Spring snowmelt peak flow （SSPF） can cause serious damage. Precipitation as rainfall directly contributes to the SSPF and influences the characteristics of the SSPF, while temperature indirectly impacts the SSPF by ...Spring snowmelt peak flow （SSPF） can cause serious damage. Precipitation as rainfall directly contributes to the SSPF and influences the characteristics of the SSPF, while temperature indirectly impacts the SSPF by shaping snowmelt rate and determining the soil frozen state which partitions snowmelt water into surface runoff and soil infiltration water in spring. It is necessary to identify the important and significant paths of climatic factors influencing the SSPF and provide estimates of the magnitude and significance of hypothesized causal connections between climatic factors and the SSPF. This study used path analysis with a selection of five factors - the antecedent precipitation index （API）, spring precipitation （SP）, winter precipitation as snowfall （WS）, 〈0℃ temperature accumulation in winter （[ATNI）, and average 〉0℃temperature accumulation in spring （AT） - to analyze their influences on the SSPF in the Kaidu River in Xinjiang, China. The results show that {ATN}, AT and WS have a significant correlation with the SSPF, while API and SP do not show a significant correlation. AT and WS directly influence the SSPF, while as the influence of[ATN] on SSPF is indirect through WS and AT. The indirect influence of [ATN[ on SSPF through WS accounts for 69% of the total influence of [ATN] on SSPF. Compared to the multiple linear regression method, path analysis provides additional valuable information, including influencing paths from independent variables to the dependent variable as well as direct and indirect impacts of external variables on the internal variable. This information can help improve the description of snow melt and spring runoff in hydrologic models as well as the planning and management of water resources.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 avalanche is a serious threat to the safety of roads in alpine mountains. In the western Tianshan Mountains, large scale avalanches occur every year and affect road safety. There is an urgent need to identify the...Snow avalanche is a serious threat to the safety of roads in alpine mountains. In the western Tianshan Mountains, large scale avalanches occur every year and affect road safety. There is an urgent need to identify the characteristics of triggering factors for avalanche activity in this region to improve road safety and the management of natural hazards. Based on the observation of avalanche activity along the national road G218 in the western Tianshan Mountains, avalanche event data in combination with meteorological, snowpack and earthquake data were collected and analyzed. The snow climate of the mountain range was examined using a recently developed snow climate classification scheme, and triggering conditions of snow avalanche in different snow climate regions were compared. The results show that snowfall is the most common triggering factor for a natural avalanche and there is high probability of avalanche release with snowfall exceeding 20.4 mm during a snowfall period. Consecutive rise in temperature within three days and daily mean temperature reaching 0.5℃ in the following day imply a high probability of temperaturerise-triggered avalanche release. Earthquakes have a significant impact on the formation of large size avalanches in the area. For the period 2011-2017, five cases were identified as a consequence of earthquake with magnitudes of 3.3≤M_L≤5.1 and source-to-site distances of 19~139 km. The Tianshan Mountains are characterized by a continental snow climate with lower snow density, lower snow shear strength and high proportion depth hoar, which explains that both the snowfall and temperature for triggering avalanche release in the continental snow climate of the Tianshan Mountains are lower than that in maritime snow climate and transitional snow climate regions. The findings help forecast avalanche release for mitigating avalanche disaster and assessing the risk of avalanche disaster.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
In the context of global warming,precipitation forms are likely to transform from snowfall to rainfall with a more pronounced trend.The change in precipitation forms will inevitably affect the processes of regional ru...In the context of global warming,precipitation forms are likely to transform from snowfall to rainfall with a more pronounced trend.The change in precipitation forms will inevitably affect the processes of regional runoff generation and confluence as well as the annual distribution of runoff.Most researchers used precipitation data from the CMIP5 model directly to study future precipitation trends without distinguishing between snowfall and rainfall.CMIP5 models have been proven to have better performance in simulating temperature but poorer performance in simulating precipitation.To overcome the above limitations,this paper used a Back Propagation Neural Network(BNN)to predict the rainfall-to-precipitation ratio(RPR)in months experiencing freezing-thawing transitions(FTTs).We utilized the meteorological(air pressure,air temperature,evaporation,relative humidity,wind speed,sunshine hours,surface temperature),topographic(altitude,slope,aspect)and geographic(longitude,latitude)data from 28 meteorological stations in the Chinese Tianshan Mountains region(CTMR)from 1961 to 2018 to calculate the RPR and constructed an index system of impact factors.Based on the BNN,decision-making trial and evaluation laboratory method(BP-DEMATEL),the key factors driving the transformation of the RPR in the CTMR were identified.We found that temperature was the only key factor affecting the transformation of the RPR in the BP-DEMATEL model.Considering the relationship between temperature and the RPR,the future temperature under different representative concentration pathways(RCPs)(RCP2.6/RCP4.5/RCP8.5)provided by 21 CMIP5 models and the meteorological factors from meteorological stations were input into the BNN model to acquire the future RPR from 2011 to 2100.The results showed that under the three scenarios,the RPR in the number of months experiencing FTTs during 2011-2100 will be higher than that in the historical period(1981-2010)in the CTMR.Furthermore,in terms of spatial variation,the RPR values on the south slope will be larger than those on the north slope under the three emission scenarios.Moreover,the RPR values exhibited different variation characteristics under different emission scenarios.Under the low-emission scenario(RCP2.6),as time passed,the RPR values changed slightly at more stations.Under the mediumemission scenario(RCP4.5),the RPR increased in the whole CTMR and stabilized on the north slope by the end of this century.Under the high-emission scenario(RCP8.5),the RPR values increased significantly through the 21 st century in the whole CTMR.This study may help to provide a scientific management basis for agricultural production and hydrology.展开更多
Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect ...Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R^2=0.55 vs.R^2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.展开更多
基金financially supported by the Project of State Key Basic R & D Program of China (973 Program, Grant No. 2010CB951002)the key deployment project of Chinese Academy of Sciences (Grant No. KZZD-EW-12-2)Chinese Academy of Sciences Visiting Professorship for Senior International Scientists (Grant No. 2011T2Z40)
文摘Spring snowmelt peak flow （SSPF） can cause serious damage. Precipitation as rainfall directly contributes to the SSPF and influences the characteristics of the SSPF, while temperature indirectly impacts the SSPF by shaping snowmelt rate and determining the soil frozen state which partitions snowmelt water into surface runoff and soil infiltration water in spring. It is necessary to identify the important and significant paths of climatic factors influencing the SSPF and provide estimates of the magnitude and significance of hypothesized causal connections between climatic factors and the SSPF. This study used path analysis with a selection of five factors - the antecedent precipitation index （API）, spring precipitation （SP）, winter precipitation as snowfall （WS）, 〈0℃ temperature accumulation in winter （[ATNI）, and average 〉0℃temperature accumulation in spring （AT） - to analyze their influences on the SSPF in the Kaidu River in Xinjiang, China. The results show that {ATN}, AT and WS have a significant correlation with the SSPF, while API and SP do not show a significant correlation. AT and WS directly influence the SSPF, while as the influence of[ATN] on SSPF is indirect through WS and AT. The indirect influence of [ATN[ on SSPF through WS accounts for 69% of the total influence of [ATN] on SSPF. Compared to the multiple linear regression method, path analysis provides additional valuable information, including influencing paths from independent variables to the dependent variable as well as direct and indirect impacts of external variables on the internal variable. This information can help improve the description of snow melt and spring runoff in hydrologic models as well as the planning and management of water resources.
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 Science and Technology Service Network Initiative of the Chinese Academy of Science (Grant No.KFJSTSZDTP-015)the National Project of Investigation of Basic Resources for Science and Technology (Grant No.2017FY100501)the supports in field and laboratory work from the Tianshan Station for Snow cover and Avalanche Research,Chinese Academy of Sciences
文摘Snow avalanche is a serious threat to the safety of roads in alpine mountains. In the western Tianshan Mountains, large scale avalanches occur every year and affect road safety. There is an urgent need to identify the characteristics of triggering factors for avalanche activity in this region to improve road safety and the management of natural hazards. Based on the observation of avalanche activity along the national road G218 in the western Tianshan Mountains, avalanche event data in combination with meteorological, snowpack and earthquake data were collected and analyzed. The snow climate of the mountain range was examined using a recently developed snow climate classification scheme, and triggering conditions of snow avalanche in different snow climate regions were compared. The results show that snowfall is the most common triggering factor for a natural avalanche and there is high probability of avalanche release with snowfall exceeding 20.4 mm during a snowfall period. Consecutive rise in temperature within three days and daily mean temperature reaching 0.5℃ in the following day imply a high probability of temperaturerise-triggered avalanche release. Earthquakes have a significant impact on the formation of large size avalanches in the area. For the period 2011-2017, five cases were identified as a consequence of earthquake with magnitudes of 3.3≤M_L≤5.1 and source-to-site distances of 19~139 km. The Tianshan Mountains are characterized by a continental snow climate with lower snow density, lower snow shear strength and high proportion depth hoar, which explains that both the snowfall and temperature for triggering avalanche release in the continental snow climate of the Tianshan Mountains are lower than that in maritime snow climate and transitional snow climate regions. The findings help forecast avalanche release for mitigating avalanche disaster and assessing the risk of avalanche disaster.
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financially supported by the National Natural Science Foundation of China(41761014,42161025,42101096)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA20020201)the Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University,and the Excellent Platform of Lanzhou Jiaotong University。
文摘In the context of global warming,precipitation forms are likely to transform from snowfall to rainfall with a more pronounced trend.The change in precipitation forms will inevitably affect the processes of regional runoff generation and confluence as well as the annual distribution of runoff.Most researchers used precipitation data from the CMIP5 model directly to study future precipitation trends without distinguishing between snowfall and rainfall.CMIP5 models have been proven to have better performance in simulating temperature but poorer performance in simulating precipitation.To overcome the above limitations,this paper used a Back Propagation Neural Network(BNN)to predict the rainfall-to-precipitation ratio(RPR)in months experiencing freezing-thawing transitions(FTTs).We utilized the meteorological(air pressure,air temperature,evaporation,relative humidity,wind speed,sunshine hours,surface temperature),topographic(altitude,slope,aspect)and geographic(longitude,latitude)data from 28 meteorological stations in the Chinese Tianshan Mountains region(CTMR)from 1961 to 2018 to calculate the RPR and constructed an index system of impact factors.Based on the BNN,decision-making trial and evaluation laboratory method(BP-DEMATEL),the key factors driving the transformation of the RPR in the CTMR were identified.We found that temperature was the only key factor affecting the transformation of the RPR in the BP-DEMATEL model.Considering the relationship between temperature and the RPR,the future temperature under different representative concentration pathways(RCPs)(RCP2.6/RCP4.5/RCP8.5)provided by 21 CMIP5 models and the meteorological factors from meteorological stations were input into the BNN model to acquire the future RPR from 2011 to 2100.The results showed that under the three scenarios,the RPR in the number of months experiencing FTTs during 2011-2100 will be higher than that in the historical period(1981-2010)in the CTMR.Furthermore,in terms of spatial variation,the RPR values on the south slope will be larger than those on the north slope under the three emission scenarios.Moreover,the RPR values exhibited different variation characteristics under different emission scenarios.Under the low-emission scenario(RCP2.6),as time passed,the RPR values changed slightly at more stations.Under the mediumemission scenario(RCP4.5),the RPR increased in the whole CTMR and stabilized on the north slope by the end of this century.Under the high-emission scenario(RCP8.5),the RPR values increased significantly through the 21 st century in the whole CTMR.This study may help to provide a scientific management basis for agricultural production and hydrology.
基金supported by Projects of International Cooperation and Exchanges NSFC (grant: 41361140361)the Special fund project of Chinese Academy of Sciences (grant: Y371164001)the key deployment project of Chinese Academy of Sciences (Grant No. KZZD-EW-12-2, KZZD-EW12-3)
文摘Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R^2=0.55 vs.R^2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.
