What question did this study set out to answer?

The aim is to evaluate flood risk in coastal zones using a new risk index that accounts for various driving factors.

February 14, 2026

Dynamic assessment of compound flooding through a risk index approach

FYFarhad YazdandoostK.N.Toosi University of Technology NINeda IzanlooK.N.Toosi University of Technology

Puntos clave

The aim is to evaluate flood risk in coastal zones using a new risk index that accounts for various driving factors.
Developed the Compound Dynamic Risk Index combining probabilistic modeling and curvature-based diagnostics.
Classified CDRI values into categories based on risk levels.
Applied the index to the Fraser River and Potomac River regions for validation.
Utilized four supervised learning algorithms to assess predictive capability.
Demonstrated that curvature-driven transitions occurred prior to increases in flood risk as indicated by CDRI.
Achieved classification accuracy of 88.95% for Fraser River and 81.87% for Potomac system using Deep Echo State Network.
Showed CDRI can serve as both a descriptive index and an anticipatory flood risk indicator.

Resumen

Assessing flood risk in estuarine and coastal zones is challenging when river discharge, tidal peaks, and weather-driven forces coincide. Traditional models–based on fixed dependency patterns or isolated extreme events–tend to overlook the rapidly evolving, non-linear interactions among these drivers over short time scales, leading to systematic underestimation of compound flooding severity. The Compound Dynamic Risk Index overcomes these limitations by integrating daily-resolution probabilistic dependence modeling (via copula-derived joint exceedance probabilities of river discharge and storm surge) with a curvature-based diagnostic that captures second-order temporal dynamics. Unlike conventional indicators activated only upon threshold exceedance, the curvature component enables early detection of shifts in hazard trajectory, providing an anticipatory signal of risk escalation before peak intensity is reached. CDRI values are classified into four categories: Low (CDRI \ (<0. 30\) ), Medium (0. 30–0. 60), High (0. 60–0. 90), and Severe (CDRI \ (0. 90\) ). Consequently, the CDRI functions both as a descriptive index and an anticipatory indicator of emerging compound floods. Application to two hydroclimatically distinct regions–the Fraser River in British Columbia and the Potomac River in the eastern United States–demonstrated that curvature-driven transitions generally occurred ahead of subsequent increases in CDRI, indicating their role as an anticipatory precursor of compound-risk intensification. For predictive evaluation, daily CDRI time series were supplied to four supervised learning algorithms: Random Forest, XGBoost, LSTM, and Deep Echo State Network. Among these, the DeepESN achieved the highest classification accuracy: 88. 95% in the Fraser River and 81. 87% in the Potomac system. By focusing on curvature-driven escalation, daily-scale resolution, and predictive reliability, the index addresses critical gaps in early warning systems and supports more adaptive flood management under growing hydroclimatic uncertainty.

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