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At least 271 records · Page 15Linked to original sources
USGS2018

Multi-Resource Analysis—Methodology and synthesis

This document introduces the Multi-Resource Analysis (MRA), a set of products that are being designed to integrate information on multiple natural resources in a region, combine that information with models of resource interrelationships and scenarios of change, and provide meaningful insights on the implications of those changes to people and the resources they value. The MRA builds from and enhances a wide range of existing U.S. Geological Survey assessment products. These enhancements will help natural resource managers better understand the connections among the resources they manage and the changes that might occur due to natural events and human decisions. This knowledge will help them identify solutions to landscape-scale management issues that best meet their objectives. MRA products are developed through a structured process that brings together scientists, decision makers, and other stakeholders to address relevant issues and decisions for a specified geographic region, ensuring that the analysis is directly relevant. This circular introduces the MRA, describes the envisioned process for developing a region-specific MRA, and discusses the various MRA components and products. The MRA process and products are shown through descriptions and examples drawn from two proof-of-concept studies and related work.

Circular↗
USGS2018

Integrating adaptive management and ecosystem services concepts to improve natural resource management: Challenges and opportunities

Executive Summary—Overview Natural resource managers must make decisions that affect broad-scale ecosystem processes involving large spatial areas, complex biophysical interactions, numerous competing stakeholder interests, and highly uncertain outcomes. Natural and social science information and analyses are widely recognized as important for informing effective management. Chief among the systematic approaches for improving the integration of science into natural resource management are two emergent science concepts, adaptive management and ecosystem services. Adaptive management (also referred to as “adaptive decision making”) is a deliberate process of learning by doing that focuses on reducing uncertainties about management outcomes and system responses to improve management over time. Ecosystem services is a conceptual framework that refers to the attributes and outputs of ecosystems (and their components and functions) that have value for humans. This report explores how ecosystem services can be moved from concept into practice through connection to a decision framework—adaptive management—that accounts for inherent uncertainties. Simultaneously, the report examines the value of incorporating ecosystem services framing and concepts into adaptive management efforts. Adaptive management and ecosystem services analyses have not typically been used jointly in decision making. However, as frameworks, they have a natural—but to date underexplored—affinity. Both are policy and decision oriented in that they attempt to represent the consequences of resource management choices on outcomes of interest to stakeholders. Both adaptive management and ecosystem services analysis take an empirical approach to the analysis of ecological systems. This systems orientation is a byproduct of the fact that natural resource actions affect ecosystems—and corresponding societal outcomes—often across large geographic scales. Moreover, because both frameworks focus on resource systems, both must confront the analytical challenges of systems modeling—in terms of complexity, dynamics, and uncertainty. Given this affinity, the integration of ecosystem services analysis and adaptive management poses few conceptual hurdles. In this report, we synthesize discussions from two workshops that considered ways in which adaptive management approaches and ecosystem service concepts may be complementary, such that integrating them into a common framework may lead to improved natural resource management outcomes. Although the literature on adaptive management and ecosystem services is vast and growing, the report focuses specifically on the integration of these two concepts rather than aiming to provide new definitions or an indepth review or primer of the concepts individually. Key issues considered include the bidirectional links between adaptive decision making and ecosystem services, as well as the potential benefits and inevitable challenges arising in the development and use of an integrated framework. Specifically, the workshops addressed the following questions: How can application of ecosystem service analysis within an adaptive decision process improve the outcomes of management and advance understanding of ecosystem service identification, production, and valuation? How can these concepts be integrated in concept and practice? What are the constraints and challenges to integrating adaptive management and ecosystem services? And, should the integration of these concepts be moved forward to wider application—and if so, how?

Circular↗
USGS2012

Estimating landscape carrying capacity through maximum clique analysis

Habitat suitability (HS) maps are widely used tools in wildlife science and establish a link between wildlife populations and landscape pattern. Although HS maps spatially depict the distribution of optimal resources for a species, they do not reveal the population size a landscape is capable of supporting--information that is often crucial for decision makers and managers. We used a new approach, "maximum clique analysis," to demonstrate how HS maps for territorial species can be used to estimate the carrying capacity, N(k), of a given landscape. We estimated the N(k) of Ovenbirds (Seiurus aurocapillus) and bobcats (Lynx rufus) in an 1153-km2 study area in Vermont, USA. These two species were selected to highlight different approaches in building an HS map as well as computational challenges that can arise in a maximum clique analysis. We derived 30-m2 HS maps for each species via occupancy modeling (Ovenbird) and by resource utilization modeling (bobcats). For each species, we then identified all pixel locations on the map (points) that had sufficient resources in the surrounding area to maintain a home range (termed a "pseudo-home range"). These locations were converted to a mathematical graph, where any two points were linked if two pseudo-home ranges could exist on the landscape without violating territory boundaries. We used the program Cliquer to find the maximum clique of each graph. The resulting estimates of N(k) = 236 Ovenbirds and N(k) = 42 female bobcats were sensitive to different assumptions and model inputs. Estimates of N(k) via alternative, ad hoc methods were 1.4 to > 30 times greater than the maximum clique estimate, suggesting that the alternative results may be upwardly biased. The maximum clique analysis was computationally intensive but could handle problems with < 1500 total pseudo-home ranges (points). Given present computational constraints, it is best suited for species that occur in clustered distributions (where the problem can be broken into several, smaller problems), or for species with large home ranges relative to grid scale where resampling the points to a coarser resolution can reduce the problem to manageable proportions.

Ecological Applications↗
USGS2020

Approaches to highly parameterized inversion: PEST++ Version 5, a software suite for parameter estimation, uncertainty analysis, management optimization and sensitivity analysis

PEST++ Version 5 extends and enhances the functionality of the PEST++ Version 3 software suite, providing environmental modeling practitioners access to updated Version 3 tools as well as new tools to support decision making with environmental models. Version 5 of PEST++ includes tools for global sensitivity analysis (PESTPP-SEN); least-squares parameter estimation with integrated first-order, second-moment parameter and forecast uncertainty estimation (PESTPP-GLM); an iterative, localized ensemble smoother (PESTPP-IES); and a tool for management optimization under uncertainty (PESTPP-OPT). Additionally, all PEST++ Version 5 tools have a built-in fault-tolerant, multithreaded parallel run manager and are model independent, using the same protocol as the widely used PEST software suite. PEST++ Version 5 is consistent with PEST++ Version 3 conventions and design philosophy. The software’s emphasis continues to target efficient and optimized algorithms that have proven beneficial in decision-support settings and can accommodate large, highly parameterized problems. Expanded and new capabilities are now available to express uncertainty using Monte Carlo and analytical uncertainty approaches and allow evaluation of thousands to millions of parameters. New management optimization capabilities in Version 5 also allow environmental models to be used to answer management questions using multiple societal constraints in a risk-based framework. The PEST++ Version 5 software suite can be compiled for Microsoft Windows® and Unix-based operating systems such as Apple and Linux®; the source code is available with a Microsoft Visual Studio® 2019 solution; and CMake support for all three operating system is also provided. PEST++ Version 5 continues to build a foundation for an open-source framework capable of producing model-independent, robust, and efficient decision-support tools for large environmental models. The functionality of each of the PEST++ tools are demonstrated on a simple example problem. Implications of decisions used when using the PEST++ suite tools are also discussed.

Techniques and Methods↗
USGS2006

Dealing with uncertainty and sensitivity issues in process-based models of carbon and nitrogen cycles in northern forest ecosystems

Many process-based models on carbon (C) and nitrogen (N) cycles have been developed for northern forest ecosystems. These models are widely used to evaluate the long-term decisions in forest management dealing with effects like particulate pollution, productivity and climate change. Regarding climate change, one of the key questions that have sensitive political implications is whether northern forests will sequester atmospheric C or not. Whilst many process-based models have been tested for accuracy by evaluating or validating against observed data, few have dealt with the complexity of the incorporated procedures to estimate uncertainties associated with model predictions or the sensitivity of these predictions to input factors in a systematic, inter-model comparison fashion. In general, models differ in their underlying attempts to match natural complexities with assumed or imposed model structure and process formulations to estimate model parameters, to gather data and to address issues on scope, scale and natural variations. Uncertainties may originate from model structure, estimation of model parameters, data input, representation of natural variation and scaling exercises. Model structure relates to the mathematical representation of the processes modelled and the type of state variables that a model contains. The modelling of partitioning among above- and below-ground C and N pools and the interdependence among these pools remain a major source of uncertainty in model structure and error propagation. Most soil C models use at least three state variables to represent the different types of soil organic matter (SOM). This approach results in creating three artificial SOM pools, assuming that each one contains C compounds with same turnover rate. In reality, SOM consists of many different types of C compounds with widely different turnover rates. Uncertainty in data and parameter estimates are closely linked. Data uncertainties are associated with high variations in estimating forest biomass, productivity and soil organic matter and may be incomplete for model initialization, calibration, validation and sensitivity analysis of generalized predictor models. The scale at which a model is being used also affects the level of uncertainty, as the errors in the prediction of the C and N dynamics differ from the site to the landscape levels and across climatic regions. If the spatial or temporal scale of a model application is changed, additional uncertainty arises from neglecting natural variability in system variables in time and space. Uncertainty issues are also intimately related to model validation and sensitivity analysis. The estimation of uncertainties is needed to inform decision process, in order to detect the possible corridor of development. Uncertainty in this context is an essential measure of quality for stakeholder and decision makers.

Conference Paper↗
USGS2008

Uncertainty and sensitivity issues in process-based models of carbon and nitrogen cycles in terrestrial ecosystems

Many process-based models of carbon (C) and nitrogen (N) cycles have been developed for northern forest ecosystems. These models are widely used to evaluate the long-term decisions in forest management dealing with effects like particulate pollution, productivity and climate change. Regarding climate change, one of the key questions that have sensitive political implications is whether northern forests will sequester atmospheric C or not. Whilst many process-based models have been tested for accuracy by evaluating or validating against observed data, few have dealt with the complexity of the incorporated procedures to estimate uncertainties associated with model predictions or the sensitivity of these predictions to input factors in a systematic, inter-model comparison fashion. In general, models differ in their underlying attempts to match natural complexities with assumed or imposed model structure and process formulations to estimate model parameters, to gather data and to address issues on scope, scale and natural variations. Uncertainties may originate from model structure, estimation of model parameters, data input, representation of natural variation and scaling exercises. Model structure relates to the mathematical representation of the processes modelled and the type of state variables that a model contains. The modelling of partitioning among above- and below-ground C and N pools and the interdependence among these pools remain a major source of uncertainty in model structure and error propagation. For example, most soil C models use at least three state variables to represent the different types of soil organic matter (SOM). This approach results in creating three artificial SOM pools, assuming that each one contains C compounds with the same turnover rate. In reality, SOM consists of many different types of C compounds with widely different turnover rates. Uncertainty in data and parameter estimates are closely linked. Data uncertainties are associated with high variations in estimating forest biomass, productivity and soil organic matter and their estimates may be incomplete for model initialisation, calibration, validation and sensitivity analysis of generalised predictor models. The scale at which a model is being used also affects the level of uncertainty, as the errors in the prediction of the C and N dynamics differ from site to landscape levels and across climatic regions. If the spatial or temporal scale of a model application is changed, additional uncertainty arises from neglecting natural variability in system variables in time and space. Uncertainty issues are also intimately related to model validation and sensitivity analysis. The estimation of uncertainties is needed to inform decision processes in order to detect the possible corridor of development. Uncertainty in this context is an essential measure of quality for stakeholder and decision makers.

Book chapter↗
USGS2014

How much is new information worth? Evaluating the financial benefit of resolving management uncertainty

Conservation decision-makers face a trade-off between spending limited funds on direct management action, or gaining new information in an attempt to improve management performance in the future. Value-of-information analysis can help to resolve this trade-off by evaluating how much management performance could improve if new information was gained. Value-of-information analysis has been used extensively in other disciplines, but there are only a few examples where it has informed conservation planning, none of which have used it to evaluate the financial value of gaining new information. We address this gap by applying value-of-information analysis to the management of a declining koala Phascolarctos cinereus population. Decision-makers responsible for managing this population face uncertainty about survival and fecundity rates, and how habitat cover affects mortality threats. The value of gaining new information about these uncertainties was calculated using a deterministic matrix model of the koala population to find the expected population growth rate if koala mortality threats were optimally managed under alternative model hypotheses, which represented the uncertainties faced by koala managers. Gaining new information about survival and fecundity rates and the effect of habitat cover on mortality threats will do little to improve koala management. Across a range of management budgets, no more than 1&middot;7% of the budget should be spent on resolving these uncertainties. The value of information was low because optimal management decisions were not sensitive to the uncertainties we considered. Decisions were instead driven by a substantial difference in the cost efficiency of management actions. The value of information was up to forty times higher when the cost efficiencies of different koala management actions were similar. Synthesis and applications . This study evaluates the ecological and financial benefits of gaining new information to inform a conservation problem. We also theoretically demonstrate that the value of reducing uncertainty is highest when it is not clear which management action is the most cost efficient. This study will help expand the use of value-of-information analyses in conservation by providing a cost efficiency metric by which to evaluate research or monitoring.

Journal of Applied Ecology↗
USGS1981

From ecological test site to geographic information system: lessons for the 1980's

Geographic information systems were common elements in two kinds of interdisciplinary regional demonstration projects in the 1970's. Ecological test sits attempted to provide for more efficient remote-sensing data delivery for regional environmental management. Regional environmental systems analysis attempted to formally describe and model the interacting regional social and environmental processes, including the resource-use decision making process. Lessons for the 1980's are drawn from recent evaluations and assessments of these programs, focusing on cost, rates of system development and technology transfer, program coordination, integrative analysis capability, and the involvement of system users and decision makers.

Pecora VII Symposium↗
USGS2026

Harmonization of aggregated freshwater biotic data to support continental and global assessment

Biodiversity loss and conservation are increasingly coming into focus in global policy fora, requiring information and assessments at wider spatial and temporal scales than previously considered. However, the monitoring framework required to support such data collection and assessment is lacking in many countries and is not harmonized across countries, hampering these efforts. Aggregation of existing freshwater data offers a solution to the problem of assessing status and trends of ecosystems and biodiversity at large spatial scales in the absence of nationally coordinated monitoring efforts. Analysis of aggregated data from different sources, collected using different protocols and with varying levels of metadata and supporting data, can be challenging and requires decisions regarding data comparability. In this paper, we identify the challenges inherent in harmonizing aggregated freshwater data for analysis, including general concerns related to research goals, spatial and temporal scale, sample selection, sampling effort, and site integrity. We also discuss the challenges related to measured parameters, sampled habitats, sample collection and processing methods, and data integrity for phytoplankton, benthic algae, macrophytes, zooplankton, benthic macroinvertebrates, fish, and supporting variables such as water and sediment chemistry. We provide a workflow to evaluate each of these challenges and make decisions about how best to work with the data. Finally, we review a case study from a large-scale analysis of freshwater data from the circumpolar Arctic region that exemplifies the encountered challenges and the chosen solutions. Through the description of the case study, we provide practical solutions to support aggregation and analysis of existing freshwater data. As global conversations about biodiversity status and trends continue, the demand for large-scale analyses of data from different sources will only grow. In the absence of globally harmonized monitoring, we are faced with the need to ensure comparability of data, making expert judgements where needed to support sound conclusions.

PLOS Water↗
USGS2021

Tools and technologies for quantifying spread and impacts of invasive species

The need for tools and technologies for understanding and quantifying invasive species has never been greater. Rates of infestation vary on the species or organism being examined across the United States, and notable examples can be found. For example, from 2001 to 2003 alone, ash ( Fraxinus spp.) mortality progressed at a rate of 12.97 km year −1 (Siegert et al. 2014), and cheatgrass ( Bromus tectorum ) is expected to increase dominance on 14% of Great Basin rangelands (Boyte et al. 2016). The magnitude and scope of problems that invasive species present suggest novel approaches for detection and management are needed, especially those that enable more cost-effective solutions. The advantages of using technologically advanced approaches and tools are numerous, and the quality and quantity of available information can be significantly enhanced by their use. They can also play a key role in development of decision-support systems; they are meant to be integrated with other systems, such as inventory and monitoring, because often the tools are applied after a species of interest has been detected and a threat has been identified. In addition, the inventory systems mentioned in Chap. 10 are regularly used in calibrating and validating models and decision-support systems. For forested areas, Forest Inventory and Analysis (FIA) data are most commonly used (e.g., Václavík et al. 2015) given the long history of the program. In non-forested systems, national inventory datasets have not been around as long (see Chap. 10), but use of these data to calibrate and validate spatial models is growing. These inventory datasets include the National Resources Inventory (NRI) (e.g., Duniway et al. 2012) and the Assessment Inventory and Monitoring program (AIM) (e.g., McCord et al. 2017). Similarly, use of the Nonindigenous Aquatic Species (NAS) database is growing as well (e.g., Evangelista et al. 2017). The consistent protocols employed by these programs prove valuable for developing better tools, but the data they afford are generally limited for some tools because the sampling intensity is too low.

Book chapter↗
USGS1992

Improved first-order uncertainty method for water-quality modeling

Uncertainties are unavoidable in water-quality modeling and subsequent management decisions. Monte Carlo simulation and first-order uncertainty analysis (involving linearization at central values of the uncertain variables) have been frequently used to estimate probability distributions for water-quality model output due to their simplicity. Each method has its drawbacks: Monte Carlo simulation's is mainly computational time; and first-order analysis are mainly questions of accuracy and representativeness, especially for nonlinear systems and extreme conditions. An improved (advanced) first-order method is presented, where the linearization point varies to match the output level whose exceedance probability is sought. The advanced first-order method is tested on the Streeter-Phelps equation to estimate the probability distribution of critical dissolved-oxygen deficit and critical dissolved oxygen using two hypothetical examples from the literature. The advanced first-order method provides a close approximation of the exceedance probability for the Streeter-Phelps model output estimated by Monte Carlo simulation using less computer time - by two orders of magnitude - regardless of the probability distributions assumed for the uncertain model parameters.

Journal of Environmental Engineering↗
USGS2010

An optimum reduction of gauges to meet data program constraints

Budget or manpower constraints may force a reduction in data collection activities. However, information may be transferred from continued gauge sites to discontinued gauge sites provided there is ‘sufficient’ correlation between flow sequences. Information defined as the reciprocal of variance (of the parameter estimator for which the gauge has been established) is provided at ungauged sites by regression techniques. A set of gauge sites is chosen to satisfy the budget constraint and to maximize the information content. The information content based on regression is a function of the correlations between flow sequences and is subject to the errors in their estimators. Post optimal analysis gives estimates of correlation error effects on the decision process as to which gauges to continue and which to discontinue.

Hydrological Sciences Bulletin↗
USGS1999

New reporting procedures based on long-term method detection levels and some considerations for interpretations of water-quality data provided by the U.S. Geological Survey National Water Quality Laboratory

This report describes the U.S. Geological Survey National Water Quality Laboratory's approach for determining long-term method detection levels and establishing reporting levels, details relevant new reporting conventions, and provides preliminary guidance on interpreting data reported with the new conventions. At the long-term method detection level concentration, the risk of a false positive detection (analyte reported present at the long-term method detection level when not in sample) is no more than 1 percent. However, at the long-term method detection level, the risk of a false negative occurrence (analyte reported not present when present at the long-term method detection level concentration) is up to 50 percent. Because this false negative rate is too high for use as a default 'less than' reporting level, a more reliable laboratory reporting level is set at twice the determined long-term method detection level. For all methods, concentrations measured between the laboratory reporting level and the long-term method detection level will be reported as estimated concentrations. Non-detections will be censored to the laboratory reporting level. Adoption of the new reporting conventions requires a full understanding of how low-concentration data can be used and interpreted and places responsibility for using and presenting final data with the user rather than with the laboratory. Users must consider that (1) new laboratory reporting levels may differ from previously established minimum reporting levels, (2) long-term method detection levels and laboratory reporting levels may change over time, and (3) estimated concentrations are less certain than concentrations reported above the laboratory reporting level. The availability of uncensored but qualified low-concentration data for interpretation and statistical analysis is a substantial benefit to the user. A decision to censor data after they are reported from the laboratory may still be made by the user, if merited, on the basis of the intended use of the data.

Open-File Report↗
USGS2012

Survey of hydrologic models and hydrologic data needs for tracking flow in the Rio Grande, north-central New Mexico, 2010

The six Middle Rio Grande Pueblos have prior and paramount rights to deliveries of water from the Rio Grande for their use. When the pueblos or the Bureau of Indian Affairs Designated Engineer identifies a need for additional flow on the Rio Grande, the Designated Engineer is tasked with deciding the timing and amount of releases of prior and paramount water from storage at El Vado Reservoir to meet the needs of the pueblos. Over the last three decades, numerous models have been developed by Federal, State, and local agencies in New Mexico to simulate, understand, and (or) manage flows in the Middle Rio Grande upstream from Elephant Butte Reservoir. In 2008, the Coalition of Six Middle Rio Grande Basin Pueblos entered into a cooperative agreement with the U.S. Geological Survey to conduct a comprehensive survey of these hydrologic models and their capacity to quantify and track various components of flow. The survey of hydrologic models provided in this report will help water-resource managers at the pueblos, as well as the Designated Engineer, make informed water-resource-management decisions that affect the prior and paramount water use. Analysis of 4 publicly available surface-water models and 13 publicly available groundwater models shows that, although elements from many models can be helpful in tracking flow in the Rio Grande, numerous data gaps and modeling needs indicate that accurate, consistent, and timely tracking of flow on the Rio Grande could be improved. Deficient or poorly constrained hydrologic variables are sources of uncertainty in hydrologic models that can be reduced with the acquisition of more refined data. Data gaps need to be filled to allow hydrologic models to be run on a real-time basis and thus ensure predictable water deliveries to meet needs for irrigation, domestic, stock, and other water uses. Timeliness of flow-data reporting is necessary to facilitate real-time model simulation, but even daily data are sometimes difficult to obtain because the data come from multiple sources. Each surface-water model produces results that could be helpful in quantifying the flow of the Rio Grande, specifically by helping to track water as it moves down the channel of the Rio Grande and by improving the understanding of river hydraulics for the specified reaches. The ability of each surface-water model to track flow on the Rio Grande varies according to the purpose for which each model was designed. The purpose of Upper Rio Grande Water Operations Model (URGWOM) - to simulate water storage and delivery operations in the Rio Grande - is more applicable to tracking flow on the Rio Grande than are any of the other surface-water models surveyed. Specifically, the strengths of URGWOM in relation to modeling flow are the details and attention given to the accounting of Rio Grande flow and San Juan-Chama flow at a daily time step. The most significant difficulty in using any of the surveyed surface-water models for the purpose of predicting the need for requested water releases is that none of the surface-water models surveyed consider water accounting on a real-time basis. Groundwater models that provide detailed simulations of shallow groundwater flow in the vicinity of the Rio Grande can provide large-scale estimates of flow between the Rio Grande and shallow aquifers, which can be an important component of the Rio Grande water budget as a whole. The groundwater models surveyed for this report cannot, however, be expected to provide simulations of flow at time scales of less than the simulated time step (1 month to 1 year in most cases). Of those of the currently used groundwater models, the purpose of model 13 - to simulate the shallow riparian groundwater environment - is the most appropriate for examining local-scale surface-water/groundwater interactions. The basin-scale models, however, are also important in understanding the large-scale water balances between the aquifers and the surface water. In the case of the Upper and Middle Rio Grande Valley, models 6, 10, and 12 are the most accurate and current groundwater models available.

Colorado;New Mexico↗
USGS2015

Evaluation of groundwater levels in the South Platte River alluvial aquifer, Colorado, 1953-2012, and design of initial well networks for monitoring groundwater levels

The South Platte River and underlying alluvial aquifer form an important hydrologic resource in northeastern Colorado that provides water to population centers along the Front Range and to agricultural communities across the rural plains. Water is regulated based on seniority of water rights and delivered using a network of administration structures that includes ditches, reservoirs, wells, impacted river sections, and engineered recharge areas. A recent addendum to Colorado water law enacted during 2002-2003 curtailed pumping from thousands of wells that lacked authorized augmentation plans. The restrictions in pumping were hypothesized to increase water storage in the aquifer, causing groundwater to rise near the land surface at some locations. The U.S. Geological Survey (USGS), in cooperation with the Colorado Water Conservation Board and the Colorado Water Institute, completed an assessment of 60 years (yr) of historical groundwater-level records collected from 1953 to 2012 from 1,669 wells. Relations of "high" groundwater levels, defined as depth to water from 0 to 10 feet (ft) below land surface, were compared to precipitation, river discharge, and 36 geographic and administrative attributes to identify natural and human controls in areas with shallow groundwater. Averaged per decade and over the entire aquifer, depths to groundwater varied between 24 and 32 ft over the 60-yr record. The shallowest average depth to water was identified during 1983-1992, which also recorded the highest levels of decadal precipitation. Average depth to water was greatest (32 ft) during 1953-1962 and intermediate (30 ft) in the recent decade (2003-2012) following curtailment of pumping. Between the decades 1993-2002 and 2003-2012, groundwater levels declined about 2 ft across the aquifer. In comparison, in areas where groundwater levels were within 20 ft of the land surface, observed groundwater levels rose about 0.6 ft, on average, during the same period, which demonstrated preferential rise in areas with shallow groundwater. Approximately 29 percent of water-level observations were identified as high groundwater in the South Platte River alluvial aquifer over the 60-yr record. High groundwater levels were found in 17 to 33 percent of wells examined by decade, with the largest percentages occurring over three decades from 1963 to 1992. The recent decade (2003-2012) exhibited an intermediate percentage (25 percent) of wells with high groundwater levels but also had the highest percentage (30 percent) of high groundwater observations, although results by observations were similar (26-29 percent) over three decades prior, from 1963 to 1992. Major sections of the aquifer from north of Sterling to Julesburg and areas near Greeley, La Salle, and Gilcrest were identified with the highest frequencies of high groundwater levels. Changes in groundwater levels were evaluated using Kendal line and least trimmed squares regression methods using a significance level of 0.01 and statistical power of 0.8. During 2003-2012, following curtailment of pumping, 88 percent of wells and 81 percent of subwatershed areas with significant trends in groundwater levels exhibited rising water levels. Over the complete 60-yr record, however, 66 percent of wells and 57 percent of subwatersheds with significant groundwater-level trends still showed declining water levels; rates of groundwater-level change were typically less than 0.125 ft/yr in areas near the South Platte River, with greater declines along the southern tributaries. In agreement, 58 percent of subwatersheds evaluated between 1963-1972 and 2003-2012 showed net declines in average decadal groundwater levels. More areas had groundwater decline in upgradient sections to the west and rise in downgradient sections to the east, implying a redistribution of water has occurred in some areas of the aquifer. Precipitation was identified as having the strongest statistically significant correlations to river discharge over annual and decadal periods (Pearson correlation coefficients of 0.5 and 0.8, respectively, and statistical significance defined by p-values less than 0.05). Correlation coefficients between river discharge and frequency of high groundwater levels were statistically significant at 0.4 annually and 0.6 over decadal periods, indicating that periods of high river flow were often coincident with high groundwater conditions. Over seasonal periods in five of the six decades examined, peak high groundwater levels occurred after spring runoff from July to September when administrative structures were most active. Between 1993-2002 and 2003-2012, groundwater levels rose while river discharge decreased, in part from greater reliance on surface water and curtailed pumping from wells without augmentation plans. Geographic attributes of elevation and proximity to streams and rivers showed moderate correlations to high groundwater levels in wells used for observing groundwater levels (correlation coefficients of 0.3 to 0.4). Local depressions and regional lows within the aquifer were identified as areas of potential shallow groundwater. Wells close to the river regularly indicated high groundwater levels, while those within depleted tributaries tended to have low frequencies of high groundwater levels. Some attributes of administrative structures were spatially correlated to high groundwater levels at moderate to high magnitudes (correlation coefficients of 0.3 to 0.7). The number of affected river reaches or recharge areas that surround a well where groundwater levels were observed and its distance from the nearest well field showed the strongest controls on high groundwater levels. Influences of administrative structures on groundwater levels were in some cases local over a mile or less but could extend to several miles, often manifesting as diffuse effects from multiple surrounding structures. A network of candidate monitoring wells was proposed to initiate a regional monitoring program. Consistent monitoring and analysis of groundwater levels will be needed for informed decisions to optimize beneficial use of water and to limit high groundwater levels in susceptible areas. Finalization of the network will require future field reconnaissance to assess local site conditions and discussions with State authorities.

Colorado↗
USGS2024

A metapopulation strategy to support long term conservation of genetic diversity in Department of the Interior bison

Once numbering in the tens of millions, plains bison ( Bison bison bison ) were nearly driven to extinction with only a few hundred individuals remaining by the late 19th century. Plains bison have since recovered to approximately 20,000 animals managed in conservation herds throughout North America, yet substantial challenges to their recovery remain. The Department of the Interior (DOI) is working with diverse partners to steward approximately 11,000 bison in 18 conservation herds across 12 states. Most herds exist in areas without native predators, and removals are required to keep herd sizes at or below carrying capacity. The loss of genetic diversity within bison, and the fact that most DOI herds are relatively small and isolated from each other with no opportunity for natural gene flow, raises concerns about maintaining genetic diversity over the long term. Connecting populations through gene flow (i.e., creating a metapopulation) can minimize loss of genetic diversity, both within and across populations. Management of DOI bison conservation herds has historically varied across bureaus and conservation units. Adopting a national perspective on bison conservation was identified as a priority in the 2008 Department of the Interior Bison Conservation Initiative (BCI). The concept of metapopulation management as a potential tool to maximize the conservation of genetic diversity among DOI herds was first described in this 2008 Initiative and was specifically encouraged in the 2010 DOI Bison Conservation Genetics Workshop report (Dratch and Gogan 2010). In the 2020 BCI, the DOI re-affirmed its commitment to conserving bison as native, North American wildlife. This document establishes a framework for a nationally coordinated strategy for bison managed by the DOI to support the genetic conservation goals outlined in the 2020 BCI. This is a decisionmaking framework that guides managers through the process of determining when and how to consider translocations. Decisions and actions within the framework are informed by analysis and interpretation of data housed in an integrated, relational database that will be initially populated with the most current data and updated annually thereafter. It provides science-based guidance on how to conserve DOI bison genetic diversity through strategic translocations, while also considering cattle introgression and bison health. We illustrate how this Strategy can be used to guide the establishment of new conservation herds and discuss what it means to be a DOI partner. Finally, this is intended to be used as a living document that will evolve as needs and technologies change.

Science Report↗
USGS1997

Implications of the Bangor Hydro decision on FERC relicensings

In the next 15 years, over 400 existing nonfederal dams will require new licenses by the Federal Energy Regulatory Commission (FERC) in order to continue operations. In many cases, state or federal agencies have a unique opportunity to condition the hydropower licenses to protect environmental values. In 1996, the Court of Appeals for the District of Columbia Circuit issued an opinion striking down a mandatory license condition imposed by the U.S. Department of the Interior in a FERC relicensing. The case, Bangor Hydro-Electric Company v.Federal Energy Regulatory Commission, is instructive for agencies regarding the procedures to be followed in imposing mandatory FERC licensing conditions in the future. After Bangor Hydro, agencies should: (1) support their decisions by substantial evidence in the FERC record, (2) request applicant studies to support agency decisions and allow public comment on the decisions as a means to test the facts and analysis, and (3) consider intervening in a case to defend the condition on appeal. Bangor Hydro also raises, but does not decide, the issue of whether the agency imposing the mandatory condition should engage in balancing the economics of the hydropower project with the public resources affected by the project.

Rivers↗