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1、Ground-waterGround-waterGround-water modelmodelmodel calibrationcalibrationcalibration andandand uncertaintyuncertaintyuncertainty analysisanalysisanalysis Course for Geologists and Engineers by Mary C. Hill, U.S. Geological SurveyOffered through CU-Boulder Civil, Environmental and Architectural Eng
2、ineeringDept., Fall 2000Course Description: Models are used extensively to evaluate ground-water systems and to predict their response to such things as changes in pumpage and proposed remediation efforts. Because many aspects of ground-water systems are unknown, most models are calibrated. Calibrat
3、ion commonly is achieved by trial and error alone, but these methods provide less insight than is possible. This course teaches how sensitivity analysis, nonlinear regression, and associated statistics can be used to greatly improve how data is used to calibrate and test ground- water models. For ex
4、ample, parameters that can not be estimated accurately and uniquely with the available data can be quickly identified. Parameter values that produce the best fit between simulated and observed hydraulic heads, concentrations, and so on can be estimated by nonlinear regression. Measures of prediction
5、 uncertainty and measures of the importance of existing and potential observations are a natural consequence of regression methods.Prerequisites: Basic statistics, computer usage. Ideas are taught using ground-water modeling but apply to any modeling; ground-water model experience is useful but is n
6、ot a prerequisite. . Program Schedule: Course participants will learn nonlinear regression methods using the U.S. Geological Survey computer programs MODFLOW-2000, an inverse ground-water flow model that is numerically sophisticated but applies to limited situations; and UCODE, a universal inverse c
7、ode that can be used with any model. MT3DMS, a forward ground-water transport program, also will be discussed in the course. These public domain programs are well-documented, tested, and suitable for complex field application. Class hours will be from 12:30 to 1:45, Tuesday and Thursday, August 29 t
8、o Decedmber 14, room ECCE1B41. There is no class 10/5 and 11/23 because of CU breaks. The dates of the classes are as follows: ClassDateClassDateClassDateClassDate 18/2999/261710/262511/28 28/31109/281810/312611/30 39/51110/31911/22712/5 49/71210/102011/72812/7 59/121310/122111/92912/12 69/141410/17
9、2211/143012/14 79/191510/192311/16 89/211610/242411/21Final exam12/18 1:30-4 pmOffice hours and location: Office hours will be Tuesday and Thursday, 1:45-4:00. I will be available immediately after class at 1:45 the classroom, and shortly thereafter at my office at 3215 Marine St., Room E137Grades:
10、Homework, 25%; exams, 50%; class participation and presentation, 25%. Except for mathematical proofs, homework must be typed (if this is a problem, please see the instructor).Texts: The main text for the course is a draft of the text book “Methods and Guidelines for Effective Model Calibration” by H
11、ill and Tiedeman (M exercises are from M exercise 8c, 8d, 9, 10. Use UCODE generated data sets to plot the objective function for pairs of parameters from the steady-state class problem to study why the prior information was needed in exercise 8. (11)Calculate measures of parameter uncertainty and c
12、orrelation. HW: exercises 11. (12)Test for model nonlinearity. HW: exercise 12. (13)Predictions and prediction uncertainty. Evauate potential new observations. HW: exercises 13-14. (14)Review session. HW: read DAgnese and others (1999) (due class 16) (15).Midterm exam. (16)Field example: Modeling th
13、e Death Valley regional flow system. HW: Prepare idea for class presentation (due class 19) (17)Using hydrogeologic data to constrain ground-water model development, methods and examples Dr. Claudia C. Faunt (18)optional Model calibration and evaluation using transient observations. HW: exercises 15
14、-22 (due class 19). (19)Assign student presentations. HW: Read Christensen and Cooley (1999) (20)Model nonlinearity and its effect on sensitivity analysis, regression, and uncertainty analysis Dr. Richard L. Cooley, guest lecture (21)Objective function design. HW: read Mehl and Hill (1999). (22)Cali
15、brate with concentration data from a laboratory experiment using UCODE, MODFLOW, and different solution methods from MT3DMS Mr. Steffen Mehl. HW: read Barlebo and others (1998). (23)Calibrate with heads alone, and heads and with concentrations in the Grindsted landfill site, Denmark. HW: read Sanfor
16、d and others (2004, USGS SRIR 03-4286) (24)Calibrate with age dates using UCODE and MODPATH in the Rio Grande basin. HW: read Tiedeman and others (1997, GW) Due: First draft of project. First regression attempt. Sens, model fit. HW: Hill + (1998, GW)More applications of the methods and guidelines (25)Characterizing a fractured rock ground-water system in Mirror Lake, New Hampshire; investigation of regression using a synthetic test case. HW: re
