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This study analysed the response in the size of wetlands to a sudden turn from drought to flood along the Yangtze River system in China. The wetlands and their spatial distributions in the provinces of Hubei, Hunan, Jiangxi, Anhui and Jiangsu for three periods (April and May 2010, April and May 2011 and June and July 2011) were identified by the artificial visual interpretation of charge-coupled device images obtained in 2010 and 2011 by the HJ-1A and HJ-1B satellites. The sizes of the wetland areas of all five provinces in the three periods were 30,087.10, 23,427.68 and 31,346.49 km2, respectively, with the largest changes occurring in Jiangxi. Changes in the mean standardized precipitation index (SPI) and its patterns of spatial distribution had similar trends to the changes in the size of the wetlands of each province in the three periods. SPI was positively correlated (R2 = 0.360, p < 0.05) with changes in the proportions of the wetlands during the drought. The mean SPI of the final period after the flood was positively correlated (R2 = 0.406, p < 0.01) with changes in the proportions of wetlands. Poyang Lake and Dongting Lake were impacted most by the flood, and Hongze Lake and Caohu Lake were the least affected. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims to develop a novel hybrid model for assessing landslide susceptibility at the regional scale using multisource data to produce a landslide susceptibility map of the Zigui–Badong area near the Three Gorges Reservoir, China. This area is subject to anthropogenic influences because the reservoir's water level fluctuates cyclically between 145 and 175m; in addition, the area suffers from extreme rainfall events due to the local climate. The area has experienced significant and widespread landslide events in recent years. In our study, a novel hybrid model is proposed to produce landslide susceptibility maps using geographical information systems (GIS) and remote sensing. The hybrid model is based on rough set (RS) theory and a support vector machine (SVM). RS theory is employed as an attribute reduction tool to identify the significant environmental parameters of a landslide, and an SVM is used to predict landslide susceptibility. Four data domains were considered in this research: geological, geomorphological, hydrology, and land cover. The original group of 20 environmental parameters and 202 landslides were used as the inputs to produce a landslide susceptibility map. According to the map, 19.7% of the study area was identified as medium- and high-susceptibility zones encompassing 89.5% of the historical landslides. The results indicate high levels of landslide hazard in and around the main inhabited areas, such as Badong County and other towns, as well as in rural residential areas and transportation areas along the Yangtze River and its tributaries. The predicted map indicates a good correlation between the classified high hazard areas and slope failures confirmed in the field. Furthermore, the quality of the proposed model was comprehensively evaluated, including the degree of model fit, the robustness of the model, the uncertainty associated with the probabilistic estimate, and the model prediction skill. The proposed model was also compared with the general SVM, which demonstrated that the hybrid model has superior prediction skill and higher reliability and confirmed the usefulness of the proposed model for landslide susceptibility mapping at a regional scale. [Copyright &y& Elsevier]