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USGS2007

Ground-water recharge in the arid and semiarid southwestern United States

Ground-water recharge in the arid and semiarid southwestern United States results from the complex interplay of climate, geology, and vegetation across widely ranging spatial and temporal scales. Present-day recharge tends to be narrowly focused in time and space. Widespread water-table declines accompanied agricultural development during the twentieth century, demonstrating that sustainable ground-water supplies are not guaranteed when part of the extracted resource represents paleorecharge. Climatic controls on ground-water recharge range from seasonal cycles of summer monsoonal and winter frontal storms to multimillennial cycles of glacial and interglacial periods. Precipitation patterns reflect global-scale interactions among the oceans, atmosphere, and continents. Large-scale climatic influences associated with El Niño and Pacific Decadal Oscillations strongly, but irregularly, control weather in the study area, so that year-to-year variations in precipitation and ground-water recharge are large and difficult to predict. Proxy data indicate geologically recent periods of naturally occurring multidecadal droughts unlike any in the modern instrumental record. Any anthropogenically induced climate change will likely reduce ground-water recharge through diminished snowpack at higher elevations. Future changes in El Niño and monsoonal patterns, both crucial to precipitation in the study area, are highly uncertain in current models. Current land-use modifications influence ground-water recharge through vegetation, irrigation, and impermeable area. High mountain ranges bounding the study area—the San Bernadino Mountains and Sierra Nevada to the west, and the Wasatch and southern Colorado Rocky Mountains to the east—provide external geologic controls on ground-water recharge. Internal geologic controls stem from tectonic processes that led to numerous, variably connected alluvial-filled basins, exposure of extensive Paleozoic aquifers in mountainous recharge areas, and distinct modes of recharge in the Colorado Plateau and Basin and Range subregions. The chapters in this professional paper present (first) an overview of climatic and hydrogeologic framework (chapter A), followed by a regional analysis of ground-water recharge across the entire study area (chapter B). These are followed by an overview of site-specific case studies representing different subareas of the geographically diverse arid and semiarid southwestern United States (chapter C); the case studies themselves follow in chapters D–K. The regional analysis includes detailed hydrologic modeling within the framework of a high-resolution geographic-information system (GIS). Results from the regional analysis are used to explore both the distribution of ground-water recharge for mean climatic conditions as well as the influence of two climatic patterns—the El Niño-Southern Oscillation and Pacific Decadal Oscillation—that impart a high degree of variability to the hydrologic cycle. Individual case studies employ a variety of geophysical and geochemical techniques to investigate recharge processes and relate the processes to local geologic and climatic conditions. All of the case studies made use of naturally occurring tracers to quantify recharge. Thermal and geophysical techniques that were developed in the course of the studies are presented in appendices. The quantification of ground-water recharge in arid settings is inherently difficult due to the generally low amount of recharge, its spatially and temporally spotty nature, and the absence of techniques for directly measuring fluxes entering the saturated zone from the unsaturated zone. Deep water tables in arid alluvial basins correspond to thick unsaturated zones that produce up to millennial time lags between changes in hydrologic conditions at the land surface and subsequent changes in recharge to underlying ground water. Recent advances in physical, chemical, isotopic, and modeling techniques have fostered new types of recharge assessments. Chemical and isotopic techniques include an increasing variety of environmental tracers that are useful and robust. Physically based techniques include the use of heat as a tracer and computationally intensive geophysical imaging tools for characterizing hydrologic conditions in the unsaturated zone. Modeling-based techniques include spatially distributed water-budget computations using high-resolution remotely sensed and ground-based geographic data. Application of these techniques to arid and semiarid settings in the southwestern United States reveals distinct patterns of recharge corresponding to geologic setting, climatic and vegetative history, and land use. Analysis of recharge patterns shows that large expanses of alluvial basin floors are drying out under current climatic conditions, with little to no recharge to underlying ground water. Ground-water recharge occurs mainly beneath upland catchments in which thin soils overlie permeable bedrock, ephemeral channels in which flow may average only several hours per year, and active agricultural areas. The chapters in this professional paper represent a coordinated attempt to develop a better understanding of one of the Nation's most critical yet difficult-to-quantify renewable resources.

Professional Paper↗

USGS2021

Testing a generalizable machine learning workflow for aquatic invasive species on Rainbow Trout (Oncorhynchus mykiss) in northwest Montana

Biological invasions are accelerating worldwide, causing major ecological and economic impacts in aquatic ecosystems. The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and data-driven products. Remotely sensed data products can be combined with existing invasive species occurrence data via machine learning models to provide the proactive spatial risk analysis necessary for implementing coordinated and agile management paradigms across large scales. We present a workflow that generates rapid spatial risk assessments on aquatic invasive species using occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, we tested this workflow using extensive spatial and temporal hybridization and occurrence data from a well-studied, ongoing, and climate-driven species invasion in the upper Flathead River system in northwestern Montana, USA. Rainbow Trout (RBT; Oncorhynchus mykiss), an introduced species in the Flathead River basin, compete and readily hybridize with native Westslope Cutthroat Trout (WCT; O. clarkii lewisii), and the spread of RBT individuals and their alleles has been tracked for decades. We used remotely sensed and other geospatial data as key environmental predictors for projecting resultant habitat suitability to geographic space. The ensemble modeling technique yielded high accuracy predictions relative to 30-fold cross-validated datasets (87% 30-fold cross-validated accuracy score). Both top predictors and model performance relative to these predictors matched current understanding of the drivers of RBT invasion and habitat suitability, indicating that temperature is a major factor influencing the spread of invasive RBT and hybridization with native WCT. The congruence between more time-consuming modeling approaches and our rapid machine-learning approach suggest that this workflow could be applied more broadly to provide data-driven management information for early detection of potential invaders.

Alberta, British Columbia, Montana↗

USGS2000

Using structural equation modeling to investigate relationships among ecological variables

Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude -0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0.1258. Natural variability had a positive direct effect on biodiversity of magnitude 0.5347 and a negative indirect effect mediated through growth potential of magnitude -0.1105 yielding a positive total effects of magnitude 0.4242. Sediment contamination had a negative direct effect on biodiversity of magnitude -0.1956 and a negative indirect effect on growth potential via biodiversity of magnitude -0.067. Biodiversity had a positive effect on growth potential of magnitude 0.8432, and growth potential had a positive effect on biodiversity of magnitude 0.3398. The correlation between biodiversity and growth potential was estimated at 0.7658 and that between sediment contamination and natural variability at -0.3769.

Environmental and Ecological Statistics↗

USGS2020

Conceptual framework and approach for conducting a geoenvironmental assessment of undiscovered uranium resources

This report presents a novel conceptual framework and approach for conducting a geologically based environmental assessment, or geoenvironmental assessment, of undiscovered uranium resources within an area likely to contain uranium deposits. The framework is based on a source-to-receptor model that prioritizes the most likely contaminant sources, contaminant pathways, and affected environmental media for three common uranium extraction methods—open pit or underground mining with milling and in situ recovery (ISR). Data on regional geology, hydrology, and climate, as well as historical uranium mining and milling records are used to estimate the probable amounts of waste rock, tailings, wastewater, surface land disturbance, and subsurface aquifer disturbance for likely mining methods. Constituents of concern that might take the form of leachates, dust, radon, and sediments formed by chemical and physical weathering are also identified in the geoenvironmental assessment. Finally, areas where constituents of concern are likely to occur and persist in air, land, surface water, and groundwater are indicated by the potential for dispersion of dust by wind, accumulation of radon because of air stagnation, dispersion of sediments and wastewater by runoff, and infiltration of wastewater or leachates with consideration of the likely mobility of contaminants in surface water and groundwater. The geoenvironmental assessment output can be summarized in the following primary products: (1) a descriptive geoenvironmental model; (2) maps and statistics of variables that indicate the potential for constituents of concern to occur and persist in air, land, surface water, and groundwater within a tract that is geologically permissive for the occurrence of uranium; and (3) tables providing estimated or indicated quantities of waste rock, tailings, wastewater, dust, and radon emissions that could be associated with undiscovered uranium resources, if extracted, for each permissive tract. The uranium geoenvironmental assessment could help natural resource managers to prioritize and (or) identify (1) important potential contaminant pathways, (2) management practices required depending on the types of constituents that could be of concern, (3) areas for response in the event of accidental release, and (4) future directions for study. Furthermore, indicators of rock and water volumes potentially associated with an undiscovered uranium deposit may be evaluated to make quantitative comparisons of water required for uranium production or potential waste products generated during uranium extraction from areas permissive for uranium resource occurrence throughout the United States.

Scientific Investigations Report↗

USGS2006

Dermal insecticide residues from birds inhabiting an orchard

The US Environmental Protection Agency conducts risk assessments of insecticide applications to wild birds using a model that is limited to the dietary route of exposure. However, free-flying birds are also exposed to insecticides via the inhalation and dermal routes. We measured azinphos-methyl residues on the skin plus feathers and the feet of brown-headed cowbirds ( Molothrus ater ) in order to quantify dermal exposure to songbirds that entered and inhabited an apple ( Malus x domestica ) orchard following an insecticide application. Exposure to azinphos-methyl was measured by sampling birds from an aviary that was built around an apple tree. Birds sampled at 36 h and 7-day post-application were placed in the aviary within 1 h after the application whereas birds exposed for 3 days were released into the aviary 4-day post-application. Residues on vegetation and soil were also measured. Azinphos-methyl residues were detected from the skin plus feathers and the feet from all exposure periods. Our results underscore the importance of incorporating dermal exposure into avian pesticide risk assessments.

Environmental Monitoring and Assessment↗

USGS2008

Land use and the structure of western US stream invertebrate assemblages: Predictive models and ecological traits

Inferences drawn from regional bioassessments could be strengthened by integrating data from different monitoring programs. We combined data from the US Geological Survey National Water-Quality Assessment (NAWQA) program and the US Environmental Protection Agency Wadeable Streams Assessment (WSA) to expand the scope of an existing River InVertebrate Prediction and Classification System (RIVPACS)-type predictive model and to assess the biological condition of streams across the western US in a variety of landuse classes. We used model-derived estimates of taxon-specific probabilities of capture and observed taxon occurrences to identify taxa that were absent from sites where they were predicted to occur (decreasers) and taxa that were present at sites where they were not predicted to occur (increasers). Integration of 87 NAWQA reference sites increased the scope of the existing WSA predictive model to include larger streams and later season sampling. Biological condition at 336 NAWQA test sites was significantly (p < 0.001) associated with basin land use and tended to be lower in basins with intensive landuse modification (e.g., mixed, urban, and agricultural basins) than in basins with relatively undisturbed land use (e.g., forested basins). Of the 437 taxa observed among reference and test sites, 180 (41%) were increasers or decreasers. In general, decreasers had a different set of ecological traits (functional traits or tolerance values) than did increasers. We could predict whether a taxon was a decreaser or an increaser based on just a few traits, e.g., desiccation resistance, timing of larval development, habit, and thermal preference, but we were unable to predict the type of basin land use from trait states present in invertebrate assemblages. Refined characterization of traits might be required before bioassessment data can be used routinely to aid in the diagnoses of the causes of biological impairment. ?? 2008 by The North American Benthological Society.

Arizona, California, Colorado, Idaho, Montana, New↗

USGS2014

Assessing and managing freshwater ecosystems vulnerable to global change

Freshwater ecosystems are important for global biodiversity and provide essential ecosystem services. There is consensus in the scientific literature that freshwater ecosystems are vulnerable to the impacts of environmental change, which may trigger irreversible regime shifts upon which biodiversity and ecosystem services may be lost. There are profound uncertainties regarding the management and assessment of the vulnerability of freshwater ecosystems to environmental change. Quantitative approaches are needed to reduce this uncertainty. We describe available statistical and modeling approaches along with case studies that demonstrate how resilience theory can be applied to aid decision-making in natural resources management. We highlight especially how long-term monitoring efforts combined with ecological theory can provide a novel nexus between ecological impact assessment and management, and the quantification of systemic vulnerability and thus the resilience of ecosystems to environmental change.

Florida Everglades↗

USGS2016

A modeling approach to compare ΣPCB concentrations between congener-specific analyses

Changes in analytical methods over time pose problems for assessing long-term trends in environmental contamination by polychlorinated biphenyls (PCBs). Congener-specific analyses vary widely in the number and identity of the 209 distinct PCB chemical configurations (congeners) that are quantified, leading to inconsistencies among summed PCB concentrations (ΣPCB) reported by different studies. Here we present a modeling approach using linear regression to compare ΣPCB concentrations derived from different congener-specific analyses measuring different co-eluting groups. The approach can be used to develop a specific conversion model between any two sets of congener-specific analytical data from similar samples (similar matrix and geographic origin). We demonstrate the method by developing a conversion model for an example data set that includes data from two different analytical methods, a low resolution method quantifying 119 congeners and a high resolution method quantifying all 209 congeners. We used the model to show that the 119-congener set captured most (93%) of the total PCB concentration (i.e., Σ 209 PCB) in sediment and biological samples. ΣPCB concentrations estimated using the model closely matched measured values (mean relative percent difference = 9.6). General applications of the modeling approach include (a) generating comparable ΣPCB concentrations for samples that were analyzed for different congener sets; and (b) estimating the proportional contribution of different congener sets to ΣPCB. This approach may be especially valuable for enabling comparison of long-term remediation monitoring results even as analytical methods change over time.

Integrated Environmental Assessment and Management↗

USGS1996

Uncertainty of groundwater vulnerability assessments for agricultural regions in Hawaii: Review

There are important challenges associated with assessing potential groundwater vulnerability hazards that may result from regional scale applications of agrochemicals. The increasing availability of Geographic Information System (GIS) software to those involved in assisting with landuse decisions has resulted in the widespread production of multicolored risk management maps for many environmentally sensitive issues. Soil-based GIS's have recently been coupled to various solute-leaching models to make near-surface groundwater vulnerability assessments for guidance in pesticide regulation in several states. In general, these assessments rest on soil, climatic, and chemical data that are extremely sparse and contain considerable uncertainty. It is also important to acknowledge the uncertainty associated with the transport/fate processes that are not accounted for by the modeling approach used to make the assessment. In this paper, we review the results from a series of papers that have focused on characterization of uncertainty in pesticide mobility estimates, using the attenuation and retardation indices (AF and RF), for the Pearl Harbor Basin on the Hawaiian island of Oahu. Relative to data error uncertainties, we discuss the impacts of: (i) soil, climatic, and chemical data base uncertainties, (ii) reductions in data base uncertainties, (iii) extrapolation of soil data base information based on soil taxonomy and soil survey, and (iv) importing information from outside the region of interest. Relative to model error uncertainties, we compare pesticide leaching estimates from the simple AF and RF mobility indices with simulations from the EPA's Pesticide Root Zone Model (PRZM) and field observations. Finally, we outline a Regional Integrated Risk Assessment approach for characterizing regional scale groundwater vulnerability for near-surface nonpoint sources.

Hawaii↗

USGS2024

Strong variation in Brook Trout trends across geology, elevation, and stream size in Shenandoah National Park

Objective Landscape context structures fish abundance and dynamics, and understanding trends in fish abundance across the landscape is often prerequisite for effective conservation. In this study, we evaluated the status and trends of Brook Trout Salvelinus fontinalis in Shenandoah National Park to understand how these are structured across bedrock geology, elevation, and stream size. Methods We used long-term monitoring data from 94 sites in Shenandoah National Park to evaluate trends in Brook Trout abundance over a 27-year period (1996–2022) and assess the importance of local environmental covariates using a hierarchical Bayesian N-mixture model based on depletion sampling. Focal covariates were chosen for their demonstrated importance in structuring fish populations in Shenandoah National Park and elsewhere. Bedrock geology controls sensitivity to acid deposition, watershed area is related to stream habitat features such as complexity and flow variability, and elevation creates gradients in temperature. Result Models revealed significant decreases in adult Brook Trout abundance over time (95% credible intervals < 0) for 31 of 94 sites (33%), and at least three sites exhibited apparent extirpations over the study period. Estimated Brook Trout abundance declined by 50% or more in approximately 70% of streams across the park over the study period. Sites with the warmest water temperatures exhibited the fastest declines in abundance. However, large watersheds on poorly buffered bedrock exhibited significant gains in abundance over time, suggesting some recovery from acid deposition due to improvements in air quality. Conclusion Our analysis revealed large and divergent changes in Brook Trout abundance over recent decades and suggests the importance of local water temperature and acid sensitivity as probable causal mechanisms. These results highlight the importance of considering local factors when evaluating long-term trends in stream fish populations. Results of this study can assist the development of targeted conservation actions within Shenandoah National Park and elsewhere.

Virginia↗

USGS2016

The National Wind Erosion Research Network: Building a standardized long-term data resource for aeolian research, modeling and land management

The National Wind Erosion Research Network was established in 2014 as a collaborative effort led by the United States Department of Agriculture’s Agricultural Research Service and Natural Resources Conservation Service, and the United States Department of the Interior’s Bureau of Land Management, to address the need for a long-term research program to meet critical challenges in wind erosion research and management in the United States. The Network has three aims: (1) provide data to support understanding of basic aeolian processes across land use types, land cover types, and management practices, (2) support development and application of models to assess wind erosion and dust emission and their impacts on human and environmental systems, and (3) encourage collaboration among the aeolian research community and resource managers for the transfer of wind erosion technologies. The Network currently consists of thirteen intensively instrumented sites providing measurements of aeolian sediment transport rates, meteorological conditions, and soil and vegetation properties that influence wind erosion. Network sites are located across rangelands, croplands, and deserts of the western US. In support of Network activities, http://winderosionnetwork.org was developed as a portal for information about the Network, providing site descriptions, measurement protocols, and data visualization tools to facilitate collaboration with scientists and managers interested in the Network and accessing Network products. The Network provides a mechanism for engaging national and international partners in a wind erosion research program that addresses the need for improved understanding and prediction of aeolian processes across complex and diverse land use types and management practices.

Arizona, California, Colorado, Idaho, North Dakota↗

USGS2009

Exploring subtle land use and land cover changes: A framework for future landscape studies

Land cover and land use changes can have a wide variety of ecological effects, including significant impacts on soils and water quality. In rural areas, even subtle changes in farming practices can affect landscape features and functions, and consequently the environment. Fine-scale analyses have to be performed to better understand the land cover change processes. At the same time, models of land cover change have to be developed in order to anticipate where changes are more likely to occur next. Such predictive information is essential to propose and implement sustainable and efficient environmental policies. Future landscape studies can provide a framework to forecast how land use and land cover changes is likely to react differently to subtle changes. This paper proposes a four step framework to forecast landscape futures at fine scales by coupling scenarios and landscape modelling approaches. This methodology has been tested on two contrasting agricultural landscapes located in the United States and France, to identify possible landscape changes based on forecasting and backcasting agriculture intensification scenarios. Both examples demonstrate that relatively subtle land cover and land use changes can have a large impact on future landscapes. Results highlight how such subtle changes have to be considered in term of quantity, location, and frequency of land use and land cover to appropriately assess environmental impacts on water pollution (France) and soil erosion (US). The results highlight opportunities for improvements in landscape modelling.

Landscape Ecology↗

USGS2020

Trends in oyster populations in the northeastern Gulf of Mexico: An assessment of river discharge and fishing effects over time and space

Within the Big Bend region of the northeastern Gulf of Mexico, one of the least developed coastlines in the continental USA, intertidal and subtidal populations of eastern oyster Crassostrea virginica (hereafter referred to as “oyster”) are a critical ecosystem and important economic constituent. We assessed trends in intertidal oyster populations, river discharge, and commercial fishing activity in the Suwannee River estuary within the Big Bend region using fisheries‐independent data from irregular monitoring efforts and publicly available environmental data. We used generalized linear models to evaluate counts of oysters from line‐transect surveys over time and space. We assessed model performance using simulation to understand potential bias and then evaluated whether these counts were related to freshwater inputs from the Suwannee River and commercial oyster fishing effort and landings at different time lags. We found that intertidal oyster counts have declined over time and that most of these declines are found in inshore intertidal oyster bars, which are becoming degraded. We also found a significant relationship between oyster counts and a 1‐year lag on mean daily Suwannee River discharge, but including commercial fishery trips or landings did not improve model fit. It is unclear whether declines in intertidal oyster bars are offset by formation of new oyster reefs elsewhere. These results quantify rapid declines in intertidal oyster reefs in a region of coastline with high conservation value that can be used to inform ongoing and proposed restoration projects in the region.

Florida↗

USGS2015

Predicting the risk of toxic blooms of golden alga from cell abundance and environmental covariates

Golden alga ( Prymnesium parvum ) is a toxic haptophyte that has caused considerable ecological damage to marine and inland aquatic ecosystems worldwide. Studies focused primarily on laboratory cultures have indicated that toxicity is poorly correlated with the abundance of golden alga cells. This relationship, however, has not been rigorously evaluated in the field where environmental conditions are much different. The ability to predict toxicity using readily measured environmental variables and golden alga abundance would allow managers rapid assessments of ichthyotoxicity potential without laboratory bioassay confirmation, which requires additional resources to accomplish. To assess the potential utility of these relationships, several a priori models relating lethal levels of golden alga ichthyotoxicity to golden alga abundance and environmental covariates were constructed. Model parameters were estimated using archived data from four river basins in Texas and New Mexico (Colorado, Brazos, Red, Pecos). Model predictive ability was quantified using cross-validation, sensitivity, and specificity, and the relative ranking of environmental covariate models was determined by Akaike Information Criterion values and Akaike weights. Overall, abundance was a generally good predictor of ichthyotoxicity as cross validation of golden alga abundance-only models ranged from &sim; 80% to &sim; 90% (leave-one-out cross-validation). Environmental covariates improved predictions, especially the ability to predict lethally toxic events (i.e., increased sensitivity), and top-ranked environmental covariate models differed among the four basins. These associations may be useful for monitoring as well as understanding the abiotic factors that influence toxicity during blooms.

New Mexico, Texas↗

USGS2005

Contaminants as viral cofactors: assessing indirect population effects

Current toxicological methods often miss contaminant effects, particularly when immune suppression is involved. The failure to recognize and evaluate indirect and sublethal effects severely limits the applicability of those methods at the population level. In this study, the Vitality model is used to evaluate the population level effects of a contaminant exerting only indirect, sublethal effects at the individual level. Juvenile rainbow trout ( Oncorhynchus mykiss ) were injected with 2.5 or 10.0 mg/kg doses of the model CYP1A inducer, β-naphthoflavone (BNF) as a pre-stressor, then exposed to a challenge dose of 10 2 or 10 4 pfu/fish of infectious hematopoietic necrosis virus (IHNV), an important viral pathogen of salmonids in North America. At the end of the 28-d challenge, the mortality data were processed according to the Vitality model which indicated that the correlation between the average rate of vitality loss and the pre-stressor dose was strong: R 2 = 0.9944. Average time to death and cumulative mortality were dependent on the BNF dose, while no significant difference between the two viral dosages was shown, implying that the history of the organism at the time of stressor exposure is an important factor in determining the virulence or toxicity of the stressor. The conceptual framework of this model permits a smoother transfer of results to a more complex stratum, namely the population level, which allows the immunosuppressive results generated by doses of a CYP1A inducer that more accurately represent the effects elicited by environmentally-relevant contaminant concentrations to be extrapolated to target populations. The indirect effects of other environmental contaminants with similar biotransformation pathways, such as polycyclic aromatic hydrocarbons (PAH), could be assessed and quantified with this model and the results applied to a more complex biological hierarchy.

Aquatic Toxicology↗

USGS2023

Banking on strong rural livelihoods and the sustainable use of natural capital in post-conflict Colombia

In post-conflict Colombia, the government has prioritized resettlement of displaced people through development of strong rural livelihoods and the sustainable use of natural capital. In this paper, we considered government proposals for expanding payment for ecosystem services (PES) and sustainable silvopastoral systems, and private-sector investment in habitat banking. We coupled the Integrated Economic-Environmental Model (IEEM) with spatially explicit land use and land cover change and ecosystem services models to assess the potential impacts of these programs through the lens of wealth and sustainable economic development. This innovative workflow integrates dynamic endogenous feedbacks between natural capital, ecosystem services and the economic system, and can be applied to other country contexts. Results show that PES and habitat banking programs are strong investment propositions (Net Present Value of US$4.4 and $4.9 billion, respectively), but only when moving beyond conventional economic analysis to include non-market ecosystem services. Where a portfolio investment approach is taken and PES is implemented with sustainable silvopastoral systems, investment returns would reach US$7.1 billion. This paper provides a detailed evaluation of the benefits of investing in rural livelihoods and enhancing Colombia’s natural capital base, with empirical evidence to inform the spatial targeting of policies to maximize economic, environmental and social outcomes.

Environment, Development, and Sustainability↗

USGS2013

Review of revised Klamath River Total Maximum Daily Load models from Link River Dam to Keno Dam, Oregon

Flow and water-quality models are being used to support the development of Total Maximum Daily Load (TMDL) plans for the Klamath River downstream of Upper Klamath Lake (UKL) in south-central Oregon. For riverine reaches, the RMA-2 and RMA-11 models were used, whereas the CE-QUAL-W2 model was used to simulate pooled reaches. The U.S. Geological Survey (USGS) was asked to review the most upstream of these models, from Link River Dam at the outlet of UKL downstream through the first pooled reach of the Klamath River from Lake Ewauna to Keno Dam. Previous versions of these models were reviewed in 2009 by USGS. Since that time, important revisions were made to correct several problems and address other issues. This review documents an assessment of the revised models, with emphasis on the model revisions and any remaining issues. The primary focus of this review is the 19.7-mile Lake Ewauna to Keno Dam reach of the Klamath River that was simulated with the CE-QUAL-W2 model. Water spends far more time in the Lake Ewauna to Keno Dam reach than in the 1-mile Link River reach that connects UKL to the Klamath River, and most of the critical reactions affecting water quality upstream of Keno Dam occur in that pooled reach. This model review includes assessments of years 2000 and 2002 current conditions scenarios, which were used to calibrate the model, as well as a natural conditions scenario that was used as the reference condition for the TMDL and was based on the 2000 flow conditions. The natural conditions scenario included the removal of Keno Dam, restoration of the Keno reef (a shallow spot that was removed when the dam was built), removal of all point-source inputs, and derivation of upstream boundary water-quality inputs from a previously developed UKL TMDL model. This review examined the details of the models, including model algorithms, parameter values, and boundary conditions; the review did not assess the draft Klamath River TMDL or the TMDL allocations. Attention to the details of a model is one of the best ways to identify potential problems, correct them if possible, and begin to assess the magnitude of potential model errors and uncertainty. Model users need to determine the level of acceptable uncertainty associated with their objectives, identify all sources of potential uncertainty (model uncertainty, data uncertainty, etc.), and assess their approach and results accordingly. In the draft Klamath River TMDL, the Oregon Department of Environmental Quality identified the upstream boundary conditions as the largest source of uncertainty for both the current and natural conditions scenarios, not the model algorithms or choice of model parameters. We agree that the upstream boundary conditions are one of the largest, if not the largest, source of model uncertainty; therefore, the derivation of upstream boundary conditions may be more important to the TMDL than some other model-related issues identified in this review. The revised models contain a number of changes, some of which were done to solve small problems and are largely inconsequential to model results, but others of which are important and affect model predictions of instream concentrations. A consistent version of the model is now applied to all scenarios, and an error in the source code was corrected that had inadvertently discarded 20 percent of the incoming solar radiation in the original model. The baseline light-extinction coefficient for water was decreased and set to a consistent and defensible value across all models of reservoir reaches. Inconsistencies among the values of certain parameters in the original models, such as the ammonia nitrification rate and the decomposition rates of organic matter, have been eliminated, although the reasoning behind the final selections was not documented. The dependence of the rate of sediment oxygen demand (SOD) on temperature was modified such that the SOD rate was substantially decreased at temperatures less than 20°C, causing the model to predict higher dissolved oxygen (DO) concentrations in spring, autumn, and winter. Although that change to the temperature dependence function was done to make the function more similar to the model’s default, this change was not accompanied by any documentation of recalibration or sensitivity exercises. The maximum SOD rate for the 2002 current conditions scenario was decreased from 3.0 grams per square meter per day (g/m 2 /d) in the original model to 2.0 g/m 2 /d in the revised model, a considerable adjustment that appears to have been needed to offset effects of a change to another variable (O2LIM) that would have resulted in a substantial increase in the effective SOD rate for 2002. A 50-percent decrease in the SOD rate over a 2-year period, however, is not likely to be mirrored by field measurements, so this change may be compensating for some process that is not represented correctly in the DO budget for the current conditions scenarios. Several important changes were made to the natural conditions scenario. First, the elevation of the Keno reef was corrected; the elevation specified in the original model was 1 foot too high, which affected the volume of the pooled reach and the travel time through it. The most important changes to this scenario were to the upstream boundary inputs of organic matter and algae, which affect incoming fluxes of nitrogen and phosphorus. Algal biomass inputs were increased by approximately 60 percent during summer because of a change in the way those inputs were derived from results of the UKL TMDL model. Non-algal organic matter inputs were decreased, particularly in summer to correct a problem attributed to double-counting of phosphorus in the original inputs. The distribution of non-algal organic matter was changed from 20 percent dissolved in the original model to 90 percent dissolved in the revised model in response to review comments and published data. The overall sum of algal biomass and non-living organic matter was decreased, which resulted in lower inputs of total phosphorus and nitrogen. Total phosphorus inputs were less than 0.03 mg/L, and although the inputs were derived from selected results of the UKL TMDL model, these concentrations seem too low to be representative of a historically eutrophic system surrounded by extensive wetlands, peat soils, and a groundwater system high in phosphorus. The draft TMDL states that the upstream boundary conditions are the greatest source of uncertainty, greater than any uncertainty associated with the models. Efforts to improve existing models of algal growth and nutrient cycling in UKL, therefore, would provide a substantial benefit to downstream modeling efforts on the Klamath River. Although many improvements were made in revising the Klamath River TMDL models, some issues and uncertainties remain. Several errors in the model source code remain, but do not affect model results for this application as long as certain options and rates are not changed; future users of these models should be aware of these issues. Although the distribution of dissolved and particulate organic matter was modified for the natural conditions scenario, that distribution was not changed for the current conditions scenarios. Recent data on that distribution and the likely rates of organic matter decomposition could be used to improve these models in the future. Nitrate predictions at Keno (Highway 66) still are too high for the current conditions scenarios; future efforts should re-evaluate the model’s denitrification rates and the release rate of ammonia from anoxic sediments. Possibly the most important of the remaining issues are tied to the two-state (healthy/unhealthy) hypothesis for the algae population that was coded into the model. Some of the rates and conversion functions could be refined to make them more acceptable; currently, the published literature does not support the concept of moderately low dissolved-oxygen concentrations as a stressor of algae in the ranges used by the model. More research is needed before these algorithms can be truly tested. The algorithms currently appear to help the model fit the patterns in the available data, and that is useful and perhaps sufficient for some purposes, but those algorithms are not truly predictive or reliable for certain purposes until they can be tested through well-designed experiments and research. In summary, the TMDL models used to simulate Link and Klamath Rivers from Link River Dam to Keno Dam were revised to fix several problems and address various issues. The resulting models are an improvement over those that were reviewed by USGS in 2009, and represent a useful advance in the simulation of a complex system that is difficult to model. However, several issues remain that cause increased uncertainty in the model results. Depending on the objectives of the modeling, now or in the future, these remaining issues could be more or less important. For the Klamath River TMDL, the upstream boundary conditions may be a larger source of uncertainty than the concerns with model algorithms and model parameters identified in this review. Efforts to re-evaluate the available models of algal growth and nutrient cycling in UKL would be highly beneficial to downstream modeling efforts in the Klamath River.

Oregon;California↗

USGS1983

Habitat Suitability Index Models: Lewis' woodpecker

This document is part of the Habitat Suitability Index (HSI) Model Series (FWS/OBS-82/10), which provides habitat information useful for impact assessment and habitat management. Several types of habitat i nformat i on are provided. The Habitat Use Information Section is largely constrained to those data that can be used to derive quantitative relationships between key environmental variables and habitat suitability. The habitat use information provides the foundation for HSI models that follow. In addition, this same information may be useful in the development of other models more appropriate to specific assessment or evaluation needs.

FWS/OBS↗