Can Trend Tests Detect Changes in Design-Flood Quantiles under Changing Climate?

Abstract

To estimate design-flood quantiles, such as the 100-year flood, the observed annual maximum flood (AMF) series is fitted to a probability distribution and then the design flood quantile is estimated from the fitted distribution. This is because, in most cases, historical records are not long enough to observe rare, design-flood events. Changes in the AMF series, which are usually detected using simple trend tests (e.g., Mann-Kendall test (MKT)), are hypothesized to result in changes in  design-flood estimates. This hypothesis is tested by using an alternate framework to detect significant changes in design-flood between two periods – rather than changes in the AMF series – and then evaluated using synthetically generated AMF series from the Log-Pearson Type-3 (LP3) distribution due to changes in moments associated with flood distribution. Synthetic experiments show that the MKT does not consider changes in all three moments of the LP3 distribution and incorrectly detects changes in design-flood. We applied the framework on 31 river basins spread across the United States. Statistically significant changes in design-flood quantiles were observed even without a significant trend in the AMF series and basins with statistically significant trends did not necessarily exhibit statistically significant changes in design-flood. If changes to design-flood quantiles are of interest, this framework can be useful rather than simple trend tests on the AMF series which may or may not indicate changes in the design-flood quantiles have occurred. We are now extending the application of the developed framework to mixed population scenarios where floods are generated from more than one causal mechanism under the hypothesis that two more causal mechanisms result in statistically different design-flood quantile estimates at the same river.