Understanding the relationship between rainfall and flood probabilities through combined intensity-duration-frequency analysis

Breinl, Korbinian; Lun, David; Müller-Thomy, Hannes; Blöschl, Günter

doi:10.1016/j.jhydrol.2021.126759

Cited by 45 publications

(17 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The use of extreme rainfall frequency as a proxy of extreme flood frequency is quite the standard in urban flooding analysis, where the unavailability of observations is systemic, hindering a proper probability-based interpretation of flood features. However, given the high nonlinearity of the rainfall-runoff transformation in urban areas, the relation between rainfall and flood frequencies is complex (Breinl et al, 2021;Viglione & Blöschl, 2009). In a climate change perspective, Padulano et al (2019) showed that the variability in rainfall intensity, such as the variability induced by the use of multiple climate projections, is higher than the variability in flood features (such as the mean and standard deviation of spatially distributed surface water depth and flow velocity under peal conditions) caused by using those rainfall values as input in an inundation model.…”

Section: Discussionmentioning

confidence: 99%

Using the present to estimate the future: A simplified approach for the quantification of climate change effects on urban flooding by scenario analysis

Padulano

Costabile

Costanzo

et al. 2021

Hydrological Processes

View full text Add to dashboard Cite

Understanding and modelling pluvial flood patterns is pivotal for the estimation of flood impacts in urban areas, especially in a climate change perspective. However, urban flood modelling under climate change conditions poses several challenges. On one hand, the identification and collection of climate change data suitable for floodrelated evaluations requires consistent computational and scientific effort. On the other hand, large difficulties can arise in the reproduction of the rainfall-runoff transformation process in cases when only little information about the subsurface processes is known. In this perspective, a simplified approach is proposed to address the challenges regarding the quantitative estimation of climate change effects on urban flooding for real case applications. The approach is defined as "bottom-up" because climate change information is not included in flood modelling, but it is only invoked for the interpretation of results. In other words, the challenge faced in this work is the development of a modelling strategy that is expeditious, because it does not require flood simulations for future rainfall scenarios, but only under current climate conditions, thus reducing the overall computational effort; and it is flexible, because results can be easily updated once new climate change data, scenarios or methods become available, without the need of additional flood simulations. To simulate real case applications, the approach is tested for a scenario analysis, where different return periods and hyetograph shapes are used as input for urban inundation modelling in Naples, Italy. The approach can support public and private stakeholders, such as land administrators and water systems managers; moreover, it represents a valuable and effective basis for climate change risk communication strategies.

show abstract

Section: Discussionmentioning

confidence: 99%

Using the present to estimate the future: A simplified approach for the quantification of climate change effects on urban flooding by scenario analysis

Padulano

Costabile

Costanzo

et al. 2021

Hydrological Processes

View full text Add to dashboard Cite

show abstract

“…Another factor which considerable mentioned in the literature and plays a pivotal role in flood susceptibility mapping is rainfall (Breinl et al, 2021;Das and Scaringi, 2021;Sharma et al, 2017). There is a significant association between the rainfall and flooding throughout the area under study.…”

Section: Rainfallmentioning

confidence: 99%

Novel Hybrid Models by Coupling Support Vector Regression (SVR) With Meta-heuristic Algorithms (WOA and GWO) for Urban Flood Risk Mapping

Rezaie

Panahi

Bateni

et al. 2022

Preprint

View full text Add to dashboard Cite

Schools as social bases and children’s centers are among the most vulnerable areas to flooding. Flood risk mapping (FRM) is very important for flood preparedness and adopting preventive plans for reducing the school vulnerability to flooding. To achieve this, there is a need for the models that can be used in vast areas with high predictive accuracy. This study aims to develop the innovative hybrid models by coupling the support vector regression (SVR), statistical approaches, and two meta-heuristic algorithms, whale optimization algorithms (WOA) as well as grey wolf optimizer (GWO). According to the proposed methodology, a hybrid feature of SVR and frequency ratio (FR-SVR) is optimized by applying the GWO and WOA optimization algorithms to generate the maps related to flood susceptibility (FSMs). The method was utilized for the Ardabil Province located in southwestern Caspian Sea precincts of which faced devastating floods. The GIS database including 147 ground control locations of flooded zones and nine factors which influence flood were utilized to learn and ascertain the validity of the models. The statistical measures of RMSE, MAE, AUC, and ROC curve were then applied for the developed models in order to estimate prophetically. The results indicated that the meta-optimized FR-SVR-GWO as well as FR-SVR-WOA models exceeded the FR-SVR and FR models in training (RMSEFR-SVR-WOA = 0.2016, RMSEFR-SVR-GWO = 0.1885, AUCFR-SVR-WOA = 0.87, AUCFR-SVR-GWO = 0.88) and validation (RMSEFR-SVR-WOA = 0.2025, RMSEFR-SVR-GWO = 0.1986, AUCFR-SVR-WOA = 0.87, AUCFR-SVR-GWO = 0.87) phases. The FR-SVR-WOA and FR-SVR-GWO models were very competitive regarding AUC and RMSE values, but the FR-SVR-WOA model reproduced greater flood susceptibility rates and was considered for school flood risk mapping (SFRM).

show abstract

“…The results of a spatial analysis based on a survey in 364 villages show that the frequency of flooding is associated with land use in the catchment area, including forest cover and condition, and area of wetlands [21]. The mechanism of rainfall and catchment characteristics forming the relationship between rainfall and the probability of flooding [22]. One of the causes of flooding is seasonal shifts and unpredictable rain patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting rainfall and potential for repeated events to predict flood areas in Banten province, Indonesia

Ruhiat¹

2022

J. meas. eng.

View full text Add to dashboard Cite

In a period of ten years, from 2011-2020 rainfall in Indonesia is relatively high, with annual rainfall between 460.5-4,627.4 mm. The high rainfall has implications for flooding in several provinces. During this period, almost every year several areas in Banten Province experienced floods. To predict areas of Banten Province that have the potential for flooding, forecasts of rainfall and the potential for repeated occurrences of high rainfall are carried out. In making the forecast, observations were made at the Serang Meteorological Station, the Budiarto Curug Meteorological Station, the South Tangerang Climatology Station, and the Tangerang Geophysics Station. Rainfall data from the four stations were analyzed by Fourier transform, Gumbel method and Mononobe method. Distribution analysis results obtained rainfall in Banten Province between 0.0-607.9 mm with the length of rainy days per month between 0-26 days. Then, the results of the Fourier transform analysis; Banten Province included a monsoon rain pattern with unimodial rainfall. Furthermore, the results of the analysis of the Gumbel method and the Mononobe method, Banten Province included the category of moderate rain and tended to be heavy, even extreme. Based on the results of the analysis using these two methods, in 2025 in Banten Province, it is predicted that 11 % heavy rain, 3 % very heavy rain and 1 % extreme rain are predicted. In that year, it is predicted that there will be 65 sub-districts in Banten Province that have the potential for flooding. The sub-districts that have the potential for flooding are mostly located in Serang Regency, Serang City, Tangerang City, and South Tangerang City. This potential flood is predicted to occur in: January, February, March, April, May, October and November.

show abstract

Understanding the relationship between rainfall and flood probabilities through combined intensity-duration-frequency analysis

Cited by 45 publications

References 88 publications

Using the present to estimate the future: A simplified approach for the quantification of climate change effects on urban flooding by scenario analysis

Using the present to estimate the future: A simplified approach for the quantification of climate change effects on urban flooding by scenario analysis

Novel Hybrid Models by Coupling Support Vector Regression (SVR) With Meta-heuristic Algorithms (WOA and GWO) for Urban Flood Risk Mapping

Forecasting rainfall and potential for repeated events to predict flood areas in Banten province, Indonesia

Contact Info

Product

Resources

About