Spatial Precipitation Mapping Based on Geostatistical Analysis from Co-Located Elevation, Humidity, and Temperature Data in the Northern Chao Phraya River Basin

This paper serves 2 objectives: (i) to establish a spatial representative rainfall stations from existing nextwork in Northern Chao Phraya River Basin; and (ii) to use of multivariate geostatistical algorithm for incorporating relatively cheaper elevation, humidity, and temperature data into the spatial prediction of rainfall at the study site. Ping, Wang, Yom, and Nan Basins are located in the upstream Chao Phraya River Basin and serve as an upstream control for the main Chao Phraya River Basin. The technique was illustrated using monthly and annual rainfall observations measured at 326 rain gauge stations covering the entire Basin and its vicinity. The precipitation prediction maps, generated by Thiessen polygon, inverse square distance, and ordinary Kriging were used t o determine the sensitivity of the rainfall data to the prediction results by constructing the covariance surface map. Digital elevation, humidity, and temperature models were incorporated into the spatial prediction of rainfall using multivariate geostatistical algorithms. The prediction performance of geostatistical interpolation were cross validated with the straightforward linear regression of rainfall against elevation, humidity, and temperature. The results revealed that the multivariate geostatistical algorithm outperformed the linear regression approach, stressing the importance of accounting for spatially dependent rainfall observations in addition to the co-located elevation. The digital elevation data were highly correlated to monthly monsoon-induced precipitation in the study area. Humidity and temperature data exhibited a lower degree of correlation to the monthly precipitation data, especially when the Basin size is considerably larger.