Changes

[[File:H4CH12FG1.JPG|thumb|300px|{{figure number|1}}Illustration of the importance of considering uncertainty in an analysis. The "High Most Likely" case (green) has a most likely charge greater than the minimum and the "Low Most Likely" case (red) has a most likely charge less than the minimum. However, a consideration of the probability distributions (triangular distributions in this example) can alter our perception of what is "low risk" and what is "high risk."]]

[[File:H4CH12FG1.JPG|thumb|300px|{{figure number|1}}Illustration of the importance of considering uncertainty in an analysis. The "High Most Likely" case (green) has a most likely charge greater than the minimum and the "Low Most Likely" case (red) has a most likely charge less than the minimum. However, a consideration of the probability distributions (triangular distributions in this example) can alter our perception of what is "low risk" and what is "high risk."]]

Why consider uncertainty? Is not a single deterministic case sufficient for analysis? Consider the simple case of estimating charge volume to a trap. The necessary minimum charge volume required for success (i.e., low charge risk) and a range associated with this minimum charge have been defined and are illustrated by the vertical black solid and dashed lines, respectively, in [[:file:H4CH12FG1.JPG|Figure 1]]. If a model predicts a charge volume greater than the minimum, then it might be said that little or no charge risk exists. Similarly, if a model predicts a charge volume less than the minimum, then we might say that a significant charge risk exists. These cases are illustrated in Figure 1 and are labeled “low risk” and “high risk,” respectively. However, the perception of what is low risk and what is high risk can change greatly when the probability of an outcome is considered. In this example, the difference between low risk and high risk becomes less definitive, as indicated in [[:file:H4CH12FG1.JPG|Figure 1]]. Although this is a simplistic illustration, all of the key input parameters in a basin model have the potential to cause this degree of ambiguity in the final results. For that reason, estimates of the range of possible outcomes are as important to the final analysis as estimates of the most likely outcome.

Why consider uncertainty? Is not a single deterministic case sufficient for analysis? Consider the simple case of estimating [[charge volume]] to a trap. The necessary minimum charge volume required for success (i.e., low charge risk) and a range associated with this minimum charge have been defined and are illustrated by the vertical black solid and dashed lines, respectively, in [[:file:H4CH12FG1.JPG|Figure 1]]. If a model predicts a charge volume greater than the minimum, then it might be said that little or no charge risk exists. Similarly, if a model predicts a charge volume less than the minimum, then we might say that a significant charge risk exists. These cases are illustrated in Figure 1 and are labeled “low risk” and “high risk,” respectively. However, the perception of what is low risk and what is high risk can change greatly when the probability of an outcome is considered. In this example, the difference between low risk and high risk becomes less definitive, as indicated in [[:file:H4CH12FG1.JPG|Figure 1]]. Although this is a simplistic illustration, all of the key input parameters in a basin model have the potential to cause this degree of ambiguity in the final results. For that reason, estimates of the range of possible outcomes are as important to the final analysis as estimates of the most likely outcome.

==Handling uncertainty==

==Handling uncertainty==