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 145 records · Page 8Linked to original sources

USGS2013

Effects of error covariance structure on estimation of model averaging weights and predictive performance

When conducting model averaging for assessing groundwater conceptual model uncertainty, the averaging weights are often evaluated using model selection criteria such as AIC, AICc, BIC, and KIC (Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, and Kashyap Information Criterion, respectively). However, this method often leads to an unrealistic situation in which the best model receives overwhelmingly large averaging weight (close to 100%), which cannot be justified by available data and knowledge. It was found in this study that this problem was caused by using the covariance matrix, C E , of measurement errors for estimating the negative log likelihood function common to all the model selection criteria. This problem can be resolved by using the covariance matrix, C ek , of total errors (including model errors and measurement errors) to account for the correlation between the total errors. An iterative two-stage method was developed in the context of maximum likelihood inverse modeling to iteratively infer the unknown C ek from the residuals during model calibration. The inferred C ek was then used in the evaluation of model selection criteria and model averaging weights. While this method was limited to serial data using time series techniques in this study, it can be extended to spatial data using geostatistical techniques. The method was first evaluated in a synthetic study and then applied to an experimental study, in which alternative surface complexation models were developed to simulate column experiments of uranium reactive transport. It was found that the total errors of the alternative models were temporally correlated due to the model errors. The iterative two-stage method using C ek resolved the problem that the best model receives 100% model averaging weight, and the resulting model averaging weights were supported by the calibration results and physical understanding of the alternative models. Using C ek obtained from the iterative two-stage method also improved predictive performance of the individual models and model averaging in both synthetic and experimental studies.

Water Resources Research↗

USGS2016

San Pedro River Aquifer Binational Report

The United States and Mexico share waters in a number of hydrological basins and aquifers that cross the international boundary. Both countries recognize that, in a region of scarce water resources and expanding populations, a greater scientific understanding of these aquifer systems would be beneficial. In light of this, the Mexican and U.S. Principal Engineers of the International Boundary and Water Commission (IBWC) signed the “Joint Report of the Principal Engineers Regarding the Joint Cooperative Process United States-Mexico for the Transboundary Aquifer Assessment Program" on August 19, 2009 (IBWC-CILA, 2009). This IBWC “Joint Report” serves as the framework for U.S.-Mexico coordination and dialogue to implement transboundary aquifer studies. The document clarifies several details about the program such as background, roles, responsibilities, funding, relevance of the international water treaties, and the use of information collected or compiled as part of the program. In the document, it was agreed by the parties involved, which included the IBWC, the Mexican National Water Commission (CONAGUA), the U.S. Geological Survey (USGS), and the Universities of Arizona and Sonora, to study two priority binational aquifers, one in the San Pedro River basin and the other in the Santa Cruz River basin. This report focuses on the Binational San Pedro Basin (BSPB). Reasons for the focus on and interest in this aquifer include the fact that it is shared by the two countries, that the San Pedro River has an elevated ecological value because of the riparian ecosystem that it sustains, and that water resources are needed to sustain the river, existing communities, and continued development. This study describes the aquifer’s characteristics in its binational context; however, most of the scientific work has been undertaken for many years by each country without full knowledge of the conditions on the other side of the border. The general objective of this study is to use new and existing research to define the general hydrologic framework of the Binational San Pedro Aquifer (BSPA), to gather hydrogeological and other relevant data in preparation for future work such as an updated groundwater conceptual model and budget and to establish the basis for a binational numerical model. The specific objectives are as follows: Understand the current state of knowledge with respect to climate, geology, soils, land cover, land use, and hydrology of the aquifer in its binational context; Compile and create a database of scientific information from both countries; Identify data gaps and identify what data would be necessary to update, in a subsequent phase, the hydrologic model of the aquifer system, including surface- and groundwater interactions on a binational level. The BSPB is one of the most studied basins in the region, and a database of publications has been compiled as part of this project. Previous studies include topics that range from geophysics and hydrogeology to biology and ecosystem services. The economic drivers on each side of the border are quite different. In the Arizona 4 portion of the basin military and tourism dominate while in the Sonoran portion, mining is the most important industry. Water management is also different in the two countries. In Mexico, primary authority for management of water resources devolves from the federal government. In the United States, primary authority rests with the states except in cases of interstate surface waters. Binational waters are not currently jointly managed by the two countries except in cases where treaties have been negotiated such as for the Rio Grande and Colorado Rivers. Thus, there is currently no binational coordination or treaty governing the management of groundwater.

Report↗

USGS2010

Parameter-expanded data augmentation for Bayesian analysis of capture-recapture models

Data augmentation (DA) is a flexible tool for analyzing closed and open population models of capture-recapture data, especially models which include sources of hetereogeneity among individuals. The essential concept underlying DA, as we use the term, is based on adding "observations" to create a dataset composed of a known number of individuals. This new (augmented) dataset, which includes the unknown number of individuals N in the population, is then analyzed using a new model that includes a reformulation of the parameter N in the conventional model of the observed (unaugmented) data. In the context of capture-recapture models, we add a set of "all zero" encounter histories which are not, in practice, observable. The model of the augmented dataset is a zero-inflated version of either a binomial or a multinomial base model. Thus, our use of DA provides a general approach for analyzing both closed and open population models of all types. In doing so, this approach provides a unified framework for the analysis of a huge range of models that are treated as unrelated "black boxes" and named procedures in the classical literature. As a practical matter, analysis of the augmented dataset by MCMC is greatly simplified compared to other methods that require specialized algorithms. For example, complex capture-recapture models of an augmented dataset can be fitted with popular MCMC software packages (WinBUGS or JAGS) by providing a concise statement of the model's assumptions that usually involves only a few lines of pseudocode. In this paper, we review the basic technical concepts of data augmentation, and we provide examples of analyses of closed-population models ( M 0, M h , distance sampling, and spatial capture-recapture models) and open-population models (Jolly-Seber) with individual effects.

Journal of Ornithology↗

USGS2021

A new approach for representing agent-environment feedbacks: Coupled agent-based and state-and-transition simulation models

Context Agent-based models (ABMs) and state-and-transition simulation models (STSMs) have proven useful for understanding processes underlying social-ecological systems and evaluating practical questions about how systems might respond to different scenarios. ABMs can simulate a variety of agents (autonomous units, such as wildlife or people); agent characteristics, decision-making, adaptive behavior, and mobility; and agent-environment interactions. STSMs are flexible and intuitive stochastic landscape models that can track scenarios and integrate diverse data. Both can be run spatially and track metrics of management success. Objectives Due to the complementarity of these approaches, we sought to couple them through a dynamic linkage and demonstrate the relevance of this advancement for modeling landscape processes and patterns. Methods We developed analytical techniques and software tools to couple these modeling approaches using NetLogo, R, and the ST-Sim package for SyncroSim. We demonstrated the capabilities and value of this coupled approach through a proof-of-concept case study of bison-vegetation interactions in Badlands National Park. Results The coupled ABM-STSM: (1) streamlined handling of model inputs and outputs; (2) allowed representation of processes at multiple temporal scales; (3) minimized assumptions; and (4) generated spatial and temporal patterns that better reflected agent-environment interactions. Conclusions These developments constitute a new approach for representing agent-environment feedbacks; modelers can now use output from an ABM to dictate landscape changes within an STSM that in turn influence agents. This facilitates experimentation across domains (agent and environment) and creation of more realistic and management-relevant projections, and opens new opportunities for communicating models and linking to other methods.

Landscape Ecology↗

USGS2007

Regional analysis of ground-water recharge

A modeling analysis of runoff and ground-water recharge for the arid and semiarid southwestern United States was performed to investigate the interactions of climate and other controlling factors and to place the eight study-site investigations into a regional context. A distributed-parameter water-balance model (the Basin Characterization Model, or BCM) was used in the analysis. Data requirements of the BCM included digital representations of topography, soils, geology, and vegetation, together with monthly time-series of precipitation and air-temperature data. Time-series of potential evapotranspiration were generated by using a submodel for solar radiation, taking into account topographic shading, cloudiness, and vegetation density. Snowpack accumulation and melting were modeled using precipitation and air-temperature data. Amounts of water available for runoff and ground-water recharge were calculated on the basis of water-budget considerations by using measured- and generated-meteorologic time series together with estimates of soil-water storage and saturated hydraulic conductivity of subsoil geologic units. Calculations were made on a computational grid with a horizontal resolution of about 270 meters for the entire 1,033,840 square-kilometer study area. The modeling analysis was composed of 194 basins, including the eight basins containing ground-water recharge-site investigations. For each grid cell, the BCM computed monthly values of potential evapotranspiration, soil-water storage, in-place ground-water recharge, and runoff (potential stream flow). A fixed percentage of runoff was assumed to become recharge beneath channels operating at a finer resolution than the computational grid of the BCM. Monthly precipitation and temperature data from 1941 to 2004 were used to explore climatic variability in runoff and ground-water recharge. The selected approach provided a framework for classifying study-site basins with respect to climate and dominant recharge processes. The average climate for all 194 basins ranged from hyperarid to humid, with arid and semiarid basins predominating (fig. 6, chapter A, this volume). Four of the 194 basins had an aridity index of dry subhumid; two of the basins were humid. Of the eight recharge-study sites, six were in semiarid basins, and two were in arid basins. Average-annual potential evapotranspiration showed a regional gradient from less than 1 m/yr in the northeastern part of the study area to more than 2 m/yr in the southwestern part of the study area. Average-annual precipitation was lowest in the two arid-site basins and highest in the two study-site basins in southern Arizona. The relative amount of runoff to in-place recharge varied throughout the study area, reflecting differences primarily in soil water-holding capacity, saturated hydraulic conductivity of subsoil materials, and snowpack dynamics. Climatic forcing expressed in El Niño and Pacific Decadal Oscillation indices strongly influenced the generation of precipitation throughout the study area. Positive values of both indices correlated with the highest amounts of runoff and ground-water recharge.

Professional Paper↗

USGS2020

Parameter estimation for multiple post-wildfire hydrologic models

Predictions of post‐wildfire flooding and debris flows are needed, typically with short lead times. Measurements of soil‐hydraulic properties necessary for model parameterization are, however, seldom available. This study quantified soil‐hydraulic properties, soil‐water retention, and selected soil physical properties within the perimeter of the 2017 Thomas Fire in California. The Thomas Fire burn scar produced catastrophic debris flows in January 2018, highlighting the need for improved prediction capability. Soil‐hydraulic properties were also indirectly estimated using relations tied to soil‐water retention. These measurements and estimates are examined in the context of parameterizing post‐wildfire hydrologic models. Tension infiltrometer measurements showed significant decreases ( p < .05) in field‐saturated hydraulic conductivity ( K fs ) and sorptivity ( S ) in burned areas relative to unburned areas. Wildfire effects on soil water‐retention were dominated by significant decreases in saturated soil‐water content ( θ S ). The van Genuchten parameters α , N , and residual water content did not show significant wildfire effects. The impacts of the wildfire on hydraulic and physical soil properties were greatest in the top 1 cm, emphasizing that measurements of post‐fire soil properties should focus on the near‐surface. Reductions in K fs , θ s , and soil‐water retention in burned soils were attributed to fire‐induced decreases in soil structure evidenced by increases in dry bulk density. Sorptivity reductions in burned soils were attributed to increases in soil‐water repellency. Rapid post‐fire assessments of flash flood and debris flow hazards using physically‐based hydrologic models are facilitated by similarities between K fs , S , and the Green–Ampt wetting front potential ( ψ f ) with measurements at other southern CA burned sites. We suggest that ratios of burned to unburned K fs (0.37), S (0.36), and ψ f (0.66) could be used to scale unburned values for model parameterization. Alternatively, typical burned values ( K fs = 20 mm hr −1 ; S = 6 mm hr −0.5 ; ψ f = 1.6 mm) could be used for model parameterization.

California↗

USGS2019

Try, try again: Lessons learned from success and failure in participatory modeling

Participatory Modeling (PM) is becoming increasingly common in environmental planning and conservation, due in part to advances in cyberinfrastructure as well as to greater recognition of the importance of engaging a diverse array of stakeholders in decision making. We provide lessons learned, based on over 200 years of the authors’ cumulative and diverse experience, about PM processes. These include successful and, perhaps more importantly, not-so-successful trials. Our collective interdisciplinary background has supported the development, testing, and evaluation of a rich range of collaborative modeling approaches. We share here what we have learned as a community of participatory modelers, within three categories of reflection: a) lessons learned about participatory modelers; b) lessons learned about the context of collaboration; and c) lessons learned about the PM process. First, successful PM teams encompass a variety of skills beyond modeling expertise. Skills include: effective relationship-building, openness to learn from local experts, awareness of personal motivations and biases, and ability to translate discussions into models and to assess success. Second, the context for collaboration necessitates a culturally appropriate process for knowledge generation and use, for involvement of community co-leads, and for understanding group power dynamics that might influence how people from different backgrounds interact. Finally, knowing when to use PM and when not to, managing expectations, and effectively and equitably addressing conflicts is essential. Managing the participation process in PM is as important as managing the model building process. We recommend that PM teams consider what skills are present within a team, while ensuring inclusive creative space for collaborative exploration and learning supported by simple yet relevant models. With a realistic view of what it entails, PM can be a powerful approach that builds collective knowledge and social capital, thus helping communities to take charge of their future and address complex social and environmental problems.

Elementa: Science of the Anthropocene↗

USGS2016

Estimating abundance: Chapter 27

This chapter provides a non-technical overview of ‘closed population capture–recapture’ models, a class of well-established models that are widely applied in ecology, such as removal sampling, covariate models, and distance sampling. These methods are regularly adopted for studies of reptiles, in order to estimate abundance from counts of marked individuals while accounting for imperfect detection. Thus, the chapter describes some classic closed population models for estimating abundance, with considerations for some recent extensions that provide a spatial context for the estimation of abundance, and therefore density. Finally, the chapter suggests some software for use in data analysis, such as the Windows-based program MARK, and provides an example of estimating abundance and density of reptiles using an artificial cover object survey of Slow Worms ( Anguis fragilis ).

Book chapter↗

USGS2024

The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy

Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts. We detail the process of generating 17 rounds of scenarios and projections at different stages of the COVID-19 pandemic, and disseminating results to the public health community and lay public. We also highlight how SMH was expanded to generate influenza projections during the 2022–23 season. We identify key impacts of SMH results on public health and draw lessons to improve future collaborative modeling efforts, research on scenario projections, and the interface between models and policy.

Epidemics↗

USGS2021

Indicators of volcanic eruptions revealed by global M4+ earthquakes

Determining whether seismicity near volcanoes is due primarily to tectonic or magmatic processes is a challenging but critical endeavor for volcanic eruption forecasting and detection, especially at poorly monitored volcanoes. Global statistics on the occurrence and timing of earthquakes near volcanoes both within and outside of eruptive periods reveal patterns in eruptive seismicity that may improve our ability to discern magmatically driven seismicity from purely tectonic seismicity. In this paper, we catalog magnitude four and greater (M4+) earthquakes near volcanoes globally and compute statistics on their occurrence with respect to various eruptive and volcanic attributes, evaluating their utility as diagnostic indicators of eruptions. Using a 2‐week time window and a 30 km radius around the volcanoes, we find that 11% of eruptions are preceded by at least one M4+ earthquake, but only 1% of such earthquakes is followed by eruption. However, earthquakes located 5–15 km from the volcano, those with normal faulting mechanisms and/or large nondouble‐couple components, and those occurring as groups are more commonly associated with eruptions, providing significant forecasting utility in some cases. Similarly, certain volcanoes are more likely to exhibit such precursors, such as those with long repose periods. We illustrate the use of these data in eruption forecasting scenarios, including rapid identification of analogous earthquake sequences at other volcanoes. When integrated within the context of multiparametric, multidisciplinary probabilistic assessments of volcanic activity, global earthquake statistics can improve eruption forecasts, and our work provides a model for use on other rapidly expanding global volcanological databases.

Journal of Geophysical Research↗

USGS2020

Estimating late 19th century hydrology in the Greater Everglades Ecosystem: An integration of paleoecologic data and models

Determining hydrologic conditions prior to instrumental records is a challenge for restoration of freshwater ecosystems worldwide. Paleoecologic data provide this information on past conditions and when these data are used to adjust hydrologic models, allow conditions to be hindcast that may not be directly estimated from the paleo-data alone. In this context, the paleo-data provide real-world estimates as input to the models. Restoration of the Greater Everglades Ecosystem requires this understanding of the hydrology of the natural system prior to significant alterations due to water management and land use. Large scale models such as the Natural Systems Model (NSM 4.6.2) have been used by the South Florida Water Management District and other agencies responsible for restoration to estimate past hydrologic conditions; however, these models typically portray a drier natural system for the beginning of the 20th century than what is indicated by paleoecologic analyses and historical data. The purpose of this study is to estimate pre-20th century water levels, hydroperiods and flow in the freshwater wetlands of the Everglades by using pollen assemblage data in three sediment cores to adjust the Natural Systems Model. This study is designed to further test estimates of flow through the Everglades derived from analysis of sediment cores collected in Florida Bay. The results demonstrate that the NSM 4.6.2 underestimates water levels and hydroperiods in the Everglades compared to the paleo-adjusted NSM 4.6.2 model outputs. Flow models that use the paleo-adjusted water levels as input indicate flow through Shark River Slough in the late 19th century was approximately two times flow between 1990 and 2000, and flow through Taylor Slough was approximately three times flow between 1990 and 2000. The flow estimates derived from this study agree with the estimates derived from earlier studies using estuarine cores. This integration of paleoecologic information and hydrologic models provides resource managers with the best available estimates of past conditions and allows them to set realistic targets for restoration of freshwater ecosystems.

Florida↗

USGS2021

What's left before participatory modeling can fully support real-world environmental planning processes: A case study review

In environmental participatory modeling (PM), both computer and non-computer-based modeling techniques are used to aid participatory problem description, solution, and decision-making actions in environmental contexts. Although many PM case studies have been published, few efforts have sought to systematically describe and understand dominant PM processes or establish best practices for PM. As a first step, we have reviewed a random sample of environmental PM case study articles (n = 60) using a novel PM process evaluation instrument. We found that significant work likely remains for PM to fully support participatory and integrated planning processes. While PM reports systematically address knowledge integration and learning, they often neglect the facilitation of a multi-value perspective within a democratic process , and the integration across organizations within a governance system. If not reported, we suspect these aspects are also neglected in practice. We conclude with key research and practice issues for improving PM as an approach for real-world participatory planning and governance.

Environmental Modelling & Software↗

USGS2025

Do watershed conditions or local climate play a larger role in determining regional stream salamander distributions?

Anthropogenic influences like land use and climate variability interact with natural heterogeneity to influence the persistence of stream salamanders. Using occupancy modeling in the southern Appalachian Mountains, we investigated the influence of land use, climate, and physical context (e.g., drainage area, elevation) on stream salamander occupancy, noting species, and life stage specific responses. Our results illustrate that forest loss is a better predictor of salamander occupancy than physical context (elevation) or climate. Across the gradients in this dataset, precipitation did not have a significant influence on salamander occupancy, potentially due to the observed narrow, wet gradient. Temperature had little effect on Eurycea wilderae occupancy; however, temperature negatively affected adult but not larval Desmognathus amphileucus occupancy . Spatial thermal variability in this study was larger than projected increases due to climate change, suggesting that local mechanisms (e.g., behavior or physiological plasticity) may facilitate salamander resilience to climate change. However, the negative effects of forest loss coupled with rising temperatures (e.g., increased solar radiation, warmer stream runoff) underscore the importance of riparian forests in mitigating climate stressors. Preserving forest cover is critical for maintaining stream salamander populations and may offer opportunities for maintaining resilience in the face of additional stressors like rising temperatures or drought.

Georgia, North Carolina↗

USGS2023

Synergistic soil, land use, and climate influences on wind erosion on the Colorado Plateau: Implications for management

Two decades of drought in the southwestern USA are spurring concerns about increases in wind erosion, dust emissions, and associated impacts on ecosystems, agriculture, human health, and water supply. Different avenues of investigation into primary drivers of wind erosion and dust have yielded mixed results depending on the spatial and temporal sensitivity of the evidence. We monitored passive aeolian sediment traps from 2017 to 2020 across eighty-one sites near Moab UT to understand patterns of sediment flux. At measurement sites we collated climate, soil, topography and vegetation spatial layers to better understand the context of wind erosion and then combine these data with field observations of land use in models to characterize the influence of cattle grazing, oil and gas well pads, and vehicle/heavy equipment disturbance that potentially drive both exposure of bare soil and increases in erodible sediment supply that increase vulnerability to erosion. Disturbed areas with low soil calcium carbonate content yielded high sediment transport in dry years, but notably areas with little disturbance and low bare soil exposure had much less activity. Cattle grazing had the largest land use association with erosional activity with analyses suggesting that both herbivory and trampling from cattle could be drivers. The amount and distribution of bare soil exposure from new sub-annual fractional cover remote sensing products proved very helpful in mapping erosion, and new predictive maps informed by field data are presented to help depict spatial patterns of wind erosion activity. Our results suggest that despite the magnitude of current droughts, minimizing surface disturbance in vulnerable soils can mitigate a large portion of dust emissions. Results can help managers identify eroding areas where disturbance reduction and soil surface protection measures can be prioritized.

Science of the Total Environment↗

USGS2013

Temporal variation and scale in movement-based resource selection functions

A common population characteristic of interest in animal ecology studies pertains to the selection of resources. That is, given the resources available to animals, what do they ultimately choose to use? A variety of statistical approaches have been employed to examine this question and each has advantages and disadvantages with respect to the form of available data and the properties of estimators given model assumptions. A wealth of high resolution telemetry data are now being collected to study animal population movement and space use and these data present both challenges and opportunities for statistical inference. We summarize traditional methods for resource selection and then describe several extensions to deal with measurement uncertainty and an explicit movement process that exists in studies involving high-resolution telemetry data. Our approach uses a correlated random walk movement model to obtain temporally varying use and availability distributions that are employed in a weighted distribution context to estimate selection coefficients. The temporally varying coefficients are then weighted by their contribution to selection and combined to provide inference at the population level. The result is an intuitive and accessible statistical procedure that uses readily available software and is computationally feasible for large datasets. These methods are demonstrated using data collected as part of a large-scale mountain lion monitoring study in Colorado, USA.

Statistical Methodology↗

USGS2014

On the effects of scale for ecosystem services mapping

Ecosystems provide life-sustaining services upon which human civilization depends, but their degradation largely continues unabated. Spatially explicit information on ecosystem services (ES) provision is required to better guide decision making, particularly for mountain systems, which are characterized by vertical gradients and isolation with high topographic complexity, making them particularly sensitive to global change. But while spatially explicit ES quantification and valuation allows the identification of areas of abundant or limited supply of and demand for ES, the accuracy and usefulness of the information varies considerably depending on the scale and methods used. Using four case studies from mountainous regions in Europe and the U.S., we quantify information gains and losses when mapping five ES - carbon sequestration, flood regulation, agricultural production, timber harvest, and scenic beauty - at coarse and fine resolution (250 m vs. 25 m in Europe and 300 m vs. 30 m in the U.S.). We analyze the effects of scale on ES estimates and their spatial pattern and show how these effects are related to different ES, terrain structure and model properties. ES estimates differ substantially between the fine and coarse resolution analyses in all case studies and across all services. This scale effect is not equally strong for all ES. We show that spatially explicit information about non-clustered, isolated ES tends to be lost at coarse resolution and against expectation, mainly in less rugged terrain, which calls for finer resolution assessments in such contexts. The effect of terrain ruggedness is also related to model properties such as dependency on land use-land cover data. We close with recommendations for mapping ES to make the resulting maps more comparable, and suggest a four-step approach to address the issue of scale when mapping ES that can deliver information to support ES-based decision making with greater accuracy and reliability.

PLoS ONE↗

USGS2018

Vegetation cover, tidal amplitude and land area predict short-term marsh vulnerability in Coastal Louisiana

The loss of coastal marshes is a topic of great concern, because these habitats provide tangible ecosystem services and are at risk from sea-level rise and human activities. In recent years, significant effort has gone into understanding and modeling the relationships between the biological and physical factors that contribute to marsh stability. Simulation-based process models suggest that marsh stability is the product of a complex feedback between sediment supply, flooding regime and vegetation response, resulting in elevation gains sufficient to match the combination of relative sea-level rise and losses from erosion. However, there have been few direct, empirical tests of these models, because long-term datasets that have captured sufficient numbers of marsh loss events in the context of a rigorous monitoring program are rare. We use a multi-year data set collected by the Coastwide Reference Monitoring System (CRMS) that includes transitions of monitored vegetation plots to open water to build and test a predictive model of near-term marsh vulnerability. We found that despite the conclusions of previous process models, elevation change had no ability to predict the transition of vegetated marsh to open water. However, we found that the processes that drive elevation change were significant predictors of transitions. Specifically, vegetation cover in prior year, land area in the surrounding 1 km2 (an estimate of marsh fragmentation), and the interaction of tidal amplitude and position in tidal frame were all significant factors predicting marsh loss. This suggests that 1) elevation change is likely better a predictor of marsh loss at time scales longer than we consider in this study and 2) the significant predictive factors affect marsh vulnerability through pathways other than elevation change, such as resistance to erosion. In addition, we found that, while sensitivity of marsh vulnerability to the predictive factors varied spatially across coastal Louisiana, vegetation cover in prior year was the best single predictor of subsequent loss in most sites followed by changes in percent land and tidal amplitude. The model’s predicted land loss rates correlated well with land loss rates derived from satellite data, although agreement was spatially variable. These results indicate 1) monitoring the loss of small scale vegetation plots can inform patterns of land loss at larger scales 2) the drivers of land loss vary spatially across coastal Louisiana, and 3) relatively simple models have potential as highly informative tools for bioassessment, directing future research, and management planning.

Louisiana↗

USGS2017

Skagit River coho salmon life history model—Users’ guide

Natural resource management is conducted in the context of multiple anthropogenic stressors and is further challenged owing to changing climate. Experiments to determine the effects of climate change on complex ecological systems are nearly impossible. However, using a simulation model to synthesize current understanding of key ecological processes through the life cycle of a fish population can provide a platform for exploring potential effects of and management responses to changing conditions. Potential climate-change scenarios can be imposed, responses can be observed, and the effectiveness of potential actions can be evaluated. This approach is limited owing to future conditions likely deviating in range and timing from conditions used to create the model so that the model is expected to become obsolete. In the meantime, however, the modeling process explicitly states assumptions, clarifies information gaps, and provides a means to better understand which relationships are robust and which are vulnerable to changing climate by observing whether and why model output diverges from actual observations through time. The purpose of the model described herein is to provide such a decision-support tool regarding coho ( Oncorhynchus kisutch ) salmon for the Sauk-Suiattle Indian Tribe of Washington State. The Skagit coho salmon model is implemented in a system dynamics format and has three primary stocks—(1) predicted smolts, (2) realized smolts, and (3) escapement. “Predicted smolts” are the number of smolts expected based on the number of spawners in any year and the Ricker production curve. Pink salmon ( Oncorhynchus gorbuscha ) return to the Skagit River in odd years, and when they overlap with juvenile rearing coho salmon, coho smolt production is substantially higher than in non-pink years. Therefore, the model uses alternative Ricker equations to predict smolts depending on whether their juvenile year was a pink or non-pink year. The stock “realized smolts” is calculated based on the expected effect of streamflow conditions to alter the productivity predicted by the Ricker curve. Adverse conditions include scouring flow events that occur when redds are present; high-flow events during winter on juveniles, which can cause fish displacement and adverse water turbidity; and extremely low flows in summer. The stock “escapement” represents the fish remaining after accounting for ocean mortality and harvest. Ocean mortality has been linked with indices of ocean conditions, which are related to ocean biological productivity. Ocean survival also may have a density-dependent component such that lower survival is associated with higher numbers of smolts. The model allows the user to change certain model parameters and inputs, and choose among alternative predictors for certain modeled relations.

Washington↗