USE OF FIRST-ORDER UNCERTAINTY ANALYSIS TO OPTIMIZE SUCCESSFUL STREAMWATER-QUALITY SIMULATION

A first-order uncertainty technique is developed to quantify the relat ionship between field data collection and a modeling exercise involvin g both calibration and subsequent verification. A simple statistic (LT OTAL) is used to quantify the total likelihood (probability) of succes sfully calibrating and verifying the model. Results from the first-ord er technique are compared with those from a traditional Monte Carlo si mulation approach using a simple Streeter-Phelps dissolved oxygen mode l. The largest single difference is caused by the filtering or removal of unrealistic outcomes within the Monte Carlo framework. The amount of bias inherent in the first-order approach is also a function of the magnitude of input variability and sampling location. The minimum bia s of the first-order technique is approximately 20 percent for a case involving relatively large uncertainties. However the bias is well beh aved (consistent) so as to allow for correct decision making regarding the relative efficacy of various sampling strategies. The utility of the first-order technique is demonstrated by linking data collection c osts with modeling performance. For a simple and inexpensive project, a wise and informed selection resulted in an LTOTAL value of 86 percen t, while an uninformed selection could result in an LTOTAL value of on ly 55 percent.