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AbstractLandslides can result in extensive casualties and huge economic losses. The accurate discrimination of the main slip direction and deformation trajectory is an important prerequisite for studying landslide formation mechanisms and designing landslide control schemes. In the process of landslide evolution over time, the main slip direction also changes dynamically and provides a comprehensive reflection of the landslide displacement state. However, few studies on this topic have been published. In this paper, a new methodology for analyzing slope stability is proposed based on three techniques: interferometric synthetic aperture radar (InSAR), unmanned aerial vehicle (UAV), and ground-based interferometric synthetic aperture radar (GB-InSAR). The Small Baseline Subset Interferometric SAR (SBAS-InSAR) technique is combined with an overall analysis of the study area to identify the regions of interest (ROIs) with large deformation and the starting target points, and the fusion results of radar deformation data (RDD) and digital surface model (DSM) data are used to fit the deformation surface field of the ROIs. The gradient descent approach is executed to obtain the running trajectory points of the target masses so that the main slip direction and displacement trajectory in the study area can be predicted at small scales. The measured data for the Hongshiyan landslide in Yunnan Province are used to verify the effectiveness of the method, and the predicted results are consistent with the actual landslide direction. The experimental results show that the method can exactly identify the deformation area, especially in the case of a fast-changing deformation trend. This approach can provide more accurate monitoring area results to support the rapid control and prevention of landslide hazards by analyzing the minimum pixel grid (i.e. points), as the smallest spatial unit at a time interval of minutes. The study shows that the method can efficiently combine space–Earth multisource monitoring data to clarify the main slide direction and improve the postslide trajectory prediction of the slope, which is beneficial for assessing disaster risks and improving landslide prevention and control effects, reflecting the engineering application value of the approach.

AbstractIn the recent decades, mass casualties and property losses are caused by flood events. A combination of satellite and hydrological methods has been promoted owing to the advantages of satellite precipitation products (SPPs), such as their wide coverage and high spatiotemporal resolution; these benefits may make up for the deficiencies of gauge data and make SPPs a good supplementary data source, especially for sparsely gauged areas. The present study employs the double-kernel smoothing technique (DS) to integrate TRMM precipitation data and gauge data and evaluates the performance of this method in identifying extreme flood events via the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) in the humid Xiangjiang River Basin, China. Results show that the HEC-HMS driven by TRMM precipitation data can capture the 9 selected flood events accurately both before and after the merging process despite some deficiencies in the TRMM precipitation data. In addition, the merging method generally improved the consistency (CC increased from 0.04 to 0.24) and rainfall-detection capability of the TRMM precipitation data (FAR decreased by 9.12%, POD increased by 56.91% and HSS increased by 15.43%) and also promoted the overall hydrological simulation accuracy and reliability (the average CC increased from 0.86 to 0.96, the average NSE increased from 0.58 to 0.73). However, the blended TRMM precipitation data did not always outperform the nonblended data in terms of certain flood feature simulation details, such as the flood volume, flood peak and peak time.

The optimized location and allocation of earthquake emergency shelters are critical for improving the resilience of communities. However, the existing disaster shelters location–allocation optimization models usually select new shelters from candidate shelters neglecting the existing or planned shelters. The research into comparisons before and after location–allocation optimization between the current planning scenario (CLA scenario) and the supplementary new candidate shelter scenario (SLA scenario) is thus imperative for disaster risk reduction. This article develops a multi-criteria multi-objective multi-scenario supplemental location–allocation optimization model that addresses the concerns related to optimization comparison based on the above two scenarios and provides a higher degree of realism by integrating multi-criteria evaluation and optimization comparison into location–allocation problems. The solutions obtained under the two scenarios are then compared with a case study in Huairou Science City. The results show that the solution under the SLA scenario is superior to the corresponding CLA scenario regardless of the minimal-distance scheme or the minimal-area scheme. The proposed model is proven to be useful for the location–allocation optimization of earthquake emergency shelters, and the presented results can be used as a reference for balancing the interests of the government and residents during the reconstruction and expansion projects of shelters.

Many empirical typhoon economic loss models consider that the losses caused by typhoons mainly depend on the intensity of the hazards and the exposure in the affected areas. Few studies have attracted attention to the role of disaster-formative environmental factors in typhoon losses. In this study, we chose land use and land cover (LULC) as disaster-formative environmental factors together with typhoon wind speed, rainfall, and gross domestic product (GDP) as predictive factors for typhoon economic losses in Guangdong Province, China. The results showed that the intensity of wind speed was the most important factor, while LULC played weak roles in typhoon economic losses for 23 typical typhoons in terms of county level losses in Guangdong. Subregionally, typhoon economic loss models performed better in coastal areas than in noncoastal areas.

Technological hazards induced by natural hazards have a significant impact on human life and economy. Their power exceeds the impact of the natural hazards that caused them. However, most of the multi-hazard risk maps failed to incorporate technological hazards when mapping multi hazard risk. To address this problem, we established a hybrid multi-hazard risk assessment methodology that calculates the risk caused by natural and technological hazards in a three-step process. First, the possible natural and technological hazards in a given region are identified by hazard-forming environment analysis and hazard source investigation respectively. A scenario simulation is adopted to analyze the intensity and impact scope of these hazards. Second, the triggered relationships among natural and technological hazards are analyzed by a hazard matrix. Then, the radar graph is adopted to calculate the multi-hazard intensity under the coupling effect of all possible hazards in this region. Third, the multi-hazard risk is calculated by aggregating land use data and multi-hazard intensity together. This methodology was applied in Huairou Science City (HSC), Beijing. The result shows that the joint action of earthquake and liquid ammonia leakage is the main reason for the high risk in the central area of HSC. The methodology developed in this study can clearly show the different impacts of each hazard on a given region under the action of hazard coupling, and the calculated results can better reflect the actual disaster situation.