Leveraging Multi-Source Data and Digital Technology to Support the Monitoring of Localized Water Changes in the Mekong Region

The limited availability of high-resolution monitoring systems for the drought phenomena and water dynamics affected by weather anomalies hinders policy decisions in a multitude of ways. This paper introduces the availability of the high-resolution Water Monitoring System (WMS) developed from a mix of sophisticated multi-spectral satellite imageries, analytic and data sciences, and cloud computing, for monitoring the changes in water levels and vegetation water stress at the local scale. The WMS was tested in the Lower Mekong Region (LMR) case basin, Thailand's Chi River Basin, in the period from January 2021 to April 2021, the dry season. The overall quality of the VHI, VCI, TCI, and NDVI drought simulation results showed a statistically positive Pearson correlation with the reservoir and dam water volume data (ranged between 0.399 and 0.575) but demonstrated a strong negative correlation with the groundwater level data (between -0.355 and -0.504). Further investigation and more detailed analysis of the influence of different physical environmental conditions related to change in groundwater level should be considered to increase scientific knowledge and understanding about the changing nature of the local system from local perspectives with the alternative use of drought indices in data-poor areas. Our result suggests that the WMS can provide quantitative spatiotemporal variations of localized and contextualized surface water changes as a preliminary analysis. The WMS results can offer guidance for finding a better smaller unit management that suits the local conditions, such as water resource management, disaster risk reduction measures (i.e., drought and flood), irrigation practice, land use planning, and crop management. The existing WMS is geared toward the early warning of water and agricultural development, progress on the SDGs, utilization of digital innovation, and improved abilities of decision-makers to monitor and foresee extreme weather events earlier and with high spatial accuracy