A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical dis...A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical distribution of the two variables may show multimodal characteristics.In this work,a finite mixture bivariate statistical model was designed to describe the statistical properties,which is composed of several components,each with a Weibull distribution and a normal distribution for wind speed and wind shear,respectively,with a Gaussian copula to describe the dependency structure between the two variables.To confirm the developed model,reanalysis data from six positions in the coastal sea areas of China were used.Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions,giving acceptable predictions of the joint probability distributions.Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics.Importantly,unlike traditional methods that are limited to specific hub heights,the model developed here can estimate wind energy potential across different hub heights,enhancing the economic viability assessment of wind power projects.展开更多
Nepal was hit by a 7.8 magnitude earthquake on 25^(th) April,2015.The main shock and many large aftershocks generated a large number of coseismic landslips in central Nepal.We have developed a landslide susceptibility...Nepal was hit by a 7.8 magnitude earthquake on 25^(th) April,2015.The main shock and many large aftershocks generated a large number of coseismic landslips in central Nepal.We have developed a landslide susceptibility map of the affected region based on the coseismic landslides collected from remotely sensed data and fieldwork,using bivariate statistical model with different landslide causative factors.From the investigation,it is observed that most of the coseismic landslides are independent of previous landslides.Out of 3,716 mapped landslides,we used 80% of them to develop a susceptibility map and the remaining 20% were taken for validating the model.A total of 11 different landslide-influencing parameters were considered.These include slope gradient,slope aspect,plan curvature,elevation,relative relief,Peak Ground Acceleration(PGA),distance from epicenters of the mainshock and major aftershocks,lithology,distance of the landslide from the fault,fold,and drainage line.The success rate of 87.66% and the prediction rate of86.87% indicate that the model is in good agreement between the developed susceptibility map and theexisting landslides data.PGA,lithology,slope angle and elevation have played a major role in triggering the coseismic mass movements.This susceptibility map can be used for relocating the people in the affected regions as well as for future land development.展开更多
基金supported by the Key R&D Program of Shandong Province,China(No.2021ZLGX04)the National Natural Science Foundation of China(No.52171284)。
文摘A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical distribution of the two variables may show multimodal characteristics.In this work,a finite mixture bivariate statistical model was designed to describe the statistical properties,which is composed of several components,each with a Weibull distribution and a normal distribution for wind speed and wind shear,respectively,with a Gaussian copula to describe the dependency structure between the two variables.To confirm the developed model,reanalysis data from six positions in the coastal sea areas of China were used.Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions,giving acceptable predictions of the joint probability distributions.Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics.Importantly,unlike traditional methods that are limited to specific hub heights,the model developed here can estimate wind energy potential across different hub heights,enhancing the economic viability assessment of wind power projects.
基金the Chinese Academy of Sciences Presidents International Fellowship Initiative(Grant No.2015PEO23)External Cooperation Program of BIC,15 Chinese Academy of Sciences(Grant No.131551KYSB20150009)hundred talents program of Chinese Academy of Sciences(Su Lijun)for supporting for this research
文摘Nepal was hit by a 7.8 magnitude earthquake on 25^(th) April,2015.The main shock and many large aftershocks generated a large number of coseismic landslips in central Nepal.We have developed a landslide susceptibility map of the affected region based on the coseismic landslides collected from remotely sensed data and fieldwork,using bivariate statistical model with different landslide causative factors.From the investigation,it is observed that most of the coseismic landslides are independent of previous landslides.Out of 3,716 mapped landslides,we used 80% of them to develop a susceptibility map and the remaining 20% were taken for validating the model.A total of 11 different landslide-influencing parameters were considered.These include slope gradient,slope aspect,plan curvature,elevation,relative relief,Peak Ground Acceleration(PGA),distance from epicenters of the mainshock and major aftershocks,lithology,distance of the landslide from the fault,fold,and drainage line.The success rate of 87.66% and the prediction rate of86.87% indicate that the model is in good agreement between the developed susceptibility map and theexisting landslides data.PGA,lithology,slope angle and elevation have played a major role in triggering the coseismic mass movements.This susceptibility map can be used for relocating the people in the affected regions as well as for future land development.
