General Information
    • ISSN: 2010-0264
    • Frequency: Bimonthly (2010-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJESD
    • Editor-in-Chief: Prof. Richard Haynes
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: Chemical Abstracts Services (CAS), CABI, Ulrich Periodicals Directory, Electronic Journals Library, Crossref, ProQuest.
    • E-mail:
  • Jun 22, 2018 News! [CFP] 2018 the annual meeting of IJESD Editorial Board, ACESD 2018, will be held in Singapore during November 2-4, 2018.    [Click]
  • Jan 15, 2019 News! Vol.10, No.2 has been published with online version!   [Click]
The University of Queensland, Australia
It is my honor to be the editor-in-chief of IJESD. The journal publishes good papers in the field of environmental science and development.
IJESD 2012 Vol.3(2): 124-129 ISSN: 2010-0264
DOI: 10.7763/IJESD.2012.V3.201

Optimal Rain Gauge Network Design and Spatial Precipitation Mapping based on Geostatistical Analysis from Colocated Elevation and Humidity Data

Aksara Putthividhya and Kenji Tanaka

Abstract—The accurate estimation of the spatial rainfall distribution requires a dense network of instruments, which entails large installation and operational costs. It is thus necessary to optimize the number of rainfall stations and estimate point precipitation at unrecorded locations from existing data. This paper serves 2 objectives: i) to establish a spatial representative rainfall stations from the entire existing network in the study area (i.e., rainfall-data optimization); and ii) to use of multivariate geostatistical algorithm for incorporating relatively cheaper hydrological data into the spatial prediction of rainfall. The technique was illustrated using annual and monthly rainfall observations measured at 326 rainfall stations covering Yom river basin and its vicinity in Thailand. Optimal rain gauge network was designed based on the station redundancy and the homogeneity of the rainfall distribution. Digital elevation, humidity, and temperature models were incorporated into the spatial rainfall prediction using multivariate geostatistical algorithms. The results revealed that the multivariate geostatistical algorithm outperform the linear regression, stressing the importance of accounting for spatially dependent rainfall observations in addition to the collocated elevation. The digital elevation data were highly correlated to monthly monsoon-induced precipitation. Humidity and temperature data exhibited a higher degree of correlation to the monthly precipitation data.

Index Terms—Geostatistics, kriging, multivariate, rain gauge network, spatial interpolation.

A. Putthividhya is with the Water Resources Engineering Department, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand (e-mail:
K. Tanaka is with the Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan (e-mail:


Cite: Aksara Putthividhya and Kenji Tanaka, "Optimal Rain Gauge Network Design and Spatial Precipitation Mapping based on Geostatistical Analysis from Colocated Elevation and Humidity Data," International Journal of Environmental Science and Development vol. 3, no. 2, pp. 124-129, 2012.

Copyright © 2008-2018. International Journal of Environmental Science and Development. All rights reserved.