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首页> 外文期刊>Theoretical and applied climatology >DEM-based delineation for improving geostatistical interpolation of rainfall in mountainous region of Central Himalayas, India
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DEM-based delineation for improving geostatistical interpolation of rainfall in mountainous region of Central Himalayas, India

机译：基于DEM的轮廓线可改善印度喜马拉雅山中部山区的降雨地统计插值

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摘要

In mountainous region with heterogeneous topography, the geostatistical modeling of the rainfall using global data set may not confirm to the intrinsic hypothesis of stationarity. This study was focused on improving the precision of the interpolated rainfall maps by spatial stratification in complex terrain. Predictions of the normal annual rainfall data were carried out by ordinary kriging, universal kriging, and co-kriging, using 80-point observations in the Indian Himalayas extending over an area of 53,484 km(2). A two-step spatial clustering approach is proposed. In the first step, the study area was delineated into two regions namely lowland and upland based on the elevation derived from the digital elevation model. The delineation was based on the natural break classification method. In the next step, the rainfall data was clustered into two groups based on its spatial location in lowland or upland. The terrain ruggedness index (TRI) was incorporated as a co-variable in co-kriging interpolation algorithm. The precision of the kriged and co-kriged maps was assessed by two accuracy measures, root mean square error and Chatfield's percent better. It was observed that the stratification of rainfall data resulted in 5-20 % of increase in the performance efficiency of interpolation methods. Co-kriging outperformed the kriging models at annual and seasonal scale. The result illustrates that the stratification of the study area improves the stationarity characteristic of the point data, thus enhancing the precision of the interpolated rainfall maps derived using geostatistical methods.
机译：在地形不均的山区，使用全球数据集对降雨进行地统计建模可能无法证实平稳性的内在假设。这项研究的重点是通过复杂地形中的空间分层来提高插值降雨图的精度。正常年降水量数据的预测是通过普通克里格法，通用克里格法和共同克里格法进行的，使用了印度喜马拉雅山的80点观测资料，面积超过53484 km（2）。提出了一种两步空间聚类方法。第一步，根据数字高程模型得出的高程，将研究区域划分为低地和高地两个区域。划定是基于自然休息分类法。下一步，根据降雨数据在低地或高地上的空间位置将其分为两类。地形坚固性指数（TRI）被作为协变量纳入了共同克里格插值算法中。克雷格图和共同克雷德图的精度通过两种准确性度量来评估，即均方根误差和Chatfield更好的百分比。可以看出，降雨数据的分层导致插值方法的执行效率提高了5-20％。在年度和季节尺度上，共同克里金法优于克里金模型。结果表明，研究区域的分层改善了点数据的平稳性，从而提高了用地统计方法得出的插值降雨图的精度。

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  • 来源
    《Theoretical and applied climatology》 |2017年第2期|51-58|共8页
  • 作者

    Kumari Madhuri; Singh Chander Kumar; Bakimchandra Oinam; Basistha Ashoke;

  • 作者单位

    TERI Univ, Dept Nat Resources, New Delhi, India|Amity Univ, Amity Sch Engn & Technol, Dept Civil Engn, Noida, Uttar Pradesh, India;

    TERI Univ, Dept Reg Water Studies, New Delhi, India;

    Natl Inst Technol, Dept Civil Engn, Imphal, Manipur, India;

    Egis India Consulting Engineers Pvt Ltd, New Delhi, India;

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