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Identification of homogeneous rainfall regions in New South Wales, Australia Cover

Identification of homogeneous rainfall regions in New South Wales, Australia

Open Access
|Jan 2021

Figures & Tables

Fig. 1.

Map of New South Wales (NSW), Australia, surrounded by Queensland, Victoria, South Australia, Coral, and Tasman seas.

Fig. 2.

Four distinct climate zones: the coastal belt, the ranges and tablelands of the Great Dividing Range, the Western slopes and plains, and the arid plains of New South Wales, Australia.

Fig. 3.

(a) Location of 226 rainfall stations in New South Wales, Australia. (b) A close look at the location of 223 stations, excluding two stations from Lord Howe Island and one from Norfolk Island, Australia.

Fig. 4.

A flow chart of iterative steps of the Partition Around Medoid (PAM) algorithm.

Fig. 5.

(a) Map of allocations of 226 rainfall stations to 10 clusters by Partition Around Medoid (PAM) using G-plane coordinates, the different colors represent the memberships of stations to a specific cluster. (b) A close look into the allocations of 223 rainfall stations to 10 clusters by Partition Around Medoid (PAM) using G-plane coordinates.

Fig. 6.

Average silhouette width comparison for selecting the optimal number of clusters.

Fig. 7.

(a) Map of allocations of 226 rainfall stations to 10 clusters by Partition Around Medoid (PAM) using rectangular coordinates; the different colors represent the memberships of sites to a specific cluster. (b) A close look into allocations of 223 rainfall stations to 10 clusters by Partition Around Medoid (PAM) using rectangular coordinates.

Table 1.

Summary information of all ten cluster’s medoids using Partition Around Medoid.

Cluster no.Medoid clusterStation nameG-LatG-LonD-LatD-LonAverage annual rainfallCluster 1201Uriarra Forest−35.2994148.9222523.0008−292.438814.2Cluster 2216Wollombi (Blair)−32.9667151.1333331.8256−25.2381825.2Cluster 365Dunedoo Post office−32.0165149.3956498.469373.11653612.4Cluster 485Green Pigeon (Morning View)−28.4738153.0861158.0061477.32651632.2Cluster 5200Uralla (Lana)−30.6417151.3002324.6122232.7578773.3Cluster 6196Upper Mongogarie (Kimberley)−28.9667152.8167183.3333422.15921075.9Cluster 7176Sydney (Observatory Hill)−33.8607151.205321.889124.00431215.7Cluster 887Griffith Airport Aws−34.2487146.0695790.0565−193.941397.6Cluster 935Cabramurra Smhea−35.9383148.3842567.3771−365.9591681.5Cluster 10116Lord Howe Island Aero−31.5421159.0786−412.978129.64961478.6
Table 2.

Summary statistics of all ten clusters by Partition Around Medoid using rectangular coordinates.

Cluster no.Sample sizeAverageStandard deviationCV (%)MinimumMaximumCluster 134797.76140.1417.57566.501058.60Cluster 236839.81123.9114.75585.801035.50Cluster 329590.9579.4913.45411.80785.30Cluster 4171686.14165.209.801421.302015.30Cluster 537785.77128.7916.39538.801080.70Cluster 6161060.93122.1111.51856.101262.30Cluster 7311192.03106.958.971002.201424.70Cluster 816381.38107.8728.28225.80577.50Cluster 971628.63173.7410.671288.701808.90Cluster 1031468.90175.0511.921289.201638.90Complete Data226921.20362.3439.33225.802015.30
Fig. 8.

Fitted Gaussian variogram for Cluster 3 using rectangular coordinates.

Fig. 9.

(a) Prediction (b) Variance map of precipitation for Cluster 3 using ordinary kriging.

Language: English
Page range: 1907979 - 1907979
Published on: Jan 1, 2021
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services

© 2021 Shahid Khan, Ijaz Hussain, Ataur Rahman, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.