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
Precipitation uncertainty estimation and rainfall-runoff model calibration using iterative ensemble smoothers
Comparing single and multiple objective constrained optimization algorithms for tuning a groundwater remediation system
Editorial: Rapid, reproducible, and robust environmental modeling for decision support: worked examples and open-source software tools
Community cloud computing infrastructure to support equitable water research and education
Modflow-setup: Robust automation of groundwater model construction
Simulation of regional groundwater flow and groundwater/lake interactions in the Central Sands, Wisconsin
Flopy: The Python interface for MODFLOW
Book review: Analytical groundwater modeling: Theory and applications using Python
Assessing spatial transferability of a random forest metamodel for predicting drainage fraction
Areas contributing recharge to priority wells in valley-fill aquifers in the Neversink River and Rondout Creek drainage basins, New York
A model-independent tool for evolutionary constrained multi-objective optimization under uncertainty
A scalable model-independent iterative data assimilation tool for sequential and batch estimation of high dimensional model parameters and states
Using Jupyter Notebooks to tell data stories and create reproducible workflows
TC Chamberlin Modeling Center
Edwards Aquifer Groundwater Model Uncertainty Analysis
Flocks of a feather dock together: Using Docker and HTCondor to link high-throughput computing across the USGS
Empowering decision-makers: A dynamic web interface for running Bayesian networks
Hunting Invasive Species with HTCondor: High Throughput Computing for Big Data and Next Generation Sequencing
A digital crust to advance continental‐scale modeling of subsurface fluid flow in climate, crustal process, and Earth system models
MODFLOW 6 models for simulating groundwater flow and a proposed remediation system in the sole-source aquifer system in southeastern Nassau County, New York
Parameter Estimation, Uncertainty Analysis, and Optimization with the PEST++ Family of codes: Tutorial Jupyter Notebooks
Lake Biogeochemical Model Output for One Retrospective and 12 Future Climate Runs in Northern Wisconsin & Michigan, USA
Soil-Water-Balance model developed to simulate net infiltration, irrigation water requirements, and other water budget components in support of the Central Sands Lakes Study, Wisconsin
Groundwater Model Archive and Workflow for Neversink/Rondout Basin, New York, Source Water Delineation
Towards reproducible environmental modeling for decision support: a worked example
Data for Three-dimensional distribution of groundwater residence time metrics in the glaciated United States using metamodels trained on general numerical simulation models
Data and Scripts for Metamodeling for Groundwater Age Forecasting in the Lake Michigan Basin
MODFLOW-NWT model data sets for simulating effects of groundwater withdrawals on streamflows in Northwestern Chippewa County, Wisconsin
Science and Products
- Publications
Filter Total Items: 87
Precipitation uncertainty estimation and rainfall-runoff model calibration using iterative ensemble smoothers
The introduction of iterative ensemble smoothers (IES) for parameter calibration opens avenues for expanding parameter space in surface water hydrologic modeling. Here, we have introduced independent parameters into a model calibration experiment to estimate errors in rainfall forcing data. This approach has the potential to estimate rainfall errors using other hydrological observations and to impAuthorsDavide Zoccatelli, Daniel B. Wright, Jeremy T. White, Michael N. Fienen, Guo YuComparing single and multiple objective constrained optimization algorithms for tuning a groundwater remediation system
Groundwater flow and particle tracking models are critical tools to simulate the natural system, contaminant fate and transport, and effects of remediation. Constrained optimization uses models to systematically explore the interplay between remedial design and contaminant fate, considering uncertainty. Sequential Linear Programming (SLP) provides a design alternative addressing a single goal (e.gAuthorsMichael N. Fienen, Nicholas Corson-Dosch, Kalle L Jahn, Jeremy T. WhiteEditorial: Rapid, reproducible, and robust environmental modeling for decision support: worked examples and open-source software tools
No abstract available.AuthorsJeremy White, Michael N. Fienen, Catherine R. Moore, Anneli GuthkeCommunity cloud computing infrastructure to support equitable water research and education
No abstract available.AuthorsAnthony M. Castronova, Ayman Nassar, Wouter Knoben, Michael N. Fienen, Louise Arnal, Martyn ClarkModflow-setup: Robust automation of groundwater model construction
In an age of both big data and increasing strain on water resources, sound management decisions often rely on numerical models. Numerical models provide a physics-based framework for assimilating and making sense of information that by itself only provides a limited description of the hydrologic system. Often, numerical models are the best option for quantifying even intuitively obvious connectionAuthorsAndrew T. Leaf, Michael N. FienenSimulation of regional groundwater flow and groundwater/lake interactions in the Central Sands, Wisconsin
A multiscale, multiprocess modeling approach was applied to the Wisconsin Central Sands region in central Wisconsin to quantify the connections between the groundwater system, land use, and lake levels in three seepage lakes in Waushara County, Wisconsin: Long and Plainfield (The Plainfield Tunnel Channel Lakes), and Pleasant Lakes. A regional groundwater-flow model, the Newton Raphson formulationAuthorsMichael N. Fienen, Megan J. Haserodt, Andrew T. Leaf, Stephen M. WestenbroekFlopy: The Python interface for MODFLOW
No abstract available.AuthorsAndrew T. Leaf, Michael N. FienenBook review: Analytical groundwater modeling: Theory and applications using Python
No abstract available.AuthorsMichael N. FienenAssessing spatial transferability of a random forest metamodel for predicting drainage fraction
Fully distributed hydrological models are widely used in groundwater management, but model speed and data requirements impede their use for decision support purposes. Metamodels provide a simpler and faster model which emulates the underlying complex model using machine learning techniques. However, metamodel predictions beyond the ranges, in space and/or time, of training data are highly uncertaiAuthorsElisa Bjerre, Michael N. Fienen, Raphael Schneider, Julian Koch, Anker L. HøjbergAreas contributing recharge to priority wells in valley-fill aquifers in the Neversink River and Rondout Creek drainage basins, New York
In southeastern New York, the villages of Ellenville, Wurtsboro, Woodridge, the hamlet of Mountain Dale, and surrounding communities in the Neversink River and Rondout Creek drainage basins rely on wells that pump groundwater from valley-fill glacial aquifers for public water supply. Glacial aquifers are vulnerable to contamination because they are highly permeable and have a shallow depth to wateAuthorsNicholas Corson-Dosch, Michael N. Fienen, Jason S. Finkelstein, Andrew T. Leaf, Jeremy T. White, Joshua C. Woda, John H. WilliamsA model-independent tool for evolutionary constrained multi-objective optimization under uncertainty
An open-source tool has been developed to facilitate constrained single- and multi-objective optimization under uncertainty (CMOU) analyses. The tool uses the well-known PEST interface protocols to communicate with the underlying forward simulation, making it non-intrusive. The tool contains a built-in parallel run manager to make use of heterogeneous and distributed computing resources. Several pAuthorsJeremy White, Matthew Knowling, Michael N. Fienen, Adam Siade, Otis Rea, Guillermo MartinezA scalable model-independent iterative data assimilation tool for sequential and batch estimation of high dimensional model parameters and states
Ensemble-based data assimilation (DA) methods have displayed strong potential to improve model state and parameter estimation across several disciplines due to their computational efficiency, scalability, and ability to estimate uncertainty in the dynamic states and the parameters. However, a barrier to adoption of ensemble DA methods remains. Namely, there is currently a lack of available tools tAuthorsAyman H. Alzraiee, Jeremy T. White, Matthew Knowling, Randall J. Hunt, Michael N. Fienen - Science
Using Jupyter Notebooks to tell data stories and create reproducible workflows
Increasingly, USGS scientists seek to share and collaborate while working on data and code. Furthermore, these scientists often require advanced computing resources. Jupyter Notebooks are one such tool for creating these workflows. The files are interactive, code “notebooks” which allow users to combine code and text in one document, enabling scientists to share the stories held within their data.TC Chamberlin Modeling Center
The TC Chamberlin Modeling Center provides one-stop access to advanced computing so no project is limited by a lack of computer power. The Center can provide hardware access, assistance with migration and implementation, and training. We also develop, test, and disseminate state-of-the-art computational and analytical techniques and tools so models can be more effectively used in decision-making.Edwards Aquifer Groundwater Model Uncertainty Analysis
USGS Texas Water Science Center (TXWSC) is undertaking a 1.5-year study to assess parameter and predictive uncertainty in the Edwards Aquifer Authority MODFLOW Model using both linear and non-linear techniques. The Edwards Aquifer Authority (EAA) uses two models simulating the periods from 2001 to 2015 (verification model) and the drought of record of 1947-1958 (drought of record model).Flocks of a feather dock together: Using Docker and HTCondor to link high-throughput computing across the USGS
USGS scientists often face computationally intensive tasks that require high-throughput computing capabilities. Several USGS facilities use HTCondor to run their computational pools but are not necessarily connected to the larger USGS pool. This project demonstrated how to connect HTCondor pools by flocking, or coordinating, within the USGS. In addition to flocking the Upper Midwest EnvironmentalEmpowering decision-makers: A dynamic web interface for running Bayesian networks
U.S. Geological Survey (USGS) scientists are at the forefront of research that is critical for decision-making, particularly through the development of models (Bayesian networks, or BNs) that forecast coastal change. The utility of these tools outside the scientific community has been limited because they rely on expensive, technical software and a moderate understanding of statistical analyses. WHunting Invasive Species with HTCondor: High Throughput Computing for Big Data and Next Generation Sequencing
Large amounts of data are being generated that require hours, days, or even weeks to analyze using traditional computing resources. Innovative solutions must be implemented to analyze the data in a reasonable timeframe. The program HTCondor (https://research.cs.wisc.edu/htcondor/) takes advantage of the processing capacity of individual desktop computers and dedicated computing resources as a singA digital crust to advance continental‐scale modeling of subsurface fluid flow in climate, crustal process, and Earth system models
Fluid circulation in the Earth’s crust plays an essential role in surface, near surface, and crustal dynamics. Near the surface, soil water and groundwater interact with each other and with rivers, lakes and wetlands, affecting weathering, soil formation, ecosystem evolution and biogeochemical cycles. Further down (1km), fluid flow affects diagenesis, hydrocarbon maturation and migration, ore depo - Data
MODFLOW 6 models for simulating groundwater flow and a proposed remediation system in the sole-source aquifer system in southeastern Nassau County, New York
This model archive contains files for a set of groundwater flow, particle tracking, and management optimization models that simulate the area around the Navy-Northrop-Grumman contamination plume on Long Island, New York. These models were developed as in insets from the Long Island Regional “parent” Model, from which perimeter boundary conditions were inherited. In addition to input and output filParameter Estimation, Uncertainty Analysis, and Optimization with the PEST++ Family of codes: Tutorial Jupyter Notebooks
A series of Jupyter notebooks documenting a self-guided, interactive curriculum for the PEST++ family of software codes for uncertainty analysis, parameter estimation, and management optimization. For a currently maintained version of these materials, please visit https://github.com/gmdsi/GMDSI_notebooks.Lake Biogeochemical Model Output for One Retrospective and 12 Future Climate Runs in Northern Wisconsin & Michigan, USA
This dataset contains modeled daily lake area, volume, constituent mass, and biogeochemical rates for 3,692 lakes in the Northern Highlands Lake District (NHLD) for one retrospective model run (1986-2010) and 12 model runs under future climate scenarios. This dataset was created using published tools developed to simulate detailed hydrological and biogeochemical fluxes for thousands of lakes and rSoil-Water-Balance model developed to simulate net infiltration, irrigation water requirements, and other water budget components in support of the Central Sands Lakes Study, Wisconsin
This model archive provides input and output for Soil-Water-Balance (SWB) models developed for the Central Sands Lake study in central Wisconsin; this archive supplements the technical appendix in a report to the Wisconsin State Legislature written by the Wisconsin Department of Natural Resources (WDNR) in response to 2017 Wisconsin Act 10. This legislation directed DNR to determine whether existiGroundwater Model Archive and Workflow for Neversink/Rondout Basin, New York, Source Water Delineation
This model archive contains model files and a complete repeatable workflow for source water delineation for groundwater supply wells in the Neversink/Rondout Basins of New York. The U.S. Geological Survey, in cooperation with the NYSDEC and NYSDOH, began an investigation in 2019 with the general objectives of (1) improving understanding of the regional groundwater flow system (2) delineating areasTowards reproducible environmental modeling for decision support: a worked example
Supporting datasets for the associated journal publication "Towards reproducible environmental modeling for decision support: a worked example". Includes source codes for the version of PEST++ and MODFLOW-2005 used, the pyEMU and FloPy python modules and the driver script "eaa.py". Also included are the existing MODFLOW-2005 models supplied the Edwards Aquifer AuthorityData for Three-dimensional distribution of groundwater residence time metrics in the glaciated United States using metamodels trained on general numerical simulation models
Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large-scale numericData and Scripts for Metamodeling for Groundwater Age Forecasting in the Lake Michigan Basin
Groundwater age is an important indicator of groundwater susceptibility to anthropogenic contamination and a key input to statistical models for forecasting water quality. Numerical models can provide estimates of groundwater age, enabling interpretation of measured age tracers. However, to extend to national-scale groundwater systems where numerical models are not routinely available, a more effiMODFLOW-NWT model data sets for simulating effects of groundwater withdrawals on streamflows in Northwestern Chippewa County, Wisconsin
A new groundwater flow model for western Chippewa County, Wisconsin has been developed by the Wisconsin Geological and Natural History Survey (WGNHS) and the U.S. Geological Survey (USGS). An analytic element GFLOW model was constructed and calibrated to generate hydraulic boundary conditions for the perimeter of the more detailed three-dimensional MODFLOW-NWT model. This three-dimensional model u - News