For the zoning of snow hazard in China, on the principles of (a) comprehensive analysis integrated with dominant factors, (b) multi-level division, and (c) serving the agriculture and stock-raising, transportation and...For the zoning of snow hazard in China, on the principles of (a) comprehensive analysis integrated with dominant factors, (b) multi-level division, and (c) serving the agriculture and stock-raising, transportation and communication, we first classified China into two large zones according to the situation of snow or no snow distribution. Secondly, based on the climate and landform, properties of snow cover and main features of snow hazard, the large zone of snow hazard can be classified into three second-level regions. In order to obviously reflect the difference of snow cover quantity and snow hazard type as well as hazardous degree, twenty subregions (third-level) of snow hazard are further divided in detail. In addition, the boundaries and the principal features of the differences between the various snow hazard regions are provided.展开更多
Extreme snow loads can collapse roofs.This load is calculated based on the ground snow load(that is,the snow water equivalent on the ground).However,snow water equivalent(SWE) measurements are unavailable for most sit...Extreme snow loads can collapse roofs.This load is calculated based on the ground snow load(that is,the snow water equivalent on the ground).However,snow water equivalent(SWE) measurements are unavailable for most sites,while the ground snow depth is frequently measured and recorded.A new simple practical algorithm was proposed in this study to evaluate the SWE by utilizing ground snow depth,precipitation data,wind speed,and air temperature.For the evaluation,the precipitation was clas sified as snowfall or rainfall according to the air temperature,the snowfall or rainfall was then corrected for measurement error that is mainly caused by wind-induced undercatch,and the effect of snow water loss was considered.The developed algorithm was applied and validated using data from57 meteorological stations located in the northeastern region of China.The annual maximum SWE obtained based on the proposed algorithm was compared with that obtained from the actual SWE measurements.The return period values of the annual maximum ground snow load were estimated and compared to those obtained according to the procedure suggested by the Chinese structural design code.The comparison indicated that the use of the proposed algorithm leads to a good estimated SWE or ground snow load.Its use allowed the estimation of the ground snow load for sites without SWE measurement and facilitated snow hazard mapping.展开更多
Avalanches are one of the most natural hazard in the mountain areas and therefore, identification of avalanche hazard is necessary for planning future development activities. The study area falls under the internation...Avalanches are one of the most natural hazard in the mountain areas and therefore, identification of avalanche hazard is necessary for planning future development activities. The study area falls under the international boundary region which generally covered by the snow(38%) on high altitude regions of the western part of Himalayas. Avalanches are triggered in study area during snowfall resulting in loss of human life, property and moreover the transportation and communication affected by the debris which ultimately delays the relief measures. Therefore in this study three major causative parameters i.e terrain, ground cover and meteorological have been incorporated for the identification of avalanche hazard zones(AHZ) by integrating Analytical Hierarchical Process(AHP) method in Geographical Information System(GIS). In the first part of study, avalanche sites have been identified by the criteria related to terrain(slope, aspect and curvature) and ground cover. Weights and ratings to these causative factors and their cumulative effects have been assigned on the basis of experience and knowledge of field. In the second part of the study, single point interpolation and Inverse Distance Weighted(IDW) method has been employed as only one weather station falls in study area. Accordingly, it has been performed to generate the meteorological parameter maps(viz. air temperature and relative humidity) from the field observatories and Automatic Weather Stations(AWS) located at Baaj OP in Uri sector. Finally, the meteorological parameter maps were superimposed on the terrain-based avalanche hazard thematic layers to identify the dynamic avalanche hazard sites. Conventional weighted approach and Analytical Hierarchical Process(AHP) method have been implemented for the identification of AHZ that shows approximately 55% area under maximum hazard zone. Further, the results were validated by overlapping the existing registered avalanche sites. The sites were identified through field survey and avalanche data card followed by its delineation from the toposheet(1:50,000 scale). Interestingly study found that 28% area under moderate and maximum AHZ correlated well with registered avalanche sites when they were overlapped. The accuracy for such works can be increased by field survey under favorable weather condition and by adding data from more number of AWS for predicting avalanche hazards in mountainous regions.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
文摘For the zoning of snow hazard in China, on the principles of (a) comprehensive analysis integrated with dominant factors, (b) multi-level division, and (c) serving the agriculture and stock-raising, transportation and communication, we first classified China into two large zones according to the situation of snow or no snow distribution. Secondly, based on the climate and landform, properties of snow cover and main features of snow hazard, the large zone of snow hazard can be classified into three second-level regions. In order to obviously reflect the difference of snow cover quantity and snow hazard type as well as hazardous degree, twenty subregions (third-level) of snow hazard are further divided in detail. In addition, the boundaries and the principal features of the differences between the various snow hazard regions are provided.
基金Financial support from the National Natural Science Foundation of China(Grant Nos.51808169 and 51927813)the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.2020083)are gratefully acknowledged.
文摘Extreme snow loads can collapse roofs.This load is calculated based on the ground snow load(that is,the snow water equivalent on the ground).However,snow water equivalent(SWE) measurements are unavailable for most sites,while the ground snow depth is frequently measured and recorded.A new simple practical algorithm was proposed in this study to evaluate the SWE by utilizing ground snow depth,precipitation data,wind speed,and air temperature.For the evaluation,the precipitation was clas sified as snowfall or rainfall according to the air temperature,the snowfall or rainfall was then corrected for measurement error that is mainly caused by wind-induced undercatch,and the effect of snow water loss was considered.The developed algorithm was applied and validated using data from57 meteorological stations located in the northeastern region of China.The annual maximum SWE obtained based on the proposed algorithm was compared with that obtained from the actual SWE measurements.The return period values of the annual maximum ground snow load were estimated and compared to those obtained according to the procedure suggested by the Chinese structural design code.The comparison indicated that the use of the proposed algorithm leads to a good estimated SWE or ground snow load.Its use allowed the estimation of the ground snow load for sites without SWE measurement and facilitated snow hazard mapping.
文摘Avalanches are one of the most natural hazard in the mountain areas and therefore, identification of avalanche hazard is necessary for planning future development activities. The study area falls under the international boundary region which generally covered by the snow(38%) on high altitude regions of the western part of Himalayas. Avalanches are triggered in study area during snowfall resulting in loss of human life, property and moreover the transportation and communication affected by the debris which ultimately delays the relief measures. Therefore in this study three major causative parameters i.e terrain, ground cover and meteorological have been incorporated for the identification of avalanche hazard zones(AHZ) by integrating Analytical Hierarchical Process(AHP) method in Geographical Information System(GIS). In the first part of study, avalanche sites have been identified by the criteria related to terrain(slope, aspect and curvature) and ground cover. Weights and ratings to these causative factors and their cumulative effects have been assigned on the basis of experience and knowledge of field. In the second part of the study, single point interpolation and Inverse Distance Weighted(IDW) method has been employed as only one weather station falls in study area. Accordingly, it has been performed to generate the meteorological parameter maps(viz. air temperature and relative humidity) from the field observatories and Automatic Weather Stations(AWS) located at Baaj OP in Uri sector. Finally, the meteorological parameter maps were superimposed on the terrain-based avalanche hazard thematic layers to identify the dynamic avalanche hazard sites. Conventional weighted approach and Analytical Hierarchical Process(AHP) method have been implemented for the identification of AHZ that shows approximately 55% area under maximum hazard zone. Further, the results were validated by overlapping the existing registered avalanche sites. The sites were identified through field survey and avalanche data card followed by its delineation from the toposheet(1:50,000 scale). Interestingly study found that 28% area under moderate and maximum AHZ correlated well with registered avalanche sites when they were overlapped. The accuracy for such works can be increased by field survey under favorable weather condition and by adding data from more number of AWS for predicting avalanche hazards in mountainous regions.
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.
