# Application of Principal Component Analysis and Cluster Analysis in Regional Flood Frequency Analysis: A Case Study in New South Wales, Australia

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area

^{2}for small to medium sized catchments [6], which appears to be rational to select candidate catchments for this study. From New South Wales (NSW), Australia, a total of 88 catchments are selected to carry out this study. These are natural catchments and free from any major storage and land use change. These selected catchments have catchment areas varying from 8 to 1010 km

^{2}. The mean of catchment area is found to be 352 km

^{2}and median is found to be 260 km

^{2}. It is recommended in Rahman et al. [57] to select catchments that have at least 20 years of flood data to develop the RFFA models in Australia. For this study, the selected catchments show a record length of annual maximum (AM) flow data in the range from 25 to 82 years (mean of 41.5 years and median 37 years). The catchments selected, vary from mountain to coastal region. The mean annual rainfall for the chosen catchments ranges from 625–1955 mm with a mean of 1000 mm and a median of 910 mm. Figure 1 shows the location of the selected 88 catchments.

_{62}) at each catchment centroid is obtained from Australian Bureau of Meteorology website. The shape factor (SF) is defined as the shortest distance between a catchment’s centroid and outlet divided by the square root of catchment area (A). Stream density (sden) is obtained as the sum of all the streamlines on a 1:100000 topographic map divided by catchment area (A). Mean annual rainfall (MAR) and mean annual evapotranspiration (MAE) data for each catchment are obtained from Australian Bureau of Meteorology website. The fraction forest (forest) is obtained as the total forested area shown on a 1:100000 topographic map divided by catchment area (A). The mainstream slope (S1085) is obtained as the difference in elevations at 10% and 85% of the mainstream length (measured from the catchment outlet) divided by 0.75 of mainstream length. The PCs are extracted using these characteristics to develop the PCR models using quantile regression technique (QRT) and to form regions using cluster analysis.

## 3. Methods

#### 3.1. Principal Component Analysis

_{1}, x

_{2}, … x

_{n}are the original variables and a

_{jj}are the eigenvectors. The eigenvalues are the variances of the PCs. The covariance or correlation matrix of the data set is used to derive the coefficients a

_{jj}, which are the eigenvectors. The eigenvalues of the data matrix can be calculated by Equation (3):

#### 3.2. Cluster Analysis

_{k}sites, the ESS is calculated as:

_{j}= [x

_{1}, x

_{2}, …, x

_{p}]

^{T}is a vector of p characteristics measured at site j, and where each element denotes the mean value of a characteristic across the N

_{k}sites in the region. ESS

_{k}is calculated for the theoretical fusion of any two clusters at each step, and the actual fusions selected are those which minimize the increment in the total ESS across all regions.

#### 3.3. Region of Influence Approach

_{i,j}is the weighted Euclidean distance between site i and j, M is the number of features incorporated in the distance measure, and the X terms represent standardized values for feature m at site i and site j, and W

_{m}is a weight applied to attribute m, which reflects the relative significance of the feature. Standardization of attributes is performed to remove units and therefore bias due to scaling of the attributes can be avoided.

#### 3.4. Homogeneity Assessment

_{1}is based on L coefficient of variation, H

_{2}is based on L coefficient of variation, and L coefficient of skewness and H

_{3}is based on L coefficient of skewness and L coefficient of kurtosis [15].

#### 3.5. Evaluation Statistics

_{pred}) and observed flood quantile (Q

_{obs}): relative error (RE), median absolute relative error (med_RE

_{r}), Q

_{pred}/Q

_{obs}ratio, median Q

_{pred}/Q

_{obs}ratio (med_Q

_{pred}/Q

_{obs}), mean square error (MSE), root mean square error (RMSE), bias (BIAS), relative bias (RBIAS), relative root mean square error (RRMSE), and root mean square normalized error (RMSNE):

_{obs}is the observed flood quantile at site i. Q

_{obs}is obtained by carrying out at-site FFA using LP3 distribution by FLIKE software [66]. In this study, six flood quantiles with annual exceedance probabilities (AEPs) of 50%, 20%, 10%, 5%, 2%, and 1% are considered as dependent variables.

## 4. Results and Discussion

#### 4.1. Principal Component Analysis

_{62}’, ‘SF’, ‘sden’, ‘MAR’, ‘MAE’, ‘S1085’, and ‘forest’, respectively) are calculated using the method described in methods section. Looking into Figure 2 and Table 2, it can be seen that catchment area has a negative correlation (−0.208; p-value 0.052) with the rainfall intensity indicating if the catchment area is large, rainfall intensity decreases. Rainfall intensity has a positive correlation with the variables shape factor, stream density, fraction forest, mean annual rainfall and mean annual evapo-transpiration, where the maximum positive correlation is between mean annual rainfall and rainfall intensity being 0.83 (p-value ≈ 0) and mean annual evapo-transpiration and rainfall intensity being 0.67 (p-value ≈ 0). This indicates that, if the rainfall intensity increases, mean annual rainfall also increases. Although these values are not close to ± 1, they have a very small p values (<0.10) indicating that these correlations are significant. Slope has a positive correlation of 0.387 with fraction forest and a negative correlation of −0.286 with mean annual evapo-transpiration where both the coefficients have smaller p values (<0.10). The variable fraction forest also has a positive correlation with the variable mean annual rainfall (0.405; p-value ≈ 0) and mean annual evapo-transpiration has positive correlations with stream density and mean annual rainfall (0.392 and 0.533; p-values ≈ 0). All the other correlation coefficients range from −0.007 to 0.303, which are statistically insignificant.

_{62}’, ‘SF’, ‘sden’, ‘MAR’, ‘MAE’, ‘S1085’, and ‘forest’, respectively) to achieve the uncorrelated eight PCs. Figure 3 shows the transformed PCs without any correlation. The eigenvalues with their percentage of contribution represent the quantity of variability in the data and they are presented in Table 3. Table 3 confirms that the first two PCs explain the maximum degree of variability of the data set with the proportion of PC1 and PC2 being 35.3% and 20.5%, respectively. The proportions of other PCs (PC3, PC4, PC5, PC6, PC7, and PC8) range 1.8%–13.4%. Although, PC1 and PC2 have the highest percentages among all the PCs, however, the cumulative of these two PCs only accounts for 55.8% variance, meaning these two PCs can only explain half of the variability in the dataset. To explain at least 85% of the variability in the data the first five PCs are required though the individual percentage is quite low for some PCs.

#### 4.2. Development and Testing of Regression Equation in Fixed Region and ROI Framework

^{2}) and adjusted coefficient of determination (adj_R

^{2}) are found to be quite small for the regression equations, 0.46 and 0.43, respectively. The developed regression equation is then tested in both fixed region and ROI framework with leave-one-out validation. To apply fixed region (FR) approach, all the 88 sites are grouped together as ‘one region’ and all the flood quantiles for each site for the six AEPs (50%, 20%, 10%, 5%, 2%, and 1%) are estimated by leave-one-out validation.

_{r}, and med_Q

_{pred}/Q

_{obs}based on observed and predicted values for 5% AEP flood. Looking into Table 4, it is found that for 5% AEP flood, fixed region has the lowest MSE. The lowest R

_{r}is found in case of KNN80 (46.59%) and med_Q

_{pred}/Q

_{obs}close to 1 is found in case of KNN15. 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. The results show RMSE values being 41 m

^{3}/s to 51 m

^{3}/s for 10% AEP flood, and 49 m

^{3}/s to 70 m

^{3}/s for 1% AEP flood. 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.

_{pred}/Q

_{obs}ratio values for both the fixed region and ROI framework.

_{pred}/Q

_{obs}ratio values for both the fixed region and ROI framework for 5% AEP flood. Both Figure 4 and Figure 5 starts with fixed region approach and have all the ROI approaches presented one by one after the fixed region. Looking at Table 3, one can see that fixed region performs better than the rest of the ROI models. However, Figure 4 and Figure 5 show that, although the box size is smaller (i.e., a smaller error range), the median line is not close to the expected line (expected lines are set at zero and one for Figure 4 and Figure 5, respectively) for the fixed region. KNN10 shows a similar performance as presented in Table 4. KNN15 and KNN25 both show promising results in Figure 4 and Figure 5 with a smaller box size and median value being very close to the median line. However, it seems that KNN25 has smaller error bars than KNN15. There are number of outliers for all the models as shown by small circles, but they are not all visible in the figures as the range for the boxplots are set in the range of −300 to +300 for the RE and −2 to +3 for Q

_{pred}/Q

_{obs}ratio values to have a greater visibility. In Figure 5, it is seen that none of the top error bars are visible in the set range bringing in the question of how well they fit the regression analysis. The rest of the ROI models show that they represent a poorer fit with bigger box size and median RE being far away from the expected line.

_{pred}/Q

_{obs}ratio values for all the AEPs for both the fixed region and ROI, respectively. Rows 2 to 18 of Table 5 and Table 6 show the mean, median and standard deviation (Std_Dev) of the selected AEPs for both the fixed region and ROI models, respectively. The last three rows show the overall mean, median, and Std_Dev for the AEPs. All the RE values are transformed to their absolute values by ignoring their sign. All the lowest values in case of both Q

_{pred}/Q

_{obs}ratio values and RE values are presented with blue color in both Table 5 and Table 6. Although KNN25 comes out as the best model out of all of them leaving KNN15 behind, however from Table 5 and Table 6, it seems that KNN15 outperforms KNN25 especially in the case of Q

_{pred}/Q

_{obs}ratio values. A fixed region approach or KNN80 does not show any better performance in this case. As seen earlier, KNN10 shows the worst results. The other models generate a mixture of results. In some cases, a very large RE (%) and Q

_{pred}/Q

_{obs}ratios are also found (i.e., for stations 206026, 210068, 210076, and 222016). As seen from the R

^{2}and adj_R

^{2}values, this regression model is found to be representing a poor fit. The analysis for both the fixed region and ROI framework also supports this finding.

#### 4.3. Application of Cluster Analysis

#### 4.3.1. Cluster Formation

^{2}with a median value of 835.5 km

^{2}). The median values of area for the other clusters are in the range 156–365 km

^{2}. Looking into design rainfall intensity i.e., I

_{62}, the median values range 38 mm–59 mm for all the clusters. The highest rainfall intensity is found in case of cluster 3, in the range 76 mm–133 mm. The variable ‘SF’ is found to be similar for all the five clusters; whereas ‘sden’ is found to be higher for clusters 1 and 3 and minimum for cluster 4. The variables ‘MAR’ and ‘MAE’ are found to be higher for cluster 1, i.e., 1480.2 mm and 1382.7 mm (median), respectively. Cluster 2 shows a relatively higher slope. Finally, fraction forest area is found to be relatively higher for clusters 1 and 2.

#### 4.3.2. Homogeneity Analysis of the Clusters

_{i}≥ 3 is considered to be discordant. Based on this criterion, no discordant station is found for clusters 1, 2, 4, and 5, yet one discordant station (D

_{i}= 3.01) is found for cluster 3. The heterogeneity measure is applied to the five clusters to calculate H-statistics (H

_{1}, H

_{2}, and H

_{3}). For cluster 3, although the D

_{i}value is not very large, the heterogeneity measure is applied twice; firstly, with all the discordant station in the cluster and secondly, removing the discordant station.

_{2}and H

_{3}for some clusters are smaller, H

_{1}is mostly indicative of the heterogeneity in the group, which is much higher than 1.00. It is of interest to check how these heterogeneous clusters perform in regional flood estimation. Hence, QRT is applied to each cluster in the next section with leave-one-out validation to validate the QRT.

#### 4.3.3. Development of Prediction Equation and Performance Testing

_{20}) and the regression coefficients for each variable for each cluster are given in Table 9.

^{2}and adj_R

^{2}for each model for all the clusters are found to be quite high except for cluster 5. In case of cluster 1, R

^{2}and adj_R

^{2}values are 0.93 and 0.89, respectively, for the selected model; for cluster 2 they are 0.98 and 0.96, respectively; for cluster 3 they are 0.82 and 0.77, respectively; for cluster 4 these are 0.68 and 0.63, respectively, and for cluster 5 these are 0.66 and 0.48, respectively. It is evident that except cluster 5 the other four clusters generate regression models with satisfactory goodness-of-fit.

_{pred}/Q

_{obs}ratio values. The expected line in Figure 10 is set at zero as it indicates an un-biased model. For Figure 12, the expected line is set at one as the ratio being one is indicative of an unbiased model. Figure 11 has a set boundary of −300 to +300, whereas Figure 12 has a set boundary of −3 to 3 to have better visibility.

_{1}value does not perform as good as cluster 2 in the leave-one-out validation.

^{3}/s to 200 m

^{3}/s and in case of larger observations ranging from 1200 m

^{3}/s to 1800 m

^{3}/s, there are some under-estimations by the regression model. For smaller discharges, cluster 2 seems to be performing well, although as the discharge gets larger the prediction by the model gets more erroneous, which is also visible from the boxplots. In case of clusters 3 and 4, the models perform well for smaller discharges; for the larger discharges, the models provide gross under-estimation. Cluster 5 is the worst performing group as seen from Figure 13. Cluster 2 performs the best in case of 5% AEP flood. As 5% AEP is the most frequently adopted flood quantile in design flood estimation, it can be said that regions formed based on small to medium sized area with a small range in other catchment characteristics will generate better prediction than other groups. Looking into the homogeneity analysis for all the five clusters, it can be concluded that homogeneity does not play a vital role in enhancing the prediction accuracy.

_{abs}) and absolute Q

_{pred}/Q

_{obs}ratio for all the clusters and the selected AEPs. Table 10 and Table 11 again prove the worst performance by cluster 5 with very large values for both RE

_{abs}and Q

_{pred}/Q

_{obs}ratio values. Clusters 3 and 4 also show a mixture of under- and over-estimation. Clusters 1 and 2 seem to show promising results, although in the case of cluster 1, the mean RE

_{abs}and mean Q

_{pred}/Q

_{obs}ratio values for 1% AEP flood are quite high. Cluster 2 shows the lowest values for both the overall REabs and Q

_{pred}/Q

_{obs}further confirming the better performance of cluster 2.

_{r}, and med_Q

_{pred}/Q

_{obs}based on observed and predicted flood values for all the five clusters in case of 5% AEP flood. A value close to zero is preferable for MSE as zero indicates no error in prediction. However, from Table 12 it is seen that all the MSE values for the five clusters are very large, in the range of 25,000 to 35,000,000. The smallest MSE is found in case of cluster 2 and the value is 25,309. The range of RMSE for all the clusters fall between 159 m

^{3}/s to 5800 m

^{3}/s. Cluster 2 shows the lowest RMSE with a value of 159 m

^{3}/s proving cluster 2 being the best performing group. Cluster 2 also shows the smallest values in case of RRMSE, RMSNE, BIAS, and RBIAS. Cluster 1 has large value for both BIAS and RBIAS (1480.61 and 388.4, respectively). 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. It should be noted that the values of MSE, RMSE, and BIAS depend on catchment size, a larger catchment generally has a larger discharge which is likely to result in higher MSE, RMSE, and BIAS.

_{r}and med_Q

_{pred}/Q

_{obs}both show, cluster 2 has the smallest values. Cluster 5 is the worst performing group with a high RE

_{r}and median_Q

_{pred}/Q

_{obs}(403.44 and −3.03, respectively). Hence, it can be said that group of stations having smaller catchment areas and lower range of other catchment characteristics such as cluster 2 is likely to generate more accurate flood prediction in QRT in the study region.

#### 4.4. Comparison with ARR RFFA Model

_{r}values between ARR RFFA model [32] and PCR KNN15, PCR KNN25, and QRT models for cluster 2 is presented in this section. ARR RFFA model is developed using a Bayesian generalized least square based parameter regression technique to estimate regional flood quantiles using Australian flood data [32]. The RE

_{r}values for ARR RFFA model and PCR KNN15, PCR KNN25 and QRT models for cluster 2 are compared in Table 13. It is apparent from Table 13 that, the RE

_{r}values for ARR RFFA model (ranging from 56% to 64%) is greater than PCR KNN15, PCR KNN25 and QRT models for cluster 2 (RE

_{r}values ranging from 42% to 69%, 42% to 60% and 22% to 37%, respectively). ARR RFFA model is developed with data from 558 stations from NSW, Victoria and Queensland [58] and PCR KNN15, PCR KNN25 and QRT models for cluster 2 are developed for 15, 25 and 16 stations from NSW. This may be a possible reason for these differences in RE values. However, it is promising to see that the RE

_{r}values from the PCR KNN15, PCR KNN25, and QRT models for cluster 2 are analogous to the RE

_{r}values of ARR RFFA model. From this study, it may be argued that PCR may not be a good choice in RFFA in case of NSW. Moreover, a group of stations with smaller catchment areas such as cluster 2 may generate a better RFFA grouping. Further research with additional catchment characteristics data may enhance the reliability of PCR and cluster analysis based RFFA models in the study region.

## 5. Conclusions

^{2}and adj_R

^{2}values of the developed regression equations, it is found that the principal component regression based RFFA models perform quite poorly.

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Blöschl, G.; Sivapalan, M.; Wagener, T.; Savenije, H.; Viglione, A. (Eds.) Runoff prediction in Ungauged Basins: Synthesis across Processes, Places and Scales; Cambridge University Press: Cambridge, UK, 2013. [Google Scholar]
- Ouarda, T.B.M.J.; St-Hilaire, A.; Bobée, B. A review of recent developments in regional frequency analysis of hydrological extremes. Revue des Sciences de l’eau
**2008**, 21, 219–232. [Google Scholar] [CrossRef][Green Version] - Ouarda, T.B.M.J.; Bâ, K.M.; Diaz-Delgado, C.; Carsteanu, A.; Chokmani, K.; Gingras, H.; Quentin, E.; Trujillo, E.; Bobée, A.B. Intercomparison of regional flood frequency estimation methods at ungauged sites for a Mexican case study. J. Hydrol.
**2008**, 348, 40–58. [Google Scholar] [CrossRef] - Haddad, K.; Rahman, A.; Ling, F. Regional flood frequency analysis method for Tasmania, Australia: A case study on the comparison of fixed region and region-of-influence approaches. Hydrol. Sci. J.
**2015**, 60, 2086–2101. [Google Scholar] [CrossRef] - Ouarda, T.B.M.J. Regional hydrological frequency analysis. In Encyclopedia of Environmetrics; El-Shaarawi, A.H., Piegorsch, W.W., Eds.; Wiley: New York, NY, USA, 2013. [Google Scholar]
- Rahman, A.S.; Khan, Z.; Rahman, A. Application of Independent Component Analysis in Regional Flood Frequency Analysis: Comparison between Quantile Regression and Parameter Regression Techniques. J. Hydrol.
**2019**, 581, 124372. [Google Scholar] [CrossRef] - Acreman, M.C. Regional Flood Frequency Analysis in the UK: Recent Research-New Ideas; Institute of Hydrology: Wallingford, UK, 1987. [Google Scholar]
- Acreman, M.C.; Sinclair, C.D. Classification of drainage basins according to their physical characteristics; an application for flood frequency analysis in Scotland. J. Hydrol.
**1986**, 84, 365–380. [Google Scholar] [CrossRef] - Eng, K.; Tasker, G.D.; Milly, P.C.D. An analysis of region-of-influence methods for flood regionalisation in the-Gulf-Atlantic rolling plains. J. Am. Water Resour. Assoc.
**2005**, 41, 135–143. [Google Scholar] [CrossRef] - Pilgrim, D.H. Australian Rainfall and Runoff; Institution of Engineers: Barton, Australia, 1987. [Google Scholar]
- Tasker, G.D.; Hodge, S.A.; Barks, C.S. Region of influence regression for estimating the 50 year flood at ungauged sites. J. Am. Water Resour. Assoc.
**1996**, 32, 163–170. [Google Scholar] [CrossRef] - Burn, D.H. An appraisal of the “region of influence” approach to flood frequency analysis. Hydrol. Sci. J.
**1990**, 35, 149–165. [Google Scholar] [CrossRef][Green Version] - Burn, D.H. Evaluation of regional flood frequency analysis with a region of influence approach. Water Resour. Res.
**1990**, 26, 2257–2265. [Google Scholar] [CrossRef] - Chebana, F.; Ouarda, T.B.M.J. Depth and homogeneity in regional flood frequency analysis. Water Resour. Res.
**2008**, 44, W11422. [Google Scholar] [CrossRef][Green Version] - Hosking, J.R.M.; Wallis, J.R. Some statistics useful in regional frequency analysis. Water Resour. Res.
**1993**, 29, 271–281. [Google Scholar] [CrossRef] - Merz, R.; Blöschl, G. Flood frequency regionalisation—Spatial proximity vs. catchment attributes. J. Hydrol.
**2005**, 302, 283–306. [Google Scholar] [CrossRef] - Burn, D.H. Cluster analysis as applied to regional flood frequency. J. Water Res. Plan. Man.
**1989**, 115, 567–582. [Google Scholar] [CrossRef] - Burn, D.H.; Boorman, D.B. Estimation of hydrological parameters at ungauged catchments. J. Hydrol.
**1993**, 143, 429–454. [Google Scholar] [CrossRef] - Himeidan, Y.E.S.; Hamid, E.E.H. Rainfall variability in New Halfa agricultural scheme (Sudan). Univ. Khartoum J. Agric. Sci.
**2019**, 14, 383–391. [Google Scholar] - Hughes, J.M.R.; James, B. A hydrological regionalization of streams in Victoria, Australia, with implications for stream ecology. Mar. Freshw. Res.
**1989**, 40, 303–326. [Google Scholar] [CrossRef] - Mosley, M.P. Delimitation of New Zealand hydrologic regions. J. Hydrol.
**1981**, 49, 173–192. [Google Scholar] [CrossRef] - Nathan, R.J.; McMahon, T.A. Identification of homogeneous regions for the purposes of regionalisation. J. Hydrol.
**1990**, 121, 217–238. [Google Scholar] [CrossRef] - Rasheed, A.; Egodawatta, P.; Goonetilleke, A.; McGree, J. A Novel Approach for Delineation of Homogeneous Rainfall Regions for Water Sensitive Urban Design—A Case Study in Southeast Queensland. Water
**2019**, 11, 570. [Google Scholar] [CrossRef][Green Version] - Santos, C.A.G.; Moura, R.; da Silva, R.M.; Costa, S.G.F. Cluster Analysis Applied to Spatiotemporal Variability of Monthly Precipitation over Paraíba State Using Tropical Rainfall Measuring Mission (TRMM) Data. Remote Sens.
**2019**, 11, 637. [Google Scholar] [CrossRef][Green Version] - Tasker, G.D. Comparing methods of hydrologic regionalisation. J. Am. Water Resour. Assoc.
**1982**, 18, 965–970. [Google Scholar] [CrossRef] - Hosking, J.R.M.; Wallis, J.R. Regional Frequency Analysis: An Approach based on L-moments; Cambridge University Press: New York, NY, USA, 1997. [Google Scholar]
- Eng, K.; Milly, P.C.; Tasker, G.D. Flood regionalisation: A hybrid geographic and predictor-variable region-of-influence regression method. J. Hydrol. Eng.
**2007**, 12, 585–591. [Google Scholar] [CrossRef] - Eng, K.; Stedinger, J.R.; Gruber, A.M. Regionalisation of streamflow characteristics for the Gulf-Atlantic rolling plains using leverage-guided region-of-influence regression. In Proceedings of the World Environmental and Water Resources Congress 2007: Restoring Our Natural Habitat, Tampa, Florida, 15–19 May 2007; pp. 1–11. [Google Scholar]
- Gaál, L.; Kyselý, J.; Szolgay, J. Region-of-influence approach to a frequency analysis of heavy precipitation in Slovakia. Hydrol. Earth Sys. Sci. Discuss.
**2008**, 12, 825–839. [Google Scholar] [CrossRef][Green Version] - Haddad, K.; Rahman, A. Regional flood frequency analysis in eastern Australia: Bayesian GLS regression-based methods within fixed region and ROI framework: Quantile regression vs. parameter regression technique. J. Hydrol.
**2012**, 430–431, 142–161. [Google Scholar] [CrossRef] - Micevski, T.; Hackelbusch, A.; Haddad, K.; Kuczera, G.; Rahman, A. Regionalisation of the parameters of the log-Pearson 3 distribution: A case study for New South Wales, Australia. Hydrol. Process.
**2015**, 29, 250–260. [Google Scholar] [CrossRef] - Rahman, A.; Haddad, K.; Kuczera, G.; Weinmann, P.E. Regional flood methods. Aust. Rainfall Runoff.
**2019**, 3, 105–146. [Google Scholar] - Zrinji, Z.; Burn, D.H. Regional flood frequency with hierarchical region of influence. J. Water Res. Plan. Man.
**1996**, 122, 245–252. [Google Scholar] [CrossRef] - Burn, D.H.; Goel, N.K. The formation of groups for regional flood frequency analysis. Hydrol. Sci. J.
**2000**, 45, 97–112. [Google Scholar] [CrossRef] - Castellarin, A.; Burn, D.H.; Brath, A. Assessing the effectiveness of hydrological similarity measures for regional flood frequency analysis. J. Hydrol.
**2001**, 241, 270–285. [Google Scholar] [CrossRef] - Burn, D.H. Catchment similarity for regional flood frequency analysis using seasonality measures. J. Hydrol.
**1997**, 202, 212–230. [Google Scholar] [CrossRef] - Lim, Y.H.; Lye, L.M. Regional flood estimation for ungauged basins in Sarawak, Malaysia. Hydrol. Sci. J.
**2003**, 48, 79–94. [Google Scholar] [CrossRef][Green Version] - Zrinji, Z.; Burn, D.H. Flood frequency analysis for ungauged sites using a region of influence approach. J. Hydrol.
**1994**, 153, 1–21. [Google Scholar] [CrossRef] - Bates, B.C.; Rahman, A.; Mein, R.G.; Weinmann, P.E. Climatic and physical factors that influence the homogeneity of regional floods in south-eastern Australia. Water Resour. Res.
**1998**, 34, 3369–3382. [Google Scholar] [CrossRef] - Fill, H.D.; Stedinger, J.R. Using regional regression within IF procedures and an empirical Bayesian estimator. J. Hydrol.
**1998**, 210, 128–145. [Google Scholar] [CrossRef] - Haddad, K.; Rahman, A.; Stedinger, J.R. Regional flood frequency analysis using Bayesian generalized least squares: A comparison between quantile and parameter regression techniques. Hydrol. Process.
**2012**, 26, 1008–1021. [Google Scholar] [CrossRef] - Griffis, V.W.; Stedinger, J.R. The use of GLS regression in regional hydrologic analyses. J. Hydrol.
**2007**, 344, 82–95. [Google Scholar] [CrossRef] - Micevski, T.; Kuczera, G. Combining site and regional flood information using a Bayesian Monte Carlo approach. Water Resour. Res.
**2009**, 45. [Google Scholar] [CrossRef][Green Version] - Ouali, D.; Chebana, F.; Ouarda, T.B.M.J. Quantile regression in regional frequency analysis: A better exploitation of the available information. J. Hydrometeorol.
**2016**, 17, 1869–1883. [Google Scholar] [CrossRef] - Rahman, A.; Charron, C.; Ouarda, T.B.M.J.; Chebana, F. Development of regional flood frequency analysis techniques using generalized additive models for Australia. Stoch. Environ. Res. Risk A
**2018**, 32, 123–139. [Google Scholar] [CrossRef] - Rahman, A. A quantile regression technique to estimate design floods for ungauged catchments in south-east Australia. Australas. J. Water Resour.
**2005**, 9, 81–89. [Google Scholar] [CrossRef] - Chebana, F.; Charron, C.; Ouarda, T.B.M.J.; Martel, B. Regional frequency analysis at ungauged sites with the generalized additive model. J. Hydrometeorol.
**2014**, 15, 2418–2428. [Google Scholar] [CrossRef][Green Version] - Burn, D.H. Delineation of groups for regional flood frequency analysis. J. Hydrol.
**1988**, 104, 345–361. [Google Scholar] [CrossRef] - DeCoursey, D.G.; Deal, R.B. General Aspects of Multivariate Analysis with Applications. Misc. Publ.
**1974**, 1275, 47. [Google Scholar] - Hawley, M.E.; McCuen, R.H. Water yield estimation in western United States. J. Irrig. Drain. Div.
**1982**, 108, 25–34. [Google Scholar] - Kar, A.K.; Goel, N.K.; Lohani, A.K.; Roy, G.P. Application of clustering techniques using prioritized variables in regional flood frequency analysis—Case study of Mahanadi Basin. J. Hydrol. Eng.
**2011**, 17, 213–223. [Google Scholar] [CrossRef] - Choi, T.H.; Kwon, O.E.; Koo, J.Y. Water demand forecasting by characteristics of city using principal component and cluster analyses. Environ. Eng. Res.
**2010**, 15, 135–140. [Google Scholar] [CrossRef][Green Version] - Haque, M.M.; de Souza, A.; Rahman, A. Water demand modelling using independent component regression technique. Water Resour. Res.
**2017**, 31, 299–312. [Google Scholar] [CrossRef] - Haque, M.M.; Rahman, A.; Hagare, D.; Kibria, G. Principal component regression analysis in water demand forecasting: An application to the Blue Mountains, NSW, Australia. J. Hydrol. Environ. Res.
**2013**, 1, 49–59. [Google Scholar] - Koo, J.Y.; Yu, M.J.; Kim, S.G.; Shim, M.H.; Koizumi, A. Estimating regional water demand in Seoul, South Korea, using principal component and cluster analysis. Water Sci. Tech. Water Supply
**2005**, 5, 1–7. [Google Scholar] [CrossRef] - Ball, J.; Babister, M.; Nathan, R.; Weeks, W.; Weinmann, P.E.; Retallick, M.; Testoni, I. Australian Rainfall and Runoff-A Guide to Flood Estimation; Engineers Australia: Canberra, Australia, 2019. [Google Scholar]
- Rahman, A.; Haddad, K.; Haque, M.; Kuczera, G.; Weinmann, P.E. Australian Rainfall and Runoff Project 5: Regional Flood Methods: Stage 3 Report; (No. P5/S3, p. 025). technical report; Engineers Australia: Canberra, Australia, 2015. [Google Scholar]
- Çamdevýren, H.; Demýr, N.; Kanik, A.; Keskýn, S. Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs. Ecol. Model.
**2005**, 181, 581–589. [Google Scholar] [CrossRef] - Olsen, R.L.; Chappell, R.W.; Loftis, J.C. Water quality sample collection, data treatment and results presentation for principal components analysis–literature review and Illinois River watershed case study. Water Res.
**2012**, 46, 3110–3122. [Google Scholar] [CrossRef] [PubMed] - Pires, J.C.M.; Martins, F.G.; Sousa, S.I.V.; Alvim-Ferraz, M.C.M.; Pereira, M.C. Selection and validation of parameters in multiple linear and principal component regressions. Environ. Modell. Softw.
**2008**, 23, 50–55. [Google Scholar] [CrossRef] - Johnson, R.A.; Wichern, D.W. Applied Multivariate Statistical Analysis; PrenticeHall International. Inc.: New Jersey, NJ, USA, 2007. [Google Scholar]
- Baeriswyl, P.A.; Rebetez, M. Regionalization of precipitation in Switzerland by means of principal component analysis. Theor. Appl. Climatol.
**1997**, 58, 31–41. [Google Scholar] [CrossRef][Green Version] - Bhaskar, N.R.; O’Connor, C.A. Comparison of method of residuals and cluster analysis for flood regionalization. J. Water Resour. Plan. Manag.
**1989**, 115, 793–808. [Google Scholar] [CrossRef] - Dinpashoh, Y.; Fakheri-Fard, A.; Moghaddam, M.; Jahanbakhsh, S.; Mirnia, M. Selection of variables for the purpose of regionalization of Iran’s precipitation climate using multivariate methods. J. Hydrol.
**2004**, 297, 109–123. [Google Scholar] [CrossRef] - Rao, A.R.; Srinivas, V.V. Regionalization of watersheds by hybrid-cluster analysis. J. Hydrol.
**2006**, 318, 37–56. [Google Scholar] [CrossRef] - Kuczera, G. FLIKE HELP; Chapter 2 FLIKE Notes; University of Newcastle: Callaghan, Australia, 1999. [Google Scholar]
- Durocher, M.; Burn, D.H.; Zadeh, S.M. A nationwide regional flood frequency analysis at ungauged sites using ROI/GLS with copulas and super regions. J. Hydrol.
**2018**, 567, 191–202. [Google Scholar] [CrossRef][Green Version] - Chokmani, K.; Ouarda, T.B.M.J. Physiographical space-based kriging for regional flood frequency estimation at ungauged sites. Water Resour. Res.
**2004**, 40. [Google Scholar] [CrossRef] - Shu, C.; Ouarda, T.B.M.J. Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space. Water Resour. Res.
**2007**, 43. [Google Scholar] [CrossRef][Green Version]

**Figure 2.**Correlation between the predictor variables before applying principal component analysis (PCA).

**Figure 6.**Result of Ward’s hierarchical clustering method by using eight selected catchment characteristics (dendrogram). Figure 7 and Figure 8 show the boxplots representing the distribution of the eight predictors for the clusters. Figure 7 shows the boxplots for ‘A’, ‘I

_{62}’, ‘SF’, and ‘sden’ and Figure 8 shows the boxplots for ‘MAR’, ‘MAE’, ‘S1085’, and ‘forest’. From Figure 7 and Figure 8, it can be said that the stations in cluster 1 have the smallest catchment area range with the largest design rainfall intensity, as well as larger ‘MAR’ and ‘MAE’ values. These stations seem to have higher percentages of forest areas as well as along with higher stream density. Stations in cluster 2 have moderate catchment areas, along with ‘I

_{62}’, ‘SF’, ‘sden’, and ‘MAR’. However, the ‘MAE’ is the highest in case of cluster 2. Cluster 3 seems to have all the predictors relatively uniformly distributed. Cluster 4 is characterized by medium catchment area, smaller ‘I

_{62}’, ‘SF’, ‘sden’, ‘MAR’, ‘MAE’, ‘S1085’, and ‘forest’. The largest area is found for cluster 5, although the other predictors are quite small with large variance in fraction forest area.

**Figure 7.**Boxplots of area, rainfall intensity, shape factor and stream density for all clusters: (

**a**) boxplot of area; (

**b**) boxplot of rainfall intensity; (

**c**) boxplot of shape factor; and (

**d**) boxplot of stream density.

**Figure 8.**Boxplots of mean annual rainfall, mean annual evapo-transpiration, slope and fraction forest for all the clusters: (

**a**) boxplot of mean annual rainfall; (

**b**) boxplot of mean annual evapo-transpiration; (

**c**) boxplot of slope; and (

**d**) boxplot of fraction forest.

**Figure 11.**Boxplots of RE for all clusters and AEPs: (

**a**) cluster 1 (C1); (

**b**) cluster 2 (C2); (

**c**) cluster 3 (C3); (

**d**) cluster 4 (C4); and (

**e**) cluster 5 (C5).

**Figure 12.**Boxplots of Q

_{pred}/Q

_{obs}ratios for all clusters and AEPs: (

**a**) cluster 1 (C1); (

**b**) cluster 2 (C2); (

**c**) cluster 3 (C3); (

**d**) cluster 4 (C4); and (

**e**) cluster 5 (C5).

Variables | Range | Median | Mean | Standard Deviation |
---|---|---|---|---|

Catchment area (A) in km^{2} | 8–1010 | 260 | 351.9 | 281.4 |

Rainfall intensity (I_{62}) in mm/h | 31.3–87.3 | 43.1 | 45.4 | 11.3 |

Shape factor (SF) | 0.3–1.6 | 0.8 | 0.8 | 0.2 |

Stream density (sden) in /km | 0.5–5.5 | 2.8 | 2.7 | 1.1 |

Mean annual rainfall (MAR) in mm | 626.2–1953.2 | 1000.3 | 909.9 | 304.5 |

Mean annual evapo-transpiration (MAE) in mm/y | 980.4–1543.3 | 1223.7 | 1185.6 | 126.3 |

Fraction forest (forest) | 0–1 | 0.5 | 0.5 | 0.3 |

Mainstream slope (S1085) in m/km | 1.5–49.8 | 12.9 | 9.1 | 10.8 |

**Table 2.**Correlation coefficients with their corresponding p-values between the independent variables.

A | I_{62} | SF | sden | forest | MAR | MAE | |
---|---|---|---|---|---|---|---|

I_{62} | −0.208 | ||||||

0.052 | |||||||

SF | −0.054 | 0.035 | |||||

0.619 | 0.746 | ||||||

sden | −0.175 | 0.367 | 0.037 | ||||

0.102 | 0 | 0.733 | |||||

forest | −0.116 | 0.33 | −0.007 | 0.046 | |||

0.283 | 0.002 | 0.951 | 0.667 | ||||

MAR | −0.314 | 0.83 | −0.058 | 0.361 | 0.405 | ||

0.003 | 0 | 0.592 | 0.001 | 0 | |||

MAE | −0.094 | 0.671 | 0.136 | 0.392 | −0.031 | 0.533 | |

0.381 | 0 | 0.206 | 0 | 0.771 | 0 | ||

S1085 | −0.331 | −0.121 | 0.051 | −0.081 | 0.387 | −0.021 | −0.286 |

0.002 | 0.262 | 0.637 | 0.451 | 0 | 0.844 | 0.007 |

Name | PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 |
---|---|---|---|---|---|---|---|---|

Eigenvalue | 2.822 | 1.641 | 1.070 | 0.915 | 0.702 | 0.432 | 0.278 | 0.141 |

Proportion | 0.353 | 0.205 | 0.134 | 0.114 | 0.088 | 0.054 | 0.035 | 0.018 |

Cumulative | 0.353 | 0.558 | 0.692 | 0.806 | 0.894 | 0.948 | 0.982 | 1 |

**Table 4.**Statistical evaluation for fixed region (FR) and region of influence (ROI) for 5% annual exceedance probability (AEP) flood.

5% AEP | KNN10 | KNN15 | KNN20 | KNN25 | KNN30 | KNN40 | KNN50 | KNN60 | KNN70 | KNN80 | FR |
---|---|---|---|---|---|---|---|---|---|---|---|

MSE | 443296.69 | 228884.66 | 183799.06 | 187180.23 | 179135.55 | 181519.00 | 170279.10 | 166945.45 | 163644.38 | 163850.83 | 163447.17 |

RMSE | 665.81 | 478.42 | 428.72 | 432.64 | 423.24 | 426.05 | 412.65 | 408.59 | 404.53 | 404.78 | 404.29 |

BIAS | −65.51 | 1.20 | −11.94 | −13.12 | −0.82 | −20.07 | −14.73 | −21.27 | −18.68 | −6.20 | −0.29 |

RBIAS | 22.24 | −0.44 | 55.31 | 63.94 | 54.40 | 61.69 | 65.54 | 56.98 | 54.34 | 69.90 | 65.48 |

RRMSE | 0.11 | 0.00 | 0.02 | 0.02 | 0.00 | 0.03 | 0.03 | 0.04 | 0.03 | 0.01 | 0.00 |

RMSNE | 5.40 | 3.15 | 3.28 | 3.03 | 2.38 | 2.77 | 2.28 | 2.05 | 2.05 | 2.69 | 2.43 |

med_R_{r} | 59.03 | 53.41 | 54.46 | 52.61 | 55.12 | 51.48 | 49.01 | 50.74 | 47.15 | 46.59 | 48.08 |

med_Q_{pred}/Q_{obs} | 1.03 | 1.01 | 1.16 | 1.07 | 1.14 | 1.19 | 1.20 | 1.16 | 1.16 | 1.18 | 1.17 |

RE | FR | KNN10 | KNN15 | KNN20 | KNN25 | KNN30 | KNN40 | KNN50 | KNN60 | KNN70 | KNN80 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Mean_abs | 139.75 | 229.93 | 114.81 | 152.82 | 123.56 | 133.99 | 122.83 | 122.97 | 127.89 | 131.03 | 133.88 | |

50% | Median_abs | 51.76 | 63.84 | 42.70 | 45.76 | 42.70 | 48.91 | 42.19 | 44.84 | 46.70 | 49.10 | 50.12 |

Std Dev_abs | 356.39 | 623.98 | 156.38 | 310.14 | 204.57 | 238.60 | 218.48 | 247.15 | 227.90 | 266.56 | 323.67 | |

Mean_abs | 122.18 | 206.85 | 117.88 | 134.93 | 132.02 | 126.40 | 117.35 | 112.29 | 111.24 | 113.35 | 125.28 | |

20% | Median_abs | 48.32 | 51.22 | 51.25 | 54.02 | 43.64 | 50.44 | 46.03 | 50.88 | 50.09 | 46.27 | 45.36 |

Std Dev_abs | 276.38 | 530.31 | 164.84 | 242.12 | 211.61 | 243.99 | 264.68 | 230.54 | 244.19 | 246.78 | 286.58 | |

Mean_abs | 118.13 | 208.47 | 130.45 | 140.34 | 135.02 | 127.57 | 125.56 | 115.12 | 105.84 | 107.25 | 122.45 | |

10% | Median_abs | 48.62 | 55.19 | 55.39 | 54.23 | 48.77 | 50.32 | 49.57 | 51.92 | 53.24 | 47.21 | 41.70 |

Std Dev_abs | 223.95 | 504.74 | 182.24 | 260.47 | 236.11 | 225.52 | 247.62 | 200.09 | 197.48 | 200.13 | 247.30 | |

Mean_abs | 118.95 | 213.73 | 153.93 | 143.57 | 135.83 | 127.49 | 132.75 | 121.48 | 111.44 | 108.40 | 124.36 | |

5% | Median_abs | 48.09 | 59.03 | 53.41 | 54.46 | 52.62 | 55.13 | 51.48 | 49.02 | 50.74 | 47.15 | 46.59 |

Std Dev_abs | 213.32 | 498.85 | 276.47 | 297.00 | 272.61 | 201.63 | 244.73 | 193.92 | 172.80 | 174.95 | 239.47 | |

Mean_abs | 130.02 | 225.32 | 197.48 | 154.64 | 144.06 | 140.61 | 150.67 | 137.73 | 126.73 | 118.43 | 134.54 | |

2% | Median_abs | 52.16 | 66.95 | 58.87 | 56.14 | 58.25 | 60.52 | 54.35 | 52.14 | 50.76 | 53.10 | 53.28 |

Std Dev_abs | 258.97 | 516.14 | 542.05 | 374.04 | 359.08 | 251.26 | 308.61 | 256.72 | 218.79 | 194.39 | 279.09 | |

Mean_abs | 143.45 | 239.70 | 248.66 | 174.50 | 164.45 | 163.20 | 170.96 | 156.71 | 144.20 | 130.90 | 147.16 | |

1% | Median_abs | 53.56 | 71.95 | 68.67 | 61.21 | 59.66 | 59.80 | 52.97 | 50.99 | 49.75 | 47.46 | 51.92 |

Std Dev_abs | 322.07 | 548.06 | 848.84 | 458.31 | 459.05 | 351.10 | 400.20 | 341.21 | 294.90 | 239.05 | 335.53 | |

Overall mean | 128.75 | 220.67 | 160.53 | 150.13 | 139.16 | 136.54 | 136.69 | 149.15 | 121.22 | 131.30 | 133.48 | |

Overall median | 49.77 | 61.39 | 54.90 | 54.42 | 51.31 | 55.06 | 50.63 | 47.42 | 50.17 | 45.15 | 45.95 | |

Overall Std Dev | 278.68 | 536.23 | 443.19 | 330.54 | 303.33 | 255.48 | 286.29 | 350.60 | 228.51 | 255.40 | 264.04 |

**Table 6.**Mean, median and standard deviation of Q

_{pred}/Q

_{obs}ratio for fixed region (FR) and ROI.

Ratio | FR | KNN10 | KNN15 | KNN20 | KNN25 | KNN30 | KNN40 | KNN50 | KNN60 | KNN70 | KNN80 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Mean_abs | 2.00 | 2.72 | 1.67 | 2.09 | 1.84 | 1.96 | 1.81 | 1.84 | 1.86 | 1.87 | 1.94 | |

50% | Median_abs | 1.16 | 1.09 | 1.10 | 1.12 | 1.15 | 1.12 | 1.10 | 1.12 | 1.17 | 1.16 | 1.14 |

Std Dev_abs | 3.44 | 6.20 | 1.60 | 2.95 | 2.02 | 2.39 | 2.10 | 2.33 | 2.12 | 2.56 | 3.12 | |

Mean_abs | 1.85 | 2.53 | 1.69 | 1.90 | 1.90 | 1.85 | 1.79 | 1.77 | 1.71 | 1.73 | 1.89 | |

20% | Median_abs | 1.14 | 1.14 | 1.09 | 1.19 | 1.20 | 1.23 | 1.09 | 1.18 | 1.09 | 1.11 | 1.16 |

Std Dev_abs | 2.68 | 5.23 | 1.60 | 2.38 | 2.08 | 2.36 | 2.57 | 2.20 | 2.36 | 2.40 | 2.80 | |

Mean_abs | 1.83 | 2.53 | 1.81 | 1.93 | 1.92 | 1.87 | 1.88 | 1.81 | 1.72 | 1.73 | 1.89 | |

10% | Median_abs | 1.15 | 1.17 | 1.24 | 1.19 | 1.13 | 1.33 | 1.12 | 1.22 | 1.17 | 1.14 | 1.13 |

Std Dev_abs | 2.23 | 4.98 | 1.70 | 2.62 | 2.38 | 2.22 | 2.44 | 1.97 | 1.94 | 1.98 | 2.46 | |

Mean_abs | 1.88 | 2.58 | 2.04 | 1.97 | 1.94 | 1.87 | 1.98 | 1.90 | 1.80 | 1.76 | 1.93 | |

5% | Median_abs | 1.19 | 1.26 | 1.19 | 1.24 | 1.12 | 1.21 | 1.22 | 1.22 | 1.21 | 1.18 | 1.20 |

Std Dev_abs | 2.18 | 4.93 | 2.61 | 3.02 | 2.79 | 2.07 | 2.47 | 1.99 | 1.77 | 1.79 | 2.44 | |

Mean_abs | 2.00 | 2.67 | 2.45 | 2.08 | 2.07 | 2.04 | 2.16 | 2.07 | 1.96 | 1.86 | 2.04 | |

2% | Median_abs | 1.17 | 1.28 | 1.20 | 1.13 | 1.21 | 1.23 | 1.22 | 1.21 | 1.26 | 1.21 | 1.21 |

Std Dev_abs | 2.70 | 5.12 | 5.29 | 3.83 | 3.68 | 2.61 | 3.16 | 2.68 | 2.30 | 2.07 | 2.89 | |

Mean_abs | 2.15 | 2.77 | 2.95 | 2.29 | 2.27 | 2.27 | 2.37 | 2.25 | 2.14 | 1.99 | 2.17 | |

1% | Median_abs | 1.27 | 1.22 | 1.29 | 1.22 | 1.33 | 1.33 | 1.17 | 1.20 | 1.26 | 1.23 | 1.26 |

Std Dev_abs | 3.33 | 5.47 | 8.36 | 4.68 | 4.69 | 3.62 | 4.09 | 3.54 | 3.08 | 2.54 | 3.47 | |

Overall mean | 1.95 | 2.63 | 2.10 | 2.04 | 1.99 | 1.98 | 2.00 | 1.94 | 1.87 | 1.82 | 1.98 | |

Overall median | 1.17 | 1.18 | 1.14 | 1.17 | 1.19 | 1.19 | 1.16 | 1.18 | 1.18 | 1.17 | 1.20 | |

Overall Std Dev | 2.79 | 5.31 | 4.34 | 3.32 | 3.08 | 2.59 | 2.87 | 2.50 | 2.29 | 2.23 | 2.87 |

Cluster 1 | Cluster 2 | Cluster 3 | Cluster 4 | Cluster 5 | |
---|---|---|---|---|---|

No. of stations | 15 | 16 | 16 | 23 | 18 |

Period of records (median) | 29–80 (36) | 26–71 (40) | 30–82 (36) | 25–70 (37) | 32–56 (37.5) |

Area (km^{2})(median) | 20–363 (156) | 103–673 (194.5) | 8–391 (161) | 14–740 (365) | 454–1010 (835.5) |

I_{62} (mm)(median) | 50–88 (58.4) | 31–54 (43.1) | 76–133 (41.3) | 31–48 (38.4) | 34–54 (44.9) |

SF (median) | 0.4–1.02 (0.8) | 0.4–1.02 (0.8) | 0.4–1.7 (0.8) | 0.2–1.2 (0.7) | 0.4–1 (0.8) |

sden (median) | 2.2–5.2 (3.9) | 1–3 (1.9) | 3.2–5.5 (3.9) | 0.5–3.1 (1.7) | 2–5 (3.1) |

MAR (mm) (median) | 1128–1954 (1480.2) | 672–1310 (937.5) | 744–1289 (815.2) | 656–1204 (851.2) | 626–1265 (791.9) |

MAE (mm) (median) | 1280–1544 (1382.7) | 980–1341 (1094.6) | 1069–1378 (1200.5) | 1044–1342 (1165.2) | 1107–1396 (1245.9) |

S1085 (median) | 3–23 (9.9) | 8–50 (29.6) | 4–28 (11.2) | 1–18 (6.7) | 3–19 (7.2) |

forest (median) | −1 (0.9) | 0.5–1 (0.9) | 0.05–1 (0.3) | 0–0.83 (0.2) | 0.03–1 (0.5) |

Number of Stations | H_{1} | H_{2} | H_{3} | |
---|---|---|---|---|

Cluster 1 | 15 | 5.11 | 4.93 | 3.71 |

Cluster 2 | 16 | 7.38 | 2.92 | −0.05 |

Cluster 3 | 16 | 7.59 | 6.13 | 3.62 |

15 | 7.55 | 5.26 | 2.74 | |

Cluster 4 | 23 | 1.93 | 1.16 | 0.64 |

Cluster 5 | 18 | 5.54 | 4.06 | 2.70 |

**Table 9.**Coefficients of each variable for each cluster in the regression (Equation (16)) for design flood estimation.

Cluster | β_{0} | β_{1} | β_{2} | β_{3} | β_{4} | β_{5} | β_{6} | β_{7} | β_{8} |
---|---|---|---|---|---|---|---|---|---|

1 | 4.29 | 0.94 | 1.53 | 0 | 0.75 | 0 | −2.14 | −0.15 | 0 |

2 | −9.84 | 0.29 | 7.26 | −1.80 | −0.52 | −3.93 | 3.75 | −0.80 | 0 |

3 | −4.62 | 0.81 | 3.19 | −1.21 | 0 | 0 | 0 | 0 | 0 |

4 | −0.94 | 0.72 | 0.96 | 0 | 0.52 | 0 | 0 | 0 | 0 |

5 | 4.10 | 0.49 | 4.42 | −0.34 | 0.74 | −0.62 | −2.65 | 0 | −0.22 |

**Table 10.**Comparison of mean, median and standard deviation of RE

_{abs}for all five clusters (blue indicates the lowest value).

RE | Cluster 1 | Cluster 2 | Cluster 3 | Cluster 4 | Cluster 5 | |
---|---|---|---|---|---|---|

50% | Mean_abs | 76.25 | 56.02 | 104.51 | 64.69 | 471.67 |

Median_abs | 48.41 | 36.56 | 54.86 | 33.76 | 183.76 | |

Std Dev_abs | 89.91 | 70.76 | 108.34 | 76.86 | 580.00 | |

20% | Mean_abs | 89.88 | 36.57 | 71.11 | 82.74 | 542.75 |

Median_abs | 43.06 | 25.92 | 42.39 | 39.87 | 424.41 | |

Std Dev_abs | 210.38 | 29.72 | 89.01 | 113.28 | 317.63 | |

10% | Mean_abs | 173.56 | 27.91 | 66.26 | 90.36 | 503.40 |

Median_abs | 31.84 | 24.90 | 35.48 | 45.83 | 422.76 | |

Std Dev_abs | 558.54 | 25.44 | 79.88 | 130.68 | 254.32 | |

5% | Mean_abs | 412.96 | 27.82 | 66.62 | 94.14 | 453.41 |

Median_abs | 28.01 | 22.86 | 32.80 | 46.33 | 403.45 | |

Std Dev_abs | 1500.24 | 24.66 | 76.09 | 140.28 | 208.10 | |

2% | Mean_abs | 1427.02 | 34.25 | 71.15 | 96.05 | 403.47 |

Median_abs | 21.34 | 26.55 | 46.46 | 47.05 | 380.24 | |

Std Dev_abs | 5427.65 | 30.44 | 82.16 | 145.37 | 170.96 | |

1% | Mean_abs | 3637.99 | 44.37 | 78.85 | 96.41 | 376.95 |

Median_abs | 21.17 | 30.53 | 42.31 | 40.13 | 342.35 | |

Std Dev_abs | 13984.54 | 35.89 | 94.50 | 146.08 | 154.74 | |

Overall mean | 969.61 | 37.82 | 76.42 | 87.40 | 458.61 | |

Overall median | 33.56 | 26.16 | 42.92 | 43.07 | 387.51 | |

Overall Std Dev | 6121.12 | 39.71 | 87.63 | 125.93 | 313.49 |

**Table 11.**Comparison of mean, median and standard deviation of Q

_{pred}/Q

_{obs}ratios for all five clusters (blue indicates the lowest value).

Cluster 1 | Cluster 2 | Cluster 3 | Cluster 4 | Cluster 5 | ||
---|---|---|---|---|---|---|

50% | Mean_abs | 1.38 | 1.21 | 1.52 | 1.35 | 4.68 |

Median_abs | 1.08 | 0.97 | 0.78 | 1.00 | 1.85 | |

Std Dev_abs | 1.13 | 0.89 | 1.43 | 0.95 | 6.10 | |

20% | Mean_abs | 1.60 | 1.09 | 1.33 | 1.44 | 4.43 |

Median_abs | 1.03 | 0.85 | 0.89 | 0.88 | 3.24 | |

Std Dev_abs | 2.21 | 0.47 | 1.11 | 1.34 | 3.18 | |

10% | Mean_abs | 2.47 | 1.07 | 1.30 | 1.49 | 4.03 |

Median_abs | 1.02 | 1.02 | 0.91 | 0.90 | 3.23 | |

Std Dev_abs | 5.66 | 0.38 | 1.00 | 1.52 | 2.54 | |

5% | Mean_abs | 4.88 | 1.07 | 1.31 | 1.52 | 3.53 |

Median_abs | 1.04 | 1.01 | 0.98 | 0.89 | 3.03 | |

Std Dev_abs | 15.07 | 0.37 | 0.98 | 1.62 | 2.08 | |

2% | Mean_abs | 15.02 | 1.10 | 1.36 | 1.54 | 3.03 |

Median_abs | 0.95 | 0.98 | 1.01 | 0.87 | 2.80 | |

Std Dev_abs | 54.35 | 0.45 | 1.04 | 1.67 | 1.71 | |

1% | Mean_abs | 37.11 | 1.15 | 1.42 | 1.54 | 2.77 |

Median_abs | 0.99 | 0.93 | 1.08 | 0.91 | 2.42 | |

Std Dev_abs | 139.92 | 0.56 | 1.17 | 1.67 | 1.55 | |

Overall mean | 10.41 | 1.12 | 1.37 | 1.48 | 3.75 | |

Overall median | 1.00 | 0.96 | 0.96 | 0.94 | 2.88 | |

Overall Std Dev | 61.26 | 0.54 | 1.10 | 1.46 | 3.25 |

MSE | RMSE | BIAS | RBIAS | RRMSE | RMSNE | RE_{r} | med_Q_{pred}/Q_{obs} | |
---|---|---|---|---|---|---|---|---|

Cluster 1 | 34034166.24 | 5833.88 | 1480.61 | 388.4 | 2.34 | 15.07 | 28.01 | 1.04 |

Cluster 2 | 25309.52 | 159.09 | 5.54 | 7.30 | 0.01 | 0.37 | 22.86 | 1.01 |

Cluster 3 | 58766.74 | 242.42 | 11.39 | 30.91 | 0.04 | 0.99 | 32.79 | 0.98 |

Cluster 4 | 91284.23 | 302.13 | −47.02 | 51.92 | 0.12 | 1.66 | 45.54 | 0.89 |

Cluster 5 | 17065693.55 | 4131.06 | −4056.77 | −453.41 | 3.61 | 4.96 | 403.44 | −3.03 |

**Table 13.**Comparison of absolute RE (%) values between ARR RFFA model and PCR KNN15, PCR KNN25, and QRT models for cluster 2.

AEPs | ARR RFFA Model Absolute RE (%) | PCR_KNN15 Absolute RE (%) | PCR_KNN25 Absolute RE (%) | Cluster 2 Absolute RE (%) |
---|---|---|---|---|

50% | 63.07 | 42.7 | 42.70 | 36.56 |

20% | 57.25 | 51.25 | 43.64 | 25.92 |

10% | 57.48 | 55.39 | 48.77 | 24.9 |

5% | 58.85 | 53.41 | 52.62 | 22.86 |

2% | 60.39 | 58.87 | 58.25 | 26.55 |

1% | 64.06 | 68.67 | 59.66 | 30.53 |

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**MDPI and ACS Style**

Rahman, A.S.; Rahman, A. Application of Principal Component Analysis and Cluster Analysis in Regional Flood Frequency Analysis: A Case Study in New South Wales, Australia. *Water* **2020**, *12*, 781.
https://doi.org/10.3390/w12030781

**AMA Style**

Rahman AS, Rahman A. Application of Principal Component Analysis and Cluster Analysis in Regional Flood Frequency Analysis: A Case Study in New South Wales, Australia. *Water*. 2020; 12(3):781.
https://doi.org/10.3390/w12030781

**Chicago/Turabian Style**

Rahman, Ayesha S, and Ataur Rahman. 2020. "Application of Principal Component Analysis and Cluster Analysis in Regional Flood Frequency Analysis: A Case Study in New South Wales, Australia" *Water* 12, no. 3: 781.
https://doi.org/10.3390/w12030781