The evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) in drought monitoring in the Lancang River Basin
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- Zeng, H., Li, L. & Li, J. J. Geogr. Sci. (2012) 22: 273. doi:10.1007/s11442-012-0926-1
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Drought is one of the most destructive disasters in the Lancang River Basin, which is an ungauged basin with strong heterogeneity on terrain and climate. Our validation suggested the version-6 monthly TRMM multi-satellite precipitation analysis (TMPA; 3B43 V.6) product during the period 1998 to 2009 is an alternative precipitation data source with good accuracy. By using the standard precipitation index (SPI), at the grid point (0.25°×0.25°) and sub-basin spatial scales, this work assessed the effectiveness of TMPA in drought monitoring during the period 1998 to 2009 at the 1-month scale and 3-months scale; validated the monitoring accuracy of TMPA for two severe droughts happened in 2006 and 2009, respectively. Some conclusions are drawn as follows. (1) At the grid point spatial scale, in comparison with the monitoring results between rain gauges (SPI1g) and TMPA grid (SPI1s), both agreed well at the 1-month scale for most of the grid points and those grid points with the lowest critical success index (CSI) are distributed in the middle stream of the Lancang River Basin. (2) The same as SPI1s, the consistency between SPI3s and SPI3g is good for most of the grid points at the 3-months scale, those grid points with the lowest were concentrated in the middle stream and downstream of the Lancang River Basin. (3) At the 1-month scale and 3-months scale, CSI ranged from 50% to 76% for most of the grid points, which demonstrated high accuracy of TMPA in drought monitoring. (4) At the 3-months scale, based on TMPA basin-wide precipitation estimates, though we tended to overestimate (underestimate) the peaks of dry or wet events, SPI3s detected successfully the occurrence of them over the five sub-basins at the most time and captured the occurrence and development of the two severe droughts happened in 2006 and 2009. This analysis shows that TMPA has the potential for drought monitoring in data-sparse regions.