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Nonstationary flood coincidence risk analysis using time-varying copula functions
Ying Feng1,2, Peng Shi1,2, Simin Qu3,4
1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing, 210098, China.
Scientific Reports
|February 27, 2020
Summary
Climate change increases flood risks. Nonstationary models reveal that traditional stationary models underestimate or overestimate flood coincidence, especially during extreme wet or dry years, highlighting the need for advanced climate change adaptation strategies.
Area of Science:
- Hydrology and Climate Science
- Environmental Risk Assessment
Background:
- Concurrent flood flows in main rivers and tributaries can cause catastrophic flooding.
- Climate change introduces nonstationary conditions, altering historical flood patterns and risks.
Purpose of the Study:
- To investigate flood coincidence risk under nonstationary climate change conditions.
- To compare flood coincidence probabilities calculated by nonstationary and stationary models.
- To identify the most likely flood coincidence scenarios using copula theory.
Main Methods:
- Calculated coincidence probabilities using nonstationary multivariate models, considering flood occurrence dates and magnitudes.
- Compared results with those from stationary models.
- Employed copula theory to determine the most likely flood coincidence scenarios.
Main Results:
- Highest flood coincidence probabilities observed in mid-July for the Huai and Hong Rivers.
- Flood magnitude distributions were found to be nonstationary.
- Time-varying copulas demonstrated a superior fit for flood magnitude dependence compared to stationary copulas.
Conclusions:
- Stationary models may inaccurately assess flood coincidence risk, underestimating it in wet years and overestimating in dry years.
- Nonstationary models are essential for accurate flood risk assessment under climate change scenarios.
- Utilizing nonstationary multivariate models and time-varying copulas is crucial for effective flood management and adaptation.
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