Statistical downscaling model was used to generate 30-year climate trend of Kedah – the state which has the largest cultivation area in Malaysia, resulting from climate changes. To obtain a better predictors set, multicorrelation matrix analysis was added in the climate model as a screening tool to explain the multiple correlation relationship among 26 predictors and 20 predictands. The performance of the predictor set was evaluated statistically in terms of mean absolute error, mean square error, and standard deviation. The simulation results depict the climatic changing trend in this region in terms of temperature, rainfall, and wet and dry length compared to historical data captured from 1961 to 2008. Annual temperature and rainfall depth are expected to increase 0.2 °C per decade and 0.9% per year, respectively, from the historical record. The months of November and January are expected to receive the highest and lowest rainfall depth, respectively, because of the two monsoon seasons. The wet spell is estimated to be from May to November in the middle of Kedah. The annual dry spell shall be from January to March, and is expected to shorten yearly.

INTRODUCTION

Climate refers to the average weather recorded in a year; it is generally affected by geographic factors such as the oceans and the altitude of the region. Changes in climate occur when the accumulated heat absorbed by the existing greenhouse gases, such as nitrous oxide, oxygen, methane, water vapor, carbon dioxide, and tropospheric ozone, increases and accelerates global warming. As reported by the Intergovernmental Panel on Climate Change AR4 (IPCC 2011), the global average surface temperature for the past 100 years has increased from 0.6 °C (1901–2000) to 0.74 °C (1906–2005). The World Meteorological Organization claimed the year 2010 as the warmest year where an increase of 1.2–1.4 °C had been recorded particularly in Africa, parts of Asia, and parts of the Arctic. Since 1961, more than 80% of the heat had been added to the climate system, causing the average temperature of the ocean to increase at 3,000 m depth; the rate of observed sea level rise is estimated to be 0.17 m in the 20th century.

In Malaysia, global warming has a serious impact on the land and water resources, agricultural activities, and hydrological cycle. As claimed by the Department of Irrigation and Drainage, Malaysia, more than 70% of water use in Malaysia is allocated for irrigation purposes and the remaining 30% is for domestic, industry, and other demands. Unfavorable calamitous events caused by climate changes jeopardize the agricultural sectors by, for example, affecting the cultivation growth, destroying machinery, inducing losses, and affecting agricultural production. Data have shown that a previous calamity on the irrigation area during years 1989–2010 destroyed thousands hectares of paddies, rubbers, and vegetables in several states in Malaysia such as Perlis, Kedah, and Penang which then escalated into food scarcity problems and millions Ringgit losses.

In terms of annual precipitation, Bates et al. (2008) have proved that the volume of water resources for our society and ecosystems is strongly correlated to climate change. Unusual warms change the water volume stored in reservoirs due to unpredictable rain availability, timing, and water quality. In the United States, Izuka et al. (2005) stated the storage of the Garlinghouse Tunnel had decreased around 50% since the 1980s; the storage depends fully on the precipitation and river runoff during wet periods. When global warming escalates, it increases the water evaporation rate and may easily lead to water storage loss of more than 20% of the average annual runoff. This becomes especially worrying since the valley width is impounded and has larger water open area (FAO 1991). This also means that water storage has turned into an uncontrollable factor when rainfall patterns become unpredictable, and the compounded effects are felt by all sectors.

For the agriculture sector, more uncertainties in climate make it increasingly difficult to predict and plan the irrigation demand even for the following month. New approaches are therefore needed to gauge changes in rainfall directions, duration, trending, and depth. This has been largely addressed using various climate models developed to primarily understand the changes in present/future climatic conditions with due consideration on greenhouse gases and aerosols emission (Goosse et al. 2013). Such models also assist in evaluating regional surface water response and reservoir system capacity to meet local demands (Yano et al. 2007).

This case study introduces a statistical downscaling model (SDSM) which has been used to generate the current/future local climate trend. SDSM models have been widely applied for hydrological issues caused by climate scenarios because it provides station-scale climate information at general circulation model (GCM) scale between atmospheric circulation pattern (predictors) and local-scale parameters (predictands) using multiple regression techniques. It is a popular tool among researchers since it can portray an easily understandable relationship pattern between predictor and predictand. Besides, the model does not require high computational demand to view the simulation result because the output is presented in finer resolutions. To conclude, it is cost-effective and gives satisfactory climate simulation with its capability and reliability proven by researchers such as Sharma et al. (2011), Khan et al. (2006), Guttman et al. (2012), Lopes (2009), and Spak et al. (2007).

METHODOLOGY

Climate model

Statistical downscaling is analogous to the model output statistic and perfect program approach used for short-range numerical weather prediction (Wilby & Dawson 2007). The model was developed by Robert L. Wilby and Christian W. Dawson from the United Kingdom and uses a weather generator method to produce a multiple realization of the synthetic daily weather sequence. This software calculates the statistical relationship based on multiple regression techniques between large scale (predictor) and local climate (predictand). Table 1 gives the list of predictor and predictand combinations used in this study.

Table 1

List of predictor and predictand combinations

No.Predictor variablesPredictor descriptionPredictand variables (station)Predictor description
Mlsp Mean sea level pressure GM Gajah Mati 
p_f Surface airflow strength IBT Ibu Bekalan Tupah 
p_u Surface zonal velocity KP Kedah Peak 
p_v Surface meridional velocity KT Keretapi Tokai 
p_z Surface vorticity Kod Kodiang 
p_th Surface wind direction KSS Kota Sarang Semut 
p_zh Surface divergence Pen Pendang 
p5_f 500 hPa airflow strength SL Sungai Limau 
p5_u 500 hPa zonal velocity TC Telok Chengai 
10 p5_v 500 hPa meridional velocity LTP Ladang Tanjung Pauh 
11 p5_z 500 hPa vorticity KN Kuala Nerang 
12 p500 500 hPa geopotential height Kg.T Kg. Terabak 
13 p5th 500 hPa wind direction Sik Sik 
14 p5zh 500 hPa divergence Kg.LB Kg. Lubok Badak 
15 p8_f 850 hPa airflow strength Kg.LS Kg. Lubok Segintah 
16 p8_u 850 hPa zonal velocity KSS Kuala Sala 
17 p8_v 850 hPa meridional velocity LH Ladang Henrietta 
18 p8_z 850 hPa vorticity SG SM Gurun 
19 p850 850 hPa geopotential height Jit Jitra 
20 p8th 850 hPa wind direction AP Ampang Pedu 
21 p8zh 850 hPa divergence   
22 r500 Relative humidity at 500 hPa   
23 r850 Relative humidity at 850 hPa   
24 Rhum Near surface relative humidity   
25 Shum Surface specific humidity   
26 Temp Mean temperature   
No.Predictor variablesPredictor descriptionPredictand variables (station)Predictor description
Mlsp Mean sea level pressure GM Gajah Mati 
p_f Surface airflow strength IBT Ibu Bekalan Tupah 
p_u Surface zonal velocity KP Kedah Peak 
p_v Surface meridional velocity KT Keretapi Tokai 
p_z Surface vorticity Kod Kodiang 
p_th Surface wind direction KSS Kota Sarang Semut 
p_zh Surface divergence Pen Pendang 
p5_f 500 hPa airflow strength SL Sungai Limau 
p5_u 500 hPa zonal velocity TC Telok Chengai 
10 p5_v 500 hPa meridional velocity LTP Ladang Tanjung Pauh 
11 p5_z 500 hPa vorticity KN Kuala Nerang 
12 p500 500 hPa geopotential height Kg.T Kg. Terabak 
13 p5th 500 hPa wind direction Sik Sik 
14 p5zh 500 hPa divergence Kg.LB Kg. Lubok Badak 
15 p8_f 850 hPa airflow strength Kg.LS Kg. Lubok Segintah 
16 p8_u 850 hPa zonal velocity KSS Kuala Sala 
17 p8_v 850 hPa meridional velocity LH Ladang Henrietta 
18 p8_z 850 hPa vorticity SG SM Gurun 
19 p850 850 hPa geopotential height Jit Jitra 
20 p8th 850 hPa wind direction AP Ampang Pedu 
21 p8zh 850 hPa divergence   
22 r500 Relative humidity at 500 hPa   
23 r850 Relative humidity at 850 hPa   
24 Rhum Near surface relative humidity   
25 Shum Surface specific humidity   
26 Temp Mean temperature   

These relationships were developed using observed weather data and previously captured relationships among GCM-derived predictors. This produces the maximum, mean, and minimum temperature, the precipitation and humidity of site-specific daily scenarios for a selected region, and the range of statistical parameters such as variance and frequencies of extremes. The SDSM downscaling used two types of data, viz. predictand and predictor at the grid box of 28X × 33Y.

The historical rainfall data (1961–2008) recorded at 20 locations were used as the predictands while the National Center for Environmental Prediction (NCEP) reanalysis data and GCM outputs of the Hadley Center General Circulation Model (HadCM3) under A2 scenario were used as the predictor to simulate climate trend. The HadCM3 model was a modified version of HadCM2, done to improve the accuracy of the climate projection results without the application of flux adjustments. It has wider coarse spatial resolutions of 2.5° × 3.75° (latitude by longitude) which can be applied in many climatic regional studies. Samadi et al. (2010) consider HadCM3 to be the best GCM model, i.e., superior to other models such as CCSNIES, CSRIO (Australian Government), and Geophysical Fluid Dynamics Laboratory (GFDL). In this study, the A2 scenario had been chosen to give an upper bound on future emissions, and was selected from an impacts-and-adaptation point of view – if a system could adapt to large climate change, it would have no problem with smaller climate change and a lower end scenario. The only setback is that low emissions scenario will have less information when such is the case (NARCCAP 2007).

Fundamentally, the screening process in the SDSM model uses the multiple linear regression (MLR) concept to express the predictor–predictand relationship, but it can only analyse multiple predictors with a single predictand in a single simulation run. With this, it poses a challenge in choosing the predictors that will correlate well with all climate stations (predictand) in a study region. In this study, there were 20 rainfall stations surrounding the study area that could forecast different climate trends at different locations. To screen, analyse, and select the predictor set that could represent all rainfall stations would be a tedious task. Besides, the rainfall pattern in Malaysia is non-uniform and very sensitive to the GCM parameters that may influence the rainfall depth. As such, though the MLR concept was adopted, the focus was on multiple-predictand to multiple-predictor relationships, presented in a multiple correlation matrix or M-CM analysis. The purpose was to measure and clearly state the empirical relationship between 26 predictors and 20 predictands. Figure 1 illustrates the schematic diagram of SDSM model that was adopted in this study (see Table 1 for full list of predictors and predictands).

Figure 1

Schematic diagram of SDSM analysis (Wilby & Dawson 2007).

Figure 1

Schematic diagram of SDSM analysis (Wilby & Dawson 2007).

Predictor selection in SDSM model – multicorrelation matrix method

The M-CM method is a multiple regression method famous for defining the input variables between historical seasonal average rainfall occurrence probabilities and GCM's simulated seasonal mean rainfall depth. It can measure the linear relationship between multiple dependent variables and multiple independent variables, and therefore assist in gauging the interaction of data from two sets of variables and their inter-relationship. M-CM was applied in this study to screen the potential of multiple predictands and predictors and the associations between them. The concept was to show the physical interpretation of the connection between local surface climate (predictand) and possible predictors in multi-site. The generated correlation value shows the percentage of variance that can be explained in the form of multi-dependent variables using the multi-independent variables. It also gives the criterion variables (product innovation variables) among the relationships. Thus, the predictor selection was based on the inter-variable correlation value showed in m × m matrix form. The formula for the correlation matrix is: 
formula
1
 
formula
2
 
formula
3
where and refer to the predictands and predictors data at i and j raw; is the mean value of both variables, and and refer to their standard deviation (StD). Basically, the capability among variables was interpreted as values between −1 and 1 to show positive/negative association. The positive value illustrates how strong the predictand–predictor relationship is in the transformation and vice versa.

Performance evaluations

To evaluate the calibration and validation performance, the mean absolute error (MAE), mean square error (MSE), and StD were computed between observed and simulated climate variables over the 100 simulations. The function of MAE is to measure the accuracy of continuous variables through the average of errors between the two datasets. It is a relatively simple and common mathematic measurement that has been widely employed in forecasts. In this case, the average error represents the general disparity of two datasets, measured through quadratic scoring rules. MSE measures the average squared difference between the estimated and observed value (accuracy), and the variability of the actual data (precision). The difference between MAE and MSE is that each error in MSE is presented in square value; a larger error has a greater influence on the total square error and vice versa (Willmott & Matsuura 2005). MSE provides partial information to choosing better estimators. On the other hand, StD measures the spread and distribution of a data estimation. It can show the range of variation from the observed mean value for the entire dataset. Smaller values of StD indicate that the majority of estimation data are very close to the mean of the estimation dataset. The formulae of these mathematical statistics are presented in Table 2.

Table 2

List of statistical test equations

NameFormulaDescription
MAE  Average error between two variables 
MSE  Average squared error between two variables 
StD  Standard deviation 
NameFormulaDescription
MAE  Average error between two variables 
MSE  Average squared error between two variables 
StD  Standard deviation 

STUDY AREA

The study area is the Malaysian Muda Irrigation Scheme under the Muda Agricultural Development Authority (MADA, Kedah state), which is made up of important areas that cultivate paddy – the staple food of Malaysians. Covering 97,000 ha, it is the largest double paddy cultivation area in Malaysia.

Geographically, the area lies between 5°45′–6°30′N latitude and 100°10′–100°30′E longitude. The topography of the area is almost flat with a slope ranging from 1 in 5,000 to 1 in 10,000. The climate of the area, like other parts of Malaysia, can be classified into four seasons viz. south-west monsoon (May–September), north-east monsoon (November–March), and two inter-monsoon seasons. December–February and June–July are warmer seasons while April–May and September–November are humid seasons. The type of soil in the study area is heavy clay in nature. The mean temperature varies between 27 and 32 °C. The average rainfall depth is 199 mm/month and 2,390 mm/year. The relative humidity in this area fluctuates between 54 and 94%. The 20 rainfall stations that had been identified were chosen from the quality of available rainfall records and their location in the Muda Irrigation Scheme area. To support irrigated water demand efficiently, two important reservoirs were built by the government to store and supply water for the paddy cultivation area (96,558 ha), namely the Pedu Reservoir and the Muda Reservoir. These systems are jointly called the Pedu-Muda reservoir (see Figure 2).

Figure 2

Geographic map of Kedah state.

Figure 2

Geographic map of Kedah state.

RESULTS AND DISCUSSION

Temperature simulation result

The simulation of temperature data (predictand) referred to the meteorological station at Alor Setar. It was assumed that the recorded temperature at Alor Setar meteorological station could represent the temperature trend at Kedah state. Contrary to the rainfall trend, the temperature difference between districts in Kedah is very small and almost distributed uniformly across all surrounding states. The pattern of temperature is also consistent even at different locations. Because of the very small difference in temperature, the temperature reading at Alor Setar station was taken to represent the temperature trend at Kedah.

In the SDSM screening results, five predictors were selected to generate the temperature trend at the study site: surface airflow strength (p_f); 500 hPa geopotential height (p500); relative humidity at 500 hPa (r500); relative humidity at 850 hPa (r850); and mean temperature at 2 m (temp). The rationale was that the temperature record had better interconnection with these five atmospheric characteristics since it produced higher correlation values within the range of 0.3–0.5 compared to the remaining 21 available predictors. Figure 3 shows the potential of selected predictors associated with the local temperature station in calibrated (1972–1986) and validated (1987–2001) process using predictor sets from NCEP for three conditions – maximum, mean, and minimum temperature.

Figure 3

Calibrated and validated results of temperature at station Alor Setar.

Figure 3

Calibrated and validated results of temperature at station Alor Setar.

The line graphs showed that the selected predictors were close to the monthly observed data. However, the estimated minimum temperature during the validation process was slightly lower than observed record during January to September. Table 3 summarizes the performances of calibration and validation results. The error value was within the range of 0.1 °C (October and December) to 0.9 °C (January). As for the max and mean temperature, the error range was only within the range of 0.1–0.4 °C. These results produced very small values in MAE and MSE in the whole analysis, ranging from 0.0 to 0.5 °C. The correlation values had been estimated higher, recorded at 0.9 and 1.0 for calibrated and validated analyses, respectively. This shows that the calibrated and validated values were in good agreement with historical record. Therefore, the projection analysis results produced by the SDSM model are reliable and acceptable in this stage.

Table 3

Performance of calibrated and validated results for temperature at station Alor Setar

 Maximum
Mean
Minimum
CalibrationValidationCalibrationValidationCalibrationValidation
r 0.9 1.0 0.9 0.9 0.9 0.9 
MAE 0.5 0.1 0.3 0.4 0.2 0.2 
MSE 0.4 0.1 0.2 0.1 
 Maximum
Mean
Minimum
CalibrationValidationCalibrationValidationCalibrationValidation
r 0.9 1.0 0.9 0.9 0.9 0.9 
MAE 0.5 0.1 0.3 0.4 0.2 0.2 
MSE 0.4 0.1 0.2 0.1 

To generate the future temperature trend in this area, the GCM output type HadCM3-A2 was used with the constant predictors set. These predictors represent the changes of the atmospheric pattern response to the greenhouse gases effect based on the level of regional development. The temperature simulation and projection trend are showed in Figure 4. The future temperature shows that the monthly temperature reading is estimated to increase in all temperature conditions; minimum (+1.8%), mean (+3.6%), and maximum (+4.5%). The annual mean temperature in the future (2040–2069) is expected to increase to 28.3 °C with +0.2 °C from the current temperature reading in year 2010. A higher temperature is predicted in February and March; this may be affected by the interchange of north-east monsoon to the south-west monsoon. Therefore, it can be concluded that the average temperature of the study area will continue to rise by 0.2 °C per decade. The results are consistently similar to the peninsular Malaysia climatic report by Meteorological Department Malaysia, where the warmest season will be in December–January–February (DJF) at the end of the century and the temperature increment will be 1.1–3.0 °C.

Figure 4

Simulated and projected (years 2040–2069) temperature trend at Alor Setar meteorological station.

Figure 4

Simulated and projected (years 2040–2069) temperature trend at Alor Setar meteorological station.

Rainfall simulation result

The analysis of rainfall trend was more complex than the temperature analysis. The reason is Malaysia has a non-uniform distribution of rainfall which produces different water quantity at different locations in the same day. Thus, the 20 rainfall stations selected surrounding Kedah state became significant in recognizing the rainfall distribution pattern of Kedah. While selecting the associated predictors for these rainfall stations, M-CM analysis was added into the SDSM model to screen the relationships among the 26 predictors and 20 predictands already presented in a correlation matrix form. The purpose was to view the physical interpretation of the connection between local surface climate (predictand) and multiple sites in order to identify better predictors for climate projection.

Table 4 shows that five predictors have been selected to develop the statistical relationship between the local and regional scale of climate association, i.e., 500 hPa zonal velocity (p5_u); airflow strength (p_f); 500 hPa relative humidity (r500); 850 hPa meridional velocity (8_v); and specific humidity (shum). The combination of these five selected predictors successfully modeled the relationships with the local stations, with a correlation range of between −0.08 and +0.20, and was better than the remaining 21 predictors. Figure 5 shows the calibrated and validated results for 20 rainfall stations in this region. Results showed that most stations had been calibrated and validated close to observed values except at a few rainfall stations, namely IBT, KT, SL, SIK, SG, and Kg.LS. These were attributed to the underestimated correlation of the predictor–predictand relationship. These areas are expected to have poor validation during February, March, April, and December; this will influence the pattern of climatic trend in future years. Even though the simulated rainfall did not correlate well with the observed rainfall, the simulated patterns were able to consistently preserve the historical pattern throughout the year. Thus, it is concluded that the selected predictors can accurately simulate the rainfall with local predictands.

Table 4

Correlation values between rainfall station and five climate variables

 p8_vr500p_fShump5_u p8_vr500p_fShump5_u
IBT 0.13 0.13 0.15 0.17 0.17 TC 0.17 0.13 0.20 0.17 0.18 
KT −0.01 0.09 0.02 0.12 0.08 KLB −0.01 0.12 0.02 0.13 0.10 
Kg.T 0.15 0.12 0.17 0.16 0.17 KP 0.04 0.05 0.05 0.03 0.02 
LTP 0.13 0.12 0.15 0.17 0.18 Jit 0.09 0.06 0.12 0.12 0.10 
AP 0.17 0.14 0.18 0.15 0.17 Kg.LS 0.02 0.07 0.03 0.05 0.05 
SL 0.18 0.12 0.19 0.17 0.20 KN 0.10 0.12 0.11 0.17 0.14 
GM 0.10 0.11 0.13 0.17 0.15 KS 0.12 0.07 0.16 0.13 0.10 
KOD 0.14 0.10 0.16 0.16 0.16 LH 0.05 0.04 0.08 0.10 0.07 
KSS 0.16 0.13 0.18 0.17 0.19 SIK 0.03 0.11 0.06 0.12 0.11 
PEN 0.12 0.11 0.15 0.18 0.18 SG −0.08 −0.04 −0.07 −0.10 −0.02 
 p8_vr500p_fShump5_u p8_vr500p_fShump5_u
IBT 0.13 0.13 0.15 0.17 0.17 TC 0.17 0.13 0.20 0.17 0.18 
KT −0.01 0.09 0.02 0.12 0.08 KLB −0.01 0.12 0.02 0.13 0.10 
Kg.T 0.15 0.12 0.17 0.16 0.17 KP 0.04 0.05 0.05 0.03 0.02 
LTP 0.13 0.12 0.15 0.17 0.18 Jit 0.09 0.06 0.12 0.12 0.10 
AP 0.17 0.14 0.18 0.15 0.17 Kg.LS 0.02 0.07 0.03 0.05 0.05 
SL 0.18 0.12 0.19 0.17 0.20 KN 0.10 0.12 0.11 0.17 0.14 
GM 0.10 0.11 0.13 0.17 0.15 KS 0.12 0.07 0.16 0.13 0.10 
KOD 0.14 0.10 0.16 0.16 0.16 LH 0.05 0.04 0.08 0.10 0.07 
KSS 0.16 0.13 0.18 0.17 0.19 SIK 0.03 0.11 0.06 0.12 0.11 
PEN 0.12 0.11 0.15 0.18 0.18 SG −0.08 −0.04 −0.07 −0.10 −0.02 
Figure 5

Validation result between observed and simulated data for 20 rainfall stations.

Figure 5

Validation result between observed and simulated data for 20 rainfall stations.

Since these predictors correlated well with 13 other rainfall stations, for example, the simulation of GM, KSS, Kod, LTP, Pen, KS, Kg.T, KN, KS, and AP were very close to observed values with minor errors, they were retained. And besides, atmospheric characters such as specific humidity, airflow indices, zonal velocity, and relative humidity have always been used as precipitation predictors (Crane & Hewitson 1998; Goodess & Palutikof 1998; Wilby & Wigley 2000; Willems & Vrac 2011).

In general, the rainfall distribution is non-uniform because each location receives different rainfall depth, but within the range of 5–30 mm/day or 1,645–3,335 mm/year. To evaluate the predictor set's performance, Table 5 shows the MAE, MSE, and StD for each rainfall station between the historical data and simulated results. As expected in correlation results, stations Kg.LS, SIK, Kg.LB, and SG had higher errors in the simulated results of more than 2.0 mm/day in MAE and 7.0 mm/day in MSE. The error was around ±20%, and might have been caused by the frail association with p_5u and 8_v due to the smaller coefficient values produced at these rainfall stations. The attributes of U and V in the atmospheric parameters represent the direction of wind speed that would directly influence the advection moisture at the region (Jinqiang & Simona 2008). Report by the Meteorological Department Malaysia (MDM, Malaysia) has stated that the rainfall amount in Malaysia is influenced by the strength of the wind flow coming from western Pacific toward the South China Sea during the winter monsoon. Therefore, these predictors sets were still used to develop the future climate trend at this region.

Table 5

Statistical performance comparison MAE, MSE, and StD (mm/day)

Rainfall stationKg.LBAPGMIBTJitKg.LSKg.TKodKPKSS
MAE 2.4 0.6 0.9 1.9 0.9 2.0 1.4 0.5 1.9 0.8 
MSE 6.7 0.8 1.1 5.2 3.5 10.8 2.2 0.5 5.2 1.2 
StD 1.7 0.4 0.7 1.3 0.7 1.4 1.0 0.4 1.3 0.6 
Rainfall stationKTKNKSLHLTPPenSIKSLSGTC
MAE 1.8 1.0 1.1 1.4 0.6 0.8 3.1 1.6 2.1 1.2 
MSE 5.2 1.7 1.2 3.4 0.6 1.1 10.9 3.7 7.3 1.9 
StD 1.2 0.7 0.7 1.0 0.5 0.6 2.2 1.1 1.5 0.8 
Rainfall stationKg.LBAPGMIBTJitKg.LSKg.TKodKPKSS
MAE 2.4 0.6 0.9 1.9 0.9 2.0 1.4 0.5 1.9 0.8 
MSE 6.7 0.8 1.1 5.2 3.5 10.8 2.2 0.5 5.2 1.2 
StD 1.7 0.4 0.7 1.3 0.7 1.4 1.0 0.4 1.3 0.6 
Rainfall stationKTKNKSLHLTPPenSIKSLSGTC
MAE 1.8 1.0 1.1 1.4 0.6 0.8 3.1 1.6 2.1 1.2 
MSE 5.2 1.7 1.2 3.4 0.6 1.1 10.9 3.7 7.3 1.9 
StD 1.2 0.7 0.7 1.0 0.5 0.6 2.2 1.1 1.5 0.8 

Based on the StD results, the simulated value spread was to the mean of historical data. The biggest value was 2.2 mm/day, which was obtained from station SIK, and the lowest value was 0.4 mm/day, recorded at station AP and Kod. The results illustrated that the rainfall stations estimated correlated well with the observed data, except for stations Kg.LS, SIK, Kg.LB, and SG. It would appear that the predictor selection based on M-CM analysis was satisfactory. The error produced was also low and within reasonable range at most rainfall stations. The accuracy of the simulated data also proved that the selection of predictors at each location was important for the calibration process. These predictors shall be used to develop climate trend for future years.

Figure 6 shows the projection of annual rainfall distribution from year 2040 to 2069 using the GCMs model which represented the physical atmosphere using numerical data. The future trending was generated at every rainfall station using the predictor set provided by the HadCM3-A2 scenario. As can be seen in the map (Figure 6), the maximum annual rainfall is estimated to reach 4680 mm/year at Kuala Muda and west of Sik district. The rainfall depth is estimated to spread from Kuala Muda to the nearby areas such as Pendang, Sik, several parts in Kota Setar, Padang Terap, and Baling. Most of the districts are anticipated to experience higher rainfall amounts from the previous interval year, except at Yan and west of Pendang. The minimum rainfall amount is predicted to be about 1,900 mm/year, an 11.0% increase from the historical minimum rainfall. The average annual rainfall for this interval year is predicted to be 2,944 mm/year, a 22% rise compared to the historical year. The monthly rainfall of this year is predicted to be consistent with the historical year, but with high monthly rainfall depth. November is expected to receive the highest depth compared to September (this is the peak month in historical data). The main reason for this transition of trend will be the wind during the north-east monsoon in east Malaysia. The minimum rainfall is predicted to fall in January (16 mm/month) at station Kodiang and the maximum rainfall to be in November (892 mm/month) at station Ibu Bekalan Tupah.

Figure 6

Annual rainfall trend at Kedah state during years 2040–2069.

Figure 6

Annual rainfall trend at Kedah state during years 2040–2069.

Rainfall projection at the Pedu-Muda reservoir area

The projection of climate for the purpose of better reservoir management was based on stations 61 and 66, which represent the Pedu reservoir and Muda reservoir areas, respectively. Generally, the monthly rainfall trend at these regions is predicted to be different because of the reservoir locations, even when both reservoirs are only 7 km apart. Figure 7(a) and 7(b) shows the average monthly rainfall trend during years 2040–2069 at stations 61 and 66, respectively. The projection result in station 61 shows that the future rainfall depth will drop, except in August and November. The highest and lowest concentration will be in August and January, respectively; this is different from the historical record where the highest amount is in October. The average annual rainfall during this year is predicted to achieve 2,565 mm/year, 8% more than the historical record.

Figure 7

Annual rainfall trend at (a) station 61 (Pedu reservoir), and (b) station 66 (Muda reservoir).

Figure 7

Annual rainfall trend at (a) station 61 (Pedu reservoir), and (b) station 66 (Muda reservoir).

Meanwhile, the rainfall projection results at station 66 shows a different pattern from that of station 61. In the future, the rainfall depth will increase in February, April, May, and November while decrease slightly in other months. The highest rainfall depth will be in November and not October as indicated in the historical data. The future rainfall trend will affect the expected increment in contaminant into the earth systems due to development. However, January will still receive the least rainfall depth; the estimated average annual rainfall is 2,382 mm/year, −5% from previous record (1997–2008).

Wet and dry length

A wet day is defined as a day with at least 1 mm of rainfall depth meanwhile a dry day receives less than 0.1 mm of rainfall depth. The depth and pattern of rainfall in Malaysia are influenced by the south-west monsoon (May–September), the north-east monsoon (November–March), and two inter-monsoon seasons. December–February and June–July are the warmer seasons while April–May and September–November are the humid seasons.

Future wet length is estimated to be longer than the historical record at several months. Figure 8 illustrates the wet length distribution in the region on average from year 2040 to 2069. The frequency of rainy days is quite similar to the historical pattern, whereas the rainy season comes from May to November and the peak time is from September to October (mostly 30 days). Then, it is expected to decrease from January to March with only 5–20 days of rain. The rainfall stations at Kodiang and Kedah Peak had the least wet days in a month while the rainfall stations at Ibu Bekalan Tupah, Keretapi Tokai, Kuala Nerang, and Pendang had the most rainy days.

Figure 8

Wet length distribution at Kedah for years 2040–2069.

Figure 8

Wet length distribution at Kedah for years 2040–2069.

Exposure to calamitous events increases with prolonged wet length, gauged in terms of rainfall volume per hour. The Meteorological Department in Malaysia categorizes rainfall depth into four groups, namely light (1–10 mm), moderate (11–30 mm), heavy (31–60 mm), and very heavy (more than 60 mm). Flash flood is expected to occur if the convective rainfall is more than 60 mm in 2–4 hours. Cheang et al. (1986) stated that most rainfall in Malaysia occurs in short spells. However, other supported indices are required in measuring the potential of flood or drought at the study area, such as stream flow, groundwater indices, and at-site indices (Henny et al. 2008).

Figure 9 shows the average maximum dry spell length predicted at the 20 locations from year 2040 to 2069. Generally, the dry spell length is estimated to reduce annually at most locations. This pattern is similar to the historical record, which showed that December to March had longer dry length than other months. The longest dry length is expected at station Kodiang with 116 days recorded in a year and February being the hottest month in years 2057, 2060, 2062, 2065, and 2066. A similar trend is also expected at station Jitra, where more than 90 days of dry spell is expected in a year. Station LTP is expected to only have 10 days of dry spell a year. Based on the average simulated result, no dry day is expected from May to November at this location.

Figure 9

Max monthly dry length at 20 locations during years 2040–2069.

Figure 9

Max monthly dry length at 20 locations during years 2040–2069.

CONCLUSION

This study had used the SDSM model to generate climate patterns in terms of temperature, rainfall, and wet and dry length for the next 30 years (2040–2069). Results showed that future climate pattern will still be interrelated to the historical record, but with greater magnitudes. The average future annual temperature and rainfall are expected to increase by 0.2 °C per decade and 0.9% per year, respectively. The months of January to March will be the hottest and August to November will be the wettest months throughout a year with an average of 11 days and 30 days, respectively. The month of November will receive the highest rainfall depth; this prediction differs slightly from historical pattern due to expected changes in the north-east monsoon trend.

Increment in water depth at the Muda Irrigation Scheme area is expected to change the historical reservoir management policies in quantifying the water supply for cultivation purposes. A rise in temperature in the crop land is expected to escalate evapotranspiration by 0.04% annually, as estimated by Tukimat et al. (2012). Meanwhile, increment in rainfall depth is expected to decrease water demand.

Uncertainties in climate change due to global warming are expected to impact upcoming atmospheric transition. With this, the management of water resource systems will be more important than ever to meet all demands as well as brace for calamitous events. The Muda Irrigation Scheme is the largest paddy cultivation zone and the main suppliers of rice, the staple food of Malaysians. The cultivation area is fully dependent on the Pedu-Muda reservoir for irrigation purposes during the two cultivation seasons in a year. Therefore, the current rule-curve operation used to predict rainfall has to be amended to cater for climate change issues. A better projection of future climatic variability shall enrich the updated hydrological information, which will become useful to operators in preparing or reinventing their strategies.

ACKNOWLEDGEMENTS

This research is supported by the Ministry of Higher Education (MOHE), Jabatan Pengairan dan Saliran (JPS), Universiti Malaysia Pahang (UMP), and Universiti Teknologi Malaysia (UTM).

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