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Clustering Data and Incorporating Topographical Variables for Improving Spatial Interpolation of Rainfall in Mountainous Region

Author

Listed:
  • Madhuri Kumari

    (TERI University
    Amity University Uttar Pradesh)

  • Chander Kumar Singh

    (TERI University)

  • Ashoke Basistha

    (Egis India Consulting Engineers Pvt. Ltd. New Delhi)

Abstract

This study was an attempt to quantify the improvement in the accuracy of rainfall interpolation in the mountainous terrain by clustering of rainfall data at the data preparation stage and incorporating topographical variables at interpolation stage. The univariate kriging techniques, ordinary kriging (OK), simple kriging and universal kriging (UK) were compared with multivariate kriging method of ordinary cokriging (OCK). The elevation, slope and terrain ruggedness index (TRI) computed from digital elevation model were incorporated as explanatory variable in OCK. These algorithms were applied to the normal annual and seasonal rainfall data points located in Central Himalayas of Uttarakhand region. The study area was divided into two different zones of lowland and upland based on the elevation variability. This zonation was then used as a basis for clustering the rainfall data. The performance of the interpolation techniques was assessed for subdivided regions and compared with the results obtained for complete region. The evaluation was based on absolute error metrics of root mean square error (RMSE) in combination with RMSE-observations standard deviation ratio. The absolute percentage error (APE) statistics was calculated for every observation points and then the percentage of data for with APE ≤ 30% (APE30) was analyzed. It was observed that performance efficiency of the interpolation methods improves by 5-20% if the rainfall data is clustered based on homogeneity of terrain elevation as against considering the complete set of data. In complex terrain, the inclusion of topographical variables improves the cokriging based rainfall prediction if it is correlated with rainfall.

Suggested Citation

  • Madhuri Kumari & Chander Kumar Singh & Ashoke Basistha, 2017. "Clustering Data and Incorporating Topographical Variables for Improving Spatial Interpolation of Rainfall in Mountainous Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 425-442, January.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1534-0
    DOI: 10.1007/s11269-016-1534-0
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    References listed on IDEAS

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    1. Nazzareno Diodato & Gianni Tartari & Gianni Bellocchi, 2010. "Geospatial Rainfall Modelling at Eastern Nepalese Highland from Ground Environmental Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2703-2720, September.
    2. Ashoke Basistha & D. Arya & N. Goel, 2008. "Spatial Distribution of Rainfall in Indian Himalayas – A Case Study of Uttarakhand Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(10), pages 1325-1346, October.
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