Geology topics

Michael N. Fienen

Publications and source records attributed to Michael N. Fienen.

98 records · Page 6Linked to original sources

USGS2011

Using models for the optimization of hydrologic monitoring

Hydrologists are often asked what kind of monitoring network can most effectively support science-based water-resources management decisions. Currently (2011), hydrologic monitoring locations often are selected by addressing observation gaps in the existing network or non-science issues such as site access. A model might then be calibrated to available data and applied to a prediction of interest (regardless of how well-suited that model is for the prediction). However, modeling tools are available that can inform which locations and types of data provide the most 'bang for the buck' for a specified prediction. Put another way, the hydrologist can determine which observation data most reduce the model uncertainty around a specified prediction. An advantage of such an approach is the maximization of limited monitoring resources because it focuses on the difference in prediction uncertainty with or without additional collection of field data. Data worth can be calculated either through the addition of new data or subtraction of existing information by reducing monitoring efforts (Beven, 1993). The latter generally is not widely requested as there is explicit recognition that the worth calculated is fundamentally dependent on the prediction specified. If a water manager needs a new prediction, the benefits of reducing the scope of a monitoring effort, based on an old prediction, may be erased by the loss of information important for the new prediction. This fact sheet focuses on the worth or value of new data collection by quantifying the reduction in prediction uncertainty achieved be adding a monitoring observation. This calculation of worth can be performed for multiple potential locations (and types) of observations, which then can be ranked for their effectiveness for reducing uncertainty around the specified prediction. This is implemented using a Bayesian approach with the PREDUNC utility in the parameter estimation software suite PEST (Doherty, 2010). The techniques briefly described earlier are described in detail in a U.S. Geological Survey Scientific Investigations Report available on the Internet (Fienen and others, 2010; http://pubs.usgs.gov/sir/2010/5159/). This fact sheet presents a synopsis of the techniques as applied to a synthetic model based on a model constructed using properties from the Lake Michigan Basin (Hoard, 2010).

Fact Sheet↗

USGS2011

cloudPEST - A python module for cloud-computing deployment of PEST, a program for parameter estimation

This report documents cloudPEST-a Python module with functions to facilitate deployment of the model-independent parameter estimation code PEST on a cloud-computing environment. cloudPEST makes use of low-level, freely available command-line tools that interface with the Amazon Elastic Compute Cloud (EC2(TradeMark)) that are unlikely to change dramatically. This report describes the preliminary setup for both Python and EC2 tools and subsequently describes the functions themselves. The code and guidelines have been tested primarily on the Windows(Registered) operating system but are extensible to Linux(Registered).

Open-File Report↗

USGS2011

Approaches to highly parameterized inversion: Pilot-point theory, guidelines, and research directions

Pilot points have been used in geophysics and hydrogeology for at least 30 years as a means to bridge the gap between estimating a parameter value in every cell of a model and subdividing models into a small number of homogeneous zones. Pilot points serve as surrogate parameters at which values are estimated in the inverse-modeling process, and their values are interpolated onto the modeling domain in such a way that heterogeneity can be represented at a much lower computational cost than trying to estimate parameters in every cell of a model. Although the use of pilot points is increasingly common, there are few works documenting the mathematical implications of their use and even fewer sources of guidelines for their implementation in hydrogeologic modeling studies. This report describes the mathematics of pilot-point use, provides guidelines for their use in the parameter-estimation software suite (PEST), and outlines several research directions. Two key attributes for pilot-point definitions are highlighted. First, the difference between the information contained in the every-cell parameter field and the surrogate parameter field created using pilot points should be in the realm of parameters which are not informed by the observed data (the null space). Second, the interpolation scheme for projecting pilot-point values onto model cells ideally should be orthogonal. These attributes are informed by the mathematics and have important ramifications for both the guidelines and suggestions for future research.

Scientific Investigations Report↗

USGS2011

Simulation of the shallow groundwater-flow system near Mole Lake, Forest County, Wisconsin

The shallow groundwater system near Mole Lake, Forest County, Wis. was simulated using a previously calibrated regional model. The previous model was updated using newly collected water-level measurements and refinements to surface-water features. The updated model was then used to calculate the area contributing recharge for one existing and two proposed pumping locations on lands of the Sokaogon Chippewa Community. Delineated 1-, 5-, and 10-year areas contributing recharge for existing and proposed wells extend from the areas of pumping to the northeast of the pumping locations. Steady-state pumping was simulated for two scenarios: a base pumping scenario using pumping rates that reflect what the Tribe expects to pump and a high pumping scenario, in which the rate was set to the maximum expected from wells installed in this area. In the base pumping scenario, pumping rates of 32 gallons per minute (gal/min; 46,000 gallons per day (gal/d)) from the existing well and 30 gal/min (43,000 gal/d) at each of the two proposed wells were simulated. The high pumping scenario simulated a rate of 70 gal/min (101,000 gal/d) from each of the three pumping wells to estimate of the largest areas contributing recharge that might be expected given what is currently known about the shallow groundwater system. The areas contributing recharge for both the base and high pumping scenarios did not intersect any modeled surface-water bodies; however, the high pumping scenario had a larger areal extent than the base pumping scenario and intersected a septic separator.

Wisconsin↗

USGS2010

Simulation of groundwater flow and effects of groundwater irrigation on stream base flow in the Elkhorn and Loup River basins, Nebraska, 1895-2055: Phase Two

Regional groundwater-flow simulations for a 30,000-square-mile area of the High Plains aquifer, referred to collectively as the Elkhorn-Loup Model, were developed to predict the effects of groundwater irrigation on stream base flow in the Elkhorn and Loup River Basins, Nebraska. Simulations described the stream-aquifer system from predevelopment through 2005 [including predevelopment (pre-1895), early development (1895-1940), and historical development (1940 through 2005) conditions] and future hypothetical development conditions (2006 through 2033 or 2055). Predicted changes to stream base flow that resulted from simulated changes to groundwater irrigation will aid development of long-term strategies for management of hydrologically connected water supplies. The predevelopment through 2005 simulation was calibrated using an automated parameter-estimation method to optimize the fit to pre-1940 groundwater levels and base flows, 1945 through 2005 decadal groundwater-level changes, and 1940 through 2005 base flows. The calibration results of the pre-1940 period indicated that 81 percent of the simulated groundwater levels were within 30 feet of the measured water levels. The results did not indicate large areas of simulated groundwater levels that were biased too high or too low, indicating that the simulation generally captures the regional trends. Calibration results using 1945 through 2005 decadal groundwater-level changes indicated that a majority of the simulated groundwater-level changes were within 5 feet of the changes calculated from measured groundwater levels. Simulated groundwater-level rises generally were smaller than measured rises near surface-water irrigation districts. Simulated groundwater-level declines were larger than measured declines in several parts of the study area having large amounts of irrigated crops. Base-flow trends and volumes generally were reproduced by the simulation at most sites. Exceptions include downward trends of simulated base flow from the 1970s to the end of the calibration period for the Elkhorn River at Norfolk, Beaver Creek at Genoa, and Cedar River near Fullerton. Effects of groundwater irrigation on stream base flow were predicted using several methods: (1) simulated base-flow depletion was mapped to represent the percentage of water pumped from a hypothetical well during 2006 through 2055 that corresponds to base-flow depletions at the end of that 50-year period; (2) the groundwater-flow simulation predicted changes in stream base flow that result from modifying the number of irrigated acres in a 25-year period (2009 through 2033); and (3) a simulation-optimization model determined the minimum reduction of groundwater pumpage that would be necessary in the Elkhorn River Basin in a 25-year period (2009 through 2033) to comply with various hypothetical base-flow requirements for the Elkhorn River. The results are not intended to determine specific management plans that must be adopted, but rather to improve the understanding of how base flow is affected by irrigation. A 50-year simulation (2006-55) indicated that depletions of less than 10 percent of pumpage mainly occur in areas that are about 10 miles or farther from the Elkhorn and Loup Rivers and their tributaries. The calibrated simulation was used to predict the 25-year effect on base flow of a 10 percent decrease in irrigated acres and the effect of increasing acres at the presently (2010) allowed rate. Hypothesized changes to irrigated acres were applied only to areas where mapped base-flow depletions were at least 10 percent of pumpage. The effect of changes in irrigated acres includes the combined effects of changes to pumpage and additional recharge from irrigated acres. When irrigated acres were decreased by 10 percent within selected areas of four Natural Resources Districts (a total reduction of about 120,000 acres and a 5 percent reduction in irrigation pumpage), simulated base flow was predicted to inc

Nebraska↗

USGS2010

Using prediction uncertainty analysis to design hydrologic monitoring networks: Example applications from the Great Lakes water availability pilot project

The importance of monitoring networks for resource-management decisions is becoming more recognized, in both theory and application. Quantitative computer models provide a science-based framework to evaluate the efficacy and efficiency of existing and possible future monitoring networks. In the study described herein, two suites of tools were used to evaluate the worth of new data for specific predictions, which in turn can support efficient use of resources needed to construct a monitoring network. The approach evaluates the uncertainty of a model prediction and, by using linear propagation of uncertainty, estimates how much uncertainty could be reduced if the model were calibrated with addition information (increased a priori knowledge of parameter values or new observations). The theoretical underpinnings of the two suites of tools addressing this technique are compared, and their application to a hypothetical model based on a local model inset into the Great Lakes Water Availability Pilot model are described. Results show that meaningful guidance for monitoring network design can be obtained by using the methods explored. The validity of this guidance depends substantially on the parameterization as well; hence, parameterization must be considered not only when designing the parameter-estimation paradigm but also-importantly-when designing the prediction-uncertainty paradigm.

Scientific Investigations Report↗

USGS2009

Obtaining parsimonious hydraulic conductivity fields using head and transport observations: A Bayesian geostatistical parameter estimation approach

Flow path delineation is a valuable tool for interpreting the subsurface hydrogeochemical environment. Different types of data, such as groundwater flow and transport, inform different aspects of hydrogeologic parameter values (hydraulic conductivity in this case) which, in turn, determine flow paths. This work combines flow and transport information to estimate a unified set of hydrogeologic parameters using the Bayesian geostatistical inverse approach. Parameter flexibility is allowed by using a highly parameterized approach with the level of complexity informed by the data. Despite the effort to adhere to the ideal of minimal a priori structure imposed on the problem, extreme contrasts in parameters can result in the need to censor correlation across hydrostratigraphic bounding surfaces. These partitions segregate parameters into facies associations. With an iterative approach in which partitions are based on inspection of initial estimates, flow path interpretation is progressively refined through the inclusion of more types of data. Head observations, stable oxygen isotopes ( 18 O/ 16 O ratios), and tritium are all used to progressively refine flow path delineation on an isthmus between two lakes in the Trout Lake watershed, northern Wisconsin, United States. Despite allowing significant parameter freedom by estimating many distributed parameter values, a smooth field is obtained.

Water Resources Research↗

USGS2008

An interactive Bayesian geostatistical inverse protocol for hydraulic tomography

Hydraulic tomography is a powerful technique for characterizing heterogeneous hydrogeologic parameters. An explicit trade-off between characterization based on measurement misfit and subjective characterization using prior information is presented. We apply a Bayesian geostatistical inverse approach that is well suited to accommodate a flexible model with the level of complexity driven by the data and explicitly considering uncertainty. Prior information is incorporated through the selection of a parameter covariance model characterizing continuity and providing stability. Often, discontinuities in the parameter field, typically caused by geologic contacts between contrasting lithologic units, necessitate subdivision into zones across which there is no correlation among hydraulic parameters. We propose an interactive protocol in which zonation candidates are implied from the data and are evaluated using cross validation and expert knowledge. Uncertainty introduced by limited knowledge of dynamic regional conditions is mitigated by using drawdown rather than native head values. An adjoint state formulation of MODFLOW-2000 is used to calculate sensitivities which are used both for the solution to the inverse problem and to guide protocol decisions. The protocol is tested using synthetic two-dimensional steady state examples in which the wells are located at the edge of the region of interest.

Water Resources Research↗