Hydrometeorological analysis of a flash flood event in an ungauged Mediterranean watershed under an operational forecasting and monitoring context

Giannaros, Christos; Dafis, Stavros; Stefanidis, Stefanos; Giannaros, Theodore M.; Koletsis, Ioannis; Oikonomou, Christina

doi:10.1002/met.2079

Cited by 28 publications

(18 citation statements)

References 64 publications

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“…On the use of new technologies, implementing a robust weather forecasting system is a vital component of any effective disaster risk management plan and of evacuation. Ensemble flood forecasting techniques [39] and operational forecasting and monitoring systems [40], have emerged as promising approaches to enhance the prediction of potential threats, such as hurricanes and floods. By employing these techniques, it becomes possible to disseminate more timely and reliable messages regarding the potential level of hazard to the affected population.…”

Section: Discussionmentioning

confidence: 99%

Emergency Evacuation Behavior in Small Island Developing States: Hurricane Irma in Sint Maarten

Medina

Sánchez

Vojinović

2023

Water

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Disasters triggered by natural hazards are becoming more frequent and more intense, causing damage to infrastructure and causing loss of life. One way to reduce disaster risk is by evacuating the hazardous area. However, despite the amount of literature that exists on evacuation behavior, there is still a lack of agreement on which variables can be used as predictors for individuals (or households) to actually evacuate. This lack of agreement can be related to the many variables that can affect the evacuation decision, from demographics, geographic, the hazard itself, and also local or cultural differences that may influence evacuation. Hence, it is essential to analyze and understand these variables based on the specifics of a case study. This study aims to find the most significant variables to be used as predictors of evacuation on the island of Sint Maarten, using data collected after the disaster caused by Hurricane Irma in September 2017. The results suggest that the variables gender, homeownership, percentage of property damage, quality of information, number of storeys of the house, and the vulnerability index are the most significant variables influencing evacuation decisions on the island. We believe the results of this paper offer a clear view to risk managers on the island as to which variables are most important in order to increase evacuation rates on Sint Maarten and to plan more efficiently for future evacuations. In addition, the variables found in this study have the potential to be the base information to set up, validate, and calibrate evacuation models.

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Section: Discussionmentioning

confidence: 99%

Emergency Evacuation Behavior in Small Island Developing States: Hurricane Irma in Sint Maarten

Medina

Sánchez

Vojinović

2023

Water

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“…Severe floods frequently occur in Northeast China, which may cause huge socioeconomic losses [1,2], such as the flood in the Songliao River Basin in 1995, the flood in the Songneng River Basin in 1998, and the mega-flood in the Raoyang River Basin in 2022. In terms of short-term forecasts, a strong indication of the forthcoming flash flood could have been provided at least 2 days in advance based on the WRF-based HEC-HMS-simulated flood peak [3]. Additionally, the WRF model assimilating radar data and in situ weather stations showed very good predictive capability for rainfall timing and location, thus supporting accurate river flow peak forecasting with the hydrological model Continuum [4].…”

Section: Introductionmentioning

confidence: 99%

Evaluation on the Forecast Skills of Precipitation and Its Influencing Factors in the Flood Season in Liaoning Province of China

et al. 2023

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To clarify the precipitation forecast skills of climate forecast operations in the flood season in Liaoning Province of China, this study examines the forecast accuracies of China’s national and provincial operational climate prediction products and the self-developed objective prediction methods and climate model products by Shenyang Regional Climate Center (SRCC) in the flood season in Liaoning. Furthermore, the forecast accuracies of the main influencing factors on the precipitation in the flood season of Liaoning are assessed. The results show that the SRCC objective methods have a relatively high accuracy. The European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal forecast initialized at the sub-nearest time has the best performance in June. The National Climate Center (NCC) Climate System Model sub-seasonal forecast initialized at the sub-nearest time, and the ECMWF seasonal and sub-seasonal forecasts initialized at the nearest time, perform the best in July. The NCC sub-seasonal forecast initialized at the sub-nearest time has the best performance in August. For the accuracy of the SRCC objective method, the more significant the equatorial Middle East Pacific sea surface temperature (SST) anomaly is, the higher the evaluation score of the dynamic–analogue correction method is. The more significant the North Atlantic SST tripole is, the higher the score of the hybrid downscaling method is. For the forecast accuracy of the main influencing factors of precipitation, the tropical Atlantic SST and the north–south anti-phase SST in the northwest Pacific can well predict the locations of the southern vortex and the northern vortex in early summer, respectively. The warm (clod) SST in China offshore has a good forecast performance on the weak (strong) southerly wind in midsummer in Northeast China. The accuracy of using the SST in the Nino 1+2 areas to predict the north–south location of the western Pacific subtropical high is better than that of using Kuroshio SST. The accuracy of predicting northward-moving typhoons from July to September by using the SST in the west-wind-drift area is better than using the SST in the Nino 3 area. The above conclusions are of great significance for improving the short-term climate prediction in Liaoning.

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“…Precipitation is an important way to supply water resources to a basin or region. The accurate precipitation forecasts are valuable and rather important for the integration of natural hazards forecasting [1,2]. Precipitation is affected by many factors [3,4], such as topography, atmospheric circulation, the underlying surface, and human activities.…”

Section: Introductionmentioning

confidence: 99%

Combined Forecasting Model of Precipitation Based on the CEEMD-ELM-FFOA Coupling Model

Zhang

2023

Water

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Precipitation prediction is an important technical mean for flood and drought disaster early warning, rational utilization, and the development of water resources. Complementary ensemble empirical mode decomposition (CEEMD) can effectively reduce mode aliasing and white noise interference; extreme learning machines (ELM) can predict non-stationary data quickly and easily; and the fruit fly optimization algorithm (FFOA) has better local optimization ability. According to the multi-scale and non-stationary characteristics of precipitation time series, a new prediction approach based on the combination of complementary ensemble empirical mode decomposition (CEEMD), extreme learning machine (ELM), and the fruit fly optimization algorithm (FFOA) is proposed. The monthly precipitation data measured in Zhengzhou City from 1951 to 2020 was taken as an example to conduct a prediction experiment and compared with three prediction models: ELM, EMD-HHT, and CEEMD-ELM. The research results show that the sum of annual precipitation predicted by the CEEMD-ELM-FFOA model is 577.33 mm, which is higher than the measured value of 572.53 mm with an error of 4.80 mm. The average absolute error is 0.81 and the average relative error is 1.39%. The prediction value of the CEEMD-ELM-FFOA model can closely follow the changing trend of precipitation, which shows a better prediction effect than the other three models and can be used for regional precipitation prediction.

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Hydrometeorological analysis of a flash flood event in an ungauged Mediterranean watershed under an operational forecasting and monitoring context

Cited by 28 publications

References 64 publications

Emergency Evacuation Behavior in Small Island Developing States: Hurricane Irma in Sint Maarten

Emergency Evacuation Behavior in Small Island Developing States: Hurricane Irma in Sint Maarten

Evaluation on the Forecast Skills of Precipitation and Its Influencing Factors in the Flood Season in Liaoning Province of China

Combined Forecasting Model of Precipitation Based on the CEEMD-ELM-FFOA Coupling Model

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