Single Shift Segmentation Improves Moderate Flood Estimates under Nonstationary Conditions across the United States

Abstract

Precipitation, particularly at high quantiles, has been reported to increase in various regions across the globe, raising pluvial flood risk. One of the main challenges in reliable flood frequency analysis is handling nonstationarity arising from climate variability or anthropogenic disturbances such as land use/cover change or river regulation. To separate these nonstationary footprints, we analyzed annual maximum peak flow records from 18 reference (minimally disturbed) and 66 non-reference stream gages, each with more than 100 years of flood records across the United States. Next, we used a nonparametric Pettitt test to identify statistically significant change points. When present, the flood record was split into pre- and post-change segments with a Log-Pearson III distribution fitted to each. Depending on the region and site type, using a segmented record improved the quantile estimate. At the majority of reference sites, post-change data produced the highest flood quantiles, reflecting recent climate-driven nonstationarity. Conversely, at several non-reference sites, pre-change data returned larger estimates, indicating that long-standing anthropogenic disturbances can attenuate the signal of climatic variations. Our study confirms that fitting a flood frequency model to the segment that minimizes nonstationarity, rather than the entire record, returns more reliable estimates for moderate flood magnitudes of up to a 25-year return interval. The approach highlights the need to understand the population from which flood records are extracted, to separate those populations where appropriate, and then fit a statistical distribution. This practical approach offers a simple thought process for updating moderate flood forecasts to guide infrastructure design or rehabilitation in the current dynamic environment, an era of constant change that needs flexibility in everything we design.

Keywords

Flood frequency analysis / Nonstationary / Petit change point detection / Log Pearson Type III (LP3) / Reference versus non-reference basins / Anthropogenic disturbance / Climate variability / United States

Supplementary Materials

The following supporting information can be found at: https://www.sciepublish.com/article/pii/623, Table S1: The description of 18 reference and 66 nonreference study sites with more than 100 years of peak flow data across the contiguous United States.

Acknowledgments

Authors would like to acknowledge and extend their gratitude for the support provided by Kansas State University for the current research. The work was conducted between 2018-2019, during Berton’s tenure as a postdoctoral fellow and Rahmani’s tenure as an assistant professor in the Department of Biological and Agricultural Engineering at Kansas State University.

Author Contributions

R.B.: Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Data Curation, Writing—Original Draft Preparation, Writing-Review & Editing, Visualization; V.R.: Conceptualization, Validation, Resources, Writing—Review & Editing, Supervision, Project Administration, Funding Acquisition.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding authors.

Funding

This research was funded by the Department of Biological and Agricultural Engineering at Kansas State University through the contribution number of “19-102-J” of the Kansas Agricultural Experiment Station.

Declaration of Competing Interes

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Additionally, the views expressed in this paper are those of the authors and do not necessarily reflect the official positions or opinions of their affiliated institutions.

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