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Claire R. Tiedeman

Publications and source records attributed to Claire R. Tiedeman.

30 records · Page 2Linked to original sources
USGS2007

Model calibration and issues related to validation, sensitivity analysis, post-audit, uncertainty evaluation and assessment of prediction data needs

When simulating natural and engineered groundwater flow and transport systems, one objective is to produce a model that accurately represents important aspects of the true system. However, using direct measurements of system characteristics, such as hydraulic conductivity, to construct a model often produces simulated values that poorly match observations of the system state, such as hydraulic heads, flows and concentrations (for example, Barth et al., 2001 ). This occurs because of inaccuracies in the direct measurements and because the measurements commonly characterize system properties at different scales from that of the model aspect to which they are applied. In these circumstances, the conservation of mass equations represented by flow and transport models can be used to test the applicability of the direct measurements, such as by comparing model simulated values to the system state observations. This comparison leads to calibrating the model, by adjusting the model construction and the system properties as represented by model parameter values, so that the model produces simulated values that reasonably match the observations.

Book chapter↗
USGS2007

Contamination in fractured-rock aquifers: Research at the former Naval Air Warfare Center, West Trenton, New Jersey

The U.S. Geological Survey and cooperators are studying chlorinated solvents in a fractured sedimentary rock aquifer underlying the former Naval Air Warfare Center (NAWC), West Trenton, New Jersey. Fractured-rock aquifers are common in many parts of the United States and are highly susceptible to contamination, particularly at industrial sites. Compared to 'unconsolidated' aquifers, there can be much more uncertainty about the direction and rate of contaminant migration and about the processes and factors that control chemical and microbial transformations of contaminants. Research at the NAWC is improving understanding of the transport and fate of chlorinated solvents in fractured-rock aquifers and will compare the effectiveness of different strategies for contaminant remediation.

New Jersey↗
USGS2007

OPR-PPR, a computer program for assessing data importance to model predictions using linear statistics

The OPR-PPR program calculates the Observation-Prediction (OPR) and Parameter-Prediction (PPR) statistics that can be used to evaluate the relative importance of various kinds of data to simulated predictions. The data considered fall into three categories: (1) existing observations, (2) potential observations, and (3) potential information about parameters. The first two are addressed by the OPR statistic; the third is addressed by the PPR statistic. The statistics are based on linear theory and measure the leverage of the data, which depends on the location, the type, and possibly the time of the data being considered. For example, in a ground-water system the type of data might be a head measurement at a particular location and time. As a measure of leverage, the statistics do not take into account the value of the measurement. As linear measures, the OPR and PPR statistics require minimal computational effort once sensitivities have been calculated. Sensitivities need to be calculated for only one set of parameter values; commonly these are the values estimated through model calibration. OPR-PPR can calculate the OPR and PPR statistics for any mathematical model that produces the necessary OPR-PPR input files. In this report, OPR-PPR capabilities are presented in the context of using the ground-water model MODFLOW-2000 and the universal inverse program UCODE_2005. The method used to calculate the OPR and PPR statistics is based on the linear equation for prediction standard deviation. Using sensitivities and other information, OPR-PPR calculates (a) the percent increase in the prediction standard deviation that results when one or more existing observations are omitted from the calibration data set; (b) the percent decrease in the prediction standard deviation that results when one or more potential observations are added to the calibration data set; or (c) the percent decrease in the prediction standard deviation that results when potential information on one or more parameters is added.

Techniques and Methods↗
USGS2006

USGS science in Menlo Park -- a science strategy for the U.S. Geological Survey Menlo Park Science Center, 2005-2015

In the spring of 2004, the U.S. Geological Survey (USGS) Menlo Park Center Council commissioned an interdisciplinary working group to develop a forward-looking science strategy for the USGS Menlo Park Science Center in California (hereafter also referred to as "the Center"). The Center has been the flagship research center for the USGS in the western United States for more than 50 years, and the Council recognizes that science priorities must be the primary consideration guiding critical decisions made about the future evolution of the Center. In developing this strategy, the working group consulted widely within the USGS and with external clients and collaborators, so that most stakeholders had an opportunity to influence the science goals and operational objectives. The Science Goals are to: Natural Hazards: Conduct natural-hazard research and assessments critical to effective mitigation planning, short-term forecasting, and event response. Ecosystem Change: Develop a predictive understanding of ecosystem change that advances ecosystem restoration and adaptive management. Natural Resources: Advance the understanding of natural resources in a geologic, hydrologic, economic, environmental, and global context. Modeling Earth System Processes: Increase and improve capabilities for quantitative simulation, prediction, and assessment of Earth system processes. The strategy presents seven key Operational Objectives with specific actions to achieve the scientific goals. These Operational Objectives are to: Provide a hub for technology, laboratories, and library services to support science in the Western Region. Increase advanced computing capabilities and promote sharing of these resources. Enhance the intellectual diversity, vibrancy, and capacity of the work force through improved recruitment and retention. Strengthen client and collaborative relationships in the community at an institutional level. Expand monitoring capability by increasing density, sensitivity, and efficiency and reducing costs of instruments and networks. Encourage a breadth of scientific capabilities in Menlo Park to foster interdisciplinary science. Communicate USGS science to a diverse audience.

Circular↗
USGS2005

Guidelines 13 and 14—Prediction uncertainty

An advantage of using optimization for model development and calibration is that optimization provides methods for evaluating and quantifying prediction uncertainty. Both deterministic and statistical methods can be used. Guideline 13 discusses using regression and post-audits, which we classify as deterministic methods. Guideline 14 discusses inferential statistics and Monte Carlo methods, which we classify as statistical methods.

Book chapter↗
USGS2005

Calibrating transient and transport models and recalibrating existing models

The methods presented in Chapters 3 to 8 are applicable to models of any system. However, there are special considerations when applying the methods to certain types of models. This chapter discusses three types of models that are of special interest to many scientific and engineering fields: transient models, transport models, and existing models that are to be recalibrated.

Book chapter↗
USGS2004

Evaluation of longitudinal dispersivity estimates from simulated forced‐ and natural‐gradient tracer tests in heterogeneous aquifers

We simulate three types of forced‐gradient tracer tests (converging radial flow, unequal strength two well, and equal strength two well) and natural‐gradient tracer tests in multiple realizations of heterogeneous two‐dimensional aquifers with a hydraulic conductivity distribution characterized by a spherical variogram. We determine longitudinal dispersivities (α L ) by analysis of forced‐gradient test breakthrough curves at the pumped well and by spatial moment analysis of tracer concentrations during the natural‐gradient tests. Results show that among the forced‐gradient tests, a converging radial‐flow test tends to yield the smallest α L , an equal strength two‐well test tends to yield the largest α L , and an unequal strength two‐well test tends to yield an intermediate value. This finding is qualitatively explained by considering the aquifer area sampled by a particular test. A converging radial‐flow test samples a small area, and thus the tracer undergoes a low degree of spreading and mixing. An equal strength two‐well test samples a much larger area, so the tracer is spread and mixed to a greater degree. Results also suggest that if the distance between the tracer source well and the pumped well is short relative to the lengths over which velocity is correlated, then the α L estimate can be highly dependent on local heterogeneities in the vicinity of the wells. Finally, results indicate that α L estimated from forced‐gradient tracer tests can significantly underestimate the α L needed to characterize solute dispersion under natural‐gradient flow. Only a two‐well tracer test with a large well separation in an aquifer with a low degree of heterogeneity can yield a value of α L that characterizes natural‐gradient tracer spreading. This suggests that a two‐well test with a large well separation is the preferred forced‐gradient test for characterizing solute dispersion under natural‐gradient flow.

Water Resources Research↗
USGS2003

Using sensitivity analysis in model calibration efforts

In models of natural and engineered systems, sensitivity analysis can be used to assess relations among system state observations, model parameters, and model predictions. The model itself links these three entities, and model sensitivities can be used to quantify the links. Sensitivities are defined as the derivatives of simulated quantities (such as simulated equivalents of observations, or model predictions) with respect to model parameters. We present four measures calculated from model sensitivities that quantify the observation-parameter-prediction links and that are especially useful during the calibration and prediction phases of modeling. These four measures are composite scaled sensitivities (CSS), prediction scaled sensitivities (PSS), the value of improved information (VOII) statistic, and the observation prediction (OPR) statistic. These measures can be used to help guide initial calibration of models, collection of field data beneficial to model predictions, and recalibration of models updated with new field information. Once model sensitivities have been calculated, each of the four measures requires minimal computational effort. We apply the four measures to a three-layer MODFLOW-2000 (Harbaugh et al., 2000; Hill et al., 2000) model of the Death Valley regional ground-water flow system (DVRFS), located in southern Nevada and California. D’Agnese et al. (1997, 1999) developed and calibrated the model using nonlinear regression methods. Figure 1 shows some of the observations, parameters, and predictions for the DVRFS model. Observed quantities include hydraulic heads and spring flows. The 23 defined model parameters include hydraulic conductivities, vertical anisotropies, recharge rates, evapotranspiration rates, and pumpage. Predictions of interest for this regional-scale model are advective transport paths from potential contamination sites underlying the Nevada Test Site and Yucca Mountain.

California, Nevada↗
USGS2001

Preliminary evaluation of the importance of existing hydraulic-head observation locations to advective-transport predictions, Death Valley regional flow system, California and Nevada

When a model is calibrated by nonlinear regression, calculated diagnostic statistics and measures of uncertainty provide a wealth of information about many aspects of the system. This report presents a method of ranking the likely importance of existing observation locations using measures of prediction uncertainty. It is suggested that continued monitoring is warranted at more important locations, and unwarranted or less warranted at less important locations. The report develops the methodology and then demonstrates it using the hydraulic-head observation locations of a three-layer model of the Death Valley regional flow system. The predictions of interest are subsurface transport from beneath Yucca Mountain and 14 Underground Test Areas. The advective component of transport is considered because it is the component most affected by the system dynamics represented by the scale model being used. The problem is addressed using the capabilities of the U.S. Geological Survey computer program MODFLOW-2000, with its ADVective-Travel Observation (ADV) Package, and an additional computer program developed for this work. The methods presented in this report are used in three ways. (1) The ratings for individual observations are obtained by manipulating the measures of prediction uncertainty, and do not involve recalibrating the model. In this analysis, observation locations are each omitted individually and the resulting increase in uncertainty in the predictions is calculated. The uncertainty is quantified as standard deviations on the simulated advective transport. The increase in uncertainty is quantified as the percent increase in the standard deviations caused by omitting the one observation location from the calculation of standard deviations. In general, observation locations associated with larger increases are rated as more important. (2) Ratings for largely geographically based groups are obtained using a straightforward extension of the method used for individual observation locations. This analysis is needed where observations are clustered to determine whether the area is important to the predictions of interest. (3) Finally, the method is used to evaluate omitting a set of 100 observation locations. The locations were selected because they had low individual ratings and were not one of the few locations at which hydraulic heads from deep in the system were measured. The major results of the three analyses, when applied to the three-layer DVRFS ground-water flow system, are described in the following paragraphs. The discussion is labeled using the numbers 1 to 3 to clearly relate it to the three ways the method is used, as listed above. (1) The individual observation location analysis indicates that three observation locations are most important. They are located in Emigrant Valley, Oasis Valley, and Beatty. Of importance is that these and other observations shown to be important by this analysis are far from the travel paths considered. This displays the importance of the regional setting within which the transport occurs, the importance of including some sites throughout the area in the monitoring network, and the importance of including sites in these areas in particular. The method considered in this report indicates that the 19 observation locations that reflect hydraulic heads deeper in the system (in model layers 1, 2, and 3) are not very important. This appears to be because the locations of these observations are in the vicinity of shallow observation locations that also generally are rated as low importance, and because the model layers are hydraulically well connected vertically. The value of deep observations to testing conceptual models, however, is stressed. As a result, the deep observations are rated higher than is consistent with the results of the analysis presented, and none of these observations are omitted in the scenario discussed under (3) below. (2) The geographic grouping of th

California, Nevada↗
USGS1997

Numerical simulation of ground-water flow through glacial deposits and crystalline bedrock in the Mirror Lake area, Grafton County, New Hampshire

This report documents the development of a computer model to simulate steady-state (long-term average) flow of ground water in the vicinity of Mirror Lake, which lies at the eastern end of the Hubbard Brook valley in central New Hampshire. The 10-km2 study area includes Mirror Lake, the three streams that flow into Mirror Lake, Leeman's Brook, Paradise Brook, and parts of Hubbard Brook and the Pemigewasset River. The topography of the area is characterized by steep hillsides and relatively flat valleys. Major hydrogeologic units include glacial deposits, composed of till containing pockets of sand and gravel, and fractured crystalline bedrock, composed of schist intruded by granite, pegmatite, and lamprophyre. Ground water occurs in both the glacial deposits and bedrock. Precipitation and snowmelt infiltrate to the water table on the hillsides, flow downslope through the saturated glacial deposits and fractured bedrock, and discharge to streams and to Mirror Lake. The model domain includes the glacial deposits, the uppermost 150m of bedrock, Mirror Lake, the layer of organic sediments on the lake bottom, and streams and rivers within the study area. A streamflow routing package was included in the model to simulate baseflow in streams and interaction between streams and ground water. Recharge from precipitation is assumed to be areally uniform, and riparian evapotranspiration along stream banks is assumed negligible. The spatial distribution of hydraulic conductivity is represented by dividing the model domain into several zones, each having uniform hydraulic properties. Local variations in recharge and hydraulic conductivities are ignored; therefore, the simulation results characterize the general ground-water system, not local details of ground-water movement. The model was calibrated using a nonlinear regression method to match hydraulic heads measured in piezometers and wells, and baseflow in three inlet streams to Mirror Lake. Model calibration indicates that recharge from precipitation to the water table is 26 to 28 cm/year. Hydraulic conductivities are 1.7 x 10-6 to 2.7 x 10-6 m/s for glacial deposits, about 3 x 10-7 m/s for bedrock beneath lower hillsides and valleys, and about 6x10-8 m/s for bedrock beneath upper hillsides and hilltops. Analysis of parameter uncertainty indicates that the above values are well constrained, at least within the context of regression analysis. In the regression, several attributes of the ground-water flow model are assumed perfectly known. The hydraulic conductivity for bedrock beneath upper hillsides and hilltops was determined from few data, and additional data are needed to further confirm this result. Model fit was not improved by introducing a 10-to-1 ration of horizontal-to-vertical anisotropy in the hydraulic conductivity of the glacial deposits, or by varying hydraulic conductivity with depth in the modeled part (uppermost 150m) of the bedrock. The calibrated model was used to delineate the Mirror Lake ground-water basin, defined as the volumes of subsurface through which ground water flows from the water table to Mirror Lake or its inlet streams. Results indicate that Mirror Lake and its inlet streams drain an area of ground-water recharge that is about 1.5 times the area of the surface-water basin. The ground-water basin extends far up the hillside on the northwestern part of the study area. Ground water from this area flows at depth under Norris Brook to discharge into Mirror Lake or its inlet streams. As a result, the Mirror Lake ground-water basin extends beneath the adjacent ground-water basin that drains into Norris Brook. Model simulation indicates that approximately 300,000 m3/year of precipitation recharges the Mirror Lake ground-water basin. About half the recharge enters the basin in areas where the simulated water table lies in glacial deposits; the other half enters the basin in areas where the simulated water table lies in be

New Hampshire↗