A Super Region Approach to Improve Pooled Flood Frequency Analysis

Zadeh, Shabnam Mostofi; Burn, Donald H.

doi:10.1080/07011784.2018.1548946

Cited by 10 publications

(7 citation statements)

References 47 publications

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“…Therefore, although the use of macroregions is found to be relevant in the flood context (Durocher et al, ; Mostofi Zadeh & Burn, ), results of the present study do not support their use in rainfall pooling group formation across Canada. This may be attributed to the difficulty in defining appropriate macroregions for rainfall durations, as well as to the marked spatial character of rainfall.…”

Section: Resultscontrasting

confidence: 80%

“…The number of stations with at least 40 years of data varies from 60 to 65, depending on the rainfall duration (e.g., Figure a). Robson and Reed () used a minimum record length of 20 years for computing the PUM for floods regarding T = 20 and 50 years, Formetta, Bell, and Stewart () employed a minimum record length of 30 years when estimating quantiles for return periods from 5 to 100 years, whereas Mostofi Zadeh and Burn () used a minimum record length of 90 years for return periods from 2 to 100 years.…”

Section: Resultsmentioning

confidence: 99%

“…Macroregions may be delimited on the basis of available climatic/physiographic descriptors at the rainfall stations, as well as based on existing climate‐related classifications. The first type of macroregions, henceforth “superregions,” is motivated by rainfall and flood regional studies from the literature (e.g., Durocher, Mostofi Zadeh, Burn, & Ashkar, ; Gabriele & Chiaravalloti, ; Mostofi Zadeh & Burn, ). The Köppen–Geiger climate classification based on temperature and precipitation (Peel, Finlayson, & McMahon, ), as well as Canadian ecozones (Ecological Stratification Working Group, ; http://sis.agr.gc.ca/cansis/nsdb/ecostrat/index.html), are considered for the second type of macroregions.…”

Section: Methodsmentioning

confidence: 99%

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Pooled frequency analysis for intensity–duration–frequency curve estimation

Requena

Burn

Coulibaly

2019

Hydrological Processes

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Rainfall intensity–duration–frequency (IDF) curves are used in the design of urban infrastructure. Their estimation is based on rainfall frequency analysis, usually performed on rainfall records from a single gauged station. However, available at‐site record length is often too short to provide accurate estimates for long return periods. In the present study, a general framework for pooled rainfall frequency analysis based on the index‐event model is proposed for IDF estimation at gauged stations. Pooling group formation is defined by the region of influence approach on the basis of the geographical distance similarity measure. Several pooled approaches are defined and evaluated by a procedure through which quantile estimation and uncertainty are assessed. Alternate approaches for the definition of a pooling group are based on different criteria regarding initial pooling group size (and the relationship between size and return period), approaches for assessing pooling group homogeneity, and the use of macroregions in pooling group formation. The proposed framework is applied to identify the preferred approach for pooled rainfall intensity frequency analysis in Canada. Pooled approaches are found to provide more precise estimates than the at‐site approach, especially for long return periods. Pooled parent distribution selection supported the use of the generalized extreme value distribution across the country. Recommendations for pooling group formation include increasing the pooling group size with increases in return period and identifying an appropriate trade‐off between pooling group homogeneity and size for long return periods.

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Section: Resultscontrasting

confidence: 80%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Pooled frequency analysis for intensity–duration–frequency curve estimation

Requena

Burn

Coulibaly

2019

Hydrological Processes

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“…A possible explanation is the limited sample size, and consequently limited training set size, in our analyses, as only respectively 48, 117 and 184 flood events occurred between 1988 and 2016 in the Maarkebeek, Bellebeek and Demer subbasins. A regional analysis, pooling flood polygons from more subbasins than the three study areas included in this research, may improve model accuracy [58]. Overall, a lower RMSE was achieved with the SVR models, which were also found to be more sensitive to variations in the input data.…”

Section: Plos Onementioning

confidence: 85%

A data-driven analysis, and its limitations, of the spatial flood archive of Flanders, Belgium to assess the impact of soil sealing on flood volume and extent

2020

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Soil sealing increases surface runoff in a watershed and decreases infiltration into the soil. Consequently, urbanization poses a significant challenge for watershed management to mitigate faster runoff accumulation downstream and associated floods. Hydrological models are often employed to assess the impact of land-use dynamics on flood events. Alternatively, data-driven approaches combining time series of land use geodatasets and georeferenced flooded zones also allow to assess the relationship between soil sealing and flood severity. This study presents such data-driven analysis using a spatially explicit archive of flooded areas dating back to 1988 in the Flanders region of Belgium, which is characterized by urban sprawl. This archived data, along with time series of rainfall and land use, were analyzed for three middle-sized river subbasins using two machine learning methods: boosted regression trees and support vector regression. The machine learning methods were found suitable for this type of analysis, since their flexibility allows for spatially explicit models with larger sample sizes. However, the relationship between soil sealing and flood volume and extent could not be conclusively confirmed by our models. This may be due to data limitations, such as the limited number of recorded historical floods, inaccuracies in recorded historical flood polygons and inconsistencies in the land use classifications. It is therefore stressed that continued consistent monitoring of floods and land use changes is required.

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“…Furthermore, each of the regionalization approaches (ROI, CCA, CA) considered in this study has several variants. One could attempt preparing ensembles corresponding to different variants of ROI (e.g., Cunderlik & Burn, 2006; Durocher et al., 2018; Formetta et al., 2018; Mostofi & Burn, 2019; Zrinji & Burn, 1996), CCA (e.g., Han et al., 2020; Ouali et al., 2016; Ouarda et al., 2000; Ribeiro‐Corréa et al., 1995; Shu & Ouarda, 2007), and CA (e.g., Basu & Srinivas, 2014, 2016; Cassalho et al., 2019; Farsadnia et al., 2014; Rao & Srinivas, 2006a; Wazneh et al., 2015) for investigating improvement in regions derived by application of FEC to those individual ensembles and their possible combinations.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy Ensemble Clustering Approach to Address Regionalization Uncertainties in Flood Frequency Analysis

Kiran

Srinivas

2021

Water Resources Research

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Estimates of flood quantiles are necessary at various locations/hotspots in river basins for applications related to hydrological design and risk assessment of water resources systems. Desired flood quantiles could be estimated by frequency analysis of the available at-site data for locations having sufficient historical information on flows. However, for an ungauged or sparsely gauged location, the flood quantiles' prediction remains one of the challenging problems in hydrology. A variety of regional flood frequency analysis (RFFA) approaches are available for addressing this challenge. The approaches generally involve (i) regionalization, which refers to identification of a group of watersheds (i.e., region) resembling the target site's watershed in terms of attributes/characteristics influencing flood, and (ii) regional frequency analysis (RFA), which involves pooling of flood-related information from sites in the region and using it to estimate desired flood quantile(s) at the target site. Approaches to watershed regionalization for RFFA include those based on regression analysis (method of residuals), canonical correlation analysis (CCA), region of influence (ROI) and its extensions, hierarchical approach, and cluster analysis. Several articles and books provide an overview of the literature on regionalization of watersheds and issues related to RFA (e.g.,

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A Super Region Approach to Improve Pooled Flood Frequency Analysis

Cited by 10 publications

References 47 publications

Pooled frequency analysis for intensity–duration–frequency curve estimation

Pooled frequency analysis for intensity–duration–frequency curve estimation

A data-driven analysis, and its limitations, of the spatial flood archive of Flanders, Belgium to assess the impact of soil sealing on flood volume and extent

Fuzzy Ensemble Clustering Approach to Address Regionalization Uncertainties in Flood Frequency Analysis

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