Abstract
This study aims to present a new imputation method for missing precipitation records by fusing its spatio-temporal information. On the basis of extending simple kriging model, a nonstationary kriging method which assumes that the mean or trend is known and varies in whole study area was proposed. It obtains precipitation trend of each station at a given time by analyzing its time series data, and then performs geostatistical analysis on the residual between the trend and measured values. Finally, these spatio-temporal information is integrated into a unified imputation model. This method was illustrated using monthly total precipitation data from 671 meteorological stations of China in April, spanning the period of 2001-2010. Four different methods, including moving average, mean ratio, expectation maximization and ordinary kriging were introduced to compare with. The results show that: Among these methods, the mean absolute error, mean relative error and root mean square error of the proposed method are the smallest, so it produces the best imputation result. That is because: (1) It fully takes into account the spatio-temporal information of precipitation. (2) It assumes that the mean varies in whole study area, which is more in line with the actual situation for rainfall.
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