Levee reliability analyses for various flood return periods – a case study in southern Taiwan

Huang, Win‐Gee; Yu, Hong Wen; Weng, Meng-Chia

doi:10.5194/nhess-15-919-2015

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…In this study, the 2‐year return period flood, which was derived from global flood observation and regional flood frequency analysis (Zhao, Bates, et al., 2021) was used to represent the magnitude of BD along river networks. The 2‐year return period flood is also applied as an index in levee reliability analysis (Huang et al., 2015).…”

Section: Study Area and Data Preparationmentioning

confidence: 99%

Flood Defense Standard Estimation Using Machine Learning and Its Representation in Large‐Scale Flood Hazard Modeling

Zhao

Bates

Neal

et al. 2023

Water Resources Research

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Floods are the most frequent type of natural hazards worldwide, and have caused significant loss of life and severe economic impacts on populations and property during the past two decades (CRED and UNDRR, 2020). To reduce the negative impact of floods, numerous types of flood defenses, such as levee systems, have been built to protect cities, towns, and farms in almost every country (Ubilla et al., 2008;Z. Wang & Liu, 2019). According to a report by the United States (U.S.) Army Corps of Engineers (USACE), approximately 11 million people and $1.3 trillion of property value existed in flood-defended areas in the U.S. as of 2018 (USACE, 2018). The number and standard of flood defenses continuously improve over time to meet societal needs and keep pace with rapid urbanization in floodplains (Leonard, 2008;T. Zhu et al., 2007). Flood defenses have significantly changed the regional flooding distribution and also residents' exposure (Di Baldassarre et al., 2009;Ludy & Kondolf, 2012), and this needs to be considered in flood hazard assessment.With the increase of computing power and advances in remote sensing techniques, it is now possible to map flood hazards on a large scale at high resolution (<100 m) (Ward et al., 2015). High resolution global hydrography datasets, such as MERIT Hydro (Yamazaki et al., 2019) and HydroBASINS (Lehner & Grill, 2013) have been released; however, information on detailed flood defenses for most rivers in the world is severely limited (Aerts et al., 2020;Sampson et al., 2015). Existing state-of-the-art global flood hazard models either assume a simplified high flood defense standard (FDS) or assume no protection when applied (Aerts et al., 2020;Scussolini et al., 2016;Ward et al., 2015). This assumption causes the misestimations of flood hazards when the flooded areas are actually protected by existing flood defenses, and therefore induces a distorted flood hazard and risk assessment. The first global flood defense database (called FLOPROS) was built by collecting FDS data worldwide at the sub-country scale (Scussolini et al., 2016). FLOPROS assumes that the FDSs in a vast area are the same (i.e., most states in the US or all of Australia have the same FDS) and ignores the heterogeneity of FDSs between rivers. The coarse resolution of the FDS data in FLOPROS cannot meet the requirement of large-scale flood hazard modeling at high resolution.

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Section: Study Area and Data Preparationmentioning

confidence: 99%

Flood Defense Standard Estimation Using Machine Learning and Its Representation in Large‐Scale Flood Hazard Modeling

Zhao

Bates

Neal

et al. 2023

Water Resources Research

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“…With reference to the latter issue, major civil engineering works subject to risk of flooding, such as bridges (Benedict & Knight, 2021), storm sewers (Sun et al, 2011), dam-drainage systems (Khaddor et al, 2021), levees (Huang et al, 2015), and other hydraulic structures for river flood control (Cipollini et al, 2021;Lendering et al, 2019;Scussolini et al, 2016) are all designed to withstand relatively extreme events, with large return periods (Ponce, 1989;Rasekh et al, 2010;Sayers et al, 2013), depending on their strategic importance and the threat that their failure would pose to human lives and properties (Cipollini et al, 2021;Lendering et al, 2019;Morrison et al, 2018;Shah et al, 2018;Tung, 2005;Vogel & Castellarin, 2017). However, there are many other applications where accurate prediction of frequent floods is critical.…”

Section: Introductionmentioning

confidence: 99%

Underestimation of Frequent Floods when Using Annual Maxima for Frequency Analysis: Drivers, Spatial Variability, and Possible Solutions

Dell'Aira

Cancelliere

Meier

2023

Preprint

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Because they are conceptually unable to consider events at the sub-annual scale, probabilistic flood analyses based on annual maxima (AM) underestimate the actual frequency of frequent floods (with return periods under 5 years), so that peaks-over-threshold (POT) approaches should be preferred. While this has been acknowledged for decades, frequent floods are still estimated too often using AM, probably because the procedure is simpler, and AM series are longer and easier to obtain. However, the negative bias incurred when performing flood frequency with AM can be severe. This affects fields such as river restoration, stream ecology, and fluvial geomorphology, which require a correct characterization of frequent floods. Using hundreds of U.S. watersheds with natural flow regimes, across different climatic and geomorphic conditions, we systematically study the variability in how AM frequency analyses underestimate frequent floods, finding clear spatial patterns. Exploiting the duality between the Generalized Extreme Value and the Generalized Pareto distributions (used for modeling AM and POT, respectively), we identify the drivers of frequent-flood underestimation, studying the influence of the distributions’ shapes. In turn, with the support of an optimal feature-selection technique, we determine the physical drivers explaining underestimation, from a wide spectrum of basin descriptors, investigating their linkages with the distributional characteristics that affect underestimation. A theoretical relationship is derived to infer the underestimation rate, allowing for post-hoc correction of AM-predicted frequent floods, without the need to perform POT frequency analyses. However, this approach underperforms at sites with mixed flood populations.

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“…The BSTEM can be used to estimate the timing of riverbank collapse and riverbank retreat from noncohesive and cohesive fluvial erosion. Huang et al [9,10] employed a limit equilibrium method to assess river embankment stability in southern Taiwan for various flood return periods. In their adopted method, three failure mechanisms can be studied: overtopping, levee foundation scouring, and slope sliding.…”

Section: Introductionmentioning

confidence: 99%

Predicting River Embankment Failure Caused by Toe Scour Considering 1D and 2D Hydraulic Models: A Case Study of Da-An River, Taiwan

Chang

Chen

Guo

et al. 2020

Water

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Physically based numerical models can predict scour depth at embankments located in bend reaches. However, methodologies for utilizing these numerical models to assess the risk of reinforced concrete embankment failure are rarely investigated. Therefore, a new assessment methodology is proposed to predict the riverbank failure caused by bend scour. The methodology is primarily based on a bend scour simulation model that integrates a one-dimensional (1D) hydraulic model, a two-dimensional (2D) hydrodynamic finite-volume model, and an empirical equation of bend scour prediction. The model was first applied to the Shuiwei Embankment located in a river bend reach of Da-An River in Taiwan and verified against results from the 1D hydraulic model and field data. The model was then used to simulate 2D flow field and the temporal evolution of bend scour depth under different return period flood events to examine the relationships between river discharge, water level, shear stress, and bend scour depth. The influence of shear stress on the stability of toe protections was also investigated. The field data (from two events) and numerical solutions (four scenarios) were assessed to conceive two empirical equations for predicting shear stress and bend scour depth. A new assessment methodology was proposed using these two equations to predict the risk of river embankment failure during flood periods. The proposed methodology can be easily applied in other disaster-prone regions to mitigate the effects of disasters caused by bend scouring.

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Levee reliability analyses for various flood return periods – a case study in southern Taiwan

Cited by 6 publications

References 19 publications

Flood Defense Standard Estimation Using Machine Learning and Its Representation in Large‐Scale Flood Hazard Modeling

Flood Defense Standard Estimation Using Machine Learning and Its Representation in Large‐Scale Flood Hazard Modeling

Underestimation of Frequent Floods when Using Annual Maxima for Frequency Analysis: Drivers, Spatial Variability, and Possible Solutions

Predicting River Embankment Failure Caused by Toe Scour Considering 1D and 2D Hydraulic Models: A Case Study of Da-An River, Taiwan

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