# Rainfall Characteristics and Regionalization in Peninsular Malaysia Based on a High Resolution Gridded Data Set

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}is composed of highlands, floodplains and coastal zones. The Titiwangsa range forms the backbone of the Peninsula, running approximately south-southeast from southern Thailand over a distance of 480 km and separating the eastern part from the western part. Surrounding the central high regions are the coastal lowlands. In general, the Peninsula experiences a warm and humid tropical climate all year round, with uniform temperatures ranging from 25 °C to 32 °C. Rainfall is characterized by two rainy seasons associated with the southwest monsoon (SWM) from May to September and the northeast monsoon (NEM) from November to March [7,16,28].

#### 2.2. Data Sources

#### 2.3. Methodology

#### 2.3.1. Data Interpolation

#### 2.3.2. Climatic Regions Delineation

_{i}shows the ith cluster (i = 1, 2, …, k) and μ

_{i}is the centroid of all the cluster points ${x}_{j}\in {C}_{i}$. The algorithm however requires the number of clusters, k, to be set a priori. Here, the algorithm was applied to the 4289 data items, setting the value of k from 3 to 10. The validity of the optimum number of cluster was then determined by the silhouette width [33] for each data items is calculated as:

#### 2.3.3. Trend Detection

_{0}is that there is no trend in the series (the data are independent and identically distributed). The alternative hypothesis H

_{1}is that a trend exists in the data. The Kendall S-statistic is obtained from comparison between all possible (x, y) pairs of data and is given by:

_{i}is the number of ties of extent i in either the x or y data. The summation term in the numerator is used only if the data series contains tied values. The standardized test statistic Z is obtained as

_{0}should be accepted if |Z| is greater than Z

_{α/2}, where α represents the chosen significance level (e.g., 5% with Z

_{0.025}= 1.96) then the null hypothesis is rejected implying that the trend is significant. A positive Z value indicates an upward trend, whereas a negative Z value indicates a downward trend. If a significant trend is present, the average rate of increase or decrease can be obtained from the slope of a simple linear regression.

#### 2.3.4. Spatial Variation of Rainfall

_{i}is the rainfall depth in each grid cell. The average areal rainfall and the spatial variance are computed for each region. The coefficient of variation of rainfall which is used to characterize the monthly spatial variability of rainfall is defined as the standard deviation divided by the average areal rainfall.

#### 2.3.5. Rainfalls Correlation Analysis with El Ninõ-Southern Oscillation (ENSO)

## 3. Results and Discussion

#### 3.1. Characteristics of Delineated Climatic Regions

#### 3.2. Rainfall Trends

#### 3.3. Spatial Variability

#### 3.4. Influence of ENSO

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Box and whisker plot of areal average mean monthly rainfall (1976–2006) of the eight delineated rainfall regions in Malaysia. The solid line is the median, the height of the box is the difference between the third and first quartiles (IQR). The zones delineated by Lim [5] are shown in red lines.

**Figure 2.**Box and whisker plot of mean monthly coefficients of spatial variations (1976–2006) of the eight delineated rainfall regions (Panel

**a**to

**h**). The solid line is the median, the height of the box is the difference between the third and first quartiles (IQR). Any data observation which lies 1.5 IQR lower than the first quartile or 1.5 IQR higher than the third quartile is considered an outlier in the statistical sense, indicated by open circles.

**Figure 3.**Mean monthly rainfall coefficients of variation over the eight delineated rainfall regions (Panel

**a**to

**h**) for the period of 1976–2006.

**Figure 4.**The correlation of concurrent monthly rainfall (mm/month) and Multivariate El Ninõ-Southern Oscillation (ENSO) Index (MEI) for Peninsular Malaysia. The dotted line indicates significance at 95% level.

**Figure 5.**The correlation of concurrent monthly rainfall (mm/month) and Multivariate ENSO Index (MEI) of eight distinct climatic regions. The dotted line indicates significance at 95% level.

K, Non-Overlapping Regions | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|

Averaged silhouette width, $\overline{S}$ | 0.35 | 0.41 | 0.43 | 0.46 | 0.43 | 0.53 | 0.50 | 0.48 |

Region | Mean Elevation (m) (min, max) | Annual Rainfall (mm/Year) | Standard Deviation | Northeast Monsoon (Nov.–Mar.) Rainfall | Southwest Monsoon (May–Sep.) Rainfall | Total Monsoon Rainfall | |||
---|---|---|---|---|---|---|---|---|---|

mm/Year | % | mm/Year | % | mm/Year | % | ||||

R1 | 272 (0, 860) | 2118 | 440 | 567 | 26 | 1035 | 48 | 1602 | 75 |

R2 | 116 (0, 773) | 2229 | 314 | 922 | 41 | 806 | 36 | 1728 | 77 |

R3 | 326 (16, 1475) | 2154 | 347 | 921 | 42 | 765 | 35 | 1686 | 78 |

R4 | 74 (0, 189) | 1807 | 464 | 705 | 39 | 743 | 41 | 1448 | 80 |

R5 | 50 (0, 183) | 2206 | 604 | 1004 | 45 | 794 | 35 | 1798 | 81 |

R6 | 457 (68, 1280) | 2174 | 329 | 868 | 39 | 868 | 39 | 1736 | 79 |

R7 | 266 (0, 1044) | 2940 | 321 | 1530 | 52 | 993 | 33 | 2523 | 85 |

R8 | 128 (0, 352) | 2383 | 550 | 1268 | 53 | 735 | 30 | 2004 | 84 |

**Table 3.**Annual and monsoons rainfall trends (mm/year) of eight distinct climatic regions for the period of 1976–2006.

Region | Annual | NEM | SWM |
---|---|---|---|

R1 | 6.3 | 7.5 | −5.1 |

R2 | 3.7 | 4.3 | −1.5 |

R3 | 7.7 | 6.7 | 0.0 |

R4 | 2.1 | 3.1 | −1.8 |

R5 | 7.1 | 4.9 | −0.8 |

R6 | 5.8 | 5.0 | −1.0 |

R7 | 13.3 | 11.4 | −1.1 |

R8 | 9.1 | 5.8 | −0.3 |

Region | Jan. | Feb. | Mar. | Apr. | May | Jun. | Jul. | Aug. | Sep. | Oct. | Nov. | Dec. |
---|---|---|---|---|---|---|---|---|---|---|---|---|

R1 | 1.3 | 1.3 | 2.5 | 0.7 | −3.5 | 0.8 | −0.8 | 0.5 | −2.0 | 2.6 | 0.2 | 2.8 |

R2 | 1.6 | 0.3 | −0.2 | 0.1 | −1.7 | 0.3 | −0.3 | 0.5 | −0.2 | 0.3 | 1.7 | 1.5 |

R3 | 2.2 | 0.2 | 1.2 | 0.8 | −1.0 | 0.3 | −0.1 | 0.5 | 0.4 | −0.7 | 1.6 | 2.5 |

R4 | 2.0 | −1.3 | 1.1 | −0.2 | −0.3 | −0.2 | −0.6 | 1.1 | −2.0 | 0.1 | −0.9 | 3.1 |

R5 | 5.6 | −1.1 | 1.5 | −0.2 | −0.0 | −0.9 | 0.0 | 0.8 | −0.7 | 1.1 | −0.4 | 1.5 |

R6 | 2.2 | 0.2 | 1.7 | 0.3 | −1.4 | 0.9 | −0.9 | 0.9 | −0.6 | 0.1 | −0.5 | 2.8 |

R7 | 5.0 | 2.4 | 3.5 | −0.4 | −0.6 | 1.1 | −1.4 | 0.0 | −0.2 | 1.0 | −1.2 | 4.2 |

R8 | 5.8 | 0.1 | 1.1 | −0.1 | −0.5 | −0.1 | −0.2 | 0.9 | −0.4 | 0.8 | −0.3 | 2.0 |

**Table 5.**Correlation coefficient values between the NEM rainfall and the MEI at various concurrent and leading months.

Region | Mar. | Apr. | May | Jun. | Jul. | Aug. | Sep. | Oct. | Nov. | Dec. | Jan. | Feb. | Mar. |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

R1 | 0.232 | 0.215 | 0.269 | 0.053 | −0.118 | −0.222 | −0.267 | −0.342 | −0.435 | −0.478 | −0.489 | −0.537 | −0.536 |

R2 | 0.489 | 0.563 | 0.585 | 0.453 | 0.230 | 0.045 | −0.051 | −0.057 | −0.153 | −0.223 | −0.243 | −0.284 | −0.287 |

R3 | 0.308 | 0.344 | 0.348 | 0.203 | 0.002 | −0.143 | −0.244 | −0.236 | −0.319 | −0.393 | −0.394 | −0.444 | −0.470 |

R4 | 0.293 | 0.392 | 0.408 | 0.414 | 0.280 | 0.146 | 0.030 | 0.055 | −0.023 | −0.087 | −0.071 | −0.114 | −0.128 |

R5 | 0.396 | 0.439 | 0.395 | 0.363 | 0.259 | 0.166 | 0.100 | 0.147 | 0.059 | 0.045 | 0.056 | 0.012 | −0.011 |

R6 | 0.542 | 0.527 | 0.559 | 0.300 | 0.048 | −0.106 | −0.212 | −0.265 | −0.383 | −0.394 | −0.401 | −0.486 | −0.478 |

R7 | 0.426 | 0.451 | 0.523 | 0.335 | 0.153 | 0.046 | −0.036 | −0.112 | −0.247 | −0.235 | 0.201 | −0.258 | −0.264 |

R8 | 0.538 | 0.582 | 0.589 | 0.529 | 0.341 | 0.222 | 0.145 | 0.136 | 0.022 | 0.040 | 0.052 | −0.007 | −0.016 |

**Table 6.**Correlation coefficient values between the SWM rainfall and the MEI at various concurrent and leading months.

Region | Sep. | Oct. | Nov. | Dec. | Jan. | Feb. | Mar. | Apr. | May | Jun. | Jul. | Aug. | Sep. |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

R1 | 0.395 | 0.411 | 0.381 | 0.398 | 0.405 | 0.355 | 0.330 | 0.280 | 0.255 | 0.218 | 0.086 | −0.024 | −0.090 |

R2 | 0.462 | 0.423 | 0.425 | 0.451 | 0.448 | 0.419 | 0.401 | 0.380 | 0.382 | 0.213 | 0.040 | −0.117 | −0.219 |

R3 | 0.373 | 0.323 | 0.285 | 0.341 | 0.332 | 0.265 | 0.250 | 0.253 | 0.249 | 0.061 | −0.128 | −0.249 | −0.308 |

R4 | 0.527 | 0.450 | 0.371 | 0.389 | 0.371 | 0.328 | 0.322 | 0.288 | 0.201 | −0.060 | −0.205 | −0.375 | −0.464 |

R5 | 0.576 | 0.563 | 0.539 | 0.590 | 0.637 | 0.589 | 0.565 | 0.558 | 0.465 | 0.201 | −0.085 | −0.270 | −0.321 |

R6 | 0.292 | 0.279 | 0.212 | 0.252 | 0.254 | 0.223 | 0.242 | 0.209 | 0.205 | 0.040 | −0.156 | −0.280 | −0.326 |

R7 | 0.356 | 0.346 | 0.263 | 0.258 | 0.225 | 0.217 | 0.202 | 0.054 | −0.035 | −0.189 | −0.335 | −0.472 | −0.499 |

R8 | 0.505 | 0.446 | 0.340 | 0.381 | 0.390 | 0.329 | 0.294 | 0.239 | 0.130 | −0.094 | −0.254 | −0.378 | −0.423 |

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## Share and Cite

**MDPI and ACS Style**

Wong, C.L.; Liew, J.; Yusop, Z.; Ismail, T.; Venneker, R.; Uhlenbrook, S.
Rainfall Characteristics and Regionalization in Peninsular Malaysia Based on a High Resolution Gridded Data Set. *Water* **2016**, *8*, 500.
https://doi.org/10.3390/w8110500

**AMA Style**

Wong CL, Liew J, Yusop Z, Ismail T, Venneker R, Uhlenbrook S.
Rainfall Characteristics and Regionalization in Peninsular Malaysia Based on a High Resolution Gridded Data Set. *Water*. 2016; 8(11):500.
https://doi.org/10.3390/w8110500

**Chicago/Turabian Style**

Wong, Chee Loong, Juneng Liew, Zulkifli Yusop, Tarmizi Ismail, Raymond Venneker, and Stefan Uhlenbrook.
2016. "Rainfall Characteristics and Regionalization in Peninsular Malaysia Based on a High Resolution Gridded Data Set" *Water* 8, no. 11: 500.
https://doi.org/10.3390/w8110500