Ground Water Modeling Concepts CE EN 547 – BRIGHAM YOUNG UNIVERSITY Ground Water Modeling Concepts

Types of Models Predictive Interpretive Generic Used to make predictions based on hypothetical future scenarios Interpretive Used to gain a better understanding of an aquifer Generic Based on a simple classical case, rather than on a real system. Used for academic purposes

Model Development Protocol Define purpose of model Conceptual model development Code selection Data collection Model design Calibration Verification Prediction Postaudit Adapted from Anderson & Woessner, 1992

1. Define Purpose of Model Types of models Interpretive Predictive How much accuracy is needed? How much $$ is available for study? How will answers be used?

2. Develop Conceptual Model A high level description of the principal features of the system to be modeled Includes aquifer units, boundary conditions, sources, and sinks Parsimony - Keep the model simple enough to be manageable, yet complex enough to be useful

Characterize Aquifer Units Try to determine principal hydrogeologic units Hydrogeologic unit = zone that exhibits common hydraulic properties May include multiple geologic units

3. Code Selection Does the code model all of the processes in the conceptual model? Use the simplest code possible in light of conceptual model and the purpose of the modeling study Has the code been thoroughly tested and verified?

4. Data Collection Well/borehole logs River/lake stages Pumping data Maps, aerial photos Elevations Observation well data

5. Model Design Construct numerical model Find reasonable set of parameters and initial conditions

6. Calibration Modify input parameters until model output matches field-observed values Observations Heads at wells Flows at sources/sinks (rivers, lakes) Can be automated in some cases with MF2K PES, PEST, UCODE Should include sensitivity analysis

7. Verification Calibrate to multiple sets of observation data if possible In each case, adjust boundary conditions and model stresses to be consistent with conditions present when observations were made

8. Prediction Alter model, run simulation, and make predictions Can include stochastic analyses

9. Postaudit When possible, review the model in later years to determine accuracy of predictions Can be used to improve model

One of the most insidious and nefarious properties of scientific models is their tendency to take over, and sometimes supplant, reality. — Erwin Chargaff Quoted in J. J. Zuckerman, 'The Coming Renaissance of Descriptive Chemistry', Journal of Chemical Education, 1986, 63, 830. …all models are approximations. Essentially, all models are wrong, but some are useful. — George E.P. Box In George E. P. Box and Norman R. Draper, Empirical Model-Building and Response Surfaces (2007), 414.

Case Study Global Climate Change Modeling

Legitimate critique of science? Classic case of misunderstanding science

System being modeled is highly variable. Need an ensemble System being modeled is highly variable. Need an ensemble. Even then it is highly dependent on assumptions.

Skeptic plot. Are the models useless? Skeptic graphic: http://www.drroyspencer.com/2013/04/global-warming-slowdown-the-view-from-space/ http://www.drroyspencer.com/2013/04/global-warming-slowdown-the-view-from-space/

Data/facts are easily manipulated http://www.skepticalscience.com/ipcc-model-gw-projections-done-better-than-you-think.html Skeptic skeptic plot. Data/facts are easily manipulated

The IPCC FAR 'Best' BAU projected rate of warming from 1990 to 2012 was 0.25°C per decade. However, that was based on a scenario with higher emissions than actually occurred. When accounting for actual GHG emissions, the IPCC average 'Best' model projection of 0.2°C per decade is within the uncertainty range of the observed rate of warming (0.15 ± 0.08°C) per decade since 1990, though a bit higher than the central estimate. http://www.skepticalscience.com/contary-to-contrarians-ipcc-temp-projections-accurate.html

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