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At least 631 records · Page 35Linked to original sources
USGS2015

Local climate and cultivation, but not ploidy, predict functional trait variation in Bouteloua gracilis (Poaceae)

Efforts to improve the diversity of seed 18 resources for important restoration species has become a high priority for land managers in many parts of the world. Relationships between functional trait values and the environment from which seed sources are collected can provide important insights into patterns of local adaptation and guidelines for seed transfer. However, little is known about which functional traits exhibit genetic differentiation across populations of restoration species and thus may contribute to local adaptation. Here, we report the results of a common garden experiment aimed at assessing genetic (including ploidy level) and environmental regulation of several functional traits among populations of Bouteloua gracilis, a dominant C4 grass and the most highly utilized restoration species across much of the Colorado Plateau. We found that leaf size and specific leaf area (SLA) varied significantly among populations, and were strongly correlated with the source population environment from which seeds were collected. However, variation in ploidy level had no significant effect on functional traits. Leaves of plants grown from commercial seed releases were significantly larger and had lower SLA than those from natural populations, a result that is concordant with the overall relation between climate and these two functional traits. We suggest that the patterns of functional trait variation shown here may extend to other grass species in the western USA, and may serve as useful proxies for more extensive genecology research. Furthermore, we argue that care should be taken to develop commercial seed lines with functional trait values that match those of natural populations occupying climates similar to target restoration sites.

Plant Ecology↗
USGS2022

Development of projected depth-duration frequency curves (2050–89) for south Florida

Planning stormwater projects requires estimates of current and future extreme precipitation depths for events with specified return periods and durations. In this study, precipitation data from four downscaled climate datasets are used to determine changes in precipitation depth-duration-frequency curves from the period 1966–2005 to the period 2050–89 primarily on the basis of Representative Concentration Pathways 4.5 and 8.5 emission scenarios from the Coupled Model Intercomparison Project Phase 5. The four downscaled climate datasets are (1) the Coordinated Regional Downscaling Experiment (CORDEX) dataset, (2) the Localized Constructed Analogs (LOCA) dataset, (3) the Multivariate Adaptive Constructed Analogs (MACA) dataset, and (4) the Jupiter Intelligence Weather Research and Forecasting Model (JupiterWRF) dataset. Change factors—multiplicative changes in expected extreme precipitation magnitude from current to future period—were computed for grid cells from the downscaled climate datasets containing National Oceanic and Atmospheric Administration Atlas 14 stations in central and south Florida. Change factors for specific durations and return periods may be used to scale the National Oceanic and Atmospheric Administration Atlas 14 historical depth-duration-frequency values to the period 2050–89 on the basis of changes in extreme precipitation derived from downscaled climate datasets. Model culling was implemented to select downscaled climate models that best captured observed historical patterns of precipitation extremes in central and south Florida. Overall, a large variation in change factors across downscaled climate datasets was found, with change factors generally greater than one and increasing with return period. In general, median change factors were higher for the south-central Florida climate region (1.05–1.55 depending on downscaled climate dataset, duration, and return period) than for the south Florida climate region (1–1.4 depending on downscaled climate dataset, duration, and return period) when considering best performing models for both areas, indicating a projected overall increase in future extreme precipitation events.

Florida↗
USGS2012

GRACEnet: addressing policy needs through coordinated cross-location research

GRACEnet (Greenhouse gas Reduction through Agricultural Carbon Enhancement network) was conceived to build upon ongoing USDA Agricultural Research Service (ARS) research to improve soil productivity, while addressing the challenges and opportunities of interest in C sequestration from a climate change perspective. The vision for GRACEnet was and remains: Knowledge and information used to implement scientifically based agricultural management practices from the field to national policy scales on C sequestration, greenhouse gas (GHG) emissions, and environmental benefits. The national focus of GRACEnet uses a standardized approach by ARS laboratories and university and land manager (e.g. farmer and rancher) cooperators to assess C sequestration and GHG emission from different crop and grassland systems. Since 2002, GRACEnet has significantly expanded GHG mitigation science and delivered usable information to agricultural research and policy organizations. Recent developments suggest GRACEnet will have international impact by contributing leadership and technical guidance for the Global Research Alliance on Agricultural Greenhouse Gases.

Book chapter↗
USGS2026

Combining individual and close-kin mark–recapture to design an effective wildlife population survey

Close-kin mark–recapture (CKMR) is a promising approach for assessing population size of species that have been difficult to survey using more traditional methods. Here, we combine individual and close-kin mark–recapture in a single modeling framework (ICKMR) and provide an example of study design using this approach for Pacific walrus ( Odobenus rosmarus divergens ). We develop the ICKMR model and test it using simulated datasets, then use properties of the pseudo-likelihood to investigate the expected precision in estimates of abundance with different proposed survey designs. Our motivating example, the Pacific walrus, is an ice-associated marine mammal found in the Bering and Chukchi seas, where it is an important resource for Indigenous peoples. Pacific walrus abundance declined in the late 20th century, and it is currently a species of conservation concern due to potential impacts of climate change, particularly the loss of sea ice. To reduce uncertainty in population size estimates, researchers undertook a genetic mark–recapture sampling campaign from 2013 to 2017 and collected tissue samples from over 8000 individuals. Another campaign of a similar scale is ongoing (2023–2028). While sample collection was designed for individual mark–recapture, advances in CKMR methods and associated molecular techniques mean that these samples could also be suitable for CKMR. The advantages of CKMR over mark–recapture include an increased effective sample size (because each individual tags itself and its parents, siblings, and offspring) and additional insights into demographic quantities of interest. To make best use of genetic samples, we combine individual mark–recapture (IMR) with CKMR (ICKMR) and investigate whether different sampling strategies can increase precision in estimates of abundance. Our modeling approach includes special considerations for walrus life history, including a multi-year inter-birth interval. We found that expected coefficients of variation (CVs) of the ICKMR estimates of abundance, adult female survival, juvenile female survival, and proportion of breeding females are lower than those expected from IMR alone, and with ICKMR, fewer years of sampling can be conducted to obtain sufficient precision in estimates of abundance. This work demonstrates the utility of ICKMR and could be applicable across a variety of taxa.

Ecology↗
USGS2022

GeoImageNet: A multi-source natural feature benchmark dataset for GeoAI and supervised machine learning

The field of GeoAI or Geospatial Artificial Intelligence has undergone rapid development since 2017. It has been widely applied to address environmental and social science problems, from understanding climate change to tracking the spread of infectious disease. A foundational task in advancing GeoAI research is the creation of open, benchmark datasets to train and evaluate the performance of GeoAI models. While a number of datasets have been published, very few have centered on the natural terrain and its landforms. To bridge this gulf, this paper introduces a first-of-its-kind benchmark dataset, GeoImageNet, which supports natural feature detection in a supervised machine-learning paradigm. A distinctive feature of this dataset is the fusion of multi-source data, including both remote sensing imagery and DEM in depicting spatial objects of interest. This multi-source dataset allows a GeoAI model to extract rich spatio-contextual information to gain stronger confidence in high-precision object detection and recognition. The image dataset is tested with a multi-source GeoAI extension against two well-known object detection models, Faster-RCNN and RetinaNet. The results demonstrate the robustness of the dataset in aiding GeoAI models to achieve convergence and the superiority of multi-source data in yielding much higher prediction accuracy than the commonly used single data source.

GeoInformatica↗
USGS2017

Reconstructing the response of C3 and C4 plants to decadal-scale climate change during the late Pleistocene in southern Illinois using isotopic analyses of calcified rootlets

The ??13C and ??18O values of well-preserved carbonate rhizoliths (CRs) provide detailed insights into changes in the abundance of C3 and C4 plants in response to approximately decadal-scale changes in growing-season climate. We performed stable isotope analyses on 35-40 CRs sampled at 1-cm intervals from an 18-cm-thick paleosol formed in southern Illinois during Wisconsin interstadial 2. Minimum ??13C values show little variation with depth, whereas maximum values vary dramatically, and average values show noticeable variability; maximum ??18O values vary less than the minimum ??18O values. These findings indicate that a diverse and stable C3 flora with a limited number of C4 grass species prevailed during this interval, and suggest that the maximum growing-season temperatures were relatively stable, but minimum growing-season temperatures varied considerably. Two general patterns characterize the relationships between the ??13C and ??18O values obtained from the 1-cm samples. In some cases, low ??13C values correspond to low ??18O values and high ??13C values correspond to high ??18O values, suggesting that cooler growing-season temperatures favored C3 and warmer growing-season temperatures favored C4 plants. In other cases, low ??13C values correspond to high ??18O values, likely suggesting that wetter growing-season conditions were favorable to C3 plants. The high density of well-preserved CRs in this paleosol provides a unique opportunity to study detailed ecological responses to high-resolution variability in growing-season climate. ?? 2006 University of Washington.

Quaternary Research↗
USGS2006

Climate model biases in seasonality of continental water storage revealed by satellite gravimetry

Satellite gravimetric observations of monthly changes in continental water storage are compared with outputs from five climate models. All models qualitatively reproduce the global pattern of annual storage amplitude, and the seasonal cycle of global average storage is reproduced well, consistent with earlier studies. However, global average agreements mask systematic model biases in low latitudes. Seasonal extrema of low‐latitude, hemispheric storage generally occur too early in the models, and model‐specific errors in amplitude of the low‐latitude annual variations are substantial. These errors are potentially explicable in terms of neglected or suboptimally parameterized water stores in the land models and precipitation biases in the climate models.

Water Resources Research↗
USGS2010

Simulation of lake evaporation with application to modeling lake level variations of Harney‐Malheur Lake, Oregon

A physically based eddy diffusion model for simulating the seasonal variation in lake temperature and evaporation is presented and validated. Because no lake‐specific fitting of the parameters of the model is necessary, the model can be used to simulate evaporation in studies of climate change and lake hydrology in a variety of settings. The eddy diffusion model is used to simulate evaporation for input to a simple lake level model that is applied to reconstruct recent fluctuations in the level of Harney‐Malheur Lake caused by climatic variations.

Oregon↗
USGS2021

A global dataset of inland fisheries expert knowledge

Inland fisheries and their freshwater habitats face intensifying effects from multiple natural and anthropogenic pressures. Fish harvest and biodiversity data remain largely disparate and severely deficient in many areas, which makes assessing and managing inland fisheries difficult. Expert knowledge is increasingly used to improve and inform biological or vulnerability assessments, especially in data-poor areas. Integrating expert knowledge on the distribution, intensity, and relative influence of human activities can guide natural resource management strategies and institutional resource allocation and prioritization. This paper introduces a dataset summarizing the expert-perceived state of inland fisheries at the basin (fishery) level. An electronic survey distributed to professional networks (June-September 2020) captured expert perceptions (n = 536) of threats, successes, and adaptive capacity to fisheries across 93 hydrological basins, 79 countries, and all major freshwater habitat types. This dataset can be used to address research questions with conservation relevance, including: demographic influences on perceptions of threat, adaptive capacities for climate change, external factors driving multi-stressor interactions, and geospatial threat assessments.

Scientific Data↗
USGS2017

Engaging the user community for advancing societal applications of the Surface Water Ocean Topography mission

Scheduled for launch in 2021, the Surface Water and Ocean Topography (SWOT) mission will be a truly unique mission that will provide high-temporal-frequency maps of surface water extents and elevation variations of global water bodies (lakes/reservoirs, rivers, estuaries, oceans, and sea ice) at higher spatial resolution than is available with current technologies (Biancamaria et al. 2016; Alsdorf et al. 2007). The primary instrument on SWOT is based on a Ka-band radar interferometer (KaRIN), which uses radar interferometery technology. The satellite will fly two radar antennas at either end of a 10-m (33 ft) mast, allowing it to measure the elevation of the surface along a 120-km (75 mi)-wide swath below. The availability of high-frequency and high-resolution maps of elevations and extents for surface water bodies and oceans will present unique opportunities to address numerous societally relevant challenges around the globe (Srinivasan et al. 2015). These opportunities may include such diverse and far-ranging applications as fisheries management, flood inundation mapping/risk mitigation/forecasting, wildlife conservation, global data assimilation for improving forecast of ocean tides and weather, reservoir management, climate change impacts and adaptation, and river discharge estimation, among others. Although SWOT is a research mission and not scheduled for launch for another 4 years, there is a need to build engagement within the application community now and to explore how best to advance the societal relevance and benefits of the SWOT mission from concept to reality. The SWOT Applications Working Group organized a workshop on 5–6 April 2017 at the U.S. Geological Survey (USGS) headquarters in Reston, Virginia. The goal of the workshop was to understand and communicate how the applications community can use SWOT data to address problems of profound societal relevance.

Bulletin of the American Meteorological Society↗
USGS2017

Fena Valley Reservoir watershed and water-balance model updates and expansion of watershed modeling to southern Guam

In 2014, the U.S. Geological Survey, in cooperation with the U.S. Department of Defense’s Strategic Environmental Research and Development Program, initiated a project to evaluate the potential impacts of projected climate-change on Department of Defense installations that rely on Guam’s water resources. A major task of that project was to develop a watershed model of southern Guam and a water-balance model for the Fena Valley Reservoir. The southern Guam watershed model provides a physically based tool to estimate surface-water availability in southern Guam. The U.S. Geological Survey’s Precipitation Runoff Modeling System, PRMS-IV, was used to construct the watershed model. The PRMS-IV code simulates different parts of the hydrologic cycle based on a set of user-defined modules. The southern Guam watershed model was constructed by updating a watershed model for the Fena Valley watersheds, and expanding the modeled area to include all of southern Guam. The Fena Valley watershed model was combined with a previously developed, but recently updated and recalibrated Fena Valley Reservoir water-balance model. Two important surface-water resources for the U.S. Navy and the citizens of Guam were modeled in this study; the extended model now includes the Ugum River watershed and improves upon the previous model of the Fena Valley watersheds. Surface water from the Ugum River watershed is diverted and treated for drinking water, and the Fena Valley watersheds feed the largest surface-water reservoir on Guam. The southern Guam watershed model performed “very good,” according to the criteria of Moriasi and others (2007), in the Ugum River watershed above Talofofo Falls with monthly Nash-Sutcliffe efficiency statistic values of 0.97 for the calibration period and 0.93 for the verification period (a value of 1.0 represents perfect model fit). In the Fena Valley watershed, monthly simulated streamflow volumes from the watershed model compared reasonably well with the measured values for the gaging stations on the Almagosa, Maulap, and Imong Rivers—tributaries to the Fena Valley Reservoir—with Nash-Sutcliffe efficiency values of 0.87 or higher. The southern Guam watershed model simulated the total volume of the critical dry season (January to May) streamflow for the entire simulation period within –0.54 percent at the Almagosa River, within 6.39 percent at the Maulap River, and within 6.06 percent at the Imong River. The recalibrated water-balance model of the Fena Valley Reservoir generally simulated monthly reservoir storage volume with reasonable accuracy. For the calibration and verification periods, errors in end-of-month reservoir-storage volume ranged from 6.04 percent (284.6 acre-feet or 92.7 million gallons) to –5.70 percent (–240.8 acre-feet or –78.5 million gallons). Monthly simulation bias ranged from –0.48 percent for the calibration period to 0.87 percent for the verification period; relative error ranged from –0.60 to 0.88 percent for the calibration and verification periods, respectively. The small bias indicated that the model did not consistently overestimate or underestimate reservoir storage volume. In the entirety of southern Guam, the watershed model has a “satisfactory” to “very good” rating when simulating monthly mean streamflow for all but one of the gaged watersheds during the verification period. The southern Guam watershed model uses a more sophisticated climate-distribution scheme than the older model to make use of the sparse climate data, as well as includes updated land-cover parameters and the capability to simulate closed depression areas. The new Fena Valley Reservoir water-balance model is useful as an updated tool to forecast short-term changes in the surface-water resources of Guam. Furthermore, the now spatially complete southern Guam watershed model can be used to evaluate changes in streamflow and recharge owing to climate or land-cover changes. These are substantial improvements to the previous models of the Fena Valley watershed and Reservoir. Datasets associated with this report are available as a U.S. Geological Survey data release (Rosa and Hay, 2017; DOI:10.5066/F7HH6HV4).

Scientific Investigations Report↗
USGS2022

Quality assurance report for Loch Vale Watershed, 2010–19

The Loch Vale Watershed Research and Monitoring Program collects long-term datasets of ecological and biogeochemical parameters in Rocky Mountain National Park to support both (1) management of this protected area and (2) research into watershed-scale ecosystem processes as those processes respond to atmospheric deposition and climate variability. The program collects data on precipitation depth and atmospheric deposition chemistry—as well as surface water biogeochemistry—within the watershed and in other areas of the park. These data are used by resource managers, scientists, policy makers, and students, so it is important that all collected data meet high quality standards. This report presents an evaluation of data quality for precipitation, atmospheric ammonia, and surface water quality samples collected from 2010 to 2019. This report also presents changes made to the monitoring and laboratory equipment used during the study period and describes new data streams added to the project, including atmospheric ammonia, surface water chlorophyll-a, and dissolved oxygen in two lakes: The Loch and Sky Pond. Quality-assurance procedures looked at the accuracy and precision of measurements made over the study period and found that precipitation and surface water chemistry data were 99 percent accurate and precise. Records that failed to meet quality standards were removed from published databases. From 2010 to 2014, a colocated precipitation gauge and deposition collector were installed on site as quality checks. From 2014 to 2018, power loss at the site resulted in significant loss of precipitation data records during the snow seasons. Those problems were addressed by installing new solar-power equipment in 2019. Measurements of deposition chemistry, atmospheric ammonia deposition, and surface water biogeochemistry were all sufficiently complete and consistent to support project data needs.

Colorado↗
USGS2018

Analysis of the impact of African dust storms on the presence of enteric viruses in the atmosphere in Tenerife, Spain

Airborne viruses and their relation to dust storms, as a possible route for dispersion, have not been widely investigated. There are, however, studies that have described the airborne dispersal of pathogenic viruses and their potential impact on public and agronomical health. Atmospheric samples were collected in an urban area of Tenerife during 2009, 2010, 2012 and 2013 and screened for the presence of enteric viruses using PCR and sequencing. The potential relationship of viral data with African dust storms and other climatic variables (viz., the seasonality, origin of the air mass and PM levels) was analyzed. Enteroviruses and Rotaviruses were detected in 15.4% (20/130) and 36.9% (48/130) of the samples, respectively. No significant statistical relationships were observed with African dust storms or the origin of the air masses, although higher percentages of positives were obtained for dust storm days. Enterovirus detection was significantly linked to warmer seasons, and PM 2.5 levels showed an inverse correlation with a rotaviral presence. This is the first multi-year report to describe the presence of Enterovirus and Rotavirus genetic sequences in air samples collected in an outdoor urban environment. The data illustrates the need for source region sampling to determine links and the influence of the weather and climatic and regional wind patterns on long-range atmospheric dispersion of viruses in future research efforts.

Tenerife↗
USGS1957

Water problems in the present trend towards greater aridity

In the past few days we have heard a number of scientists, gathered here at Scripps Institution of Oceanography, discuss research work which, in one manner or another, bears on problems related to water resources. They have been discussing, particularly, problems in the field of climatology, and have speculated on the meaning of the results of this research. One of the problems under discussion was the nature, and possible cause, of the climatic fluctuation which we have experienced in recent decades, and its relation to climatic changes in recent geologic time since the last glaciation. These discussions have given me the courage, or perhaps the recklessness, to indulge in some speculation relative to various aspects of water resource problems in the United States.

Conference Paper↗
USGS2018

Development of the Wildlife Adaptation Menu for Resource Managers

The Climate Change Response Framework is an example of a collaborative, cross-boundary approach to create a set of tools, partnerships, and actions to support climate-informed conservation and land management. Historically, this effort has focused on the needs of forest managers and forestry professionals. In recent years, however, there has been increasing demand for science and tools to address climate change adaptation in wildlife management and conservation. Not only do wildlife and resource managers need the best available science, it must also be presented in a usable format with feasible options within the purview of an individual manager. The research team is first completing a comprehensive review of peer-reviewed studies to summarize what wildlife-related management actions currently exist in climate change adaptation. They will then develop and test a “menu” of climate change adaptation actions that are suitable for wildlife management in terrestrial ecosystems. This Wildlife Adaptation Menu will be modeled off existing adaptation menus for Forestry and Urban Forestry, and it will be designed to be used in conjunction with the Adaptation Workbook . In addition to a menu of adaption strategies and approaches, the scientists will also identify site-level tactics and develop case studies demonstrating the use and implementation of the menu. To ensure that information and tools meet the needs of managers, the team is involving and integrating input from wildlife managers at every step of the process. Managers will be involved in scoping the project, testing the menu, and implementing the menu.

Final Report↗
USGS1993

Integration of environmental simulation models with satellite remote sensing and geographic information systems technologies: case studies

Environmental modelers are testing and evaluating a prototype land cover characteristics database for the conterminous United States developed by the EROS Data Center of the U.S. Geological Survey and the University of Nebraska Center for Advanced Land Management Information Technologies. This database was developed from multi temporal, 1-kilometer advanced very high resolution radiometer (AVHRR) data for 1990 and various ancillary data sets such as elevation, ecological regions, and selected climatic normals. Several case studies using this database were analyzed to illustrate the integration of satellite remote sensing and geographic information systems technologies with land-atmosphere interactions models at a variety of spatial and temporal scales. The case studies are representative of contemporary environmental simulation modeling at local to regional levels in global change research, land and water resource management, and environmental simulation modeling at local to regional levels in global change research, land and water resource management and environmental risk assessment. The case studies feature land surface parameterizations for atmospheric mesoscale and global climate models; biogenic-hydrocarbons emissions models; distributed parameter watershed and other hydrological models; and various ecological models such as ecosystem, dynamics, biogeochemical cycles, ecotone variability, and equilibrium vegetation models. The case studies demonstrate the important of multi temporal AVHRR data to develop to develop and maintain a flexible, near-realtime land cover characteristics database. Moreover, such a flexible database is needed to derive various vegetation classification schemes, to aggregate data for nested models, to develop remote sensing algorithms, and to provide data on dynamic landscape characteristics. The case studies illustrate how such a database supports research on spatial heterogeneity, land use, sensitivity analysis, and scaling issues involving regional extrapolations and parameterizations of dynamic land processes within simulation models.

Pecora 12 Symposium↗
USGS2015

Determining climate change management priorities: A case study from Wisconsin

A burgeoning dialogue exists regarding how to allocate resources to maximize the likelihood of long-term biodiversity conservation within the context of climate change. To make effective decisions in natural resource management, an iterative, collaborative, and learning-based decision process may be more successful than a strictly consultative approach. One important, early step in a decision process is to identify priority species or systems. Although this promotes the conservation of select species or systems, it may inadvertently alter the future of non-target species and systems. We describe a process to screen terrestrial wildlife for potential sensitivity to climate change and then use the results to engage natural resource professionals in a process of identifying priorities for monitoring, research, and adaptation strategy implementation. We demonstrate this approach using a case study from Wisconsin. In Wisconsin, experts identified 23 out of 353 species with sufficient empirical research and management understanding to inform targeted action. Habitat management and management of hydrological conditions were the common strategies for targeted action. Although there may be an interest in adaptation strategy implementation for many species and systems, experts considered existing information inadequate to inform targeted action. According to experts, 40% of the vertebrate species in Wisconsin will require near-term intervention for climate adaptation. These results will inform state-wide conservation planning as well as regional efforts.

Wisconsin↗
USGS2023

Ten best practices for effective phenological research

The number and diversity of phenological studies has increased rapidly in recent years. Innovative experiments, field studies, citizen science projects, and analyses of newly available historical data are contributing insights that advance our understanding of ecological and evolutionary responses to the environment, particularly climate change. However, many phenological data sets have peculiarities that are not immediately obvious and can lead to mistakes in analyses and interpretation of results. This paper aims to help researchers, especially those new to the field of phenology, understand challenges and practices that are crucial for effective studies. For example, researchers may fail to account for sampling biases in phenological data, struggle to choose or design a volunteer data collection strategy that adequately fits their project’s needs, or combine data sets in inappropriate ways. We describe ten best practices for designing studies of plant and animal phenology, evaluating data quality, and analyzing data. Practices include accounting for common biases in data, using effective citizen or community science methods, and employing appropriate data when investigating phenological mismatches. We present these best practices to help researchers entering the field take full advantage of the wealth of available data and approaches to advance our understanding of phenology and its implications for ecology.

International Journal of Biometeorology↗