Abstract
Climate change will have a significant impact on the water cycle and will lead to severe environmental problems and disasters in humid tropical river basins. Examples include river basins in Sumatra Island, Indonesia, where the coastal lowland areas are mostly composed of peatland that is a wetland environment initially sustained by flooding from rivers. Climate change may alter the frequency and magnitude of flood inundation in these lowland areas, disturbing the peatland environment and its carbon dynamics and damaging agricultural plantations. Consequently, projecting the extent of inundation due to future flooding events is considered important for river basin management. Using dynamically downscaled climate data obtained by the Non-Hydrostatic Regional Climate Model (NHRCM), the Rainfall-Runoff-Inundation (RRI) model was applied to the Batanghari River Basin (42,960 km2) in Sumatra Island, Indonesia, to project the extent of flood inundation in the latter part of the twenty-first century. In order to obtain reasonable estimates of the extent of future flood inundation, this study compared two bias correction methods: a Quantile Mapping (QM) method and a combination of QM and Variance Scaling (VS) methods. The results showed that the bias correction obtained by the QM method improved the simulated flow duration curve (FDC) obtained from the RRI model, which facilitated comparison with the simulated FDC using reference rainfall data. However, the high spatial variability observed in daily and 15-day rainfall data remained as the spatial variation bias, and this could not be resolved by simple QM bias correction alone. Consequently, the simulated extreme variables, such as annual maximum flood inundation volume, were overestimated compared to the reference data. By introducing QM-VS bias correction, the cumulative density functions of annual maximum discharge and inundation volumes were improved. The findings also showed that flooding will increase in this region; for example, the flood inundation volume corresponding to a 20-year return period will increase by 3.3 times. River basin management measures, such as land use regulations for plantations and wetland conservation, should therefore consider increases in flood depth and area, the extents of which under a future climate scenario are presented in this study.
Highlights
Climate change will have a significant impact on waterrelated disasters and environmental problems in Indonesia
2.1 Dataset This study uses climate data produced by the NonHydrostatic Regional Climate Model (NHRCM) that was developed by the Japan Meteorological Research Institute (MRI)
The Non-Hydrostatic Regional Climate Model (NHRCM) data show higher spatial variability in annual rainfall compared to the reference data
Summary
Climate change will have a significant impact on waterrelated disasters and environmental problems in Indonesia. GCMs have limitations in reproducing reasonable rainfall patterns in humid tropics, especially in areas with many islands such as those in the so-called Indonesian Maritime Continent (MC) (Ramage et al 1968), which is surrounded by the Indian and Pacific oceans (Neale and Slingo 2003) These limitations can be attributed to the imperfect parameterization of physical processes (e.g., convection, cloud generation, local land sea breeze circulation, and regional variations in the diurnal cycle) (Gianotti et al 2012; Ulate et al 2014), as well as the coarse resolution of topography (Neale and Slingo 2003; Schiemann et al 2014; Hertwig et al 2015). Several studies have shown that RCMs can successfully reproduce local rainfall patterns in space and time in the MC (Juneng et al 2016; Cruz and Sasaki 2017; Rashid and Hirst 2017; Ratna et al 2017; Kang et al 2018)
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