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Combined effects of snow depth and nitrogen addition on ephemeral growth at the southern edge of the Gurbantunggut Desert,China

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Journal of Arid Land 2013年第4期5卷 500-510页

作者： LianLian FAN Yan LI LiSong TANG Jian MA State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of Sciences University of Chinese Academy of Sciences

Water and nitrogen （N） inputs are considered as the two main limiting factors affecting plant *** in these inputs are expected to alter the structure and composition of the plant community,thereby influencing biodiversity and ecosystem *** is a form of precipitation in winter,and snow melting can recharge soil water and result in a flourish of ephemerals during springtime in the Gurbantunggut Desert,China.A bi-factor experiment was designed and deployed during the snow-covering season from 2009 to *** experiment aimed to explore the effects of different snow-covering depths and N addition levels on *** indicated that deeper snow cover led to the increases in water content in topsoil as well as density and coverage of ephemeral plants in the same N treatment; by contrast,N addition sharply decreased the density of ephemerals in the same snow ***,N addition exhibited a different effect on the growth of ephemeral plants：in the 50％ snow treatment,N addition limited the growth of ephemeral plants,showing that the height and the aboveground biomass of the ephemeral plants were lower than in those without N addition; while with the increases in snow depth （100％ and 150％ snow treatments）,N addition benefited the growth of the dominant individual *** richness was not significantly affected by snow in the same N ***,N addition significantly decreased the species richness in the same snow-covering *** primary productivity of ephemerals in the N addition increased with the increase of snow *** variations indicated that the effect of N on the growth of ephemerals was restricted by water *** plenty of water （100％ and 150％ snow treatments）,N addition contributed to the growth of ephemeral plants; while with less water （50％ snow treatment）,N addition restricted the growth of ephemeral plants.

关键词： snow depth soil water content N addition ephemeral plant plant density species richness

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Machine learning model for snow depth estimation using a multisensory ubiquitous platform

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Journal of Mountain Science 2022年第9期19卷 2506-2527页

作者： Sofeem NASIM Mourad OUSSALAH Björn KLÖVE Ali Torabi HAGHIGHI Department of Computer Science and Engineering University of OuluOulu 90570Finland Water Energy and Environmental EngineeringUniversity of OuluOulu 90570Finland

snow depth estimation is an important parameter that guides several hydrological applications and climate change *** advances in remote sensing technology and enhanced satellite observations,the estimation of snow depth at local scale still requires improved accuracy and *** advances in ubiquitous and wearable technology promote new prospects in tackling this *** this paper,a wearable IoT platform that exploits pressure and acoustic sensor readings to estimate and classify snow depth classes using some machine-learning models have been put ***,the results of Random Forest classifier showed an accuracy of 94%,indicating a promising alternative in snow depth measurement compared to in situ,LiDAR,or expensive large-scale wireless sensor network,which may foster the development of further affordable ecological monitoring systems based on cheap ubiquitous sensors.

关键词： snow depth Wearable technology Machine learning Random forest classifier

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Spatial and Temporal Variability of snow depth Derived from Passive Microwave Remote Sensing Data in Kazakhstan

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Journal of Meteorological Research 2016年第6期30卷 1033-1043页

作者： MASHTAYEVA Shamshagul DAI Liyun CHE Tao SAGINTAYEV Zhanay SADVAKASOVA Saltanat KUSSAINOVA Marzhan ALIMBAYEVA Danara AKYNBEKKYZY Meerzhan Lev Gumilev Eurasian National University Astana 010000 Kazakhstan Key Laboratory of Remote Sensing of Gansu Province Heihe Remote Sensing Experimental Research Station Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of Sciences Lanzhou 730000 China Nazarbayev University Astana 01000 Kazakhstan National Hydrometeorological Service of the Republic of Kazakhstan Astana 010000 Kazakhstan Center for Excellence in Tibetan Plateau Earth Sciences Chinese Academy of Sciences Beijing 100101 China

snow cover plays an important role in the hydrological cycle and water management in Kazakhstan. However, traditional observations do not meet current needs. In this study, a snow depth retrieval equation was developed based on passive microwave remote sensing data. The average snow depth in winter （ASDW）, snow cover duration （SCD）, monthly maximum snow depth （MMSD）, and annual average snow depth （AASD） were derived for each year to monitor the spatial and temporal snow distributions. The SCD exhibited significant spatial variations from 30 to 250 days. The longest SCD was found in the mountainous area in eastern Kazakhstan, reaching values between 200 and 250 days in 2005. The AASD increased from the south to the north and maintained latitudinal zonality. The MMSD in most areas ranged from 20 to 30 cm. The ASDW values ranged regularity of latitudinal zonality from 15 to 20 cm in the eastern region and were characterized by spatial The ASDW in the mountainous area often exceeded 20 cm.

关键词： snow cover snow depth remote sensing passive microwave spatial and temporal variations Kazakhstan

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Historical and real-time estimation of snow depth in Eurasia based on multiple passive microwave data

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气候变化研究进展:英文版 2023年第4期14卷 537-545页

作者： Li-Yun DAI Li-Juan MA Su-Ping NIE Si-Yu WEI Tao CHE Key Laboratory of Remote Sensing of Gansu Province Heihe Remote Sensing Experimental Research StationNorthwest Institute of Eco-Environment and ResourcesChinese Academy of SciencesLanzhou 730000China National Climate Centre China Meteorological AdministrationBeijing 1000101China University of Chinese Academy of Sciences Beijing 1000101China

Current snow depth datasets demonstrate large discrepancies in the spatial pattern in Eurasia,and the lagging updates of datasets do not meet the operational requirements of the meteorological service *** study developed a dynamic retrieval method for daily snow depth over Eurasia based on cross-sensor calibrated microwave brightness temperatures to enhance retrieval accuracy and meet the requirements of operational *** brightness temperatures were detected by microwave radiometer imager carried on the FengYun 3(FY-3)satellite and the special sensor microwave imager/sounder carried on the USA Defense Meteorological Satellite Program series satellites,which use the fewest sensors to provide the longest data and consequently introduce minimal errors during inter-sensor ***,inter-sensor calibration was conducted amongst brightness temperatures collected by the three sensors.A spatiotemporal dynamic relationship between snow depth and microwave brightness temperature gradient was then established,overcoming the large uncertainties induced by varying snow *** relationship can be utilised in FY-3 satellite data for operational service to obtain real-time snow *** generated daily snow depth dataset from 1988 to 2021 presents similar spatial patterns of snow depth to those observed in *** in situ snow depth,the overall bias and root mean square error are−2.04 and 6.49 cm,respectively,facilitating considerable improvements in accuracy compared with the Advanced Microwave Scanning Radiometer 2 snow depth product,which adopts the static *** analysis shows an overall decreasing trend from 1988 to 2021 for annual and monthly mean snow depths,demonstrating a noticeable reduction since around *** reduction in monthly mean snow depth started earlier in shallow snow months than in deep snow months.

关键词： snow depth Passive microwave remote sensing Eurasia

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Cross-calibration of brightness temperature obtained by FY-3B/MWRI using Aqua/AMSR-E data for snow depth retrieval in the Arctic

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Acta Oceanologica Sinica 2021年第1期40卷 43-53页

作者： Haihua Chen Lele Li Lei Guan College of Information Science and Engineering Ocean University of ChinaQingdao 266100China

This study cross-calibrated the brightness temperatures observed in the Arctic by using the FY-3B/MWRI L1 and the Aqua/AMSR-E *** monthly parameters of the cross-calibration were determined and evaluated using robust linear *** snow depth in case of seasonal ice was calculated by using parameters of the crosscalibration of data from the MWRI *** correlation coefficients of the H/V polarization among all channels Tb of the two sensors were higher than *** parameters of the monthly cross-calibration were useful for the snow depth retrieval using the *** from the MWRI Tb were cross-calibrated to the AMSR-E *** in the data of the two sensors were optimized to approximately 0 K through the cross-calibration,the standard deviations decreased significantly in the range of 1.32 K to 2.57 K,and the correlation coefficients were as high as 99%.An analysis of the statistical distributions of the histograms before and after cross-calibration indicated that the FY-3B/MWRI Tb data had been well ***,the results of the cross-calibration were evaluated by data on the daily average Tb at 18.7 GHz,23.8 GHz,and 36.5 GHz(V polarization),and at 89 GHz(H/V polarization),and were applied to the snow depths retrieval in the *** parameters of monthly cross-calibration were found to be effective in terms of correcting the daily average *** results of the snow depths were compared with those of the calibrated MWRI and AMSR-E *** of 0.18 cm to 0.38 cm were observed in the monthly snow depths,with the standard deviations ranging from 4.19 cm to 4.80 cm.

关键词： FY-3B AMSR-E brightness temperature(T^(b)) cross-calibration snow depth Arctic

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Evaluation of snow depth and snow Cover Fraction Simulated by Two Versions of the Flexible Global Ocean–Atmosphere–Land System Model

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Advances in Atmospheric Sciences 2014年第2期31卷 407-420页

作者： XIA Kun WANG Bin LI Lijuan SHEN Si HUANG Wenyu XU Shiming DONG Li LIU Li Ministry of Education Key Laboratory for Earth System Modeling Center of Earth System Sciences Tsinghua University State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences

Based on historical runs,one of the core experiments of the fifth phase of the Coupled Model Intercomparison Project （CMIP5）,the snow depth （SD） and snow cover fraction （SCF） simulated by two versions of the Flexible Global OceanAtmosphere-Land System （FGOALS） model,Grid-point Version 2 （g2） and Spectral Version 2 （s2）,were validated against observational *** results revealed that the spatial pattern of SD and SCF over the Northern Hemisphere （NH） are simulated well by both models,except over the Tibetan Plateau,with the average spatial correlation coefficient over all months being around 0.7 and 0.8 for SD and SCF,*** the onset of snow accumulation is captured wellby the two models in terms of the annual cycle of SD and SCF,g2 overestimates SD/SCF over most mid-and high-latitude areas of the *** showed that g2 produces lower temperatures than s2 because it considers the indirect effects of aerosols in its atmospheric component,which is the primary driver for the SD/SCF difference between the two *** addition,both models simulate the significant decreasing trend of SCF well over （30°-70°N） in winter during the period ***,as g2 has a weak response to an increase in the concentration of CO2 and lower climate sensitivity,it presents weaker interannual variation compared to s2.

关键词： snow depth snow cover fraction FGOALS-s2 FGOALS-g2

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Increased Tibetan Plateau snow depth:An Indicator of the Connection between Enhanced Winter NAO and Late-Spring Tropospheric Cooling over East Asia

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Advances in Atmospheric Sciences 2010年第4期27卷 788-794页

作者：辛晓歌周天军宇如聪 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric Physics Chinese Academy of Sciences Division of Climate System Modeling National Climate CenterChina Meteorological Administration State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Meteorological Administration

The authors present evidence to suggest that variations in the snow depth over the Tibetan Plateau （TP） are connected with changes of North Atlantic Oscillation （NAO） in winter （JFM）. During the positive phase of NAO, the Asian subtropical westerly jet intensifies and the India-Myanmar trough deepens. Both of these processes enhance ascending motion over the TP. The intensified upward motion, together with strengthened southerlies upstream of the India-Myanmar trough, favors stronger snowfall over the TP, which is associated with East Asian tropospheric cooling in the subsequent late spring （April-May）. Hence, the decadal increase of winter snow depth over the TP after the late 1970s is proposed to be an indicator of the connection between the enhanced winter NAO and late spring tropospheric cooling over East Asia.

关键词： Tibetan Plateau snow depth North Atlantic Oscillation (NAO) tropospheric cooling

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Assessment of snow depth over Arctic Sea Ice in CMIP6 Models Using Satellite Data

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Advances in Atmospheric Sciences 2021年第2期38卷 168-186页

作者： Shengzhe CHEN Jiping LIU Yifan DING Yuanyuan ZHANG Xiao CHENG Yongyun HU Department of Atmospheric and Environmental Sciences University at AlbanyState University of New YorkAlbanyNY 12222USA College of Global Change and Earth System Science and State Key Laboratory of Remote Sensing ScienceBeijing Normal UniversityBeijing 100875China School of Geospatial Engineering and Science Sun Yat-sen UniversityZhuhai 519000China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519000China Department of Atmospheric and Oceanic Sciences School of PhysicsPeking UniversityBeijing 100871China University Corporation for Polar Research Beijing 100875China

snow depth over sea ice is an essential variable for understanding the Arctic energy *** this study,we evaluate snow depth over Arctic sea ice during 1993-2014 simulated by 31 models from phase 6 of the Coupled Model Intercomparison Project(CMIP6)against recent satellite *** CMIP6 models capture some aspects of the observed snow depth climatology and *** observed variability lies in the middle of the models’*** the models show negative trends of snow depth during ***,substantial spatiotemporal discrepancies are *** to the observation,most models have late seasonal maximum snow depth(by two months),remarkably thinner snow for the seasonal minimum,an incorrect transition from the growth to decay period,and a greatly underestimated interannual variability and thinning trend of snow depth over areas with frequent occurrence of multi-year sea *** models are unable to reproduce the observed snow depth gradient from the Canadian Arctic to the outer areas and the largest thinning rate in the central *** projections suggest that snow depth in the Arctic will continue to decrease from 2015 to *** the SSP5-8.5 scenario,the Arctic will be almost snow-free during the summer and fall and the accumulation of snow starts from *** investigation into the possible causes of the issues for the simulated snow depth by some models based on the same family of models suggests that resolution,the inclusion of a hightop atmospheric model,and biogeochemistry processes are important factors for snow depth simulation.

关键词： snow depth Arctic sea ice CMIP6 satellite projection

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Retrieval of snow depth on Antarctic sea ice from the FY-3D MWRI data

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Acta Oceanologica Sinica 2023年第12期42卷 105-117页

作者： Zhongnan Yan Xiaoping Pang Qing Ji Yizhuo Chen Chongxin Luo Pei Fan Zeyu Liang Chinese Antarctic Center of Surveying and Mapping Wuhan UniversityWuhan 430079China Key Laboratory of Polar Surveying and Mapping Science Ministry of Natural ResourcesWuhan 430079China

The snow depth on sea ice is an extremely critical part of the *** and understanding changes of snow depth on Antarctic sea ice is beneficial for research on sea ice and global climate *** Microwave Radiation Imager(MWRI)sensor aboard the Chinese FengYun-3D(FY-3D)satellite has great potential for obtaining information of the spatial and temporal distribution of snow depth on the sea *** comparing in-situ snow depth measurements during the 35th Chinese Antarctic Research Expedition(CHINARE-35),we took advantage of the combination of multiple gradient ratio(GR(36V,10V)and GR(36V,18V))derived from the measured brightness temperature of FY-3D MWRI to estimate the snow *** method could simultaneously introduce the advantages of high and low GR in the snow depth retrieval model and perform well in both deep and shallow snow *** on this,we constructed a novel model to retrieve the FY-3D MWRI snow depth on Antarctic sea *** new model validated by the ship-based observational snow depth data from CHINARE-35 and the snow depth measured by snow buoys from the Alfred Wegener Institute(AWI)suggest that the model proposed in this study performs better than traditional models,with root mean square deviations(RMSDs)of 8.59 cm and 7.71 cm,respectively.A comparison with the snow depth measured from Operation IceBridge(OIB)project indicates that FY-3D MWRI snow depth was more accurate than the released snow depth product from the *** snow and Ice Data Center(NSIDC)and the National Tibetan Plateau Data Center(NTPDC).The spatial distribution of the snow depth from FY-3D MWRI agrees basically with that from ICESat-2;this demonstrates its reliability for estimating Antarctic snow depth,and thus has great potential for understanding snow depth variations on Antarctic sea ice in the context of global climate change.

关键词： snow depth Antarctic sea ice passive microwave FY-3D

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Retrieval snow depth by Artificial Neural Network Methodology from Integrated AMSR-E and In-situ Data——A Case Study in Qinghai-Tibet Plateau

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Chinese Geographical Science 2008年第4期18卷 356-360页

作者： CAO Yungang YANG Xiuchun ZHU Xiaohua Department of Survey Engineering School of Civil Engineering Southwest Jiaotong University Chengdu 610031 China Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences Beijing 100081 China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China

On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002-2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.

关键词： artificial neural network Bayesian regularization snow depth brightness temperature Qinghai-Tibet Plateau

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