50603, Kuala Lumpur, Malaysia c Department of Surveying Science and Geomatics, Faculty of Architecture, Planning. Shah Alam, Selangor, Malaysia

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1 This article was downloaded by: [University of Malaya] On: 13 August 2012, At: 19:45 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK Geodinamica Acta Publication details, including instructions for authors and subscription information: Homogeneous Climate Divisions for Peninsular Malaysia Fariza Yunus a, Aziz Shafie b, Jasmee Jaafar c & Zamalia Mahmud d a Malaysian Meteorological Department, Jalan Sultan, 46667, Petaling Jaya, Selangor, Malaysia b Department of Geography, Faculty of Arts & Social Sciences, University of Malaya, 50603, Kuala Lumpur, Malaysia c Department of Surveying Science and Geomatics, Faculty of Architecture, Planning and Surveying, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia d Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia Version of record first published: 13 Aug 2012 To cite this article: Fariza Yunus, Aziz Shafie, Jasmee Jaafar & Zamalia Mahmud (2011): Homogeneous Climate Divisions for Peninsular Malaysia, Geodinamica Acta, 24:2, To link to this article: PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

2 Geodinamica Acta 24/2 (2011) Geodinamica Acta Homogeneous Climate Divisions for Peninsular Malaysia Fariza Yunus 1*, Aziz Shafie 2, Jasmee Jaafar 3, Zamalia Mahmud 4 1 Malaysian Meteorological Department, Jalan Sultan, Petaling Jaya, Selangor, Malaysia 2 Department of Geography, Faculty of Arts & Social Sciences, University of Malaya, Kuala Lumpur, Malaysia 3 Department of Surveying Science and Geomatics, Faculty of Architecture, Planning and Surveying, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia 4 Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor Malaysia Abstract Classification of Peninsular Malaysia was delineated by integrating in-situ temperature elements data and Geographical Information System (GIS) raster data. The principal component (PC) analysis was applied to long-term mean monthly temperature elements data for monsoon seasons. The first three principal components were chosen to be statistically significant, accounted for 96.5% of the variability in the 27 variables. These three components are related to the mean monthly variation in minimum temperature during monsoon season (first PC), the mean monthly variation in maximum and the mean temperature in southwest monsoon (second PC), and the mean monthly variation in maximum temperature during northeast monsoon (third PC). Cluster analyses were applied to create clusters of meteorological stations, of which six classes were formed. To determine cluster boundaries, interpolation analysis was applied to generate GIS raster data of factor scores. The supervised classification analysis was then performed to the generated GIS factor data. The result of a maximum likelihood classification produced three clusters when summarized by districts. Final classification results of climate divisions show rational climate regionalization that reveals control on temperature. The use of factor score GIS raster data effectively assists the generation of meteorological station clusters, grouped using only in-situ data Lavoisier SAS. All rights reserved Keywords: Geographic Information System, principal component analysis, temperature, climate divisions, Peninsular Malaysia. 1. Introduction This paper focuses on the temporal and spatial variability of temperature elements over Peninsular Malaysia. It is aim to define homogenous temperature elements area in order to develop temperature elements modeling. In general, climate division refers to an area that has similar characteristic of climatic elements (AMS, 2010). This climate division is important for planning, managing and critical decision making for climate related projects. The most prominent example of climate division referred has been the work of Thornthwaite (1931), who uses a variety of climate elements. Most standard approaches used in previous studies are directly based on temperature and precipitation (Rhee et al., 2007, DeGaetano, 2001, Paruelo et al., 1995, Fovel and Fovel, 1993). These approaches frequently employ a combination of principal component analysis (PCA) and cluster analysis (DeGaetano, 1996; Gong and Richman, 1995). Many studies (Rhee et al., 2008, Gerstengarbe et al., 1999, Fovell 1997, DeGaetano, 1996) use a combination of hierarchical and nonhierarchical clustering analysis to overcome the unrealistic assumption of hierarchical clustering analysis (Hair et al., 2009). Compared to other countries, limited studies have been carried out in delineating of climate divisions for Peninsular Malaysia. The existing climate division delineated by Dale (1959) only uses * Corresponding author. address : fariza@met.gov.my doi: /ga Lavoisier SAS. All rights reserved

3 90 Fariza Yunus et al. / Geodinamica Acta 24/2 (2011) precipitation data to divide the area into five divisions (Tick and Abu Samah, 2004). Therefore, it is important to derive a classification for Peninsular Malaysia, using temperature data. This paper presents an approach to classify Peninsular Malaysia based on temperature data towards climate homogenous divisions. Most previous studies (DeGaetano, 2001; Fovel, 1997) define climate homogenous divisions based on meteorological stations data. In Peninsular Malaysia, most meteorological stations are located in low lying areas. However, using only in-situ data makes it impossible to cluster hilly areas with very limited meteorological stations. Furthermore, it may introduce bias as the uneven distribution of meteorological stations can create problems in determining borderlines among groups of stations and in identifying climate characteristic of divisions with low density of stations (Rhee et al., 2008; DeGaetano, 1996). To place the cluster border, DeGaetano (1996) used a discriminant function analysis by linking a group of stations with locations variables. This technique, however, can introduce bias as the variables are indirectly related to climate variables. Another study by Rhee et al. (2008) used remotely sensed data of monthly land surface temperature in determining climate cluster border. Although surface air temperature and land surface temperature are highly correlated, it measures different entities. In fact, land surface temperature is not a climate variable and the study utilized different periods for both types of data. GIS raster data of factor scores may be useful to solve this problem. These factor scores are only arbitrary values, but they are important information for measuring temperature elements. Furthermore, this study used the same set of PC data for clustering meteorological stations and determining the cluster boundaries. The latent bias caused by the sparse and irregular distribution of stations can be reduced. 2. Study area and in-situ temperature elements data Peninsular Malaysia is located in south of Thailand, north of Singapore and east of Indonesian island of Sumatra (Fig. 1). The main mountain range is Titiwangsa Mountain where the highest point is the Mount Tahan (2,187m). Generally, the climate is hot and humid throughout the year, with an annual mean temperature of 26.5 o C, and average annual rainfall exceeding 2000mm (Tick and Abu Samah, 2004). Figure 1 shows a network of active meteorological stations in the study area. From Figure 1, the distribution of meteorological stations is in irregular pattern. Meteorological stations distribution is relatively dense over the low lying areas where 60 out of 62 stations are located at between 0 to 250m from mean sea level (MSL). Temperature elements data of maximum, minimum and mean values were used in this study. Daily data were obtained for a period of 10 years (1999 to 2008). All stations had sufficient data, in which, 55 stations (89%) had at least Fig. 1: Distribution of meteorological stations in study area of Peninsula Malaysia 80% of completed daily data (Malmgren, 1999). These data have gone through rigorous quality control procedures at the Climate Division of the Malaysian Meteorological Department. Since the seasonal and spatial temperature variations are relatively small, and that there is a definite variation during the monsoon seasons (MMD, 2010), this study used temperature data during the monsoon seasons. There are two monsoon seasons in Peninsular Malaysia (Tick and Abu Samah, 2004); southwest monsoon which occurs from May to September (five months), and northeast monsoon which occurs from November to February (four months). The monthly means per station for these selected months were computed over the period of ten years. Hence, the analysis of this study focuses on 27 variables, derived from the three temperature elements in the nine months of monsoon seasons. 3. Methodology Climatologically homogenous divisions were generated using a multi-step approach based on the long-term monthly average of temperature elements for monsoon seasons (Rhee et al, 2008). First, a principal component analysis (PCA)

4 Fariza Yunus et al. / Geodinamica Acta 24/2 (2011) Fig. 2: Scree Plot Result of PCA was performed (DeGaetano, 1996; Fovell and Fovell, 1993) among the 27 variables. Although the use of long-term means data and PCA will forfeit some information (Rhee et al., 2008), the effect is not significant for Peninsular Malaysia as the temperature is almost uniform, with annual variation of less than 3 o C (MMD, 2010). The scree plot and latent root criterion were also used to select the appropriate PC (Hair et al., 2009). To be statistically significant, varimax rotation was employed to the selected PCs (Hair et al., 2009; Malmgern, 1999). Next, the study applied hierarchical and nonhierarchical of cluster analysis (Rhee et al., 2008; Hair, et al, 2009; Fovell, 1997; DeGeatano, 1996) to the PCA results. In hierarchical cluster analysis, the average linkage method was utilized as it is the most popular hierarchical clustering method used in previous studies (Rhee et al., 2008; DeGaetano, 1996). The appropriate numbers of clusters were determined using a number prior to the tremendous increase in the coefficient values, where large increase in heterogeneity exists (Hair et al, 2009). Also, in this analysis, it is essential to determine the initial seeding points for each cluster. A nonhierarchical analysis was implemented to the same truncated PCA data, but using the results of the previous hierarchical analysis for the number of clusters and cluster seed points. Using these initial cluster seed points, the study conducted nonhierarchical clustering by adopting the algorithm used by DeGaetano (1996). Another step in generating the climatologically homogenous divisions is the determination of cluster boundaries for clusters of meteorological stations. To place cluster boundaries, GIS raster data for PC truncated results were initially generated using widely used interpolation analysis of inverse distance technique (IDW) (Mitas and Mitasova, 1999; Tomczak, 1998). Then, supervised classification was performed to the generated raster GIS data of factor 4. Results and discussions scores using the results of previous nonhierarchical clustering analysis as training data. In this analysis, since the number of sample is large enough, a traditional technique of maximum likelihood classification (Walter, 2003; Walter, 1998; Huang and Jensen, 1997) was used as the supervised classification method. In this method, data were assumed to be normally distributed using central limit theorem. Finally, climatologically homogenous divisions were delineated using the district boundaries of Peninsular Malaysia using zonal analysis for management purposes (Rhee et al., 2008, Fovell, 1997, DeGaetano, 2001). The correlations between the original 27 variables were relatively high, ranging from 0.59, in the case of January mean monthly minimum temperature (mintt 1 ) and February mean monthly maximum temperature (maxtt 2 ), to 0.998, in the case of June mean monthly mean temperature (meantt 6 ) and July mean monthly mean temperature (meantt 7 ). In most cases, the correlation values for the data were high with values exceeding 0.7. In fact, it was found that 34% of these data had correlations of more than 0.9, suggesting the presence of redundant information. It thus requires the result of PCA in order to reduce information bias in the final clusters caused by these information redundancies. Figure 2 shows the scree plot result of the PCA. From Figure 2, component number greater than or equal to four has very slight change in Eigen values. Only the first three PCs have Eigen values greater than one. Therefore, from the scree plot and latent root criterion, the first three PCs were selected for analysis. To enhance the interpretation of selected principal components, varimax orthogonal rotation was applied (DeGaetano, 1996). For each of the PC, variables with factor loadings greater than or equal to 0.7 were considered (Hair et al., 2009). Table 1 shows factor loadings of varimax orthogonal rotated principal component for the first PCs. The first three PCs selected in Table 1 accounted for 96.5% of the variation in the data (Table 1). The first PC (PC1), which explained the most variation in the data (83.7%), contains significant contribution of mean monthly variation in minimum temperature during monsoon seasons (mintt 11, mintt 12, mintt 1 mintt 2 and mintt 5, mintt 6, mintt 7, mintt 8 mintt 9 ). The second PC (PC2), which explained 8.3% of variance, was represented by mean monthly variation of

5 92 Fariza Yunus et al. / Geodinamica Acta 24/2 (2011) maximum (maxtt 5, maxtt 6, maxtt 7, maxtt 8, maxtt 9 ) and mean temperature (meantt 5, meantt 6, meantt 7, meantt 8, meantt 9 ) in southwest monsoon. The third PC (PC3) accounted for 4.5% variance and was related to the mean monthly distribution of maximum temperature (maxtt 11, maxtt 12, maxtt 1, maxtt 2 ) during northeast monsoon. Average linkage hierarchical clustering method was applied to factor scores of the three PCs. Table 2 shows agglomeration schedule of the PC1, PC2 and PC3. From Table 2, the average proportionate increase for stage 50 to 61 is 57.99%. The largest increase in coefficient occurred when moving to final stage (25.55), where the solution result was two clusters. However, Principal Component (PC) Table 1: Varimax rotated principal component factor loadings for three selected PCs. Only variables corresponding to factor loadings of more than 0.7 were considered. Cumulative variance is given in parenthesis PC1 (83.7%) PC2 (92.0%) PC3 (96.5%) Var Load Var Load Var Load mintt maxtt maxtt mintt maxtt maxtt mintt maxtt maxtt mintt maxtt maxtt mintt maxtt mintt meantt mintt meantt mintt meantt mintt meantt meantt Table 2: Agglomeration schedule for PC1, PC2 and PC3 Stage Cluster Combined Coefficients Number of Cluster After Combining this was not an appropriate solution as it involved the largest change in heterogeneity (Hair et al., 2009). The second largest increase in coefficient was 2.37 or 75.86% of proportionate increase. However, the difference in proportionate increase between the third largest and second largest was not distinct. The increase in the coefficient for the third largest was 1.26 (68.14% of proportionate increase). It occurred when moving from stage 56 to 57. In addition, the six-cluster solution was associated with proportionately less heterogeneous than the five-cluster solution. Based on these results, the stopping point for cluster analysis was at stage 56, in which case, six-cluster solution was selected as the appropriate number Increase in Coefficient to Next Stage of cluster. The locations of meteorological station clusters using six-cluster solution were examined. Figure 3 shows the six-cluster solution for the 62 meteorological stations. From Figure 3, cluster one consists of 28 stations and located in the west coast. Cluster two consists of 15 stations, and majority of these stations are located in the northeastern and central parts of Peninsular Malaysia. Cluster three and cluster four, each had one single station located in hilly areas. Cluster five had 12 stations located in the inland areas of the western Proportionate Increase in Heterogeneity to Next Stage Cluster 1 Cluster % % % % % % % % % % % Average 57.99%

6 Fariza Yunus et al. / Geodinamica Acta 24/2 (2011) Fig. 3: Six-cluster solution of meteorological stations part of Peninsular Malaysia. Cluster six had five stations which located in the coastal areas of the eastern part of Peninsular Malaysia. Centroids for each of the six clusters were obtained and used as the seed points for k-means nonhierarchical clustering. The reassignment of meteorological stations was then examined. Most stations were grouped in the same cluster as in the hierarchical cluster analysis, with the exception of the Parit Buntar s Hospital station and the Kota Tinggi s Hospital station. Both stations were regrouped in cluster five in k-means nonhierarchical cluster analysis. Initially, the Parit Buntar s Hospital station was in cluster one; while the Kota Tinggi s Hospital station was in cluster two. To place cluster boundaries, first, simple linear regression models of discrete data for factor scores and elevation at every station were developed for every PC in order to estimate lapse rate values for every PC. In addition, the gradient values for regression models of , and were utilized to estimate the lapse rate value for PC1, PC2 and PC3 respectively. Factor score at zero elevation at all stations were interpolated using IDW interpolation method to develop surface of factor score at zero elevation for every PC. To generate raster factor scores, the respective lapse rate was timed with digital elevation model (DEM), and this surface was then subtracted to raster data of factor scores at zero elevation. Results from nonhierarchical cluster analysis were utilized in supervised classification, where all 62 meteorological stations were used as training data for factor scores. The maximum likelihood classification of supervised classification was then applied to the corresponding raster data of factor scores. Although there were six classes in the Fig. 4: PCs classed based on maximum likelihood classification training data, classification results in only four classes in analysis area (Fig. 4). The single station cluster three and cluster four were not shown in the classification analysis. These clusters were merged with cluster five, becoming Fig. 5: Climate divisions (CD) as a result of the three CDs classes using GIS raster data of factor scores and summarized by district

7 94 Fariza Yunus et al. / Geodinamica Acta 24/2 (2011) into climate division three. Cluster six was still exists but, only in small fraction areas of the eastern part of Peninsular Malaysia. The used of GIS raster factor scores data assists in identifying the climate characteristic of locations with no meteorological stations, and the delineation between cluster of low density distribution stations. To illustrate, some inland areas of the eastern part of Peninsular Malaysia does not have any meteorological station. However, using GIS raster data, these areas can be classified into climate division three. In this study, the delineation unit was the district boundaries, as the country boundaries are too coarse (Rhee et al., 2008, Fovell, 1997, DeGaetano, 2001). This delineation is useful for planning, managing and critical making decision in any climate-related projects. Zonal analyses were performed with a majority statistical option (Rhee et al., 2008). Figure 5 presents the three classes of Peninsular Malaysia s climate divisions which were correspond to the west coast (CD1), the east coast (CD2), and the main range area (CD3). In Figure 5, the areas of climate division four were not individually shown as they were too small. Instead, they were merged into climate division two. The three classification References [1] American Meteorological Society. (2010), Glossary of meteorology. Retrieved April1, from glossary/ search?p=1&query= climatic+classifycation&submit=search [2] Dale W. L. (1959), The rainfall of Malaya part I. Journal of Tropical Geography, 13, [3] DeGaetano A. T. (1996), Delineation of mesoscale climate zones in the Northeastern United States using a novel approach to cluster analysis. Journal of Climate, 9, [4] DeGaetano A. T. (2001), Spatial grouping of United States climate stations using a hybrid clustering approach. International Journal of Climate, 21, [5] Fovell R. G. (1997), Consensus clustering of U.S temperature and precipitation data. Journal of Climate, 10, [6] Fovell R. G., Fovell M-Y. (1993), Climate zones of the conterminous United States defined using cluster analysis. Journal of Climate, 6, [7] Gerstengarbe F. W., Werner P. C., Fraedrich K. (1999), Applying non-hierarchical cluster analysis algorithms to climate classification: some problems and their solution. Theor. Appl. Climatol., 64, [8] Gong X., Richman M. B. (1995), On the application of cluster analysis to growing season precipitation data in North America East of the Rockies. Journal of Climate, 8, [9] Hair J. F., Black W. C., Babin J. B., Anderson R. E. (2009), Multivariate data analysis. Pearson Education Inc. New Jersey, , [10] Huang X., Jensen J. R. (1997), A Machine-Learning Approach to Automated Knowledge Base Building for Remote Sensing Image Analysis with GIS Data. Photogrammetric Engineering & Remote Sensing, 63, areas of climate divisions are expected to play an important role in determining the pattern of temperature distribution in Peninsular Malaysia. As the Main Range is the hilly areas, this area is relatively colder as compared to the other two divisions which are located at low lying areas. These three divisions are conform with climate pattern suggested by climate record where annual variation of temperature is relatively higher in the east coast areas as compared to other areas, as the former is often affected by cold surges originating from Siberia during the northeast monsoon (MMD, 2010). In addition, the average daily temperature to the east of the Main Range is lower than that of the west of the Main Range (MMD, 2010). In general, the ascending orders of temperature element values for these divisions are Main Range, east coast and west coast. In essence, the generated climate divisions conform to the climate control of Peninsular Malaysia, including topography, latitudinal locations and distance from coastal areas. These delineated climate divisions shall be integrated with existing climate divisions (Dale, 1959) by using consensus clustering suggested by Fovell (1997) to obtain a complete climate regions for Peninsular Malaysia. Accurate climate divisions can be used in many other applied climate studies in the future. [11] Malmgren B. A. (1999), Climate zonation in Puerto Rico based on principal component analysis and an artificial neural network. Journal of Climate, 12, [12] Mitas L., Mitasova H. (1999), Spatial Interpolation. In Longley P A. Goodchild M F, Maguire DJ and Rhind DW. Geographical Information Systems: Principles, Techniques, Management and Applications. New York. Wiley, [13] Malaysian Meteorological Department (2010), General climate of Malaysia. 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(1998), Automatic classification of remote sensing data for GIS database revision. IAPRS, 32, [20] Walter V. (2004), Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry & Remote Sensing, 58,