首页> 外文期刊>寒旱区科学:英文版 >Spatial-temporal variability of snow cover over the Amur River Basin inferred from MODIS daily snow products in recent decades
【24h】

Spatial-temporal variability of snow cover over the Amur River Basin inferred from MODIS daily snow products in recent decades

机译:近几十年来从Modis Daily Snow Product推断Amur River盆地的雪覆盖的空间变异性

获取原文
获取原文并翻译 | 示例
       

摘要

MODIS snow products MOD10A1MYD10A1 provided us a unique chance to investigate snow cover as well as its spatial-temporal variability in response to global changes from regional and global perspectives. By means of MODIS snow products MOD10A1MYD10A1 derived from an extensive area of the Amur River Basin, mainly located in the Northeast part of China, some part in far east area of the former USSR and a minor part in Republic of Mongolia, the reproduced snow datasets after removal of cloud effects covering the whole watershed of the Amur River Basin were generated by using 6 different cloud-effect-removing algorithms. The accuracy of the reproduced snow products was evaluated with the time series of snow depth data observed from 2002 to 2010 within the Chinese part of the basin, and the results suggested that the accuracies for the reproduced monthly mean snow depth datasets derived from 6 different cloud-effect-removing algorithms varied from 82% to 96%, the snow classification accuracies(the harmonic mean of Recall and Precision) was higher than 80%, close to the accuracy of the original snow product under clear sky conditions when snow cover was stably accumulated. By using the reproduced snow product dataset with the best validated cloud-effect-removing algorithm newly proposed, spatial-temporal variability of snow coverage fraction(SCF), the date when snow cover started to accumulate(SCS) as well as the date when being melted off(SCM) in the Amur River Basin from 2002 to 2016 were investigated. The results indicated that the SCF characterized the significant spatial heterogeneity tended to be higher towards East and North but lower toward West and South over the Amur River Basin. The inter-annual variations of SCF showed an insignificant increase in general with slight fluctuations in majority part of the basin. Both SCS and SCM tended to be slightly linear varied and the inter-annual differences were obvious. In addition, a clear decreasing trend in snow cover is observed in the region. Trend analysis(at 10% significance level) showed that 71% of areas between2,000 and 2,380 m a.s.l. experienced a reduction in duration and coverage of annual snow cover. Moreover, a severe snow cover reduction during recent years with sharp fluctuations was investigated. Overall spatial-temporal variability of Both SCS and SCM tended to coincide with that of SCF over the basin in general.
机译:Modis Snow Products Mod10A1 MyD10A1为我们提供了一个独特的机会,以应对区域和全球视角的全球变化,为雪覆盖进行调查以及其空间变异性。通过Modis Snow Products Mod10A1 MyD10A1源于Amur River盆地的广泛面积,主要位于中国东北部,部分苏联的远东地区和蒙古共和国的一部分,转载通过使用6种不同的云效应除去算法产生覆盖Amur河流域的整个流域后的雪地数据集。通过从2002年到2010年在盆地的2002年到2010年观察到的时间序列评估了再现的雪产品的准确性,结果表明,转发月用平均雪深度数据集的精度达到了6种不同的云-Effect除去算法从82%变化到96%,雪分类精度(召回和精度的谐波平均值)高于80%,接近雪覆盖稳定的清澈天空条件下原始雪产品的准确性积累。通过使用具有最佳验证的云效应移除算法的再现的雪产品数据集新增的,雪覆盖率分数(SCF)的空间时间变异性,雪覆盖开始积聚(SCS)以及日期的日期从2002年到2016年,阿穆尔河流域融化了(SCM)被调查。结果表明,SCF表征了大量的空间异质性往往朝向东部和北方更高,但在阿穆尔河流域朝向西部和南部。 SCF的年间变化表明,一般的一般增加,盆地的多数部分略有波动。 SCS和SCM都倾向于略微线性变化,并且年度差异是显而易见的。此外,在该地区观察到雪覆盖的明显降低趋势。趋势分析(10%的意义水平)显示,71%的区域为2,000至2,380米A.L.经历了减少的持续时间和覆盖年度雪覆盖。此外,近年来,调查了近年来急剧波动的严重雪盖。 SCS和SCM的总空间 - 时间可变性倾向于与盆地的SCF相互作用。

著录项

  • 来源
    《寒旱区科学:英文版》 |2020年第006期|P.418-429|共12页
  • 作者单位

    Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China;

    Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China;

    National Engineering Laboratory for Lake Pollution Control and Ecological Restoration Chinese Research Academy of Environmental Science Beijing 100012 China;

    National Engineering Laboratory for Lake Pollution Control and Ecological Restoration Chinese Research Academy of Environmental Science Beijing 100012 China;

    Lanzhou University of Technology Lanzhou Gansu 730050 China;

    Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 ChinaUniversity of Chinese Academy of Sciences Beijing 100049 China;

    Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 ChinaUniversity of Chinese Academy of Sciences Beijing 100049 China;

    Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 ChinaUniversity of Chinese Academy of Sciences Beijing 100049 China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 chi
  • 中图分类 海洋基础科学;
  • 关键词

    MODIS; SCF; SCS; SCM; Amur River Basin; cloud effect removal;

    机译:modis;scf;scs;scm;amur河流域;云效果去除;
  • 入库时间 2022-08-19 04:55:53
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号