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Impact of forcing data and land surface properties on snow simulation in a regional climate model:a case study over the Tianshan Mountains,Central Asia

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Journal of Mountain Science 2021年第12期18卷 3147-3164页

作者： li Qian YANG Tao li lan-hai State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqi 830011China Ili Station for Watershed Ecosystem Research Chinese Academy of SciencesXinyuan 835800China Key Laboratory for Mountain Hazards and Earth Surface Process Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China CAS Research Centre for Ecology and Environment of Central Asia Urumqi 830011China Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone Urumqi 830011China

Snow is a key variable that influences hydrological and climatic *** surface models employing snow physics-modules can simulate the snow accumulation and ablation ***,there are still uncertainties in modeling snow resources over complex terrain such as *** 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 *** tests were conducted based on different reanalysis forcing datasets and different land surface *** results indicated that the snow dynamics were reproduced in a snow hydrological year by the WRF/Noah-MP model for all of the *** model produced a low bias in snow depth and snow water equivalent(SWE)regardless of the forcing ***,the underestimation of snow depth and SWE could be relatively alleviated by modifying the land cover and vegetation ***,no significant improvement in accuracy was found in the date of snow depth maximum and melt *** 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.

关键词： WRF/Noah-MP model Initial and lateral boundary conditions land surface properties Snow depth Snow water equivalent

Using Path Analysis to Identify the Influence of Climatic Factors on Spring Peak Flow Dominated by Snowmelt in an Alpine Watershed

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Journal of Mountain Science 2014年第4期11卷 990-1000页

作者： ZHANG Fei-Yun li lan-hai AHMAD Sajjad li Xue-Mei State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Graduate School University of Chinese Academy of Sciences Department of Civil and Environmental Engineering University of Nevada Las Vegas NV 89154-4015 USA Faculty of Geomatics Lanzhou Jiaotong University

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.

关键词： Snowmelt Peak flow Path analysis Alpine watershed

Snow cover estimation from MODIS and Sentinel-1 SAR data using machine learning algorithms in the western part of the Tianshan Mountains

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Journal of Mountain Science 2020年第4期17卷 884-897页

作者： liU Yang CHEN Xi HAO Jian-Sheng li lan-hai State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqi 830011China Ili Station for Watershed Ecosystem Research Chinese Academy of SciencesXinyuan 835800China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumqi 830011China Xinjiang Regional Center of Resources and Environmental Science Instrument Chinese Academy of SciencesUrumqi 830011China

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.

关键词： Snow cover Estimation Sentinel-1/2 MODIS Machine learning

Projected change in precipitation forms in the Chinese Tianshan Mountains based on the Back Propagation Neural Network Model

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Journal of Mountain Science 2022年第3期19卷 689-703页

作者： REN Rui li Xue-mei li Zhen li lan-hai HUANG Yi-yu Faculty of Geomatics Lanzhou Jiaotong UniversityLanzhou 730070China Key Laboratory of Regional Climate-Environment in Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Xinjiang Institute of Ecology and Geography Chinese Academy of SciencesUrumqi 830011China National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring Lanzhou 730070China Gansu Provincial Engineering Laboratory for National Geographic State Monitoring Lanzhou 730070China

In the context of global warming,precipitation forms are likely to transform from snowfall to rainfall with a more pronounced *** change in precipitation forms will inevitably affect the processes of regional runoff generation and confluence as well as the annual distribution of *** researchers used precipitation data from the CMIP5 model directly to study future precipitation trends without distinguishing between snowfall and ***5 models have been proven to have better performance in simulating temperature but poorer performance in simulating *** 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 *** 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 *** found that temperature was the only key factor affecting the transformation of the RPR in the BP-DEMATEL *** 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 *** 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 ***,in terms of spatial variation,

关键词： Global warming Tianshan Mountains region Precipitation forms CMIP5 models Back Propagation Neural Network Model

Evaluation of spatiotemporal variability of temperature and precipitation over the Karakoram Highway region during the cold season by a Regional Climate Model

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Journal of Mountain Science 2020年第9期17卷 2108-2122页

作者： YANG Tao li Qian CHEN Xi YIN Gang li lan-hai Philippe De MAEYER State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqi 830011China Ili Station for Watershed Ecosystem Research Chinese Academy of SciencesXinyuan 835800China University of Chinese Academy of Sciences Beijing 100049China Department of Geography Ghent UniversityGhent 9000Belgium Sino-Belgian Joint Laboratory of Geo-information Urumqi 830011China Sino-Belgian Joint Laboratory of Geo-information Ghent 9000Belgium Research Centre for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumqi 830011China College of Resources and Environmental Sciences Xinjiang UniversityUrumqi 830046China

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 *** 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 *** 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 *** results of WRF were compared with the in-situ observations and Multi-Source WeightedEnsemble Precipitation(MSWEP)*** 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 *** rain bands were exhibited in a northwest-to-southeast direction over the study *** wet day frequency was found in January,February,and March in the section between Hunza and *** addition,the areas with extreme values are mainly located in the Dasu-Islamabad section in February,March,and *** 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.

关键词： Karakoram Highway WRF model Precipitation Temperature Snow hazards

Reference evapotranspiration concentration and its relationship with precipitation concentration at southern and northern slopes of Tianshan Mountains, China

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Journal of Mountain Science 2019年第6期16卷 1381-1395页

作者： HUANG Fa-rong YANG Tao li Qian li Si-si li lan-hai Suwannee ADSAVAKULChai State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqi 830011China Ili station for Watershed Ecosystem Research Chinese Academy of SciencesXinyuan 835800China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumqi 830011China University of Chinese Academy of Sciences Beijing 100049China School of Engineering University of the Thai Chamber of CommerceBangkok 10400Thailand

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.

关键词： Concentration Reference evapotranspiration Precipitation Trend analysis Tianshan Mountains

Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China

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Journal of Mountain Science 2018年第1期15卷 33-45页

作者： liU Yang li lan-hai CHEN Xi YANG Jin-Ming HAO Jian-Sheng State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi 830011 China CAS Research Center for Ecology and Environment of Central Asia No.818 South Beijing Road Urumqi 830011 China Institute of Arid Ecology and Environment Xinjiang University Urumqi 830046 China

Snow depth is a general input variable in many models of agriculture,hydrology,climate and *** 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 *** 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 *** estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is *** 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 ***=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow *** 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 ***,the GWRK resulted in more detailed information on snow depth distribution than the *** final conclusion is that GWRK is better suited for estimating regional snow depth distribution.

关键词： Snow depth Spatial distribution Regression kriging Geographically weighted regression kriging

Avalanche activity and characteristics of its triggering factors in the western Tianshan Mountains,China

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Journal of Mountain Science 2018年第7期15卷 1397-1411页

作者： HAO Jian-sheng HUANG Fa-rong liU Yang Amobichukwu Chukwudi AMANAMBU li lan-hai State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences No. 818 South Beijing Road Urumqi 830011 China. University of Chinese Academy of sciences Bering 100049 China Yili Station for Watershed Ecosystem Research Chinese Academy of Sciences No. 818 South Beijing Road Urumqi 830011 China. Xinjiang Regional Center of Resources and Environmental Science Instrument Chinese Academy of Sciences Urumqi 830011 China CAS Research Center for Ecology and Environment in Central Asia No. 818 South Beijing Road Urumqi Xinjiang 830011 China

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.

关键词： Snow avalanche Snow properties Snow climate Triggering conditions Risk management

Snowpack variations and their hazardous effects under climate warming in the central Tianshan Mountains

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Advances in Climate Change Research 2024年第3期15卷 442-451页

作者： Jian-Sheng HAO Yan WANG lan-hai li Key Laboratory of land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of Sciences(CAS)Beijing 100101China Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone Urumqi 830011China State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of SciencesUrumqi 830011China Tianshan Snowcover and Avalanche Observation and Research Station of Xinjiang Xinyuan 835800China CAS Research Center for Ecology and Environment of Central Asia Urumqi 830011China

Climate change alters snowpack evolution,which in turn influences the likelihood of snow avalanches and flood *** lack of systemic observational data on key snow characteristics in high mountains remains a scientific challenge in terms of systematically elucidating the dynamic chain of variations in climate-snowpack-snow *** restricts our understanding and poses challenges in the prediction of snow-related disaster *** such,this study analysed the variations of temperature and snowfall and the physical characteristics of snowpacks based on ground-based observations from the Kunse River Valley situated in the Tianshan Mountains from 1967 to *** results reveal that the temperature increased significantly by 0.32°C per decade(plier ***,average and maximum snow depths increased along with an increase in peak snow water equivalent,thus indicating a higher frequency of extremely scarce or abundant snow *** low snowpack temperature gradient and earlier snowmelt dates in spring lead to earlier occurrences of snowmelt floods and wet *** the risks of these events increase,they pose greater threats to farmlands,road transportation,water-electricity infrastructure and several other human ***,these insights are critical for providing vital information that can deepen our understanding of the impact of climate change on snowpack characteristics and improve management strategies for snow-related disaster prevention and mitigation.

关键词： Climate change Snowpack variations Snow-related disaster Tianshan Mountains