the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Combining statistical and hydrodynamic models to assess compound flood hazards from rainfall and storm surge: a case study of Shanghai
Abstract. Coastal regions have experienced significant environmental changes and increased vulnerability to floods caused by the combined effect of multiple flood drivers such as storm surge, heavy rainfall, and river discharge, i.e., compound floods. Hence, for a sustainable development of coastal cities, it is necessary to understand the spatiotemporal dynamics and future trends of compound flood hazard. While the statistical dependence between flood drivers, i.e., rainfall and storm surges, has been extensively studied, the sensitivity of the inundated areas to the relative timing of driver’s individual peaks is less understood and location dependent. To fill this gap, here we propose a framework combining a statistical dependence model for compound event definition and a hydrodynamic model to assess inundation maps of compound flooding from storm surge and rainfall during typhoon season in Shanghai. First, we determine the severity of the joint design event, i.e., peak surge and precipitation, based on the copula model. Second, we use the Same Frequency Amplification (SFA) method to transform the design event values in hourly timeseries so that they represent boundary conditions to force hydrodynamic models. Third, we assess the sensitivity of inundation maps to the time lag between storm surge peak and rainfall. Finally, we define flood zones based on the primary flood driver and we delineate flood zones under the worst compound flood scenario. The study highlights that the temporal delay between storm surge and rainfall plays a pivotal role in shaping the dynamics of flooding events. More specifically, the worst conditions in terms of cumulative inundation depth occur when rainfall precedes the storm surge peak. At the same time, the results show that in Shanghai surge is the primary flood driver. High storm surge at the eastern part of the city (Wusongkou tidal gauge) propagate upstream in the Huangpu River resulting in fluvial flooding in Shanghai city center and several surrounding districts. This calls for a better fluvial flooding control system hinging on the backwater effect during high surge in the upper and middle Huangpu River and in the newly added urbanized areas to ensure flood resilience. The proposed framework is useful to evaluate and predict flood hazard in coastal cities, and the results can provide guidance for urban disaster prevention and mitigation.
- Preprint
(2204 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on hess-2023-261', Anonymous Referee #1, 26 Feb 2024
The presented study looks to assess compound flood hazards in Shanghai from rainfall and storm surge events using a statistical dependence model for compound event definition and a hydrodynamic model to represent inundation. The authors in particular look at the sensitivity of inundation to the timing of storm surge peaks and rainfall, which is valuable information for flood risk management. The study is well organized and the objectives are clearly defined and well addressed. I do have a number of relatively minor issues that should be addressed before the paper is published. Please find them below:
- Add quantitive results to Abstract.
- “TC” events, tropical cyclone acronym should be spelt out the first time
- Paragraph starting line 67 is missing some references. The example provided line 71, are the authors saying this is something that can happen in theory, or that has been documented to happen in the case study area (reference?).
- Line 135. A little more detail on what is meant by a “local typical storm surge pattern and rainfall hyetograph” would be beneficial. What is a typical storm and how is it defined?
- a follow up remark is that total sea level during a surge is refrerred to as “storm surge” in the paper, including in the result figures. How are tides considered in this study? Is the surge residual set at high tide?
- Bilskie and Hagen (2018) defines zones as “coastal”, “hydrologic” and “transition”. The use of the term “fluvial” here to refer to surge-induced only might be misleading as fluvial flooding in its typical definition can be rainfall-induced. Unless a clearer explanation is provided, “Tidal zone” or “coastal zone” might be more accurate. Jumping forward, the Bilskie and Hagen terminology is used in Figure 7 so there needs to be consistency on this throughout the paper.
- How was the hydrodynamic model calibrated and validated? I could not find Ke et al. (2021) in the references.
- Figure 6 b and d, add scale and north arrow.
- Line 229: “represented by red” in Figure 7?
- While the discussions provide insights on the study’s limitations and its wider implications on flood mitigation, there is a lack of references to past studies that have looked at flooding risk in Shanghai and how the author’s findings relate to them.
Citation: https://doi.org/10.5194/hess-2023-261-RC1 - AC1: 'Reply on RC1', Hanqing Xu, 25 May 2024
-
RC2: 'Comment on hess-2023-261', Anonymous Referee #2, 14 May 2024
1 please add protection information along the Huangpu River and coastlines.
2 There is little inundation along the coastline, is it because of coastal protection?
3 Line 155-160, the description of how to develop a flood inundation model looks quite simple, but it is a complicated process and did not describe how to validate. I suggest adding more information here, at least, to put some information in the supplementary.
for example, "River, flood wall and discharge data obtained from the Shanghai Municipal Water Authority, to develop the urban inundation model", it is too simple.
Citation: https://doi.org/10.5194/hess-2023-261-RC2 - AC2: 'Reply on RC2', Hanqing Xu, 25 May 2024
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
341 | 141 | 32 | 514 | 23 | 28 |
- HTML: 341
- PDF: 141
- XML: 32
- Total: 514
- BibTeX: 23
- EndNote: 28
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1