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At least 415 records · Page 23Linked to original sources
USGS2014

Hydrogeology and simulation of groundwater flow in the Central Oklahoma (Garber-Wellington) Aquifer, Oklahoma, 1987 to 2009, and simulation of available water in storage, 2010–2059

The Central Oklahoma (Garber-Wellington) aquifer underlies about 3,000 square miles of central Oklahoma. The study area for this investigation was the extent of the Central Oklahoma aquifer. Water from the Central Oklahoma aquifer is used for public, industrial, commercial, agricultural, and domestic supply. With the exception of Oklahoma City, all of the major communities in central Oklahoma rely either solely or partly on groundwater from this aquifer. The Oklahoma City metropolitan area, incorporating parts of Canadian, Cleveland, Grady, Lincoln, Logan, McClain, and Oklahoma Counties, has a population of approximately 1.2 million people. As areas are developed for groundwater supply, increased groundwater withdrawals may result in decreases in long-term aquifer storage. The U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, investigated the hydrogeology and simulated groundwater flow in the aquifer using a numerical groundwater-flow model. The purpose of this report is to describe an investigation of the Central Oklahoma aquifer that included analyses of the hydrogeology, hydrogeologic framework of the aquifer, and construction of a numerical groundwater-flow model. The groundwater-flow model was used to simulate groundwater levels and for water-budget analysis. A calibrated transient model was used to evaluate changes in groundwater storage associated with increased future water demands.

Oklahoma↗
USGS2009

DayCent-Chem simulations of ecological and biogeochemical processes of eight mountain ecosystems in the United States

Atmospheric deposition of nitrogen (N) and sulfur (S) cause complex responses in ecosystems, from fertilization to forest ecosystem decline, freshwater eutrophication to acidification, loss of soil base cations, and alterations of disturbance regimes. DayCent-Chem, an ecosystem simulation model that combines ecosystem nutrient cycling and plant dynamics with aqueous geochemical equilibrium calculations, was developed to address ecosystem responses to combined atmospheric N and S deposition. It is unique among geochemically-based models in its dynamic biological cycling of N and its daily timestep for investigating ecosystem and surface water chemical response to episodic events. The model was applied to eight mountainous watersheds in the United States. The sites represent a gradient of N deposition across locales, from relatively pristine to N-saturated, and a variety of ecosystem types and climates. Overall, the model performed best in predicting stream chemistry for snowmelt-dominated sites. It was more difficult to predict daily stream chemistry for watersheds with deep soils, high amounts of atmospheric deposition, and a large degree of spatial heterogeneity. DayCent-Chem did well in representing plant and soil carbon and nitrogen pools and fluxes. Modeled stream nitrate (NO 3 - ) and ammonium (NH 4 + ) concentrations compared well with measurements at all sites, with few exceptions. Simulated daily stream sulfate (SO 4 2- ) concentrations compared well to measured values for sites where SO 4 2- deposition has been low and where SO 4 2- adsorption/desorption reactions did not seem to be important. The concentrations of base cations and silica in streams are highly dependent on the geochemistry and weathering rates of minerals in each catchment, yet these were rarely, if ever, known. Thus, DayCent-Chem could not accurately predict weathering products for some catchments. Additionally, few data were available for exchangeable soil cations or the magnitude of base cation deposition as a result of dry and fog inputs. The uncertainties related to weathering reactions, deposition, soil cation exchange capacity, and groundwater contributions influenced how well the simulated acid neutralizing capacity (ANC) and pH estimates compared to observed values. Daily discharge was well represented by the model for most sites. The chapters of this report describe the parameterization for each site and summarize model results for ecosystem variables, stream discharge, and stream chemistry. This intersite comparison exercise provided insight about important and possibly not well understood processes.

Scientific Investigations Report↗
USGS2018

GenEst statistical models—A generalized estimator of mortality

Introduction GenEst (a generalized estimator of mortality) is a suite of statistical models and software tools for generalized mortality estimation. It was specifically designed for estimating the number of bird and bat fatalities at solar and wind power facilities, but both the software (Dalthorp and others, 2018) and the underlying statistical models are general enough to be useful in various situations to estimate the size of open populations when detection probabilities and search coverages are less than 1. In this report, we outline the statistical models and data structures underlying the estimator. The models are numerous, complex, and intricately interwoven. Discussion begins with broad, high-level overviews of the general models. The lower-level technical details are then gradually added. Broader and less technical discussions on the general context and applications of the models and the use of the software are available in the software user guide (Simonis and others, 2018), vignettes bundled with the software, and the help files within the software itself.

Techniques and Methods↗
USGS2020

Apatite trace element geochemistry and cathodoluminescent textures—Acomparison between regional magmatism and the Pea Ridge IOA-REE andBoss IOCG deposits, southeastern Missouri iron metallogenic province, USA

The southeast Missouri iron metallogenic province contains a remarkable wealth of historically important Fe, Cu, Au, and rare earth element (REE) deposits including the Pea Ridge iron oxide-apatite-rare earth element (IOA-REE) deposit and the Boss iron oxide-copper-gold (IOCG) deposit. These deposits are coeval with silicic and intermediate composition magmatism in the St. Francois Mountains terrane. Magmatism, iron-oxide (±Cu, Au, Co) and apatite formation, and REE mineralization overlapped in space and time, but the specific role of regional magmatism in the metallogenesis of these deposits remains unclear and basic petrogenetic models are still debated. We report results from high-spatial resolution textural and geochemical analyses of apatite from regional igneous and ore rocks to elucidate their petrogenetic histories and evaluate deposit models. Backscattered electron and spectral cathodoluminescence imaging of apatite reveal no primary igneous zoning, but show different domains with intricate rims and dissolution/reprecipitation textures, each with distinctive REE patterns in many samples. Apatite from all samples are nearly endmember fluorapatite containing up to ~1.3 wt% Cl and F/Cl ratios span nearly three orders of magnitude. Fresh igneous fluorapatite contain low Na2O (0.15 wt%) while most Pea Ridge ore samples contain higher Na2O (up to ~0.45 wt%), and concentrations of sulfur in fluorapatite of all types are generally moderate to low (0.3 wt% SO3). Significant amounts of Fe (60,000 ppm), Mg (30,000 ppm), Mn (7,000 ppm), and Sr (12,000 ppm) are contained in fluorapatite of all sample types, and they also have moderate amounts of As (4,000 ppm), Ba (2,000 ppm), Th (400 ppm), and U (80 ppm). Fluorapatite show an extraordinarily large range of REE (~0.1-2.0 wt%) and Y (~100-7000 ppm) concentrations. While fresh igneous fluorapatite share many geochemical features with metasomatized igneous fluorapatite and ore-stage fluorapatite from the Pea Ridge IOA and Boss IOCG ore zones, they also have distinct geochemical signatures that are indicative of unique trace element partitioning and substitution mechanisms. These distinguishing textural and geochemical signatures preclude ore-zone fluorapatite genesis directly from a magma (i.e., crystallization directly from a silicate melt) but are permissive of ore-zone fluorapatite formation by magmatic-hydrothermal fluids derived from the regional magmas. Basinal brines may play an important role in the formation of fluorapatite, especially from the Pea Ridge hematite and Boss magnetite-rich zones. Fluorapatite from different ore zones likely formed by crystallization during pulses of hydrothermal fluids with varying Cl-, Na-, and F-contents, which fundamentally controlled the carrying capacity and solubility of REE+Y and generated geochemically distinctive generations of fluorapatite. Exploration geologists using fluorapatite trace element geochemistry to identify IOA and IOCG deposits should proceed with caution, as more high-quality data from these deposits are needed to improve multivariate discrimination analysis. Fluorapatite from IOA/IOCG deposits can be reasonably discriminated from that of other mineral deposit types (e.g., porphyry/epithermal, skarn, orogenic), but no criteria successfully discriminate yet between IOA and IOCG deposits.

Missouri↗
USGS2019

Stochastic model for simulating Souris River Basin regulated streamflow upstream from Minot, North Dakota

The Souris River Basin is a 24,000 square-mile basin in the Provinces of Saskatchewan and Manitoba in Canada, and the State of North Dakota in the United States. Above-average snowpack during the winter of 2010–11, along with record-setting rains in May and June of 2011, led to record flooding that caused extensive damage to Minot, North Dakota, and numerous smaller communities in Saskatchewan, Manitoba, and North Dakota. As a result, the International Souris River Board created the Souris River Flood Task Force to evaluate potential reservoir operation changes and flood control measures to manage future floods and droughts. Part of this evaluation involved identifying a need for a stochastic streamflow model to estimate the likelihood of future flooding or drought. A stochastic natural (unregulated) streamflow simulation model described in a previous report was built upon in this report to include the effects of regulation of four reservoirs (Rafferty, Alameda, and Boundary Reservoirs and Lake Darling) and their operation guidelines. First, a regulated reservoir storage/streamflow routing model was developed and calibrated from when all four reservoirs were in operation until the end of the reconstructed natural streamflow dataset provided by the U.S. Army Corps of Engineers (1992–2011). The regulated reservoir storage/streamflow routing model then was combined with the stochastic natural (unregulated) streamflow model to provide a stochastic regulated streamflow simulation model for the Souris River Basin upstream from Minot, North Dakota. The stochastic regulated streamflow simulation model was used to estimate regulated flood frequency curves, which are useful for feasibility and design of critical structures such as levees or bridges. Three potential future climatic conditions were considered in this analysis: condition A (wet equilibrium), representing wet (similar to 1970–2017) climatic conditions; condition B (transition), representing transition from wet to dry (similar to 1912–69) climatic conditions; and condition C (dry equilibrium), representing dry climatic conditions. Comparison of the estimated flood frequency curves for regulated flow among the three climatic conditions indicated large differences in flood magnitudes for the more extreme (1-percent or less) annual exceedance probabilities. The estimated 0.2-percent annual exceedance probability flood magnitude for the Souris River upstream from Minot, N. Dak., was 29,300 cubic feet per second for condition A (wet equilibrium), compared to 14,800 cubic feet per second for condition C (dry equilibrium). For comparison, the recorded peak streamflow for 2011 for the Souris River upstream from Minot, N. Dak., was 26,900 cubic feet per second. Although it is not possible to predict how long the current (1970–2017) wet climatic conditions may persist, flood risk for at least the next 25 years, or until about 2040, may be represented best by climatic condition A.

North Dakota↗
USGS2021

Improving inferences about private land conservation by accounting for incomplete reporting

Private lands provide key habitat for imperiled species and are core components of function protected area networks; yet, their incorporation into national and regional conservation planning has been challenging. Identifying locations where private landowners are likely to participate in conservation initiatives can help avoid conflict and clarify trade-offs between ecological benefits and sociopolitical costs. Empirical, spatially explicit assessment of the factors associated with conservation on private land is an emerging tool for identifying future conservation opportunities. However, most data on private land conservation are voluntarily reported and incomplete, which complicates these assessments. We used a novel application of occupancy models to analyze the occurrence of conservation easements on private land. We compared multiple formulations of occupancy models with a logistic regression model to predict the locations of conservation easements based on a spatially explicit social-ecological systems framework. We combined a simulation experiment with a case study of easement data in Idaho and Montana (United States) to illustrate the utility of the occupancy framework for modeling conservation on private land. Occupancy models that explicitly accounted for variation in reporting produced estimates of predictors that were substantially less biased than estimates produced by logistic regression under all simulated conditions. Occupancy models produced estimates for the 6 predictors we evaluated in our case study that were larger in magnitude, but less certain than those produced by logistic regression. These results suggest that occupancy models result in qualitatively different inferences regarding the effects of predictors on conservation easement occurrence than logistic regression and highlight the importance of integrating variable and incomplete reporting of participation in empirical analysis of conservation initiatives. Failure to do so can lead to emphasizing the wrong social, institutional, and environmental factors that enable conservation and underestimating conservation opportunities in landscapes where social norms or institutional constraints inhibit reporting.

Idaho, Montana↗
USGS2010

Computer simulation of reservoir depletion and oil flow from the Macondo well following the Deepwater Horizon blowout

This report describes the application of a computer model to simulate reservoir depletion and oil flow from the Macondo well following the Deepwater Horizon blowout. Reservoir and fluid data used for model development are based on (1) information released in BP's investigation report of the incident, (2) information provided by BP personnel during meetings in Houston, Texas, and (3) calibration by history matching to shut-in pressures measured in the capping stack during the Well Integrity Test. The model is able to closely match the measured shut-in pressures. In the simulation of the 86-day period from the blowout to shut in, the simulated reservoir pressure at the well face declines from the initial reservoir pressure of 11,850 pounds per square inch (psi) to 9,400 psi. After shut in, the simulated reservoir pressure recovers to a final value of 10,300 psi. The pressure does not recover back to the initial pressure owing to reservoir depletion caused by 86 days of oil discharge. The simulated oil flow rate declines from 63,600 stock tank barrels per day just after the Deepwater Horizon blowout to 52,600 stock tank barrels per day just prior to shut in. The simulated total volume of oil discharged is 4.92 million stock tank barrels. The overall uncertainty in the simulated flow rates and total volume of oil discharged is estimated to be + or - 10 percent.

Open-File Report↗
USGS2019

Revisiting “An Exercise in Groundwater Model Calibration and Prediction” after 30 years: Insights and New Directions

In 1988, an important publication moved model calibration and forecasting beyond case studies and theoretical analysis. It reported on a somewhat idyllic graduate student modeling exercise where many of the system properties were known; the primary forecasts of interest were heads in pumping wells after a river was modified. The model was calibrated using manual trial‐and‐error approaches where a model's forecast quality was not related to how well it was calibrated. Here, we investigate whether tools widely available today obviate the shortcomings identified 30 years ago. A reconstructed version of the 1988 true model was tested using increasing parameter estimation sophistication. The parameter estimation demonstrated the inverse problem was non‐unique because only head data were available for calibration. When a flux observation was included, current parameter estimation approaches were able to overcome all calibration and forecast issues noted in 1988. The best forecasts were obtained from a highly parameterized model that used pilot points for hydraulic conductivity and was constrained with soft knowledge. Like the 1988 results, however, the best calibrated model did not produce the best forecasts due to parameter overfitting. Finally, a computationally frugal linear uncertainty analysis demonstrated that the single‐zone model was oversimplified, with only half of the forecasts falling within the calculated uncertainty bounds. Uncertainties from the highly parameterized models had all six forecasts within the calculated uncertainty. The current results outperformed those of the 1988 effort, demonstrating the value of quantitative parameter estimation and uncertainty analysis methods.

Groundwater↗
USGS2015

Understanding the Day Cent model: Calibration, sensitivity, and identifiability through inverse modeling

The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N 2 O, and soil 3 NO − compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil 3 NO − and 4 NH + . Post-processing analyses provided insights into parameter–observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent.

Environmental Modelling and Software↗
USGS2018

Modeling White Sucker (Catostomus commersonii) populations to assess commercial harvest influence on age structure

Commercial harvest of White Suckers Catostomus commersonii for bait in the American Lobster Homarus americanus industry is minimally regulated in Maine and there are concerns as to the influence of increasing harvest. We built a population model using parameters (i.e. age-specific mortality, age at maturity, and size-specific fecundity) from literature and field studies to investigate the theoretical effects of harvest mortality on age structure. Because stock-recruitment relations are poorly characterized for this species, we explored the influence of both Beverton-Holt and Ricker recruitment processes. Our base model closely resembled the empirical age structures reported from three unfished lakes in Maine, with four percent of fish in the modeled spawning run being age-10 or older. We assessed the additive effects of harvest mortality on age structure using the full range of possible mortalities. As expected, increased harvest mortality in the model resulted in a decline and disappearance of older age-classes such that few fish greater than age-10 remained in the population under a realistic harvest mortality scenario. This age-truncation was qualitatively comparable to data from aggregate age distributions reported from three commercially harvested lakes in Maine. Because the loss of older fish may compromise population viability, this model is a valuable guidance tool for managers to craft regulation of this growing fishery.

Maine↗
USGS2013

Construction of 3-D geologic framework and textural models for Cuyama Valley groundwater basin, California

Groundwater is the sole source of water supply in Cuyama Valley, a rural agricultural area in Santa Barbara County, California, in the southeasternmost part of the Coast Ranges of California. Continued groundwater withdrawals and associated water-resource management concerns have prompted an evaluation of the hydrogeology and water availability for the Cuyama Valley groundwater basin by the U.S. Geological Survey, in cooperation with the Water Agency Division of the Santa Barbara County Department of Public Works. As a part of the overall groundwater evaluation, this report documents the construction of a digital three-dimensional geologic framework model of the groundwater basin suitable for use within a numerical hydrologic-flow model. The report also includes an analysis of the spatial variability of lithology and grain size, which forms the geologic basis for estimating aquifer hydraulic properties. The geologic framework was constructed as a digital representation of the interpreted geometry and thickness of the principal stratigraphic units within the Cuyama Valley groundwater basin, which include younger alluvium, older alluvium, and the Morales Formation, and underlying consolidated bedrock. The framework model was constructed by creating gridded surfaces representing the altitude of the top of each stratigraphic unit from various input data, including lithologic and electric logs from oil and gas wells and water wells, cross sections, and geologic maps. Sediment grain-size data were analyzed in both two and three dimensions to help define textural variations in the Cuyama Valley groundwater basin and identify areas with similar geologic materials that potentially have fairly uniform hydraulic properties. Sediment grain size was used to construct three-dimensional textural models that employed simple interpolation between drill holes and two-dimensional textural models for each stratigraphic unit that incorporated spatial structure of the textural data.

California↗
USGS2003

Simulation of net infiltration and potential recharge using a distributed-parameter watershed model of the Death Valley region, Nevada and California

This report presents the development and application of the distributed-parameter watershed model, INFILv3, for estimating the temporal and spatial distribution of net infiltration and potential recharge in the Death Valley region, Nevada and California. The estimates of net infiltration quantify the downward drainage of water across the lower boundary of the root zone and are used to indicate potential recharge under variable climate conditions and drainage basin characteristics. Spatial variability in recharge in the Death Valley region likely is high owing to large differences in precipitation, potential evapotranspiration, bedrock permeability, soil thickness, vegetation characteristics, and contributions to recharge along active stream channels. The quantity and spatial distribution of recharge representing the effects of variable climatic conditions and drainage basin characteristics on recharge are needed to reduce uncertainty in modeling ground-water flow. The U.S. Geological Survey, in cooperation with the Department of Energy, developed a regional saturated-zone ground-water flow model of the Death Valley regional ground-water flow system to help evaluate the current hydrogeologic system and the potential effects of natural or human-induced changes. Although previous estimates of recharge have been made for most areas of the Death Valley region, including the area defined by the boundary of the Death Valley regional ground-water flow system, the uncertainty of these estimates is high, and the spatial and temporal variability of the recharge in these basins has not been quantified. To estimate the magnitude and distribution of potential recharge in response to variable climate and spatially varying drainage basin characteristics, the INFILv3 model uses a daily water-balance model of the root zone with a primarily deterministic representation of the processes controlling net infiltration and potential recharge. The daily water balance includes precipitation (as either rain or snow), snow accumulation, sublimation, snowmelt, infiltration into the root zone, evapotranspiration, drainage, water content change throughout the root-zone profile (represented as a 6-layered system), runoff (defined as excess rainfall and snowmelt) and surface water run-on (defined as runoff that is routed downstream), and net infiltration (simulated as drainage from the bottom root-zone layer). Potential evapotranspiration is simulated using an hourly solar radiation model to simulate daily net radiation, and daily evapotranspiration is simulated as an empirical function of root zone water content and potential evapotranspiration. The model uses daily climate records of precipitation and air temperature from a regionally distributed network of 132 climate stations and a spatially distributed representation of drainage basin characteristics defined by topography, geology, soils, and vegetation to simulate daily net infiltration at all locations, including stream channels with intermittent streamflow in response to runoff from rain and snowmelt. The temporal distribution of daily, monthly, and annual net infiltration can be used to evaluate the potential effect of future climatic conditions on potential recharge. The INFILv3 model inputs representing drainage basin characteristics were developed using a geographic information system (GIS) to define a set of spatially distributed input parameters uniquely assigned to each grid cell of the INFILv3 model grid. The model grid, which was defined by a digital elevation model (DEM) of the Death Valley region, consists of 1,252,418 model grid cells with a uniform grid cell dimension of 278.5 meters in the north-south and east-west directions. The elevation values from the DEM were used with monthly regression models developed from the daily climate data to estimate the spatial distribution of daily precipitation and air temperature. The elevation values were also used to simulate atmosp

Water-Resources Investigations Report↗
USGS2020

An update of USGS bear-real-time earthquake shaking and impact products

We report on advancements in both hazard and consequence modeling that form the core of the U.S. Geological Survey’s (USGS) strategy to improve rapid earthquake shaking and loss estimates. Whereas our primary goal is to improve our operational capabilities of the USGS National Earthquake Information Center, the science, software, and datasets behind these systems continue to advance uses and studies of earthquake shaking and impact by the seismological, engineering, financial, and risk modeling communities. Several important updates to our integrated shaking and impact products are outlined and we introduce new earthquake information products that have recently been brought online, including rapid ground failure estimates and more spatially refined loss estimates domestically (in the U.S). We continue to compile, develop, and refine key openly available models and datasets that contribute to calibrating these systems and report on the collection and storage of new inventories. We also describe some of the basic operational considerations in the current generation of these shaking and loss-estimation systems. A key aspect of the product integration and development is leveraging earthquake-hazard and loss-modeling science done internally (within the USGS) and by external researchers and collaborators. Lastly, we outline new opportunities for further research and development by emphasizing scientific, data, and application gaps and challenges that must be solved in order to improve our shaking and impact information tools.

Conference Paper↗
USGS2022

A practical guide to understanding and validating complex models using data simulations

Biologists routinely fit novel and complex statistical models to push the limits of our understanding. Examples include, but are not limited to, flexible Bayesian approaches (e.g. BUGS, stan), frequentist and likelihood-based approaches (e.g. packages lme4 ) and machine learning methods. These software and programs afford the user greater control and flexibility in tailoring complex hierarchical models. However, this level of control and flexibility places a higher degree of responsibility on the user to evaluate the robustness of their statistical inference. To determine how often biologists are running model diagnostics on hierarchical models, we reviewed 50 recently published papers in 2021 in the journal Nature Ecology & Evolution , and we found that the majority of published papers did not report any validation of their hierarchical models, making it difficult for the reader to assess the robustness of their inference. This lack of reporting likely stems from a lack of standardized guidance for best practices and standard methods. Here, we provide a guide to understanding and validating complex models using data simulations. To determine how often biologists use data simulation techniques, we also reviewed 50 recently published papers in 2021 in the journal Methods Ecology & Evolution . We found that 78% of the papers that proposed a new estimation technique, package or model used simulations or generated data in some capacity (18 of 23 papers); but very few of those papers (5 of 23 papers) included either a demonstration that the code could recover realistic estimates for a dataset with known parameters or a demonstration of the statistical properties of the approach. To distil the variety of simulations techniques and their uses, we provide a taxonomy of simulation studies based on the intended inference. We also encourage authors to include a basic validation study whenever novel statistical models are used, which in general, is easy to implement. Simulating data helps a researcher gain a deeper understanding of the models and their assumptions and establish the reliability of their estimation approaches. Wider adoption of data simulations by biologists can improve statistical inference, reliability and open science practices.

Methods in Ecology and Evolution↗
USGS2002

Simulation of ground-water flow and potential contaminant transport at Area 6 Landfill, Naval Air Station Whidbey Island, Island County, Washington

A three-dimensional finite-difference steady-state ground-water flow model was developed to simulate hydraulic conditions at the Area 6 Landfill, Naval Air Station Whidbey Island, near Oak Harbor, Washington. Remediation efforts were started in 1995 in an attempt to contain trichloroethene and other contaminants in the ground water. The model was developed as a tool to test the effectiveness of the pump-and-treat remediation efforts as well as alternative remediation strategies. The model utilized stratigraphic data from approximately 76 Navy and 19 private wells to define the geometry of the shallow, intermediate, and deep aquifers and the intervening confining layers. Initial aquifer parameters and recharge estimates from aquifer tests and published remedial investigation reports were used in the model and then adjusted until simulated water levels closely matched observed water-level data collected prior to the onset of remediation in 1995. The calibrated model was then modified to depict the remedial pump-and-treat system, in which contaminated ground water is extracted, treated, and returned to the ground surface for infiltration. The water levels simulated by the modified model were compared with observed water levels for the 1998 calendar year, during which time the pump-and-treat system was in nearly continuous operation and the ground-water system had equilibrated to steady-state conditions. Although artificial boundaries were used in the model, the choice of model boundary conditions was simulation in the area of primary concern surrounding the western contaminant plume and extraction wells. Particle tracking results indicate that the model can effectively simulate the advective transport of contaminants from the source area to the pumping wells and thus be used to test alternative remedial pumping strategies.

Water-Resources Investigations Report↗
USGS2025

Updating and recalibrating the integrated Santa Rosa Plain Hydrologic Model to assess stream depletion and to simulate future climate and management scenarios in Santa Rosa, Sonoma County, California

The Santa Rosa Plain Hydrologic Model (SRPHM) was developed and published in 2014 through a collaboration between the U.S. Geological Survey (USGS) and Sonoma Water to analyze the hydrologic system in the Santa Rosa Plain watershed, help meet the increasing demand for fresh water, and prepare for future uncertainties in water resources. The original model simulated hydrological conditions and water use from water years 1975 to 2010. Recently (2023), the USGS, in cooperation with Sonoma Water and the California State Water Resources Control Board, updated the SRPHM model to extend its simulation period to the end of the 2018 calendar year, incorporate new estimates of rural and agricultural water use, and use efficient input format for climate variables. The updated model was recalibrated, and evaluation of the new model calibration is included in this report. This report presents the results of comparing the hydraulic heads, streamflow, and groundwater budget simulated by the updated model with those generated by the original model and observed data. The main difference in the simulated budget between the original and updated SRPHM is the estimates of agricultural pumping, rural domestic pumping, and return flow generated from rural water use that was not simulated in the original model. The revised agricultural pumping is simulated using the agricultural package, which constrains pumping to available groundwater. The use of the agricultural package leads to a more realistic estimation of agricultural water use, with revised agricultural pumping being one-third less than that in the original model. The revised rural pumping is about half of the pumping in the original model because of using detailed parcel data to estimate population density in rural areas instead of coarse census tracts. Overall, average total inflows for water years 2006–10 simulated by the updated model were about 2 percent less than the original model, and the average total updated outflows were nearly 5 percent less than the original model. The updated model was then used to generate stream depletion maps, simulate climate change scenarios during 2019–99, and simulate water rights allocation using the Model for Decision Support in Integrated River Basin Management (MODSIM). The results from simulating eight future climate scenarios indicated either an increase in groundwater storage or no significant change in the next 80 years, along with an increase in recharge, an increase in actual evapotranspiration in six out of eight climate projections, and an increase in surface runoff. The increases in the simulated future groundwater storage, recharge, evapotranspiration, and runoff in most climate projections are mainly driven by the projected increase in precipitation in most of the future climate scenarios. The updated model also was used to test a pilot case study demonstrating water-resource allocation among different users with different water rights using the integrated MODSIM-Groundwater and Surface-Water Flow Model (GSFLOW) platform. The updated SRPHM serves as a valuable tool for analyzing historical and future hydrologic conditions in the Santa Rosa Plain watershed and preparing for future uncertainties.

California↗
USGS2017

Groundwater flow model for the Little Plover River basin in Wisconsin’s Central Sands

The Little Plover River is a groundwater-fed stream in the sand plains region of central Wisconsin. In this region, sandy sediment deposited during or soon after the last glaciation forms an important unconfined sand and gravel aquifer. This aquifer supplies water for numerous high-capacity irrigation, municipal, and industrial wells that support a thriving agricultural industry. In recent years the addition of many new wells, combined with observed diminished flows in the Little Plover and other nearby rivers, has raised concerns about the impacts of the wells on groundwater levels and on water levels and flows in nearby lakes, streams, and wetlands. Diverse stakeholder groups, including well operators, Growers, environmentalists, local land owners, and regulatory and government officials have sought a better understanding of the local groundwater-surface water system and have a shared desire to balance the water needs of the he liagricultural, industrial, and urban users with the maintenance and protection of groundwater-dependent natural resources. To help address these issues, the Wisconsin Department of Natural Resources requested that the Wisconsin Geological and Natural History Survey and U.S. Geological Survey cooperatively develop a groundwater flow model that could be used to demonstrate the relationships among groundwater, surface water, and well withdrawals and also be a tool for testing and evaluating alternative water management strategies for the central sands region. Because of an abundance of previous studies, data availability, local interest, and existing regulatory constraints the model focuses on the Little Plover River watershed, but the modeling methodology developed during this study can apply to much of the larger central sands of Wisconsin. The Little Plover River groundwater flow model simulates three-dimensional groundwater movement in and around the Little Plover River basin under steady-state and transient conditions. This model explicitly includes all high-capacity wells in the model domain and simulates seasonal variations in recharge and well pumping. The model represents the Little Plover River, and other significant streams and drainage ditches in the model domain, as fully connected to the groundwater system, computes stream base flow resulting from groundwater discharge, and routes the flow along the stream channel. A separate soil-water-balance (SWB) model was used to develop groundwater recharge arrays as input for the groundwater flow model. The SWB model uses topography, soils, land use, and climatic data to estimate recharge as deep drainage from the soil zone. The SWB model explicitly includes recharge originating as irrigation water, and computes irrigation using techniques similar to those used by local irrigation operators. The groundwater flow model uses the U.S. Geological Survey’s MODFLOW modeling code which is freely available, widely accepted, and commonly used by the groundwater community. The groundwater flow model and the SWB model use identical high-resolution numerical grids having model cells 100 feet on a side, with physical properties assigned to each grid cell. This grid allows accurate geographic placement of wells, streams, and other model features. The 3-dimensional grid has three layers; layers 1 and 2 represent the sand and gravel aquifer and layer 3 represents the underlying sandstone. The distribution of material properties in the model (hydraulic conductivity, aquifer thickness, etc.) comes from previous published geologic studies of the region, updated by calibration to recent streamflow and groundwater level data. The SWB model operates on a daily time step. The groundwater flow model was calibrated to monthly stress periods with time steps ranging from 1 to 16 days. More detailed time discretization is possible. The groundwater model was calibrated to water-level and streamflow data collected during 2013 and 2014 by adjusting model parameters (primarily hydraulic conductivity, storage, and recharge) until the model produced a conditionally optimal fit between field observations and model output, subject to consistency with previously published geologic studies. Calibration was performed under both steady and transient conditions, and used a sophisticated parameter-estimation procedure (PEST) for the calibration process and to identify important model parameters. For the Little Plover River, the two most important parameters are the global recharge multiplier and the hydraulic conductivity of the stream bed. The calibrated model produces water-level and mass-balance results that are consistent with field observations and previous studies of the area. The completed model is a powerful tool for testing and demonstrating alternative water-management scenarios. Example model applications described in this report include simulating how the cumulative impacts of pumping and land-use change have affected average baseflow in the Little Plover River. Depletion-potential mapping represents a method for predicting which wells and well locations have the greatest impact on nearby surface-water resources. The completed model is publicly available, along with a companion user’s guide to assist with its operation, at http://wgnhs.org/littleplover- river-groundwater-model.

Wisconsin↗
USGS1985

Maximum likelihood methods for investigating reporting rates of rings on hunter-shot birds

It is well known that hunters do not report 100% of the rings that they find on shot birds. Reward studies can be used to estimate what this reporting rate is, by comparison of recoveries of rings offering a monetary reward, to ordinary rings. A reward study of American Black Ducks (Anas rubripes) is used to illustrate the design, and to motivate the development of statistical models for estimation and for testing hypotheses of temporal and geographic variation in reporting rates. The method involves indexing the data (recoveries) and parameters (reporting, harvest, and solicitation rates) by geographic and temporal strata. Estimates are obtained under unconstrained (e.g., allowing temporal variability in reporting rates) and constrained (e.g., constant reporting rates) models, and hypotheses are tested by likelihood ratio. A FORTRAN program, available from the author, is used to perform the computations.

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