Enhancing Fluvial Flood Inundation Mapping Through Stochastic Rating Curve Representation

A stochastic rating curve approach using copula-based Bayesian frameworks to characterize channel geometry-discharge relationships, generating probabilistic flood inundation maps that capture uncertainties in stage-discharge variations.

Problem Statement

In fluvial flooding, where rivers overflow their banks, channel geometry-discharge relationships play a crucial role in flood prediction. Conventionally, this relationship is estimated deterministically using rating curves that adopt the power-law relationship between stage and discharge. The deterministic approach cannot adequately explain or account for the inherent uncertainties in this relationship, which arise from climate variability, morphological alterations in the riverbed, and anthropogenic modifications.

Proposed Solution

This study proposes a stochastic approach to better characterize channel geometry-discharge relationships and account for these uncertainties. Different copula-based Bayesian frameworks, along with the Inventory of Field Measurements for Hydraulic Attributes (IFMHA) dataset, were used to describe stage-discharge relationships. The resulting probabilistic rating curves are integrated with the Office of Water Prediction (OWP) Height Above Nearest Drainage (HAND) Flood Inundation Mapping (OWP HAND-FIM) model to generate probabilistic flood inundation maps.

Tools & Techniques

• Copula-based Bayesian frameworks for probabilistic analysis
• Height Above Nearest Drainage (HAND) Flood Inundation Mapping model
• Bayesian statistical modeling for hydrological applications

Research Team & Collaborations