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Results for “Environmental Modeling & Assessment”

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At least 271 records · Page 15Linked to original sources

USGS2011

Building on crossvalidation for increasing the quality of geostatistical modeling

The random function is a mathematical model commonly used in the assessment of uncertainty associated with a spatially correlated attribute that has been partially sampled. There are multiple algorithms for modeling such random functions, all sharing the requirement of specifying various parameters that have critical influence on the results. The importance of finding ways to compare the methods and setting parameters to obtain results that better model uncertainty has increased as these algorithms have grown in number and complexity. Crossvalidation has been used in spatial statistics, mostly in kriging, for the analysis of mean square errors. An appeal of this approach is its ability to work with the same empirical sample available for running the algorithms. This paper goes beyond checking estimates by formulating a function sensitive to conditional bias. Under ideal conditions, such function turns into a straight line, which can be used as a reference for preparing measures of performance. Applied to kriging, deviations from the ideal line provide sensitivity to the semivariogram lacking in crossvalidation of kriging errors and are more sensitive to conditional bias than analyses of errors. In terms of stochastic simulation, in addition to finding better parameters, the deviations allow comparison of the realizations resulting from the applications of different methods. Examples show improvements of about 30% in the deviations and approximately 10% in the square root of mean square errors between reasonable starting modelling and the solutions according to the new criteria.

Stochastic Environmental Research and Risk Assessm↗

USGS2008

Simulating the impact of cholinesterase-inhibiting pesticides on non-target wildlife in irrigated crops

We present a simulation model for risk assessment of the impact of insecticide inhibitors of cholinesterase (ChE) applied in irrigated agricultural fields on non-target wildlife. The model, which we developed as a compartment model based on difference equations (??t = 1 h), consists of six submodels describing the dynamics of (1) insecticide application, (2) insecticide movement into floodable soil, (3) irrigation and rain, (4) insecticide dissolution in water, (5) foraging and insecticide intake from water, and (6) ChE inhibition and recovery. To demonstrate application of the model, we simulated historical and "worst-case" scenarios of the impact of ChE-inhibiting insecticides on white-winged doves (Zenaida asiatica) inhabiting natural brushland adjacent to cotton and sugarcane fields in the Lower Rio Grande Valley of Texas, USA. Only when a rain event occurred just after insecticide application did predicted levels of ChE inhibition surpass the diagnostic level of 20% exposure. The present model should aid in assessing the effect of ChE-inhibiting insecticides on ChE activity of different species that drink contaminated water from irrigated agricultural fields, and in identifying specific situations in which the juxtaposition of environmental conditions and management schemes could result in a high risk to non-target wildlife. ?? 2007 Elsevier B.V. All rights reserved.

Ecological Modelling↗

USGS2021

Adaptive monitoring in action: Reconsidering design-based estimators reveals underestimation of whitebark pine disease prevalence in the Greater Yellowstone Ecosystem

Identifying and understanding status and trends in ecological indicators motivates continual monitoring over decades. Many programs rely on probability surveys and their companion design-based estimators for status assessments (e.g. Horvitz–Thompson). Design-based estimators do not easily extend to trend estimation nor situations with observation errors. Field-based monitoring efforts inevitably have turnover of field crew members which may affect consistency and accuracy of data collection over time. Additionally, design-based estimators ignore the complexities of spatial and temporal heterogeneity in an ecological indicator and how this variability may be linked to environmental or biological dynamics. We propose monitoring programs should re-evaluate their prescribed statistical methods, consider model-based approaches and adapt their sampling designs as needed to improve inferences. The Greater Yellowstone Ecosystem, home to two of the most iconic U.S. National Parks, has experienced significant declines in whitebark pine Pinus albicaulis communities due to forest pathogens, insect outbreaks, wildland fires and drought. Whitebark pine is a keystone species found in mountainous environments throughout the Western U.S. and Canada. We assessed the design-based ratio estimator originally recommended for estimating prevalence of white pine blister rust Cronartium ribicola . We compared the design-based estimator to a model-based approach that accounts for the sampling design, imperfect detection and allows for infection probabilities to vary over space and time. Ignoring observation errors led to lower estimated prevalence of white pine blister rust in the general population. Using model-based approaches, we found that the probability of infection has increased since 2004. However, overall prevalence likely has not changed because of the mountain pine beetle Dendroctonus ponderosae -induced shift towards smaller diameter trees that have a lower probability of infection compared to their larger cohorts. Synthesis and Applications . Using a design-based approach to detect change in ecological indicators falls short because of the inability to account for observation errors or to explore environmental or biological factors explaining temporal dynamics. Inherently understanding the mechanisms leading to changes in an ecological indicator over time informs potential management actions. Our assessment underscores the need for continued evaluation and updating of a monitoring program's sampling design and analytical procedures to maintain relevancy.

Wyoming↗

USGS2003

A proposed coast-wide reference monitoring system for evaluating Wetland restoration trajectories in Louisiana

Wetland restoration efforts conducted in Louisiana under the Coastal Wetlands Planning, Protection and Restoration Act require monitoring the effectiveness of individual projects as well as monitoring the cumulative effects of all projects in restoring, creating, enhancing, and protecting the coastal landscape. The effectiveness of the traditional paired-reference monitoring approach in Louisiana has been limited because of difficulty in finding comparable reference sites. A multiple reference approach is proposed that uses aspects of hydrogeomorphic functional assessments and probabilistic sampling. This approach includes a suite of sites that encompass the range of ecological condition for each stratum, with projects placed on a continuum of conditions found for that stratum. Trajectories in reference sites through time are then compared with project trajectories through time. Plant community zonation complicated selection of indicators, strata, and sample size. The approach proposed could serve as a model for evaluating wetland ecosystems.

Environmental Monitoring and Assessment↗

USGS2021

Re‐purposing groundwater flow models for age assessments: Important characteristics

Groundwater flow model construction is often time‐consuming and costly, with development ideally focused on a specific purpose, such as quantifying well capture from water bodies or providing flow fields for simulating advective transport. As environmental challenges evolve, the incentive to re‐purpose existing groundwater flow models may increase. However, few studies have evaluated which characteristics of groundwater flow models deserve greatest consideration when re‐purposing models for groundwater age and advective transport simulations. In this paper, we compare simulated age metrics produced by three MODFLOW‐MODPATH models of the same area but with differing levels of complexity (layering and heterogeneity). Comparisons are made at three watershed scales (HUC 8 to HUC 12). Groundwater age metrics, specifically the young fraction and median age of the young and old fractions, are used for evaluation because they relate to intrinsic susceptibility of aquifers and are simpler to interpret than full age distributions used for advective transport. Results indicate that: 1. the young fraction is less sensitive to model layering than the median age of young and old fractions, suggesting that simple models may suffice for basic intrinsic susceptibility assessments; 2. water table mounding and associated discharge into partially penetrating boundaries, such as head‐water streams, is important for simulating both the young fraction and the median age of the young fraction; and 3. the influence of partially penetrating head‐water streams is maintained regardless of the porosity distribution. Results of this work should aid modelers with evaluating the appropriateness of re‐purposing existing groundwater flow models for age simulations.

Groundwater↗

USGS2016

MODIS imagery improves pest risk assessment: A case study of wheat stem sawfly (Cephus cinctus, Hymenoptera: Cephidae) in Colorado, USA

Wheat stem sawfly ( Cephus cinctus Norton, Hymenoptera: Cephidae) has long been a significant insect pest of spring, and more recently, winter wheat in the northern Great Plains. Wheat stem sawfly was first observed infesting winter wheat in Colorado in 2010 and, subsequently, has spread rapidly throughout wheat production regions of the state. Here, we used maximum entropy modeling (MaxEnt) to generate habitat suitability maps in order to predict the risk of crop damage as this species spreads throughout the winter wheat-growing regions of Colorado. We identified environmental variables that influence the current distribution of wheat stem sawfly in the state and evaluated whether remotely sensed variables improved model performance. We used presence localities of C. cinctus and climatic, topographic, soils, and normalized difference vegetation index and enhanced vegetation index data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery as environmental variables. All models had high performance in that they were successful in predicting suitable habitat for C. cinctus in its current distribution in eastern Colorado. The enhanced vegetation index for the month of April improved model performance and was identified as a top contributor to MaxEnt model. Soil clay percent at 0–5 cm, temperature seasonality, and precipitation seasonality were also associated with C. cinctus distribution in Colorado. The improved model performance resulting from integrating vegetation indices in our study demonstrates the ability of remote sensing technologies to enhance species distribution modeling. These risk maps generated can assist managers in planning control measures for current infestations and assess the future risk of C. cinctus establishment in currently uninfested regions.

Colorado↗

USGS2018

Harnessing big data to rethink land heterogeneity in Earth system models

The continual growth in the availability, detail, and wealth of environmental data provides an invaluable asset to improve the characterization of land heterogeneity in Earth system models – a persistent challenge in macroscale models. However, due to the nature of these data (volume and complexity) and computational constraints, these data are underused for global applications. As a proof of concept, this study explores how to effectively and efficiently harness these data in Earth system models over a 1/4° ( ∼ 25 km) grid cell in the western foothills of the Sierra Nevada in central California. First, a novel hierarchical multivariate clustering approach (HMC) is introduced that summarizes the high-dimensional environmental data space into hydrologically interconnected representative clusters (i.e., tiles). These tiles and their associated properties are then used to parameterize the sub-grid heterogeneity of the Geophysical Fluid Dynamics Laboratory (GFDL) LM4-HB land model. To assess how this clustering approach impacts the simulated water, energy, and carbon cycles, model experiments are run using a series of different tile configurations assembled using HMC. The results over the test domain show that (1) the observed similarity over the landscape makes it possible to converge on the macroscale response of the fully distributed model with around 300 sub-grid land model tiles; (2) assembling the sub-grid tile configuration from available environmental data can have a large impact on the macroscale states and fluxes of the water, energy, and carbon cycles; for example, the defined subsurface connections between the tiles lead to a dampening of macroscale extremes; (3) connecting the fine-scale grid to the model tiles via HMC enables circumvention of the classic scale discrepancies between the macroscale and field-scale estimates; this has potentially significant implications for the evaluation and application of Earth system models.

Hydrology and Earth System Sciences↗

USGS2026

Seasonal drivers of density in a subarctic population of northern red-backed voles

Northern red-backed voles ( Clethrionomys rutilus ) are an important species in the boreal forest ecosystem, both as herbivores and as a key food source for many mammalian and avian predators. They exhibit dramatic inter- and intra-annual population fluctuations, for which causes are not entirely known. We monitored northern red-backed vole densities in Denali National Park and Preserve through time with the goal of examining how environmental factors influenced density over time. Using a 30-year record of mark-recapture data, we used spatially explicit capture-recapture methods to estimate autumn and early summer densities each year. We assessed cyclic patterns in density, variation in amplitude, and any periodicity of population fluctuations using post hoc linear modeling. We found that the vole population appeared to be cyclic with a 2–4 year period, although the pattern varied somewhat among sampling sites. Our results indicated an association between white spruce ( Picea glauca ) seed production and vole density, implying white spruce seeds were either an important source of food during winter seasons, or that the environmental triggers that promote high seed fall were also associated with increased vole density. We also found a negative effect of an autumn harshness index, indicating winter conditions play a role in vole density in the following season. Finally, we found evidence of a negative density-dependent relationship between autumn and early summer. Together, these findings suggest a system in which density dependence and cyclic relationships are irregular but highly influential, with environmental effects capable of enhancing or moderating their impact. Continued monitoring of voles, alongside more thorough assessments of environmental conditions, may provide additional insight into the complex population dynamics of this species.

Alaska↗

USGS2019

Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models

Gully erosion susceptibility mapping is a fundamental tool for land-use planning aimed at mitigating land degradation. However, the capabilities of some state-of-the-art data-mining models for developing accurate maps of gully erosion susceptibility have not yet been fully investigated. This study assessed and compared the performance of two different types of data-mining models for accurately mapping gully erosion susceptibility at a regional scale in Chavar, Ilam, Iran. The two methods evaluated were: Certainty Factor (CF), a bivariate statistical model; and Maximum Entropy (ME), an advanced machine learning model. Several geographic and environmental factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 63 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. Accuracy assessments completed with the receiver operating characteristic curve method showed that the ME-based regional gully susceptibility map has an area under the curve (AUC) value of 88.6% whereas the CF-based map has an AUC of 81.8%. According to jackknife tests that were used to investigate the relative importance of predictor variables, aspect, distance to river, lithology and land use are the most influential factors for the spatial distribution of gully erosion susceptibility in this region of Iran. The gully erosion susceptibility maps produced in this study could be useful tools for land managers and engineers tasked with road development, urbanization and other future development.

Science of the Total Environment↗

USGS2026

Early Pliocene (Zanclean) sea surface temperature for PlioMIP3

Paleoclimate researchers have been comparing Pliocene environmental data to paleoclimate model results since the 1980s. The Pliocene Model Intercomparison Project (PlioMIP) began in 2008 with a focus on the Late Pliocene. Here we assess the availability and utility of sea surface temperature (SST) data for verification of Pliocene Model Intercomparison Project (PlioMIP3) Early Pliocene (Zanclean) experiments. We analyze published data in terms of quantity and spatial distribution. Only SST estimates derived using alkenone paleo thermometry are reported, and all estimates are based upon the same temperature calibration. Sea surface temperature data are selected from within three distinct time intervals: The early Zanclean 5.3 Ma – 4.2 Ma time slab, and two time slices within the early Zanclean, chosen by PlioMIP3 at 4.870 Ma and 4.474 Ma. Results show the early Zanclean time slab contains 2055 individual estimates. Approximately ∼ 80% of these estimates come from Sites 609, 642, 846, 847, 882, 907, and 1146. There are 17 sites with a total of 42 estimates within the 4.474 Ma ±10 kyr time slice, and 15 sites with a total of 47 data points within the 4.870 Ma ±10 kyr interval. The sparse spatial and temporal distribution of Zanclean data, relative to the data available for the mid Piacenzian, makes point-by-point data model comparison suspect. We suggest interpreting model output against lower resolution long term trends in proxy data, and comparison of models through temperature gradients, may be the most useful application of currently available data. Integrating Zanclean age coastal plain sequences within data model comparison schemes, for increased understanding of regional climate impacts, also holds great potential.

Global and Planetary Change↗

USGS2002

Response of benthic algae to environmental gradients in an agriculturally dominated landscape

Benthic algal communities were assessed in an agriculturally dominated landscape in the Central Columbia Plateau, Washington, to determine which environmental variables best explained species distributions, and whether algae species optima models were useful in predicting specific water-quality parameters. Land uses in the study area included forest, range, urban, and agriculture. Most of the streams in this region can be characterized as open-channel systems influenced by intensive dryland (nonirrigated) and irrigated agriculture. Algal communities in forested streams were dominated by blue-green algae, with communities in urban and range streams dominated by diatoms. The predominance of either blue-greens or diatoms in agricultural streams varied greatly depending on the specific site. Canonical correspondence analysis (CCA) indicated a strong gradient effect of several key environmental variables on benthic algal community composition. Conductivity and % agriculture were the dominant explanatory variables when all sites ( n = 24) were included in the CCA; water velocity replaced conductivity when the CCA included only agricultural and urban sites. Other significant explanatory variables included dissolved inorganic nitrogen (DIN), orthophosphate (OP), discharge, and precipitation. Regression and calibration models accurately predicted conductivity based on benthic algal communities, with OP having slightly lower predictability. The model for DIN was poor, and therefore may be less useful in this system. Thirty-four algal taxa were identified as potential indicators of conductivity and nutrient conditions, with most indicators being diatoms except for the blue-greens Anabaena sp. and Lyngbya sp.

Journal of the North American Benthological Societ↗

USGS2008

Analysis of Pulsed Flow Modification Alternatives, Lower Missouri River, 2005

The graphical, tabular, and statistical data presented in this report resulted from analysis of alternative flow regime designs considered by a group of Missouri River managers, stakeholders, and scientists during the summer of 2005. This plenary group was charged with designing a flow regime with increased spring flow pulses to support reproduction and survival of the endangered pallid sturgeon. Environmental flow components extracted from the reference natural flow regime were used to design and assess performance of alternative flow regimes. The analysis is based on modeled flow releases from Gavins Point Dam (near Yankton, South Dakota) for nine design alternatives and two reference scenarios; the reference scenarios are the run-of-the-river and the water-control plan implemented in 2004. The alternative designs were developed by the plenary group with the goal of providing pulsed spring flows, while retaining traditional social and economic uses of the river.

Open-File Report↗

USGSDate not supplied

Development of stochastic modeling systems using deterministic models and GIS: Principles and a case study in the Atlantic Zone of Costa Rica

The most important requirements for large-area environmental modeling are a tight integration between models and data, and a close match of the spatial scale at which the model is developed with the scale at which the model is to be applied. To better match the scale of data with that of the model, we propose a set of principles for the development of stochastic modeling systems based on linkage of deterministic models with GIS data. For modeling purposes, a region is usually rasterized into cells and the environmental conditions of those cells are specified by ranges or classes using GIS data layers. It is not necessary to simulate each and every GIS cell in the study area because many cells may have similar environmental conditions and can be grouped together to form cohorts. We define a cohort as the assembly of the cells sharing a unique combination of environmental conditions within the study region. Multiple model simulations can be performed for any given cohort. For each simulation, some of the parameter values can be randomly generated within the specified environmental conditions of the cohort according to a certain statistical distribution which, in turn, can be specified by GIS data layers. By this method the variance and covariance of environmental variables in space and time are integrated into the simulation processes with these modeling systems to make full use of the available data and to assess the uncertainties of the simulated results. An integrated simulation system between CENTURY model and GIS was developed to demonstrate the value of the concepts imbedded in stochastic simulation systems for large area studies.

Conference Paper↗

USGS2021

Aquatic ecosystem metabolism as a tool in environmental management

Recent advances in high-frequency environmental sensing and statistical approaches have greatly expanded the breadth of knowledge regarding aquatic ecosystem metabolism - the measurement and interpretation of gross primary productivity (GPP) and ecosystem respiration (ER). Aquatic scientists are poised to take advantage of widely available datasets and freely-available modeling tools to apply functional information gained through ecosystem metabolism to better environmental management. Historically, several logistical and conceptual factors have limited the widespread application of metabolism in management settings. Benefitting from new instrumental and modeling tools, it is now relatively straightforward to extend routine monitoring of dissolved oxygen (DO) to dynamic measures of aquatic ecosystem function (GPP & ER) and key physical processes such as gas exchange with the atmosphere (G). We review the current approaches for using DO data in environmental management with a focus on the United States, but briefly describe management frameworks in Europe and Canada. We highlight new applications of diel DO data and metabolism in regulatory settings and explore how they can be applied to managing and monitoring ecosystems. We then review existing data types and provide a short guide for implementing field measurements and modeling of ecosystem metabolic processes using currently available tools. Finally, we discuss research needed to overcome current conceptual limitations of applying metabolism in management settings. Despite challenges associated with modeling metabolism in rivers and lakes, rapid developments in this field have moved us closer to utilizing real-time estimates of GPP, ER and G to improve the assessment and management of environmental change.

WIREs Water↗

USGS2023

Simulation of groundwater flow at the former Badger Army Ammunition Plant, Sauk County, Wisconsin

To help support remedial efforts at the former Badger Army Ammunition Plant the U.S. Geological Survey built and calibrated a transient groundwater flow model using the Newton Raphson formulation (MODFLOW–NWT) of the U.S. Geological Survey’s modular three-dimensional finite-difference code. The model simulates the groundwater flow system at the site from 1984 to 2020. The former Badger Army Ammunition Plant is a 7,275-acre site in Sauk County, Wisconsin. The plant produced smokeless gunpower and solid rocket propellent as munitions components. Peak production periods were during World War II, the Korean War, and the Vietnam War. Subsequent groundwater contamination investigations have found four plumes at the site. A health risk assessment identified at least one contaminant of concern for human health risk present in three of the plumes: the propellant burning ground plume, the deterrent burning ground plume, and the central plume. A cooperative study began between the U.S. Army Environmental Command and U.S. Geological Survey to better understand the groundwater flow system at the former Badger Army Ammunition Plant. Field data, including aquifer tests, streamflow measurements, continuous groundwater elevations, and groundwater gradients with the Wisconsin River were collected and used to inform and calibrate the groundwater flow model. The model was used to assess the variability of the groundwater system over the study period, the components of the groundwater budget, and groundwater flow directions from identified source areas towards the Wisconsin River. Model performance assessment focused on using particle tracking to compare groundwater flowpaths that originate in the contaminant source areas to the observed plume footprints. This focus on plume behavior geometry should help constrain the advective component of a future groundwater transport model of the site.

Wisconsin↗

USGS2021

Toward improved decision-support tools for Delta Smelt management actions

The Collaborative Science and Adaptive Management Program (CSAMP) has endorsed a goal of reversing the recent downward trajectory of the Delta Smelt population within 5-10 generations, with the long-term aim of establishing a self-sustaining population. An ambitious agenda of management actions is planned, and more management actions are being considered. This White Paper furthers one of the recommendations in the 2019 Delta Smelt Science Plan – the need to predict the potential ecological effects of taking a management action. Existing statistical models can be highly informative in assessing the response of Delta Smelt to changing system conditions and management actions. However, management actions can shift or alter conditions in ways that models based on analysis of historical data may not be able to represent, and short-term or localized effects may be missed with models designed to assess effects at the population level. Decision support tools (DSTs) are computer-based tools developed to assist decision-making, often combining computationally intensive analysis and spatial mapping of environmental relationships. DSTs can be used in planning processes that evaluate an array of actions, such as in Structured Decision Making (SDM), where DSTs are needed to compare among alternatives. DSTs can also be used to explore the potential effects of different approaches to implementing management actions. The goal of this White Paper is to identify plausible options for DSTs that could be developed for future use to evaluate management actions that seek to either reverse the decline of Delta Smelt or minimize or mitigate the effects of other water management actions. Different types of management actions lead to different needs for DSTs. This White Paper was developed using three types of actions currently being considered to enhance the Delta Smelt population: Supplementation with Hatchery Fish, Summer-Fall Habitat, and Food Enhancement actions. These three management actions target different parts of the estuary and different processes, with a variety of possible metrics to gauge performance. Three DSTs are proposed that collectively address management questions related to the management actions considered, with each requiring a slightly different set of processes to be included and producing an array of outputs at varying spatial and temporal scales: DST 1. Modeling Fish Movement, Survival, and Reproduction Across Their Range. This DST can address management questions that require information about Delta Smelt spatial distribution and movement. • DST 1 could be used to compare conditions with and without management actions in place, how the management action performs among different types of water years (with varied flow and associated abiotic conditions), and to assess relative change with different variations and strategies of the management actions. • DST 2. Changes in Habitat Conditions and Delta Smelt Response. This DST is intended to evaluate combinations of conditions that are considered to provide suitable habitat for Delta Smelt, and Delta Smelt response. Delta Smelt habitat is generally described as open water with low salinity (0 to 6), turbidity of at least 12 NTU, suitable temperature conditions, and sufficient food availability to support growth. • DST 3. Regional Effects of Food Subsidy. This DSTs seeks to evaluate effectiveness of food enhancement actions by providing information on responses of the immediate targets of the action (i.e., phytoplankton or zooplankton) and tracing those to projected growth responses of Delta Smelt. There is not a single DST that adequately addresses management questions relevant to all management actions, although there is some overlap in the management questions each of the three DSTs can address. For each of the DSTs a substantial foundation of models and approaches already exists and modeling has already been applied to several of the management actions described. However, a number of outstanding issues remain for further development of the proposed DSTs. These are summarized in this White Paper together with potential approaches that could be applied or tested. Some components for the DSTs are already available and thus development could be relatively easy. However, for several of the topics identified there are gaps in knowledge that currently limit formulation of model structure and process representations. This presents challenges to readily incorporate some needed mechanisms into the models. Eleven next steps, aligned with relevant DSTs, are outlined. The next steps vary in their complexity or technical ‘lift’ required. Many build on existing work, or methods and approaches that have already been developed or are underway, while others require additional thinking to establish a viable approach. Some interim utility for decisions could be gained during initial development of the DSTs with further features added over time. Development of a DST requires engagement of both managers and scientists. Identifying the outputs and resolution needed for management purposes early in development of any DST is essential for effective pursuit of next steps and suitable approaches to address challenges. Dialog between managers and technical experts also informs what process-based simulation can do, and what tradeoffs are acceptable to meet a given purpose. To further develop the DSTs outlined here for application in the estuary requires: - Engagement of a committed group of technical experts with appropriate expertise. - The development of a coordinated workplan including appropriate project management and tracking. - Dialog between potential users (i.e., managers and policy makers) and technical experts. - Resources to pursue DST development including personnel and computational resources. This White Paper demonstrates the potential for moving toward DSTs for a variety of management actions in support of Delta Smelt that include mechanistic representations of physical and biological processes. Through focused effort from technical experts, managers and policy makers, DSTs can be developed to provide quantitative predictions of management effects on the ecosystem, targeting the changes the management actions seek to achieve, how these effects compare to ambient conditions, and how the effects vary among water year types or with timing and location of actions. Importantly, solid foundations exist which can be leveraged, refined, and built upon to specifically inform current and future management decisions.

White Paper↗

USGS2003

Development and testing of method for assessing and mapping agricultural areas susceptible to atrazine leaching in the state of Washington

In a joint effort by the Washington State Department of Agriculture, the Washington Department of Ecology, and the U.S. Geological Survey, the Environmental Protection Agency's Pesticide Root Zone Model and a Geographic Information System were used to develop and test a method for screening and mapping the susceptibility of ground water in agricultural areas to pesticide contamination. The objective was to produce a map that would be used by the Washington State Department of Agriculture to allocate resources for monitoring pesticide levels in ground water. The method was tested by producing a map showing susceptibility to leaching of the pesticide atrazine for the Columbia Basin Irrigation Project, which encompasses an area of intensive agriculture in eastern Washington. The reliability of the atrazine map was assessed by using statistical procedures to determine whether the median of the percentage of atrazine simulated to leach below the root zone in wells where atrazine was detected was statistically greater than the median percentage at wells where atrazine was not detected (at or above 0.001 microgram per liter) in 134 wells sampled by the U.S. Geological Survey. A statistical difference in medians was not found when all 134 wells were compared. However, a statistical difference was found in medians for two subsets of the 134 wells that were used in land-use studies (studies examining the quality of ground water beneath specific crops). The statistical results from wells from the land-use studies indicate that the model potentially can be used to map the relative susceptibility of agricultural areas to atrazine leaching. However, the distinction between areas of high and low susceptibility may not yet be sufficient to use the method for allocating resources to monitor water quality. Several options are offered for improving the reliability of future simulations.

Water-Resources Investigations Report↗

USGS2016

A stochastic bioenergetics model based approach to translating large river flow and temperature in to fish population responses: The pallid sturgeon example

In managing fish populations, especially at-risk species, realistic mathematical models are needed to help predict population response to potential management actions in the context of environmental conditions and changing climate while effectively incorporating the stochastic nature of real world conditions. We provide a key component of such a model for the endangered pallid sturgeon ( Scaphirhynchus albus ) in the form of an individual-based bioenergetics model influenced not only by temperature but also by flow. This component is based on modification of a known individual-based bioenergetics model through incorporation of: the observed ontogenetic shift in pallid sturgeon diet from marcroinvertebrates to fish; the energetic costs of swimming under flowing-water conditions; and stochasticity. We provide an assessment of how differences in environmental conditions could potentially alter pallid sturgeon growth estimates, using observed temperature and velocity from channelized portions of the Lower Missouri River mainstem. We do this using separate relationships between the proportion of maximum consumption and fork length and swimming cost standard error estimates for fish captured above and below the Kansas River in the Lower Missouri River. Critical to our matching observed growth in the field with predicted growth based on observed environmental conditions was a two-step shift in diet from macroinvertebrates to fish.

Missouri River↗