Probabilistic early warning of flash floods using Monte Carlo simulation and hydrological modelling
Accurate early warning of flash floods is critical for prompt decision-making in mitigating disaster impact. However, most current applications of flash-flood warning are based on deterministic approaches, and the inherent uncertainty that exists has not been fully considered. This study proposed a...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2025.2523423 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839647404273434624 |
|---|---|
| author | Xuemei Wu Yuting Zhao Wenjiang Zhang Xiaodong Li Guanghua Qin Hongxia Li |
| author_facet | Xuemei Wu Yuting Zhao Wenjiang Zhang Xiaodong Li Guanghua Qin Hongxia Li |
| author_sort | Xuemei Wu |
| collection | DOAJ |
| description | Accurate early warning of flash floods is critical for prompt decision-making in mitigating disaster impact. However, most current applications of flash-flood warning are based on deterministic approaches, and the inherent uncertainty that exists has not been fully considered. This study proposed a probabilistic flash-flood warning approach by incorporating hydrological modelling uncertainty. The Monte Carlo (MC)-based parameter selection method, together with probability density analysis, was used in assessing the probability of warning criteria being exceeded. Moreover, an optimal decision rule was introduced to enhance the reliability of the flash-flood warning. The results show that the proposed approach provides more informative results by generating the probability distribution estimation and probabilistic thresholds, enabling the user to choose their own decision rule. The probabilistic approach with the optimal threshold has a better performance (CSI = 0.58) than the deterministic approach (CSI = 0.41), especially in the reduction of the number of false alarms (from 37 to 19 events), which shows better reliability and confidence. The results highlight the improvement of the proposed approach by incorporating the uncertainty in hydrological modelling, which can effectively quantify the potential impact risk and aid decision-making to issue warnings. Specifically, a range of possible outcomes are transformed into actionable decisions for issuing reasonable flash-flood warnings with a lead time of 1–3 h. This study provides new insights into the application of the probabilistic approach in flash-flood warning and is expected to enhance practical applications. |
| format | Article |
| id | doaj-art-692e3f3ece984b1e98e1658cb7f45c83 |
| institution | Matheson Library |
| issn | 1994-2060 1997-003X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj-art-692e3f3ece984b1e98e1658cb7f45c832025-06-30T06:52:49ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2025-12-0119110.1080/19942060.2025.2523423Probabilistic early warning of flash floods using Monte Carlo simulation and hydrological modellingXuemei Wu0Yuting Zhao1Wenjiang Zhang2Xiaodong Li3Guanghua Qin4Hongxia Li5State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, People’s Republic of ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, People’s Republic of ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, People’s Republic of ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, People’s Republic of ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, People’s Republic of ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu, People’s Republic of ChinaAccurate early warning of flash floods is critical for prompt decision-making in mitigating disaster impact. However, most current applications of flash-flood warning are based on deterministic approaches, and the inherent uncertainty that exists has not been fully considered. This study proposed a probabilistic flash-flood warning approach by incorporating hydrological modelling uncertainty. The Monte Carlo (MC)-based parameter selection method, together with probability density analysis, was used in assessing the probability of warning criteria being exceeded. Moreover, an optimal decision rule was introduced to enhance the reliability of the flash-flood warning. The results show that the proposed approach provides more informative results by generating the probability distribution estimation and probabilistic thresholds, enabling the user to choose their own decision rule. The probabilistic approach with the optimal threshold has a better performance (CSI = 0.58) than the deterministic approach (CSI = 0.41), especially in the reduction of the number of false alarms (from 37 to 19 events), which shows better reliability and confidence. The results highlight the improvement of the proposed approach by incorporating the uncertainty in hydrological modelling, which can effectively quantify the potential impact risk and aid decision-making to issue warnings. Specifically, a range of possible outcomes are transformed into actionable decisions for issuing reasonable flash-flood warnings with a lead time of 1–3 h. This study provides new insights into the application of the probabilistic approach in flash-flood warning and is expected to enhance practical applications.https://www.tandfonline.com/doi/10.1080/19942060.2025.2523423Forecasting uncertaintydeterministic approachprobabilistic approachrainfall thresholdMonte Carloflash-flood warning |
| spellingShingle | Xuemei Wu Yuting Zhao Wenjiang Zhang Xiaodong Li Guanghua Qin Hongxia Li Probabilistic early warning of flash floods using Monte Carlo simulation and hydrological modelling Engineering Applications of Computational Fluid Mechanics Forecasting uncertainty deterministic approach probabilistic approach rainfall threshold Monte Carlo flash-flood warning |
| title | Probabilistic early warning of flash floods using Monte Carlo simulation and hydrological modelling |
| title_full | Probabilistic early warning of flash floods using Monte Carlo simulation and hydrological modelling |
| title_fullStr | Probabilistic early warning of flash floods using Monte Carlo simulation and hydrological modelling |
| title_full_unstemmed | Probabilistic early warning of flash floods using Monte Carlo simulation and hydrological modelling |
| title_short | Probabilistic early warning of flash floods using Monte Carlo simulation and hydrological modelling |
| title_sort | probabilistic early warning of flash floods using monte carlo simulation and hydrological modelling |
| topic | Forecasting uncertainty deterministic approach probabilistic approach rainfall threshold Monte Carlo flash-flood warning |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2025.2523423 |
| work_keys_str_mv | AT xuemeiwu probabilisticearlywarningofflashfloodsusingmontecarlosimulationandhydrologicalmodelling AT yutingzhao probabilisticearlywarningofflashfloodsusingmontecarlosimulationandhydrologicalmodelling AT wenjiangzhang probabilisticearlywarningofflashfloodsusingmontecarlosimulationandhydrologicalmodelling AT xiaodongli probabilisticearlywarningofflashfloodsusingmontecarlosimulationandhydrologicalmodelling AT guanghuaqin probabilisticearlywarningofflashfloodsusingmontecarlosimulationandhydrologicalmodelling AT hongxiali probabilisticearlywarningofflashfloodsusingmontecarlosimulationandhydrologicalmodelling |