SEARCH · Search USGS

Results for “Modeling and Using Context”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6Linked to original sources

USGS2019

Grounding simulation models with qualitative case studies: Toward a holistic framework to make climate science usable for US public land management

Policies directing agencies and public land managers to incorporate climate change into management face several barriers. These stem, in part, from a disconnect between the information that is produced and the information needs of local resource managers. A disproportionate focus on the natural and physical sciences in climate vulnerability and adaptation assessment obscure understandings of complex social systems and the interactions and feedbacks in social-ecological systems. We use a qualitative case study of bison management on Department of the Interior-managed and tribal lands to explore how a social-science driven Determinants and Analogue Vulnerability Assessment (DAVA) can inform ecological response models, specifically simulation models that account for multiple drivers of change. First, we illustrate how a DAVA approach can help to: 1) identify key processes, entities, and interactions across scales; 2) document local impacts, indicators, and monitoring efforts of drought and climate; and 3) identify major tradeoffs and uncertainties. We then demonstrate how qualitative narratives can inform simulation models by: 1) prioritizing model components included in modeling efforts; 2) framing joint management and climate scenarios; and 3) parameterizing and evaluating model performance. We do this by presenting a conceptual joint agent-based/state-and-transition simulation modeling framework. Simulation models can represent multiple interacting variables and can identify surprising, emergent outcomes that might not be evident from qualitative analysis alone, and we argue that qualitative case studies can ground simulation models in local contexts and help make them more structurally realistic and useful. Together, these can provide a step toward developing actionable climate change adaptation strategies.

Climate Risk Management↗

USGS2022

Leveraging rangeland monitoring data for wildlife: From concept to practice

Available rangeland data, from field-measured plots to remotely sensed landscapes, provide much needed information for mapping and modeling wildlife habitats. Better integration of wildlife habitat characteristics into rangeland monitoring schemes is needed for most rangeland wildlife species at varying spatial and temporal scales. Here, we aim to stimulate use of and inspire ideas about rangeland monitoring data in the context of wildlife habitat modeling and species conservation.

Colorado, Kansas, Montana, Nebraska, New Mexico, N↗

USGS2009

Recent declines in western U.S. snowpack in the context of twentieth-century climate variability

A monthly snow accumulation and melt model was used with monthly Precipitation-elevation Regressions on Independent Slopes Model (PRISM) temperature and precipitation data to generate time series of 1 April snow water equivalent (SWE) for 1900 through 2008 in the western United States. Averaged across the western United States, SWE generally was higher than long-term (1900–2008) average conditions during the periods 1900–25, 1944–55, and 1966–82; SWE was lower than long-term average conditions during the periods 1926–43, 1957–65, and 1984–2008. During the period 1900–2008, the temporal pattern in winter precipitation exhibited wetter-than-average and drier-than-average decadal-scale periods with no long-term increasing or decreasing trend. Winter temperature generally was below average from 1900 to the mid-1950s, close to average from the mid-1950s to the mid-1980s, and above average from the mid-1980s to 2008. In general, periods of higher-than-average SWE have been associated with higher precipitation and lower temperature. Since about 1980, western U.S. winter temperatures have been consistently higher than long-term average values, and the resultant lower-than-average SWE values have been only partially offset by periods of higher-than-average precipitation. The post-1980 lower-than-average SWE conditions in the western United States are unprecedented within the context of twentieth-century climate and estimated SWE.

Arizona, California, Idaho, Montana, New Mexico, N↗

USGS2008

The human footprint in the west: a large-scale analysis of anthropogenic impacts.

Anthropogenic features such as urbanization, roads, and power lines, are increasing in western United States landscapes in response to rapidly growing human populations. However, their spatial effects have not been evaluated. Our goal was to model the human footprint across the western United States. We first delineated the actual area occupied by anthropogenic features, the physical effect area. Next, we developed the human footprint model based on the ecological effect area, the zone influenced by features beyond their physical presence, by combining seven input models: three models quantified top-down anthropogenic influences of synanthropic predators (avian predators, domestic dog and cat presence risk), and four models quantified bottom-up anthropogenic influences on habitat (invasion of exotic plants, human-caused fires, energy extraction, and anthropogenic wildland fragmentation). Using independent bird population data, we found bird abundance of four synanthropic species to correlate positively with human footprint intensity and negatively for three of the six species influenced by habitat fragmentation. We then evaluated the extent of the human footprint in relation to terrestrial (ecoregions) and aquatic systems (major rivers and lakes), regional management and conservation status, physical environment, and temporal changes in human actions. The physical effect area of anthropogenic features covered 13% of the western United States with agricultural land (9.8%) being most dominant. High-intensity human footprint areas (class 8 – 10) overlapped highly productive low-elevation private landholdings and covered 7% of the western United States compared to 48% for low-intensity areas (class 1 – 3), which were confined to low-productivity high-elevation federal landholdings. Areas within 1 km of rivers were more affected by the human footprint compared to lakes. Percentage human population growth was higher in low-intensity human footprint areas. The disproportional regional effects of the human footprint on landscapes in the western United States create a challenge to management of ecosystems and wildlife populations. Using footprint models, managers can plan land use actions, develop restoration scenarios, and identify areas of high conservation value at local landscapes within a regional context. Moreover, human footprint models serve as a tool to stratify landscapes for studies investigating floral and faunal response to human disturbance intensity gradients.

Ecological Applications↗

USGS2019

Selecting ecological models using multi-objective optimization

Choices in ecological research and natural resource management require balancing multiple, often competing objectives. Examples include maximizing species persistence in a wildlife conservation context, while minimizing cost, or balancing opposing stakeholder objectives when managing wildlife populations. Multiple-objective optimization (MOO) provides a unifying framework for solving multiple objective problems. Model selection is a critical component of ecological inference and prediction and requires balancing the competing objectives of model fit and model complexity. The tradeoff between model fit and model complexity provides a basis for describing the model-selection problem within the MOO framework. We discuss MOO and two strategies for solving the MOO problem; modeling preferences pre-optimization and post-optimization. Most conventional model selection methods can be formulated as solutions of MOO problems via specification of pre-optimization preferences. We reconcile model selection within the MOO framework. We also consider model selection using post-optimization specification of preferences. That is, by first identifying Pareto optimal solutions, and then selecting among them. We demonstrate concepts with an ecological application of model selection using avian species richness data in the continental United States.

Ecological Modelling↗

USGS1987

On the functional optimization of a certain class of nonstationary spatial functions

Procedures are developed in order to obtain optimal estimates of linear functionals for a wide class of nonstationary spatial functions. These procedures rely on well-established constrained minimum-norm criteria, and are applicable to multidimensional phenomena which are characterized by the so-called hypothesis of inherentity. The latter requires elimination of the polynomial, trend-related components of the spatial function leading to stationary quantities, and also it generates some interesting mathematics within the context of modelling and optimization in several dimensions. The arguments are illustrated using various examples, and a case study computed in detail. ?? 1987 Plenum Publishing Corporation.

Journal of Optimization Theory and Applications↗

USGS2017

Challenges and solutions for applying the travel cost demand model to geographically remote visitor destinations: A case study of bear viewing at Katmai National Park and Preserve

Remote and unique destinations present difficulties when attempting to construct traditional travel cost models to value recreation demand. The biggest limitation comes from the lack of variation in the dependent variable, defined as the number of trips taken over a set time frame. There are various approaches that can be used for overcoming limitations of the traditional travel cost model in the context of remote destinations. This study applies an adaptation of the standard model to estimate recreation benefits of bear viewing at Katmai National Park and Preserve in Alaska, which represents a once-in-a-lifetime experience for many visitors. Results demonstrate that visitors to this park’s Brooks Camp area are willing to pay an average of US$287 per day of bear viewing. Implications of these findings for valuing recreation at other remote destinations are discussed.

Alaska↗

USGS2023

BioLake: A first assessment of lake temperature-derived bioclimatic predictors for aquatic invasive species

Aquatic invasive species (AIS) present major ecological and economic challenges globally, endangering ecosystems and human livelihoods. Managers and policy makers thus need tools to predict invasion risk and prioritize species and areas of concern, and they often use native range climate matching to determine whether a species could persist in a new location. However, climate matching for AIS often relies on air temperature rather than water temperature due to a lack of global water temperature data layers, and predictive power of models is seldom evaluated. We developed 12 global lake (water) temperature-derived “BioLake” bioclimatic layers for distribution modeling of aquatic species and compared “climatch” climate matching predictions (from climatchR package) from BioLake with those based on BioClim temperature layers and with a null model. We did this for 73 established AIS in the United States, training the models on their ranges outside of the United States and Canada. Models using either set of climate layers outperformed the null expectation by a similar (but modest) amount on average, but some species were occasionally found in locations with low climatch scores. Mean US climatch scores were higher for most species when using air temperature. Including additional climate layers in models reduced mean climatch scores, indicating that commonly used climatch score thresholds are not absolute but can be context specific and may require calibration based upon climate data used. Although finer resolution global lake temperature data would likely improve predictions, our BioLake layers provide a starting point for aquatic species distribution modeling. Climate matching was most effective for some species that originated at low latitudes or had small ranges. Climatch scores remain useful but limited for predicting AIS risk, perhaps because current ranges seldom fully reflect climatic tolerances (fundamental niches). Managers could consider climate matching as one of a suite of tools that can be used in AIS prioritization.

Ecosphere↗

USGS2011

Adaptive finite volume methods with well-balanced Riemann solvers for modeling floods in rugged terrain: Application to the Malpasset dam-break flood (France, 1959)

The simulation of advancing flood waves over rugged topography, by solving the shallow-water equations with well-balanced high-resolution finite volume methods and block-structured dynamic adaptive mesh refinement (AMR), is described and validated in this paper. The efficiency of block-structured AMR makes large-scale problems tractable, and allows the use of accurate and stable methods developed for solving general hyperbolic problems on quadrilateral grids. Features indicative of flooding in rugged terrain, such as advancing wet-dry fronts and non-stationary steady states due to balanced source terms from variable topography, present unique challenges and require modifications such as special Riemann solvers. A well-balanced Riemann solver for inundation and general (non-stationary) flow over topography is tested in this context. The difficulties of modeling floods in rugged terrain, and the rationale for and efficacy of using AMR and well-balanced methods, are presented. The algorithms are validated by simulating the Malpasset dam-break flood (France, 1959), which has served as a benchmark problem previously. Historical field data, laboratory model data and other numerical simulation results (computed on static fitted meshes) are shown for comparison. The methods are implemented in GEOCLAW, a subset of the open-source CLAWPACK software. All the software is freely available at. Published in 2010 by John Wiley & Sons, Ltd.

International Journal for Numerical Methods in Flu↗

USGS2018

Use of landscape simulation modeling to quantify resilience for ecological applications

Goals of fostering ecological resilience are increasingly used to guide U.S. public land management in the context of anthropogenic climate change and increasing landscape disturbances. There are, however, few operational means of assessing the resilience of a landscape or ecosystem. We present a method to evaluate resilience using simulation modeling. In this method, we use historical conditions (e.g., in North America, prior to European settlement), quantified using simulation modeling, to provide a comparative reference for contemporary conditions, where substantial departures indicate loss of resilience. Contemporary ecological conditions are compared statistically to the historical time series to create a resilience index, which can be used to prioritize landscapes for treatment and inform possible treatments. However, managing for resilience based on historical conditions is tenuous in the Anthropocene, which is characterized by rapid climate change, extensive human land use, altered disturbance regimes, and exotic species introductions. To account for the future variability of ecosystems resulting from climate and disturbance regime shifts, we augment historical simulations with simulations of ecosystem dynamics under projected climate and land use changes to assess the degree of departure from benchmark historical conditions. We use a mechanistic landscape model (FireBGCv2) applied to a large landscape in western Montana, USA, to illustrate the methods presented in this paper. Spatially explicit ecosystem modeling provides the vehicle to generate the historical and future time series needed to quantify potential resilience conditions associated with past and potential future conditions. Our methods show that given selection of a useful set of metrics, managers could use simulations like ours to evaluate potential future management directions.

Ecosphere↗

USGS2012

Modeling the Death Valley regional ground-water flow system

The development of a regional ground-water flow model of the Death Valley region in the southwestern United States is discussed in the context of the fourteen guidelines of Hill. This application of the guidelines demonstrates how they may be used for model calibration and evaluation, and to direct further model development and data collection.

California, Nevada↗

USGS2007

Economic decision making and the application of nonparametric prediction models

Sustained increases in energy prices have focused attention on gas resources in low permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are large. Planning and development decisions for extraction of such resources must be area-wide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm the decision to enter such plays depends on reconnaissance level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional scale cost functions. The context of the worked example is the Devonian Antrim shale gas play, Michigan Basin. One finding relates to selection of the resource prediction model to be used with economic models. Models which can best predict aggregate volume over larger areas (many hundreds of sites) may lose granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined by extraneous factors. The paper also shows that when these simple prediction models are used to strategically order drilling prospects, the gain in gas volume over volumes associated with simple random site selection amounts to 15 to 20 percent. It also discusses why the observed benefit of updating predictions from results of new drilling, as opposed to following static predictions, is somewhat smaller. Copyright 2007, Society of Petroleum Engineers.

Conference Paper↗

USGS2013

Is there room for all of us? Renewable energy and Xerospermophilus mohavensis

Mohave ground squirrels Xerospermophilus mohavensis Merriam are small ground-dwelling rodents that have a highly restricted range in the northwest Mojave Desert, California, USA. Their small natural range is further reduced by habitat loss from agriculture, urban development, military training and recreational activities. Development of wind and solar resources for renewable energy has the potential to further reduce existing habitat. We used maximum entropy habitat models with observation data to describe current potential habitat in the context of future renewable energy development in the region. While 16% of historic habitat has been impacted by, or lost to, urbanization at present, an additional 10% may be affected by renewable energy development in the near future. Our models show that X. mohavensis habitat suitability is higher in areas slated for renewable energy development than in surrounding areas. We provide habitat maps that can be used to develop sampling designs, evaluate conservation corridors and inform development planning in the region.

California, Nevada↗

USGS2019

Linking management planning for coastal wetlands to potential future wave attenuation under a range of relative sea-level rise scenarios

Understanding changes in wave attenuation by emergent vegetation as wetlands degrade or accrete over time is crucial for incorporation of wetlands into holistic coastal risk management. Linked SLAMM and XBeach models were used to investigate potential future changes in wave attenuation over a 50-year period in a degrading, subtropical wetland and a prograding, temperate wetland. These contrasting systems also have differing management contexts and were contrasted to demonstrate how the linked models can provide management-relevant insights. Morphological development of wetlands for different scenarios of sea-level rise and accretion was simulated with SLAMM and then coupled with different vegetation characteristics to predict the influence on future wave attenuation using XBeach. The geomorphological context, subsidence, and accretion resulted in large predicted reductions in the extent of vegetated land (e.g., wetland) and changes in wave height reduction potential across the wetland. These were exacerbated by increases in sea-level from +0.217 m to +0.386 m over a 50-year period, especially at the lowest accretion rates in the degrading wetland. Mangrove vegetation increased wave attenuation within the degrading, subtropical, saline wetland, while grazing reduced wave attenuation in the temperate, prograding wetland. Coastal management decisions and actions, related to coastal vegetation type and structure, have the potential to change future wave attenuation at a spatial scale relevant to coastal protection planning. Therefore, a coastal management approach that includes disaster risk reduction, biodiversity, and climate change, can be informed by coastal modeling tools, such as those demonstrated here for two contrasting case studies.

PLoS ONE↗

USGS2021

American Woodcock singing-ground survey: Comparison of four models for trend in population size

Wildlife biologists monitor the status and trends of American woodcock Scolopax minor populations in the eastern and central United States and Canada via a singing-ground survey, conducted just after sunset along roadsides in spring. Annual analyses of the survey produce estimates of trend and annual indexes of abundance for 25 states and provinces, management regions, and survey-wide. In recent years, researchers have used a log-linear hierarchical model that defines year effects as random effects in the context of a slope parameter (the S model) to model population change. Recently, researchers have proposed alternative models suitable for analysis of singing-ground survey data. Analysis of a similar roadside survey, the North American Breeding Bird Survey, has indicated that alternative models are preferable for almost all species analyzed in the Breeding Bird Survey. Here, we use leave-one-out cross-validation to compare model fit for the present singing-ground survey model to fits of three alternative models, including a model that describes population change as the difference in expected counts between successive years (the D model) and two models that include t -distributed extra-Poisson overdispersion effects (H models) as opposed to normally distributed extra-Poisson overdispersion. Leave-one-out cross-validation results indicate that the Bayesian predictive information criterion favored the D model, but a pairwise t -test indicated that the D model was not significantly better-fitting to singing-ground survey data than the S model. The H models are not preferable to the alternatives with normally distributed overdispersion. All models provided generally similar estimates of trend and annual indexes suggesting that, within this model set, choice of model will not lead to alternative conclusions regarding population change. However, as in Breeding Bird Survey analyses, we note a tendency for S model results to provide slightly more extreme estimates of trend relative to D models. We recommend use of the D model for future singing-ground survey analyses.

Eastern and Central United States and Canada↗

USGS2005

Climate science and famine early warning

Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.

Philosophical Transactions of the Royal Society B:↗

USGS2024

Modeling the effects of spatial distribution on dynamics of an invading Melaleuca quinquenervia (Cav.) Blake population

To predict the potential success of an invading non-native species, it is important to understand its dynamics and interactions with native species in the early stages of its invasion. In spatially implicit models, mathematical stability criteria are commonly used to predict whether an invading population grows in number in an early time period. But spatial context is important for real invasions as an invading population may first occur as a small number of individuals scatter spatially. The invasion dynamics are therefore not describable in terms of population level state variables. A better approach is spatially explicit individual-based modeling (IBM). We use an established spatially explicit IBM to predict the invasion of the non-native tree, Melaleuca quinquenervia (Cav.) Blake, to a native community in southern Florida. We show that the initial spatial distribution, both the spatial density of individuals and the area they cover, affects its success in growing numerically and spreading. The formation of a cluster of a sufficient number and density of individuals may be needed for the invader to locally outcompete the native species and become established. Different initial densities, identical in number and density but differing in random positions of individuals, can produce very different trajectories of the invading population through time, even affecting invasion success and failure.

Forests↗

USGS2009

The length of channelized lava flows: Insight from the 1859 eruption of Mauna Loa Volcano, Hawai‘i

The 1859 eruption of Mauna Loa Volcano, Hawai'i, produced paired 'a'ā and pāhoehoe flows of exceptional length (51 km). The 'a'ā flow field is distinguished by a long (> 36 km) and well-defined pāhoehoe-lined channel, indicating that channelized lava remained fluid to great distances from the vent. The 1859 eruption was further unusual in initiating at a radial vent on the volcano's northwest flank, instead of along the well-defined rift zone that has been the source of most historic activity. As such, it presents an opportunity both to examine controls on the emplacement of long lava channels and to assess hazards posed by future flank eruptions of Mauna Loa. Here we combine evidence from historical chronicles with analysis of bulk compositions, glass geothermometry, and microlite textures of samples collected along the 1859 lava flows to constrain eruption and flow emplacement conditions. The bulk compositions of samples from the 'a'ā and pāhoehoe flow fields are bimodally distributed and indicate tapping of two discrete magma bodies during eruption. Samples from the pāhoehoe flow field have bulk compositions similar to those of historically-erupted lavas (< 8 wt.% MgO); lava that fed the 'a'ā channel is more primitive (> 8 wt.% MgO), nearly aphyric, and was erupted at high temperatures (1194–1216 °C). We suggest that the physical properties of proximal channel-fed lava (i.e., high-temperature, low crystallinity, and low bulk viscosity) promoted both rapid flow advance and development of long pāhoehoe-lined channels. Critical for the latter was the large temperature decrease (~ 50 °C) required to reach the point at which plagioclase and pyroxene started to crystallize; the importance of phase constraints are emphasized by our difficulty in replicating patterns of cooling and crystallization recorded by high-temperature field samples using common models of flow emplacement. Placement of the 1859 eruption within the context of historic activity at Mauna Loa suggests that the formation of radial vents and eruptions of high-temperature magma may not only be linked, but may also be a consequence of periods of high magma supply (e.g., 1843–1877). Flank eruptions could therefore warrant special consideration in models and hazard mitigation efforts.

Hawai'i↗