the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 May 2024
Determining the threshold of issuing flash flood warnings based on people’s response process simulation
Abstract. The effectiveness of flash flood warnings depends on the people’s response processes to the warnings. And false warnings and missed events cause the people’s negative responses. It is crucial to find a way to determine the threshold of issuing the warnings that reduces the false warning ratio and the missed event ratio, especially for uncertain flash flood forecasting. However, most studies determine the warning threshold based on the natural processes of flash floods rather than the social processes of warning responses. Therefore, an agent-based model (ABM) was proposed to simulate the people’s response processes to the warnings. And a simulation chain of "rainstorm probability forecasting - decision on issuing warnings - warning response processes" was conducted to determine the warning threshold based on the ABM. Liulin Town in China was selected as a case study to demonstrate the proposed method. The results show that the optimal warning threshold decreases as the forecasting accuracy increases. And as the forecasting variance or the variance of the forecasting variance increases, the optimal warning threshold decreases (increases) for low (high) forecasting accuracy. Adjusting the warning threshold according to the people’s tolerance levels of the failed warnings can improve warning effectiveness, but the prerequisite is to increase the forecasting accuracy and decrease the forecasting variance. The proposed method provides valuable insights into the determination of warning threshold for improving the effectiveness of flash flood warnings.
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Status: closed
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RC1: 'Comment on hess-2024-130', Anonymous Referee #1, 09 Jun 2024
Comments:
People’s response to flood warnings is an important factor that affect the performance of flood evacuation processes. This study develops an agent-based model to simulate the people’s response processes to the warnings, and to determine the threshold for issuing flood warnings. The Liulin Town in China is selected to analyze the role of flood warning threshold and forecast variance in flood fatality rates. The modeling results provide interesting insights into effective flood management. Overall, this is a well conducted research with clear presentations. Below are some minor comments:
- Table 3 lists three parameters to represent flood forecast skills. Please add some text to describe the meaning of these parameters, and how to quantify these parameters in real-world flood warning scenarios.
- The model is quite complex with a lot of parameters. A modeling framework diagram is needed to show all the model components, the associated parameters and their relationships.
- Equation (1) describes the fatality probability as a function of flood water depth and flood water velocity. Where does this equation come from? A concise literature review on flood causality function could be helpful to make the paper more solid.
Citation: https://doi.org/10.5194/hess-2024-130-RC1 - AC1: 'Reply on RC1', ruikang zhang, 17 Jun 2024
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RC2: 'Comment on hess-2024-130', Anonymous Referee #2, 25 Jul 2024
The manuscript "Determining the threshold of issuing flash flood warnings based on people’s response process simulation" by Zhang et al. presents a novel and comprehensive approach to determining flash flood warning thresholds that considers the complexities of human response processes, which is a significant advancement over traditional methods that often focus solely on natural hydrological processes. Additionally, the study examines the uncertainties in flash flood forecasting that affect the effectiveness of warning thresholds and the role of people’s tolerance for false warnings and missed events in setting these thresholds. The manuscript is generally well-written, and I have only a few suggestions for the authors to consider before it can be accepted for publication.
1) While the introduction highlights the limitations of existing approaches, it lacks explicit statements of the specific research questions the study aims to address. I would suggest that the authors improve the introduction to more clearly frame the study's objectives and guide the reader through the subsequent sections.
2) For the methodology section, I strongly recommend adding a diagram or flowchart to illustrate the relationships between the different modules and the overall simulation chain, including key variables and processes. This will be helpful for the readers to quickly understand the complex interactions and flow of information within the model.
3) In the methods, some parameters are estimated based on author expertise and empirical data (?). I would suggest more information to justify the parameter settings. For example, although I assume that the parameters in section 2.1.3 are validated in the authors' previous study, at least the rationale behind the choice should be clearly articulated. In addition, it is not clear why the casualty probability is estimated using a logistic regression equation based on flood water depth and velocity. Please clarify the rationale for the choice of this particular equation.
4) In the case study section, it is not clear what the role of event rainfall and synthetic rainfall events are in ABM. Also, if possible, please try to improve Figure 3 to make it more informative, e.g. use a histogram and indicate the level of real event rainfall in it.
5) The results focus heavily on the simulation results without sufficient discussion of the implications and limitations of the results. For example, while the simulation results are detailed, there appears to be limited validation of these results against real-world data or historical flood events. Also, the results rely heavily on behavioral assumptions embedded in the ABM, such as confidence levels and evacuation intentions; I would suggest discussing the limitations of these assumptions and how they might affect the simulation results. In addition, the manuscript assumes that most parameters in the ABM are time-invariant, with the exception α. This simplification may overlook the dynamic nature of human behavior and environmental conditions. It would be beneficial to explore how varying these parameters over time might affect the model results.
6) Although the case study describes the town’s geomorphology and flood risk areas, it does not clearly link these local conditions to the model results and findings. Please consider to discuss how specific local conditions (e.g., topography, infrastructure) influenced the simulation results and how these findings can be generalized to other regions with similar or different conditions.
Citation: https://doi.org/10.5194/hess-2024-130-RC2 - AC2: 'Reply on RC2', ruikang zhang, 20 Aug 2024
Status: closed
-
RC1: 'Comment on hess-2024-130', Anonymous Referee #1, 09 Jun 2024
Comments:
People’s response to flood warnings is an important factor that affect the performance of flood evacuation processes. This study develops an agent-based model to simulate the people’s response processes to the warnings, and to determine the threshold for issuing flood warnings. The Liulin Town in China is selected to analyze the role of flood warning threshold and forecast variance in flood fatality rates. The modeling results provide interesting insights into effective flood management. Overall, this is a well conducted research with clear presentations. Below are some minor comments:
- Table 3 lists three parameters to represent flood forecast skills. Please add some text to describe the meaning of these parameters, and how to quantify these parameters in real-world flood warning scenarios.
- The model is quite complex with a lot of parameters. A modeling framework diagram is needed to show all the model components, the associated parameters and their relationships.
- Equation (1) describes the fatality probability as a function of flood water depth and flood water velocity. Where does this equation come from? A concise literature review on flood causality function could be helpful to make the paper more solid.
Citation: https://doi.org/10.5194/hess-2024-130-RC1 - AC1: 'Reply on RC1', ruikang zhang, 17 Jun 2024
-
RC2: 'Comment on hess-2024-130', Anonymous Referee #2, 25 Jul 2024
The manuscript "Determining the threshold of issuing flash flood warnings based on people’s response process simulation" by Zhang et al. presents a novel and comprehensive approach to determining flash flood warning thresholds that considers the complexities of human response processes, which is a significant advancement over traditional methods that often focus solely on natural hydrological processes. Additionally, the study examines the uncertainties in flash flood forecasting that affect the effectiveness of warning thresholds and the role of people’s tolerance for false warnings and missed events in setting these thresholds. The manuscript is generally well-written, and I have only a few suggestions for the authors to consider before it can be accepted for publication.
1) While the introduction highlights the limitations of existing approaches, it lacks explicit statements of the specific research questions the study aims to address. I would suggest that the authors improve the introduction to more clearly frame the study's objectives and guide the reader through the subsequent sections.
2) For the methodology section, I strongly recommend adding a diagram or flowchart to illustrate the relationships between the different modules and the overall simulation chain, including key variables and processes. This will be helpful for the readers to quickly understand the complex interactions and flow of information within the model.
3) In the methods, some parameters are estimated based on author expertise and empirical data (?). I would suggest more information to justify the parameter settings. For example, although I assume that the parameters in section 2.1.3 are validated in the authors' previous study, at least the rationale behind the choice should be clearly articulated. In addition, it is not clear why the casualty probability is estimated using a logistic regression equation based on flood water depth and velocity. Please clarify the rationale for the choice of this particular equation.
4) In the case study section, it is not clear what the role of event rainfall and synthetic rainfall events are in ABM. Also, if possible, please try to improve Figure 3 to make it more informative, e.g. use a histogram and indicate the level of real event rainfall in it.
5) The results focus heavily on the simulation results without sufficient discussion of the implications and limitations of the results. For example, while the simulation results are detailed, there appears to be limited validation of these results against real-world data or historical flood events. Also, the results rely heavily on behavioral assumptions embedded in the ABM, such as confidence levels and evacuation intentions; I would suggest discussing the limitations of these assumptions and how they might affect the simulation results. In addition, the manuscript assumes that most parameters in the ABM are time-invariant, with the exception α. This simplification may overlook the dynamic nature of human behavior and environmental conditions. It would be beneficial to explore how varying these parameters over time might affect the model results.
6) Although the case study describes the town’s geomorphology and flood risk areas, it does not clearly link these local conditions to the model results and findings. Please consider to discuss how specific local conditions (e.g., topography, infrastructure) influenced the simulation results and how these findings can be generalized to other regions with similar or different conditions.
Citation: https://doi.org/10.5194/hess-2024-130-RC2 - AC2: 'Reply on RC2', ruikang zhang, 20 Aug 2024
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