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

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.

Flood inundation mapping in data‐scarce areas: A case of Mbire District, Zimbabwe

“…">5.Flood extent validation: 30% (GPS flood locations from questionnaire survey — 91 households) of the ground data were used to validate the flood extent map generated in this study (stage 5a in Figure 2). The evaluation of the flood extent map was done as indicated in previous studies (Bennett et al, 2013; Falter et al, 2013; Khaing et al, 2019; Sayama et al, 2012; Zhang et al, 2016; Zischg et al, 2018). In our case a comprehensive validation was done by comparing the model predictions of flood extent and those reported by the study participants on a cell‐by‐cell basis.…”

“…(4) where M1D1 is the total number of pixels which were appropriately depicted by the model as flooded (correct hits); M1D0 refers to the number of pixels shown as inundated by water in the model but observed as dry during the fieldwork (false alarms); and M0D1 is the number of pixels depicted by the model as dry yet observed as flooded during the fieldwork (misses); the number of correct negative pixels refers to the areas depicted as dry in both model and fieldwork as also outlined by Khaing et al (2019).…”

GIS-based multi-criteria analysis for flood prone areas mapping in the trans-boundary Shatt Al-Arab basin, Iraq-Iran

“…On the other hand, spatial maps of historical floods can be a suitable alternative for verification (Dash and Sar 2020). Therefore, freely available multispectral imageries like Satellite images from MODIS are appropriate for extracting surface water bodies (Khaing et al 2019). In areas where rivers and streams pass through international borders, MODIS flood detection capabilities effectively provide consistent and validatable information (Brakenridge and Anderson 2006).…”

Flood inundations and risk mapping in a tidal river: a case study for the Kelani River basin, Sri Lanka

“…The evaluation of performances of model stage and the observed stage is statically evaluated with Nash-Sutcliffe Efficiency Coefficient (NSE) and the Pearson coefficient of determination (R 2 ). In addition, to evaluate the performances of inundation prediction we followed previous studies (De Silva et al 2012;Khaing et al 2019) to conduct a comprehensive evaluation by comparing the satellite observations and model predictions on a cell-by-cell basis following statistical matrix by Falter et al (2013), Bennett et al (2013), Falter et al (2015 and Khaing et al (2019).…”