Socioeconomic Impact Evaluation for Near Real-Time Flood Detection in the Lower Mekong River Basin

Abstract

Flood events pose a severe threat to communities in the Lower Mekong River Basin. The combination of population growth, urbanization, and economic development exacerbate the impacts of these events. Flood damage assessments, critical for understanding the effects of flooding on the local population and informing decision-makers about future risks, are frequently used to quantify the economic losses due to storms. Remote sensing systems provide a valuable tool for monitoring flood conditions and assessing their severity more rapidly than traditional post-event evaluations. The frequency and severity of extreme flood events are projected to increase, further highlighting the need for improved flood monitoring and impact analysis. In this study we integrate a socioeconomic damage assessment model with a near real-time flood remote sensing and decision support tool (NASA's Project Mekong). Direct damages to populations, infrastructure, and land cover are assessed using the 2011 Southeast Asian flood as a case study. Improved land use/land cover and flood depth assessments result in rapid loss estimates throughout the Mekong River Basin. Results suggest that rapid initial estimates of flood impacts can provide valuable information to governments, international agencies, and disaster responders in the wake of extreme flood events.

Evaluation of Satellite-Based Rainfall Estimates in the Lower Mekong River Basin (Southeast Asia)

“…It is important to note that the final goal of this work was not to estimate floods and extreme events, but to evaluate the performance of satellite precipitation estimates so that future studies can feel confident about using these estimates in their models. This study and further research applying in-situ observations to determine accuracy of satellite product can aid in the improvement of basin-wide decision-making, flood prediction, and management of floodwaters and drought by providing validations which suggest that satellite estimates can substitute for rain gauge measurements in areas with a sparse or absent in-situ network [26,33].…”

The Value of Near Real-Time Earth Observations for Improved Flood Disaster Response

“…produced automatic flood extents by applying a dynamic surface water classifier to data from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensors on the Aqua and Terra satellites. Oddo et al (2018) then demonstrated how the resulting flood extents can be combined with socioeconomic data to produce rapid estimates of flood impacts using depth-damage curves for different types of land cover and infrastructure. This study attempts to determine how the flood detection and impact assessment metrics produced by the preceding analyses can be further applied to emergency response (Figure 3).…”

“…Subtracting the underlying digital elevation model ("Multi-Error-Removed Improved-Terrain"-MERIT DEM) produces a raster of estimated flood depths (Figure 1c) (Yamazaki et al, 2017). For a more detailed discussion of the methodology and limitations of this approach, see sections 3 and 5 of Oddo et al (2018).…”

“…Land cover classifications were derived from atmospherically-corrected imagery from Landsat 4, 5, 7, and 8 to produce a map of the entire LMRB at ∼30-m ground resolution. Damages to specific land cover types were assessed using regionally-derived depth-damage functions (Oddo et al, 2018). Additionally, damages to populations and infrastructure were estimated by intersecting flood depths with population data from NASA's Socioeconomic Data and Applications Center (SEDAC) and open-source infrastructure data from OpenStreetMap (OSM), respectively (CIESIN, 2016;OpenStreetMap Contributors, 2019).…”

“…Therefore, the socioeconomic damage map used to identify the potential population sites (Supplementary Figure 1) also has associated uncertainties. Despite this, we find that the provisional damage estimates can still be instructive for identifying areas that may be most highly impacted [for more detail, see the discussion section 5.2-Damage Estimate Validation in Oddo et al (2018)].…”