Open access peer-reviewed chapter

Hydrological Trend Analysis Integrated with Landscape Analysis at the Watershed Scale (Case Study: Langat Basin, Malaysia)

Written By

Hadi Memarian and Siva K. Balasundram

Submitted: November 23rd, 2015 Reviewed: February 11th, 2016 Published: July 27th, 2016

DOI: 10.5772/62463

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In this study, the trends of water and sediment data collected from three hydrometer stations over the past 25 years of development in the state of Selangor, Peninsular Malaysia, were analyzed using the Mann–Kendall and Pettitt’s tests. Landscape metrics for establishing the relationship between land use changes and trends of hydrological time series were calculated. The hydrologic trends were also studied in terms of rainfall variations and man-made features. Results indicated upward trends in water discharge at the Hulu Langat sub-basin and sediment load at the Semenyih sub-basin. These increasing trends were mainly caused by rapid changes in land use. Upward trends of hydrological series at the Hulu Langat sub-basin matched its rainfall pattern. At the Lui sub-basin, however, trends of hydrological series and variations in rainfall and land use were not statistically significant.


  • trend analysis
  • Mann–Kendall test
  • Pettitt’s test
  • landscape metrics
  • water discharge
  • sediment load

1. Introduction

Globally, increased sediment load (SL) and intense flooding due to land use changes at river basins are very challenging problems [14]. The impacts of human activities and climate change on hydrological processes occurring at river systems are well documented [510]. Understanding time series trends of water discharge (WD) and SL can be a key solution in determining how hydrological systems are affected by climate change and anthropogenic disturbances [4].

Zhang et al. [4] determined time series changes in water and sediment discharge at the Zhujiang (Pearl River) Basin in China. They applied Mann–Kendall (MK) as a gradual trend test and Pettitt’s as an abrupt change test on annual WD and SL from 1950 to 2004 at nine hydrometer stations. Their study showed that long-term changes in annual WD, which were originally controlled by variation in precipitation, were not significant. SL at all main hydrometer stations showed declining trends during the study period. Declining trends were principally influenced by the construction of reservoirs and dams. In a mountainous tributary of the lower Xinjiang in China, hydrological response to changes in precipitation and anthropogenic activities were tested using MK and Pettitt’s tests [11]. The power of MK and Spearman’s rho tests to assess the significance of hydrological trends has been studied by Yue et al. [12]. They demonstrated that the power of both tests is directly proportional to trend slope, sample size, and predetermined significance level and inversely proportional to time series variation.

Ouyang et al. [6] established a relationship between soil erosion and landscape metrics at the Logliu catchment in China. They showed that landscape pattern significantly impacted soil erosion and sediment transportation. In several other studies, landscape metrics were applied at the landscape and patch levels to determine how hydrological conditions of the basin are affected by human activities such as land use change [1317].

In recent decades, the Langat Basin has experienced rapid development towards urbanisation, industrialisation, and intense agriculture [18]. The Langat Basin is also a main source of drinking water for surrounding areas and a source of hydropower and has an important role in flood mitigation. Over the past four decades, the Langat Basin has served approximately 50% of the Selangor State population. However, the Selangor State is currently facing water shortage problems, especially in urban areas [19, 20].

This study was conducted to assess the impact of land use change, rainfall variation, and other anthropogenic manipulations on hydrological trends in selected upper catchments within the Langat Basin over a period of 25 years.


2. Methodology

2.1. Study area

The Langat Basin is located at the southern part of Klang Valley, which is the most urbanised river basin in Malaysia. It is believed that the Langat Basin is currently experiencing “spillover” effects due to the excessive development in the Klang Valley. Hydrometeorologically, the Langat Basin is affected by two types of monsoons, i.e., the Northeast (November–March) and the Southwest (May–September) [21]. The average annual rainfall is approximately 2400 mm. The wettest months are April and November, with an average monthly rainfall exceeding 250 mm, whereas the driest month is June, with an average monthly rainfall not exceeding 100 mm. Topographically, the Langat Basin can be divided into three distinct areas in reference to the Langat River, i.e., mountainous area in the upstream, undulating land in the centre, and flat flood plain in the downstream (Figure 1). The Langat Basin consists of a rich diversity of landform, surface feature, and land cover [21, 22].

Sub-basin Lui Hulu
Main river Lui Langat Semenyih
3°07′–3°12′N and
3°00′–3°17′N and
2°55′–3°08′N and
Drainage area
68.25 390.26 235.62
Basin length
11.5 34.5 26.5
Average slope
35 29.4 27.4
Max. altitude
1207 1479 1070
Min. altitude
61 20 21
Ave. altitude
354 277.4 243.9
Ref. hydrometer
Sg. Lui Sg. Langat Sg. Semenyih
(×106 m3/y)
55.05 289.64 146.11
(×103 ton/y)
5.88 146.6 36.81
806.57 742.16 620.11
Sediment yield
86.22 375.65 156.22
Ref. rainfall
Kg. Lui UPM Serdang Ldg. Dominion
2188.3 2453 2548.8
Land covers* Forest 80.35%, cultivated
rubber 9.85%, orchards
2.6%, mixed horticulture
and crops,
urbanised area,
and mining land 7.2%
Forest 54.6%, cultivated
rubber 15.6%, orchards
2%, urbanised area 15%,
horticulture and crops,
oil palm, lake,
and mining land 12.8%
Forest 53.8%, cultivated rubber
17.4%, oil palm 6.3%, urban
area 5.6%, secondary forest 3.6%,
scrub land 2.4%, mining, other crops,
mixed horticulture, orchard, bare land,
marshland and aquaculture 10.9%

Table 1.

General information of the studied sub-basins.

*Based on the land use map dated 2006.

Based on the availability of hydrometric stations in the Langat Basin, three sub-basin (upstream of the Langat River) were investigated. The descriptions about these sub-basin are given in Table 1.

2.2. Data set

WD, SL, and precipitation data between 1984 and 2008 recorded at all three hydrometer and rain gauge stations under study (Table 1) were obtained from the Department of Irrigation and Drainage (DID) of Malaysia. The geographic location and general information of the hydrometer stations are presented in Figure 1 and Table 2. Land use maps dated 1984, 1988, 1990, 1995, 1997, 2001, 2002, 2005, and 2006 were obtained from the Soil Resource Management and Conservation Division, Department of Agriculture, Malaysia.

Figure 1.

Geographic locations of the three study sub-basins.

2.3. Trend analysis

In this study, non-parametric tests, such as MK and Pettitt’s, were used to detect gradual and abrupt changes in the hydrological data sets. According to Zhang et al. [4], non-parametric tests are preferred over parametric tests due to their strength in handling non-normally distributed data and missing data. The MK equation that is based on the S statistic is as follows:


where xi and xj are sequential data values, n is the length of time series, and

sgn(θ)={ 1   if θ>00  if θ=01  if θ<1 E2

Mann (1945) and Kendall (1975) (as cited by Yue et al. [12]) have posted that, when n ≥ 8, S is almost normally distributed with the following mean and variance:


where ti is number of ties of the extent i.

The standard Z statistic is calculated as follows:


Z MK pursues the standard normal distribution with μ=0 and δ=1.

The probability (P) of the S statistic is estimated by normal cumulative distribution function as follows:


Statistical significance of the data trend was based on 95% confidence level [12].

The MK test is not robust against autocorrelation [4, 11, 23]. As such, an autocorrelation test was performed on the data set to determine the degree of autocorrelation. The autocorrelation coefficient is estimated as follows [24]:


where X=t=1nXtn, n is the sample size, and k is the lag.

For a completely random series, rk ≈ 0 for all k ≠ 0. If a series of rk (for k ≠ 0) fall between the 95% confidence level estimated by ul=-1±Z1-/2n-2/n-1 (where n is the length of tested time series, l and u are the lower and upper limits, α is the significance level, and Z is the critical value of standard normal distribution for a given α), then the tested series will be independent at the 95% confidence level [23]. The WD and SL data showed significant autocorrelation. Therefore, Zhang’s method of data pre-whitening [25] was used to eliminate significant autocorrelation within the data.

Sen’s non-parametric method was used to estimate the change magnitude (i.e., slope of the linear trend). Sen’s method is robust against non-normally distributed data, missing values, and extreme outliers (Sen, 1968) (as cited by Zhang and Lu [11]).

Considering a sequence of random variables X1, X2, …, Xt, which have a change point at τ [Xt for t = 1, 2, …, τ have a common distribution function F1(x) and Xt for t=τ+1, …, T have a common distribution function F2(x) and F1(x) ≠ F2(x)], Pettitt’s test (1979) (as cited by Zhang et al. [4] and Wolfe and Schechtman [26]) was used to detect one unknown change point in the pre-whitened WD and SL time series. In the Pettitt’s test, null hypothesis (H0): no change (τ=T) is tested against alternative hypothesis (Ha): change (1 ≤ τ <T)) by the non-parametric K statistic, as follows:


where U t,T = i=1 t j=t+1 T sgn( X t X j ),sgn( θ )={ 1( ifθ>0 ) 0( ifθ=0 ) 1( ifθ<0 ) . KT+=max1tTUt,T for downward shift and KT-=-min1tTUt,T for upward shift. The significance level of KT+ or KT- is estimated by P=exp-6KT2T3+T2. When KT occurs, the time t will be the point of change. When P is smaller than the specific significance level, H0 is rejected.

The above procedures were performed using XLSTAT and R statistical packages.

2.4. Landscape analysis

To assess the changes in land use patterns over the period 1984–2006 (including nine records), Patch Analyst 3.0 (Grid) extension in ArcView 3.3 was applied to calculate landscape metrics [27], which are fundamental indices for the detection of trends in land use change [6]. A brief description of the six selected landscape metrics for this work is given below [28, 29]:

The number of patches (NUMP) is ≥1.

The patch size coefficient of variation (PSCOV) is the variability of the patch size relative to the mean patch size.


where PSCOV 0, PSSD is the standard deviation in patch size and MPS is mean patch size of the corresponding patch type.

Edge density (ED) equals the sum of lengths (m) of all edge segments involving the corresponding patch type divided by the total landscape area (m2) in meters per hectare.

The Shannon’s diversity index (SDI; at the landscape level) is a measure between 0 and 1. SDI equals 0 when the landscape comprises only one patch (i.e., no diversity) and increases with increasing number of patch types. SDI equals 1 when the different patch types are distributed proportionally.


where Pi is proportion of the landscape occupied by the patch type (class) i.

The Shannon’s evenness index (SEI; at the landscape level) is a measure between 0 and 1. SEI equals 0 when the landscape comprises only one patch (i.e., no diversity) and approaches 0 as the areal distribution of patch types becomes uneven (i.e., dominated by one type). When the areal distribution of patch types is perfectly even, SEI equals 1.


where m defines the number of patch types (classes) present in the landscape including the landscape border.

The interspersion and juxtaposition index (IJI) is the observed interspersion over the maximum possible interspersion for a given number of patch types. IJI approaches 0 when the corresponding patch type is adjacent to only one other patch type and is 100 when the corresponding patch type is equally adjacent to all other patch types.


where eik is the total length (m) of edge in landscape between the patch types (classes) i and k, and m′ defines the number of patch types (classes) presented in the landscape.


3. Results

3.1. Hydrological trend analysis

Figure 2.

Autocorrelograms, resulted from autocorrelation test on WD and SL at the selected hydrometer stations.

The autocorrelation test reveals that the WD series (except that at Sg. Lui hydrometer station) and SL series have at least one autocorrelation coefficient that is significant at the 95% confidence level (Figure 2). The autocorrelation coefficients of the WD series at both Sg. Langat and Sg. Semenyih hydrometer stations are significant at the first lag. The SL series at Sg. Langat and Sg. Semenyih hydrometer stations have one significant autocorrelation coefficient at the second and first lags, respectively. The SL series at Sg. Lui hydrometer station is autocorrelated significantly only at the second lag.

The results of gradual trend analysis based on MK and Pre-Whitening MK (PWMK) tests for WD and SL data are shown in Table 2. At Sg. Langat, WD shows an increasing trend that is significant at the 95% confidence level; however, the ascendant trend of SL is not statistically significant. There are no significant trends in the hydrological time series of Sg. Lui and Sg. Semenyih.

Gradual changes in the hydrological time series at all three hydrometer stations are given in Table 2. At Sg. Lui, WD and SL are decreasing at a rate of 0.524×106 m3/y and 0.119×103 ton/y, respectively. At Sg. Langat, however, WD and SL are increasing at a rate of 9.899×106 m3/y and 1.415×103 ton/y. At Sg. Semenyih, WD and SL show declining tendencies at 3.686×106 m3/y and 0.316×103 ton/yr.

Station name Parameter MK and PWMK trend tests Sen’s slope estimator
τ P Trend Trend Trend_P Linear Intercept
Sg. Lui WD −0.153 0.297 Decreasing −0.524 −13.105 −0.525 62.723
SL −0.072 0.637 Decreasing −0.119 −2.986 −0.471 4.596
Sg. Langat WD 0.326 0.027 Increasing 9.899 247.485 11.531 156.869
SL 0.130 0.385 Increasing 1.415 35.380 13.142 46.278
Sg. Semenyih WD −0.196 0.189 Decreasing −3.686 −92.145 −3.912 187.036
SL −0.058 0.710 Decreasing −0.316 −7.909 −3.611 20.707

Table 2.

Results of MK and PWMK tests with the Sen’s slope estimator (at α=0.05) applied on WD and SL (data in bold are significant).

Trend: Sen’s slope (trend) per unit time; Trend_P: Sen’s slope (trend) over the time period; Linear: least-squares fit trend; Intercept: intercept of the Sen’s slope (trend).

The results of abrupt changes based on the Pettitt’s test for WD and SL are shown in Figures 3 and 4 and Table 3. The results show significant drastic changes in the hydrological time series at Sg. Langat and Sg. Semenyih hydrometer stations. At Sg. Langat, the mean level of WD (after 1998) shifted upward to 392.09×106 m3/y, which corresponds to a 77% increase, whereas the mean level of SL (after 1999) shifted upward to 297.27×103 ton/y, which corresponds to a 380% increase (Figure 3). At Sg. Semenyih, the mean level of WD (after 1993) shifted downward to 111.18×106 m3/y (44% decrease) and the mean level of SL shifted downward to 14.89×103 ton/y (78% decrease; Figure 4). At Sg. Lui, however, downward shifts in hydrological time series are not significant.

Figure 3.

Abrupt changes in the mean level of WD and SL for Sg. Langat at the significance level of 0.05.

Figure 4.

Abrupt changes in the mean level of WD and SL for Sg. Semenyih at the significance level of 0.05.

Station name Parameter K T P Shift T
Sg. Lui Water 56.000 0.430 Downward 1987
Sediment 82.000 0.084 Downward 1988
Sg. Langat Water 104.000 0.012 Upward 1998
Sediment 108.000 0.009 Upward 1999
Sg. Semenyih Water 112.000 0.010 Downward 1993
Sediment 130.000 0.005 Downward 1993

Table 3.

Results of Pettitt’s test applied on WD and SL (data in bold are significant at the level of 0.05).

Based on the PWMK test, the gradual change in the Lui and Semenyih sub-basin are showing declining tendencies. It is important to know whether these declining tendencies will prevail after the change point. Table 4 shows that all hydrological series have increasing tendencies after the change point, which are statistically significant only for SL series at Sg. Semenyih. Meanwhile, decreasing tendencies before the change point are statistically significant only for SL series recorded at Sg. Langat.

Station Parameter T Time series MK trend test
τ P Trend
Sg. Lui WD 1987 Pre-T*
Post-T 0.057 0.739 Increasing
SL 1988 Pre-T −0.666 0.308 Decreasing
Post-T 0.184 0.269 Increasing
Sg. Langat WD 1998 Pre-T −0.331 0.112 Decreasing
Post-T 0.405 0.127 Increasing
SL 1999 Pre-T 0.582 0.004 Decreasing
Post-T 0.388 0.175 Increasing
Sg. Semenyih WD 1993 Pre-T 0.000 1.000
Post-T 0.143 0.511 Increasing
SL 1993 Pre-T −0.357 0.265 Decreasing
Post-T 0.450 0.028 Increasing

Table 4.

Results of the MK and PWMK tests on WD and SL before and after the change points (data in bold are significant at the level of 0.05).

*Limitation in number of records.

3.2. Landscape analysis

The relationships between landscape metrics and hydrological variables are given in Table 5. At the Lui sub-basin, all metrics with the exception of IJI and SEI are negatively correlated with WD and SL. SL correlates significantly with PSCOV and SEI, whereas WD correlates significantly with ED and NUMP.

At the Semenyih sub-basin, all metrics are negatively correlated with WD and SL. Correlations between these metrics and WD are statistically significant. Correlations between these metrics with the exception of IJI and PSCOV and SL are statistically significant. At the Hulu Langat sub-basin, on the contrary, all metrics are positively correlated with WD and SL but are not statistically significant.

In comparison, correlations between hydrological series and landscape metrics are more pronounced after the change point (Table 5). For example, at the Semenyih sub-basin, correlations between hydrological series and all metrics change from negative to positive and are only significant for PSCOV. Also, at the Hulu Langat sub-basin, correlations between WD and the metrics ED, SDI, and NUMP are statistically significant.

Table 6 shows the trend analysis of landscape metrics during the period 1984 to 2006. At the Lui sub-basin, only NUMP shows a significant increasing trend. At the Hulu Langat sub-basin, all metrics show increasing tendencies but are statistically significant only for PSCOV, ED, SDI, and NUMP. At the Semenyih sub-basin, all metrics with the exception of IJI show significant increasing trends.

All records Records after the change points
Lui Hulu Langat Semenyih Lui Hulu Langat Semenyih
PSCOV −0.625 −0.788 * 0.358 0.243 0.921** −0.584 −0.408 0.081 0.460 0.674 0.887 * 0.932 **
ED 0.680* −0.471 0.289 0.244 0.957** 0.790* −0.549 −0.026 0.941 * 0.784 0.552 0.548
IJI 0.091 0.238 0.206 0.191 0.973** −0.660 0.048 0.137 0.674 0.659 0.380 0.575
SDI −0.556 −0.099 0.257 0.228 0.885** 0.865** −0.532 −0.026 0.981 * 0.846 0.789 0.670
SEI 0.474 0.951 ** 0.340 0.379 0.940** 0.873** −0.150 0.168 0.481 0.646 0.389 0.398
NUMP 0.703* −0.481 0.362 0.523 0.954** 0.704* −0.584 −0.025 0.955 * 0.870 0.762 0.759

Table 5.

Correlations between the different landscape metrics and hydrological series using the Pearson correlation method.

*Correlation is significant at the 0.05 level.

**Correlation is significant at the 0.01 level.

Landscape metric Lui Hulu Langat Semenyih
PSCOV NS ↑ * ↑ * ↑
ED NS ↑ * ↑ * ↑
IJI NS ↓ NS ↑ NS ↑
SDI NS ↑ * ↑ * ↑
SEI NS ↓ NS ↑ * ↑
NUMP * ↑ * ↑ * ↑

Table 6.

Trend analysis of the landscape metrics during 1984 to 2006 at the studied sub-basins.

*Trend is significant at the 0.05 level.

NS, not significant. ↑, increasing; ↓, decreasing.

The categorisation of the landscape metrics using the clustering technique for all three sub-basins is given in Figures 57.

At the Lui sub-basin, PSCOV, ED, and NUMP corresponding to years 1984, 1988, and 1990 are categorised in the first cluster, whereas landscape metric values of the remaining years are classified in the second cluster. Meanwhile, SEI (1984 and 1988) and SDI (1988 and 1990) are categorised in the first cluster, whereas the values of the remaining years are classified in the second cluster (Figure 5).

Figure 5.

Change trends and classification of the landscape metrics at the Lui sub-basin during 1984 to 2006.

All landscape metric values at the Hulu Langat (except for IJI) and Semenyih sub-basins corresponding to years 1984, 1988, and 1990 are grouped in the first cluster and the values of the remaining years are grouped in the second cluster (Figure 6).

Figure 6.

Change trends and classification of the landscape metrics at the Hulu Langat Sub Basin during 1984-2006.

Figure 7.

Change trends and classification of the landscape metrics at the Semenyih sub-basin during 1984 to 2006.

3.3. Land use change detection

Change detection was based on the land use maps dated 1984 and 2006. The results show noticeable gains in agriculture, bare land, mining, oil palm, and urban acreage and a remarkable loss in rubber acreage (Table 7).

Land use Difference (2006–1984) in hectares
Lui Hulu Langat Semenyih
Agriculture 436.39 1545.32 451.57
Bare land 6.30 370.60 175.50
Forest 359.75 112.04 -237.72
Grassland 5.00 −471.70 382.30
Marshland/swamp 46.42 148.93 -39.73
Mining 16.67 395.27 147.57
Oil palm 3.44 21.81 1051.89
Rubber −809.43 −7817.72 -3860.35
Urban/built-up area 111.09 5649.90 1501.61
Water body 0.00 45.55 427.36

Table 7.

Land use change detection between 1984 and 2006 at the studied sub-basins.

Increasing gradual trends (as determined by the MK test using the 1984–2006 time series) in agriculture and mining acreages are significant only at the Hulu Langat sub-basin, whereas increasing gradual trend in oil palm acreage is significant only at the Semenyih sub-basin. Decreasing gradual trend in rubber acreage is significant at both Hulu Langat and Semenyih sub-basins. At all three sub-basins, a change trend in forest area is not significant, whereas increasing trend in the urbanised area is significant (Table 8).

Land use Lui Hulu Langat Semenyih
Agriculture NS ↑ * ↑ NS ↑
Forest NS ↓ NS ↓ NS ↓
Urban * ↑ * ↑ * ↑
Rubber NS ↓ * ↓ * ↓
Oil palm NS ↑ NS ↑ * ↑
Mining NS ↑ * ↑ NS ↑

Table 8.

Trend analysis of land use change during 1984–2006 at the studied sub-basins.

*Trend is significant at the 0.05 level.

NS, not significant. ↑, increasing; ↓, decreasing.


4. Discussion

Based on the hydrological trend analysis, the Hulu Langat sub-basin showed a significant increasing trend of WD. However, the Semenyih and Lui sub-basins showed decreasing tendencies of WD and SL. Gradual increase in hydrological series after the change points is significant only in the case of SL at Sg. Semenyih. In the following sections, hydrological alterations are discussed in relation to land use change, rainfall fluctuations, and other anthropogenic manipulations.

4.1. Effect of land use/cover change (LUCC)

Based on the data from the Department of Statistics, Malaysia (2001) and the National Urbanisation Policy of Malaysia (1981), rapid development in the state of Selangor started in 1981. The rapid development was aimed at attracting approximately 18% of Malaysia’s population to be settled in the state of Selangor by the year 2000 [30, 31]. The Langat Basin appears as a suitable barometer to measure urbanisation and agricultural/industrial development in the state of Selangor.

Based on Tables 7 and 9, rubber acreage at the Hulu Langat and Semenyih sub-basins decreased significantly between 1984 and 2006. During this period, at the Hulu Langat sub-basin, 34% of rubber acreage was transformed into urban areas, whereas another 11% was used for other agricultural production. Similarly, at the Semenyih sub-basin, 21% of rubber acreage was transformed into urban areas, whereas 15% and 6% were used for oil palm and other agricultural productions, respectively (Table 7). Based on these results and the work of Noorazuan et al. [22] and Juahir et al. [19], it is expected that these changes will affect the stream flow behaviour and characteristics.

1984 2006
Forest Rubber Agriculture Scrub/idle
industrial area
Forest 1 1 1
Rubber 21 5
47 11 33 4
Hulu Langat
Forest 4 3.5
Rubber 11 34
Agriculture 17 26
38 17 10 31
Forest 2.5 1.4 2.7
Rubber 6 15 21
Agriculture 7 10 56 15
70 10 19
Oil palm 8 8 34
7 80 13
Bare land 67

Table 9.

Land use change matrix for important transitions (frequencies in %) between the years 1984 and 2006 at the studied sub-basins.

Tables 6 shows the increasing trend in landscape change during 1984–2006, which is confirmed by correlation analysis (Table 5), especially after the change points [6, 32].

At the Hulu Langat sub-basin, cluster analysis shows that discriminant points between the clusters of landscape metrics (except for IJI) are within the period 1990–1997 (Figure 6). These points correspond to the points of change in WD and SL. At the Semenyih sub-basin, the change point in WD and SL (i.e., 1993) matches the discriminant point in all landscape metric clusters, with the exception of SDI (Figure 7). At the Lui sub-basin, points of change in WD and SL (1987 and 1988) match only the discriminant point in SEI. As indicated in Table 6, at the Lui sub-basin, change trends in landscape metrics are not statistically significant. This could have contributed to the insignificant impact of LUCC on the basin hydrological conditions.

4.2. Effect of rainfall variations

The rainfall stations (i.e., Kg. Lui, UPM Serdang, and Ldg. Dominion) were analysed for rainfall change trend. These three rainfall stations represent rainfall events at the Lui, Hulu Langat, and Semenyih sub-basins, respectively (Table 10). Increasing trend in the rainfall time series is only significant at UPM Serdang, which corresponds to increasing trends in WD and SL at Sg. Langat. Although the change point in rainfall series at UPM Serdang (i.e., 1998) is not statistically significant, it matches the change point in WD and SL at Sg. Langat. This point matches the critical water level at Langat Reservoir [33], which has been reported by Shaaban and Low [34]. At Kg. Lui, the change tendency of rainfall series after the hydrological change point matches the trend of WD and SL at Sg. Lui (Table 11). At Ldg. Dominion, the change tendency in rainfall does not match the available tendency in hydrological series at Sg. Semenyih, especially for SL after the change point.

Station name MK trend test Pettitt’s test
τ P Trend K T P Shift T
Kg. Lui 0.144 0.333 Increasing 70.000 0.188 Upward 2003
UPM Serdang 0.341 0.020 Increasing 78.000 0.079 Upward 1998
Ldg. Dominion 0.101 0.503 Increasing 68.000 0.231 Upward 1990

Table 10.

Results of the PWMK and Pettitt’s tests applied on the rainfall time series at the representative stations (data in bold are significant at α=0.05).

Rainfall stationT_Hydro series Time series PWMK trend test
τ P Trend
Kg. Lui 1988 Pre-T −0.333 0.734 Decreasing
Post-T 0.111 0.528 Increasing
UPM Serdang 1998 Pre-T 0.256 0.246 Increasing
Post-T 0.066 0.858 Increasing
Ldg. Dominion 1993 Pre-T 0.444 0.117 Increasing
Post-T 0.000 1.000

Table 11.

Results of PWMK test applied on the rainfall time series before and after the hydrological change points.

Thus far, results reveal the significant impacts of land use and rainfall variations on WD at the Hulu Langat sub-basin. However, the impacts on the sub-basin SL are not statistically significant. At the Lui sub-basin, the change trends in rainfall and landscape variables are not statistically significant. Hence, the insignificance of hydrological series trend is expected. At the Semenyih sub-basin, the impact of land use change on hydrological series is driven by the significant increasing trend in SL after 1993. However, rainfall variations do not impact the trend of hydrological series.

From the preceding discussion, two questions are important. First, have the Semenyih Reservoir and its connected water treatment facilities at the Semenyih sub-basin impacted the basin WD significantly? Secondly, despite the significant impact of land use change on the change trend in WD at the Hulu Langat sub-basin, why is the change trend in SL not statistically significant? In the following discussion, these questions are addressed.

4.3. Effect of man-made structures

There are two strategic dams in the Langat Basin. The Langat Dam, constructed in 1979, has a drainage catchment area of 41.5 km2 and a reservoir capacity of 37.5 Mm3. The Semenyih Dam, built in 1985, has a drainage catchment area of 56.7 km2 and a reservoir capacity of 62.6 Mm3. Both these dams supply domestic and industrial water. The Langat Dam is also used to generate power supply at moderate capacity for consumption within the Langat Valley. Currently, there are three major water treatment plants (WTP; operating 24 hours a day) within the study area. The Sg. Langat and Cheras WTPs along the Langat River produce 386.4 and 27 million litres per day (MLD) of clean water, respectively. The Semenyih WTP along the Semenyih River produces 545 MLD of clean water [30, 33].

Figure 8.

Cumulative double mass plot at Sg. Semenyih.

To evaluate the impact of Semenyih Dam construction on trends of hydrological time series, a double mass curve was plotted. As illustrated in Figure 8, at Sg. Semenyih, the hydrological time series trend after 1985 is seriously affected by the dam and WTP construction. The mean WD level changes from 231.8×106 m3/y before 1985 to 141×106 m3/y after 1985. The results of the Pettitt’s test for WD and SL during the period 1975–1993 were statistically significant (P=0.018 and P<0.001, respectively). This confirms 1985 as the point of change during the period 1975 to 1993.

Shaaban and Low [34] showed that drought events reduced WD at the Semenyih sub-basin, particularly in the period 1993–1998. As such, WTP and dam together with the effect of drought have been able to reduce the increasing trend of WD, especially after 1993.

At the Hulu Langat sub-basin, due to the significant trend in urbanisation and agricultural activities (Tables 7 and 9), the number and size of natural or artificial ponds are expected to increase dramatically. Field observation from this study confirms that the quantity of natural and artificial ponds is higher at Hulu Langat compared to that at Semenyih. The ponds are believed to affect the sedimentation process by increasing the deposition rate, hence resulting in the reduction in SL of the basin (Figure 9). It is clear that the Langat Dam and other sediment trapping features (i.e., natural and artificial ponds) contributed to the insignificant trend of SL at the Hulu Langat sub-basin.

Figure 9.

Ponds arisen from urban and agricultural development at the Hulu Langat sub-basin (extracted from SPOT 5 satellite images, dated 2006).


5. Conclusion

Increasing trend in WD at the Hulu Langat sub-basin was originally controlled by significant variations in land use and rainfall. However, increasing trend in SL was not significant due to dam construction and increase in the number and size of sediment trapping features, which is due to urbanisation and agricultural activities. At the Semenyih sub-basin, increasing trend in SL after 1993 was closely related to significant trends in landscape metrics and land use changes. However, WD did not increase significantly after 1993, primarily due to the impact of dam and water treatment facilities and continuous drought until 1998. At the Lui sub-basin, trends in land use and rainfall variations were mostly insignificant, thus causing an insignificant change in hydrological series.

This study demonstrates the power of the PWMK and Pettitt’s tests for trend evaluation of hydrological time series. The results obtained in this work are consistent with studies done by other researchers [3545]. Also, integrating landscape analysis with statistical analyses as emphasised in the work of several others [6, 1317, 32, 46, 47] could increase the depth of interpretation with regard to the complex hydrological conditions of developed basins.



The authors acknowledge Universiti Putra Malaysia for procuring land use maps and satellite imagery and the Department of Irrigation and Drainage, Malaysia, for supplying hydrological data.


  1. 1. Dai, Z., Liu, J. T., & Xiang, Y. (2015). Human interference in the water discharge of the Changjiang (Yangtze River), China. Hydrological Sciences Journal, 60(10), 1770–1782.
  2. 2. Zhang X., Cao W., Guo Q., & Wu S. (2010) Effects of land use change on surface runoff and sediment yield at different watershed scales on the Loess Plateau. International Journal of Sediment Research, 25, 283–293.
  3. 3. García-Ruiz J. M., Regüés D., Alvera B., Lana-Renault N., Serrano-Muela P., Nadal-Romero E., et al. (2008) Flood generation and sediment transport in experimental catchments affected by land use changes in the central Pyrenees. Journal of Hydrology, 356, 245–260.
  4. 4. Zhang S., Lu X. X., Higgitt D. L., Chen C. T. A., Han J., & Sun H. (2008) Recent changes of water discharge and sediment load in the Zhujiang (Pearl River) Basin, China. Global and Planetary Change, 60, 365–380.
  5. 5. Shi Z. H., Huang X. D., Ai L., Fang N. F., & Wu G. L. (2014). Quantitative analysis of factors controlling sediment yield in mountainous watersheds. Geomorphology, 226, 193–201.
  6. 6. Ouyang W., Skidmore A. K., Hao F., & Wang T. (2010) Soil erosion dynamics response to landscape pattern. Science of the Total Environment, 408, 1358–1366.
  7. 7. Ghaffari, G., Keesstra, S., Ghodousi, J., & Ahmadi, H. (2010). SWAT-simulated hydrological impact of land-use change in the Zanjanrood Basin, Northwest Iran. Hydrological processes, 24(7), 892–903.
  8. 8. Li Z., Liu W. Z., Zhang X. C., & Zheng F. L. (2009) Impacts of land use change and climate variability on hydrology in an agricultural catchment on the Loess Plateau of China. Journal of Hydrology, 377, 35–42.
  9. 9. He H., Zhou J., & Zhang W. (2008) Modelling the impacts of environmental changes on hydrological regimes in the Hei River Watershed, China. Global and Planetary Change, 61, 175–193.
  10. 10. Nearing M. A., Jetten V., Baffaut C., Cerdan O., Couturier A., Hernandez M., et al. (2005) Modeling response of soil erosion and runoff to changes in precipitation and cover. CATENA, 61, 131–154.
  11. 11. Zhang S. & Lu X. X. (2009) Hydrological responses to precipitation variation and diverse human activities in a mountainous tributary of the lower Xijiang, China. Catena, 77, 130–142.
  12. 12. Yue S., Pilon P., & Cavadias G. (2002) Power of the Mann-Kendall and Spearman’s ρ tests for detecting monotonic trends in hydrological series. Journal of Hydrology, 259, 254–271.
  13. 13. Lin Y. P., Hong N. M., Wu P. J., Wu C. F., & Verburg P. H. (2007) Impacts of land use change scenarios on hydrology and land use patterns in the Wu-Tu watershed in northern Taiwan. Landscape and Urban Planning, 80, 111–126.
  14. 14. Weng Y. C. (2007) Spatiotemporal changes of landscape pattern in response to urbanization. Landscape and Urban Planning, 81, 341–353.
  15. 15. Song I. J., Hong S. K., Kim H. O., Byun B., & Gin Y. (2005) The pattern of landscape patches and invasion of naturalized plants in developed areas of urban Seoul. Landscape and Urban Planning, 70, 205–219.
  16. 16. Cifaldi R. L., David Allan J., Duh J. D., & Brown D. G. (2004) Spatial patterns in land cover of exurbanizing watersheds in Southeastern Michigan. Landscape and Urban Planning, 66, 107–123.
  17. 17. Crews-Meyer K. A. (2004) Agricultural landscape change and stability in northeast Thailand: historical patch-level analysis. Agriculture, Ecosystems & Environment, 101, 155–169.
  18. 18. Mohamed A. F., Yaacob W. Z., Taha M. R., & Samsudin A. R. (2009) Groundwater and soil vulnerability in the Langat Basin Malaysia. European Journal of Scientific Research, 27, 628–635.
  19. 19. Juahir H., Zain S. M., Yusoff M. K., Hanidza T. I. T., Armi A. S. M., Toriman M. E., & Mokhtar M. (2010) Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques. Environmental Monitoring and Assessment, published online at
  20. 20. Ayub K. R., Hin L. S., & Aziz H. A. (2009) SWAT application for hydrologic and water quality modeling for suspended sediments: a case study of Sungai Langat’s Catchment in Selangor. International Conference on Water Resources (ICWR 2009), Langkawi, Kedah, Malaysia, published online at
  21. 21. Memarian H., Balasundram S. K., Talib J., Teh C. B. S., Alias M. S., Abbaspour K. C., & Haghizadeh A. (2012) Hydrologic analysis of a tropical watershed using KINEROS2. Environment Asia, 5, 84–93.
  22. 22. Noorazuan M. H., Rainis R., Juahir H., Zain S. M., & Jaafar N. (2003) GIS application in evaluating land use-land cover change and its impact on hydrological regime in Langat River Basin, Malaysia. Conference of MapAsia 2003, Malaysia, Kuala Lumpur, published online at
  23. 23. Zhang Q., Xu C. Y., Becker S., & Jiang T. (2006) Sediment and runoff changes in the Yangtze River Basin during past 50 years. Journal of Hydrology, 331, 511–523.
  24. 24. Haan C. T. (2002) Statistical Methods in Hydrology. 2nd edn. Ames, Iowa State Press, 496 pp.
  25. 25. Wang X. L. & Swail V. R. (2001) Changes of extreme wave heights in Northern Hemisphere oceans and related atmospheric circulation regimes. Journal of Climate, 14, 2204–2221.
  26. 26. Wolfe D. A. &; Schechtman E. (1984) Nonparametric statistical procedures for the changepoint problem. Journal of Statistical Planning and Inference, 9, 389–396.
  27. 27. Elkie P., Rempel R., & Carr A. (1999) Patch Analyst User’s Manual. Ontario Ministry of Natural Resources. Northwest Science and Technology, TM002. Thunder Bay, Ontario, Canada, 16.
  28. 28. Frohn R. C. & Hao Y. (2006) Landscape metric performance in analyzing two decades of deforestation in the Amazon Basin of Rondonia, Brazil. Remote Sensing of Environment, 100, 237–251.
  29. 29. McGarigal K. & Marks B. J. (1995) FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. General Technical Report. PNW-GTR-351. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 122 pp.
  30. 30. Juahir H. (2009) Water quality data analysis and modeling of the Langat River basin. Ph.D. thesis, Faculty of Science, University of Malaya, Kuala Lumpur. 68–138.
  31. 31. Hezri A. A. & Hasan M. N. (2004) Management framework for sustainable development indicators in the State of Selangor, Malaysia. Ecological Indicators, 4, 287–304.
  32. 32. Wang K., Wang H. J., Shi X. Z., Weindorf D. C., Yu D. S., Liang Y., et al. (2009) Landscape analysis of dynamic soil erosion in Subtropical China: a case study in Xingguo County, Jiangxi Province. Soil and Tillage Research, 105, 313–321.
  33. 33. Puncak Niaga Sdn. Bhd. (2008) A report on dam operations and management. Selangor, Malaysia, published online at
  34. 34. Shaaban A. J. & Low K. S. (2003) Droughts in Malaysia: a look at its characteristics, impacts, related policies and management strategies. Water and Drainage 2003 Conference, Malaysia, published online at
  35. 35. Yenilmez, F., Keskin, F., & Aksoy, A. (2011). Water quality trend analysis in Eymir Lake, Ankara. Physics and Chemistry of the Earth, Parts A/B/C, 36(5), 135–140.
  36. 36. Wilson D., Hisdal H., & Lawrence D. (2010) Has streamflow changed in the Nordic countries? Recent trends and comparisons to hydrological projections. Journal of Hydrology, 394, 334–346.
  37. 37. Yang Y. & Tian F. (2009) Abrupt change of runoff and its major driving factors in Haihe River Catchment, China. Journal of Hydrology, 374, 373–383.
  38. 38. Sahoo D. & Smith P. K. (2009) Hydroclimatic trend detection in a rapidly urbanizing semi-arid and coastal river basin. Journal of Hydrology, 367, 217–227.
  39. 39. Khaliq M. N., Ouarda T. B. M. J., Gachon P., Sushama L., & St-Hilaire A. (2009) Identification of hydrological trends in the presence of serial and cross correlations: a review of selected methods and their application to annual flow regimes of Canadian rivers. Journal of Hydrology, 368, 117–130.
  40. 40. Hamed K. H. (2009) Enhancing the effectiveness of pre-whitening in trend analysis of hydrologic data. Journal of Hydrology, 368, 143–155.
  41. 41. Chang H. (2008) Spatial analysis of water quality trends in the Han River Basin, South Korea. Water Research, 42, 3285–3304.
  42. 42. Hamed K. H. (2008) Trend detection in hydrologic data: the Mann-Kendall trend test under the scaling hypothesis. Journal of Hydrology, 349, 350–363.
  43. 43. Kahya E. & Kalayci S. (2004) Trend analysis of streamflow in Turkey. Journal of Hydrology, 289, 128–144.
  44. 44. Xu Z. X., Takeuchi K., & Ishidaira H. (2003) Monotonic trend and step changes in Japanese precipitation. Journal of Hydrology, 279, 144–150.
  45. 45. Hamed K. H. & Ramachandra Rao A. (1998) A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology, 204, 182–196.
  46. 46. Sundell-Turner N. M. & Rodewald A. D. (2008) A comparison of landscape metrics for conservation planning. Landscape and Urban Planning, 86, 219–225.
  47. 47. DiBari J. N. (2007) Evaluation of five landscape-level metrics for measuring the effects of urbanization on landscape structure: the case of Tucson, Arizona, USA. Landscape and Urban Planning, 79, 308–313.

Written By

Hadi Memarian and Siva K. Balasundram

Submitted: November 23rd, 2015 Reviewed: February 11th, 2016 Published: July 27th, 2016