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At least 73 records · Page 4Linked to original sources
USGS2007

Bayesian multimodel inference for dose-response studies

Statistical inference in dose-response studies is model-based: The analyst posits a mathematical model of the relation between exposure and response, estimates parameters of the model, and reports conclusions conditional on the model. Such analyses rarely include any accounting for the uncertainties associated with model selection. The Bayesian inferential system provides a convenient framework for model selection and multimodel inference. In this paper we briefly describe the Bayesian paradigm and Bayesian multimodel inference. We then present a family of models for multinomial dose-response data and apply Bayesian multimodel inferential methods to the analysis of data on the reproductive success of American kestrels (Falco sparveriuss) exposed to various sublethal dietary concentrations of methylmercury.

Environmental Toxicology and Chemistry↗
USGS2008

Hydrologic modeling strategy for the Islamic Republic of Mauritania, Africa

The government of Mauritania is interested in how to maintain hydrologic balance to ensure a long-term stable water supply for minerals-related, domestic, and other purposes. Because of the many complicating and competing natural and anthropogenic factors, hydrologists will perform quantitative analysis with specific objectives and relevant computer models in mind. Whereas various computer models are available for studying water-resource priorities, the success of these models to provide reliable predictions largely depends on adequacy of the model-calibration process. Predictive analysis helps us evaluate the accuracy and uncertainty associated with simulated dependent variables of our calibrated model. In this report, the hydrologic modeling process is reviewed and a strategy summarized for future Mauritanian hydrologic modeling studies.

Open-File Report↗
USGS1984

Ground-water models as a management tool in Florida

Highly sophisticated computer models provide powerful tools for analyzing historic data and for simulating future water levels, water movement, and water chemistry under stressed conditions throughout the ground-water system in Florida. Models that simulate the movement of heat and subsidence of land in response to aquifer pumping also have potential for application to hydrologic problems in the State. Florida, with 20 ground-water modeling studies reported since 1972, has applied computer modeling techniques to a variety of water-resources problems. Models in Florida generally have been used to provide insight to problems of water supply, contamination, and impact on the environment. The model applications range from site-specific studies, such as estimating contamination by wastewater injection at St. Petersburg, to a regional model of the entire State that may be used to assess broad-scale environmental impact of water-resources development. Recently, groundwater models have been used as management tools by the State regulatory authority to permit or deny development of water resources. As modeling precision, knowledge, and confidence increase, the use of ground-water models will shift more and more toward regulation of development and enforcement of environmental laws. (USGS)

Water-Resources Investigations Report↗
USGS2020

Time-series model, statistical methods, and software documentation for R–QWTREND—An R package for analyzing trends in stream-water quality

As part of a U.S. Geological Survey water-quality study started in 2018, in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality, and Minnesota Pollution Control Agency, a publicly available software package called R–QWTREND was developed for analyzing trends in stream-water quality. The R–QWTREND package is a collection of functions written in R, an open source language and a general environment for statistical computing and graphics. The package uses a parametric time-series model to express logarithmically transformed concentration in terms of flow-related variability, trend, and serially correlated model errors. Flow-related variability captures natural variability in concentration on the basis of concurrent and antecedent streamflow. The trend identifies systematic changes in concentration in terms of potential step trends, piecewise monotonic trends, or user-specified trends. Maximum likelihood estimation is used to estimate model parameters and determine the best-fit trend model. This report describes the time-series model and statistical methodology behind R–QWTREND and provides formal documentation for installing and using the package.

Open-File Report↗
USGS1976

Lakes Marion-Moultrie stream system investigation, South Carolina : Part II-Simulation Studies

A stream-reservoir model was developed to simulate the operation of the Lakes Marion-Moultrie, South Carolina, reservoir system. The reservoir system is operated under the assumption of a 3-day prior knowledge of inflows. This information is attainable from the ooperational reservoir-inflow forecasting model developed in the Part I report. The model, developed for this part of the study, incorporates an inflow component, a diversion-canal component, and a reservoir system component and is capable of evaluating the performance of the reservoir systemem for a variety of operating rule conditions. All model components were verified using observed reservoir input and output data for a 6-year (1967-72 water years) period. In addition, a long-term 31-year simulation under present rule of operation was performed and the results summarized statistically. The model may be used for simulating other rule curves of operation for planning studies of reservoir system performance. (See also W75-05646) (Woodard-USGS)

South Carolina↗
USGS2013

Refinement of regression models to estimate real-time concentrations of contaminants in the Menomonee River drainage basin, southeast Wisconsin, 2008-11

In 2008, the U.S. Geological Survey and the Milwaukee Metropolitan Sewerage District initiated a study to develop regression models to estimate real-time concentrations and loads of chloride, suspended solids, phosphorus, and bacteria in streams near Milwaukee, Wisconsin. To collect monitoring data for calibration of models, water-quality sensors and automated samplers were installed at six sites in the Menomonee River drainage basin. The sensors continuously measured four potential explanatory variables: water temperature, specific conductance, dissolved oxygen, and turbidity. Discrete water-quality samples were collected and analyzed for five response variables: chloride, total suspended solids, total phosphorus, Escherichia coli bacteria, and fecal coliform bacteria. Using the first year of data, regression models were developed to continuously estimate the response variables on the basis of the continuously measured explanatory variables. Those models were published in a previous report. In this report, those models are refined using 2 years of additional data, and the relative improvement in model predictability is discussed. In addition, a set of regression models is presented for a new site in the Menomonee River Basin, Underwood Creek at Wauwatosa. The refined models use the same explanatory variables as the original models. The chloride models all used specific conductance as the explanatory variable, except for the model for the Little Menomonee River near Freistadt, which used both specific conductance and turbidity. Total suspended solids and total phosphorus models used turbidity as the only explanatory variable, and bacteria models used water temperature and turbidity as explanatory variables. An analysis of covariance (ANCOVA), used to compare the coefficients in the original models to those in the refined models calibrated using all of the data, showed that only 3 of the 25 original models changed significantly. Root-mean-squared errors (RMSEs) calculated for both the original and refined models using the entire dataset showed a median improvement in RMSE of 2.1 percent, with a range of 0.0–13.9 percent. Therefore most of the original models did almost as well at estimating concentrations during the validation period (October 2009–September 2011) as the refined models, which were calibrated using those data. Application of these refined models can produce continuously estimated concentrations of chloride, total suspended solids, total phosphorus, E. coli bacteria, and fecal coliform bacteria that may assist managers in quantifying the effects of land-use changes and improvement projects, establish total maximum daily loads, and enable better informed decision making in the future.

Wisconsin↗
USGS2014

The future of nearshore processes research

The nearshore is the transition region between land and the continental shelf including (from onshore to offshore) coastal plains, wetlands, estuaries, coastal cliffs, dunes, beaches, surf zones (regions of wave breaking), and the inner shelf (Figure ES-1). Nearshore regions are vital to the national economy, security, commerce, and recreation. The nearshore is dynamically evolving, is often densely populated, and is under increasing threat from sea level rise, long-term erosion, extreme storms, and anthropogenic influences. Worldwide, almost one billion people live at elevations within 10 m of present sea level. Long-term erosion threatens communities, infrastructure, ecosystems, and habitat. Extreme storms can cause billions of dollars of damage. Degraded water quality impacts ecosystem and human health. Nearshore processes, the complex interactions between water, sediment, biota, and humans, must be understood and predicted to manage this often highly developed yet vulnerable nearshore environment. Over the past three decades, the understanding of nearshore processes has improved. However, societal needs are growing with increased coastal urbanization and threats of future climate change, and significant scientific challenges remain. To address these challenges, members of academia, industry, and federal agencies (USGS, USACE, NPS, NOAA, FEMA, ONR) met at the “The Past and Future of Nearshore Processes Research: Reflections on the Sallenger Years and a New Vision for the Future” workshop to develop a nearshore processes research vision where societal needs and science challenges intersect. The resulting vision is comprised of three broad research themes: Long-term coastal evolution due to natural and anthropogenic processes: As global climate change alters the rates of sea level rise and potentially storm patterns and coastal urbanization increases over the coming decades, an understanding of coastal evolution is critical. Improved knowledge of long-term morphological, ecological, and societal processes and their interactions will result in an improved ability to simulate coastal change. This will enable proactive solutions for resilient coasts and better guidance for reducing coastal vulnerability. Extreme Events: Flooding, erosion, and the subsequent recovery: Hurricane Sandy caused flooding and erosion along hundreds of miles of shoreline, flooded New York City, and impacted communities and infrastructure. Overall U.S. coastal extreme event related economic losses have increased substantially. Furthermore, climate change may cause an increase in coastal extreme events and rising sea levels could increase the occurrence of extreme events. Addressing this research theme will result in an improved understanding of the physical processes during extreme events, leading to improved models of flooding, erosion, and recovery. The resulting societal benefit will be more resilient coastal communities. The physical, biological and chemical processes impacting human and ecosystem health: Nearshore regions are used for recreation, tourism, and human habitation, and provide habitat and valuable ecosystem services. These areas must be sustained for future generations, however overall coastal water quality is declining due to microbial pathogens, fertilizers, pesticides, and heavy metal contamination, threatening ecosystem and human health. To ensure sustainable nearshore regions, predictive real-time water- and sediment-based based pollutant modeling capabilities must be developed, which requires expanding our knowledge of the physics, chemistry, and biology of the nearshore. The resulting societal benefits will include better beach safety, healthier ecosystems, and improved mitigation and regulatory policies. The scientists and engineers of the U.S. nearshore community are poised to make significant progress on these research themes, which have significant societal impact. The U.S. nearshore community, including academic, government, and industry colleagues, recommends multi-agency investment into a coordinated development of observational and modeling research infrastructure to address these themes, as discussed in the whitepaper. The observational infrastructure should include development of new sensors and methods, focused observational programs, and expanded nearshore observing systems. The modeling infrastructure should include improved process representation, better model coupling, incorporation of data assimilation techniques, and testing of real-time models. The observations will provide test beds to compare and improve models.

Report↗
USGS1993

Simulating the variable-source-area concept of streamflow generation with the watershed model TOPMODEL

This report describes TOPMODEL, which is a physically based watershed model that simulates the variable-source-area concept of streamflow generation. The report describes the theoretical background, model equations, methods to determine parameter values, Fortran computer code, and an example interactive simulation. Using TOPMODEL requires specification of soils and topographic parameters, watershed latitude, and time series of precipitation and air temperature. Model predictions include, in addition to streamflow, estimates of overland and subsurface flow, and an estimate of the spatial pattern of the depth to the water table in the watershed.

Water-Resources Investigations Report↗
USGS2011

Development of a bioenergetics model for age-0 American shad

Bioenergetics modeling can be used as a tool to investigate the impact of non-native age-0 American shad ( Alosa sapidissima ) on reservoir and estuary food webs. The model can increase our understanding of how these fish influence lower trophic levels as well as predatory fish populations that feed on juvenile salmonids. Bioenergetics modeling can be used to investigate ecological processes, evaluate alternative research hypotheses, provide decision support, and quantitative prediction. Bioenergetics modeling has proven to be extremely useful in fisheries research (Ney et al. 1993,Chips and Wahl 2008, Petersen et al. 2008). If growth and diet parameters are known, the bioenergetics model can be used to quantify the relative amount of zooplankton or insects consumed by age-0 American shad. When linked with spatial and temporal information on fish abundance, model output can guide inferential hypothesis development to demonstrate where the greatest impacts of age-0 American shad might occur. Bioenergetics modeling is particularly useful when research questions involve multiple species and trophic levels (e.g. plankton communities). Bioenergetics models are mass-balance equations where the energy acquired from food is partitioned between maintenance costs, waste products, and growth (Winberg 1956). Specifically, the Wisconsin bioenergetics model (Hanson et al. 1997) is widely used in fisheries science. Researchers have extensively tested, reviewed, and improved on this modeling approach for over 30 years (Petersen et al. 2008). Development of a bioenergetics model for any species requires three key components: 1) determine physiological parameters for the model through laboratory experiments or incorporate data from a closely related species, 2) corroboration of the model with growth and consumption estimates from independent research, and 3) error analysis of model parameters. Wisconsin bioenergetics models have been parameterized for many of the salmonids and predatory fishes encountered in the lower Columbia River (Petersen and Ward 1999). The Wisconsin bioenergetics model has not been developed for American shad, however Limburg (1996) parameterized a simplified bioenergetics growth model for this species. A common application for the Wisconsin bioenergetics model is to estimate the consumption or growth of a fish population under different temperature and feeding scenarios (Ney 1993). One advantage of the bioenergetics approach is that consumption can be estimated without direct field measurements of predation rate (prey·predator -1 · day -1 ; Petersen and Ward 1999). Field estimates of fish consumption are time consuming and costly to determine, and estimates may show wide variance due to environmental and sampling variability. However, the consumption parameters used in a newly developed bioenergetics model must be verified with field and laboratory estimates of consumption (Ney 1993). The objective of this research was to parameterize a Wisconsin bioenergetics model for age-0 American shad using published physiological data on American shad and closely related alosine species. The American shad bioenergetics model will be used as a tool to explore various hypotheses about how age-0 American shad directly and indirectly affect Columbia River salmon through ecological interactions in lower Columbia River food webs. One over-arching focus of the larger research project was to identify potential interactions between age-0 American shad and juvenile salmonids, addressing potential outcomes through bioenergetics modeling scenarios. This report contains two bioenergetics modeling applications to demonstrate how these models can be used to address management questions and direct research effort. The first modeling application uses the American shad bioenergetics model described in this report to explore prey consumption by age-0 American shad (Chapter 1, this report). Dietary data on age-0 American shad and previously published reports on the diet of juvenile fall Chinook salmon (Rondorf et al. 1990, USGS unpublished data) suggested there might be considerable dietary overlap between these species in the lower Columbia River. The U.S. Geological Survey (USGS) was interested in using the American shad bioenergetics model to explore hypotheses concerning dietary overlap between age-0 American shad and emigrating fall Chinook salmon. The second modeling application uses the fall Chinook salmon bioenergetics model (Koehler et al. 2006) to explore the growth potential of juvenile fall Chinook salmon predating on age-0 American shad in the lower Columbia River. This modeling was based dietary information on a small number of age-0 fall Chinook salmon (n = 13) collected in John Day Reservoir in 1994 - 1996 (unpublished USGS data). Analysis of this dietary data found that these salmonids were feeding primarily on age-0 American shad (> 75% by weight).

Oregon↗
USGS2010

Approaches to highly parameterized inversion: A guide to using PEST for model-parameter and predictive-uncertainty analysis

Analysis of the uncertainty associated with parameters used by a numerical model, and with predictions that depend on those parameters, is fundamental to the use of modeling in support of decisionmaking. Unfortunately, predictive uncertainty analysis with regard to models can be very computationally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system properties on the one hand (knowledge constraints) and from the necessity for the model parameters to assume values that allow the model to reproduce historical system behavior on the other hand (calibration constraints). Enforcement of knowledge and calibration constraints on parameters used by a model does not eliminate the uncertainty in those parameters. In fact, in many cases, enforcement of calibration constraints simply reduces the uncertainties associated with a number of broad-scale combinations of model parameters that collectively describe spatially averaged system properties. The uncertainties associated with other combinations of parameters, especially those that pertain to small-scale parameter heterogeneity, may not be reduced through the calibration process. To the extent that a prediction depends on system-property detail, its postcalibration variability may be reduced very little, if at all, by applying calibration constraints; knowledge constraints remain the only limits on the variability of predictions that depend on such detail. Regrettably, in many common modeling applications, these constraints are weak. Though the PEST software suite was initially developed as a tool for model calibration, recent developments have focused on the evaluation of model-parameter and predictive uncertainty. As a complement to functionality that it provides for highly parameterized inversion (calibration) by means of formal mathematical regularization techniques, the PEST suite provides utilities for linear and nonlinear error-variance and uncertainty analysis in these highly parameterized modeling contexts. Availability of these utilities is particularly important because, in many cases, a significant proportion of the uncertainty associated with model parameters-and the predictions that depend on them-arises from differences between the complex properties of the real world and the simplified representation of those properties that is expressed by the calibrated model. This report is intended to guide intermediate to advanced modelers in the use of capabilities available with the PEST suite of programs for evaluating model predictive error and uncertainty. A brief theoretical background is presented on sources of parameter and predictive uncertainty and on the means for evaluating this uncertainty. Applications of PEST tools are then discussed for overdetermined and underdetermined problems, both linear and nonlinear. PEST tools for calculating contributions to model predictive uncertainty, as well as optimization of data acquisition for reducing parameter and predictive uncertainty, are presented. The appendixes list the relevant PEST variables, files, and utilities required for the analyses described in the document.

Michigan↗
USGS2020

Hydrologic modeling for flow-ecology science in the Southeastern United States and Puerto Rico

An understanding of the applicability and utility of hydrologic models is critical to support the effective management of water resources throughout the Southeastern United States (SEUS) and Puerto Rico (PR). Hydrologic models have the capacity to provide an estimate of the quantity of available water at ungauged locations (i.e., areas of the country where a U.S. Geological Survey [USGS] continuous record gauge is not installed) and provide the baseline flow information necessary to develop the linkages between water availability and characteristics of streamflow that support ecological communities (i.e., support the development of flow-ecology response models). This report inventories and then directly examines and compares a subset of hydrologic models used to estimate streamflow at a number of gauged basins across the SEUS and PR. This effort was designed to evaluate, quantify, and compare the magnitude of error and to investigate the potential causes of error associated with predicted streamflows from seven hydrologic models of varying complexity and calibration strategy. This was accomplished by computing and then comparing classical hydrologic model fit statistics (e.g., mean bias, coefficient of determination [R2], root mean squared error [RMSE], Nash-Sutcliffe Efficiency [NSE]) and understanding the bias in the prediction in these and a subset of ecologically relevant flow metrics (ERFMs). Additionally, streamflow predictions from a larger regional-scale hydrologic model were compared to those of several fine-scale hydrologic models under a range of hypothetical climate change scenarios to determine the range of predicted streamflow responses to fixed climate perturbations. A pilot study was conducted using predicted streamflow and boosted regression trees to develop a set of predictive flow-ecology response models to assess the potential change in fish species richness in the North Carolina Piedmont under several scenarios of water availability change. This report is intended to provide a general assessment of all the tools and techniques available to support hydrologic modeling for flow-ecology science in the SEUS and PR. It is our hope that the approach used herein to understand differences in streamflow predictions among a subset of hydrologic models that have been applied in the SEUS for developing flow-ecology response models will provide water resource managers and stakeholders with an informed pathway for developing the capacity to link streamflow and ecological response and an understanding of some of the limitations associated with these type of modeling efforts.

Alabama, Florida, Georgia, Mississippi, North Caro↗
USGS2002

A three-dimensional numerical model of predevelopment conditions in the Death Valley regional ground-water flow system, Nevada and California

In the early 1990's, two numerical models of the Death Valley regional ground-water flow system were developed by the U.S. Department of Energy. In general, the two models were based on the same basic hydrogeologic data set. In 1998, the U.S. Department of Energy requested that the U.S. Geological Survey develop and maintain a ground-water flow model of the Death Valley region in support of U.S. Department of Energy programs at the Nevada Test Site. The purpose of developing this 'second-generation' regional model was to enhance the knowledge an understanding of the ground-water flow system as new information and tools are developed. The U.S. Geological Survey also was encouraged by the U.S. Department of Energy to cooperate to the fullest extent with other Federal, State, and local entities in the region to take advantage of the benefits of their knowledge and expertise. The short-term objective of the Death Valley regional ground-water flow system project was to develop a steady-state representation of the predevelopment conditions of the ground-water flow system utilizing the two geologic interpretations used to develop the previous numerical models. The long-term objective of this project was to construct and calibrate a transient model that simulates the ground-water conditions of the study area over the historical record that utilizes a newly interpreted hydrogeologic conceptual model. This report describes the result of the predevelopment steady-state model construction and calibration. The Death Valley regional ground-water flow system is situated within the southern Great Basin, a subprovince of the Basin and Range physiographic province, bounded by latitudes 35 degrees north and 38 degrees 15 minutes north and by longitudes 115 and 118 degrees west. Hydrology in the region is a result of both the arid climatic conditions and the complex geology. Ground-water flow generally can be described as dominated by interbasinal flow and may be conceptualized as having two main components: a series of relatively shallow and localized flow paths that are superimposed on deeper regional flow paths. A significant component of the regional ground-water flow is through a thick Paleozoic carbonate rock sequence. Throughout the flow system, ground water flows through zones of high transmissivity that have resulted from regional faulting and fracturing. The conceptual model of the Death Valley regional ground-water flow system used for this study is adapted from the two previous ground-water modeling studies. The three-dimensional digital hydrogeologic framework model developed for the region also contains elements of both of the hydrogeologic framework models used in the previous investigations. As dictated by project scope, very little reinterpretation and refinement were made where these two framework models disagree; therefore, limitations in the hydrogeologic representation of the flow system exist. Despite limitations, the framework model provides the best representation to date of the hydrogeologic units and structures that control regional ground-water flow and serves as an important information source used to construct and calibrate the predevelopment, steady-state flow model. In addition to the hydrogeologic framework, a complex array of mechanisms accounts for flow into, through, and out of the regional ground-water flow system. Natural discharges from the regional ground-water flow system occur by evapotranspiration, springs, and subsurface outflow. In this study, evapotranspiration rates were adapted from a related investigation that developed maps of evapotranspiration areas and computed rates from micrometeorological data collected within the local area over a multiyear period. In some cases, historical spring flow records were used to derive ground-water discharge rates for isolated regional springs. For this investigation, a process-based, numerical model was developed to estimat

Water-Resources Investigations Report↗
USGS2010

Reducing Uncertainty in the Distribution of Hydrogeologic Units within Volcanic Composite Units of Pahute Mesa Using High-Resolution 3-D Resistivity Methods, Nevada Test Site, Nevada

The U.S. Department of Energy (DOE) and the National Nuclear Security Administration (NNSA) at their Nevada Site Office (NSO) are addressing groundwater contamination resulting from historical underground nuclear testing through the Environmental Management program and, in particular, the Underground Test Area (UGTA) project. From 1951 to 1992, 828 underground nuclear tests were conducted at the Nevada Test Site (NTS) northwest of Las Vegas (DOE UGTA, 2003). Most of these tests were conducted hundreds of feet above the groundwater table; however, more than 200 of the tests were near, or within, the water table. This underground testing was limited to specific areas of the NTS including Pahute Mesa, Rainier Mesa/Shoshone Mountain, Frenchman Flat, and Yucca Flat. Volcanic composite units make up much of the area within the Pahute Mesa Corrective Action Unit (CAU) at the NTS, Nevada. The extent of many of these volcanic composite units extends throughout and south of the primary areas of past underground testing at Pahute and Rainier Mesas. As situated, these units likely influence the rate and direction of groundwater flow and radionuclide transport. Currently, these units are poorly resolved in terms of their hydrologic properties introducing large uncertainties into current CAU-scale flow and transport models. In 2007, the U.S. Geological Survey (USGS), in cooperation with DOE and NNSA-NSO acquired three-dimensional (3-D) tensor magnetotelluric data at the NTS in Area 20 of Pahute Mesa CAU. A total of 20 magnetotelluric recording stations were established at about 600-m spacing on a 3-D array and were tied to ER20-6 well and other nearby well control (fig. 1). The purpose of this survey was to determine if closely spaced 3-D resistivity measurements can be used to characterize the distribution of shallow (600- to 1,500-m-depth range) devitrified rhyolite lava-flow aquifers (LFA) and zeolitic tuff confining units (TCU) in areas of limited drill hole control on Pahute Mesa within the Calico Hills zeolitic volcanic composite unit (VCU), an important hydrostratigraphic unit in Area 20. The resistivity response was evaluated and compared with existing well data and hydrogeologic unit tops from the current Pahute Mesa framework model. In 2008, the USGS processed and inverted the magnetotelluric data into a 3-D resistivity model. We interpreted nine depth slices and four west-east profile cross sections of the 3-D resistivity inversion model. This report documents the geologic interpretation of the 3-D resistivity model. Expectations are that spatial variations in the electrical properties of the Calico Hills zeolitic VCU can be detected and mapped with 3-D resistivity, and that these changes correlate to differences in rock permeability. With regard to LFA and TCU, electrical resistivity and permeability are typically related. Tuff confining units will typically have low electrical resistivity and low permeability, whereas LFA will have higher electrical resistivity and zones of higher fracture-related permeability. If expectations are shown to be correct, the method can be utilized by the UGTA scientists to refine the hydrostratigraphic unit (HSU) framework in an effort to more accurately predict radionuclide transport away from test areas on Pahute and Rainier Mesas.

Nevada↗
USGS1982

Multi-event urban runoff quality model

A computer model is presented for simulating the quality of surface runoff from urban watersheds. The model can simulate impervious area, pervious area, and precipitation contributions to runoff quality as well as the effects of street sweeping and (or) detention storage. Within-storm variations of runoff quality are simulated for user-specified storm-runoff periods. Between these storms, a daily accounting of the accumulation and washoff of water-quality constituents on effective impervious areas is maintained. The time step of the within-storm simulations can range from 1 to 60 minutes. The model can be operated as a lumped-parameter model or as a distributed-parameter model. As a lumped-parameter model, no spatial variations in model parameters are accounted for, and input to the model requires flow hydrographs only at the outlet of the watershed. The outlet hydrographs can be either observed or simulated. As a distributed-parameter model, the model requires flow hydrographs at many points in the watershed, as defined by basin segmentation. These hydrographs will normally be simulated by the Distributed Routing Rainfall-Runoff Model. This report includes a presentation of the theory and limitations of the model, as well as a program listing, instructions for running the program, and example simulations.

Open-File Report↗
USGS2023

Updates to CE-QUAL-W2 models for select U.S. Army Corps of Engineers reservoirs in the Willamette Valley Project and an inter-reservoir reach of the Middle Fork Willamette River, northwestern Oregon

Mechanistic models capable of simulating hydrodynamics and water temperature in rivers and reservoirs are valuable tools for investigating thermal conditions and their relation to dam operations and streamflow in river basins where upstream water storage and management decisions have an important influence on river reaches with threatened fish populations. In particular, models allow managers to investigate how new, untried operations or hypothetical structures might influence streamflow and temperature conditions downstream. CE-QUAL-W2 is a two-dimensional (laterally averaged) hydrodynamic water-quality model that has previously been used to investigate the downstream effects of dam operations and other anthropogenic influences on stream temperature in the Willamette River Basin in northwestern Oregon, a region with two populations of fish species designated as threatened under the Endangered Species Act. By linking CE-QUAL-W2 river models to models of upstream, large Willamette Valley Project dams and reservoirs, these models can be used to investigate how dam operations at individual dams can influence streamflow and thermal conditions in downstream river reaches as an integrated system. Integrated model simulations that include the large dams and reservoirs linked to downstream river reaches can help managers develop a better understanding of tradeoffs associated with potential retrofits or operational changes across the multipurpose dams in the Willamette Valley Project, the effect of dam management on downstream tributaries and the Willamette River, and the resulting potential effect on threatened fish populations and habitat conditions. River models capable of simulating river corridors downstream from U.S. Army Corps of Engineers dams were previously updated and integrated to simulate conditions that occurred from March through October of 2011 (a cool and wet year), 2015 (a hot and dry year), and 2016 (a moderately hot and dry year) using CE-QUAL-W2 version 4.2. These river models encompass the following: Coast Fork Willamette and Middle Fork Willamette Rivers, the Row River, and Fall Creek downstream from Cottage Grove, Dexter, Dorena, and Fall Creek Dams, respectively; South Fork McKenzie River downstream from Cougar Dam; McKenzie River downstream from its confluence with the South Fork McKenzie River; South Santiam River downstream from Foster Dam; North Santiam River downstream from Big Cliff Dam; and Willamette River from its start at the confluence of the Middle Fork Willamette and Coast Fork Willamette Rivers to Willamette Falls (river mile 26.0; near West Linn, Oregon). This report documents model modifications, boundary condition data sources or estimation methods, and goodness-of-fit statistics for six CE-QUAL-W2 reservoir models and one river model upstream from the existing river models. These models simulate (1) Hills Creek Lake; (2) Lookout Point Lake and Dexter Reservoir on the Middle Fork Willamette River; (3) the Middle Fork Willamette River reach between Hills Creek Dam upstream and Lookout Point Lake downstream; (4) Cougar Reservoir on the South Fork McKenzie River; (5) Green Peter Lake on the Middle Santiam River and Foster Lake on the South Santiam River; and (6) Detroit Lake and (7) Big Cliff Reservoir on the North Santiam River. These CE-QUAL-W2 models were built by a variety of researchers to simulate a range of conditions in past years; this report documents their upgrade to U.S. Geological Survey (USGS) edition 7 of version 4.2 of CE-QUAL-W2 and updates each model to simulate conditions from January through December of 2011, 2015, and 2016. Also included in this report is an explanation of modifications to the CE-QUAL-W2 source code that constitute USGS edition 7 of CE-QUAL-W2 version 4.2. Each of the models described in this report can be run in isolation or linked to downstream models as a “system model” to simulate conditions in tributaries and (or) in the Willamette Valley Project as a whole. As part of the model updates described in this report, some model parameters were adjusted to improve stability or decrease model error, and boundary conditions including meteorological, hydrologic, and temperature inputs were developed and updated for model years 2011, 2015, and 2016, as necessary. In some cases, the data sources used to drive previous model versions were no longer available, which required the development and checking of new data sources or estimation techniques. Goodness-of-fit statistics for outflow from the dams and in simulated river reaches generally show a good model fit, with the models simulating subdaily water temperatures at most comparable locations with a mean absolute error of generally less than 1 degree Celsius (°C) and a reasonably low bias. Model simulation of the thermal vertical profiles in each reservoir also produced an overall mean absolute error of generally less than 1 °C for all 3 years, with the exception of the Hills Creek Lake Model and the Cougar Reservoir Model in years when the reservoirs did not fill (2015 and 2016). Both of these models have known calibration issues and tend to be sensitive to the choice of certain structural parameters in the model. Overall, the calibration process was focused on obtaining model settings that led to realistic water temperature predictions in all 3 years (2011, 2015, and 2016) without over-calibrating specifically to any single year. A complete investigation of model error for these reservoir submodels was beyond the scope of this investigation but could be undertaken in the future if better model performance for these two reservoirs is desired.

Oregon↗
USGS2011

Three-dimensional geologic model of the southeastern Espanola Basin, Santa Fe County, New Mexico

This multimedia model and report show and describe digital three-dimensional faulted surfaces and volumes of lithologic units that confine and constrain the basin-fill aquifers within the Espanola Basin of north-central New Mexico. These aquifers are the primary groundwater resource for the cities of Santa Fe and Espanola, six Pueblo nations, and the surrounding areas. The model presented in this report is a synthesis of geologic information that includes (1) aeromagnetic and gravity data and seismic cross sections; (2) lithologic descriptions, interpretations, and geophysical logs from selected drill holes; (3) geologic maps, geologic cross sections, and interpretations; and (4) mapped faults and interpreted faults from geophysical data. Modeled faults individually or collectively affect the continuity of the rocks that contain the basin aquifers; they also help define the form of this rift basin. Structure, trend, and dip data not previously published were added; these structures are derived from interpretations of geophysical information and recent field observations. Where possible, data were compared and validated and reflect the complex relations of structures in this part of the Rio Grande rift. This interactive geologic framework model can be used as a tool to visually explore and study geologic structures within the Espanola Basin, to show the connectivity of geologic units of high and low permeability between and across faults, and to show approximate dips of the lithologic units. The viewing software can be used to display other data and information, such as drill-hole data, within this geologic framework model in three-dimensional space.

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