Global Positioning System(GPS)measurements of integrated water vapor(IWV)for two years(2014 and 2015)are presented in this paper.Variation of IWV during active and break spells of Indian summer monsoon has been studie...Global Positioning System(GPS)measurements of integrated water vapor(IWV)for two years(2014 and 2015)are presented in this paper.Variation of IWV during active and break spells of Indian summer monsoon has been studied for a tropical station Hyderabad(17.4°N,78.46°E).The data is validated with ECMWF Re-Analysis(ERA)91 level data.Relationships of IWV with other atmospheric variables like surface temperature,rain,and precipitation efficiency have been established through cross-correlation studies.A positive correlation coefficient is observed between IWV and surface temperature over two years.But the coefficient becomes negative when only summer monsoon months(June,July,August,and September)are considered.Rainfall during these months cools down the surface and could be the reason for this change in the correlation coefficient.Correlation studies between IWV-precipitation,IWVprecipitation efficiency(P.E),and precipitation-P.E show that coefficients are-0.05,-0.10 and 0.983 with 95%confidence level respectively,which proves that the efficacy of rain does not depend only on the level of water vapor.A proper dynamic mechanism is necessary to convert water vapor into the rain.The diurnal variations of IWV during active and break spells have been analyzed.The amplitudes of diurnal oscillation and its harmonics of individual spell do not show clear trends but the mean amplitudes of the break spells are approximately double than those of the active spells.The amplitudes of diurnal,semidiurnal and ter-diurnal components during break spells are 1.08 kg/m^(2),0.52 kg/m;and 0.34 kg/m;respectively.The corresponding amplitudes during active spells are 0.68 kg/m^(2),0.41 kg/m;and 0.23 kg/m;.展开更多
Recycled moisture is an important indicator of the renewal capacity of regional water resources.Due to the existence of Yulong Snow Mountain,Lijiang in Yunnan Province,southeast of the Qinghai-Tibet Plateau,China,is t...Recycled moisture is an important indicator of the renewal capacity of regional water resources.Due to the existence of Yulong Snow Mountain,Lijiang in Yunnan Province,southeast of the Qinghai-Tibet Plateau,China,is the closest ocean glacier area to the equator in Eurasia.Daily precipitation samples were collected from 2017 to 2018 in Lijiang to quantify the effect of sub-cloud evaporation and recycled moisture on precipitation combined with the d-excess model during monsoon and non-monsoon periods.The results indicated that the d-excess values of precipitation fluctuated between–35.6‰and 16.0‰,with an arithmetic mean of 3.5‰.The local meteoric water line(LMWL)wasδD=7.91δ^(18)O+2.50,with a slope slightly lower than the global meteoric water line(GMWL).Subcloud evaporation was higher during the non-monsoon season than during the monsoon season.It tended to peak in March and was primarily influenced by the relative humidity.The source of the water vapour affected the proportion of recycled moisture.According to the results of the Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT)model,the main sources of water vapour in Lijiang area during the monsoon period were the southwest and southeast monsoons.During the non-monsoon period,water vapour was transported by a southwesterly flow.The recycled moisture in Lijiang area between March and October 2017 was 10.62%.Large variations were observed between the monsoon and non-monsoon seasons,with values of 5.48%and 25.65%,respectively.These differences were primarily attributed to variations in the advection of water vapour.The recycled moisture has played a supplementary role in the precipitation of Lijiang area.展开更多
The overall purpose of this paper is to post-evaluate the predictability of Hurricane Florence using the Advanced Research Weather Research Forecast (WRF) (ARW) version of a mesoscale model. This was performed over th...The overall purpose of this paper is to post-evaluate the predictability of Hurricane Florence using the Advanced Research Weather Research Forecast (WRF) (ARW) version of a mesoscale model. This was performed over the period from 0000 UTC 13 September 2018 through 0000 UTC 18 September 2018. The WRF ARW core resolution used here was the 27-km grid spacing chosen to in order to balance finer resolution against in house processing time and storage. The large-scale analysis showed that a change in the Northern Hemisphere flow regime, especially the flow in the western part of the Northern Hemisphere may have contributed partly to the reduced forward speed of the tropical cyclone. In order to measure the predictability of a system, we will use different convective and boundary layer schemes initialized from the same conditions. The results demonstrated that the sign of the local IRE tendency was similar to that of the Northern Hemisphere Integrated Enstrophy. The results also showed that when the boundary layer, convective, and cloud microphysical schemes of the model were varied, the areal coverage of heavy precipitation of Florence was under-forecast by approximately 10% or more, and the heaviest amounts were under-forecast by an average of about 20%.展开更多
In this study,the future landslide population amount risk(LPAR)is assessed based on integrated machine learning models(MLMs)and scenario simulation techniques in Shuicheng County,China.Firstly,multiple MLMs were selec...In this study,the future landslide population amount risk(LPAR)is assessed based on integrated machine learning models(MLMs)and scenario simulation techniques in Shuicheng County,China.Firstly,multiple MLMs were selected and hyperparameters were optimized,and the generated 11 models were crossintegrated to select the best model to calculate landslide susceptibility;by calculating precipitation for different extreme precipitation recurrence periods and combining the susceptibility results to assess the landslide hazard.Using the town as the basic unit,the exposure and vulnerability of the future landslide population under different Shared Socioeconomic Pathways(SSPs)scenarios in each town were assessed,and then combined with the hazard to estimate the LPAR in 2050.The results showed that the integrated model with the optimized random forest model as the combination strategy had the best comprehensive performance in susceptibility assessment.The distribution of hazard classes is similar to susceptibility,and with an increase in precipitation,the low-hazard area and high-hazard decrease and shift to medium-hazard and very high-hazard classes.The high-risk areas for future landslide populations in Shuicheng County are mainly concentrated in the three southwestern towns with high vulnerability,whereas the northern towns of Baohua and Qinglin are at the lowest risk class.The LPAR increased with the intensity of extreme precipitation.The LPAR differs significantly among the SSPs scenarios,with the lowest in the“fossil-fueled development(SSP5)”scenario and the highest in the“regional rivalry(SSP3)”scenario.In summary,the landslide susceptibility model based on integrated machine learning proposed in this study has a high predictive capability.The results of future LPAR assessment can provide theoretical guidance for relevant departments to cope with future socioeconomic development challenges and make corresponding disaster prevention and mitigation plans to prevent landslide risks from a developmental perspective.展开更多
气候变暖已经引起全球降水格局改变。土壤呼吸作为陆地生态系统向大气释放CO_(2)最大的碳库,对降水变化的响应将进一步影响碳循环,从而对全球气候变化产生反馈。尽管以往已有大量关于土壤呼吸与降水变化关系的相关研究,但存在较大争议...气候变暖已经引起全球降水格局改变。土壤呼吸作为陆地生态系统向大气释放CO_(2)最大的碳库,对降水变化的响应将进一步影响碳循环,从而对全球气候变化产生反馈。尽管以往已有大量关于土壤呼吸与降水变化关系的相关研究,但存在较大争议。因此,亟待进一步深入探究土壤呼吸对降水改变的响应。基于此,研究Meta分析方法,整合了来自Web of Science英文数据库和中国知网文献数据库(CNKI)的284篇已发表的论文和367组数据,进而分析全球中低纬度地区土壤呼吸对降水改变的响应。研究结果表明,土壤呼吸对降水改变的响应呈现出非对称特征,降水量增加能够提高16.7%的土壤呼吸,而降水量减少则会抑制17.88%的土壤呼吸。研究还发现,不同生态系统和气候区域的土壤呼吸对降水改变的响应存在较大差别。其中,降水量增加能够提高草地生态系统22%的土壤呼吸,比森林生态系统土壤呼吸高出12%;而降水量减少则会削弱草地生态系统28%的土壤呼吸,这要比森林生态系统土壤呼吸还高16%。与湿润地区相比,降水量的增加对干旱地区土壤呼吸的促进作用更加明显。而降水量的减少对干旱地区和湿润地区土壤呼吸的影响均无显著差异。此外,本研究也证实了土壤呼吸对不同降水强度和年限的响应也存在差异。在不同降水强度上,无论增加降水还是减少降水,重度增减雨的土壤呼吸均改变最大,即:重度增减雨(>75%)>中度增减雨(25%—75%)>轻度增减雨(<25%);在不同降水年限上,长期增雨对土壤呼吸的促进作用尤为突出,但长期减雨对土壤呼吸影响无显著差异。研究结果可为未来气候情景下陆地生态系统土壤呼吸变化的准确预测以及模型模拟和改进提供重要的科学依据和理论基础。展开更多
Extreme precipitation events bring considerable risks to the natural ecosystem and human life.Investigating the spatial-temporal characteristics of extreme precipitation and predicting it quantitatively are critical f...Extreme precipitation events bring considerable risks to the natural ecosystem and human life.Investigating the spatial-temporal characteristics of extreme precipitation and predicting it quantitatively are critical for the flood prevention and water resources planning and management.In this study,daily precipitation data(1957–2019)were collected from 24 meteorological stations in the Weihe River Basin(WRB),Northwest China and its surrounding areas.We first analyzed the spatial-temporal change of precipitation extremes in the WRB based on space-time cube(STC),and then predicted precipitation extremes using long short-term memory(LSTM)network,auto-regressive integrated moving average(ARIMA),and hybrid ensemble empirical mode decomposition(EEMD)-LSTM-ARIMA models.The precipitation extremes increased as the spatial variation from northwest to southeast of the WRB.There were two clusters for each extreme precipitation index,which were distributed in the northwestern and southeastern or northern and southern of the WRB.The precipitation extremes in the WRB present a strong clustering pattern.Spatially,the pattern of only high-high cluster and only low-low cluster were primarily located in lower reaches and upper reaches of the WRB,respectively.Hot spots(25.00%–50.00%)were more than cold spots(4.17%–25.00%)in the WRB.Cold spots were mainly concentrated in the northwestern part,while hot spots were mostly located in the eastern and southern parts.For different extreme precipitation indices,the performances of the different models were different.The accuracy ranking was EEMD-LSTM-ARIMA>LSTM>ARIMA in predicting simple daily intensity index(SDII)and consecutive wet days(CWD),while the accuracy ranking was LSTM>EEMD-LSTM-ARIMA>ARIMA in predicting very wet days(R95 P).The hybrid EEMD-LSTM-ARIMA model proposed was generally superior to single models in the prediction of precipitation extremes.展开更多
Identifying water vapor sources in the natural vegetation of the Tianshan Mountains is of significant importance for obtaining greater knowledge about the water cycle,forecasting water resource changes,and dealing wit...Identifying water vapor sources in the natural vegetation of the Tianshan Mountains is of significant importance for obtaining greater knowledge about the water cycle,forecasting water resource changes,and dealing with the adverse effects of climate change.In this study,we identified water vapor sources of precipitation and evaluated their effects on precipitation stable isotopes in the north slope of the Tianshan Mountains,China.By utilizing the temporal and spatial distributions of precipitation stable isotopes in the forest and grassland regions,Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT)model,and isotope mass balance model,we obtained the following results.(1)The Eurasia,Black Sea,and Caspian Sea are the major sources of water vapor.(2)The contribution of surface evaporation to precipitation in forests is lower than that in the grasslands(except in spring),while the contribution of plant transpiration to precipitation in forests(5.35%)is higher than that in grasslands(3.79%)in summer.(3)The underlying surface and temperature are the main factors that affect the contribution of recycled water vapor to precipitation;meanwhile,the effects of water vapor sources of precipitation on precipitation stable isotopes are counteracted by other environmental factors.Overall,this work will prove beneficial in quantifying the effect of climate change on local water cycles.展开更多
基金research fellowship offered by ISRO under RESPOND program[No.ISRO/RES/2/406/16-17]。
文摘Global Positioning System(GPS)measurements of integrated water vapor(IWV)for two years(2014 and 2015)are presented in this paper.Variation of IWV during active and break spells of Indian summer monsoon has been studied for a tropical station Hyderabad(17.4°N,78.46°E).The data is validated with ECMWF Re-Analysis(ERA)91 level data.Relationships of IWV with other atmospheric variables like surface temperature,rain,and precipitation efficiency have been established through cross-correlation studies.A positive correlation coefficient is observed between IWV and surface temperature over two years.But the coefficient becomes negative when only summer monsoon months(June,July,August,and September)are considered.Rainfall during these months cools down the surface and could be the reason for this change in the correlation coefficient.Correlation studies between IWV-precipitation,IWVprecipitation efficiency(P.E),and precipitation-P.E show that coefficients are-0.05,-0.10 and 0.983 with 95%confidence level respectively,which proves that the efficacy of rain does not depend only on the level of water vapor.A proper dynamic mechanism is necessary to convert water vapor into the rain.The diurnal variations of IWV during active and break spells have been analyzed.The amplitudes of diurnal oscillation and its harmonics of individual spell do not show clear trends but the mean amplitudes of the break spells are approximately double than those of the active spells.The amplitudes of diurnal,semidiurnal and ter-diurnal components during break spells are 1.08 kg/m^(2),0.52 kg/m;and 0.34 kg/m;respectively.The corresponding amplitudes during active spells are 0.68 kg/m^(2),0.41 kg/m;and 0.23 kg/m;.
基金Under the auspices of National Natural Science Foundation of China (No.42101044,42077188,52109007)。
文摘Recycled moisture is an important indicator of the renewal capacity of regional water resources.Due to the existence of Yulong Snow Mountain,Lijiang in Yunnan Province,southeast of the Qinghai-Tibet Plateau,China,is the closest ocean glacier area to the equator in Eurasia.Daily precipitation samples were collected from 2017 to 2018 in Lijiang to quantify the effect of sub-cloud evaporation and recycled moisture on precipitation combined with the d-excess model during monsoon and non-monsoon periods.The results indicated that the d-excess values of precipitation fluctuated between–35.6‰and 16.0‰,with an arithmetic mean of 3.5‰.The local meteoric water line(LMWL)wasδD=7.91δ^(18)O+2.50,with a slope slightly lower than the global meteoric water line(GMWL).Subcloud evaporation was higher during the non-monsoon season than during the monsoon season.It tended to peak in March and was primarily influenced by the relative humidity.The source of the water vapour affected the proportion of recycled moisture.According to the results of the Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT)model,the main sources of water vapour in Lijiang area during the monsoon period were the southwest and southeast monsoons.During the non-monsoon period,water vapour was transported by a southwesterly flow.The recycled moisture in Lijiang area between March and October 2017 was 10.62%.Large variations were observed between the monsoon and non-monsoon seasons,with values of 5.48%and 25.65%,respectively.These differences were primarily attributed to variations in the advection of water vapour.The recycled moisture has played a supplementary role in the precipitation of Lijiang area.
文摘The overall purpose of this paper is to post-evaluate the predictability of Hurricane Florence using the Advanced Research Weather Research Forecast (WRF) (ARW) version of a mesoscale model. This was performed over the period from 0000 UTC 13 September 2018 through 0000 UTC 18 September 2018. The WRF ARW core resolution used here was the 27-km grid spacing chosen to in order to balance finer resolution against in house processing time and storage. The large-scale analysis showed that a change in the Northern Hemisphere flow regime, especially the flow in the western part of the Northern Hemisphere may have contributed partly to the reduced forward speed of the tropical cyclone. In order to measure the predictability of a system, we will use different convective and boundary layer schemes initialized from the same conditions. The results demonstrated that the sign of the local IRE tendency was similar to that of the Northern Hemisphere Integrated Enstrophy. The results also showed that when the boundary layer, convective, and cloud microphysical schemes of the model were varied, the areal coverage of heavy precipitation of Florence was under-forecast by approximately 10% or more, and the heaviest amounts were under-forecast by an average of about 20%.
基金supported by“The National Key Research and Development Program of China(2018YFC1508804)The Key Scientific and Technology Program of Jilin Province(20170204035SF)+2 种基金The Key Scientific and Technology Research and Development Program of Jilin Province(20200403074SF)The Key Scientific and Technology Research and Development Program of Jilin Province(20180201035SF)National Natural Science Fund for Young Scholars of China(41907238)”.
文摘In this study,the future landslide population amount risk(LPAR)is assessed based on integrated machine learning models(MLMs)and scenario simulation techniques in Shuicheng County,China.Firstly,multiple MLMs were selected and hyperparameters were optimized,and the generated 11 models were crossintegrated to select the best model to calculate landslide susceptibility;by calculating precipitation for different extreme precipitation recurrence periods and combining the susceptibility results to assess the landslide hazard.Using the town as the basic unit,the exposure and vulnerability of the future landslide population under different Shared Socioeconomic Pathways(SSPs)scenarios in each town were assessed,and then combined with the hazard to estimate the LPAR in 2050.The results showed that the integrated model with the optimized random forest model as the combination strategy had the best comprehensive performance in susceptibility assessment.The distribution of hazard classes is similar to susceptibility,and with an increase in precipitation,the low-hazard area and high-hazard decrease and shift to medium-hazard and very high-hazard classes.The high-risk areas for future landslide populations in Shuicheng County are mainly concentrated in the three southwestern towns with high vulnerability,whereas the northern towns of Baohua and Qinglin are at the lowest risk class.The LPAR increased with the intensity of extreme precipitation.The LPAR differs significantly among the SSPs scenarios,with the lowest in the“fossil-fueled development(SSP5)”scenario and the highest in the“regional rivalry(SSP3)”scenario.In summary,the landslide susceptibility model based on integrated machine learning proposed in this study has a high predictive capability.The results of future LPAR assessment can provide theoretical guidance for relevant departments to cope with future socioeconomic development challenges and make corresponding disaster prevention and mitigation plans to prevent landslide risks from a developmental perspective.
文摘气候变暖已经引起全球降水格局改变。土壤呼吸作为陆地生态系统向大气释放CO_(2)最大的碳库,对降水变化的响应将进一步影响碳循环,从而对全球气候变化产生反馈。尽管以往已有大量关于土壤呼吸与降水变化关系的相关研究,但存在较大争议。因此,亟待进一步深入探究土壤呼吸对降水改变的响应。基于此,研究Meta分析方法,整合了来自Web of Science英文数据库和中国知网文献数据库(CNKI)的284篇已发表的论文和367组数据,进而分析全球中低纬度地区土壤呼吸对降水改变的响应。研究结果表明,土壤呼吸对降水改变的响应呈现出非对称特征,降水量增加能够提高16.7%的土壤呼吸,而降水量减少则会抑制17.88%的土壤呼吸。研究还发现,不同生态系统和气候区域的土壤呼吸对降水改变的响应存在较大差别。其中,降水量增加能够提高草地生态系统22%的土壤呼吸,比森林生态系统土壤呼吸高出12%;而降水量减少则会削弱草地生态系统28%的土壤呼吸,这要比森林生态系统土壤呼吸还高16%。与湿润地区相比,降水量的增加对干旱地区土壤呼吸的促进作用更加明显。而降水量的减少对干旱地区和湿润地区土壤呼吸的影响均无显著差异。此外,本研究也证实了土壤呼吸对不同降水强度和年限的响应也存在差异。在不同降水强度上,无论增加降水还是减少降水,重度增减雨的土壤呼吸均改变最大,即:重度增减雨(>75%)>中度增减雨(25%—75%)>轻度增减雨(<25%);在不同降水年限上,长期增雨对土壤呼吸的促进作用尤为突出,但长期减雨对土壤呼吸影响无显著差异。研究结果可为未来气候情景下陆地生态系统土壤呼吸变化的准确预测以及模型模拟和改进提供重要的科学依据和理论基础。
基金Under the auspices of National Key Research and Development Program of China(No.2017YFE0118100-1)。
文摘Extreme precipitation events bring considerable risks to the natural ecosystem and human life.Investigating the spatial-temporal characteristics of extreme precipitation and predicting it quantitatively are critical for the flood prevention and water resources planning and management.In this study,daily precipitation data(1957–2019)were collected from 24 meteorological stations in the Weihe River Basin(WRB),Northwest China and its surrounding areas.We first analyzed the spatial-temporal change of precipitation extremes in the WRB based on space-time cube(STC),and then predicted precipitation extremes using long short-term memory(LSTM)network,auto-regressive integrated moving average(ARIMA),and hybrid ensemble empirical mode decomposition(EEMD)-LSTM-ARIMA models.The precipitation extremes increased as the spatial variation from northwest to southeast of the WRB.There were two clusters for each extreme precipitation index,which were distributed in the northwestern and southeastern or northern and southern of the WRB.The precipitation extremes in the WRB present a strong clustering pattern.Spatially,the pattern of only high-high cluster and only low-low cluster were primarily located in lower reaches and upper reaches of the WRB,respectively.Hot spots(25.00%–50.00%)were more than cold spots(4.17%–25.00%)in the WRB.Cold spots were mainly concentrated in the northwestern part,while hot spots were mostly located in the eastern and southern parts.For different extreme precipitation indices,the performances of the different models were different.The accuracy ranking was EEMD-LSTM-ARIMA>LSTM>ARIMA in predicting simple daily intensity index(SDII)and consecutive wet days(CWD),while the accuracy ranking was LSTM>EEMD-LSTM-ARIMA>ARIMA in predicting very wet days(R95 P).The hybrid EEMD-LSTM-ARIMA model proposed was generally superior to single models in the prediction of precipitation extremes.
基金supported by the Natural Science Foundation of Hainan Province,China(420QN258)the National Natural Science Foundation of China(41630859,41761004).
文摘Identifying water vapor sources in the natural vegetation of the Tianshan Mountains is of significant importance for obtaining greater knowledge about the water cycle,forecasting water resource changes,and dealing with the adverse effects of climate change.In this study,we identified water vapor sources of precipitation and evaluated their effects on precipitation stable isotopes in the north slope of the Tianshan Mountains,China.By utilizing the temporal and spatial distributions of precipitation stable isotopes in the forest and grassland regions,Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT)model,and isotope mass balance model,we obtained the following results.(1)The Eurasia,Black Sea,and Caspian Sea are the major sources of water vapor.(2)The contribution of surface evaporation to precipitation in forests is lower than that in the grasslands(except in spring),while the contribution of plant transpiration to precipitation in forests(5.35%)is higher than that in grasslands(3.79%)in summer.(3)The underlying surface and temperature are the main factors that affect the contribution of recycled water vapor to precipitation;meanwhile,the effects of water vapor sources of precipitation on precipitation stable isotopes are counteracted by other environmental factors.Overall,this work will prove beneficial in quantifying the effect of climate change on local water cycles.