资源环境科技发展态势分析平台

Global losses caused by floods are gradually increasing under the influence of climate change and human activities. The Yangtze River (YR) economic belt has continued to experience frequent flood disasters over the years; therefore, clarifying the significant flood risk factors is highly relevant for the development of the region. In this study, we investigated the flood risk factors in the Yangtze River Basin (YRB) during 2002–2018 based on meteorological data, reconstructed terrestrial water storage (TWS) data from Gravity Recovery Climate Experiment (GRACE) (GRACE-TWS), and Landsat data. The major conclusions were as follows: (1) the principal components (i.e., the first, third, and fourth principal components (PC1, PC3, and PC4)) of Standardized Precipitation Evapotranspiration Index (SPEI) at a 12-month scale (SPEI12) were more significantly (P < 0.05) correlated with teleconnection indices (i.e., Atlantic Multi-decadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), Southern Oscillation Indices (SOI), and Multivariate El Niño-Southern Oscillation (ENSO) Index (MEI)), than the SPEI at 6-month scale (SPEI6) in the YRB during 2002–2018; (2) the changes in water bodies reflected by Landsat data (i.e., Landsat 5, Landsat 7, and Landsat 8 OLI) for the YRB confirmed the wet periods (i.e., 2002–2005, 2010, 2012–2013, and 2015–2018) monitored by SPEI (i.e., SPEI6 and SPEI12); and (3) while the Pearson correlation coefficients indicated a significant linear relationship between the major hydrological factors (e.g., precipitation, runoff, GRACE-TWS, flood potential index (FPI), and soil moisture (SM)) in the YRB, the precipitation detected by the GRACE-TWS and SM with one-month lag phase, had the maximum correlation coefficients of 0.83 and 0.85, respectively. Significant relationships (r² = 0.48 and r² = 0.92, P < 0.05) were found between variable infiltration capacity (VIC)-runoff and predicted runoffs based on these factors (i.e., precipitation, GRACE-TWS, flood potential index (FPI), and SM) and the random forest regression. This study provides significant hydrological information and contributes to approaches aimed at supporting climate resilience and investments in the YRB.