Sustainability-Based Flood Hazard Mapping of the Swannanoa River Watershed

Ahmadisharaf, Ebrahim; Kalyanapu, Alfred J.; Chung, Eun‐Sung

doi:10.3390/su9101735

Cited by 21 publications

(13 citation statements)

References 60 publications

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“…Each of these requires further research to show how these uncertainties affect the ultimate flood susceptibility maps and subsequent decision making. Future work should investigate the impact of these uncertainties by selection of other flood factors such as daily or sub-daily rainfall, HAND, stream power index and topographic wetness index, classifying the flood factors together with stakeholders [62,63], performing a sensitivity analysis on the impact of classification of observed dataset (other than 70% and 30% for training and validation) and evaluating the efficiency of the five methods via alternate goodness-of-fit measures [64].…”

Section: Discussionmentioning

confidence: 99%

Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

et al. 2019

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Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.

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Section: Discussionmentioning

confidence: 99%

Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

et al. 2019

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“…Unfortunately, due to the lack of spatial data on land-use and historical population growth, the current analysis is conducted at the national level without exploring the spatial patterns. Regarding future research, this study only looked into the economic damages, and other impacts of floods such as social and environmental impacts [43][44][45] were not investigated and could be explored in the future.…”

Section: Discussionmentioning

confidence: 99%

The Role of Hazard and Vulnerability in Modulating Economic Damages of Inland Floods in the United States Using a Survey-Based Dataset

Zhou

Leng

et al. 2019

Sustainability

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This study investigates the trends in economic damages caused by three types of inland floods (flash flood, flood, and heavy rain) in the United States and the variations in related hazard and vulnerability indicators between 1996 and 2016. We explore the underlying mechanisms based on a survey-based dataset maintained by the National Oceanic and Atmospheric Administration (NOAA) National Weather Service. An annual average of 6518 flood occurrences was reported, which caused economic damages of 3351 million USD per year. Flash flood and flood contributed to 53% and 32% of total occurrences and was associated with a larger share of damaging events (SDE). Results show that the higher impacts by flood and flash flood on property and crop are partly attributed to the greater intensity of rainfall. In addition, flood has the highest unit cost of damages. Notably, despite an upward tendency in economic damages by flash floods, no evident change trend is observed for inland floods as a whole. Further analysis shows changes in economic damages by heavy rain and flash flood are mainly governed by the increased annual frequency and hazard intensity, but the change of trend in their vulnerability indicators (i.e., SDE and Damage Per Event (DPE)) is not obvious. Regarding floods, it was not possible to attribute the variations in economic losses to hazard and vulnerability, as no significant tendency is found except for an increasing SDE. Despite limitations of length of records, data collection, and methodology, the difference in economic impacts and the related hazard and vulnerability revealed in this study can help better target future adaptation and mitigation measures.

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“…This validation revealed the excellent performance by the model with F <2> = 75.1%, 15.3% overestimation and 13.1% underestimation of the flood extent. The model has been successfully applied in simulation of riverine (Ahmadisharaf, Kalyanapu, & Bates, ; Ahmadisharaf, Kalyanapu, & Chung, 2015, , ; Ahmadisharaf, Kalyanapu, Thames, & Lillywhite, ; Kalyanapu et al, , ; Yigzaw, Hossain, & Kalyanapu, ) and dam break flooding (Ahmadisharaf, Bhuyian, & Kalyanapu, ; Kalyanapu et al, ).…”

Section: Methodsmentioning

confidence: 99%

A coupled probabilistic hydrologic and hydraulic modelling framework to investigate the uncertainty of flood loss estimates

Ahmadisharaf

Kalyanapu

2019

J Flood Risk Management

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Reliable estimation of flood loss is crucial for flood management. Since this estimation relies on models that are prone to uncertainty, it is vital to clearly understand how the uncertainties affect the appraised flood loss. This paper presents a coupled probabilistic hydrologic and hydraulic modelling framework to estimate the uncertainty of anticipated loss. A hydrologic model performs rainfall‐runoff transformation and a two‐dimensional unsteady hydraulic model simulates flood inundation. The outputs of the latter are fed into a loss estimation tool. Two sources of uncertainty—rainfall depth and antecedent moisture condition—are used to illustrate the framework on the Swannanoa River watershed in North Carolina, United States. The impact of the uncertainty of these two sources is tracked over the hydrologic model, hydraulic model and loss estimation tool. Our case study results illustrate that the estimations on the percent affected people can be four times more uncertain than the rainfall depth and two times more than the flood extent, but its uncertainty is comparable to hydrograph attributes. The appraised structural damages are nearly two times more uncertain than the affected people. These findings, however, may only be valid for a case study with hilly topography and should be cautiously extrapolated to other areas.

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Sustainability-Based Flood Hazard Mapping of the Swannanoa River Watershed

Cited by 21 publications

References 60 publications

Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

The Role of Hazard and Vulnerability in Modulating Economic Damages of Inland Floods in the United States Using a Survey-Based Dataset

A coupled probabilistic hydrologic and hydraulic modelling framework to investigate the uncertainty of flood loss estimates

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