the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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.
- Preprint
(3367 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 27 Jul 2024)
-
RC1: 'Comment on hess-2024-130', Anonymous Referee #1, 09 Jun 2024
reply
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
reply
Thank you so much to the Reviewer for the constructive comments which helped us to significantly improve the manuscript. Please see our detailed response to all comments in the attached pdf file.
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
225 | 49 | 11 | 285 | 7 | 9 |
- HTML: 225
- PDF: 49
- XML: 11
- Total: 285
- BibTeX: 7
- EndNote: 9
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1