Flood Risk Assessment and Driving Factors in the Songhua River Basin Based on an Improved Soil Conservation Service Curve Number Model

Water, Journal Year: 2025, Volume and Issue: 17(10), P. 1472 - 1472

Published: May 13, 2025

With the acceleration of urbanization and increased frequency extreme rainfall events, flooding has emerged as one most serious natural disaster problems, particularly affecting riparian cities. This study conducted a risk assessment an analysis driving factors behind flood disasters in Songhua River Basin utilizing improved Soil Conservation Service Curve Number (SCS-CN) model. First, model was by slope adjustments effective precipitation coefficient correction, with its performance evaluated using Nash–Sutcliffe efficiency (NSE) Root Mean Square Error (RMSE). Second, mapping performed based on model, distribution characteristics were analyzed. Additionally, Geographical Detector (GD), spatial statistical method for detecting factor interactions, employed to explore influence natural, economic, social detection interaction methods. The results demonstrated that improvements SCS-CN encompassed two key aspects: (1) optimization CN value through resulting optimized 50.13, (2) introduction new parameter, coefficient, calculated intensity static infiltration rate, 0.67. Compared original (NSE = 0.71, rRMSE 19.96), exhibited higher prediction accuracy 0.82, 15.88). categorized into five levels submersion depth: waterlogged areas, low-risk medium-risk high-risk extreme-risk areas. In terms land use, proportions areas ranked follows: water > wetland cropland grassland shrub forests, man-made surfaces exacerbating risks. Yilan (39.41%) Fangzheng (31.12%) faced risks, whereas A-cheng district (6.4%) Shuangcheng city (9.4%) had lower Factor from GD revealed river networks (0.404) significant driver flooding, followed Digital Elevation Model (DEM) (0.35) Normalized Difference Vegetation Index (NDVI) (0.327). explanatory power found be greater than economic factors. Interaction indicated interactions between more impact individual alone, highest observed annual DEM (q 0.762). These findings provide critical insights understanding drivers offer valuable references prevention mitigation strategies.

Language: Английский

Spatial evaluation of flood susceptibility on a national scale across Ghana using a fuzzy analytic hierarchy process and evidential belief function: an ensemble approach

Acta Geophysica, Journal Year: 2025, Volume and Issue: unknown

Published: May 16, 2025

Language: Английский