General circulation models （GCMs） are often used in assessing the impact of climate change at global and continental scales. However, the climatic factors simulated by GCMs are inconsistent at comparatively smaller ...General circulation models （GCMs） are often used in assessing the impact of climate change at global and continental scales. However, the climatic factors simulated by GCMs are inconsistent at comparatively smaller scales, such as individual river basins. In this study, a statistical downscaling approach based on the Smooth Support Vector Machine （SSVM） method was constructed to predict daily precipitation of the changed climate in the Hanjiang Basin. NCEP/NCAR reanalysis data were used to establish the statistical relationship between the larger scale climate predictors and observed precipitation. The relationship obtained was used to project future precipitation from two GCMs （CGCM2 and HadCM3） for the A2 emission scenario. The results obtained using SSVM were compared with those from an artificial neural network （ANN）. The comparisons showed that SSVM is suitable for conducting climate impact studies as a statistical downscaling tool in this region. The temporal trends projected by SSVM based on the A2 emission scenario for CGCM2 and HadCM3 were for rainfall to decrease during the period 2011–2040 in the upper basin and to increase after 2071 in the whole of Hanjiang Basin.展开更多
Landslides are widespread geomorphological phenomena with complex mechanisms that have caused extensive causalities and property damage worldwide.The scale and frequency of landslides are presently increasing owing to...Landslides are widespread geomorphological phenomena with complex mechanisms that have caused extensive causalities and property damage worldwide.The scale and frequency of landslides are presently increasing owing to the warming effects of climate change,which further increases the associated safety risks.In this study,the relationship between historical landslides and environmental variables in the Hanjiang River Basin was determined and an optimized model was used to constrain the relative contribution of variables and best spatial response curve.The optimal MaxEnt model was used to predict the current distribution of landslides and influence of future rainfall changes on the landslide susceptibility.The results indicate that environmental variables in the study area statistically correlate with landslide events over the past 20 years.The MaxEnt model evaluation was applied to landslide hazards in the Hanjiang River Basin based on current climate change scenarios.The results indicate that 25.9%of the study area is classified as a high-risk area.The main environmental variables that affect the distribution of landslides include altitude,slope,normalized difference vegetation index,annual precipitation,distance from rivers,and distance from roads,with a cumulative contribution rate of approximately 90%.The annual rainfall in the Hanjiang River Basin will continue to increase under future climate warming scenarios.Increased rainfall will further increase the extent of high-and medium-risk areas in the basin,especially when following the RCP8.5 climate prediction,which is expected to increase the high-risk area by 10.7%by 2070.Furthermore,high landslide risk areas in the basin will migrate to high-altitude areas in the future,which poses new challenges for the prevention and control of landslide risks.This study demonstrates the usefulness of the MaxEnt model as a tool for landslide susceptibility prediction in the Hanjiang River Basin caused by global warming and yields robust prediction results.This approach therefore provides an important reference for river basin management and disaster reduction and prevention.The study on landslide risks also supports the hypothesis that global climate change will further enhance the frequency and intensity of landslide activity throughout the course of the 21st Century.展开更多
基金supported financially by the National Natural Science Foundation of China (Grant Nos.50679063 and 50809049)the International CooperationResearch Fund of China (2005DFA20520)the Re-search Fund for the Doctoral Program of Higher Education(200804861062)
文摘General circulation models （GCMs） are often used in assessing the impact of climate change at global and continental scales. However, the climatic factors simulated by GCMs are inconsistent at comparatively smaller scales, such as individual river basins. In this study, a statistical downscaling approach based on the Smooth Support Vector Machine （SSVM） method was constructed to predict daily precipitation of the changed climate in the Hanjiang Basin. NCEP/NCAR reanalysis data were used to establish the statistical relationship between the larger scale climate predictors and observed precipitation. The relationship obtained was used to project future precipitation from two GCMs （CGCM2 and HadCM3） for the A2 emission scenario. The results obtained using SSVM were compared with those from an artificial neural network （ANN）. The comparisons showed that SSVM is suitable for conducting climate impact studies as a statistical downscaling tool in this region. The temporal trends projected by SSVM based on the A2 emission scenario for CGCM2 and HadCM3 were for rainfall to decrease during the period 2011–2040 in the upper basin and to increase after 2071 in the whole of Hanjiang Basin.
基金funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)National Foundation of Forestry Science and Technology Popularization(No.[2015]17)Major Fund for Natural Science of Jiangsu Higher Education Institutions(No.15KJA220004).
文摘Landslides are widespread geomorphological phenomena with complex mechanisms that have caused extensive causalities and property damage worldwide.The scale and frequency of landslides are presently increasing owing to the warming effects of climate change,which further increases the associated safety risks.In this study,the relationship between historical landslides and environmental variables in the Hanjiang River Basin was determined and an optimized model was used to constrain the relative contribution of variables and best spatial response curve.The optimal MaxEnt model was used to predict the current distribution of landslides and influence of future rainfall changes on the landslide susceptibility.The results indicate that environmental variables in the study area statistically correlate with landslide events over the past 20 years.The MaxEnt model evaluation was applied to landslide hazards in the Hanjiang River Basin based on current climate change scenarios.The results indicate that 25.9%of the study area is classified as a high-risk area.The main environmental variables that affect the distribution of landslides include altitude,slope,normalized difference vegetation index,annual precipitation,distance from rivers,and distance from roads,with a cumulative contribution rate of approximately 90%.The annual rainfall in the Hanjiang River Basin will continue to increase under future climate warming scenarios.Increased rainfall will further increase the extent of high-and medium-risk areas in the basin,especially when following the RCP8.5 climate prediction,which is expected to increase the high-risk area by 10.7%by 2070.Furthermore,high landslide risk areas in the basin will migrate to high-altitude areas in the future,which poses new challenges for the prevention and control of landslide risks.This study demonstrates the usefulness of the MaxEnt model as a tool for landslide susceptibility prediction in the Hanjiang River Basin caused by global warming and yields robust prediction results.This approach therefore provides an important reference for river basin management and disaster reduction and prevention.The study on landslide risks also supports the hypothesis that global climate change will further enhance the frequency and intensity of landslide activity throughout the course of the 21st Century.
