Flood hazard mapping and assessment in data-scarce Nyaungdon area, Myanmar

PLoS One. 2019 Nov 26;14(11):e0224558. doi: 10.1371/journal.pone.0224558. eCollection 2019.

Abstract

Torrential and long-lasting rainfall often causes long-duration floods in flat and lowland areas in data-scarce Nyaungdon Area of Myanmar, imposing large threats to local people and their livelihoods. As historical hydrological observations and surveys on the impact of floods are very limited, flood hazard assessment and mapping are still lacked in this region, making it hard to design and implement effective flood protection measures. This study mainly focuses on evaluating the predicative capability of a 2D coupled hydrology-inundation model, namely the Rainfall-Runoff-Inundation (RRI) model, using ground observations and satellite remote sensing, and applying the RRI model to produce a flood hazard map for hazard assessment in Nyaungdon Area. Topography, land cover, and precipitation are used to drive the RRI model to simulate the spatial extent of flooding. Satellite images from Moderate Resolution Imaging Spectroradiometer (MODIS) and the Phased Array type L-band Synthetic Aperture Radar-2 onboard Advanced Land Observing Satellite-2 (ALOS-2 ALOS-2/PALSAR-2) are used to validate the modeled potential inundation areas. Model validation through comparisons with the streamflow observations and satellite inundation images shows that the RRI model can realistically capture the flow processes (R2 ≥ 0.87; NSE ≥ 0.60) and associated inundated areas (success index ≥ 0.66) of the historical extreme events. The resultant flood hazard map clearly highlights the areas with high levels of risks and provides a valuable tool for the design and implementation of future flood control and mitigation measures.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Environmental Monitoring / methods*
  • Environmental Monitoring / statistics & numerical data
  • Floods / prevention & control*
  • Floods / statistics & numerical data
  • Hydrology / methods*
  • Models, Statistical
  • Myanmar
  • Rain
  • Risk Assessment / methods
  • Risk Assessment / statistics & numerical data
  • Risk Evaluation and Mitigation*
  • Rivers
  • Satellite Imagery*

Grants and funding

This study was supported by the National Key Research and Development Program of China (2018YFC1508101), National Science Foundation of China (51879067), Natural Science Foundation of Jiangsu Province (BK20180022), Six Talent Peaks Project in Jiangsu Province (NY-004), Fundamental Research Funds for the Central Universities of China (2018B42914), and the Priority Academic Program Development of Jiangsu Higher Education Institutions awarded to KZ.