Analyzing the Impact of Climate Change on Compound Flooding Under Interdecadal Variations in Rainfall and Tide
Abstract
1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data
3. Methodology
3.1. Flood Risk Analysis
3.2. SWMM Construction
- Farm and Pond: Farms and ponds are modeled as storage units interconnected by weirs to simulate flow exchange when water depths exceed the bund heights. A total of 1682 storage units and 3725 flow-exchange weirs were implemented for the farm and pond systems.
- Residential Area: To simulate surface flooding in detail, residential areas are discretized into small triangular mesh cells, each bounded by links and nodes. Rainfall-induced overland flow within each mesh cell drains to the associated node and is then conveyed through the connected links. A total of 8713 nodes and 12,360 links were implemented in the residential areas.
- Drainage Channel: Drainage channels are represented as conduits connected via junctions, which receive inflows from other components through weirs when water depths exceed embankment heights. A total of 1099 conduits, 1099 junctions, and 1233 flow-exchange weirs were established for the drainage network.
- Boundary Components: To simulate compound flooding induced by both rainfall and tides, rainfall-runoff hydrographs are assigned to each storage unit and node based on their corresponding subcatchment, while tidal levels are imposed at 12 outlet nodes located at the ends of rivers and drainage conduits.
3.3. Validation Indicators
3.4. Mann-Kendall Test
3.5. Theil–Sen Estimator
4. Results
4.1. Validation
4.2. Flood Area Trends
4.3. Flood Risk Trends
5. Discussions
5.1. Novelty
- Event selection method: Instead of selecting events based on a fixed rainfall duration, this study selects events based on variable rainfall durations. This approach helps prevent the underestimation of annual flood magnitudes.
- Enhanced simulation efficiency: By representing 2D hydraulic structures as 1D components in SWMM, surface runoff can be simulated with reasonable accuracy while maintaining computational efficiency. Compared to just a few minutes for the 1D model, the computation time required by 2D models can significantly increase by a hundred times. If a 2D model is applied to simulate a 20-year period over the entire study area under five GCMs, it would require several months of computation, making it impractical. Therefore, the application of a 1D model is essential for long-term and large-scale simulations in future research.
- Risk assessment methodology: The occurrence probability of compound flooding is directly estimated using the return periods of flood areas, offering a more intuitive and practical alternative to traditional methods that rely on the joint probabilities of marginal flood-driving factors.
5.2. Limitations
- Expansion of GCM scenarios: This study employs five GCMs to estimate future com-pound flood risks. The results reveal substantial variability among GCMs in the short- to medium-term, although their long-term projections tend to converge. Differences among various GCMs in simulating circulation patterns and intensities lead to discrepancies in their predictions of the onset, break, and revival phases of the East Asian summer monsoon, which in turn results in substantial variations in rainfall forecasts over Taiwan [31]. Incorporating a broader range of GCMs would help better capture this uncertainty.
- Storm surge modeling: In this study, tidal boundary conditions were determined by the combination of astronomical tides and sea-level rise projections without including the typhoon-induced water level setup, which may underestimate the contribution of tidal surge to flooding. Future research could incorporate ocean model simulations that more accurately represent the influence of nearshore storm surges.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Jackson, L.P.; Grinsted, A.; Jevrejeva, S. 21st Century Sea-Level Rise in Line with the Paris Accord. Earth’s Future 2018, 6, 213–229. [Google Scholar] [CrossRef]
- Kulp, S.A.; Strauss, B.H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat. Commun. 2019, 10, 4844. [Google Scholar] [CrossRef] [PubMed]
- Boumis, G.; Moftakhari, H.R.; Moradkhani, H. Coevolution of Extreme Sea Levels and Sea-Level Rise Under Global Warming. Earth’s Future 2023, 11, e2023EF003649. [Google Scholar] [CrossRef]
- Rizzo, A.; Mattei, G.; Dumon Steenssens, L.; Anzidei, M.; Aucelli, P.P.C.; Alberti, T.; Antonioli, F.; Bezzi, A.; Bonaldo, D.; Fontolan, G.; et al. Methodological advances in sea level rise vulnerability assessment: Implications for sustainable coastal management in a climate change scenario. Ocean. Coast. Manag. 2025, 268, 107751. [Google Scholar] [CrossRef]
- Prein, A.F.; Rasmussen, R.M.; Ikeda, K.; Liu, C.; Clark, M.P.; Holland, G.J. The future intensification of hourly precipitation extremes. Nat. Clim. Change 2017, 7, 48–52. [Google Scholar] [CrossRef]
- Li, C.; Zwiers, F.; Zhang, X.; Chen, G.; Lu, J.; Li, G.; Norris, J.; Tan, Y.; Sun, Y.; Liu, M. Larger Increases in More Extreme Local Precipitation Events as Climate Warms. Geophys. Res. Lett. 2019, 46, 6885–6891. [Google Scholar] [CrossRef]
- Wahl, T.; Jain, S.; Bender, J.; Meyers, S.D.; Luther, M.E. Increasing risk of compound flooding from storm surge and rainfall for major US cities. Nat. Clim. Change 2015, 5, 1093–1097. [Google Scholar] [CrossRef]
- Valle-Levinson, A.; Olabarrieta, M.; Heilman, L. Compound flooding in Houston-Galveston Bay during Hurricane Harvey. Sci. Total Environ. 2020, 747, 141272. [Google Scholar] [CrossRef]
- Zscheischler, J.; Westra, S.; van den Hurk, B.J.J.M.; Seneviratne, S.I.; Ward, P.J.; Pitman, A.; AghaKouchak, A.; Bresch, D.N.; Leonard, M.; Wahl, T.; et al. Future climate risk from compound events. Nat. Clim. Change 2018, 8, 469–477. [Google Scholar] [CrossRef]
- Shen, Y.; Morsy, M.M.; Huxley, C.; Tahvildari, N.; Goodall, J.L. Flood risk assessment and increased resilience for coastal urban watersheds under the combined impact of storm tide and heavy rainfall. J. Hydrol. 2019, 579, 124159. [Google Scholar] [CrossRef]
- Hsiao, S.-C.; Chiang, W.-S.; Jang, J.-H.; Wu, H.-L.; Lu, W.-S.; Chen, W.-B.; Wu, Y.-T. Flood risk influenced by the compound effect of storm surge and rainfall under climate change for low-lying coastal areas. Sci. Total Environ. 2021, 764, 144439. [Google Scholar] [CrossRef] [PubMed]
- Bevacqua, E.; Maraun, D.; Vousdoukas, M.I.; Voukouvalas, E.; Vrac, M.; Mentaschi, L.; Widmann, M. Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change. Sci. Adv. 2019, 5, eaaw5531. [Google Scholar] [CrossRef] [PubMed]
- Xu, H.; Tian, Z.; Sun, L.; Ye, Q.; Ragno, E.; Bricker, J.; Mao, G.; Tan, J.; Wang, J.; Ke, Q.; et al. Compound flood impact of water level and rainfall during tropical cyclone periods in a coastal city: The case of Shanghai. Nat. Hazards Earth Syst. Sci. 2022, 22, 2347–2358. [Google Scholar] [CrossRef]
- Xu, K.; Zhuang, Y.; Bin, L.; Wang, C.; Tian, F. Impact assessment of climate change on compound flooding in a coastal city. J. Hydrol. 2023, 617, 129166. [Google Scholar] [CrossRef]
- Lian, J.; Xu, H.; Xu, K.; Ma, C. Optimal management of the flooding risk caused by the joint occurrence of extreme rainfall and high tide level in a coastal city. Nat. Hazards 2017, 89, 183–200. [Google Scholar] [CrossRef]
- Jang, J.-H.; Chang, T.-H. Flood risk estimation under the compound influence of rainfall and tide. J. Hydrol. 2022, 606, 127446. [Google Scholar] [CrossRef]
- Kumbier, K.; Carvalho, R.C.; Vafeidis, A.T.; Woodroffe, C.D. Investigating compound flooding in an estuary using hydrodynamic modelling: A case study from the Shoalhaven River, Australia. Nat. Hazards Earth Syst. Sci. 2018, 18, 463–477. [Google Scholar] [CrossRef]
- Zellou, B.; Rahali, H. Assessment of the joint impact of extreme rainfall and storm surge on the risk of flooding in a coastal area. J. Hydrol. 2019, 569, 647–665. [Google Scholar] [CrossRef]
- Saharia, A.M.; Zhu, Z.; Atkinson, J.F. Compound flooding from lake seiche and river flow in a freshwater coastal river. J. Hydrol. 2021, 603, 126969. [Google Scholar] [CrossRef]
- Yuan, J.; Zheng, F.; Duan, H.-F.; Deng, Z.; Kapelan, Z.; Savic, D.; Shao, T.; Huang, W.-M.; Zhao, T.; Chen, X. Numerical modelling and quantification of coastal urban compound flooding. J. Hydrol. 2024, 630, 130716. [Google Scholar] [CrossRef]
- Rossman, L.A. Storm Water Management Model User’s Manual Version 5.1—Manual; U.S. EPA Office of Research and Development: Washington, DC, USA, 2015. [Google Scholar]
- Han, H.; Kim, D.; Kim, H.S. Inundation Analysis of Coastal Urban Area under Climate Change Scenarios. Water 2022, 14, 1159. [Google Scholar] [CrossRef]
- Bibi, T.S.; Kara, K.G.; Bedada, H.J.; Bededa, R.D. Application of PCSWMM for assessing the impacts of urbanization and climate changes on the efficiency of stormwater drainage systems in managing urban flooding in Robe town, Ethiopia. J. Hydrol. Reg. Stud. 2023, 45, 101291. [Google Scholar] [CrossRef]
- Shi, S.; Yang, B.; Jiang, W. Numerical simulations of compound flooding caused by storm surge and heavy rain with the presence of urban drainage system, coastal dam and tide gates: A case study of Xiangshan, China. Coast. Eng. 2022, 172, 104064. [Google Scholar] [CrossRef]
- Ai, P.; Yuan, D.; Xiong, C. Copula-Based Joint Probability Analysis of Compound Floods from Rainstorm and Typhoon Surge: A Case Study of Jiangsu Coastal Areas, China. Sustainability 2018, 10, 2232. [Google Scholar] [CrossRef]
- Xu, H.; Xu, K.; Lian, J.; Ma, C. Compound effects of rainfall and storm tides on coastal flooding risk. Stoch. Environ. Res. Risk Assess. 2019, 33, 1249–1261. [Google Scholar] [CrossRef]
- Jane, R.; Cadavid, L.; Obeysekera, J.; Wahl, T. Multivariate statistical modelling of the drivers of compound flood events in south Florida. Nat. Hazards Earth Syst. Sci. 2020, 20, 2681–2699. [Google Scholar] [CrossRef]
- Lu, W.; Tang, L.; Yang, D.; Wu, H.; Liu, Z. Compounding Effects of Fluvial Flooding and Storm Tides on Coastal Flooding Risk in the Coastal-Estuarine Region of Southeastern China. Atmosphere 2022, 13, 238. [Google Scholar] [CrossRef]
- Taiwan Climate Change Projection Information and Adaptation Knowledge Platform. Available online: https://tccip.ncdr.nat.gov.tw/index_eng.aspx (accessed on 12 May 2025).
- Masson-Delmotte, V. Climate Change 2021: The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1st ed.; Intergovernmental Panel on Climate Change. Working Group I, issuing body; Cambridge University Press: Cambridge, UK, 2023. [Google Scholar]
- Tung, Y.S.; Wang, S.Y.S.; Chu, J.L.; Wu, C.H.; Chen, Y.M.; Cheng, C.T.; Lin, L.Y. Projected increase of the East Asian summer monsoon (Meiyu) in Taiwan by climate models with variable performance. Meteorol. Appl. 2020, 27, e1886. [Google Scholar] [CrossRef]
- Hawkins, E.; Sutton, R. The potential to narrow uncertainty in projections of regional precipitation change. Clim. Dyn. 2011, 37, 407–418. [Google Scholar] [CrossRef]
- Morimoto, A.; Yanagi, T.; Kaneko, A. Tidal correction of altimetric data in the Japan Sea. J. Oceanogr. 2000, 56, 31–41. [Google Scholar] [CrossRef]
- Schwiderski, E.W. On charting global ocean tides. Rev. Geophys. (1985) 1980, 18, 243–268. [Google Scholar] [CrossRef]
- IPCC AR6 Sea Level Projection Tool. Available online: https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool (accessed on 12 May 2025).
- Chakravarti, I.M.; Laha, R.G.; Roy, J. Handbook of Methods of Applied Statistics: Techniques of Computation, Descriptive Methods, and Statistical Inference; Wiley: Hoboken, NJ, USA, 1967. [Google Scholar]
- Jang, J.H.; Chang, T.H.; Chen, W.B. Effect of inlet modelling on surface drainage in coupled urban flood simulation. J. Hydrol. 2018, 562, 168–180. [Google Scholar] [CrossRef]
- Jang, J.H.; Hsieh, C.T.; Chang, T.H. The importance of gully flow modelling to urban flood simulation. Urban Water J. 2019, 16, 377–388. [Google Scholar] [CrossRef]
- Marsick, A.; André, H.; Khelf, I.; Leclère, Q.; Antoni, J. Benefits of Mann–Kendall trend analysis for vibration-based condition monitoring. Mech. Syst. Signal Process. 2024, 216, 111486. [Google Scholar] [CrossRef]
- Bal, A. Improving the robustness of the theil-sen estimator using a simple heuristic-based modification. Symmetry 2024, 16, 698. [Google Scholar] [CrossRef]
Period | Rainfall (mm) | Tidal Level (m) | ||||||
---|---|---|---|---|---|---|---|---|
Min | Max | Mean | S.D. | Min | Max | Mean | S.D. | |
Baseline (1995–2014) | 8.31 | 99.89 | 39.56 | 18.65 | −2.34 | 1.98 | 0.00 | 0.94 |
Short-term (2021–2040) | 8.84 | 145.14 | 41.96 | 21.31 | −2.31 | 2.05 | 0.11 | 0.92 |
Mid-term (2041–2060) | 9.91 | 152.54 | 44.04 | 21.86 | −2.16 | 2.28 | 0.21 | 0.95 |
Medium–long-term (2061–2080) | 6.88 | 143.59 | 41.58 | 21.89 | −2.05 | 2.36 | 0.34 | 0.93 |
Long-term (2081–2100) | 9.93 | 199.59 | 46.49 | 25.13 | −1.86 | 2.54 | 0.50 | 0.93 |
Event | Time (yyyy.mm.dd–yyyy.mm.dd) | Rainfall (mm) | Tidal Level Mean/Max (m) | Max Tidal Level (m) | OCR | HTR | FAR |
---|---|---|---|---|---|---|---|
Typhoon Nari | 15 September 2001–20 September 2001 | 218.5 | 0.410/1.627 | 1.627 | 0.77 | 0.77 | 0.23 |
Typhoon Mindulle | 28 June 2004–5 July 2004 | 533.0 | 0.579/1.771 | 1.771 | 0.87 | 0.77 | 0.00 |
Rainstorm 0612 | 12 June 2005–16 June 2005 | 908.0 | 0.588/1.274 | 1.274 | 0.88 | 0.82 | 0.03 |
Rainstorm 0609 | 8 June 2006–11 June 2006 | 403.0 | 0.569/1.390 | 1.390 | 0.85 | 0.82 | 0.12 |
Typhoon Kalmaegi | 17 July 2008–20 July 2008 | 261.0 | 0.467/1.289 | 1.289 | 0.64 | 0.88 | 0.39 |
Typhoon Morakot | 5 August 2009–12 August 2009 | 521.5 | 0.693/1.838 | 1.838 | 0.80 | 0.81 | 0.20 |
Rainstorm 0823 | 23 August 2018–28 August 2018 | 553.5 | 0.674/1.634 | 1.634 | 0.74 | 0.80 | 0.29 |
GCM | Short (2021–2040) | Mid (2041–2060) | Mid to Long (2061–2080) | Long (2081–2100) | Overall (2021–2100) | |||||
---|---|---|---|---|---|---|---|---|---|---|
MK Test | TS Slope (km2/yr) | MK Test | TS Slope (km2/yr) | MK Test | TS Slope (km2/yr) | MK Test | TS Slope (km2/yr) | MK Test | TS Slope (km2/yr) | |
CanESM5 | − | −0.016 | + * | 0.359 | + | 0.171 | + | 0.089 | + ** | 0.162 |
CMCC-CM2-SR5 | + | 0.281 | + | 0.257 | + | 0.024 | + | 0.369 | + ** | 0.162 |
MIROC6 | + | 0.351 | − | −0.133 | + | 0.046 | + | 0.179 | + | 0.045 |
MPI-ESM1-2-LR | + | 0.301 | − * | −0.798 | + | 0.254 | + ** | 0.713 | + | 0.041 |
TaiESM1 | − | −0.438 | + | 0.028 | + | 0.171 | + | 0.166 | + | 0.064 |
Time | Return Period (Year) | |||||||
---|---|---|---|---|---|---|---|---|
Baseline | 2.0 | 5.0 | 10.0 | 20.0 | 50.0 | 100.0 | 200.0 | |
Short (2021–2040) | CanESM5 | 3.4 | 8.5 | 16.0 | 29.8 | 67.4 | 124.7 | 230.7 |
CMCC-CM2-SR5 | 2.4 | 4.2 | 6.3 | 9.4 | 15.9 | 23.9 | 35.9 | |
MIROC6 | 1.5 | 2.0 | 2.4 | 3.1 | 4.2 | 5.4 | 6.9 | |
MPI-ESM1-2-LR | 1.2 | 1.4 | 1.6 | 2.0 | 2.8 | 3.8 | 5.5 | |
TaiESM1 | 1.5 | 2.2 | 2.9 | 4.0 | 6.4 | 9.5 | 14.7 | |
Mid (2041–2060) | CanESM5 | 2.0 | 3.7 | 6.2 | 11.0 | 26.0 | 53.2 | 114.9 |
CMCC-CM2-SR5 | 1.3 | 1.6 | 2.1 | 2.8 | 4.6 | 7.4 | 13.0 | |
MIROC6 | 1.4 | 2.3 | 3.3 | 5.1 | 9.0 | 14.1 | 22.2 | |
MPI-ESM1-2-LR | 1.2 | 1.5 | 1.7 | 2.2 | 3.2 | 4.5 | 6.7 | |
TaiESM1 | 1.2 | 1.3 | 1.5 | 1.8 | 2.5 | 3.3 | 4.6 | |
Mid to Long (2061–2080) | CanESM5 | 1.4 | 2.4 | 4.1 | 7.8 | 21.5 | 52.3 | 139.8 |
CMCC-CM2-SR5 | 1.3 | 1.8 | 2.6 | 3.9 | 7.2 | 12.2 | 21.4 | |
MIROC6 | 1.4 | 2.3 | 3.5 | 5.4 | 9.9 | 15.7 | 25.1 | |
MPI-ESM1-2-LR | 1.2 | 1.5 | 2.0 | 2.6 | 4.0 | 5.5 | 7.8 | |
TaiESM1 | 1.1 | 1.5 | 2.0 | 2.7 | 4.1 | 5.9 | 8.5 | |
Long (2081–2100) | CanESM5 | 1.0 | 1.3 | 1.7 | 2.5 | 4.5 | 7.4 | 12.1 |
CMCC-CM2-SR5 | 1.1 | 1.3 | 1.5 | 1.9 | 2.8 | 3.8 | 5.3 | |
MIROC6 | 1.1 | 1.4 | 1.9 | 2.9 | 5.6 | 10.3 | 20.9 | |
MPI-ESM1-2-LR | 1.1 | 1.2 | 1.4 | 1.7 | 2.4 | 3.1 | 4.4 | |
TaiESM1 | 1.1 | 1.4 | 1.7 | 2.1 | 3.2 | 4.5 | 6.8 |
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Jang, J.-H.; Chang, T.-H.; Wu, Y.-M.; Liao, T.-E.; Hsu, C.-H. Analyzing the Impact of Climate Change on Compound Flooding Under Interdecadal Variations in Rainfall and Tide. Hydrology 2025, 12, 182. https://doi.org/10.3390/hydrology12070182
Jang J-H, Chang T-H, Wu Y-M, Liao T-E, Hsu C-H. Analyzing the Impact of Climate Change on Compound Flooding Under Interdecadal Variations in Rainfall and Tide. Hydrology. 2025; 12(7):182. https://doi.org/10.3390/hydrology12070182
Chicago/Turabian StyleJang, Jiun-Huei, Tien-Hao Chang, Yen-Mo Wu, Ting-En Liao, and Chih-Hung Hsu. 2025. "Analyzing the Impact of Climate Change on Compound Flooding Under Interdecadal Variations in Rainfall and Tide" Hydrology 12, no. 7: 182. https://doi.org/10.3390/hydrology12070182
APA StyleJang, J.-H., Chang, T.-H., Wu, Y.-M., Liao, T.-E., & Hsu, C.-H. (2025). Analyzing the Impact of Climate Change on Compound Flooding Under Interdecadal Variations in Rainfall and Tide. Hydrology, 12(7), 182. https://doi.org/10.3390/hydrology12070182