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.