Michael N Fienen
Mike Fienen is a Research Hydrologist with the Upper Midwest Water Science Center.
RESEARCH SUMMARY
My research mission is to provide decision-making support for environmental managers that considers uncertainty in all aspects of decisions and strives to extract the most information from the data. This mission is expressed through the main research threads of model calibration and inference of environmental systems. Specific applications include groundwater quantity and quality; statistical inference and prediction of recreational water quality on beaches; mercury in water and fish; and the groundwater and habitat impacts of sea-level rise. In support of these threads, aspects of computational efficiency, statistical analysis, and data management also play important roles.
Education and Certifications
Ph.D. Environmental Fluid Mechanics and Hydrology, Department of Civil and Environmental Engineering, Stanford University
M.Sc. Environmental Fluid Mechanics and Hydrology, Department of Civil and Environmental Engineering, Stanford University
B.A. Geology, Macalester College Spring Semester in the Rockies, National Outdoor Leadership School
Science and Products
Potential effects of sea-level rise on the depth to saturated sediments of the Sagamore and Monomoy flow lenses on Cape Cod, Massachusetts
Scripting MODFLOW model development using Python and FloPy
Evaluating the sources of water to wells: Three techniques for metamodeling of a groundwater flow model
A semi-structured MODFLOW-USG model to evaluate local water sources to wells for decision support
The effect of particle size distribution on the design of urban stormwater control measures
Predicting recreational water quality advisories: A comparison of statistical methods
The international scale of the groundwater issue
A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA
Groundwater/surface-water interactions in the Bad River Watershed, Wisconsin
Metamodels to bridge the gap between modeling and decision support
Understanding the Day Cent model: Calibration, sensitivity, and identifiability through inverse modeling
High-throughput computing vs. high-performance computing for groundwater applications
Science and Products
- Publications
Filter Total Items: 87
Potential effects of sea-level rise on the depth to saturated sediments of the Sagamore and Monomoy flow lenses on Cape Cod, Massachusetts
In 2014, the U.S. Geological Survey, in cooperation with the Association to Preserve Cape Cod, the Cape Cod Commission, and the Massachusetts Environmental Trust, began an evaluation of the potential effects of sea-level rise on water table altitudes and depths to water on central and western Cape Cod, Massachusetts. Increases in atmospheric and oceanic temperatures arising, in part, from the releAuthorsDonald A. Walter, Timothy D. McCobb, John P. Masterson, Michael N. FienenScripting MODFLOW model development using Python and FloPy
Graphical user interfaces (GUIs) are commonly used to construct and postprocess numerical groundwater flow and transport models. Scripting model development with the programming language Python is presented here as an alternative approach. One advantage of Python is that there are many packages available to facilitate the model development process, including packages for plotting, array manipulatiAuthorsMark Bakker, Vincent E. A. Post, Christian D. Langevin, Joseph D. Hughes, Jeremy T. White, Jeffrey Starn, Michael N. FienenEvaluating the sources of water to wells: Three techniques for metamodeling of a groundwater flow model
For decision support, the insights and predictive power of numerical process models can be hampered by insufficient expertise and computational resources required to evaluate system response to new stresses. An alternative is to emulate the process model with a statistical “metamodel.” Built on a dataset of collocated numerical model input and output, a groundwater flow model was emulated using aAuthorsMichael N. Fienen, Bernard T. Nolan, Daniel T. FeinsteinA semi-structured MODFLOW-USG model to evaluate local water sources to wells for decision support
In order to better represent the configuration of the stream network and simulate local groundwater-surface water interactions, a version of MODFLOW with refined spacing in the topmost layer was applied to a Lake Michigan Basin (LMB) regional groundwater-flow model developed by the U.S. Geological. Regional MODFLOW models commonly use coarse grids over large areas; this coarse spacing precludes moAuthorsDaniel T. Feinstein, Michael N. Fienen, Howard W. Reeves, Christian D. LangevinThe effect of particle size distribution on the design of urban stormwater control measures
An urban pollutant loading model was used to demonstrate how incorrect assumptions on the particle size distribution (PSD) in urban runoff can alter the design characteristics of stormwater control measures (SCMs) used to remove solids in stormwater. Field-measured PSD, although highly variable, is generally coarser than the widely-accepted PSD characterized by the Nationwide Urban Runoff ProgramAuthorsWilliam R. Selbig, Michael N. Fienen, Judy A. Horwatich, Roger T. BannermanPredicting recreational water quality advisories: A comparison of statistical methods
Epidemiological studies indicate that fecal indicator bacteria (FIB) in beach water are associated with illnesses among people having contact with the water. In order to mitigate public health impacts, many beaches are posted with an advisory when the concentration of FIB exceeds a beach action value. The most commonly used method of measuring FIB concentration takes 18–24 h before returning a resAuthorsWesley R. Brooks, Steven R. Corsi, Michael N. Fienen, Rebecca B. CarvinThe international scale of the groundwater issue
Throughout history, and throughout the world, groundwater has been a major source of water for sustaining human life. Use of this resource has increased dramatically over the last century. In many areas of the world, the balance between human and ecosystem needs is difficult to maintain. Understanding the international scale of the groundwater issue requires metrics and analysis at a commensurateAuthorsMichael Fienen, Muhammad ArshadA statistical learning framework for groundwater nitrate models of the Central Valley, California, USA
We used a statistical learning framework to evaluate the ability of three machine-learning methods to predict nitrate concentration in shallow groundwater of the Central Valley, California: boosted regression trees (BRT), artificial neural networks (ANN), and Bayesian networks (BN). Machine learning methods can learn complex patterns in the data but because of overfitting may not generalize well tAuthorsBernard T. Nolan, Michael N. Fienen, David L. LorenzGroundwater/surface-water interactions in the Bad River Watershed, Wisconsin
A groundwater-flow model was developed for the Bad River Watershed and surrounding area by using the U.S. Geological Survey (USGS) finite-difference code MODFLOW-NWT. The model simulates steady-state groundwater-flow and base flow in streams by using the streamflow routing (SFR) package. The objectives of this study were to: (1) develop an improved understanding of the groundwater-flow system in tAuthorsAndrew T. Leaf, Michael N. Fienen, Randall J. Hunt, Cheryl A. BuchwaldMetamodels to bridge the gap between modeling and decision support
No abstract available.AuthorsMichael N. Fienen, Bernard T. Nolan, Daniel T. Feinstein, J. Jeffrey StarnUnderstanding 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 improvedAuthorsMagdalena Necpálová, Robert P. Anex, Michael N. Fienen, Stephen J. Del Grosso, Michael J. Castellano, John E. Sawyer, Javed Iqbal, Jose L. Pantoja, Daniel W. BarkerHigh-throughput computing vs. high-performance computing for groundwater applications
No abstract available.AuthorsMichael N. Fienen, Randall J. Hunt - Science
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