An extended watershed-based zonal statistical AHP model for flood risk estimation: Constraining runoff converging related indicators by sub-watersheds

TL;DR

This paper introduces an extended watershed-based zonal statistical AHP model that constrains runoff-related indicators by sub-watersheds, improving flood risk estimation accuracy over pixel-based methods, validated in the Chaohu basin.

Contribution

The study develops a novel watershed-based AHP model that enhances flood risk mapping by incorporating sub-watershed scale indicators, outperforming traditional pixel-based approaches.

Findings

Improved correct ratio by up to 37% in validation.

Enhanced fit ratio by 6% compared to pixel-based AHP.

Validated effectiveness using real flood data from Chaohu basin.

Abstract

Floods are highly uncertain events, occurring in different regions, with varying prerequisites and intensities. A highly reliable flood disaster risk map can help reduce the impact of floods for flood management, disaster decreasing, and urbanization resilience. In flood risk estimation, the widely used analytic hierarchy process (AHP) usually adopts pixel as a basic unit, it cannot capture the similar threaten caused by neighborhood source flooding cells at sub-watershed scale. Thus, an extended watershed-based zonal statistical AHP model constraining runoff converging related indicators by sub-watersheds (WZSAHP-Slope & Stream) is proposed to fill this gap. Taking the Chaohu basin as test case, we validated the proposed method with a real-flood area extracted in July 2020. The results indicate that the WZSAHP-Slope & Stream model using multiple flow direction division watersheds to calculate statistics of distance from stream and slope by maximum statistic method outperformed other tested methods. Compering with pixel-based AHP method, the proposed method can improve the correct ratio by 16% (from 67% to 83%) and fit ratio by 1% (from 13% to 14%) as in validation 1, and improve the correct ratio by 37% (from 23% to 60%) and fit ratio by 6% (from 12% to 18%) as in validation 2.