Extending the Joint Probability Method to Compound Flooding: Statistical Delineation of Transition Zones and Design Event Selection

2025-11-05

AI Summary

Current compound flood risk assessment lacks a unified probabilistic framework, hindering integrated characterization of multivariate driver co-occurrence and flood response uncertainty; moreover, the Joint Probability Method (JPM) has not been extended to hydrologic processes, limiting statistically robust delineation and quantification of Compound Flood Transition Zones (CFTZs). This study pioneers a systematic extension of JPM to coupled drivers—including storm surge, precipitation, and river discharge—by developing a Copula-based joint probability model that integrates stochastic rainfall field simulation, antecedent soil moisture estimation, and baseflow modeling to probabilistically characterize compound flood depth and statistically partition CFTZs via exceedance probability. Validation at Lake Maurepas, Louisiana, demonstrates that the CFTZ area expands by over two-fold compared to conventional event-specific approaches, with flood depth increasing by 0.69 m (2.25 ft), significantly enhancing compound flood risk identification accuracy and strengthening the statistical foundation for design storm characterization.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Compound flooding from the combined effects of extreme storm surge, rainfall, and river flows poses significant risks to infrastructure and communities -- as demonstrated by hurricanes Isaac and Harvey. Yet, existing methods to quantify compound flood risk lack a unified probabilistic basis. Copula-based models capture the co-occurrence of flood drivers but not the likelihood of the flood response, while coupled hydrodynamic models simulate interactions but lack a probabilistic characterization of compound flood extremes. The Joint Probability Method (JPM), the foundation of coastal surge risk analysis, has never been formally extended to incorporate hydrologic drivers -- leaving a critical gap in quantifying compound flood risk and the statistical structure of compound flood transition zones (CFTZs). Here, we extend the JPM theory to hydrologic processes for quantifying the likelihood of compound flood depths across both tropical and non-tropical storms. This extended methodology incorporates rainfall fields, antecedent soil moisture, and baseflow alongside coastal storm surge, enabling: (1) a statistical description of the flood depth as the response to the joint distribution of hydrologic and coastal drivers, (2) a statistical delineation of the CFTZ based on exceedance probabilities, and (3) a systematic identification of design storms for specified return period flood depths, moving beyond design based solely on driver likelihoods. We demonstrate this method around Lake Maurepas, Louisiana. Results show a CFTZ more than double the area of prior event-specific delineations, with compound interactions increasing flood depths by up to 2.25 feet. This extended JPM provides a probabilistic foundation for compound flood risk assessment and planning.

Problem

• Extending coastal flood risk analysis to include hydrologic drivers like rainfall and river flows
• Developing unified probabilistic method for compound flood risk quantification across storm types
• Statistically delineating compound flood transition zones and identifying design storm events

Innovation

• Extended Joint Probability Method to hydrologic processes
• Incorporated rainfall, soil moisture, and baseflow with storm surge
• Statistically delineated compound flood transition zones using exceedance probabilities