Land use/land cover(LULC)change and climate change are two major factors affecting the provision of ecosystem services which are closely related to human well-being.However,a clear understanding of the relationships b...Land use/land cover(LULC)change and climate change are two major factors affecting the provision of ecosystem services which are closely related to human well-being.However,a clear understanding of the relationships between these two factors and ecosystem services in Central Asia is still lacking.This study aimed to comprehensively assess ecosystem services in Central Asia and analyze how they are impacted by changes in LULC and climate.The spatiotemporal patterns of three ecosystem services during the period of 2000-2015,namely the net primary productivity(NPP),water yield,and soil retention,were quantified and mapped by the Carnegie-Ames-Stanford Approach(CASA)model,Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model,and Revised Universal Soil Loss Equation(RUSLE).Scenarios were used to determine the relative importance and combined effect of LULC change and climate change on ecosystem services.Then,the relationships between climate factors(precipitation and temperature)and ecosystem services,as well as between LULC change and ecosystem services,were further discussed.The results showed that the high values of ecosystem services appeared in the southeast of Central Asia.Among the six biomes(alpine forest region(AFR),alpine meadow region(AMR),typical steppe region(TSR),desert steppe region(DSR),desert region(DR),and lake region(LR)),the values of ecosystem services followed the order of AFR>AMR>TSR>DSR>DR>LR.In addition,the values of ecosystem services fluctuated during the period of 2000-2015,with the most significant decreases observed in the southeast mountainous area and northwest of Central Asia.LULC change had a greater impact on the NPP,while climate change had a stronger influence on the water yield and soil retention.The combined LULC change and climate change exhibited a significant synergistic effect on ecosystem services in most of Central Asia.Moreover,ecosystem services were more strongly and positively correlated with precipitation than with temperature.The greening of desert areas and forest land expansion could improve ecosystem services,but unreasonable development of cropland and urbanization have had an adverse impact on ecosystem services.According to the results,ecological stability in Central Asia can be achieved through the natural vegetation protection,reasonable urbanization,and ecological agriculture development.展开更多
Precipitation and temperature are two important factors associated to snow hazards which block the transport infrastructure and cause loss of life and properties in the cold season.The in-situ observations are limited...Precipitation and temperature are two important factors associated to snow hazards which block the transport infrastructure and cause loss of life and properties in the cold season.The in-situ observations are limited in the alpine with complex topographic characteristics,while coarse satellite rainfall estimates,reanalysis rain datasets,and gridded in-situ rain gauge datasets obscure the understanding of the precipitation patterns in hazardprone areas.Considering the Karakoram Highway(KKH)region as a study area,a double nestedWeather Research and Forecasting(WRF)model with the high resolution of a 10-km horizontal grid was performed to investigate the spatial and temporal patterns of temperature and precipitation covering the Karakoram Highway region during the cold season.The results of WRF were compared with the in-situ observations and Multi-Source WeightedEnsemble Precipitation(MSWEP)datasets.The results demonstrated that the WRF model well reproduced the observed monthly temperature(R=0.96,mean bias=-3.92°C)and precipitation(R=0.57,mean bias=8.69 mm).The WRF model delineated the essential features of precipitation variability and extremes,although it overestimatedthe wet day frequency and underestimated the precipitation intensity.Two rain bands were exhibited in a northwest-to-southeast direction over the study area.High wet day frequency was found in January,February,and March in the section between Hunza and Khunjerab.In addition,the areas with extreme values are mainly located in the Dasu-Islamabad section in February,March,and April.The WRF model has the potential to compensate for the spatial and temporal gaps of the observational networks and to provide more accurate predictions on the meteorological variables for avoiding common coldweather hazards in the ungauged and high altitude areas at a regional scale.展开更多
Snow is a key variable that influences hydrological and climatic cycles.Land surface models employing snow physics-modules can simulate the snow accumulation and ablation processes.However,there are still uncertaintie...Snow is a key variable that influences hydrological and climatic cycles.Land surface models employing snow physics-modules can simulate the snow accumulation and ablation processes.However,there are still uncertainties in modeling snow resources over complex terrain such as mountains.This study employed the National Center for Atmospheric Research’s Weather Research and Forecasting(WRF)model coupled with the Noah-Multiparameterization(Noah-MP)land surface model to run one-year simulations to assess its ability to simulate snow across the Tianshan Mountains.Six tests were conducted based on different reanalysis forcing datasets and different land surface properties.The results indicated that the snow dynamics were reproduced in a snow hydrological year by the WRF/Noah-MP model for all of the tests.The model produced a low bias in snow depth and snow water equivalent(SWE)regardless of the forcing datasets.Additionally,the underestimation of snow depth and SWE could be relatively alleviated by modifying the land cover and vegetation parameters.However,no significant improvement in accuracy was found in the date of snow depth maximum and melt rate.The best performance was achieved using ERA5 with modified land cover and vegetation parameters(mean bias=−4.03 mm and−1.441 mm for snow depth and SWE,respectively).This study highlights the importance of selecting forcing data for snow simulation over the Tianshan Mountains.展开更多
Snow avalanches are a common natural hazard in many countries with seasonally snow-covered mountains.The avalanche hazard varies with snow avalanche type in different snow climate regions and at different times.The ab...Snow avalanches are a common natural hazard in many countries with seasonally snow-covered mountains.The avalanche hazard varies with snow avalanche type in different snow climate regions and at different times.The ability to understand the characteristics of avalanche activity and hazards of different snow avalanche types is a prerequisite for improving avalanche disaster management in the mid-altitude region of the Central Tianshan Mountains.In this study,we collected data related to avalanche,snowpack,and meteorology during four snow seasons(from 2015 to 2019),and analysed the characteristics and hazards of different types of avalanches.The snow climate of the mid-altitude region of the Central Tianshan Mountains was examined using a snow climate classification scheme,and the results showed that the mountain range has a continental snow climate.To quantify the hazards of different types of avalanches and describe their situation over time in the continental snow climate region,this study used the avalanche hazard degree to assess the hazards of four types of avalanches,i.e.,full-depth dry snow avalanches,full-depth wet snow avalanches,surface-layer dry snow avalanches,and surface-layer wet snow avalanches.The results indicated that surface-layer dry snow avalanches were characterized by large sizes and high release frequencies,which made them having the highest avalanche hazard degree in the Central Tianshan Mountains with a continental snow climate.The overall avalanche hazard showed a single peak pattern over time during the snow season,and the greatest hazard occurred in the second half of February when the snowpack was deep and the temperature increased.This study can help the disaster and emergency management departments rationally arrange avalanche relief resources and develop avalanche prevention strategies.展开更多
Obtaining the spatial distribution of snow cover in mountainous areas using the optical image of remote sensing technology is difficult because of cloud and fog. In this study, the object-based principle component ana...Obtaining the spatial distribution of snow cover in mountainous areas using the optical image of remote sensing technology is difficult because of cloud and fog. In this study, the object-based principle component analysis–support vector machine(PCA–SVM) method is proposed for snow cover mapping through the integration of moderateresolution imaging spectroradiometer(MODIS) snow cover products and the Sentinel-1 synthetic aperture radar(SAR) scattering characteristics. First, derived from the Sentinel-1 A SAR images, the feature parameters, including VV/VH backscatter, scattering entropy, and scattering alpha, were used to describe the variations of snow and non-snow covers. Second, the optimum feature combinations of snow cover were formed from the feature parameters using the principle component analysis(PCA) algorithm. Finally, using the optimum feature combinations, a snow cover map with a 20 m spatial resolution was extracted by means of an object-based SVM classifier. This method was applied in the study area of the Xinyuan County, which is located in the western part of the Tianshan Mountains in Xinjiang, China. The accuracies in this method were analyzed according to the data observed at different experimental sites. Results showed that the snow cover pixels of the extraction were less than those in the actual situation(FB1=93.86, FB2=59.78). The evaluation of the threat score(TS), probability of detection(POD), and false alarm ratio(FAR) for the snow-covered pixels obtained from the two-stage SAR images were different(TS1=86.84, POD1=90.10, FAR1=4.01;TS2=56.40, POD2=57.62, FAR2=3.62). False and misclassifications of the snow cover and non-snow cover pixels were found. Although the classifications were not highly accurate, the approach showed potential for integrating different sources to retrieve the spatial distribution of snow covers during a stable period.展开更多
The investigation of concentration characteristics of reference evapotranspiration(ETref) is important for water resources management. The concentration index(CI), concentration degree(CD) and concentration period(CP)...The investigation of concentration characteristics of reference evapotranspiration(ETref) is important for water resources management. The concentration index(CI), concentration degree(CD) and concentration period(CP) are used to investigate the concentration characteristics of ETref and the relationship between ETref concentration and precipitation concentration at sub-monthly timescale based on the daily climatic variables from 1966 to 2015 in 27 meteorological stations at the southern and northern slopes of Tianshan Mountains in China. It was found that the CI of ETref is about 0.40 and less concentrated than precipitation in the study area. At the southern slope, the maximum ETref appears in late June and is earlier than the maximum precipitation(early July), ETref distributes more equally than precipitation, and the CI, CD and CP of these two variables do not show significant change based on the Mann–Kendall test. At the northern slope, both the maximum ETref and precipitation appear in early July, and ETref is more dispersed than precipitation. During the study period, the maximum ETref at the northern slope tends to appear earlier due to the impacts of wind speed, relative humidity, sunshine duration, and air temperature. ETref concentration does not match the precipitation concentration in the study area, particularly at the southern slope. The mismatch between ETref and precipitation concentration within a year reveals the water resources pressure on environmental, social and economic sustainability in the study area.展开更多
Hydrothermal condition is mismatched in arid and semi-arid regions,particularly in Central Asia(including Kazakhstan,Kyrgyzstan,Tajikistan,Uzbekistan,and Turkmenistan),resulting many environmental limitations.In this ...Hydrothermal condition is mismatched in arid and semi-arid regions,particularly in Central Asia(including Kazakhstan,Kyrgyzstan,Tajikistan,Uzbekistan,and Turkmenistan),resulting many environmental limitations.In this study,we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles(MMEs)from the Coupled Model Intercomparison Project Phase 6(CMIP6)under four Shared Socioeconomic Pathway and Representative Concentration Pathway(SSP-RCP)scenarios(SSP126(SSP1-RCP2.6),SSP245(SSP2-RCP4.5),SSP460(SSP4-RCP6.0),and SSP585(SSP5-RCP8.5))during 2015-2100.The bias correction and spatial disaggregation,water-thermal product index,and sensitivity analysis were used in this study.The results showed that the hydrothermal condition is mismatched in the central and southern deserts,whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition.Compared with the historical period,the matched degree of hydrothermal condition improves during 2046-2075,but degenerates during 2015-2044 and 2076-2100.The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions.The result suggests that the optimal scenario in Central Asia is SSP126 scenario,while SSP585 scenario brings further hydrothermal contradictions.This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.展开更多
Playing an important role in global warming and plant growth,relative humidity(RH)has profound impacts on production and living,and can be used as an integrated indicator for evaluating the wet-dry conditions in the a...Playing an important role in global warming and plant growth,relative humidity(RH)has profound impacts on production and living,and can be used as an integrated indicator for evaluating the wet-dry conditions in the arid and semi-arid area.However,information on the spatial-temporal variation and the influencing factors of RH in these regions is still limited.This study attempted to use daily meteorological data during 1966–2017 to reveal the spatial-temporal characteristics of RH in the arid region of Northwest China through rotated empirical orthogonal function and statistical analysis method,and the path analysis was used to clarify the impact of temperature(T),precipitation(P),actual evapotranspiration(ETa),wind speed(W)and sunshine duration(S)on RH.The results demonstrated that climatic conditions in North Xinjiang(NXJ)was more humid than those in Hexi Corridor(HXC)and South Xinjiang(SXJ).RH had a less significant downtrend in NXJ than that in HXC,but an increasingly rising trend was observed in SXJ during the last five decades,implying that HXC and NXJ were under the process of droughts,while SXJ was getting wetter.There was a turning point for the trend of RH in Xinjiang,which occurred in 2000.Path analysis indicated that RH was negatively correlated to T,ETa,W and S,but it increased with increase of P.S,T and W had the greatest direct effects on RH in HXC,NXJ and SXJ,respectively.ETa was the factor which had the greatest indirect effect on RH in HXC and NXJ,while T was the dominant factor in SXJ.展开更多
Climate change and human activities have increased avalanche risks in alpine mountains.Therefore,strengthening the research on mitigating and controlling avalanche disasters is indispensable for sustainable socio-econ...Climate change and human activities have increased avalanche risks in alpine mountains.Therefore,strengthening the research on mitigating and controlling avalanche disasters is indispensable for sustainable socio-economic development in mountainous areas.Early avalanche warning is an essential means of avalanche disaster prevention.However,the theoretical development and application of avalanche warning strategies remain limited due to the lack of systematic understanding of the triggering mechanisms of avalanches.Based on observational data(2015–2019)of avalanches,snowpack,meteorological parameters,surface soil temperature and moisture,and topography in avalanche-prone areas in the central Tianshan Mountains,we analyzed the characteristics of different types of avalanches under a continental snow climate and the environmental factors(such as meteorological conditions and snowpack)that trigger avalanches,as well as the triggering mechanisms for different types of avalanches under the continental snow climate in terms of snow-layer shear fracture modes.We found that the snowpack parameters,weather conditions,and soil temperature and moisture varied significantly among the stages of snow accumulation,stabilization,and melting,resulting in different avalanches prevailing in different stages of snowpack evolution.Moreover,the snow-layer fractures were driven by single external factors or the combined multiple factors under the continental snow climate.Fifty-four percent of the avalanche events in the study area occurred during or after a snowfall,with 36%related to sudden increases in temperature.Then considering different triggering scenarios,snowpack evolution stages,and the coupling of intrinsic and extrinsic drivers of triggering snow-layer shear failure,we constructed five snow-layer shear fracture modes and twelve avalanche-triggering modes on mountain slopes under a continental snow climate.展开更多
Snow avalanches can repeatedly occur along the same track under diferent snowpack and meteorological conditions during the snow season in areas of snow avalanche activity.The snowfall,air temperature,and snow cover ca...Snow avalanches can repeatedly occur along the same track under diferent snowpack and meteorological conditions during the snow season in areas of snow avalanche activity.The snowfall,air temperature,and snow cover can change dramatically in a warming climate,causing signifcant changes in the snow avalanche risk.But how the risk of snow avalanche activity during the snow season will change under a warming climate remains an open question.Based on the observed meteorological and snowpack data from 1968 to 2021 and the snow avalanche activity data during the 2011–2021 snow seasons along a transportation corridor in the central Tianshan Mountains that has a typical continental snow climate,we analyzed the temporal distribution of the snow avalanche activity and the impacts of climate change on it.The results indicate that the frequency of the snow avalanche activity is characterized by a Gaussian bimodal distribution,resulting from interactions between the snowfall,air temperature,and snowpack evolution.In addition,the active period of wet snow avalanches triggered by temperature surges and high solar radiation has gradually moved forward from the second half to the frst half of March with climate warming.The frequency and size of snowfall-triggered snow avalanches showed only a slight and insignifcant increase.These fndings are important for rationally arranging snow avalanche relief resources to improve the risk management of snow avalanche disasters,and highlight the necessity to immediately design risk mitigation strategies and disaster risk policies to improve our adaptation to climate change.展开更多
基金This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences,the Pan-Third Pole Environment Study for a Green Silk Road(Pan-TPE)(XDA2004030202).
文摘Land use/land cover(LULC)change and climate change are two major factors affecting the provision of ecosystem services which are closely related to human well-being.However,a clear understanding of the relationships between these two factors and ecosystem services in Central Asia is still lacking.This study aimed to comprehensively assess ecosystem services in Central Asia and analyze how they are impacted by changes in LULC and climate.The spatiotemporal patterns of three ecosystem services during the period of 2000-2015,namely the net primary productivity(NPP),water yield,and soil retention,were quantified and mapped by the Carnegie-Ames-Stanford Approach(CASA)model,Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model,and Revised Universal Soil Loss Equation(RUSLE).Scenarios were used to determine the relative importance and combined effect of LULC change and climate change on ecosystem services.Then,the relationships between climate factors(precipitation and temperature)and ecosystem services,as well as between LULC change and ecosystem services,were further discussed.The results showed that the high values of ecosystem services appeared in the southeast of Central Asia.Among the six biomes(alpine forest region(AFR),alpine meadow region(AMR),typical steppe region(TSR),desert steppe region(DSR),desert region(DR),and lake region(LR)),the values of ecosystem services followed the order of AFR>AMR>TSR>DSR>DR>LR.In addition,the values of ecosystem services fluctuated during the period of 2000-2015,with the most significant decreases observed in the southeast mountainous area and northwest of Central Asia.LULC change had a greater impact on the NPP,while climate change had a stronger influence on the water yield and soil retention.The combined LULC change and climate change exhibited a significant synergistic effect on ecosystem services in most of Central Asia.Moreover,ecosystem services were more strongly and positively correlated with precipitation than with temperature.The greening of desert areas and forest land expansion could improve ecosystem services,but unreasonable development of cropland and urbanization have had an adverse impact on ecosystem services.According to the results,ecological stability in Central Asia can be achieved through the natural vegetation protection,reasonable urbanization,and ecological agriculture development.
基金financially supported by the project of the National Natural Science Foundation of China(U1703241)the Strategic Priority Research Program of the Chinese Academy of Sciences+2 种基金the Pan-Third Pole Environment Study for a Green Silk Road(Pan-TPE)(XDA2004030202)the Chinese Academy of Sciences President’s International Fellowship Initiative(PIFI,Grant No.2017VCA0002)the China Scholarship Council(CSC,Grant No.201904910896)。
文摘Precipitation and temperature are two important factors associated to snow hazards which block the transport infrastructure and cause loss of life and properties in the cold season.The in-situ observations are limited in the alpine with complex topographic characteristics,while coarse satellite rainfall estimates,reanalysis rain datasets,and gridded in-situ rain gauge datasets obscure the understanding of the precipitation patterns in hazardprone areas.Considering the Karakoram Highway(KKH)region as a study area,a double nestedWeather Research and Forecasting(WRF)model with the high resolution of a 10-km horizontal grid was performed to investigate the spatial and temporal patterns of temperature and precipitation covering the Karakoram Highway region during the cold season.The results of WRF were compared with the in-situ observations and Multi-Source WeightedEnsemble Precipitation(MSWEP)datasets.The results demonstrated that the WRF model well reproduced the observed monthly temperature(R=0.96,mean bias=-3.92°C)and precipitation(R=0.57,mean bias=8.69 mm).The WRF model delineated the essential features of precipitation variability and extremes,although it overestimatedthe wet day frequency and underestimated the precipitation intensity.Two rain bands were exhibited in a northwest-to-southeast direction over the study area.High wet day frequency was found in January,February,and March in the section between Hunza and Khunjerab.In addition,the areas with extreme values are mainly located in the Dasu-Islamabad section in February,March,and April.The WRF model has the potential to compensate for the spatial and temporal gaps of the observational networks and to provide more accurate predictions on the meteorological variables for avoiding common coldweather hazards in the ungauged and high altitude areas at a regional scale.
基金This study was supported by the National Natural Science Foundation of China(NSFC Grant 42001061,U1703241,and 41901087)the Strategic Priority Research Program of the Chinese Academy of Sciences,the Pan-Third Pole Environment Study for a Green Silk Road(Pan-TPE)(No.XDA2004030202).
文摘Snow is a key variable that influences hydrological and climatic cycles.Land surface models employing snow physics-modules can simulate the snow accumulation and ablation processes.However,there are still uncertainties in modeling snow resources over complex terrain such as mountains.This study employed the National Center for Atmospheric Research’s Weather Research and Forecasting(WRF)model coupled with the Noah-Multiparameterization(Noah-MP)land surface model to run one-year simulations to assess its ability to simulate snow across the Tianshan Mountains.Six tests were conducted based on different reanalysis forcing datasets and different land surface properties.The results indicated that the snow dynamics were reproduced in a snow hydrological year by the WRF/Noah-MP model for all of the tests.The model produced a low bias in snow depth and snow water equivalent(SWE)regardless of the forcing datasets.Additionally,the underestimation of snow depth and SWE could be relatively alleviated by modifying the land cover and vegetation parameters.However,no significant improvement in accuracy was found in the date of snow depth maximum and melt rate.The best performance was achieved using ERA5 with modified land cover and vegetation parameters(mean bias=−4.03 mm and−1.441 mm for snow depth and SWE,respectively).This study highlights the importance of selecting forcing data for snow simulation over the Tianshan Mountains.
基金supported by the Open Project of the Xinjiang Uygur Autonomous Region Key Laboratory(2017D04010).
文摘Snow avalanches are a common natural hazard in many countries with seasonally snow-covered mountains.The avalanche hazard varies with snow avalanche type in different snow climate regions and at different times.The ability to understand the characteristics of avalanche activity and hazards of different snow avalanche types is a prerequisite for improving avalanche disaster management in the mid-altitude region of the Central Tianshan Mountains.In this study,we collected data related to avalanche,snowpack,and meteorology during four snow seasons(from 2015 to 2019),and analysed the characteristics and hazards of different types of avalanches.The snow climate of the mid-altitude region of the Central Tianshan Mountains was examined using a snow climate classification scheme,and the results showed that the mountain range has a continental snow climate.To quantify the hazards of different types of avalanches and describe their situation over time in the continental snow climate region,this study used the avalanche hazard degree to assess the hazards of four types of avalanches,i.e.,full-depth dry snow avalanches,full-depth wet snow avalanches,surface-layer dry snow avalanches,and surface-layer wet snow avalanches.The results indicated that surface-layer dry snow avalanches were characterized by large sizes and high release frequencies,which made them having the highest avalanche hazard degree in the Central Tianshan Mountains with a continental snow climate.The overall avalanche hazard showed a single peak pattern over time during the snow season,and the greatest hazard occurred in the second half of February when the snowpack was deep and the temperature increased.This study can help the disaster and emergency management departments rationally arrange avalanche relief resources and develop avalanche prevention strategies.
基金the Open Project of Key Laboratory,Xinjiang Uygur Autonomous Region(No.2019D04003)the National Natural Science Foundation of China(NSFC Grant U1703241,41901087)+2 种基金the West Light Foundation of the Chinese Academy of Sciences(No.2018-XBQNZ-B-012)the Key International cooperation project of Chinese Academy of Sciences(No:121311KYSB20160005)the CAS Instrumental development project of Automatic Meteorological Observation System with Multifunctional Modularization(No:Y634241001).
文摘Obtaining the spatial distribution of snow cover in mountainous areas using the optical image of remote sensing technology is difficult because of cloud and fog. In this study, the object-based principle component analysis–support vector machine(PCA–SVM) method is proposed for snow cover mapping through the integration of moderateresolution imaging spectroradiometer(MODIS) snow cover products and the Sentinel-1 synthetic aperture radar(SAR) scattering characteristics. First, derived from the Sentinel-1 A SAR images, the feature parameters, including VV/VH backscatter, scattering entropy, and scattering alpha, were used to describe the variations of snow and non-snow covers. Second, the optimum feature combinations of snow cover were formed from the feature parameters using the principle component analysis(PCA) algorithm. Finally, using the optimum feature combinations, a snow cover map with a 20 m spatial resolution was extracted by means of an object-based SVM classifier. This method was applied in the study area of the Xinyuan County, which is located in the western part of the Tianshan Mountains in Xinjiang, China. The accuracies in this method were analyzed according to the data observed at different experimental sites. Results showed that the snow cover pixels of the extraction were less than those in the actual situation(FB1=93.86, FB2=59.78). The evaluation of the threat score(TS), probability of detection(POD), and false alarm ratio(FAR) for the snow-covered pixels obtained from the two-stage SAR images were different(TS1=86.84, POD1=90.10, FAR1=4.01;TS2=56.40, POD2=57.62, FAR2=3.62). False and misclassifications of the snow cover and non-snow cover pixels were found. Although the classifications were not highly accurate, the approach showed potential for integrating different sources to retrieve the spatial distribution of snow covers during a stable period.
基金funded by the West Light Foundation of the Chinese Academy of Sciences (2016–QNXZ–B–13)the open project of the Xinjiang Uygur Autonomous Region Key Laboratory (2017D04010)+1 种基金the natural science foundation of Xinjiang Uygur Autonomous Region (2017D01B52)the Pan-Third Pole Environment Study for a Green Silk Road (PanTPE) (No. XDA2004030202)
文摘The investigation of concentration characteristics of reference evapotranspiration(ETref) is important for water resources management. The concentration index(CI), concentration degree(CD) and concentration period(CP) are used to investigate the concentration characteristics of ETref and the relationship between ETref concentration and precipitation concentration at sub-monthly timescale based on the daily climatic variables from 1966 to 2015 in 27 meteorological stations at the southern and northern slopes of Tianshan Mountains in China. It was found that the CI of ETref is about 0.40 and less concentrated than precipitation in the study area. At the southern slope, the maximum ETref appears in late June and is earlier than the maximum precipitation(early July), ETref distributes more equally than precipitation, and the CI, CD and CP of these two variables do not show significant change based on the Mann–Kendall test. At the northern slope, both the maximum ETref and precipitation appear in early July, and ETref is more dispersed than precipitation. During the study period, the maximum ETref at the northern slope tends to appear earlier due to the impacts of wind speed, relative humidity, sunshine duration, and air temperature. ETref concentration does not match the precipitation concentration in the study area, particularly at the southern slope. The mismatch between ETref and precipitation concentration within a year reveals the water resources pressure on environmental, social and economic sustainability in the study area.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences,Pan-Third Pole Environment Study for a Green Silk Road(Pan-TPE)of China(XDA2004030202)Shanghai Cooperation and the Organization Science and Technology Partnership of China(2021E01019)。
文摘Hydrothermal condition is mismatched in arid and semi-arid regions,particularly in Central Asia(including Kazakhstan,Kyrgyzstan,Tajikistan,Uzbekistan,and Turkmenistan),resulting many environmental limitations.In this study,we projected hydrothermal condition in Central Asia based on bias-corrected multi-model ensembles(MMEs)from the Coupled Model Intercomparison Project Phase 6(CMIP6)under four Shared Socioeconomic Pathway and Representative Concentration Pathway(SSP-RCP)scenarios(SSP126(SSP1-RCP2.6),SSP245(SSP2-RCP4.5),SSP460(SSP4-RCP6.0),and SSP585(SSP5-RCP8.5))during 2015-2100.The bias correction and spatial disaggregation,water-thermal product index,and sensitivity analysis were used in this study.The results showed that the hydrothermal condition is mismatched in the central and southern deserts,whereas the region of Pamir Mountains and Tianshan Mountains as well as the northern plains of Kazakhstan showed a matched hydrothermal condition.Compared with the historical period,the matched degree of hydrothermal condition improves during 2046-2075,but degenerates during 2015-2044 and 2076-2100.The change of hydrothermal condition is sensitive to precipitation in the northern regions and the maximum temperatures in the southern regions.The result suggests that the optimal scenario in Central Asia is SSP126 scenario,while SSP585 scenario brings further hydrothermal contradictions.This study provides scientific information for the development and sustainable utilization of hydrothermal resources in arid and semi-arid regions under climate change.
基金This study was supported by the National Natural Science Foundation of China(U1703241)the Key International Cooperation Project of Chinese Academy of Sciences(121311KYSB20160005)the Open Project of Xinjiang Uygur Autonomous Region Key Laboratory of China(2017D04010).
文摘Playing an important role in global warming and plant growth,relative humidity(RH)has profound impacts on production and living,and can be used as an integrated indicator for evaluating the wet-dry conditions in the arid and semi-arid area.However,information on the spatial-temporal variation and the influencing factors of RH in these regions is still limited.This study attempted to use daily meteorological data during 1966–2017 to reveal the spatial-temporal characteristics of RH in the arid region of Northwest China through rotated empirical orthogonal function and statistical analysis method,and the path analysis was used to clarify the impact of temperature(T),precipitation(P),actual evapotranspiration(ETa),wind speed(W)and sunshine duration(S)on RH.The results demonstrated that climatic conditions in North Xinjiang(NXJ)was more humid than those in Hexi Corridor(HXC)and South Xinjiang(SXJ).RH had a less significant downtrend in NXJ than that in HXC,but an increasingly rising trend was observed in SXJ during the last five decades,implying that HXC and NXJ were under the process of droughts,while SXJ was getting wetter.There was a turning point for the trend of RH in Xinjiang,which occurred in 2000.Path analysis indicated that RH was negatively correlated to T,ETa,W and S,but it increased with increase of P.S,T and W had the greatest direct effects on RH in HXC,NXJ and SXJ,respectively.ETa was the factor which had the greatest indirect effect on RH in HXC and NXJ,while T was the dominant factor in SXJ.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA23090302)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(Grant No.2019QZKK0906)the National Natural Science Foundation of China(Grant No.42101080).
文摘Climate change and human activities have increased avalanche risks in alpine mountains.Therefore,strengthening the research on mitigating and controlling avalanche disasters is indispensable for sustainable socio-economic development in mountainous areas.Early avalanche warning is an essential means of avalanche disaster prevention.However,the theoretical development and application of avalanche warning strategies remain limited due to the lack of systematic understanding of the triggering mechanisms of avalanches.Based on observational data(2015–2019)of avalanches,snowpack,meteorological parameters,surface soil temperature and moisture,and topography in avalanche-prone areas in the central Tianshan Mountains,we analyzed the characteristics of different types of avalanches under a continental snow climate and the environmental factors(such as meteorological conditions and snowpack)that trigger avalanches,as well as the triggering mechanisms for different types of avalanches under the continental snow climate in terms of snow-layer shear fracture modes.We found that the snowpack parameters,weather conditions,and soil temperature and moisture varied significantly among the stages of snow accumulation,stabilization,and melting,resulting in different avalanches prevailing in different stages of snowpack evolution.Moreover,the snow-layer fractures were driven by single external factors or the combined multiple factors under the continental snow climate.Fifty-four percent of the avalanche events in the study area occurred during or after a snowfall,with 36%related to sudden increases in temperature.Then considering different triggering scenarios,snowpack evolution stages,and the coupling of intrinsic and extrinsic drivers of triggering snow-layer shear failure,we constructed five snow-layer shear fracture modes and twelve avalanche-triggering modes on mountain slopes under a continental snow climate.
基金supported by the Second Tibetan Plateau Scientifc Expedition and Research Program(STEP)(Grant nos.2019QZKK0906,2019QZKK0903)the National Natural Science Foundation of China(Grant no.42101080)the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(CAST)(2022QNRC001)。
文摘Snow avalanches can repeatedly occur along the same track under diferent snowpack and meteorological conditions during the snow season in areas of snow avalanche activity.The snowfall,air temperature,and snow cover can change dramatically in a warming climate,causing signifcant changes in the snow avalanche risk.But how the risk of snow avalanche activity during the snow season will change under a warming climate remains an open question.Based on the observed meteorological and snowpack data from 1968 to 2021 and the snow avalanche activity data during the 2011–2021 snow seasons along a transportation corridor in the central Tianshan Mountains that has a typical continental snow climate,we analyzed the temporal distribution of the snow avalanche activity and the impacts of climate change on it.The results indicate that the frequency of the snow avalanche activity is characterized by a Gaussian bimodal distribution,resulting from interactions between the snowfall,air temperature,and snowpack evolution.In addition,the active period of wet snow avalanches triggered by temperature surges and high solar radiation has gradually moved forward from the second half to the frst half of March with climate warming.The frequency and size of snowfall-triggered snow avalanches showed only a slight and insignifcant increase.These fndings are important for rationally arranging snow avalanche relief resources to improve the risk management of snow avalanche disasters,and highlight the necessity to immediately design risk mitigation strategies and disaster risk policies to improve our adaptation to climate change.
