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| 1 | National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data显示文摘China is one of the countries where landslides caused the most fatalities in the last decades. The threat that landslide disasters pose to people might even be greater in the future, due to climate change and the increasing urbanization of mountainous areas. A reliable national-scale rainfall induced landslide susceptibility model is therefore of great relevance in order to identify regions more and less prone to landsliding as well as to develop suitable risk mitigating strategies. However, relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area. The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China. In this context, it is aimed to explore the benefit of mixed effects modelling to counterbalance associated bias propagations. Six influencing factors including lithology, slope,soil moisture index, mean annual precipitation, land use and geological environment regions were selected based on an initial exploratory data analysis. Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information: Set 1(disregards the presence of incomplete landslide information), Set 2(excludes factors related to the incompleteness of landslide data), Set 3(accounts for factors related to the incompleteness via random effects). The variable sets were then introduced in a generalized additive model(GAM: Set 1 and Set 2) and a generalized additive mixed effect model(GAMM: Set 3) to establish three national-scale statistical landslide susceptibility models: models 1, 2 and 3. The models were evaluated using the area under the receiver operating characteristics curve(AUROC) given by spatially explicit and non-spatial cross-validation. The spatial prediction pattern produced by the models were also investigated. The results show that the landslide inventory incompleteness had a substantial impact on the outcomes of the statistical landslide susceptibility models. The cross-validation results provided evidence that the three established models performed well to predict model-independent landslide information with median AUROCs ranging from 0.8 to 0.9.However, although Model 1 reached the highest AUROCs within non-spatial cross-validation(median of 0.9), it was not associated with the most plausible representation of landslide susceptibility. The Model 1 modelling results were inconsistent with geomorphological process knowledge and reflected a large extent the underlying data bias. The Model 2 susceptibility maps provided a less biased picture of landslide susceptibility. However, a lower predicted likelihood of landslide occurrence still existed in areas known to be underrepresented in terms of landslide data(e.g., the Kuenlun Mountains in the northern Tibetan Plateau). The non-linear mixed-effects model(Model 3) reduced the impact of these biases best by introducing bias-describing variables as random effects. Among the three models, Model 3 was selected as the best national-scale susceptibility model for China as it produced the most plausible portray of rainfall induced landslide susceptibility and the highest spatially explicit predictive performance(median AUROC of spatial cross validation 0.84) compared to the other two models(median AUROCs of 0.81 and 0.79, respectively). We conclude that ignoring landslide inventory-based incompleteness can entail misleading modelling results and that the application of non-linear mixed-effect models can reduce the propagation of such biases into the final results for very large areas. | Qigen Lin Pedro Lima Stefan Steger Thomas Glade Tong Jiang Jiahui Zhang Tianxue Liu Ying Wang | 2021 | Geoscience Frontiers2021，12，6： | 4 |
| 2 | Projected land use changes in the Qinghai-Tibet Plateau at the carbon peak and carbon neutrality targets显示文摘Based on historical land use for eight periods from 1980 to 2020 and the projected land use under seven Shared Socioeconomic Pathways(SSPs:SSP1-1.9,SSP1-2.6,SSP2-4.5,SSP3-7.0,SSP4-3.4,SSP4-6.0,and SSP5-8.5)from 2021 to 2100,we conducted a study on past and future land use changes in the Qinghai-Tibet Plateau(QTP).This work aims to reveal the land use changes during the carbon peak(2021-2040)and carbon neutrality(2051-2070)periods and at the end of the 21st century(2081-2100).The results show that:(1)in the historical period(1980-2020),the land use types in the QTP were grassland(1475×10^(3)km^(2),58.2%),barren land(685×10^(3)km^(2),27.0%),forest land(243×10^(3)km^(2),9.6%),water(114×10^(3)km^(2),4.5%),cropland(18.6×10^(3)km^(2),0.7%)and urban land(0.3×10^(3)km^(2),0.01%).(2)Relative to the baseline period(1995-2014),the area of grassland is projected to decrease by 0.7%(SSP4-6.0)-5.4%(SSP2-4.5)(0.5-3.9%of the total area of the QTP),2.8%(SSP4-6.0)-12.5%(SSP3-7.0)(2.1-9.4%of the total area of the QTP)and 6.1%(SSP4-6.0)-21.7%(SSP4-3.4)(4.6-16.4%of the total area of the QTP)in the future three periods.In contrast,the forest land area is projected to increase,by approximately 2.5%(SSP4-6.0)to 30.1%(SSP3-7.0)(0.3-4.3%of the total area of the QTP),9.2%(SSP4-6.0)to 56.5%(SSP2-4.5)(1.3-8.0%of the total area of the QTP),and 21.2%(SSP4-6.0)to 72.8%(SSP2-4.5)(3.0-10.2%of the total area of the QTP)in the future three periods,respectively.(3)Approximately 0.4(SSP4-6.0)to 6.9%(SSP5-8.5),0.9(SSP4-6.0)to 2.7%(SSP4-3.4),and 0.04(SSP5-8.5)to 3.5%(SSP1-1.9)of land is expected to convert from grassland to forest land in the future three periods,respectively.The shift from grassland to forest land area is likely to enhance the carbon sink potential of the QTP in the future period. | Runhong XU Peijun SHI Miaoni GAO Yanjun WANG Guojie WANG Buda SU Jinlong HUANG Qigen LIN Tong JIANG | 2023 | Science China Earth Sciences2023，66，6： | 0 |
| 3 | Assessing Global Landslide Casualty Risk Under Moderate Climate Change Based on Multiple GCM Projections显示文摘Extreme precipitation-induced landslide events are projected to increase under climate change,which poses a serious threat to human lives and property.In this study,a global-scale landslide risk assessment model was established using global landslide data,by considering landslide hazard,exposure,and vulnerability.The global climate model data were then employed to drive the established global landslide risk model to explore the spatial and temporal variations in future landslide risk across the globe as a result of extreme precipitation changes.The results show that compared to the 30-year period from 1971 to 2000,the average annual frequency of landslides triggered by extreme precipitation is projected to increase by 7%and 10%,respectively,in the future 30-year periods of 2031–2060 and 2066–2095.The global average annual casualty risk of landslides is projected to increase from about 3240 to 7670 and 8380,respectively(with growth rates of 140%and 160%),during the 2031–2060 and 2066–2095 periods under the SSP2-4.5 scenario.The top 10 countries with the highest casualty risk of landslides are China,Afghanistan,India,the Philippines,Indonesia,Rwanda,Turkey,Nepal,Guatemala,and Brazil,60%of which are located in Asia.The frequency and intensity of extreme precipitation will increase under climate change,which will lead to an increase in casualties from landslides in mountainous areas globally,and this risk should be taken seriously.The present study was an attempt to investigate and quantify the impact of global landslide casualty risk under climate change,which still has uncertainty in terms of outcomes,and there remains a need for further understanding in the future of the propagation of uncertainty between the factors that affect the risk. | Xia Wang Ying Wang Qigen Lin Xudong Yang | 2023 | International Journal of Disaster Risk Science2023，14，5： | 0 |
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