This article was downloaded by: [Ondokuz Mayis Universitesine]On: 11 November 2014, At: 22:49Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

Hydrological Sciences JournalPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/thsj20

Impacts of land-use and climate variability onhydrological components in the Johor River basin,MalaysiaMou Leong Tana, Ab Latif Ibrahima, Zulkifli Yusopb, Zheng Duanc & Lloyd Lingb

a Institute of Geospatial Science and Technology, Universiti Teknologi Malaysia, Skudai, JohorBahru, Malaysiab Institute of Environmental and Water Resources Management, Universiti TeknologiMalaysia, Skudai, Johor Bahru, Malaysiac Delft University of Technology, Delft, The NetherlandsAccepted author version posted online: 25 Sep 2014.

To cite this article: Mou Leong Tan, Ab Latif Ibrahim, Zulkifli Yusop, Zheng Duan & Lloyd Ling (2014): Impacts of land-useand climate variability on hydrological components in the Johor River basin, Malaysia, Hydrological Sciences Journal, DOI:10.1080/02626667.2014.967246

To link to this article: http://dx.doi.org/10.1080/02626667.2014.967246

Disclaimer: This is a version of an unedited manuscript that has been accepted for publication. As a serviceto authors and researchers we are providing this version of the accepted manuscript (AM). Copyediting,typesetting, and review of the resulting proof will be undertaken on this manuscript before final publication ofthe Version of Record (VoR). During production and pre-press, errors may be discovered which could affect thecontent, and all legal disclaimers that apply to the journal relate to this version also.

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon andshould be independently verified with primary sources of information. Taylor and Francis shall not be liable forany losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use ofthe Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Accep

ted M

anus

cript

1

Publisher: Taylor & Francis & IAHS Press

Journal: Hydrological Sciences Journal

DOI: 10.1080/02626667.2014.967246

Impacts of land-use and climate variability

Impacts of land-use and climate variability on hydrological components in the Johor River basin, Malaysia Mou Leong TAN1, Ab Latif IBRAHIM 1, Zulkifli YUSOP2, Zheng DUAN3 and Lloyd LING2 1Institute of Geospatial Science and Technology, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Malaysia mouleong@gmail.com 2Institute of Environmental and Water Resources Management, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor Bahru, Malaysia 3Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands Received 22 October 2013; accepted 9 September 2014

Abstract This study aims to investigate separate and combined impacts of land-use and climate changes on hydrological components in the Johor River Basin (JRB), Malaysia. The Mann-Kendall and Sen’s slope test were applied to detect the trends in precipitation, temperature and streamflow of JRB. The Soil and Water Assessment Tool (SWAT) was calibrated and validated using measured monthly streamflow data. Validation results supported that SWAT was reliable in the tropical JRB. The trend analysis showed that there was an insignificant increasing trend for streamflow, whereas significant increasing trends for precipitation and temperature were found. The combined (land-use + climate change) impact caused the annual streamflow and evaporation to increase by 4.4% and 1.2%, respectively. Climate (land-use) raised annual streamflow by 4.4% (0.06%) and evaporation by 2.2% (–0.2%). Climate change imposed a stronger impact than land-use change on the streamflow and evaporation. These findings are useful for decision makers to develop better water and land-use policies.

Key words Johor River; Malaysia; land-use change; climate change; hydrology; streamflow; evaporation; SWAT; Mann-Kendall; Sen’s slope

1 INTRODUCTION Freshwater is crucial for human survival. Yet, water quality and quantity issues including pollution, water stress, flood and drought have become critical problems in many regions of the world (Kundzewicz et al. 2008). Alterations in the local hydrologic cycle would lead to a rise in intensity and frequency of tropical storms, floods and droughts (Huntington 2006). Climate variability and land-use change are often considered as two of the main factors contributing to the alteration of the hydrologic cycle. Climate variability is identified as the main contributor to changing streamflow volume, peak flow and flow routing time (IPCC 2007; Kundzewicz et al. 2008; Li et al. 2009), whereas land-use changes can cause transformation in surface runoff, flood frequency, baseflow and annual mean discharge (Huntington 2006; Brown et al. 2013; Wei et al. 2013).

Studies on the hydrological impacts of land-use and climate changes have been widely conducted in different countries such as China (Shi et al. 2013), Kenya

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

2

(Mango et al. 2011), Korea (Kim et al. 2013), United States (Wang et al. 2013) and Vietnam (Khoi and Suetsugi 2014). The applied methods can be broadly grouped into three classes: paired catchment approach, hydrological modelling and statistical analysis (Li et al. 2009; Wei et al. 2013). The paired catchment approach is only applicable in small basins which are less than 100 km2 because it is hard to find two similar moderate or large basins (Li et al. 2009). The statistical method is the simplest approach by analysing the hydro-climatic trends of monitoring stations located within and close to the studied basin, but this method does not consider the physical processes in the basin (Wei et al. 2013). Hence, hydrological modelling is considered as the most suitable method because it takes into account relationships between climate, land-use and hydrological components (Khoi and Suetsugi 2014).

The Soil and Water Assessment Tool (SWAT) has been proven as an effective tool for studies on hydrological impacts around the world (Ficklin et al. 2013; Thampi et al. 2010). In addition, numerous advantages of SWAT, such as its free availability, efficiency in data handling and user-friendly interface are among the reasons to choose this model (Arnold et al. 2012). Based on the SWAT literature database (https://www.card.iastate.edu/swat_articles/, assessed in 28 July 2014), there were only four studies that applied SWAT to simulate hydrological processes in Malaysia (Lai and Arniza 2011; Zorkeflee et al. 2012; Memarian et al. 2014; Tan et al. 2014). These studies found that SWAT showed good performances. The applicability of SWAT in other parts of Malaysia still needs to be investigated.

Knowledge on how the local hydrologic cycle and water resources will be affected by both land-use and climate changes is essential for designing reliable climate adaptation strategies and water policy. Mango et al. (2011) investigated the impact of land-use and climate change on streamflow in the upper Mara River Basin, Kenya and indicated that deforestation has reduced dry season flows and increased the peak flow. Li et al. (2011) analysed the impact of land-use and climate changes on surface hydrology in an agriculture catchment on the Loess Plateau of China, and they concluded that climate variability indicated greater impact compared to land-use change. In general, regional impacts of land-use and climate change on hydrology vary from place to place, hence local scale studies should be conducted (Wang et al. 2012). However, such detailed assessments of local hydrology are still limited in Malaysia.

The regional scale climate assessment studies especially on the water sector in Malaysia are still limited, and there is no related research listed in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) (IPCC 2007; Tangang et al. 2012). Hydrological impact research in Malaysia has been receiving much more attention since 2007, due to the increase in climate impact awareness. However, most of the studies were concerned about the changes of a single factor, i.e. either climate changes (Shaaban et al. 2011) or land-use changes (Amini et al. 2011). To our knowledge, only Adnan and Atkinson (2011) considered both impacts of land-use and climate changes in Malaysia by using the statistical analysis approach. The study was conducted in northeast Peninsular Malaysia and they found an increasing trend of streamflow and precipitation during the wet season. However, no hydrological modelling impact studies that consider both factors (land-use and climate) have been performed in southern Peninsular Malaysia. This study aims to fill this research gap. The Johor River is the main river of Johor state in southern Peninsular Malaysia. It is an important source of freshwater supply not only for Johor state but also for Singapore. The Johor River Basin (JRB) is dominated by natural forest in the

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

3

northern part, while large area of oil palm and rubber plantation cover the southern part of JRB. In the 1970s, huge forest areas in the JRB were cleared following the land development projects by the Federal Land Development Authority (FELDA) and the South East Johore Development Authority (KEJORA) land development programmes. In 1990, the Public Utility Board (PUB) of Singapore built the Linggiu Dam on the upper reaches of Johor River under the second water agreement. These large-scale projects induced the dramatic decline of forested areas, and increased the agriculture and water areas. However, no studies have investigated the consequences of these changes on the hydrological components in the JRB.

Flooding is a main water issue in JRB. Historically, there have been six major devastating flood events in the JRB that destroyed infrastructure and properties and caused loss of lives. The most destructive events were the floods which occurred during the period December 2006 and January 2007. These caused the evacuation of more than 100,000 people and 18 deaths with a total estimated loss of 0.5 billion U.S. dollars (Kia et al. 2012). The main contributing factor to these tragedies was the heavy rainfall brought on by the North-East Monsoon in December and January of those years. According to Jayawardena et al. (1997), the Linggui Dam does not have significant impact on the mitigation or reducing flooding events on downstream because it drains only 10% of the total area of the JRB. Hence, two main questions need to be investigated and are the motivation for this study: Was land-use change or climate variability the main reason for those flood events? and To what extent were these two factors causing the flood? The answers are vital for decision makers to develop better water related policies and sustainable management systems in the future.

The aim of the study is to investigate the impacts of land-use and climate variability on streamflow, evaporation and hydrological processes in the JRB in southern Peninsular Malaysia. Specific objectives are (1) to analyse and identify the trend of precipitation, temperature and streamflow in JRB; (2) to validate the performance of SWAT in streamflow simulation in JRB; (3) to evaluate the separated and combined effects of climate and land-use changes on streamflow and evaporation by running the calibrated and validated SWAT using different scenarios as inputs. The findings of this study can provide a better understanding of hydrological impacts of land-use and climate changes to assure better water resources management and effective reduction of the flood vulnerability towards sustainability practices in JRB. 2 STUDY AREA The Johor River is located in the south-eastern part of Johor province in Malaysia (Fig. 1). It is 122.7 km in length and originates from Gunung Belumut (second highest mountain in Johor) in the north of basin. The river flows in a north-south direction and then south-west into the Strait of Johor. The two major tributaries are Linggiu River and Sayong River which are located in the northern basin. The JRB is located between latitudes (1o30’~2o10’ N) and longitudes (103o20’~104o10’ E). The surface area is approximately 1652 km2. The elevation of the JRB ranges between 3 to 977 m above the mean sea level (AMSL). The major land-use types of the JRB are perennial agriculture (oil palm and rubber) and forest. The main soil type in the basin is the Ultisols series (Rengam-Jerangau). It is a yellowish-brown sandy clay, well drained and with moderate permeability, which is suitable for oil palm and rubber plantation.

The average annual rainfall of the JRB is 2500 mm. The mean annual streamflow at Rantau Panjang station (middle basin) is 37.7 m3/s. Figure 2 illustrates the mean monthly streamflow and precipitation from 1975 to 2004. The climate in the

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

4

JRB is a tropical monsoon climate which is divided into the Northeast monsoon (November–February), and the Southwest monsoon (May–August) (Tangang et al. 2012). The flooding event frequently occurs in December when highest rainfall and peak streamflow are recorded. The JRB covers four districts of the Johor state including Kota Tinggi, Kluang, Kulai Jaya and Johor Bahru. In the year 2010, the total population in the JRB was estimated by the Department of Statistics Malaysia to be about 300 000 people and 70 000 households. 3 DATA AND METHODOLOGY 3.1 SWAT description SWAT is a continuous, process-based semi-distributed model that allows the user to simulate the effects of land management on the hydrological cycle at the basin scale (Arnold et al. 1998). It is a public domain model and has a community of developers and users that drives it continually forward. In SWAT, the basin is divided into multiple sub-basins, which are then divided into unique land-use and soil group called hydrologic response units (HRUs). The water balance equation is used to simulate hydrology processes at each HRUs (Neitsch et al. 2011). There are several popular water movement equations available in SWAT such as for infiltration and runoff (Green Ampt and Curve Number) and evapotranspiration (ET) (Penman-Monteith, Hargreaves and Priestley Taylor). A detailed explanation of SWAT theory can be found in the SWAT theoretical document prepared by Neitsch et al. (2011). 3.2 Data sets

3.2.1 Streamflow data There are four stream gauges in the JRB, but only the measured monthly streamflow data from 1966 to 1999 at Rantau Panjang station (1737451) (Fig. 1) located at the outlet of sub-basin 12 were obtained from the Department on Irrigation and Drainage of Malaysia (DID). Other stations data were not available due to partly missing data or lack of maintenance, thus only the Rantau Panjang station data was used for calibration and validation of SWAT.

3.2.2 Climate data Daily temperature and precipitation data from 1975 to 2007 are available at Kluang (48672) and Senai (48679) principal stations. Daily precipitation data measured at five rain gauging stations (Fig. 1 and Table 1) were acquired from the Malaysia Meteorological Department (MMD). The Kluang and Senai principle stations are used to represent the climate condition of the upper basin (UB) and lower basin (LB), respectively. The principal stations are well known to provide good quality climate data in Malaysia because of good maintenance and strict quality control. Furthermore, precipitation data for the middle basin (MB) was acquired from the Felda Bukit Permai rainfall gauge (47146). The annual and monthly means of hydro-climatic data were calculated for trend analysis. Daily precipitation and temperature data were used as climate input. A detailed information on monitoring stations used in this study is listed in Table 1.

3.2.3 Land-use data Land-use maps (1: 250 000) of 1984 and 2002 were acquired from the Ministry of Agriculture and Agro-based Industry of Malaysia (MOA) (Fig. 4) for land-use change analysis. Land-use types were classified into forest, oil palm, rubber, agriculture land, urban and water (Fig.4 and Table 2).

3.2.4 Digital elevation model (DEM) data Based on Lin et al. (2013), a DEM with finer resolution (30m) and higher vertical accuracy did not provide a better

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

5

performance in SWAT simulation compared to the 90 m resolution of the SRTM DEM. Therefore, the 90 m resolution DEM was obtained from the National Aeronautics and Space Administration (NASA) Shuttle Radar Topography Mission (SRTM) (Fig. 3) at 90m resolution with an accuracy of about ±16m (Reuter et al. 2007) at http://srtm.csi.cgiar.org/.

3.2.5 Soil map and soil type data The soil map (1:350 000) was obtained from the MOA as shown in Fig. 3. Major soil types in the basin are Rengam-Jerangau (73.9%), followed by steepland (14.7%), Telemong-Akob-Local Alluvium (8.5%), Durian-Malacca Tavy (2.5%), Pohoi-Durian-Tavy (0.2%), Holyrood Lunas (0.1%) and Serdang-Bungor-Munchong (0.1%). Soil properties (e.g., soil texture and soil depth) were extracted from the report by Pushparajah and Amin (1977) to construct the local soil database for the SWAT model. 3.3 Trend analysis of climate A non-parametric Mann-Kendall (MK) test recommended by the World Meteorological Organization (WMO 1988) was applied to evaluate the hydro-climatic trends in UB, MB and LB of the studied basin JRB. The details and formulas for MK test can be found in Adnan and Atkinson (2011) and Shi et al. (2013). Trend magnitude was evaluated by the non-parametric, Sen’s slope approach established by Hirsch et al. (1982). The estimator � in this method is calculated as (Liu et al. 2013):

� � ������ � � � � � � , �� � � where the � and are successive values, n is data number, 1<i<j<n. The estimator � is the median of all slopes between data pairs for the entire data set. Positive � shows an increasing trend, and negative one indicates a decreasing trend. 3.4 Implementation of SWAT: calibration and validation The Arc-SWAT 2012 that integrates SWAT into ArcGIS platform was used in this study for streamflow simulation. This model is available from the SWAT official website at: http://swat.tamu.edu/software/arcswat/. Generally, the implementation of the SWAT model involves five main procedures: (1) data preparation; (2) basin delineation; (3) HRU definition; (4) parameter sensitivity analysis; (5) calibration and validation. Data sets as listed in Table 1 were used as inputs into the SWAT model. The Hargreaves approach was chosen for the ET computation because only precipitation and temperature data were available in this study. The dam was not simulated in this study due to the lack of relevant hydrological data sets.

The SWAT-CUP tool developed by Abbaspour et al. (2007) was applied for the sensitivity analysis, calibration and validation. The detailed description and processing procedure of SWAT-CUP can be found in Abbaspour (2012). The Sequential Uncertainty Fitting algorithm (SUFI-2), a semi-automatic inverse modelling procedure within SWAT-CUP, was selected because of its capability in handling and analysing many parameters in the smallest amount of model runs (Yang et al. 2008). The global sensitivity analysis that is integrated within SUFI-2 was used to test 10 parameters in parallel with the calibration procedure. The monthly streamflow data acquired from the Rantau Panjang station for 1980-1985 and the land-use map for 1984 were used for the model calibration, whereas the monthly streamflow for the period 1986-1991 was used for the model validation. The new

(1)

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

6

parameters obtained from the calibration were then applied in the SWAT model for validation purpose.

The performance of the model was determined by using several statistical indicators including the Nash-Sutcliffe efficiency (NSE) (Nash and Sutcliffe 1970), coefficient of determination (R2) and percentage of bias (PB) (Gupta et al. 1999). The NSE is widely applied in the hydrograph assessment in order to measure “goodness-of-fit” between the simulated and observed streamflows (Nash and Sutcliffe 1970). The NSE values range from 0 to 1 (ideal) are recognized as being an acceptable performance, while -∞ to 0 represents an unacceptable performance (Moriasi et al. 2007). The PB measures the average deviation of the simulated values from the observed values and 0 is the ideal value. A positive (negative) value shows an underestimation (overestimation) bias of the simulated variable compared to the observed variable (Gupta et al. 1999). The R2 evaluate the correlation between two variables and ranges from 0 to 1 (ideal). According to Moriasi et al. (2007), the performance of the SWAT model can be categorized as satisfactory (good) if NSE>0.5 (0.65) and PB<±25% (±15%). 3.5 Establishing four scenarios for assessing impacts of land-use and climate changes The one factor at a time approach (Li et al. 2009; Khoi and Suetsugi 2014) by changing one factor at each time while keeping others constant was applied to analyse impacts of land-use and climate changes on hydrological components. The considered hydrological components were streamflow, evaporation (EV), evapotranspiration (ET), soil water content (SW), percolation (PERC), surface runoff (SURQ), groundwater flow (GW_Q), water yield (WYLD) and lateral flow (LAT_Q) (Fig. 8).

The climate datasets were separated into two periods, 1975-1989 (representing the 1980s) and 1990-2004 (representing the 2000s). The land-use maps of 1984 and 2002 were used to reflect land-use pattern for the 1980s and 2000s, respectively. The year that the Linggiu dam came into operation (1991) was selected as the turning point of the climate data, because significant changes of land use may play a vital role in local hydrological components. As a result, four scenarios were established as follows: O1: climate 1980s and land use 1984 (baseline) O2: climate 1980s and land use 2002 (land-use change) O3: climate 2000s and land use 1984 (climate change) O4: climate 2000s and land use 2002 (land-use and climate change)

Four scenarios were used to run the calibrated SWAT, and their outputs were compared to investigate the separated and combined impacts of land-use changes and climate variability on various hydrological components. The outputs in O1 were considered as a baseline. The difference in outputs between O2 (O3) and O1 reflects the separate impact of land-use change (climate variability) on hydrology in the JRB. Compared with O1, O4 reflects the combined impacts. The mean annual and monthly values of simulated streamflow and evaporation of O2, O3 and O4 were subtracted from those in O1, and converted into percentages. The influences of both impacts were further analysed at the sub-basin scale by dividing the JRB into UB (sub-basin 3:SB3 and sub-basin 11:SB11), MB (sub-basin 12:SB12), and LB (sub-basin 19:SB19) (Fig. 1). For example, the SB3 output can reflect the impacts of deforestation and dam development on local hydrologic cycle. Moreover, the output of SB12 can be used to compare with streamflow data obtained from Rantau Panjang station, whereas the output of SB19 corresponds to the outlet of the entire basin.

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

7

4 RESULTS 4.1 Analysis of land-use changes The major land-use types in the JRB were oil palm, forest and rubber plantation, which occupied 39.4%, 35.7% and 18.5% of the area in 1984 and 60.4%, 24.1% and 4.3% in 2002, respectively (Fig. 4 and Table 2). Generally, three significant land-use changes were observed: (1) deforestation: decreasing of forest area by 191.4 km2, or 11.6%; (2) agricultural expansion: increasing of oil palm plantation by about 347.8 km2, or 21.1%; and (3) agriculture land loss: reduction of rubber plantation by some 234.8 km2, 14.2%. Compared with 1984, the areas of water and urban area slightly increased by 53.6 km2 (3.3%) and 6.6 km2 (0.4%) in 2002, respectively. The changes were mainly caused by the gigantic oil palm plantation project and the construction of the Linggiu dam as mentioned in section 1. 4.2 Analysis of climate trend The results on the Man-Kendall and Sen’s slope analysis of streamflow, precipitation and temperature are illustrated in Fig. 5 and presented in Tables 3, 4 and 5. The trend analysis shows increases in annual mean, peak and low flows (0.15 m3/s/year, 1.70 m3/s/year and 0.03 m3/s/year) which are not statistically significant. The highest and lowest peak flow (low flow) were recorded at 717.2 m3/s (19.11 m3/s) in year 1996 (1995) and 85.56 m3/s (2.16 m3/s) in year 1998 (1976).

The annual precipitation in the Kluang station (UB), Senai station (LB) and Felda Bukit Permai station (MB) indicated an increasing trend from 1975 to 2007 (Table 3) with 7.7 mm/year (insignificant), 12.4 mm/year (significant at 0.05) and 16.8 mm/year (significant at 0.05), respectively. Comparing the average annual precipitation between 1980s and 2000s, it rose at the LB and UB by 219 mm and 128 mm, respectively (Fig. 5a). The trend analysis was further conducted at the monthly scale as shown in Table 4. The outcome shows that for the entire basin (average of UB, MB and LB) the monthly precipitation for February (-1.55 mm/year) and April (-0.76 mm/year) had the decreasing trends, whilst the remaining months had the increasing trends, with the highest increment recorded in January (5.55 mm/year). As shown in Fig. 5 and Fig. 6, an increasing trend in annual precipitation from 1980s to 2000s was found. However, it tended to increase in the flooding months (January and December), but decrease in dry seasons (June, August and September) for both the UB and LB parts. A similar trend was reported by Adnan and Atkinson (2011) for the northern Peninsular Malaysia.

There is a monotonic trend and statistically significant change at level 0.01 in annual temperature for the LB (0.03 oC/year) and UB (0.03 oC/year) parts (Table 3). The mean annual temperature from 1980s to 2000s increased by about 0.47 oC and 0.44 oC in the UB and LB, respectively (Fig. 5b). At the monthly scale, a significant increasing trend of temperature was observed for every month for both zones as shown in Table 5. The highest increment of temperature in the UB was recorded in July (by 0.03 oC/year), whereas for the LB it was 0.03 oC/year for March. This is an important finding and evidence of a global warming signal over the entire JRB. Tangang et al. (2012) reported that the mean surface temperature in Malaysia is expected to rise by about 3 to 5 oC by the end of the 21st century. In addition, based on an ensemble of six general circulation models, Tan et al. (2014) found that the projected annual temperature in the JRB is expected to increase by 0.6 to 3.2 oC by the end of the century. Our trend analysis suggests that the JRB will get warmer in all months.

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

8

4.3 SWAT calibration and validation The JRB was divided into 19 sub-basins and 59 HRUs (Fig. 1). The global sensitivity analysis showed that the most sensitive parameters for this study were CN2 (initial SCS CN II value), followed by ESCO (soil evaporation compensation factor), GW_REVAP (groundwater “revap” coefficient), SOL_AWC (available water capacity), CH_N2 (Manning’s value for main channel), GWQMN (threshold water depth in the shallow aquifer for flow), GW_DELAY (groundwater delay), ALPHA_BNK (baseflow alpha factor for bank storage), ALPHA_BF (baseflow alpha factor) and CH_K2 (channel effective hydraulic conductivity) (Table 6). These parameters characterize surface runoff, soil properties and groundwater. Figure 7 shows the measured and simulated monthly streamflows at the Rantau Panjang station for the calibration (1980-1985) and validation (1986-1991) periods. During the calibration period, the NSE, R2 and PB were 0.66, 0.67 and -3.9%, respectively, while they were 0.62, 0.68, -15.9%, respectively, during the validation period. According to Moriasi et al. (2007), the performance of the SWAT model was considered as “good” during the calibration period while “satisfactory” during the validation period. The performance for the calibration period is better. The SWAT performance for the validation period was not as good as for the calibration period, which is most likely due to occurrence of extreme flood events in early 1984 where the SWAT poorly matched the peak flows (Fig. 7). The SWAT performance does not reach “very good” performance, and this is probably because climate data obtained from the principal stations are located outside the basin and the distribution of the climate stations is sparse. Overall, the performance evaluation indicates that the calibrated SWAT model is applicable for the JRB. Table 6 represents the calibrated parameters assigned to run scenarios established in section 3.5. 4.4 Impact of land-use change (O2 vs O1) The impact of land-use changes on the local hydrological cycle is illustrated in Fig. 8 (various components), 9 (annual streamflow and evaporation), 10 (monthly streamflow) and 11 (monthly evaporation). The conversion of forest to oil palm plantation increased the surface runoff and lateral flow dramatically by 4.8% and 12.1%, respectively, at the outlet of the JRB (SB19). Groundwater flow and percolation decreased significantly by 9.0% (SB19) and 8.8% (SB19), respectively. On the contrary, evapotranspiration, soil water content and water yield did not show much change. The main contributing reasons for these changes are deforestation and expansion of the oil palm plantations over the study site. Generally, interception and infiltration rates are higher in forest areas compared to other land cover types, hence deforestation in the JRB caused increase in surface runoff and decrease of water movement within the soil layer. The deforestation and the increase of oil palm plantations on SB12 resulted in a minor increase in annual streamflow (0.1%) and a decrease in the evaporation rate (0.2%). Regarding seasonal changes, streamflow increased by 1.8% in the wet season and decreased by 3.3% in the dry season (SB12). Similar trends were found in other sub-basins except SB3, where streamflow increased slightly in both dry (1.6%) and wet (1.5%) seasons. The Linggiu dam affected the Johor River streamflow with an increasing trend in SB3. However, Jayawardena et al. (1997) stated that it only affects streamflow in the northern regions of the upper basin, but does not reduce flood risk over the whole JRB. In the case of evaporation, it decreased for both the dry and wet season in SB12 by 1.2% and 1.0%, respectively. In the northern part of the JRB,

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

9

evaporation rate indicated an increase with the highest rate recorded in November (SB1) and August (SB3) by 23.7% and 2.2%, respectively. Accordingly, conversion of forest to oil palm plantation (water) decreased (increased) the evaporation rate.

The impact of land-use changes on the hydrological regime in different parts of Malaysia has been conducted by several researchers. Adnan and Atkinson (2011) evaluated land-use impact on streamflow in the Kelantan catchment, and concluded that the expansion of huge agricultural area (400%) in the upstream area had contribute to significant changes in streamflow with magnitude of 13.7 m3/s/year. Amini et al. (2011) reported the transformation of forest to paved area (40% urban) and urban (100%) in Damansara watershed, western Peninsular Malaysia and found an increment of peak flow by 1.37% and 14%, respectively. In general, both findings were quite similar to our results in the JRB. 4.5 Impact of climate variability (O3 vs O1) The climate variables were evaluated for two periods: 1975-1990 (1980s) and 1991-2007 (2000s) for hydrological impact assessment (Fig. 5). In the UB, annual precipitation (mean temperature) increased by 128 mm (0.47oC) or by 6.2% (1.8%), and in the LB it increased by 219 mm (0.44 oC) or by 9.3% (1.7%). The increase in precipitation was highest in December: by 72.5 mm, or 32.1% (UB) and 57.0 mm, or 23.8% (LB), followed by January by 57.2 mm, or 39.4% (UB) and 33.4 mm, or 19.5% (LB), respectively (Fig. 6). The remaining months showed a decreasing rate, where the steepest drop was recorded in September -54.8 mm, or 30.5% (UB) and -78.1 mm, or 38.4% (LB). This finding is similar to that reported by Adnan and Atkinson et al. (2011) and Shaaban et al. (2011) who analysed climate trend from 1975 to 2006 and 1984 to 2050, respectively, and showed that Malaysia’s climate system is becoming wetter in wet season and drier in dry season. Hence the frequency and intensity of dynamic natural disasters such as floods and droughts are expected to increase. As shown in Fig. 8, climate change caused an increase of 10.5 % in surface runoff, 7.4% in water yield, 1.9% in groundwater flow, 2.4% in percolation, 1.8% in lateral flow, 1.3% in evapotranspiration and 1.6% in soil water in SB12. The increments of water components are mainly due to changes of the climate system between 2000s and 1980s (Fig. 5). Precipitation and temperature play an important role in the water cycle elements pattern changes (Khoi and Suetsugi 2014). For example, increasing precipitation leads to a notable increase in surface runoff in the middle basin (SB12). Soil water content decreased due to the construction of the Linggiu dam in the upper basin area and conversion of forest area to water bodies (SB3).

The annual streamflow (evaporation) increased by 3.7% (1.4%), 3.2% (1.7%), 4.38% (2.17%) and 2.9% (8.7%) in SB3, SB11, SB12 and SB19, respectively. The rises in precipitation and temperature in the 2000s compared to 1990s were the main reasons for the increase of annual streamflow and evaporation. The rise of temperature enhances the evaporation rate (Trenberth 1999), whereas the higher amount of precipitation is leading to a higher streamflow (Shi et al. 2013). At the monthly timescale, the streamflow increased from December to January and March to June with the highest increase found in March by 31.4% (SB3), 33.1% (SB11), 24.1% (SB12) and 19.1% (SB19) (Table 7). A significant decrease in monthly streamflow under climate change was found in September by 6.1% (SB3), 7.0% (SB11), 8.4% (SB12) and 10.7% (SB19), which was caused by the reduction of precipitation amount for this month (Fig. 6). Moreover, the evaporation rate tends to dramatically

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

10

increase in March, April, October and November due to the increase in temperature during these months. 4.6 Combined impacts of land-use change and climate variability (O4 vs O1) The combined impact of land-use change and climate variability shows an increase in the annual streamflow, evaporation (Fig. 9) and hydrological components (Fig. 8) for the JRB. When land-use and climate variability cause changes in the same (opposite) direction, the combined impact on hydrological components is amplified (reduced). This situation is well demonstrated by the annual streamflow changes in SB19 where land-use changes depicted a decreased trend (0.3%) and climate changes an increase (2.9%), thus leading to a reduced increase (2.5%) under the combined impact.

The annual streamflow changes of the Rantau Panjang station (SB12) and the basin outlet (SB19) under combined impact show an increase by 4.4% and 2.5%, respectively, whereas the annual evaporation rate in SB12 and SB19 increased by 1.2% and 8.2%, respectively. Additionally, the steepest streamflow increase was recorded in March by 32.3% (SB3), 32.5% (SB11), 24.0% (SB12) and 18.8% (SB19); followed by December 15.4% (SB3), 16.7% (SB11), 14.0% (SB12) and 10.0% (SB19) (Table 7). These changes were mainly enforced by increment of dynamic rainfall events during wet season which contributed to frequent occurrence of flood events in the JRB. For example, a couple of abnormal heavy rainfall events during December 2006 and January 2007 caused extensive flooding events within the basin (Kia et al. 2012).

5 DISCUSSION The Mann-Kendall analysis shows that there is no significant trend in annual streamflow at the Rantau Panjang station. The results agree with the findings of Ramachandra et al. (2011) and Shaaban et al. (2011). They concluded that there are no significant changes in the Johor River. However, deforestation raised the peak flow (Lin et al. 2007; Mango et al. 2011) which further led to the increasing occurrence of flood events. As reported by IPCC (2007), streamflow is expected to increase about 10 to 40% in tropical regions by the mid-century, and this expected streamflow trend was already observed in the past in the JRB. However, we did not find a significant trend in the measured streamflow, probably due to the fact that the impacts of climate variability on streamflow were buffered by the impacts of land-use change.

This study shows that the annual streamflow and evaporation are influenced greater by climate variability compared to land-use changes. Hence, the adaptation to climate change should be emphasized when planning future water resources of the JRB. In addition, our study has shown that land use plays a more important role in affecting the evaporation rate than streamflow. However, the responses of streamflow at the sub-basin scale were different. An effective land-use planning can be a good way to reduce the impact of climate influence on the hydrological regime. Liu et al. (2013) reported that reforestation decreased the annual mean and peak flow. According to McGuffie et al. (1995), tropical deforestation in Southeast Asia caused a reduction in evaporation of about 5-15Wm-2. Hence, reforestation should be an adaptation strategy to reduce the impact of climate change on the JRB hydrology components.

The SWAT model performed satisfactorily over the JRB, southern Peninsular Malaysia. Nevertheless, there are several limitations arising in both model and data, which should be overcome in future studies. The main problem of this study is the

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

11

limited data availability. Other climate variables such as relative humidity, solar radiation and wind speed at daily time scale should be collected in future studies for better simulation. Longer series and other types of hydrological data should be collected and considered in the calibration process, so that the model performance for streamflow and hydrological regime simulation could be improved. 6 CONCLUSIONS The assessment of land-use and climate variability impacts on hydrological components can be conducted by using a hydrological model, SWAT. This study showed that the SWAT model can perform satisfactorily in hydrology cycle simulation for the Johor River Basin, Malaysia. The lack of simulation of the lake/reservoir is indeed a limitation, but the overall good performance during validation indicated that such limitation has a limited effect on the overall simulations from the aspect of the whole basin. The application of the SWAT model should be extended and evaluated in other parts of Malaysia. Parameters in the empirical equations within SWAT were derived from climate condition in the United States of America (Neitsch et al. 2011) which should be modified to adapt with tropical conditions, in order to obtain better hydrological simulation. The comparison of precipitation in two periods 1975-1990 (1980s) and 1991-2007 (2000s), revealed increments of 6.2% (upper basin) and 9.3% (lower basin). On the other hand, temperature has risen by 0.47oC and 0.44oC at the upper and lower sub-basins, respectively. The Mann-Kendall analysis indicated that there is a significant increase of annual precipitation in the middle and lower sub-basins. Besides that, the annual temperature also showed a significant increasing trend in the upper and lower sub-basins. Similar climate trends were observed in other related studies for the region (Tangang et al. 2007; Tan et al. 2014). These findings show that the Johor River Basin is facing climate change and the found trends may become more obvious in the future (Milly et al. 2005). The combined impacts of land-use change and climate variability resulted in the increase of surface runoff, water yield, soil water content, and evaporation, whereas the decreases in groundwater flow and percolation were found. One reason was the conversion of the huge forest zone to oil palm plantations over the JRB which caused modifications in the surface soil layer and vegetation canopy. These findings show that land-use variation plays a vital role in local water cycle changes, especially for the water movement within the soil layer. Hence the findings of this study should be considered for future land-use policy and water resources management.

More research is essential to investigate the potential impact of future climate changes on the hydrological processes with emphasis on streamflow in the Johor River Basin. The combination of the Global Circulation Model (GCMs) climate scenarios with new released emission scenarios called Representative Concentration Pathway (RCP) should be applied to drive SWAT. More climate stations data sets should be obtained in the study area to ensure better climate related research in the future.

Acknowledgements The authors acknowledge the Department Irrigation and Drainage Malaysia (DID), Malaysia Meteorological Department (MMD) and Ministry of Agriculture and Agro-based Industry Malaysia (MOA) for providing hydro-climatic data, land-use and soil maps. In addition, the authors wish to thank Dr. Valentina Krysanova for her suggestions for the SWAT modelling. Special thanks go

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

12

to two anonymous reviewers and Prof. Dr. John Van Genderen for providing constructive comments, which helped us to improve the quality of the manuscript. Funding Grant funding was provided by the Ministry of Higher Education (MOHE)

Malaysia and Universiti Teknologi Malaysia (UTM) [VOT No.

R.J130000.7827.4F213].

REFERENCES Abbaspour, K.C., Vejdani, M., and Haghighat, S., 2007. SWAT-CUP calibration and

uncertainty programs for SWAT. Modsim 2007: International Congress on Modelling and Simulation: Land, Water and Environmental Management: Integrated Systems for Sustainability, Christchurch, New Zealand.

Abbaspour, K.C., 2012. SWAT-CUP 2012: SWAT calibration and uncertainty programs - a user manual. Eawag: Swiss Federal Institute Science and Technology.

Adnan, N.A., and Atkinson, P.M., 2011. Exploring the impact of climate and land use changes on streamflow trends in a monsoon catchment. International Journal of Climatology, 31, 815-831.

Amini, A., et al., 2011. Impacts of land-use change on streamflows in the Damansara Watershed, Malaysia. Arabian Journal for Science and Engineering, 36(5), 713-720.

Arnold, J. G., et al. 1998. Large area hydrologic modeling and assessment - Part 1: Model development. Journal of the American Water Resources Association, 34(1), 73-89.

Arnold, J.G., et al., 2012. SWAT: model use, calibration, and validation. Transactions of the ASABE, 55(4), 1491-1508.

Brown, A.E., et al., 2013. Impact of forest cover changes on annual streamflow and flow duration curves. Journal of Hydrology, 483, 39-50.

Ficklin, D. L., Stewart, I. T. and Maurer, E. P. 2013. Effects of projected climate change on the hydrology in the Mono Lake Basin, California. Climatic Change, 116(1), 111-131.

Gupta, H., Sorooshian, S., and Yapo, P., 1999. Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4, 135-143.

Hirsch, R.M., Slack, J.R., and Smith, R.A., 1982. Techniques of trend analysis for monthly water quality data. Water Resources Research, 18, 107-121.

Huntington, T.G., 2006. Evidence for intensification of the global water cycle: Review and synthesis. Journal of Hydrology, 319, 83-95.

IPCC, 2007. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK.

Jayawardena, A.W., Takeuchi K., and Machbub, B., 1997. Catalogue of rivers for Southeast Asia and the pacific-volume II. The UNESCO-IHP Regional Sterring Committee for Southeast Asia and the Pacific, 2(8), 214-223.

Khoi, D.N., and Suetsugi, T., 2014. Impact of climate and land-use changes on hydrological processes and sediment yield-a case study of the Be River catchment, Vietnam. Hydrological Sciences Journal, 59(5), 1095-1108.

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

13

Kia, M.B., et al., 2012. An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental Earth Sciences, 67, 251-264.

Kim, J., et al., 2013. Impacts of changes in climate and land use/land cover under IPCC RCP scenarios on streamflow in the Hoeya River Basin, Korea. Science of The Total Environment, 452–453, 181-195.

Kundzewicz, Z.W., et al., 2008. The implications of projected climate change for freshwater resources and their management. Hydrological Sciences Journal, 53, 3-10.

Lai, S.H., and Arniza, F., 2011. Application of SWAT hydrological model to Upper Bernam River Basin (UBRB), Malaysia. The IUP Journal of Enviromental Sciences, 5, 7-18

Li, Z., et al., 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.

Lin, S., et al., 2013. Evaluating DEM source and resolution uncertainties in the Soil and Water Assessment Tool. Stochastic Environmental Research and Risk Assessment, 27, 209-221.

Liu, W.B., et al., 2013. The streamflow trend in Tangwang River basin in northeast China and its difference response to climate and land use change in sub-basins. Environmental Earth Sciences, 69, 51-62.

Mango, L.M., et al., 2011. Land use and climate change impacts on the hydrology of the upper Mara River Basin, Kenya: results of a modeling study to support better resource management. Hydrology and Earth System Sciences, 15, 2245-2258.

Mcguffie, K., et al., 1995. Global climate sensitivity to tropical deforestation. Global and Planetary Change, 10, 97-128.

Memarian, H., et al. 2014. SWAT-based Hydrological Modelling of Tropical Land Use Scenarios. Hydrological Sciences Journal, in press, doi: 10.1080/02626667.2014.892598

Milly, P.C.D., Dunne, K.A., and Vecchia, A.V. 2005. Global pattern of trends in streamflow and water availability in a changing climate. Nature, 438, 347-350.

Moriasi, D.N., et al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE 50, 885-900.

Nash, J.E., and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology, 10, 282-290.

Neitsch, S.L., et al., 2011. Soil and Water Assessment Tool theoretical documentation version 2009. Texas Water Resources Institute Technical Report No.406.

Pushparajah, E., and Amin, L.L., 1977. Soils under hevea and their management in Peninsular Malaysia. Rubber Research Institute of Malaysia.

Ramachandra Rao, A., Azli, M., and Pae, L.J., 2011. Identification of trends in Malaysian monthly runoff under the scaling hypothesis. Hydrological Sciences Journal, 56, 917-929.

Reuter, H.I., Nelson, A., and Jarvis, A., 2007. An evaluation of void‐filling interpolation methods for SRTM data. International Journal of Geographical Information Science, 21, 983-1008.

Shaaban, A.J., et al., 2011. Regional modeling of climate change impact on Peninsular Malaysia water resources. Journal of Hydrologic Engineering, 16, 1040-1049.

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

14

Shi, P., et al., 2013. Effects of land-use and climate change on hydrological processes in the upstream of Huai River, China. Water Resources Management, 27, 1263-1278.

Tan, M.L., et al., 2014 Impacts and uncertainties of climate change on streamflow of the Johor River Basin, Malaysia using a CMIP5 General Circulation Models ensemble. Journal of Water and Climate Change, in press. doi: 10.2166/wcc.2014.020.

Tangang, F. T., Juneng, L. and Ahmad, S. 2007. Trend and interannual variability of temperature in Malaysia: 1961–2002. Theoretical and Applied Climatology, 89(3-4), 127-141.

Tangang, F.T., et al., 2012. Climate change and variability over Malaysia: gaps in science and research information. Sains Malaysiana, 41, 1355-1366.

Thampi, S. G., Raneesh, K. Y. and Surya, T. V. 2010. Influence of Scale on SWAT Model Calibration for Streamflow in a River Basin in the Humid Tropics. Water Resources Management, 24(15), 4567-4578.

Trenberth, K.E., 1999. Conceptual framework for changes of extremes of the hydrological cycle with climate change. Climatic Change, 42, 327-339.

Wang, R., et al., 2013. Individual and combined effects of land use/cover and climate change on Wolf Bay watershed streamflow in southern Alabama. Hydrological Processes, in press, doi: 10.1002/hyp.10057.

Wang, Z. G., et al. 2013. Watershed Modeling of Surface Water- Groundwater Interaction under Projected Climate Change and Water Management in the Haihe River Basin, China. British Journal of Environment & Climate Change, 3(3), 421-443.

Wei, X.H., Liu, W.F., and Zhou, P.C., 2013. Quantifying the relative contributions of forest change and climatic variability to hydrology in large watersheds: a critical review of research methods. Water, 5, 728-746.

World Meteorological Organization (WMO)., 1988. Analyzing long time series of hydrological data with respect to climate variability. WCAP-3, WMO/TD- No. 224.

Yang, J., et al., 2008. Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. Journal of Hydrology, 358, 1-23.

Zorkeflee A.H., et al., 2012. Flow and sediment yield simulations for Bukit Merah Reservoir catchment, Malaysia: a case study. Water Science and Technology, 66, 2170-2176.

D

ownl

oade

d by

[O

ndok

uz M

ayis

Uni

vers

itesi

ne]

at 2

2:49

11

Nov

embe

r 20

14

Accep

ted M

anus

cript

Fig. 1 Map of the Johor River Basin with 19 suband climate gauges.

Johor River Basin with 19 sub-basins, and locations of streamflow

15

basins, and locations of streamflow

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

Fig. 2 Mean monthly streamflow (Rantau Panjang station) and precipitation from 1975 to 2004.

Mean monthly streamflow (Rantau Panjang station) and precipitation from

16

Mean monthly streamflow (Rantau Panjang station) and precipitation from

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

Fig. 3 DEM and soil map of the Johor River Basin.

DEM and soil map of the Johor River Basin.

17

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

Fig. 4 Land-use classes of the Johor River Basin in 1984 and 2002.

use classes of the Johor River Basin in 1984 and 2002.

18

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

Fig. 5 Changes in (a) annual m(UB) and lower basin (LB) during 1975

annual mean precipitation and (b) temperature at the upper basin (UB) and lower basin (LB) during 1975–2007.

19

upper basin

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

Fig. 6 Monthly precipitation and temperature differences between the 1980s and 2000s at Kluang (UB) and Senai (LB) climate stations.

Monthly precipitation and temperature differences between the 1980s and 2000s at Kluang (UB) and Senai (LB) climate stations.

20

Monthly precipitation and temperature differences between the 1980s and

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

Fig. 7 Observed and simulated monthly streamflow hydrograph at the Rantau Panjang station during the calibration (1980

Observed and simulated monthly streamflow hydrograph at the Rantau Panjang station during the calibration (1980–1985) and validation (1986–1991) periods.

21

Observed and simulated monthly streamflow hydrograph at the Rantau Panjang 1991) periods.

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

Fig. 8 Annual hydrological components changes under landand combined impact (O4) scenarios

l components changes under land-use (O2), climate (O3) scenarios.

22

use (O2), climate (O3)

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

Fig. 9 Changes in (a) annual streamflowclimate (O3) and combined impact

Changes in (a) annual streamflow and (b) evaporation under land-impact (O4) scenarios.

23

-use (O2),

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

Fig. 10 Impact of land use (O2), climate (O3) and combinedstreamflow.

Impact of land use (O2), climate (O3) and combined (O4) on monthly

24

(O4) on monthly

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

Fig. 11 Impact of land use (O2), climate (O3) and combinedevaporation.

Impact of land use (O2), climate (O3) and combined (O4) on monthly

25

(O4) on monthly

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

26

Table 1 Input data for modelling. Data Description Source Streamflow Rantau Panjang (outlet

sub-basin 12) Department of Irrigation and Drainage Malaysia

Climate data Rainfall, temperature Kluang (48672) Senai (48679) Rainfall: Hospital Kota Tinggi (47120) GRC Layang-layang (47134) Felda Taib Andak (47144) Felda Bukit Permai (47146) Felda Mardi Kluang (47158)

Malaysia Meteorological Department

DEM 90 m resolution Shuttle Radar Topography Mission Land use 1984, 2002 Ministry of Agriculture and Agro-based Industry Malaysia Soil map 2002 Ministry of Agriculture and Agro-based Industry Malaysia

Table 2 Land-use variability in the Johor River Basin between 1984 and 2002.

Type SWAT code 1984 2002 Change

(km2) (%) (km2) (%) (km2) (%)

Agriculture land AGRL 56.08 3.39 74.31 4.50 18.23 1.10

Forest FRSE 589.36 35.67 397.95 24.09 -191.41 -11.59

Oil palm OILP 650.46 39.37 998.21 60.42 347.75 21.05

Rubber RUBR 305.72 18.50 70.89 4.29 -234.83 -14.21

Urban URBN 50.52 3.06 57.14 3.46 6.62 0.40

Water WATR 0.00 0.00 53.64 3.25 53.64 3.25

1652.14 100 1652.14 100

Table 3 Mann-Kendall (Z) and Sen’s slope (�) analysis for annual streamflow (1966-1999),

precipitation and temperature (1975-2007).

Streamflow Station Precipitation Temperature

Z � S Z � S Z � S

Annual 0.68 0.15 48672(UB) 1.19 7.68 5.13 0.03 **

Peak flow 0.62 1.70 48679(LB) 2.03 12.42 * 4.11 0.03 **

Low flow 0.43 0.03 47146(MB) 2.00 16.76 *

Note S is the significance level: + significant at p < 0.1, * significant at p < 0.05, ** significant at p <

0.01.

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

27

Table 4 Analysis of Mann-Kendall statistic (Z) and Sen’s slope (�) for monthly precipitation.

Month 48672(UB) 48679(LB) 47146 (MB) Z � S Z � P Z � S Jan. 2.06 4.03 * 1.81 5.04 + 2.87 7.57 ** Feb. -1.78 -1.47 + -1.38 -2.23 -0.85 -0.96 Mar. 0.98 1.23 0.02 0.36 0.36 1.17 Apr. -0.33 -1.12 0.05 0.11 0.23 0.25 May 0.42 0.53 -0.43 -0.77 1.75 1.92 * Jun. -0.57 -0.76 1.61 2.49 0.48 0.67 Jul. 0.62 0.75 0.33 0.43 1.22 1.59 Aug. -0.36 -0.37 1.44 1.88 0.48 0.58 Sep. -0.05 -0.18 0.20 0.42 1.61 2.47 Oct. 0.76 0.54 -0.73 -1.24 0.67 1.17 Nov. -0.11 -0.28 0.17 0.58 -0.19 -0.23 Dec. 0.95 2.85 0.26 0.45 0.33 0.70

Note S is the significance level: + significant at p < 0.1, * significant at p < 0.05, ** significant at p <

0.01.

.

Table 5 Analysis of Mann-Kendall statistic (Z) and Sen’s slope (�) for monthly temperature.

UB LB

Month Z � S Z � S

Jan. 3.08 0.02 ** 2.77 0.02 **

Feb. 2.67 0.02 ** 2.70 0.03 **

Mar. 2.57 0.03 * 3.19 0.03 **

Apr. 3.35 0.03 ** 3.32 0.02 **

May 3.83 0.03 ** 3.22 0.03 **

Jun. 3.10 0.03 ** 2.31 0.02 *

Jul. 3.92 0.04 ** 3.55 0.03 **

Aug. 3.33 0.03 ** 2.74 0.02 **

Sep. 3.80 0.03 ** 3.16 0.02 **

Oct. 3.42 0.03 ** 3.92 0.03 **

Nov. 4.20 0.03 ** 3.36 0.03 **

Dec. 3.98 0.03 ** 3.61 0.03 **

Note S is the significance level: + significant at p < 0.1, * significant at p < 0.05, ** significant at p <

0.01.

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014

Accep

ted M

anus

cript

28

Table 6 SWAT sensitivity analysis (1: most sensitive) and calibrated parameters range. No. Parameter name Min Max Fitted value 1 CN2 -0.20 0.20 -0.14 2 ESCO 0.80 1.00 0.82 3 GW_REVAP 0 0.20 0.08 4 SOL_AWC -0.20 0.40 0.23 5 CH_N2 0 0.30 0.22 6 GWQMN 0 2.00 1.88 7 GW_DELAY 30.00 450.00 48.06 8 ALPHA_BNK 0 1.00 0.45 9 ALPHA_BF 0 1.00 0.65 10 CH_K2 5.00 130.00 24.88

Table 7 Simulated monthly streamflow under the impacts of land-use and climate variability at the Rantau Panjang station. Scenarios O1: baseline; O2: land-use impact; O3: climate impact; O4: combined land-use and climate impact.

Month

Scenario

O1 O2 O3 O4

(m3/s) (m3/s) (%) (m3/s) (%) (m3/s) (%)

Jan. 56.54 56.48 -0.10 59.93 6.00 60.02 6.16

Feb. 51.66 51.61 -0.10 50.04 -3.13 50.03 -3.15

Mar. 52.77 53.05 0.53 65.47 24.06 65.45 24.03

Apr. 51.24 51.54 0.57 54.56 6.47 54.67 6.68

May 36.92 36.45 -1.28 41.55 12.55 41.36 12.02

Jun. 27.07 26.44 -2.31 32.16 18.81 31.45 16.20

Jul. 36.05 35.71 -0.93 33.70 -6.51 33.46 -7.19

Aug. 46.25 46.22 -0.06 44.42 -3.95 44.36 -4.08

Sep. 50.30 50.65 0.70 46.06 -8.42 46.20 -8.15

Oct. 55.71 55.96 0.45 60.15 7.96 60.31 8.24

Nov. 74.74 75.41 0.90 68.36 -8.54 69.22 -7.39

Dec. 67.70 67.76 0.09 77.13 13.93 77.15 13.95

Dow

nloa

ded

by [

Ond

okuz

May

is U

nive

rsite

sine

] at

22:

49 1

1 N

ovem

ber

2014