Abstract:Region of influence is a common approach to estimate runoff information at ungauged locations. To estimate flood quantiles from annual maximum discharges, the Generalized Least Squares (GLS) framework has been recommended to account for unequal sampling variance and intersite correlation, which requires a proper evaluation of the sampling covariance structure. Since some jurisdictions do not have clear guidelines to perform this evaluation, a general procedure using copulas and a nonparametric intersite correl… Show more
“…Clusters 4 and 5 show a large negative BIAS and cluster 5 shows a very large negative RBIAS. The results here are notably higher than those reported in Durocher et al [67], Chokmani and Ouarda [68], Chebana et al [47], and Shu and Ouarda [69]. In Rahman et al [6], independent component regression was adopted to develop flood prediction equations using the same data set as of this study, where error values are similar to this study.…”
Section: Development Of Prediction Equation and Performance Testingsupporting
confidence: 65%
“…These studies were carried out using ordinary kriging in PCA-space, generalized additive model and single artificial neural network. Studies carried out by Durocher et al [67], Chokmani and Ouarda [68], Chebana et al [20] and Shu and Ouarda [69] show RBIAS values ranging from −5% to −20% for 10% AEP flood and −7% to −27% for 1% AEP flood. A study carried out by Rahman et al [6] found RBIAS values ranging from 22% to 69% for the six AEP floods.…”
Section: Development and Testing Of Regression Equation In Fixed Regimentioning
confidence: 92%
“…From Table 4, it is clear that KNN10 performs the poorest with largest MSE and R r , which means it is preferable to select more than ten sites to form a region to use PCR. Durocher et al [67] carried out a study in Southern Quebec (Canada) and their results show a RMSE in the range of 38 m 3 /s and 45 m 3 /s in case of 10% and 1% AEP floods using spatial copula method. For the same dataset in Québec a number of studies [47,68,69] were carried out.…”
Section: Development and Testing Of Regression Equation In Fixed Regimentioning
This paper examines the applicability of principal component analysis (PCA) and cluster analysis in regional flood frequency analysis. A total of 88 sites in New South Wales, Australia are adopted. Quantile regression technique (QRT) is integrated with the PCA to estimate the flood quantiles. A total of eight catchment characteristics are selected as predictor variables. A leave-one-out validation is applied to determine the efficiency of the developed statistical models using an ensemble of evaluation diagnostics. It is found that the PCA with QRT model does not perform well, whereas cluster/group formed with smaller sized catchments performs better (with a median relative error values ranging from 22% to 37%) than other clusters/groups. No linkage is found between the degree of heterogeneity in the clusters/groups and precision of flood quantile prediction by the multiple linear regression technique.
“…Clusters 4 and 5 show a large negative BIAS and cluster 5 shows a very large negative RBIAS. The results here are notably higher than those reported in Durocher et al [67], Chokmani and Ouarda [68], Chebana et al [47], and Shu and Ouarda [69]. In Rahman et al [6], independent component regression was adopted to develop flood prediction equations using the same data set as of this study, where error values are similar to this study.…”
Section: Development Of Prediction Equation and Performance Testingsupporting
confidence: 65%
“…These studies were carried out using ordinary kriging in PCA-space, generalized additive model and single artificial neural network. Studies carried out by Durocher et al [67], Chokmani and Ouarda [68], Chebana et al [20] and Shu and Ouarda [69] show RBIAS values ranging from −5% to −20% for 10% AEP flood and −7% to −27% for 1% AEP flood. A study carried out by Rahman et al [6] found RBIAS values ranging from 22% to 69% for the six AEP floods.…”
Section: Development and Testing Of Regression Equation In Fixed Regimentioning
confidence: 92%
“…From Table 4, it is clear that KNN10 performs the poorest with largest MSE and R r , which means it is preferable to select more than ten sites to form a region to use PCR. Durocher et al [67] carried out a study in Southern Quebec (Canada) and their results show a RMSE in the range of 38 m 3 /s and 45 m 3 /s in case of 10% and 1% AEP floods using spatial copula method. For the same dataset in Québec a number of studies [47,68,69] were carried out.…”
Section: Development and Testing Of Regression Equation In Fixed Regimentioning
This paper examines the applicability of principal component analysis (PCA) and cluster analysis in regional flood frequency analysis. A total of 88 sites in New South Wales, Australia are adopted. Quantile regression technique (QRT) is integrated with the PCA to estimate the flood quantiles. A total of eight catchment characteristics are selected as predictor variables. A leave-one-out validation is applied to determine the efficiency of the developed statistical models using an ensemble of evaluation diagnostics. It is found that the PCA with QRT model does not perform well, whereas cluster/group formed with smaller sized catchments performs better (with a median relative error values ranging from 22% to 37%) than other clusters/groups. No linkage is found between the degree of heterogeneity in the clusters/groups and precision of flood quantile prediction by the multiple linear regression technique.
“…The hydrological neighborhood‐based method is gaining popularity due to its improved performance and flexibility in selecting sites (Ouarda, ; Ouarda et al, ). CCA (Ouarda et al, ) and the region of influence (ROI) (Burn, ; Burn, ; Durocher et al, ; Haddad & Rahman, ) are among others commonly utilized and reliable approaches in the delineation of homogeneous regions. Table summarizes a few commonly utilized methods of delineating homogeneous regions.…”
Regional flood frequency analysis forms the basis for ascertaining design thresholds for extreme flow events for the purpose of resource management and design of hydraulic structures, especially at ungauged or partially gauged basins. A crucial step in this analysis is transferring available information from gauged sites to ungauged sites, which is achieved through delineation of homogeneous regions encompassing multiple catchment locations, followed by the formulation of a flood estimation model. While this process has been accomplished through a range of statistical homogenization alternatives, the present study offers a new approach rooted in the theory of complex networks, offering considerable advantages over what is traditionally followed. Data from 202 sites in Australia representing catchments of varying geographic, climatic, and vegetation attributes are used to assess the alternative proposed. The results are examined via (1) direct comparison of the location and number of homogeneous neighbors from network theory with results using canonical correlation analysis (CCA) and (2) assessing the accuracy of estimated flood quantiles by applying a common model that estimates flood quantiles using information from the two alternate groups of homogeneous sites (from network theory and CCA). Results show that network theory offers merit in delineating homogenous regions, with resulting design flood estimates showing improvements across different return periods compared to the CCA alternative used.
“…Within this network, Sandink et al and Zahmatkesh at al. [23,24] examined FFA using a quantile regression model that considered ungauged catchments across Canada. Zhang et al [39] demonstrated the generalized extreme value (GEV) distribution fits Canadian annual maximum flow data considerably better than other well-known distributions, including generalized logistic, Pearson type III, and log Pearson type III distributions.…”
The identification of homogeneous flood regions is essential for regional flood frequency analysis. Despite the type of regionalization framework considered (e.g., region of influence or hierarchical clustering), selecting flood-related attributes to reflect flood generating mechanisms is required to discriminate flood regimes among catchments. To understand how different attributes perform across Canada for identifying homogeneous regions, this study examines five distinctive attributes (i.e., geographical proximity, flood seasonality, physiographic variables, monthly precipitation pattern, and monthly temperature pattern) for their ability to identify homogeneous regions at 186 gauging sites with their annual maximum flow data. We propose a novel region revision procedure to complement the well-known region of influence and L-Moments techniques that automates the identification of homogeneous regions across continental domains. Results are presented spatially for Canada to assess patterning of homogeneous regions. Memberships of two selected regions are investigated to provide insight into membership characteristics. Sites in eastern Canada are highly likely to identify homogeneous flood regions, while the western prairie and mountainous regions are not. Overall, it is revealed that the success of identifying homogeneous regions depends on local hydrological complexities, whether the considered attribute(s) reflect primary flooding mechanism(s), and on whether catchment sites are clustered in a small geographic region. Formation of effective pooling groups affords the extension of record lengths across the Canadian domain (where gauges typically have <50 years of record), facilitating more comprehensive analysis of higher return period flood needs for climate change assessment.
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