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At least 595 records · Page 33Linked to original sources

USGS2014

Integrating research tools to support the management of social-ecological systems under climate change

Developing resource management strategies in the face of climate change is complicated by the considerable uncertainty associated with projections of climate and its impacts and by the complex interactions between social and ecological variables. The broad, interconnected nature of this challenge has resulted in calls for analytical frameworks that integrate research tools and can support natural resource management decision making in the face of uncertainty and complex interactions. We respond to this call by first reviewing three methods that have proven useful for climate change research, but whose application and development have been largely isolated: species distribution modeling, scenario planning, and simulation modeling. Species distribution models provide data-driven estimates of the future distributions of species of interest, but they face several limitations and their output alone is not sufficient to guide complex decisions for how best to manage resources given social and economic considerations along with dynamic and uncertain future conditions. Researchers and managers are increasingly exploring potential futures of social-ecological systems through scenario planning, but this process often lacks quantitative response modeling and validation procedures. Simulation models are well placed to provide added rigor to scenario planning because of their ability to reproduce complex system dynamics, but the scenarios and management options explored in simulations are often not developed by stakeholders, and there is not a clear consensus on how to include climate model outputs. We see these strengths and weaknesses as complementarities and offer an analytical framework for integrating these three tools. We then describe the ways in which this framework can help shift climate change research from useful to usable.

Ecology and Society↗

USGS2011

Adaptive resource management and the value of information

The value of information is a general and broadly applicable concept that has been used for several decades to aid in making decisions in the face of uncertainty. Yet there are relatively few examples of its use in ecology and natural resources management, and almost none that are framed in terms of the future impacts of management decisions. In this paper we discuss the value of information in a context of adaptive management, in which actions are taken sequentially over a timeframe and both future resource conditions and residual uncertainties about resource responses are taken into account. Our objective is to derive the value of reducing or eliminating uncertainty in adaptive decision making. We describe several measures of the value of information, with each based on management objectives that are appropriate for adaptive management. We highlight some mathematical properties of these measures, discuss their geometries, and illustrate them with an example in natural resources management. Accounting for the value of information can help to inform decisions about whether and how much to monitor resource conditions through time.

Ecological Modelling↗

USGS2008

An exact solution for ideal dam-break floods on steep slopes

The shallow‐water equations are used to model the flow resulting from the sudden release of a finite volume of frictionless, incompressible fluid down a uniform slope of arbitrary inclination. The hodograph transformation and Riemann's method make it possible to transform the governing equations into a linear system and then deduce an exact analytical solution expressed in terms of readily evaluated integrals. Although the solution treats an idealized case never strictly realized in nature, it is uniquely well‐suited for testing the robustness and accuracy of numerical models used to model shallow‐water flows on steep slopes.

Water Resources Research↗

USGS2017

Proceedings of the 11th United States-Japan natural resources panel for earthquake research, Napa Valley, California, November 16–18, 2016

The UJNR Panel on Earthquake Research promotes advanced research toward a more fundamental understanding of the earthquake process and hazard estimation. The Eleventh Joint meeting was extremely beneficial in furthering cooperation and deepening understanding of problems common to both Japan and the United States. The meeting included productive exchanges of information on approaches to systematic observation and modeling of earthquake processes. Regarding the earthquake and tsunami of March 2011 off the Pacific coast of Tohoku and the 2016 Kumamoto earthquake sequence, the Panel recognizes that further efforts are necessary to achieve our common goal of reducing earthquake risk through close collaboration and focused discussions at the 12th UJNR meeting.

Open-File Report↗

USGS2002

Relating net nitrogen input in the Mississippi River Basin to nitrate flux in the Lower Mississippi River--A comparison of approaches

A quantitative understanding of the relationship between terrestrial N inputs and riverine N flux can help guide conservation, policy, and adaptive management efforts aimed at preserving or restoring water quality. The objective of this study was to compare recently published approaches for relating terrestrial N inputs to the Mississippi River basin (MRB) with measured nitrate flux in the lower Mississippi River. Nitrogen inputs to and outputs from the MRB (1951 to 1996) were estimated from state-level annual agricultural production statistics and NO y (inorganic oxides of N) deposition estimates for 20 states that comprise 90% of the MRB. A model with water yield and gross N inputs accounted for 85% of the variation in observed annual nitrate flux in the lower Mississippi River, from 1960 to 1998, but tended to underestimate high nitrate flux and overestimate low nitrate flux. A model that used water yield and net anthropogenic nitrogen inputs (NANI) accounted for 95% of the variation in riverine N flux. The NANI approach accounted for N harvested in crops and assumed that crop harvest in excess of the nutritional needs of the humans and livestock in the basin would be exported from the basin. The U.S. White House Committee on Natural Resources and Environment (CENR) developed a more comprehensive N budget that included estimates of ammonia volatilization, denitrification, and exchanges with soil organic matter. The residual N in the CENR budget was weakly and negatively correlated with observed riverine nitrate flux. The CENR estimates of soil N mineralization and immobilization suggested that there were large (2000 kg N ha −1 ) net losses of soil organic N between 1951 and 1996. When the CENR N budget was modified by assuming that soil organic N levels have been relatively constant after 1950, and ammonia volatilization losses are redeposited within the basin, the trend of residual N closely matched temporal variation in NANI and was positively correlated with riverine nitrate flux in the lower Mississippi River. Based on results from applying these three modeling approaches, we conclude that although the NANI approach does not address several processes that influence the N cycle, it appears to focus on the terms that can be estimated with reasonable certainty and that are correlated with riverine N flux.

Journal of Environmental Quality↗

USGS2017

Impacts of coastal land use and shoreline armoring on estuarine ecosystems: An introduction to a special issue

The nearshore land-water interface is an important ecological zone that faces anthropogenic pressure from development in coastal regions throughout the world. Coastal waters and estuaries like Chesapeake Bay receive and process land discharges loaded with anthropogenic nutrients and other pollutants that cause eutrophication, hypoxia, and other damage to shallow-water ecosystems. In addition, shorelines are increasingly armored with bulkhead (seawall), riprap, and other structures to protect human infrastructure against the threats of sea-level rise, storm surge, and erosion. Armoring can further influence estuarine and nearshore marine ecosystem functions by degrading water quality, spreading invasive species, and destroying ecologically valuable habitat. These detrimental effects on ecosystem function have ramifications for ecologically and economically important flora and fauna. This special issue of Estuaries and Coasts explores the interacting effects of coastal land use and shoreline armoring on estuarine and coastal marine ecosystems. The majority of papers focus on the Chesapeake Bay region, USA, where 50 major tributaries and an extensive watershed (~ 167,000 km 2 ), provide an ideal model to examine the impacts of human activities at scales ranging from the local shoreline to the entire watershed. The papers consider the influence of watershed land use and natural versus armored shorelines on ecosystem properties and processes as well as on key natural resources.

Estuaries and Coasts↗

USGS2014

Thresholds for conservation and management: structured decision making as a conceptual framework

A conceptual framework is provided for considering the threshold concept in natural resource management and conservation. We define three kinds of thresholds relevant to management and conservation. Ecological thresholds are values of system state variables at which small changes bring about substantial or specified changes in system dynamics. They are frequently incorporated into ecological models used to project system responses to management actions. Utility thresholds are components of management objectives and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changes in management actions in order to reach specified management objectives. Decision thresholds are derived from the other components of the decision process.We advocate a structured decision making (SDM) approach within which the following components are identified: objectives (possibly including utility thresholds), potential actions, models (possibly including ecological thresholds), monitoring program, and a solution algorithm (which produces decision thresholds). Adaptive resource management (ARM) is described as a special case of SDM developed for recurrent decision problems that are characterized by uncertainty. We believe that SDM, in general, and ARM, in particular, provide good approaches to conservation and management. Use of SDM and ARM also clarifies the distinct roles of ecological thresholds, utility thresholds, and decision thresholds in informed decision processes.

Book↗

USGS2007

Assessment of exploration bias in data-driven predictive models and the estimation of undiscovered resources

The spatial distribution of discovered resources may not fully mimic the distribution of all such resources, discovered and undiscovered, because the process of discovery is biased by accessibility factors (e.g., outcrops, roads, and lakes) and by exploration criteria. In data-driven predictive models, the use of training sites (resource occurrences) biased by exploration criteria and accessibility does not necessarily translate to a biased predictive map. However, problems occur when evidence layers correlate with these same exploration factors. These biases then can produce a data-driven model that predicts known occurrences well, but poorly predicts undiscovered resources. Statistical assessment of correlation between evidence layers and map-based exploration factors is difficult because it is difficult to quantify the "degree of exploration." However, if such a degree-of-exploration map can be produced, the benefits can be enormous. Not only does it become possible to assess this correlation, but it becomes possible to predict undiscovered, instead of discovered, resources. Using geothermal systems in Nevada, USA, as an example, a degree-of-exploration model is created, which then is resolved into purely explored and unexplored equivalents, each occurring within coextensive study areas. A weights-of-evidence (WofE) model is built first without regard to the degree of exploration, and then a revised WofE model is calculated for the "explored fraction" only. Differences in the weights between the two models provide a correlation measure between the evidence and the degree of exploration. The data used to build the geothermal evidence layers are perceived to be independent of degree of exploration. Nevertheless, the evidence layers correlate with exploration because exploration has preferred the same favorable areas identified by the evidence patterns. In this circumstance, however, the weights for the "explored" WofE model minimize this bias. Using these revised weights, posterior probability is extrapolated into unexplored areas to estimate undiscovered deposits. ?? International Association for Mathematical Geology 2007.

Natural Resources Research↗

USGS2018

A comparison of synthetic flowpaths derived from light detection and ranging topobathymetric data and National Hydrography Dataset High Resolution Flowlines

Bathymetric and topobathymetric light detection and ranging (lidar) digital elevation models created for the Delaware River were provided to the National Geospatial Program and used to evaluate synthetic flowpath extraction from bathymetric/topobathymetric lidar survey data as a data source for improving the density, distribution, and connectivity of the National Hydrography Dataset High Resolution Flowline Network. As the surface-water component of The National Map, the National Hydrography Dataset maintains the Nation’s drainage network flow information and geometries for surface-water features used in hydrologic, hydraulic, and other science and engineering disciplines. The regional lidar survey for the Delaware River between Hancock, New York, and Trenton, New Jersey, was collected for the U.S. Geological Survey using the Experimental Advanced Airborne Research Lidar sensor system and processed by the Coastal National Elevation Database Applications Program. Using 1 percent of the maximum flow accumulation value for the surveyed Delaware River corridor as the flow accumulation threshold for grid cells at 1-, 5-, and 10-meter resolution created 223 to 283 kilometers of synthetic flowpaths potentially representing the river channel thalweg, which is the deepest point in a riverbed cross-section. There was potential for improving the High Resolution National Hydrography Dataset (HR NHD) Flowline network in places where the Delaware River channel, depicted as an Artificial Path in the HR NHD, is offset from the extracted synthetic river flowpath which sometimes appeared better positioned than the Artificial Path to represent the river thalweg. For the same area, using 0.05 percent of the maximum flow accumulation at the 1-, 5-, and 10-meter resolutions extracted 744 to 1,317 kilometers of synthetic flowpaths, with extracted synthetic flowpaths representing the main river channel and additional synthetic flowpaths representing tributaries or streams adjacent to the main channel. Overlaying these results with the HR NHDFlowline Network indicates that some of the additional synthetic flowpaths are connected to or extend HR NHD stream/river feature types. Some disconnected or isolated synthetic flowpaths not included in stream/river feature types were validated in orthoimagery and U.S. Topo Maps and provide examples of how extracted synthetic flowpaths could be used to delineate new stream/river features. Other additional extracted synthetic flowpaths depict linear features such as canals, tree lines, roads, or linear topographic depressions. For some river reaches where obstructions to flow or where low-relief topographic or bathymetric surfaces alter the flow direction, the software tool used to develop the flow direction grid did not calculate a primary flowpath for the river channel. Based on the results of this analysis, site conditions for the Delaware River corridor did not affect the quality of lidar bathymetric survey data. However, depending on the resolution of the lidar bathymetric digital elevation models (BDEMs), site conditions do have different effects on results for extracted synthetic flowpaths. We found that synthetic flowpaths extracted from 1-meter resolution lidar DEMs had more varied flow directions around in-channel landforms that obstructed flow than synthetic flowpaths extracted from 5- or 10-meter resolution lidar DEMs. As a result the 1-meter resolution DEM created some isolated or discontinuous synthetic flowpath segments where the 5- and 10-meter DEMs developed more continuous flowpaths. In this case the river bed upstream from the in-channel obstruction is shallower than the river bed downstream. Under these conditions the 1-meter resolution DEM provided synthetic flowpaths delineating a potential river thalweg. In this same area, the software solution modified (virtually raised) the river bed in the 5- and 10-meter resolution DEMs and flattened the bathymetric surface to create a continuous downstream flow direction, which caused trellis-patterned synthetic flowpaths to form. Under different site conditions and converse to the above development of synthetic flowpaths at different resolutions, at an abandoned river flood plain (terrace) with low relief that is adjacent to the river channel, the flow direction grid for the 1-meter resolution DEM developed continuous synthetic flowpath corresponding to a HR NHD Flowline network stream/river feature that connected to the main river channel but the larger resolution DEMs created isolated or disconnected synthetic flowpaths. A project to continue an evaluation of benefits of or issues caused by extracting synthetic flowpaths to enhance the HR NHD could include a study to assess the potential for merging surface-water flowpaths extracted from lidar topobathymetry and 3D Elevation Program digital elevation models. The merged DEM approach to synthetic flowpath extraction could extend the HR NHDFlowline network and enhance flow accumulations that might develop better flow direction grids in low-relief areas. Because of the confined lateral extent of the Delaware River, the lidar DEMs were not used to create catchments or watersheds; however, the merged DEM approach could also be tested as a resource for enhancing HR NHD catchments and watersheds. This lidar DEM synthetic flowpath extraction project supports the National Geospatial Program efforts to collect and produce high-quality lidar data to provide 3-dimensional representations of natural feature and aligns with the National Spatial Data Infrastructure to improve utilization of geospatial data. The results also can be useful for understanding strategies that can help maintain quality data in the HR NHD programs. KEYWORDS: bathymetric, digital elevation model, extracted synthetic flowpath, lidar, High Resolution National Hydrography Dataset, topobathymetric

New Jersey↗

USGS2001

A data management life-cycle

Documented, reliable, and accessible data and information are essential building blocks supporting scientific research and applications that enhance society's knowledge base (fig. 1). The U.S. Geological Survey (USGS), a leading provider of science data, information, and knowledge, is uniquely positioned to integrate science and natural resource information to address societal needs. The USGS Central Energy Resources Team (USGS-CERT) provides critical information and knowledge on the quantity, quality, and distribution of the Nation's and the world's oil, gas, and coal resources. By using a life-cycle model, the USGS-CERT Data Management Project is developing an integrated data management system to (1) promote access to energy data and information, (2) increase data documentation, and (3) streamline product delivery to the public, scientists, and decision makers. The project incorporates web-based technology, data cataloging systems, data processing routines, and metadata documentation tools to improve data access, enhance data consistency, and increase office efficiency

Fact Sheet↗

USGS2025

The 3D Elevation Program—Supporting Utah’s economy

Introduction High-quality elevation data for Utah inform decision making to improve the State’s economy. Light detection and ranging (lidar) data are used to support infrastructure planning and management, assess natural resources, and improve resiliency to hazards. The expanding availability of current and more accurate lidar data helps to better support natural resource conservation, wildfire risk management, geologic hazard investigation and mitigation, flood risk management, water supply planning, and urban planning and development. Critical applications that meet the State’s management needs depend on lidar data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features. The 3D Elevation Program is managed by the U.S. Geological Survey in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at quality level 2 or better to meet the many needs of the Nation and Utah. The status of available and in-progress 3DEP baseline lidar data in Utah is shown in figure 1. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 ( https://www.usgs.gov/3dep/lidarspec ) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $8.70 million in new benefits annually to the State. The top 10 Utah business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.

Utah↗

USGS2014

Introduction to selected references on fossil fuels of the central and southern Appalachian basin

The Appalachian basin contains abundant coal and petroleum resources that have been studied and extracted for at least 150 years. In this volume, U.S. Geological Survey (USGS) scientists describe the geologic framework and geochemical character of the fossil-fuel resources of the central and southern Appalachian basin. Separate subchapters (some previously published) contain geologic cross sections; seismic profiles; burial history models; assessments of Carboniferous coalbed methane and Devonian shale gas; distribution information for oil, gas, and coal fields; data on the geochemistry of natural gas and oil; and the fossil-fuel production history of the basin. Although each chapter and subchapter includes references cited, many historical or other important references on Appalachian basin and global fossil-fuel science were omitted because they were not directly applicable to the chapters.

Appalachian basin↗

USGS2007

Probabilistic prediction models for aggregate quarry siting

Weights-of-evidence (WofE) and logistic regression techniques were used in a GIS framework to predict the spatial likelihood (prospectivity) of crushed-stone aggregate quarry development. The joint conditional probability models, based on geology, transportation network, and population density variables, were defined using quarry location and time of development data for the New England States, North Carolina, and South Carolina, USA. The Quarry Operation models describe the distribution of active aggregate quarries, independent of the date of opening. The New Quarry models describe the distribution of aggregate quarries when they open. Because of the small number of new quarries developed in the study areas during the last decade, independent New Quarry models have low parameter estimate reliability. The performance of parameter estimates derived for Quarry Operation models, defined by a larger number of active quarries in the study areas, were tested and evaluated to predict the spatial likelihood of new quarry development. Population density conditions at the time of new quarry development were used to modify the population density variable in the Quarry Operation models to apply to new quarry development sites. The Quarry Operation parameters derived for the New England study area, Carolina study area, and the combined New England and Carolina study areas were all similar in magnitude and relative strength. The Quarry Operation model parameters, using the modified population density variables, were found to be a good predictor of new quarry locations. Both the aggregate industry and the land management community can use the model approach to target areas for more detailed site evaluation for quarry location. The models can be revised easily to reflect actual or anticipated changes in transportation and population features. ?? International Association for Mathematical Geology 2007.

Natural Resources Research↗

USGS2025

A case for assemblage-level conservation to address the biodiversity crisis

Traditional conservation efforts have centred on safeguarding individual species, but these strategies have limitations in a world where entire ecosystems are rapidly changing. Ecosystem conservation can maintain critical ecological functions, but often lacks the detail necessary for the effective conservation of threatened or endangered species. The conservation of such species is mandated by policies and remains a dominant focus of natural resource management. In this Perspective, we propose that assemblage-level conservation targeting groups of taxonomically related or functionally similar species can bridge the gap between species and ecosystems and help to address global biodiversity loss. This approach has previously been limited by data and methodological constraints, but the ongoing growth of biodiversity data, advances in ecological modelling and breakthroughs in computational power have now made effective assemblage-level conservation feasible. Community models provide insights at both the species level and the assemblage level while appropriately accounting for species variability in detection during sampling and uncertainty in biological inferences. Assemblage-level conservation can link both species-specific needs and broader ecological dynamics, ultimately enabling effective strategies for conserving threatened species, ecological communities and ecosystem functions.

Nature Reviews Biodiversity↗

USGS2021

Using fecal DNA and closed-capture models to estimate feral horse population size

Accurate population estimates provide the foundation for managing feral horses ( Equus caballus ferus ) across the western United States. Certain feral horse populations are protected by the Wild and Free-Roaming Horses and Burros Act of 1971 and managed by the Bureau of Land Management (BLM) or the United States Forest Service on designated herd management areas (HMAs) or wild horse territories, respectively. Horses are managed to achieve an appropriate management level (AML), which represents the number of horses determined by BLM to contribute to a thriving natural ecological balance and avoid deterioration of the range. To achieve AML for each HMA, BLM resource managers need accurate and precise population estimates. We tested the use of non-invasive fecal samples in a genetic capture-recapture framework to estimate population size in a closed horse population at the Little Book Cliffs HMA, Colorado, USA, with a known size of 153 individuals. We collected 1,957 samples over 3 independent sampling periods in 2014 and amplified them at 8 microsatellite loci. We applied mark-recapture models to determine population size using 954 samples that amplified at all 8 loci. We subsampled and reanalyzed our dataset to simulate different data collection protocols and evaluated effects on accuracy and precision of estimates using N-mixture modeling, full likelihood closed-capture modeling, and capwire single-occasion modeling that used data from all 3 sampling periods. Our model results were accurate and precise for analyses that used data from all 3 occasions; however, capwire single-occasion modeling was not accurate when we analyzed each sampling period separately. For all subsampling analysis scenarios, reducing sample size decreased precision, whether by reducing number of field staff, field days, or geographic areas surveyed on each period. Reducing spatial coverage of the survey area did not result in accurate population estimates and only marginally lowered the number of samples that would need to be collected to maintain accuracy. Because laboratory analysis contributes the greatest expense for this method ($80 U.S./sample), reducing fecal sample size is advantageous. Our results demonstrate that non-invasive sampling combined with good survey design and careful genetic and capture-recapture analyses can provide an alternative method to estimate the number of feral horses in a closed population. This method may be especially appropriate in situations where aerial inventories are not practical or accurate because of low sighting conditions. But the higher costs associated with laboratory sample analyses may reduce the method's feasibility compared to helicopter surveys.

Colorado↗

USGS2014

Threshold-dependent sample sizes for selenium assessment with stream fish tissue

Natural resource managers are developing assessments of selenium (Se) contamination in freshwater ecosystems based on fish tissue concentrations. We evaluated the effects of sample size (i.e., number of fish per site) on the probability of correctly detecting mean whole-body Se values above a range of potential management thresholds. We modeled Se concentrations as gamma distributions with shape and scale parameters fitting an empirical mean-to-variance relationship in data from southwestern West Virginia, USA (63 collections, 382 individuals). We used parametric bootstrapping techniques to calculate statistical power as the probability of detecting true mean concentrations up to 3 mg Se/kg above management thresholds ranging from 4 to 8 mg Se/kg. Sample sizes required to achieve 80% power varied as a function of management thresholds and Type I error tolerance (α). Higher thresholds required more samples than lower thresholds because populations were more heterogeneous at higher mean Se levels. For instance, to assess a management threshold of 4 mg Se/kg, a sample of eight fish could detect an increase of approximately 1 mg Se/kg with 80% power (given α = 0.05), but this sample size would be unable to detect such an increase from a management threshold of 8 mg Se/kg with more than a coin-flip probability. Increasing α decreased sample size requirements to detect above-threshold mean Se concentrations with 80% power. For instance, at an α-level of 0.05, an 8-fish sample could detect an increase of approximately 2 units above a threshold of 8 mg Se/kg with 80% power, but when α was relaxed to 0.2, this sample size was more sensitive to increasing mean Se concentrations, allowing detection of an increase of approximately 1.2 units with equivalent power. Combining individuals into 2- and 4-fish composite samples for laboratory analysis did not decrease power because the reduced number of laboratory samples was compensated for by increased precision of composites for estimating mean conditions. However, low sample sizes (<5 fish) did not achieve 80% power to detect near-threshold values (i.e., <1 mg Se/kg) under any scenario we evaluated. This analysis can assist the sampling design and interpretation of Se assessments from fish tissue by accounting for natural variation in stream fish populations.

Integrated Environmental Assessment and Management↗

USGS2021

Using social values in the prioritization of research: Quantitative examples and generalizations

Identifying critical uncertainties about ecological systems can help prioritize research efforts intended to inform management decisions. However, exclusively focusing on the ecological system neglects the objectives of natural resource managers and the associated social values tied to risks and rewards of actions. I demonstrate how to prioritize research efforts for a harvested population by applying expected value of perfect information (EVPI) to harvest decisions made with a density-independent matrix population model. Research priorities identified by EVPI diverge from priorities identified by matrix elasticity analyses that ignore social utility. Using a density-dependent harvest model, the value of information about the intrinsic productivity of a population is shown to be sensitive to the socially determined penalty for implementing a harvest rate that deviates from the goal because of imperfection in estimation. Synthesis and applications . The effect of including social values into harvest decision-making depends on the assumed population model, uncertainty in population vital rates, and the particular form of the utility function used to represent risk/reward of harvest. EVPI analyses that include perceived utility of different outcomes can be used by managers seeking to optimize monitoring and research spending. Collaboration between applied ecologists and social scientists that quantitatively measure peoples' values is needed in many structured decision-making processes.

Ecology and Evolution↗

USGS2016

Tree-ring-based estimates of long-term seasonal precipitation in the Souris River Region of Saskatchewan, North Dakota and Manitoba

Historically unprecedented flooding occurred in the Souris River Basin of Saskatchewan, North Dakota and Manitoba in 2011, during a longer term period of wet conditions in the basin. In order to develop a model of future flows, there is a need to evaluate effects of past multidecadal climate variability and/or possible climate change on precipitation. In this study, tree-ring chronologies and historical precipitation data in a four-degree buffer around the Souris River Basin were analyzed to develop regression models that can be used for predicting long-term variations of precipitation. To focus on longer term variability, 12-year moving average precipitation was modeled in five subregions (determined through cluster analysis of measures of precipitation) of the study area over three seasons (November–February, March–June and July–October). The models used multiresolution decomposition (an additive decomposition based on powers of two using a discrete wavelet transform) of tree-ring chronologies from Canada and the US and seasonal 12-year moving average precipitation based on Adjusted and Homogenized Canadian Climate Data and US Historical Climatology Network data. Results show that precipitation varies on long-term (multidecadal) time scales of 16, 32 and 64 years. Past extended pluvial and drought events, which can vary greatly with season and subregion, were highlighted by the models. Results suggest that the recent wet period may be a part of natural variability on a very long time scale.

Manitoba, North Dakota, Saskatchewan↗