1. CE 498/698 and ERS 685 (Spring 2004) Lecture 181 Lecture 18: The Modeling Environment CE 498/698 and ERS 485 Principles of Water Quality Modeling

2. CE 498/698 and ERS 685 (Spring 2004) Lecture 182 The modeling environment Models are an idealized formulation that represents the response of a physical system to external stimuli (p. 10) Models are tools that are part of an overall management process

3. CE 498/698 and ERS 685 (Spring 2004) Lecture 183 Management (or scientific) objectives, options, constraints Model development and application Make management decisions Data collection

4. CE 498/698 and ERS 685 (Spring 2004) Lecture 184 Rules of modeling RULE 1: We cannot model reality –We have to make assumptions DOCUMENT!!!! RULE 2: Real world has less precision than modeling

5. CE 498/698 and ERS 685 (Spring 2004) Lecture 185 Precision vs. accuracy Precision –Number of decimal places –Spread of repeated computations Accuracy –Error between computed or measured value and true value error of estimate = field error+ model error

6. CE 498/698 and ERS 685 (Spring 2004) Lecture 186 The problem with precise models… we get more precision from model than is real Location A Location B Difference = 10.056 deg C Model says…

7. CE 498/698 and ERS 685 (Spring 2004) Lecture 187 Figure 18.1 (Chapra 1997)

8. CE 498/698 and ERS 685 (Spring 2004) Lecture 188 Modeling in management process Problem specification Do we need to model? Model selection –Who will the users be? –What kind of data is available? –General model or specific? –Use existing model or develop a new one?

9. CE 498/698 and ERS 685 (Spring 2004) Lecture 189 Modeling in management process Model development –Develop/modify code –Input data –Determine numerical approach model resolution –Timestep –Spatial size

10. CE 498/698 and ERS 685 (Spring 2004) Lecture 1810 Figure 18.9 (Chapra 1997)

11. CE 498/698 and ERS 685 (Spring 2004) Lecture 1811 Modeling in management process Model development –Develop/modify code –Input data –Determine numerical approach model resolution –Timestep –Spatial size –Matter

12. CE 498/698 and ERS 685 (Spring 2004) Lecture 1812 Modeling in management process Preliminary application and calibration Figure 18.3 (Chapra 1997)

13. CE 498/698 and ERS 685 (Spring 2004) Lecture 1813 Modeling in management process Preliminary application and calibration –Adjust parameters –Adjust input data (where appropriate) –Compare model predictions with measured

14. CE 498/698 and ERS 685 (Spring 2004) Lecture 1814 Calibration measures Chapra: minimize sum of squares of residuals smallest sum is best! wherec p,i = model prediction for i c m,i = measured value for i

15. CE 498/698 and ERS 685 (Spring 2004) Lecture 1815 Calibration measures Chapra: minimize sum of squares of residuals R-squared: how much of the variability in the observed data is explained by the predicted data 0 ≤ r 2 ≤ 1

16. CE 498/698 and ERS 685 (Spring 2004) Lecture 1816 Calibration measures Average absolute error Calibration is a KEY process! Root mean squared error

17. CE 498/698 and ERS 685 (Spring 2004) Lecture 1817 Modeling in management process Model confirmation (validation) –Use an independent data set for measured values –Use same parameters/coefficients, same methods of estimating data Does model still work????

18. CE 498/698 and ERS 685 (Spring 2004) Lecture 1818 Modeling in management process Management application Verification Sensitivity analysis