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At least 127 records · Page 7Linked to original sources
USGS2013

Economic resilience through "One-Water" management

Disruption of water availability leads to food scarcity and loss of economic opportunity. Development of effective water-resource policies and management strategies could provide resiliance to local economies in the face of water disruptions such as drought, flood, and climate change. To accomplish this, a detailed understanding of human water use and natural water resource availability is needed. A hydrologic model is a computer software system that simulates the movement and use of water in a geographic area. It takes into account all components of the water cycle--“One Water”--and helps estimate water budgets for groundwater, surface water, and landscape features. The U.S. Geological Survey MODFLOW One-Water Integrated Hydrologic Model (MODFLOWOWHM) software and scientific methods can provide water managers and political leaders with hydrologic information they need to help ensure water security and economic resilience.

Open-File Report↗
USGS2015

Simulation of groundwater flow and analysis of the effects of water-management options in the North Platte Natural Resources District, Nebraska

The North Platte Natural Resources District (NPNRD) has been actively collecting data and studying groundwater resources because of concerns about the future availability of the highly inter-connected surface-water and groundwater resources. This report, prepared by the U.S. Geological Survey in cooperation with the North Platte Natural Resources District, describes a groundwater-flow model of the North Platte River valley from Bridgeport, Nebraska, extending west to 6 miles into Wyoming. The model was built to improve the understanding of the interaction of surface-water and groundwater resources, and as an optimization tool, the model is able to analyze the effects of water-management options on the simulated stream base flow of the North Platte River. The groundwater system and related sources and sinks of water were simulated using a newton formulation of the U.S. Geological Survey modular three-dimensional groundwater model, referred to as MODFLOW–NWT, which provided an improved ability to solve nonlinear unconfined aquifer simulations with wetting and drying of cells. Using previously published aquifer-base-altitude contours in conjunction with newer test-hole and geophysical data, a new base-of-aquifer altitude map was generated because of the strong effect of the aquifer-base topography on groundwater-flow direction and magnitude. The largest inflow to groundwater is recharge originating from water leaking from canals, which is much larger than recharge originating from infiltration of precipitation. The largest component of groundwater discharge from the study area is to the North Platte River and its tributaries, with smaller amounts of discharge to evapotranspiration and groundwater withdrawals for irrigation. Recharge from infiltration of precipitation was estimated with a daily soil-water-balance model. Annual recharge from canal seepage was estimated using available records from the Bureau of Reclamation and then modified with canal-seepage potentials estimated using geophysical data. Groundwater withdrawals were estimated using land-cover data, precipitation data, and published crop water-use data. For fields irrigated with surface water and groundwater, surface-water deliveries were subtracted from the estimated net irrigation requirement, and groundwater withdrawal was assumed to be equal to any demand unmet by surface water. The groundwater-flow model was calibrated to measured groundwater levels and stream base flows estimated using the base-flow index method. The model was calibrated through automated adjustments using statistical techniques through parameter estimation using the parameter estimation suite of software (PEST). PEST was used to adjust 273 parameters, grouped as hydraulic conductivity of the aquifer, spatial multipliers to recharge, temporal multipliers to recharge, and two specific recharge parameters. Base flow of the North Platte River at Bridgeport, Nebraska, streamgage near the eastern, downstream end of the model was one of the primary calibration targets. Simulated base flow reasonably matched estimated base flow for this streamgage during 1950–2008, with an average difference of 15 percent. Overall, 1950–2008 simulated base flow followed the trend of the estimated base flow reasonably well, in cases with generally increasing or decreasing base flow from the start of the simulation to the end. Simulated base flow also matched estimated base flow reasonably well for most of the North Platte River tributaries with estimated base flow. Average simulated groundwater budgets during 1989–2008 were nearly three times larger for irrigation seasons than for non-irrigation seasons. The calibrated groundwater-flow model was used with the Groundwater-Management Process for the 2005 version of the U.S. Geological Survey modular three-dimensional groundwater model, MODFLOW–2005, to provide a tool for the NPNRD to better understand how water-management decisions could affect stream base flows of the North Platte River at Bridgeport, Nebr., streamgage in a future period from 2008 to 2019 under varying climatic conditions. The simulation-optimization model was constructed to analyze the maximum increase in simulated stream base flow that could be obtained with the minimum amount of reductions in groundwater withdrawals for irrigation. A second analysis extended the first to analyze the simulated base-flow benefit of groundwater withdrawals along with application of intentional recharge, that is, water from canals being released into rangeland areas with sandy soils. With optimized groundwater withdrawals and intentional recharge, the maximum simulated stream base flow was 15–23 cubic feet per second (ft 3 /s) greater than with no management at all, or 10–15 ft 3 /s larger than with managed groundwater withdrawals only. These results indicate not only the amount that simulated stream base flow can be increased by these management options, but also the locations where the management options provide the most or least benefit to the simulated stream base flow. For the analyses in this report, simulated base flow was best optimized by reductions in groundwater withdrawals north of the North Platte River and in the western half of the area. Intentional recharge sites selected by the optimization had a complex distribution but were more likely to be closer to the North Platte River or its tributaries. Future users of the simulation-optimization model will be able to modify the input files as to type, location, and timing of constraints, decision variables of groundwater withdrawals by zone, and other variables to explore other feasible management scenarios that may yield different increases in simulated future base flow of the North Platte River.

Nebraska↗
USGS2024

Applying local and global sensitivity analysis to inform bigheaded carp management

Natural resource managers commonly use population-level models to aid in understanding the status of target populations or the potential implications of management actions. Sensitivity analyses, specifically, local sensitivity analysis (LSA) and global sensitivity analysis (GSA), exist as tools to improve understanding of these models, the importance of specific parameters to model outcomes, and the assumptions inherent to population models. However, applied ecology and closely related fields such as fisheries and wildlife management rarely use LSA or GSA. Here, we apply LSA and GSA to the Spatially Explicit Invasive Carp Population model, a population-level model used to simulate and compare management actions on the invasive silver carp Hypophthalmichthys molitrix population in the Illinois River. The spatial structure within the model arises from the navigation “pools” that exist between navigation lock-and-dam in the river and are also the focus of fisheries management in the river. We completed LSA and GSA for two reasons: (1) to compare the importance of model input parameters on simulated model populations and (2) to demonstrate the use of LSA and GSA for population-level models. In our system, the most important sensitivity values generally were detected in Alton Pool. This pool is the farthest downstream and includes a direct connection with the Mississippi River. These results indicate that the population dynamics in Alton Pool are an important driver of the population dynamics throughout the Illinois River. Management outcomes in this system will, therefore, be strongly dependent on the dynamics of Alton Pool.

Illinois↗
USGS2013

Analyzing the water budget and hydrological characteristics and responses to land use in a monsoonal climate river basin in South China

Hydrological models have been increasingly used by hydrologists and water resource managers to understand natural processes and human activities that affect watersheds. In this study, we use the physically based model, Soil and Water Assessment Tool (SWAT), to investigate the hydrological processes in the East River Basin in South China, a coastal area dominated by monsoonal climate. The SWAT model was calibrated using 8-year (1973–1980) record of the daily streamflow at the basin outlet (Boluo station), and then validated using data collected during the subsequent 8 years (1981–1988). Statistical evaluation shows that SWAT can consistently simulate the streamflow of the East River with monthly Nash–Sutcliffe efficiencies of 0.93 for calibration and 0.90 for validation at the Boluo station. We analyzed the model simulations with calibrated parameters, presented the spatiotemporal distribution of the key hydrological components, and quantified their responses to different land uses. Watershed managers can use the results of this study to understand hydrological features and evaluate water resources of the East River in terms of sustainable development and effective management.

Environmental Management↗
USGS2016

Report from the workshop on climate downscaling and its application in high Hawaiian Islands, September 16–17, 2015

In the subtropical and tropical Pacific islands, changing climate is predicted to influence precipitation and freshwater availability, and thus is predicted to impact ecosystems goods and services available to ecosystems and human communities. The small size of high Hawaiian Islands, plus their complex microlandscapes, require downscaling of global climate models to provide future projections of greater skill and spatial resolution. Two different climate modeling approaches (physics-based dynamical downscaling and statistics-based downscaling) have produced dissimilar projections. Because of these disparities, natural resource managers and decision makers have low confidence in using the modeling results and are therefore are unwilling to include climate-related projections in their decisions. In September 2015, the Pacific Islands Climate Science Center (PICSC), the Pacific Islands Climate Change Cooperative (PICCC), and the Pacific Regional Integrated Sciences and Assessments (Pacific RISA) program convened a 2-day facilitated workshop in which the two modeling teams, plus key model users and resource managers, were brought together for a comparison of the two approaches, culminating with a discussion of how to provide predictions that are useable by resource managers. The proceedings, discussions, and outcomes of this Workshop are summarized in this Open-File Report.

Hawaii↗
USGS1992

Applying the DRASTIC model: A review of county-scale maps

The potential for contamination of ground water has become an issue of great concern to citizens and government alike, especially in the last decade. Numerous methods for assessing ground water vulnerability to contamination have been proposed and used. These methods include field-scale deterministic models that predict the rate of migration and fate of specific chemicals, and regional models that attempt to show general trends of ground-water vulnerability to contamination. To address regional ground-water vulnerability, a model may focus on characteristics of the soil, the geologic materials at and above the unconfined water table, or on the larger part of the geologic framework containing aquifers. A model's focus significantly affects the characteristics and, therefore, the utility of maps produced by the model. For example, a model focusing on the soil or vadose zone cannot adequately characterize the contamination potential of confined aquifers. The most commonly-used method for regional assessment of ground-water vulnerability is called "DRASTIC," an acronym for the seven factors that comprise the model. The U.S. Environmental Protection Agency (EPA) funded the development of the DRASTIC model, and used it as an assessment tool in their National Pesticide Survey (USEPA, 1992). In that Survey and in other assessments of contamination potential from application of agricultural chemicals, the pesticide version of DRASTIC is used; it differs from the standard DRASTIC model only in the degree of weighting applied to the seven factors. The DRASTIC model was developed by a committee of technical advisors who used the consensus approach to specify the relative significance of each factor. DRASTIC was designed primarily as a regional tool for prioritization, or screening, to indicate those areas (of at least 100 acres in size) which are generally more sensitive to contamination and, therefore, in need of more detailed mapping and evaluation or monitoring. The committee also intended DRASTIC to support decisions on allocation of scarce monitoring or remediation resources, and to serve as an educational tool. A detailed user's manual for the DRASTIC model (Aller and others, 1987) provides descriptions of common hydrogeologic settings across the United States and expected values for the seven factors; with this information and a specified "weight" or multiplier applied to each factor, DRASTIC scores can be calculated for each setting, and a map of these DRASTIC scores generated. The authors of this model intended it to be sufficiently objective and straightforward that it could be effectively used by persons with but a rudimentary knowledge of hydrogeologic principles. By adjusting the factor values for actual or interpreted local conditions a more knowledgeable person should be able to produce a somewhat more realistic and, therefore, reliable map of ground water vulnerability than by using the expected values for a given hydrogeologic setting. Other models for evaluating regional ground-water vulnerability have been developed; of importance to this report are models developed by the Illinois State Geological Survey (ISGS), the Wisconsin Department of Natural Resources (WDNR), and a joint effort between the U.S. Geological Survey (USGS) and the ISGS. All of these models, as well as DRASTIC, can only estimate the relative potential for contamination, either with a numerical scheme or a hierarchy of contamination potential map units. The model developed by WDNR (Schmidt, 1987) is based on a numerical scheme. It was used to generate a statewide contamination susceptibility map of Wisconsin (Schmidt and Kessler, 1987); Schmidt (1987) defines ground water contamination susceptibility as the "ease with which water (and, presumably, accompanying contaminants) at the surface can reach the water table." The model is not limited to aquifers, but rather considers all unconfined ground water whether in sandy surface aquifers or low-permeability glacial till. Ground water in confined aquifers is not addressed. Contamination susceptibility is estimated by a factor-weighting scheme similar in concept to DRASTIC. The factors (soil texture, surficial deposits, depth to the water table, bedrock type, and glacial drift thickness) are assigned an arbitrary value based on perceived importance. These numbers are then weighted and summed to produce the contamination susceptibility score. This model is compared and contrasted with other models elsewhere in this report. In contrast, the ISGS method avoids the use of a numerical system to rate the relative potential for contamination, and instead orders the map units in a hierarchy from relatively low to relatively high contamination potential. Also in contrast to the two models described above, the ISGS model relies solely on the textural character of the geologic framework to a specified depth. For example, a statewide map (Berg and others, 1984, scale 1:500,000) addresses land burial of wastes, and shows the contamination potential of aquifers within geologic units in the upper 50 feet. Another ISGS map (Keefer and Berg, 1990) addresses the contamination of major, economically important aquifers, and therefore evaluates contamination potential to greater depths (to greater than 300 feet). In general, the ISGS contamination potential maps evaluate aquifers, and do not evaluate potential for contamination of ground water at the unconfined water table in geologic units that are not aquifers (for example, in glacial till or finegrained lake sediments). With the cooperation of the ISGS, I have developed a model (Seller and Berg, in press) for the regional assessment of aquifer contamination potential that is based on ISGS techniques, adapted to a broader map area where detailed information may be unavailable. This model was used to generate a map of aquifer contamination potential for an area encompassing parts of five states near southern Lake Michigan and Lake Erie; the model and map are currently being refined and evaluated.

Open-File Report↗
USGS2026

Regional conservation planning tool: A spreadsheet model to support spatial prioritization and resource allocation decisions

Prioritization is a central component of natural resource management because conservation needs routinely exceed available resources. Waterfowl and wetland conservation programs in North America are at the forefront of landscape-scale prioritization and transboundary management decisions due to the migratory nature of ducks, geese, and swans. The growing availability of geographic information systems (GIS) and geospatial technologies has accelerated the development of multi-objective landscape prioritization models, including applications of structured decision making and multi-criteria decision analysis to spatial planning for waterfowl and wetlands at the continental scale. However, regional managers and conservationists could benefit from flexibility in downscaling continental tools, selecting objectives, and assigning weights for rapid production of spatial prioritization models at smaller spatial scales without extensive computer coding or GIS analysis. We developed a spatial value model that prioritizes landscapes at sub-continental scales (e.g., states and provinces, bird conservation regions, etc.) and provides flexibility for users to select waterfowl conservation objectives of interest and weights. Our model can be used for direct downscaling of an existing continental geospatial model or further customized with region-specific geospatial data. We illustrate how regional prioritization can vary with the spatial scale selected by the user. The spatial value modeling framework and the downscaling tool presented here could increase the use of multi-criteria decision analysis and linear value modeling in spatial landscape prioritization, while also providing flexibility for selecting scales, objectives, and weights. Our spreadsheet tool was developed specifically for use by regional biologists, conservationists, and managers and does not require knowledge of GIS software (although results can be exported from the spreadsheet for spatial analysis using GIS). Together, the model outputs and the accompanying spreadsheet tool provide a bridge between continental waterfowl conservation and regional implementation, enabling rapid, stakeholder-driven, value-explicit prioritization.

Wildlife Society Bulletin↗
USGS2019

Selecting ecological models using multi-objective optimization

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

Ecological Modelling↗
USGS2012

Evaluation of Bayesian estimation of a hidden continuous-time Markov chain model with application to threshold violation in water-quality indicators

Natural resource managers require information concerning the frequency, duration, and long-term probability of occurrence of water-quality indicator (WQI) violations of defined thresholds. The timing of these threshold crossings often is hidden from the observer, who is restricted to relatively infrequent observations. Here, a model for the hidden process is linked with a model for the observations, and the parameters describing duration, return period, and long-term probability of occurrence are estimated using Bayesian methods. A simulation experiment is performed to evaluate the approach under scenarios based on the equivalent of a total monitoring period of 5-30 years and an observation frequency of 1-50 observations per year. Given constant threshold crossing rate, accuracy and precision of parameter estimates increased with longer total monitoring period and more-frequent observations. Given fixed monitoring period and observation frequency, accuracy and precision of parameter estimates increased with longer times between threshold crossings. For most cases where the long-term probability of being in violation is greater than 0.10, it was determined that at least 600 observations are needed to achieve precise estimates. An application of the approach is presented using 22 years of quasi-weekly observations of acid-neutralizing capacity from Deep Run, a stream in Shenandoah National Park, Virginia. The time series also was sub-sampled to simulate monthly and semi-monthly sampling protocols. Estimates of the long-term probability of violation were unbiased despite sampling frequency; however, the expected duration and return period were over-estimated using the sub-sampled time series with respect to the full quasi-weekly time series.

Journal of Environmental Informatics↗
USGS2025

Suspended sediment and fisheries: An exploration of empirical relationships

Objective: Sediment has an important role in aquatic ecosystems, however, excess sediment can negatively impact fish and other aquatic life. Quantifying the response of aquatic life, particularly fish, to suspended sediment is important for natural resource managers tasked with developing sediment management guidelines to protect aquatic ecosystems. Our goal was to assess the ability of established, revised, and alternate severity of ill effect (SEV) dose-response models to predict the impact of suspended sediment on fish. Methods: We synthesized existing literature to develop an expansive dataset that relates suspended sediment concentration and exposure duration to biological effects on fishes and assessed the predictive ability of established and revised SEV dose-response models. We investigated potential sources of variation in biological responses to suspended sediment dose and explored two alternative approaches for assessing the effects of suspended sediment on fish: 90th quantile SEV dose-response regression models and logistic SEV dose-response models. Results: We found that both established and revised linear SEV dose-response models poorly quantify fish biological response to suspended sediment. Quantile SEV dose-response regressions also performed poorly. More promising are logistic dose-response models that identify sediment thresholds where major effects of sediment on fish can be expected to occur. We demonstrate that fish biological response to suspended sediment is modulated by sediment particle size, water temperature, and dissolved oxygen levels, suggesting additional environmental and biological variables to consider when evaluating the effects of suspended sediment on fish. Conclusion: We contribute revised and novel empirically derived tools for predicting the effects of suspended sediment on fish and demonstrate how environmental variables and life stage may modulate fish biological response. Our work illustrates challenges associated with predictive modeling and some potential sources of variation. While empirical models integrating biological stress response to suspended sediment may help natural resource managers capture potential impacts of this stressor, a cautious approach that considers co-acting stressors may be most effective for sediment management that is protective of fish.

North American Journal of Fisheries Management↗
USGS2014

Using ecological indicators and a decision support system for integrated ecological assessment at two national park units in the Mid-Atlantic region, U.S.A.

We implemented an integrated ecological assessment using a GIS-based decision support system model for Upper Delaware Scenic and Recreational River (UPDE) and Delaware Water Gap National Recreation Area (DEWA)—national park units with the mid-Atlantic region of the United States. Our assessment examined a variety of aquatic and terrestrial indicators of ecosystem components that reflect the parks’ conservation purpose and reference condition. Our assessment compared these indicators to ecological thresholds to determine the condition of park watersheds. Selected indicators included chemical and physical measures of water quality, biologic indicators of water quality, and landscape condition measures. For the chemical and physical measures of water quality, we used a water quality index and each of its nine components to assess the condition of water quality in each watershed. For biologic measures of water quality, we used the Ephemeroptera, Plecoptera, Trichoptera aquatic macroinvertebrate index and, secondarily, the Hilsenhoff aquatic macroinvertebrate index. Finally, for the landscape condition measures of our model, we used percent forest and percent impervious surface. Based on our overall assessment, UPDE and DEWA watersheds had an ecological assessment score of 0.433 on a −1 to 1 fuzzy logic scale. This score indicates that, in general, the natural resource condition within watersheds at these parks is healthy or ecologically unimpaired; however, we had only partial data for many of our indicators. Our model is iterative and new data may be incorporated as they become available. These natural parks are located within a rapidly urbanizing landscape—we recommend that natural resource managers remain vigilant to surrounding land uses that may adversely affect natural resources within the parks.

New Jersey, New York, Pennsylvania↗
USGS2010

Modeling Climate Change and Sturgeon Populations in the Missouri River

The U.S. Geological Survey (USGS) Columbia Environmental Research Center (CERC), in collaboration with researchers from the University of Missouri and Iowa State University, is conducting research to address effects of climate change on sturgeon populations (Scaphirhynchus spp.) in the Missouri River. The CERC is conducting laboratory, field, and modeling research to identify causative factors for the responses of fish populations to natural and human-induced environmental changes and using this information to understand sensitivity of sturgeon populations to potential climate change in the Missouri River drainage basin. Sturgeon response information is being used to parameterize models predicting future population trends. These models will provide a set of tools for natural resource managers to assess management strategies in the context of global climate change. This research complements and builds on the ongoing Comprehensive Sturgeon Research Program (CSRP) at the CERC. The CSRP is designed to provide information critical to restoration of the Missouri River ecosystem and the endangered pallid sturgeon (S. albus). Current research is being funded by USGS through the National Climate Change Wildlife Science Center (NCCWSC) and the Science Support Partnership (SSP) Program that is held by the USGS and the U.S. Fish and Wildlife Service. The national mission of the NCCWSC is to improve the capacity of fish and wildlife agencies to respond to climate change and to address high-priority climate change effects on fish and wildlife. Within the national context, the NCCWSC research on the Missouri River focuses on temporal and spatial downscaling and associated uncertainty in modeling climate change effects on sturgeon species in the Missouri River. The SSP research focuses on improving survival and population estimates for pallid sturgeon population models.

Fact Sheet↗
USGS1997

Water and salt balance of Great Salt Lake, Utah, and simulation of water and salt movement through the causeway

The water and salt balance of Great Salt Lake primarily depends on the amount of inflow from tributary streams and the conveyance properties of a causeway constructed during 1957-59 that divides the lake into the south and north parts. The conveyance properties of the causeway originally included two culverts, each 15 feet wide, and the permeable rock-fill material. During 1980-86, the salt balance changed as a result of record high inflow that averaged 4,627,000 acre-feet annually and modifications made to the conveyance properties of the causeway that included opening a 300-foot-wide breach. In this study, a model developed in 1973 by Waddell and Bolke to simulate the water and salt balance of the lake was revised to accommodate the high water-surface altitude and modifications made to the causeway. This study, done by the U.S. Geological Survey in cooperation with the Utah Department of Natural Resources, Division of State Lands and Forestry, updates the model with monitoring data collected during 1980-86. This report describes the calibration of the model and presents the results of simulations for three hypothetical 10-year periods. During January 1, 1980, to July 31, 1984, a net load of 0.5 billion tons of dissolved salt flowed from the south to the north part of the lake primarily as a result of record inflows. From August 1, 1984, when the breach was opened, to December 31,1986, a net load of 0.3 billion tons of dissolved salt flowed from the north to the south part of the lake primarily as a result of the breach. For simulated inflow rates during a hypothetical 10-year period resulting in the water-surface altitude decreasing from about 4,200 to 4,192 feet, there was a net movement of about 1.0 billion tons of dissolved salt from the south to the north part, and about 1.7 billion tons of salt precipitated in the north part. For simulated inflow rates during a hypothetical 10-year period resulting in a rise in water-surface altitude from about 4,200 to 4,212 feet, there was a net movement of about 0.2 billion tons of dissolved salt from the south to the north part and no salt was precipitated in the north part of the lake.

Utah↗
USGS2021

Evaluation of hydrologic simulation models for fields with subsurface drainage to mitigated wetlands in Barnes, Dickey, and Sargent Counties, North Dakota

Proper identification of wetlands, along with a better understanding of the hydrology of mitigated wetlands, is needed to assist with conservation efforts aimed at maintaining the productivity and ecological function (wetland mitigation) of agricultural lands. The U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Natural Resources Conservation Service, completed a study to evaluate two models for simulating hydrologic conditions in fields with subsurface drainage to mitigated wetlands at several sites in North Dakota. These two models were evaluated as possible tools for water resource managers to use for designing wetland mitigation projects in the area in the future. The Soil-Plant-Atmosphere-Water (SPAW) model simulates the daily hydrologic water budgets of agricultural landscapes by two linked routines, one for farm fields (field hydrology) and one for impoundments such as wetlands and ponds (pond model). The DRAINMOD model was used in conjunction with the SPAW model because although the SPAW model can be used to simulate the hydrology of small drainage basins containing wetlands, the SPAW model does not contain routines to simulate drainage, either subsurface drainage or surface (drainage ditches), that can directly affect the wetland hydrology. The wetlands in the study areas in this report are all downstream from and adjacent to drained agricultural fields. SPAW and DRAINMOD models were developed and calibrated at three study areas (study areas B, D, and S) to evaluate how the models simulated field-scale hydrologic characteristics and the water balance in wetlands from January 1, 2003, through December 31, 2018. The SPAW model developed for study area B included five modeled fields in the field hydrology portion of SPAW that contributed inflow to one wetland simulated in the pond model portion of SPAW. Simulated wetland water depths were most similar to water depths measured at site BWET1, with an absolute mean error of 0.10 foot and a root mean square error of 0.14 foot. Site BWET2 had slightly larger errors, with an absolute mean error of 0.22 foot and a root mean square error of 0.28 foot. Simulated water depths were similar to the pattern of measured water depths at BWET1 and BWET2 from about mid-April 2018 through about mid-September 2018, but overpredicted water depths in the fall from about mid-September 2018 through about mid-October 2018. The SPAW model developed for study area D included six modeled fields in the field hydrology portion of SPAW that contributed inflow to five wetlands connected in series in the pond model portion of SPAW. Simulated water depths compared relatively well to water depths in the five wetlands, with the absolute mean error ranging from 0.17 foot (DWET1) to 0.39 foot (DWET2), and the root mean square error ranging from 0.28 foot (DWET1) to 0.56 foot (DWET5). The SPAW model developed for study area S included one modeled field in the field hydrology portion of SPAW that contributed inflow to one wetland in the pond model portion of SPAW. Among the SPAW models developed for the three study areas, the model for study area S had the best comparison between simulated and measured water depths, with an absolute mean error of 0.06 foot and a root mean square error of 0.10 foot. DRAINMOD models were developed and calibrated at the three study areas and provided inflow from subsurface drainage discharge to the SPAW models for simulating water levels in wetlands in the study areas. The calibrated DRAINMOD model for study area B showed the variability of hydrologic processes in the modeled field throughout the wide range of hydrologic conditions from January 1, 2003, through December 31, 2018. In general, the discharge through the modeled subsurface drainage system was in the spring and early summer (April through June) most years, with little to no discharge later in the year. Although the subsurface drainage system in study area D was the most complex among the three study areas and was simplified into a uniform system within DRAINMOD, simulated water table depths at study area D correlated better to measured water table depths compared to results from the model applications at the other two study areas. Simulated water table depths had an absolute mean error of 0.30 foot and root mean square error of 0.37 foot at site DGW1 and an absolute mean error of 0.29 foot and a root mean square error of 0.34 foot at site DGW2. Although the subsurface drainage system in study area S was the simplest and the modeled field was the smallest among the three study areas, simulated water table depths at study area S did not correlate as well to measured water table depths compared to results from the model applications at the other two study areas. The SPAW and DRAINMOD model applications at the three study areas in southeast North Dakota adequately simulated the hydrologic processes for fields with subsurface drainage that are connected to adjacent wetlands. However, more measured data would be needed to fully evaluate the models throughout the range of possible climatic conditions.

North Dakota↗
USGS1999

Giant panda (Ailuropoda melanoleuca) population dynamics and bamboo (subfamily Bambusoideae) life history: a structured population approach to examining carrying capacity when the prey are semelparous

The giant panda, Ailuropoda melanoleuca, is a highly specialized Ursid whose diet consists almost entirely of various species of bamboo. Bamboo (Bambusoideae) is a grass subfamily whose species often exhibit a synchronous semelparity. Synchronous semelparity can create local drops in carrying capacity for the panda. We modeled the interaction of pandas and their bamboo food resources with an age structured panda population model linked to a natural history model of bamboo biomass dynamics based on literature values of bamboo biomass, and giant panda life history dynamics. This paper reports the results of our examination of the interaction between pandas and their bamboo food resource and its implications for panda conservation. In the model all panda populations were well below the carrying capacity of the habitat. The giant panda populations growth was most sensitive to changes in birth rates and removal of reproductive aged individuals. Periodic starvation that has been documented in conjunction with bamboo die-offs is probably related to the inability to move to other areas within the region where bamboo is still available. Based on the results of this model, giant panda conservation should concentrate on keeping breeding individuals in the wild, keep corridors to different bamboo species open to pandas, and to concentrate research on bamboo life history.

Ecological Modelling↗
USGS2025

Integrating refugia modeling and decision science to support climate-adaptive conservation in the Sierra Nevada

To reduce detrimental impacts of anthropogenic change, natural resource managers often look for place-based solutions to minimize biodiversity loss. Climate-change refugia, areas buffered from contemporary climate change, can enable the persistence of valued natural resources and prolong the benefits of conservation action. Here we combine climate-change refugia modeling with structured decision-making to inform conservation decisions for the endangered foothill yellow-legged frog ( Rana boylii ) in the Sierra Nevada region of California, USA. We used an ensemble of species distribution models to identify areas projected to remain suitable into the 2040s and the 2080s under an RCP 8.5 emissions scenario, as well as areas projected to transition to suitable habitat during this time. We integrated these projections with a structured decision-making process to align management strategies with refugia model outcomes for R. boylii in a subset of the study area. Habitat suitability for R. boylii is projected to decline in the study area by over 90% by the 2040s and by a subsequent 15% by the 2080s. Climate-change refugia are projected to occupy ~7% of present-day suitable habitat, with high agreement between GCMs and model timesteps. Areas projected to transition to suitable habitat within the existing R. boylii clade boundaries are negligible. Collectively, climate-change refugia modeling and structured decision-making provide opportunities to improve resource allocation and empower conservation practitioners in climate change adaptation for at-risk species.

California↗
USGS2020

Simulation of discharge, water-surface elevations, and water temperatures for the St. Louis River estuary, Minnesota-Wisconsin, 2016–17

The St. Louis River estuary is a large freshwater estuary, next to Duluth, Minnesota, that encompasses the headwaters of Lake Superior. The St. Louis River estuary is one of the most complex and compromised near-shore systems in the upper Great Lakes with a long history of environmental contamination caused by logging, mining, paper mills, and other heavy industrial activities. Presently (2020), a widely available, science-based assessment tool capable of evaluating ecosystem-level responses to remediation and restoration projects has not existed for the estuary. To address this need, the U.S. Geological Survey (USGS) built a predictive, mechanistic, three-dimensional hydrodynamic model for the estuary using the Environmental Fluid Dynamics Code framework. In the current version, the model can simulate continuous discharge, water-surface elevations, water temperature, and flow velocity, although the modular framework allows for future additions of water-quality modeling. The model was calibrated using data collected from April 2016 through November 2016 and validated with data collected from April 2017 through November 2017. The four types of data used to evaluate model performance were water-surface elevations, discharge, water temperature, and flow velocities. Streamflow and temperature boundary condition data included a mixture of USGS streamgage data, Minnesota Department of Natural Resources gage data, and estimates derived from the gage data. The model was able to simulate the water-surface elevations with generally good agreement between the simulated and measured values for both years at the daily time step. Specifically, the model was able to demonstrate excellent agreement with the measured data with Nash-Sutcliffe efficiency coefficients greater than 0.8 for all three locations; however, the model was unable to produce hourly water-surface elevations with such accuracy for 2016–17. Discharge was more dynamic than the water-surface elevations, both for the measured and simulated data. Generally, most of the discharge ranged from −650 to 1,200 cubic meters per second, but the constantly changing flux exiting the estuary into Lake Superior (positive flows) and entering the estuary from Lake Superior (negative flows) occurred throughout the year. Even upstream at the St. Louis River at Oliver, Wisconsin, gage (USGS station 0402403250), the effect of flows into the estuary from Lake Superior did occur, demonstrating the strong effect of the Lake Superior seiche on flows for the estuary. From a performance standpoint, the model was able to simulate discharge with generally good agreement in both years, although the 2017 validation was better than the 2016 calibration period. For the daily Nash-Sutcliffe efficiency coefficients, the simulated values were 0.98, 0.62, 0.49, and 0.71 for the Oliver gage; the Superior Bay entry channel at Superior, Wisc., (USGS station 464226092005600); the Superior Bay Duluth Ship Canal at Duluth, Minn., (USGS station 464646092052900); and total entries (combination of the Superior entry and Duluth entry), respectively. For the hourly evaluation criteria, the model performed poorly, with Nash-Sutcliffe efficiency coefficients less than 0 for the two entries into Lake Superior; therefore, as a predictor of discharge at the hourly scale, the model performed worse than using the measured data average. Similar to discharge, the model was a good predictor of flow velocity at the daily time scale but had difficulty matching the measured data at the hourly scale. For discharge and flow velocity, matching at subdaily time steps for a system as complicated as the St. Louis River estuary is considered difficult because the match is highly sensitive to coordinating the exact measurement location to the simulated value. The final calibration target was water temperature, calibrated for the Oliver gage and the Duluth entry. For calibration purposes, the Duluth entry was the more important water temperature target because the Oliver gage was more of an internal check on the model. The Nash-Sutcliffe efficiency coefficients for the Duluth entry were high; hourly Nash-Sutcliffe efficiency coefficients at the Duluth entry were either at or greater than 0.7 for both years, and daily values were 0.84 and 0.82 for 2016 and 2017, respectively.

Minnesota, Wisconsin↗
USGS2023

The 3D Elevation Program—Supporting Maine’s economy

Introduction Inland flooding, sea-level rise, and pollution pose challenges for Maine’s infrastructure and natural resources. A highly detailed, three-dimensional (3D) model of the Earth’s surface is allowing the State of Maine to address these challenges in an increasingly comprehensive and timely manner. In addition, highly accurate elevation data facilitate land development, forest management, agricultural practices, and wildlife conservation, all of which are key pillars of Maine’s economy. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed 3D model of the Earth’s surface and aboveground features.

Maine↗