Regional analysis of the 2015–16 Lower Mekong River basin drought using NASA satellite observations

• Published in: Journal of hydrology. Regional studies Vol. 46; p. 101362
• Main Authors: Lakshmi, Venkataraman, Le, Manh-Hung, Goffin, Benjamin D., Besnier, Jessica, Pham, Hung T., Do, Hong-Xuan, Fang, Bin, Mohammed, Ibrahim, Bolten, John D.
• Format: Journal Article
• Language: English
• Published: Goddard Space Flight Center Elsevier B.V 01-04-2023; Elsevier
• Subjects: Downscaled SMAP; Earth Resources and Remote Sensing; GPM; GRACE; Mekong; Meteorology and Climatology; SMAP; SPI; SSI; Mekong; GPM; GRACE; SSI; SPI; Downscaled SMAP; SMAP; Gpm; Grace; Ssi; Downscaled Smap; Spi; Smap
• ISSN: 2214-5818; 2214-5818
• DOI: 10.1016/j.ejrh.2023.101362

Lower Mekong River Basin (LMRB)
Satellite remote sensing products are widely used for monitoring droughts. Using NASA satellite sensors of precipitation (Global Measurement Mission, GPM), soil moisture (Soil Moisture Active and Passive, SMAP), and terrestrial water storage (Gravity Recovery and Climate Experiment, GRACE), this study evaluates the historical drought in the LMRB during 2015–16. SMAP soil moisture was validated against in-situ soil moisture, and GPM precipitation and SMAP soil moisture were cross-validated with streamflow observations. The spatiotemporal dynamics of soil moisture were also examined in different ranges of catchment areas. In performing the analysis, we used lagged correlations between hydrological variables and the indices of the Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI).
Spatio-temporal patterns of drought in 2015–16 were examined from the entire basin to small watersheds. A mismatch occurs when using GRACE data to study droughts in small watersheds (many of the small watersheds would be a fraction of the few 100 km2 spatial resolutions of GRACE pixel). In smaller watersheds, hydrological drought (SSI) was closely defined with SMAP soil moisture downscaled to 1 km rather than the meteorological drought index (SPI). By leveraging satellite-based observations across a range of spatial scales, this study highlights the utility of Earth observations in informing water resources and land management decisions at the regional scale.
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