Abstract
With the growing concern about the failure risk of river embankments in a rapidly changing climate, this study aims to quantify the overtopping probability of river embankment in Kao-Ping River basin in southern Taiwan. A water level simulation model is calibrated and validated with historical typhoon events and the calibrated model is further used to assess overtopping risk in the future under a climate change scenario. A dynamic downscaled projection dataset, provided by Meteorological Research Institute (MRI) has been further downscaled to 5-km grids and bias-corrected with a quantile mapping method, is used to simulate the water level of Kao-Ping River in the future. Our results highlighted that the overtopping risk of Kao-Ping River increased by a factor of 5.7~8.0 by the end of the 21st century.
Highlights
A torrential rainfall alters the flow rate of rivers and puts flood prevention facilities at a higher risk of failure
A common lesson learned from the previous extreme flood events is that it is time to review the current hydraulic facilities and re-evaluate the overtopping risk of river embankments under future climate change scenarios
The The projection periods follow the Meteorological Research Institute (MRI)-atmospheric general circulation model (AGCM) setting which is comprised of Base Period (BP, 1979–2003), projection periods follow the MRI-AGCM setting which is comprised of Base Period (BP, 1979–2003), NNeeaarrFFuuttuurree((NNFF,22001155––22003399),),aannddEEnnddooffCCeenntuturryy((EECC,22007755––22009999).).AAfftteerrddoowwnnssccaalliinnggttoo55--kkmmggrriiddss, a certain degree of system bias still exists when compared to observations from rainfall gauges
Summary
A torrential rainfall alters the flow rate of rivers and puts flood prevention facilities at a higher risk of failure. A common lesson learned from the previous extreme flood events is that it is time to review the current hydraulic facilities and re-evaluate the overtopping risk of river embankments under future climate change scenarios. The The projection periods follow the MRI-AGCM setting which is comprised of Base Period (BP, 1979–2003), projection periods follow the MRI-AGCM setting which is comprised of Base Period (BP, 1979–2003), NNeeaarrFFuuttuurree((NNFF, ,22001155––22003399),),aannddEEnnddooffCCeenntuturryy((EECC,,22007755––22009999).).AAfftteerrddoowwnnssccaalliinnggttoo55--kkmmggrriiddss,, a certain degree of system bias still exists when compared to observations from rainfall gauges To cope with this problem, the MRI-WRF dataset is further bias corrected using Quantile Mapping (QM). Sustainability 2020, 12, 4511 a certain degree of system bias still exists when compared to observations from rainfall gauges To cope with this problem, the MRI-WRF dataset is further bias corrected using Quantile Mapping (QM) method by Su et al (2016) [19]. FigFuirgeu3r.eC3o. mCopmarpisaornisoofnthoef tahcecuamccuulmateudlartaeidnfraalilnoffatlyl pohf otyopnheovoenntesvienntthseitnhtrheee ptherrieoedpse(rai)o3dhs;((ab))32h4ohu. rs; (b) 24 hours
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