Data assimilation for constructing long-term gridded daily rainfall time series over Southeast Asia

The data scarcity and poor availability of observed daily rainfalls over Southeast Asia has limited the possibility to a wider range of studies in light of impacts from climate change and extreme hydro-meteorological processes such as floods, droughts, and other watershed management practices. To fill such a gap, data assimilation was carried out in this study to construct a long-term gridded daily (0.50 degrees x 0.50 degrees) rainfall time series (1951-2014) over Southeast Asia. In rainfall data assimilation, the available and globally accepted high resolution gridded datasets viz. Southeast Asia observed (SA-OBS) (1981-2014), APHRODITE (1951-2007), TRMM (1998-2018), PRINCETON (1951-2008) along with limited rain gauges-based rainfalls were utilized. In this study, eight gap filling methods were employed and tested at 20 selected rainfall grids to fill the long gaps presented in the SA-OBS gridded dataset. The strength of each method and associated uncertainties were evaluated in the computed rainfalls utilizing multiple functions at missing grids. The accuracy of each method, in case of extreme rainfalls, was tested by quantile-quantile (Q-Q) plots at different quantile intervals. The distance power method based on the Pearson correlation coefficient and the multiple linear regression method performed satisfactorily and produced minimum uncertainties in filling rainfall gaps. To test the accuracy and compatibility of gap-filled SA-OBS gridded dataset with other sources of datasets, the seasonality analysis and rainfall indices comparison were carried out. Results showed that the gap-filled SA-OBS dataset was better comparable to other sources of rainfalls. For the construction of the long-term rainfall time series (1951-2014), quantile mapping was adopted for bias correction and the quality of the final merged dataset was evaluated.