape stressors with local in-water factors. The developed framework presented to the Sustainable Fisheries Goal Implementation Team (GIT) and FHAT is informative and could potentially be used for other modeling applications in the Chesapeake Bay watershed and elsewhere. In particular the framework and modeling approach lend themselves to evaluating living resource distributions and underlying habitat conditions in shallow tidal waters and beyond, as recommended by the recent Comprehensive Evaluation of System Response (CESR) report from the Chesapeake Bay Program.

Maryland↗
USGS2018

Northeast

The distinct seasonality of the Northeast’s climate supports a diverse natural landscape adapted to the extremes of cold, snowy winters and warm to hot, humid summers. This natural landscape provides the economic and cultural foundation for many rural communities, which are largely supported by a diverse range of agricultural, tourism, and natural resource-dependent industries (see Ch. 10: Ag & Rural, Key Message 4) . The recent dominant trend in precipitation throughout the Northeast has been towards increases in rainfall intensity, with increases in intensity exceeding those in other regions of the contiguous United States. Further increases in rainfall intensity are expected, with increases in total precipitation expected during the winter and spring but with little change in the summer. Monthly precipitation in the Northeast is projected to be about 1 inch greater for December through April by end of century (2070–2100) under the higher scenario (RCP8.5). Ocean and coastal ecosystems are being affected by large changes in a variety of climate-related environmental conditions. These ecosystems support fishing and aquaculture, tourism and recreation, and coastal communities. Observed and projected increases in temperature, acidification, storm frequency and intensity, and sea levels are of particular concern for coastal and ocean ecosystems, as well as local communities and their interconnected social and economic systems. Increasing temperatures and changing seasonality on the Northeast Continental Shelf have affected marine organisms and the ecosystem in various ways. The warming trend experienced in the Northeast Continental Shelf has been associated with many fish and invertebrate species moving northward and to greater depths. Because of the diversity of the Northeast’s coastal landscape, the impacts from storms and sea level rise will vary at different locations along the coast. Northeastern cities, with their abundance of concrete and asphalt and relative lack of vegetation, tend to have higher temperatures than surrounding regions due to the urban heat island effect. During extreme heat events, nighttime temperatures in the region’s big cities are generally several degrees higher than surrounding regions, leading to higher risk of heat-related death. Urban areas are at risk for large numbers of evacuated and displaced populations and damaged infrastructure due to both extreme precipitation events and recurrent flooding, potentially requiring significant emergency response efforts and consideration of a long-term commitment to rebuilding and adaptation, and/or support for relocation where needed. Much of the infrastructure in the Northeast, including drainage and sewer systems, flood and storm protection assets, transportation systems, and power supply, is nearing the end of its planned life expectancy. Climate-related disruptions will only exacerbate existing issues with aging infrastructure. Sea level rise has amplified storm impacts in the Northeast (Key Message 2), contributing to higher surges that extend farther inland, as demonstrated in New York City in the aftermath of Superstorm Sandy in 2012. Service and resource supply infrastructure in the Northeast is at increasing risk of disruption, resulting in lower quality of life, economic declines, and increased social inequality. Loss of public services affects the capacity of communities to function as administrative and economic centers and triggers disruptions of interconnected supply chains (Ch. 16: International, Key Message 1) . Increases in annual average temperatures across the Northeast range from less than 1°F (0.6°C) in West Virginia to about 3°F (1.7°C) or more in New England since 1901. Although the relative risk of death on very hot days is lower today than it was a few decades ago, heat-related illness and death remain significant public health problems in the Northeast. For example, a study in New York City estimated that in 2013 there were 133 excess deaths due to extreme heat. These projected increases in temperature are expected to lead to substantially more premature deaths, hospital admissions, and emergency department visits across the Northeast. For example, in the Northeast we can expect approximately 650 additional premature deaths per year from extreme heat by the year 2050 under either a lower (RCP4.5) or higher (RCP8.5) scenario and from 960 (under RCP4.5) to 2,300 (under RCP8.5) more premature deaths per year by 2090. Communities, towns, cities, counties, states, and tribes across the Northeast are engaged in efforts to build resilience to environmental challenges and adapt to a changing climate. Developing and implementing climate adaptation strategies in daily practice often occur in collaboration with state and federal agencies (e.g., New Jersey Climate Adaptation Alliance 2017, New York Climate Clearinghouse 2017, Rhode Island STORMTOOLS 2017, EPA 2017, CDC 2015 ). Advances in rural towns, cities, and suburban areas include low-cost adjustments of existing building codes and standards. In coastal areas, partnerships among local communities and federal and state agencies leverage federal adaptation tools and decision support frameworks (for example, NOAA’s Digital Coast, USGS’s Coastal Change Hazards Portal, and New Jersey’s Getting to Resilience). Increasingly, cities and towns across the Northeast are developing or implementing plans for adaptation and resilience in the face of changing climate (e.g., EPA 2017 ). The approaches are designed to maintain and enhance the everyday lives of residents and promote economic development. In some cities, adaptation planning has been used to respond to present and future challenges in the built environment. Regional efforts have recommended changes in design standards when building, replacing, or retrofitting infrastructure to account for a changing climate.

Report↗
USGS1984

Hydrology of Area 62, Northern Great Plains and Rocky Mountain Coal Provinces, New Mexico and Arizona

This report summarizes available hydrologic data for Area 62 and will aid leasing decisions, and the preparation and appraisal of environmental impact studies and mine-permit applications. Area 62 is located at the southern end of the Rocky Mountain Coal Province in parts of New Mexico and Arizona and includes approximately 9,500 square miles. Surface mining alters, at least temporarily, the environment; if the areas are unreclaimed, there can be long-term environmental consequences. The land-ownership pattern in Area 62 is complicated. The checkerboard pattern created by several types of ownership makes effective management of these lands difficult. The climate generally is semiarid with average annual precipitation ranging from 10 to 20 inches. Pinons, junipers, and grasslands cover most of the area, and much of it is used for grazing by livestock. Soils vary with landscape, differing from flood plains and hillslopes to mountain slopes. The major structural features of this area were largely developed during middle Tertiary time. The main structural features are the southern San Juan Basin and the Mogollon slope. Coal-bearing rocks are present in four Cretaceous rock units of the Mesaverde Group: the Gallup Sandstone, the Dileo Coal Member, and the Gibson Coal Member of the Crevasse Canyon Formation, and the Cleary Coal Member of the Menefee Formation. Area 62 is drained by Black Creek, the Puerco River, the Zuni River, Carrizo Wash-Largo Creek, and the Rio San Jose. Only at the headwaters of the Zuni River is the flow perennial. The streamflow-gaging station network consists of 25 stations operated for a variety of needs. Streamflow changes throughout the year with variation related directly to rainfall and snowmelt. Base flow in Area 62 is zero indicating no significant ground-water discharge. Mountainous areas contribute the highest mean annual runoff of 1.0 inch. Very few water-quality data are available for the surface-water stations. Of the nine surface-water stations that have water-quality data, only one has chemical analyses from more than 10 samples. Therefore, sufficient data to characterize the area in detail are not available. Suspended sediment data are available only for a few surface- water stations in the area. Erosion rates generally are less than 1 acre-foot per square mile per year. Greater erosion rates are found within the badland areas. Water levels are periodically measured at 21 selected wells in Area 62. These observation wells are located mostly along the Rio San Jose and northeast of Gallup, New Mexico. The recharge to ground-water aquifers generally coincide with areas of greater precipitation in the mountainous areas. Depth to water below land surface is generally less than 200 feet. Well yields of 100 gallons per minute are common in most of the area. Ground-water quality is variable both within each aquifer and between aquifers. Water quality generally is best near recharge areas. Historical and current data related to stream discharge, water quality, and suspended sediment are available from computer files in the U.S. Geological Survey's National Water Data Storage and Retrieval System (WATSTORE) and through the National Water Data Exchange (NAWDEX).

Open-File Report↗
USGS1984

Hydrology of Area 62, Northern Great Plains and Rocky Mountain Coal Provinces, New Mexico and Arizona

This report summarizes available hydrologic data for Area 62 and will aid leasing decisions, and the preparation and appraisal of environmental impact studies and mine-permit applications. Area 62 is located at the southern end of the Rocky Mountain Coal Province in parts of New Mexico and Arizona and includes approximately 9,500 square miles. Surface mining alters, at least temporarily, the environment; if the areas are unreclaimed, there can be long-term environmental consequences. The land-ownership pattern in Area 62 is complicated. The checkerboard pattern created by several types of ownership makes effective management of these lands difficult. The climate generally is semiarid with average annual precipitation ranging from 10 to 20 inches. Pinons, junipers, and grasslands cover most of the area, and much of it is used for grazing by livestock. Soils vary with landscape, differing from flood plains and hillslopes to mountain slopes. The major structural features of this area were largely developed during middle Tertiary time. The main structural features are the southern San Juan Basin and the Mogollon slope. Coal-bearing rocks are present in four Cretaceous rock units of the Mesaverde Group: the Gallup Sandstone, the Dileo Coal Member, and the Gibson Coal Member of the Crevasse Canyon Formation, and the Cleary Coal Member of the Menefee Formation. Area 62 is drained by Black Creek, the Puerco River, the Zuni River, Carrizo Wash-Largo Creek, and the Rio San Jose. Only at the headwaters of the Zuni River is the flow perennial. The streamflow-gaging station network consists of 25 stations operated for a variety of needs. Streamflow changes throughout the year with variation related directly to rainfall and snowmelt. Base flow in Area 62 is zero indicating no significant ground-water discharge. Mountainous areas contribute the highest mean annual runoff of 1.0 inch. Very few water-quality data are available for the surface-water stations. Of the nine surface-water stations that have water-quality data, only one has chemical analyses from more than 10 samples. Therefore, sufficient data to characterize the area in detail are not available. Suspended sediment data are available only for a few surface- water stations in the area. Erosion rates generally are less than 1 acre-foot per square mile per year. Greater erosion rates are found within the badland areas. Water levels are periodically measured at 21 selected wells in Area 62. These observation wells are located mostly along the Rio San Jose and northeast of Gallup, New Mexico. The recharge to ground-water aquifers generally coincide with areas of greater precipitation in the mountainous areas. Depth to water below land surface is generally less than 200 feet. Well yields of 100 gallons per minute are common in most of the area. Ground-water quality is variable both within each aquifer and between aquifers. Water quality generally is best near recharge areas. Historical and current data related to stream discharge, water quality, and suspended sediment are available from computer files in the U.S. Geological Survey's National Water Data Storage and Retrieval System (WATSTORE) and through the National Water Data Exchange (NAWDEX).

Water-Resources Investigations Report↗
USGS2023

Modeling, mapping, and measuring the risk of freshwater invasive species across Alaska

Freshwater ecosystems of the Alaskan Arctic and Subarctic provide resources that are culturally, ecologically, and economically invaluable. Presently, these regions are relatively free of the impacts from invasive species compared to southern latitudes. To date, there have been relatively few verified introductions of aquatic invasive species (AIS) to freshwater ecosystems in Alaska. The expanding list and distribution of AIS has led to significant negative ecological and economic impacts (e.g., waterweed Elodea nuttalli ; E. canadensis and northern pike Esox Lucius introduced outside its native range in Alaska). Escalating human activity across Alaskan lands and waters, coupled with rapidly shifting environmental conditions, increases the potential for new species introductions and subsequent establishment. Creating a proactive framework for well-informed decision-making and action can improve the effectiveness of prevention efforts and bolster decision support tools that help resource managers direct limited resources. Prioritizing AIS that may be introduced and become established, as well as the locations at highest risk of invasion, is foundational to building a proactive invasive species management framework in Alaska. This project sought to identify and prioritize AIS known to be invasive in the contiguous United States, evaluate current and future habitat suitability for AIS in Alaska, and assess potential for AIS to be transported to habitats across Alaska, utilizing similar assessment methods as implemented for Bering Sea marine invasive species and non-native plants in Alaska. To accomplish this goal, the objectives of the project were to: 1) develop a formal ranked list of potential AIS to freshwater systems of Alaska; 2) assess the level of establishment risk for potential AIS by developing habitat suitability models for waterbodies across Alaska; and 3), identify potential pathways and specific vectors for high-risk AIS to invade Alaska and develop a framework for how vector analysis will be completed to understand transport risk. Overall, our goal is horizon scanning which is defined by Roy et al. (2019) as “a systematic examination of potential threats and opportunities, within a given context, and likely future developments, which are at the margin of current thinking and planning.” The scans include pathway analyses and risk screening of species present at pathway origin points, with a focus on identifying species at high risk of being introduced, becoming established, spreading, and causing harm. We refined a list of 28 AIS from a list of hundreds based on characterizations of species’ invasiveness and species’ proximity to Alaska (USGS 2020; GBIF 2022). Next, we evaluated the relative invasiveness of individual species to create an initial AIS ranking. We sought to characterize habitat suitability of AIS by selecting variables that were continental in scale, covering North America to include Alaska as well as the lower 48 states comparing natural discharge, sub-basin average terrain slope (degrees), average silt fraction, average organic carbon, lithological class, and human footprint in sub-basin in 2009. We estimated AIS habitat suitability across the entire state of Alaska using the physiological tolerances of the AIS (Appendix 2). We also evaluated pathways and vectors for the introduction of AIS (Appendix 2). Many pathways and vectors considered did not meet the criteria for Alaska or freshwater systems. Of the 28 ranked species that we categorized as very high, high, and moderate levels of invasiveness; all three risk groups included fish and mollusks (Appendix 2). One commonality of the very high-invasiveness-ranked species was the availability of Ecological Risk Screening Summary documents (USFWS, 2022) produced by U.S. Fish and Wildlife Service (USFWS), except for the goldfish ( Carassius auratus ) and the New Zealand mudsnail ( Potamopyrgus antipodarum ). The Ecological Risk Screening Summary is now available for New Zealand mudsnails. In general, fish species often ranked very high or high in invasiveness and included sportfish and aquarium fish, suggesting the importance of pathways such as aquarium trade, fishing industry, intentional (but illegal) introductions of sportfishes and aquarium fishes for establishment. The technique we used for habitat suitability models necessitated aquatic environmental datasets that were continental in scale, which was often interpolated from very coarse resolution source data layers, particularly in Alaska. Better spatial data representing aquatic environments would likely improve this approach. While the lack of introductions in Alaska and nearby provinces and states is encouraging, the lack of occurrence data for the focal species also created complications for habitat suitability modeling. Despite the challenges, the habitat suitability models indicated limited suitability for warmwater species while some species, such as Brook trout (S alvelinus fontinalis ), have high habitat suitability across Alaska no matter what threshold approach is taken. Some environmental predictors were more important than others. Specifically, the most important predictor variable, ‘frost free days,’ was critical for 15 out of 28 species as expected due to harsh winter conditions in Arctic and Subarctic regions. The second most important predictor was ‘subbasin land surface runoff’, a variable that indicates the amount of discharge and runoff, while the third most important predictor was ‘snow cover’ another indication of winter conditions. Overall, the ability to understand the effect of future climate scenarios on the establishment of AIS was challenging. A detailed dataset of freshwater temperatures and water chemistry (e.g., pH, calcium) would greatly improve the ability to predict invasiveness of freshwater species to Alaska’s ecosystems on a regional basis. Future studies may benefit from a more focused geographic scope examining a group of subbasins or a regional basin rather than the entire state. These drainages could be selected based upon the mostly likely locations of introduction pathways. The two most prevalent pathway risks for AIS are in-state transfer and stowaways/contaminants. Although there are examples of introductions from other pathways, the risk is somewhat mitigated by Alaska’s climate and regulations. However, variable application of protocols for inspection and cleaning of fishing gear, watercraft, and other similar items while traveling into Alaska as well as transferring from waterbody to waterbody within the state creates a substantial risk in introducing invasive species. We plot cumulative invasive vulnerability for all subbasins and for the top 10% of subbasins (Appendix 3).

Alaska↗
USGS2010

Southeast Regional Assessment Project for the National Climate Change and Wildlife Science Center, U.S. Geological Survey

The Southeastern United States spans a broad range of physiographic settings and maintains exceptionally high levels of faunal diversity. Unfortunately, many of these ecosystems are increasingly under threat due to rapid human development, and management agencies are increasingly aware of the potential effects that climate change will have on these ecosystems. Natural resource managers and conservation planners can be effective at preserving ecosystems in the face of these stressors only if they can adapt current conservation efforts to increase the overall resilience of the system. Climate change, in particular, challenges many of the basic assumptions used by conservation planners and managers. Previous conservation planning efforts identified and prioritized areas for conservation based on the current environmental conditions, such as habitat quality, and assumed that conditions in conservation lands would be largely controlled by management actions (including no action). Climate change, however, will likely alter important system drivers (temperature, precipitation, and sea-level rise) and make it difficult, if not impossible, to maintain recent historic conditions in conservation lands into the future. Climate change will also influence the future conservation potential of non-conservation lands, further complicating conservation planning. Therefore, there is a need to develop and adapt effective conservation strategies to cope with the effects of climate and landscape change on future environmental conditions. Congress recognized this important issue and authorized the U.S. Geological Survey (USGS) National Climate Change and Wildlife Science Center (NCCWSC; http://nccw.usgs.gov/) in the Fiscal Year 2008. The NCCWSC will produce science that will help resource management agencies anticipate and adapt to climate change impacts to fish, wildlife, and their habitats. With the release of Secretarial Order 3289 on September 14, 2009, the mandate of the NCCWSC was expanded to address climate change-related impacts on all Department of the Interior (DOI) resources. The NCCWSC will establish a network of eight DOI Regional Climate Science Centers (RCSCs) that will work with a variety of partners to provide natural resource managers with tools and information that will help them anticipate and adapt conservation planning and design for projected climate change. The forecasting products produced by the RCSCs will aid fish, wildlife, and land managers in designing suitable adaptive management approaches for their programs. The DOI also is developing Landscape Conservation Cooperatives (LCCs) as science and conservation action partnerships at subregional scales. The USGS is working with the Southeast Region of the U.S. Fish and Wildlife Service (FWS) to develop science collaboration between the future Southeast RCSC and future LCCs. The NCCWSC Southeast Regional Assessment Project (SERAP) will begin to develop regional downscaled climate models, land cover change models, regional ecological models, regional watershed models, and other science tools. Models and data produced by SERAP will be used in a collaborative process between the USGS, the FWS (LCCs), State and federal partners, nongovernmental organizations, and academia to produce science at appropriate scales to answer resource management questions. The SERAP will produce an assessment of climate change, and impacts on land cover, ecosystems, and priority species in the region. The predictive tools developed by the SERAP project team will allow end users to better understand potential impacts of climate change and sea level rise on terrestrial and aquatic populations in the Southeastern United States. The SERAP capitalizes on the integration of five existing projects: (1) the Multi-State Conservation Grants Program project "Designing Sustainable Landscapes," (2) the USGS multidisciplinary Science Thrust project "Water Availability for Ecological Needs," (3) the USGS Southeast Pilot Project "Climate Change in the Southeastern U.S. and its Impacts on Bird Distributions and Habitats," (4) a sea-level rise impacts study envisioned jointly with the National Oceanic and Atmospheric Administration (NOAA), and (5) two USGS sea-level rise impact assessment projects that address inundation hazards and provide probabilistic forecasts of coastal geomorphic change. The SERAP will expand on these existing projects and include the following tasks, which were initiated in summer 2009: * Regionally downscaled probabilistic climate-change projections * Integrated coastal assessment * Integrated terrestrial assessment * Multi-resolution assessment of potential climate change effects on biological resources: aquatic and hydrologic dynamics * Optimal conservation strategies to cope with climate change The SERAP seeks to formally integrate these tasks to aid conservation planning and design so that ecosystem management decisions can be optimized for providing desirable outcomes across a range of species and environments. The following chapters detail SERAP's efforts in providing a suite of regional climate, watershed, and landscape-change analyses and develop the interdisciplinary framework required for the biological planning phases of adaptive management and strategic conservation. The planning phase will include the identification of conservation alternatives, development of predictive models and decision support tools, and development of a template to address similar challenges and goals in other regions. The project teams will explore and develop ways to link the various ecological models arising from each component. The SERAP project team also will work closely with members of the LCCs and other partnerships throughout the life of the project to ensure that the objectives of the project meet resources mangers needs in the Southeast.

Open-File Report↗
USGS2021

Oyster model inventory: Identifying critical data and modeling approaches to support restoration of oyster reefs in coastal U.S. Gulf of Mexico waters

Executive Summary Along the coast of the U.S. Gulf of Mexico, the eastern oyster ( Crassostrea virginica ) plays important ecological and economic roles. Commercial landings from this region account for more than 50 percent of all U.S. landings; these oyster reefs also provide varied ecosystem services, including nursery habitat for many fish and macroinvertebrate species, shoreline protection, and water-quality maintenance. Declining trends in both total oyster production and functional reef area across this region have spurred investment in restoration of oyster resources, with specific calls for restoration projects to develop a network of reefs and identify broodstock and sanctuary reef restoration sites. Decision making related to restoration and establishment of a network of oyster reefs in the Gulf of Mexico requires information on both the environment and the effects of the environment on the oyster life cycle (including larval movement, survival, oyster recruitment, reproduction, growth, and mortality). Here, we examined the current state of data and model development in this region with the goal of providing an overview of oyster modeling approaches and an inventory of available data and existing oyster models. This report is meant to provide an overview to managers for understanding existing efforts and identify a path forward to most efficiently inform oyster resource management and restoration planning in moving from a single reef management approach to a reef network management approach. Numerous models related to some aspect of the oyster life cycle have been built, calibrated, and validated for various Gulf of Mexico estuaries over the last few decades (over 30 models identified). These models, which could inform site restoration, can be classified into four approaches: (1) oyster Habitat Suitability Index (HSI) models; (2) larval transport models; (3) on-reef oyster models that may include oyster growth, mortality and reproduction, and substrate persistence; and (4) coupled larval transport on-reef metapopulation models that simulate the entire oyster life cycle. The data requirements, model complexity and assumptions, and transferability vary by approach. Specifically, some approaches may offer greater accessibility, flexibility, and transferability spatially or temporally, with minimal data input, but only provide broad information to support site selection. In contrast, other approaches may require significant site-specific data for their construction and validation but may provide more accurate and location-specific data to support site selection for broodstock reefs. Regardless of modeling approach used, data on environmental drivers, such as salinity, water temperature, or water flow impacting oyster metabolism and movement, are required at appropriate spatial and temporal scales. While numerous data collection platforms, environmental models, and research products exist within Gulf of Mexico estuaries to provide important environmental data to use as drivers in the oyster models, significant variability in temporal and spatial coverage of the data, and variation in the availability of future condition models, exists across estuaries. This variation influences the spatial and temporal scales at which oyster models may be developed and impacts the calibration and validation of the oyster models within a given estuary, affecting its potential ability to address specific management or restoration questions. While multiple modeling approaches exist for informing site selection of broodstock or sanctuary oyster reefs, the development, calibration, and validation of a single modeling platform presents the most efficient, transferable, and useful tool for managers across the Gulf of Mexico. The development of a single modeling platform would involve using standardized input variables, governing equations, and assumptions for the modeled oyster processes and outputs, and for standardized calibration and validation procedures that could be applied within each estuary. The differences among estuary applications would require substituting only estuary-specific environmental data, and calibrating and validating the modeling approach with local oyster data. Two modeling approaches likely to be useful include (1) development of a general geospatial HSI modeling framework that could be applied consistently across estuaries and (2) a mechanistic coupled larval transport on-reef metapopulation model requiring only estuarine specific calibration and hydrodynamic models. Both approaches benefit from existing work across multiple Gulf of Mexico estuaries and could provide valuable support for oyster restoration, but may differ in their ability to address specific questions related to oyster restoration. HSI models specifically guide restoration practitioners in determining suitable habitat based on available data. The HSI approach, while currently more widely used and accessible, requires more development of larval suitability and larval input and output components in order to inform reef connectivity. A metapopulation approach considering the full oyster life cycle that simulates both on-reef oyster growth, mortality, reproduction, substrate persistence, and larval transport (ideally with larval growth and mortality) would provide the greatest detail and level of understanding but requires significant up-front investment. The larval oyster model and on-reef oyster model are usually developed independently for systems, although the two approaches can be coupled to represent the entire oyster life cycle in order to characterize and assess a reef metapopulation. This approach may be less accessible and much more data-intensive, however, and it requires some expertise to run and apply to inform oyster resource management. Ultimately, the development of single modeling platforms for each of these approaches would provide flexible tools applicable across all Gulf of Mexico oyster supporting estuaries. By using a single platform for model development, testing, calibrating and validating, and evaluation of modeled future scenarios, oyster restoration scientists and managers would not only be able to examine different scenario outcomes within a single estuary, but could also have comparable modeled results to evaluate potential outcomes, across estuaries and regions, that are not confounded by varying modeled data inputs, governing equations, assumptions, or user judgement.

Alabama, Florida, Louisiana, Mississippi, Texas↗
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↗
USGS2015

In-reservoir behavior, dam passage, and downstream migration of juvenile Chinook salmon and juvenile steelhead from Detroit Reservoir and Dam to Portland, Oregon, February 2013-February 2014

In the second year of 2 years of study, the movements of juvenile spring Chinook salmon ( Oncorhynchus tshawytscha ) and juvenile summer steelhead ( Oncorhynchus mykiss ) through Detroit Reservoir, passing Detroit Dam, and migrating downstream to Portland, Oregon, were studied during a 1-year-long period beginning in February 2013. The primary purpose of the study was to provide empirical data to inform decisions about future alternatives for improving downstream passage of salmonids at Detroit Dam. A secondary purpose was to design and assess the performance of a system to detect juvenile salmonids implanted with acoustic transmitters migrating in the Willamette River. Inferences about fish migration were made from detections of juvenile fish of hatchery origin at least 95 millimeters in fork length surgically implanted with an acoustic transmitter and released during the spring (March&ndash;May) and fall (September&ndash;November) of 2013. Detection sites were placed throughout the reservoir, near the dam, and at two sites in the North Santiam River and at three sites in the Willamette River culminating at Portland, Oregon. We based most inferences on an analysis period up to the 90th percentile of tag life (68&ndash;78 days after release, depending on species and season), although a small number of fish passed after that period as late as April 8, 2014. Chinook salmon migrated from the tributaries of release to the reservoir in greater proportion than steelhead, particularly in the fall. The in-reservoir migration behaviors and dam passage of the two species were similar during the spring study, but during the fall study, few steelhead reached the reservoir and none passed the dam within the analysis period. Migrations in the reservoir were directed and non-random, except in the forebay. Depths of fish within 25 meters of the dam were deeper in the day than at night for Chinook salmon and similar in the day and night for steelhead; steelhead generally were at shallower depths than Chinook salmon. The primary factors affecting dam passage rates were seasonal dam operating conditions and diel period. Fish passage rates were much greater during the spring and summer than in the fall and winter, and the difference was attributed to the availability and use of the spillway near the top of the dam during the spring and summer. The flood-control purpose of the reservoir prevented spillway use during much of the fall and winter because of the low forebay elevation. Passage rates at night were greater than in the day during spring and summer (4.2 times) and during the fall and winter (14.9 times). Fish length, dam discharge, and forebay elevation also affected dam passage rates. Travel times from Detroit Dam passage to the downstream sites were shorter during the fall and winter than during the spring and summer, and were less than a median of 8.68 days to Portland. The estimated survival in the 11 kilometers (km) between Detroit Dam and the Minto Dam forebay was lower than in the remaining 241 km to the Portland site. Estimated survival per 100 km in the free-flowing reach from Minto Dam to Portland was 0.675&ndash;0.836, depending on species and season, and was similar to other free-flowing rivers in the Western United States. The high probability of fish in the reservoir reaching the dam, the chance for repeated presence near the dam, the fish depths, and the factors known to affect passage rates suggest that a properly designed surface passage route could be a viable downstream passage alternative for juvenile Chinook salmon and steelhead at Detroit Dam. As part of the evaluations conducted at Detroit Dam, we continued to refine and improve methods for monitoring fish movements in the Willamette River. The goal was to develop stable, cost-effective, long-term monitoring arrays suitable for detection of any Juvenile Salmon Acoustic Telemetry System (JSATS)-tagged fish in the Willamette River. These data then could be used to estimate timing, migration rates, and survival of JSATS-tagged fish from various studies in the Willamette River Basin. The challenge, however, is that acoustic telemetry generally performs poorly in shallow, turbulent water, like that found in the Willamette River. We successfully designed, deployed, and maintained a series of monitoring sites near the Oregon cities of Salem, Wilsonville, and Portland. In the spring, detection probabilities at these sites ranged from 0.900 to 1.000. In the fall, the detection probabilities decreased and ranged from 0.526 to 1.000. The lower detection probabilities, particularly at the Salem site (0.526), were owing to loss of data caused by abnormally high flows as well as the 2013 Federal government shutdown, which prevented us from servicing the equipment. The monitoring sites that we installed seem to be robust and enable the efficient use of acoustic-tagged fish for studies of migration or survival in the Willamette River and similar environments.

Oregon↗
USGS2025

Calibration of the Stream Salmonid Simulator (S3) model to estimate annual survival, movement, and food consumption by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the restoration reach of the Trinity River, California, 2006–18

Executive Summary The Trinity River is managed in two sections: (1) from the upper 64-kilometer “restoration reach” downstream from Lewiston Dam to the confluence with the North Fork Trinity River, and (2) the 120-kilometer lower Trinity River downstream from the restoration reach. The Stream Salmonid Simulator (S3) has been previously applied to these reaches and the Klamath River. To estimate fish growth, past S3 calibration efforts in the Trinity and Klamath Rivers used maximum likelihood methods that considered only the abundance of juvenile Chinook salmon ( Oncorhynchus tshawytscha ) passing a fish trap to estimate survival and movement parameters, but not fish consumption. Previous calibrations did not estimate the average proportion of maximum consumption ( C y ) when estimating survival ( S y ) and movement ( M 0 y ) parameters across years ( y ) of data, but because no other information was available in the literature a fixed value of C y =0.66 was assumed. Therefore, the goal of this report is to present an alternative approach that calibrates the S3 model to multivariate data (that is, abundance and size), enabling the estimation of the average proportion of maximum consumption, in conjunction with survival and movement parameters for a particular migration year. We fit the S3 model to individual years of weekly trap abundance estimates and mean fish sizes (fork length) at the Pear Tree Gulch (hereafter referred to as Pear Tree) fish trap representing the restoration reach. We used the Earth Mover’s Distance (EMD) as the objective value to be minimized in parameter optimization. This approach estimated survival, movement, and consumption parameters for each migration year. Because we had information on the abundance of natural and hatchery produced juvenile salmon at the fish traps, we estimated survival and movement for natural and hatchery fish. S3 is a deterministic life-stage-structured population model that tracks daily growth, movement, and survival of juvenile Chinook Salmon. A key theme of the model is that river discharge affects habitat availability and capacity, which in turn drives density-dependent population dynamics. To explicitly link population dynamics to habitat quality and quantity, the river environment is constructed as a one-dimensional series of linked habitat units, each of which has an associated daily timeseries of discharge, water temperature, and useable habitat area or carrying capacity. In turn, the physical characteristics of each habitat unit and the number of fish occupying each unit drive survival and growth within each habitat unit and movement of fish among habitat units. The physical template of the restoration reach of the Trinity River was classified into 356 meso-habitat units comprised of runs, riffles, and pools. For each habitat unit, we developed a timeseries of daily discharge, water temperature, amount of available spawning habitat, and fry and parr carrying capacity. Capacity time series were constructed using state-of-the-art models of spatially explicit hydrodynamics and quantitative fish habitat relationships developed for the Trinity River. These variables were then used to drive population dynamics such as egg maturation and survival, and in turn, juvenile movement, growth, and survival. We estimated movement, survival, and consumption parameters by calibrating the model to 12 years of weekly juvenile abundance estimates and fish sizes at the Pear Tree fish trap near the downstream end of the restoration reach. We estimated parameters for 12 years that included a wide range of female spawner abundances (1,414–11,494) and water year types (critically dry–extremely wet). We contrast the estimated parameters to the corresponding number of female spawners and the total annual volume of water discharged for the Trinity River (Trinity River Restoration Program; https://www.trrp.net/restoration/flows/summary/ ). The calibration consisted of replicating historical conditions as closely as possible (for example, discharge; temperature; spawner abundance, spawning location and timing, and hatchery releases), and then running the model to predict weekly abundance passing the trap location from each brood year of adults and subsequent migration year of their juvenile progeny. Because density-dependent movement was favored in past evaluations, we estimated S3 parameters based on density-independent survival and density-dependent movement. Likewise, each year’s estimated survival parameter for natural ( S N y ) and hatchery ( S H y ) fish may be interpreted as the mean daily survival probability from emergence or hatchery release to the Pear Tree fish trap. Under density dependence, the estimated movement parameter for natural ( M 0N y ) and hatchery ( M 0H y ) fish represents the intercept of the Beverton-Holt model; the probability of remaining in a habitat at near-zero abundance. We estimated C y by using EMD and incorporating abundance and fish size into model calibration. Average daily proportions of maximum consumption, , across the years were generally high (=0.640; standard deviation (SD) SD=0.176), suggesting that fish were feeding at about two-thirds of expected maximum consumption rates. This average proportion of maximum consumption,is very similar to what has been assumed (=0.66) in previous Trinity and Klamath River S3 calibration and simulation efforts. In 2017, we estimated the lowest C y , suggesting lower average consumption for juvenile salmon in high-discharge water years. When this high discharge year was excluded, there was no apparent trend in C y with annual water volume. Estimates of survival showed little trend over the range in spawner abundances, but a trend towards higher natural and hatchery fish survival with higher annual volumes of water was apparent. Over the 12 years, the average survival of hatchery fish was =0.888 (SD=0.079) and the average survival natural fish was=0.969 (SD=0.01). With respect to fish movement, we estimated higher M 0N y and M 0H y with higher annual volumes of water in the Trinity River. Higher M N0 y or M H0 y suggest greater probability of remaining in a habitat at low fish densities, with potential for density-dependent processes in movement to occur. The highest M 0N y = 0.676 was estimated during brood year 2012, and the overall average for natural fish was =0.276 (SD=0.188) and for hatchery fish was=0.467 (SD=0.235). Under the Beverton-Holt model, as M 0N y or M 0H y approach zero, there is less capacity for change in fish movement as fish density increases. The S3 model was initialized with only the spatiotemporal distribution of spawners, so it performed well at capturing the essential outmigration features that are ultimately governed by rates of growth, movement, and mortality. We used a new optimization method that could accommodate multivariate data on abundance and fish size collected at the Pear Tree fish trap, enabling the calibration of S3 to estimate five parameters for 12 separate years of data. Incorporating weekly fish size data for each year in our parameter optimization process made the estimation of C y possible and represents a step forward in the fitting of the S3 model to fish trap data for the purposes of parameter calibration and the estimation of growth parameters with respect to annual conditions. We identified lack of fit and adding important effects into the S3 model may improve the S3 estimation and simulation of water scenarios. The Trinity River Restoration Program (TRRP) Science Advisory Board recommended that the TRRP focus on developing core elements of a decision support system (DSS; Buffington and others, 2014). Toward that end, the habitat and S3 models described in this report are both core elements of the DSS. The structure of S3 makes it a particularly useful fish production model for the DSS because population dynamics are sensitive to (1) water temperature, (2) daily discharge management, and (3) habitat quality and quantity. Each of these variables are key management parameters under consideration in the TRRP. As such, the S3 model may provide valuable insights into the potentially variable effects of different management decisions on the Trinity River.

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