An Extended Watershed-Based AHP Model for Flood Hazard Estimation: Constraining Runoff Converging Indicators via MFD-Derived Sub-Watershed by Maximum Zonal Statistical Method

Abstract

Floods threaten the sustainable development of areas with a high probability of hazard. A typical analytic hierarchy process (pixel-based AHP) based flood hazard estimation method may ignore the similar threat caused by neighborhood cells at the sub-watershed scale. This study proposed an extended Watershed-based Zonal Statistical AHP for flood hazard estimation: Constraining converging related indicators by the sub-watersheds (WZSAHP-RC) model to improve this gap. Before calculating the flood hazard index, the proposed model uses the sub-watershed derived by the multiple flow direction method as a based unit to calculate the maximum zonal statistical value of runoff converging indicators. Moreover, taking the Chaohu basin of Anhui in China as the case study, the validation flooding ground-truthing was constructed from GF-3, and Landsat OLI images of the flood event from 20 July to 24 July 2020, which is the biggest flood recorded by the Zhongmiao station, which recorded a new water level, 0.82 m higher than the historical record. Compared with the validation, the results indicated the proposed method could improve the correct ratio by 38% (from 22% to 60%) and the fit ratio by 17% (from 18% to 35%) when considering the predicted flood hazard levels of “High” and “Very High” as flooded areas. Moreover, the flood hazard map derived by WZSAHP-RC demonstrated greater consistency in the flooded districts filtered by Baidu News than the pixel-based AHP. It revealed that considering two- or even multi-dimensional homogeneity may help to improve the accuracy of flood hazard maps on a catchment scale.

An Improved Flow Direction Algorithm That Considers Mass Conservation for Sediment Transport Simulations

“…Current flow direction algorithms can be classified into single flow direction algorithms (SFDs), multiple flow direction algorithms (MFDs), and hybrid flow direction algorithms (HFDs) [14,15]. Under the SFD model, the water flow in a central cell is assumed to flow out of the central cell from only one direction, followed by a judgment of the flow direction based on the elevation, enabling each cell to flow to, at most, one downstream cell [16,17]. D8 is the earliest classical SFD algorithm, also known as the maximum slope-drop method, where the water flow direction is judged by the slope-drop value between the central cell element and the adjacent cell elements [18].…”