the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Feb 2024
Development of a Novel Storm Surge Inundation Model Framework for Efficient Prediction
Abstract. Storm surge is a natural process that generates flood disasters in coastal zone and causes massive casualties and property losses. Therefore, the storm surge inundation is of major concern in formulating appropriate strategies for disaster prevention and mitigation. However, traditional storm surge hydrodynamic models have large limits on computational efficiency and stability in practical applications. In this study, a novel storm surge inundation model was developed based on a wetting and drying algorithm established from simplified shallow water momentum equation. The wetting and drying algorithm was applied to rectangle grid that iterates through cellular automata algorithm to improve computational efficiency. The model, referred to as the Hydrodynamical Cellular Automata Flood Model (HCA‐FM), was evaluated by comparing the simulations to regional field observations and that from a widely used hydrodynamic numerical model, respectively. The comparisons demonstrated that HCA‐FM can reproduce the observed inundation distributions, and predict consistent results with the numerical simulation in terms of the inundation extent and submerged depth, with much improved computational efficiency (predict inundation within a few minutes) and high stability. The results reflect significant advancement of HCA‐FM toward efficient predictions of storm surge inundation and applications at large space scales.
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RC1: 'Comment on gmd-2024-12', Anonymous Referee #1, 26 Feb 2024
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- Can authors explain the limitations of traditional storm surge hydrodynamic models that prompted the development of the Hydrodynamical Cellular Automata Flood Model (HCA-FM), particularly regarding computational efficiency and stability?
- How does the wetting and drying algorithm derived from simplified shallow water momentum equations contribute to the computational efficiency and stability of the HCA-FM model?
- Could authors elaborate on how the rectangle grid and cellular automata algorithm are utilized within the HCA-FM model to improve computational efficiency, and what advantages does this approach offer over traditional methods?
- In evaluating the HCA-FM model, what were the critical criteria used to assess its performance, and how did the model compare to regional field observations and simulations from a widely used hydrodynamic numerical model?
- Can authors provide insights into validating the HCA-FM model against regional field observations, including the specific data sources and methodologies used for comparison?
- What were the main findings regarding the ability of the HCA-FM model to reproduce observed inundation distributions, and how did its predictions compare to those of the hydrodynamic numerical model regarding inundation extent and submerged depth?
- The study mentions significantly improved computational efficiency with the HCA-FM model, allowing for inundation predictions within a few minutes. Can authors discuss the implications of this improved efficiency for real-time storm surge forecasting and disaster management?
- How does the high stability of the HCA-FM model contribute to its reliability in predicting storm surge inundation, particularly under varying environmental conditions and input parameters?
- Considering the significant advancements demonstrated by the HCA-FM model, what are the potential applications at large space scales, and how might they contribute to more effective disaster prevention and mitigation strategies in coastal regions?
- Lastly, based on the findings of this study, what are the critical areas for future research or refinement of the HCA-FM model, and how might it be further optimized for broader practical use in storm surge prediction and coastal zone management?
Citation: https://doi.org/10.5194/gmd-2024-12-RC1 -
AC1: 'Reply on RC1', Xuanxuan Gao, 22 Mar 2024
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Dear reviewer:
Thank you for your concerns and comments on our manuscript. Reply to comments, point-to-point, are given as follows:
1. Can authors explain the limitations of traditional storm surge hydrodynamic models that prompted the development of the Hydrodynamical Cellular Automata Flood Model (HCA-FM), particularly regarding computational efficiency and stability?
Traditional storm surge hydrodynamic models, such as the ADCIRC model, simulate the storm surge inundation by numerically solving full shallow water equations. However, it is hard to balance scientific accuracy, numerical stability, and computational efficiency in computer modeling. The reasons are detailed as follows:
High Computational Cost: The time step of the hydrodynamic model is restricted by the grid resolution to ensure stability, because the stability of the solution is limited by the CFL restriction on the gravity wave speed (√gH ∆t/∆x<Cr, in which a practical Cr upper bound of is 0.5 or smaller). To guarantee the spatial accuracy and computational stability of the storm surge inundation simulation, the grid resolution must be increased, and the time step must be reduced to ensure model stability, which significantly increases the computational cost.
As a result, most hydrodynamic modeling applications require significant computational resources. In addition, many complicated preparations must be done before using a hydrodynamic model, particular for a unstructured mesh, including mesh generation, mesh quality adjustment, and input file generation. It is not convenient for practical use. These limitations have prompted the development of the Hydrodynamical Cellular Automata Flood Model (HCA-FM) to provide a computationally efficient and easy-to-use storm surge flood model.
2. How does the wetting and drying algorithm derived from simplified shallow water momentum equations contribute to the computational efficiency and stability of the HCA-FM model?
The traditional numerical models require a significant amount of time to solve hydrodynamic equations using discretization methods. However, in HCA-FM, we transform the shallow water equation into a straightforward wetting and drying algorithm and dynamic calculation formula. Based on this, we have developed the efficient grid iteration algorithm of HCA-FM using cellular automata. Therefore, on the premise of ensuring the accuracy of the calculation results, this approach improves computational efficiency and avoids instability that may arise when solving complex equations using discretization methods.
3. Could authors elaborate on how the rectangle grid and cellular automata algorithm are utilized within the HCA-FM model to improve computational efficiency, and what advantages does this approach offer over traditional methods?
A cellular automata (CA) is typically composed of a group of cells that represent a discretized space, each of which has a state, a distribution of neighboring cells, a discrete time step, and a set of transition rules. The transition rules dominate the update process of CA by determining the new state of each cell in terms of its current state and the states of the cells in its neighborhood. The computational efficiency of the CA algorithm depends on the complexity of the transition rules. The wetting and drying algorithm (transition rules) of HCA-FM, which is derived from the simplified shallow water equations, is very simple and therefore computationally efficient. In comparison, traditional methods like numerical model solve hydrodynamic equations using discretization methods and thus require a significant amount of time. Therefore, HCA-FM has an advantage in terms of computational efficiency.
4. In evaluating the HCA-FM model, what were the critical criteria used to assess its performance, and how did the model compare to regional field observations and simulations from a widely used hydrodynamic numerical model?
In the comparison of HCA-FM simulations with field observations (Section 3.2), the visual image comparisons were made for the line or polygon features of inundation extent. The experiment contains two typhoon storm surge processes, and the study areas are Cangzhou, Hebei and Shenzhen, Guangdong. The model performance is tested by the fitness of line or polygon features of inundation extent by visual comparison.
In the experiment of comparing simulations between HCA-FM and numerical simulations from ADCIRC+SWAN model (Section 3.3), comparisons were made for inundation extent and depth in Laizhou Bay for two typhoon storm surge processes (Lekima and Polly). The two models are using the same topo data. The index of agreement in the comparison includes the fit ratio δ of inundation extent, squared correlation coefficient R2 and root mean squared error RMSE for water depth (page 12, line 255-265). The fit ratio δ ranges from 0 for no overlap to 1 for perfect fit. R2 varies between 0 and 1 which a computed value of 1 indicates perfect agreement. A smaller RMSE indicates better agreement.
5. Can authors provide insights into validating the HCA-FM model against regional field observations, including the specific data sources and methodologies used for comparison?
In the experiments of comparing HCA-FM simulations with field observations, visual image comparisons were made for the line or polygon features of inundation extent. It is important to note that due to the limitations of rough survey data, validation was based solely on inundation extent. Detailed comparisons for water depth had been made between HCA-FM and ADCIRC+SWAN coupled model.
The first experiment compared the inundation extent caused by Typhoon Lekima in Cangzhou, Hebei. Investigation team from the National Marine Environmental Forecasting Center investigated disasters around the south coast of the Bohai Bay. The second experiment compared the inundation extent caused by Typhoon Hato in Shenzhen, Guangdong. The Marine Monitoring and Forecasting Center of Shenzhen organized teams to investigate disasters in key regions. The field survey data in Shenzhen after Typhoon Hato included several locations that were severely affected, which indirectly reflected the inundation extent. Relevant complement will be made in revised manuscript.
6. What were the main findings regarding the ability of the HCA-FM model to reproduce observed inundation distributions, and how did its predictions compare to those of the hydrodynamic numerical model regarding inundation extent and submerged depth?
In Section 3.2, the simulated ability of HCA-FM is validated comparing its simulation with the observed inundation distribution, as given in Fig. 4 & 5, it is able to reproduce the actual inundation area for both two TC events.
In Section 3.3, as a result in the comparisons between HCA-FM and ADCIRC+SWAN (Fig. 6), the fit ratio δ of inundation extent was 0.92 for TC Lekima and 0.95 for TC Polly. For the submerged depth, the R2 were 0.96 for both Lekima and Polly, the RMSE were 0.13 m for Lekima and 0.12 m for Polly. The results indicates a good consistency between the two models. In addition, sensitivity experiments associated with wind force and bottom friction were designed (Table 2), and the results shows that consideration of external forces ensures model’s accuracy towards the storm surge inundation simulation.
7. The study mentions significantly improved computational efficiency with the HCA-FM model, allowing for inundation predictions within a few minutes. Can authors discuss the implications of this improved efficiency for real-time storm surge forecasting and disaster management?
Hydrodynamic models are generally considered as unviable for areas larger than 1000 km2 when the resolution required is less than 10 m. Forecast timeliness is often not met for larger scale forecasts. But the HCA-FM model requires significantly less computer effort than hydrodynamic models. Runtime savings portend that the model is suitable for large floodplains larger than 2000 km2. In real-time storm surge forecasting and disaster management, the HCA-FM model can be used in conjunction with the hydrodynamic models. The HCA-FM can quickly forecast the potential regions and hazard level affected by the storm surge to identify the most serious regions, which leaves more and sufficient time for the government to make decisions.
8. How does the high stability of the HCA-FM model contribute to its reliability in predicting storm surge inundation, particularly under varying environmental conditions and input parameters?
Since the iterative solving process of the HCA-FM is concise, there do not exist many constraints on the stability as those of the numerical model, which depends on factors such as the environment, mesh, and model parameters when solving complex differential equations. Therefore, different environmental conditions, grids and parameters do not affect the stability of the model, thus ensuring the reliability of the model in predicting storm surge inundation. As seen, we perform experiments conducted in different region, and the HCA-FM model performed well in all regions, including Laizhou Bay where it was tested against two typhoons with different tracks. These results suggest that the model is accurate and stable across different study areas and typhoon processes.
9. Considering the significant advancements demonstrated by the HCA-FM model, what are the potential applications at large space scales, and how might they contribute to more effective disaster prevention and mitigation strategies in coastal regions?
HCA-FM can be used not only for real-time disaster prevention and mitigation by rapidly identifying key affected areas on a large scale, but also for regional storm surge hazard and risk assessment. To conduct a probabilistic risk assessment, it is necessary to analyze the probability statistical characteristics of storm surge disaster-causing factors from their long-term time series. As long-term storm surge flood observations are sparse, a large number of simulations are required. Additionally, risk assessments often cover larger study areas, which can make running simulations using hydrodynamic models prohibitively time-consuming. Therefore, HCA-FM can leverage its computational efficiency to significantly reduce the time spent on probabilistic risk assessment and contribute to more effective disaster prevention and mitigation strategies in coastal regions.
10. Lastly, based on the findings of this study, what are the critical areas for future research or refinement of the HCA-FM model, and how might it be further optimized for broader practical use in storm surge prediction and coastal zone management?
Since computing the new state of a cell in CA depends only on the state of the neighboring cells at the previous time step, CA algorithms are well suited to parallel computation. It will be considered to apply parallel computation for HCA-FM model for further enhancement of computational efficiency in the future. Additional, consideration will also be given to designing an interactive operating system for the model to cater for more intuitive and easy use. In addition to improvement of efficiency, the principles of the model will also be improved in the future to take into account more comprehensive hydrodynamic mechanisms to improve the simulation accuracy. Relevant complement will be made in revised manuscript.We have also revised the manuscript to more clearly address the above issues.
Thank you very much for your attention and time.
Yours sincerely,
Xuanxuan GaoCitation: https://doi.org/10.5194/gmd-2024-12-AC1 -
RC2: 'Reply on AC1', Anonymous Referee #1, 22 Mar 2024
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The authors have well addressed my comments this manuscript is acceptable for publication in its present form.
Citation: https://doi.org/10.5194/gmd-2024-12-RC2
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RC2: 'Reply on AC1', Anonymous Referee #1, 22 Mar 2024
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Data sets
Data for HCA-FM Manuscript Submitted to GMD X. Gao, S. Li, D. Mo, Y. Liu, and P. Hu https://doi.org/10.5281/zenodo.10596631
Model code and software
HCA-FM code and instruction X. Gao https://doi.org/10.5281/zenodo.10596826
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