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Since the Ming Dynasty, the flood disaster has been one of the important natural disasters affecting our country. However, relatively few studies on the evolution law of flood disaster at large spatial–temporal scales have been reported. Based on the datasets of flood stations from 1470 to 2000, this paper quantitatively analyzes and discusses the spatial and temporal evolution pattrens of floods in China in the past 500 years by using regional gravity center model, wavelet analysis, Daniel index, and M–K test, and predicts the trend of flood disasters in the future. The results show that: (1) Since 1470, there has been an increasing trend of flood intensity index in China;(2) From 1470 to 2000, there were two high centers of flood disaster in 1560 and 1925, respectively, and one low center appeared in 1745; (3) Flood risk changes from easy occurrence in the eastern coastal area to uniform spatial distribution over the whole study region; (4) In the past 500 years, the graver center of flood disaster is located in Baokang County, Hubei Province, and the gravity center of flood disaster shows a trend of moving to northwest both at time scales of 50-year and 100 year; (5) The flood disaster in the studied area will still show an overall upward trend in the future.The research can provide decision support for the precise prevention and control of flood disasters in China. [ABSTRACT FROM AUTHOR]

This paper proposes an eigenvector spatial filtering-based (ESF-based) regression model for land cover pattern simulation in China's Hubei province. The significance and influence of biophysical, climatic, and socio-economic factors have been detected and analyzed in the study region. The ESF-based multinomial logistic regression (spatial model) is constructed for discrete choices to take spatial autocorrelation into consideration. For the massive raster pixels, a segmentation processing (grid-based partition) approach is employed to resolve the large datasets to smaller ones to improve calculation efficiency. Both 32 × 32 and 64 × 64 cell sizes are used to compare the differences and influence of these approaches. For the 32 × 32 cell size, the hitting ratio increased from 0.70 to 0.89 and the deviance decreased 65.6%. For the 64 × 64 cell size, the hitting ratio increased from 0.68 to 0.77 and the deviance decreased 33.2%. The fitted results and maps show that spatial autocorrelation (SA) plays an important role in land cover patterns. Besides, the ESF-based spatial model can isolate SA in land cover pattern simulation, and therefore can improve the fitting accuracy and decrease the model uncertainty. The experiment shows that ESF-based multinomial logistic regression method provides a promising approach for discrete choice regression for massive raster datasets. [ABSTRACT FROM AUTHOR]