5. Applications

#### 5.1. Study region

Peninsular Malaysia is located between 1 and 7 north of the equator which is the tropical area. Generally, these areas experience a wet and humid tropical climate throughout the year; this country has characteristic such as high annual rainfall, humidity, and temperature. Peninsular Malaysia has a stable temperature year-round from 25.5 to 32C. Normally, the annual rainfall is between 2000 and 4000 mm, whereas the annual number of wet days ranges from 150 to 200.

The climate of Peninsular Malaysia describes two monsoons separated by two inter-monsoons. In May through September, the southwest monsoon (SWM) occurs and the northeast monsoon (NEM) occurs from November to March. The two inter-monsoons occur in April (FIM) and October (SIM). In Peninsular Malaysia, the main range mountains, widely known at the circumstances as Banjaran Titiwangsa, run southward from the Malaysia-Thai border in the north, spanning a distance of 483 km and separating the eastern part of the peninsula which receives heavy rainfall. By contrast, regions sheltered by the main range, as shown in Figure 1, are more or less free from its influence.

#### 5.2. Goodness of fit of NSRP

Table 2 provides information about the parameters of the NSRP model for rainfall occurring in November dan December for 48 terminals in Peninsular Malaysia. The NSRP model with parameters, which is identified for every terminal, and various statistic values are the initial foundation for contraction of the rainfall data. In particular, the mean and probability values


the number of statistics used in this model. whereas n is 8 representing the average of an hour rainfall; the variance used for the rainfall includes periods of 1, 6, and 24 h, autocorrelation lag 1 for 1-h rainfall, autocorrelation lag 1 for a 24-h rainfall scale, a probability of 1-h rainfall, and

Peninsular Malaysia is located between 1 and 7 north of the equator which is the tropical area. Generally, these areas experience a wet and humid tropical climate throughout the year; this country has characteristic such as high annual rainfall, humidity, and temperature. Peninsular Malaysia has a stable temperature year-round from 25.5 to 32C. Normally, the annual rainfall is between 2000 and 4000 mm, whereas the annual number of wet days ranges from 150 to 200.

The climate of Peninsular Malaysia describes two monsoons separated by two inter-monsoons. In May through September, the southwest monsoon (SWM) occurs and the northeast monsoon (NEM) occurs from November to March. The two inter-monsoons occur in April (FIM) and October (SIM). In Peninsular Malaysia, the main range mountains, widely known at the circumstances as Banjaran Titiwangsa, run southward from the Malaysia-Thai border in the north, spanning a distance of 483 km and separating the eastern part of the peninsula which receives heavy rainfall. By contrast, regions sheltered by the main range, as shown in Figure 1,

Table 2 provides information about the parameters of the NSRP model for rainfall occurring in November dan December for 48 terminals in Peninsular Malaysia. The NSRP model with parameters, which is identified for every terminal, and various statistic values are the initial foundation for contraction of the rainfall data. In particular, the mean and probability values

Southwest S1 0.025 93.70 2.56 0.116 2.23 0.012 56.82 7.88 0.078 1.48

East S5 0.012 12.69 4.34 0.068 3.03 0.021 11.94 3.22 0.197 3.08

λ E (X) E (C) β ηλ E (X) E (C) β η

S2 0.028 221.46 1.46 0.020 2.08 0.021 70.67 4.58 0.109 2.56 S3 0.033 15.58 1.39 0.221 2.22 0.012 5.96 5.70 0.081 2.16 S4 0.003 74.40 16.28 0.001 1.48 0.015 85.30 5.41 0.112 1.96

S6 0.022 5.28 5.77 0.098 2.23 0.012 4.31 15.23 0.081 2.13 S7 0.012 8.65 13.68 0.034 1.50 0.009 4.93 22.14 0.026 1.03 S8 0.027 89.30 2.80 0.193 2.31 0.012 94.99 10.56 0.097 2.22 S9 0.017 6.24 7.69 0.062 1.64 0.011 5.39 16.08 0.061 1.60

Region Station November December

a probability of 24-h rainfall scale.

64 Engineering and Mathematical Topics in Rainfall

are more or less free from its influence.

5.2. Goodness of fit of NSRP

5. Applications

5.1. Study region



Region Station November December

AO AE PO PE PO2 PE2 AO AE PO PE PO2 PE2

Analysis of Storm Rainfall in Peninsular Malaysia Using Neyman‐Scott Rectangular Pulse Modeling

http://dx.doi.org/10.5772/intechopen.70043

67

S19 0.40 0.52 0.14 0.13 0.71 0.61 0.48 0.69 0.20 0.23 0.64 0.53 S20 0.47 0.48 0.12 0.12 0.64 0.64 0.70 0.76 0.17 0.17 0.61 0.55

S22 0.38 0.38 0.11 0.11 0.65 0.66 0.16 0.16 0.05 0.05 0.39 0.39 S24 0.29 0.29 0.10 0.15 0.58 0.57 0.19 0.19 0.08 0.08 0.44 0.45 S25 3.80 3.74 0.12 0.12 0.68 0.71 2.37 2.45 0.08 0.08 0.48 0.46 S26 0.28 0.29 0.12 0.11 0.56 0.57 0.17 0.18 0.09 0.09 0.41 0.40 S28 0.43 0.43 0.16 0.15 0.75 0.79 0.27 0.26 0.11 0.11 0.55 0.58 S29 0.49 0.48 0.13 0.12 0.70 0.74 0.37 0.37 0.09 0.09 0.60 0.61 S30 2.70 2.63 0.09 0.09 0.56 0.58 1.81 1.79 0.10 0.10 0.51 0.51 S31 0.44 0.43 0.13 0.13 0.68 0.73 0.36 0.38 0.12 0.09 0.63 0.63 S32 4.14 4.13 0.11 0.11 0.73 0.73 2.16 2.13 0.07 0.07 0.51 0.52 S33 2.42 2.40 0.10 0.10 0.59 0.59 1.97 1.94 0.08 0.08 0.49 0.50 S34 3.21 3.17 0.14 0.14 0.65 0.66 2.43 2.45 0.12 0.12 0.56 0.55 S35 0.36 0.35 0.13 0.13 0.64 0.67 0.21 0.21 0.08 0.08 0.48 0.50 S36 4.67 4.64 0.11 0.11 0.77 0.78 3.15 3.15 0.10 0.10 0.63 0.63 S37 0.40 0.39 0.14 0.13 0.67 0.69 0.30 0.29 0.09 0.09 0.54 0.56 S38 2.99 2.98 0.11 0.11 0.63 0.63 2.47 2.42 0.09 0.09 0.53 0.55 S39 0.37 0.37 0.11 0.11 0.60 0.59 0.15 0.16 0.05 0.05 0.35 0.34 S40 0.24 0.23 0.12 0.11 0.56 0.59 0.16 0.16 0.08 0.08 0.40 0.41 S41 0.26 0.26 0.10 0.10 0.59 0.60 0.24 0.24 0.08 0.08 0.50 0.49 S42 0.37 0.37 0.12 0.12 0.65 0.66 0.20 0.20 0.07 0.07 0.43 0.44 S43 0.40 0.39 0.13 0.13 0.63 0.65 0.32 0.32 0.09 0.09 0.53 0.53 S44 0.45 0.44 0.15 0.15 0.71 0.74 0.26 0.25 0.09 0.09 0.57 0.58

West S21 0.18 0.18 0.09 0.09 0.46 0.45 0.12 0.13 0.05 0.05 0.33 0.32

Northwest S45 0.11 0.11 0.09 0.09 0.28 0.29 0.12 0.11 0.06 0.06 0.29 0.30

AO ¼ mean of 1-h rainfall (observed data), AE ¼ mean of 1-h rainfall (NSRP model), PO ¼ probability of 1-h rainfall (observed data), PE ¼ probability of 1-h rainfall (NSRP model), and PO2 ¼ probability of 24-h rainfall (observed data,

Table 3. The representation of the statistics which are estimated from the NSRP model compared with the statistic

PE2 ¼ probability of 24-h rainfall (NSRP model).

obtained from the analyzed data for the 48 raingauge terminal.

S46 0.37 0.38 0.17 0.17 0.57 0.57 0.23 0.28 0.10 0.10 0.40 0.34 S47 0.15 0.15 0.08 0.08 0.38 0.38 0.10 0.10 0.04 0.04 0.25 0.25 S48 0.25 0.25 0.13 0.13 0.58 0.57 0.13 0.13 0.07 0.07 0.36 0.35 S49 2.45 2.08 0.10 0.10 0.51 0.85 0.96 0.90 0.07 0.07 0.21 0.22 S50 0.25 0.25 0.10 0.10 0.50 0.50 0.09 0.09 0.04 0.04 0.29 0.29

Table 2. List of NSRP parameter for the 48 raingauge stations.

of the 1- and 24-h rainfall amount are then calculated. To describe the condition of a data set, these statistics are chosen.

To control how well the representation of the rainfall data is made by the NSRP model obtained, the mean of the 1-h rainfall and the probabilities of the 1- and 24-h rainfall estimated from the model are compared with these statistics values calculated from the observed data. Part of the results, focusing on the month of November and December only, is displayed in Table 3. It can be seen that there are no major differences between the estimated and the observed values of the statistics of interest.


Analysis of Storm Rainfall in Peninsular Malaysia Using Neyman‐Scott Rectangular Pulse Modeling http://dx.doi.org/10.5772/intechopen.70043 67


of the 1- and 24-h rainfall amount are then calculated. To describe the condition of a data set,

λ E (X) E (C) β ηλ E (X) E (C) β η

AO AE PO PE PO2 PE2 AO AE PO PE PO2 PE2

S2 3.45 4.38 0.11 0.06 0.64 0.61 2.73 2.67 0.11 0.11 0.56 0.58 S3 0.29 0.32 0.10 0.06 0.58 0.58 0.19 0.19 0.08 0.08 0.44 0.45 S4 2.61 2.58 0.09 0.08 0.55 0.69 2.80 3.52 0.11 0.10 0.49 0.47

S6 0.29 0.30 0.14 0.14 0.66 0.63 0.32 0.36 0.16 0.17 0.60 0.52 S7 0.89 0.93 0.20 0.20 0.70 0.69 0.96 0.98 0.27 0.27 0.72 0.72 S8 2.91 2.96 0.09 0.09 0.58 0.57 4.23 5.62 0.13 0.13 0.58 0.48 S9 0.47 0.50 0.16 0.16 0.69 0.64 0.54 0.61 0.18 0.19 0.66 0.57 S10 0.70 0.76 0.21 0.21 0.73 0.65 0.71 0.76 0.21 0.22 0.64 0.58 S11 0.36 0.37 0.12 0.12 0.68 0.66 0.32 0.34 0.14 0.14 0.56 0.52 S12 6.68 7.66 0.20 0.20 0.70 0.59 8.31 9.42 0.26 0.27 0.68 0.58 S13 3.18 3.25 0.10 0.10 0.65 0.62 3.43 3.60 0.11 0.11 0.56 0.53 S14 0.45 0.45 0.15 0.15 0.73 0.73 0.58 0.69 0.19 0.20 0.69 0.57 S15 0.78 0.80 0.22 0.22 0.79 0.78 1.00 1.04 0.29 0.29 0.73 0.73 S16 0.53 0.58 0.19 0.20 0.65 0.57 0.74 0.88 0.22 0.23 0.57 0.46 S17 0.28 0.28 0.10 0.10 0.60 0.60 0.30 0.29 0.12 0.12 0.57 0.58 S18 0.80 0.83 0.21 0.21 0.75 0.74 0.97 1.45 0.25 0.26 0.75 0.74

S47 0.012 5.04 5.97 0.127 2.39 0.006 9.60 5.57 0.097 3.34 S48 0.022 4.11 5.16 0.147 1.89 0.008 3.62 6.92 0.080 1.51 S49 0.078 67.53 1.01 0.179 2.57 0.003 16.50 22.20 0.048 1.09 S50 0.011 6.89 8.10 0.061 2.43 0.008 7.68 4.09 0.098 2.86

Region Station November December

Region Station November December

To control how well the representation of the rainfall data is made by the NSRP model obtained, the mean of the 1-h rainfall and the probabilities of the 1- and 24-h rainfall estimated from the model are compared with these statistics values calculated from the observed data. Part of the results, focusing on the month of November and December only, is displayed in Table 3. It can be seen that there are no major differences between the estimated and the

Southwest S1 2.74 2.72 0.08 0.08 0.57 0.57 3.31 3.54 0.11 0.12 0.51 0.47

East S5 0.23 0.22 0.06 0.06 0.42 0.44 0.27 0.26 0.08 0.07 0.47 0.49

these statistics are chosen.

66 Engineering and Mathematical Topics in Rainfall

observed values of the statistics of interest.

Table 2. List of NSRP parameter for the 48 raingauge stations.

AO ¼ mean of 1-h rainfall (observed data), AE ¼ mean of 1-h rainfall (NSRP model), PO ¼ probability of 1-h rainfall (observed data), PE ¼ probability of 1-h rainfall (NSRP model), and PO2 ¼ probability of 24-h rainfall (observed data, PE2 ¼ probability of 24-h rainfall (NSRP model).

Table 3. The representation of the statistics which are estimated from the NSRP model compared with the statistic obtained from the analyzed data for the 48 raingauge terminal.
