Climate change impacts under CMIP5 RCP scenarios on water resources of the Kelantan River Basin, Malaysia

    Climate change impacts under CMIP5 RCP scenarios on water resources of the Kelantan River Basin, Malaysia Mou Leong Tan, Ab Latif Ibrahim, Zulkifli Yusop, Vivien P. Chua, Ngai Weng Chan PII: DOI: Reference:

S0169-8095(17)30083-2 doi:10.1016/j.atmosres.2017.01.008 ATMOS 3864

To appear in:

Atmospheric Research

Received date: Revised date: Accepted date:

4 August 2016 9 January 2017 18 January 2017

Please cite this article as: Tan, Mou Leong, Ibrahim, Ab Latif, Yusop, Zulkifli, Chua, Vivien P., Chan, Ngai Weng, Climate change impacts under CMIP5 RCP scenarios on water resources of the Kelantan River Basin, Malaysia, Atmospheric Research (2017), doi:10.1016/j.atmosres.2017.01.008

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Climate change impacts under CMIP5 RCP scenarios on water resources of the

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Kelantan River Basin, Malaysia

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Mou Leong Tan1,2*, Ab Latif Ibrahim2, Zulkifli Yusop3, Vivien P. Chua1, Ngai Weng Chan4

Department of Civil and Environmental Engineering, National University of Singapore, 1

Geoscience and Digital Earth Centre, Research Institute for Sustainable Environment,

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Engineering Drive 2, Singapore.

Centre for Environmental Sustainability and Water Security, Research Institute for

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Universiti Teknologi Malaysia, Johor Bahru, Malaysia.

Geography Section, School of Humanities, Universiti Sains Malaysia, Penang, Malaysia.

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Sustainable Environment, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.

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Abstract

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This study aims to evaluate the potential impacts of climate change on water

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resources of the Kelantan River Basin in north-eastern Peninsular Malaysia using the Soil and

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Water Assessment Tool (SWAT) model. Thirty-six downscaled climate projections from five General Circulation Models (GCMs) under the three Representative Concentration Pathways

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(RCPs) 2.6, 4.5 and 8.5 scenarios for the periods of 2015-2044 and 2045-2074 were incorporated into the calibrated SWAT model. Differences of these scenarios were calculated

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by comparing to the 1975-2004 baseline period. The results showed that the SWAT model performed well in monthly streamflow simulation, with the Nash-Sutcliffe efficiency values

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of 0.75 and 0.63 for calibration and validation, respectively. Based on the ensemble of five

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GCMs, the annual rainfall and maximum temperature are projected to increase by 1.2-8.8%

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and 0.6-2.1oC, respectively. This corresponds to the increases in annual streamflow (14.627.2%), evapotranspiration (0.3-2.7%), surface runoff (46.8-90.2%) and water yield (14.226.5%) components. The study shows an increase of monthly rainfall during the wet season,

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and decrease during the dry season. Therefore, the monthly streamflow and surface runoff are likely to increase significantly in November, December and January. In addition, slight decreases in monthly water yield are found between June and October (1.9-8.9%) during the 2015-2044 period. These findings could act as a scientific reference to develop better climate adaptation strategies.

Keywords Climate change, CMIP5, RCP, SWAT, Kelantan, Malaysia.

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Introduction

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Water resources are among the most vital natural resources, as humans depend heavily on water for survival and a wide spectrum of usage (Chan, 2012). The

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Intergovernmental Panel on Climate Change (IPCC) reported increase in water-related risks (e.g. droughts and floods) that may be attributed to climate changes such as in local

reported

between

2005

and

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precipitation and temperature. Roughly two thousand natural drought and flood cases were 2015

in

the

Emergency

Disaster

Database

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(http://www.emdat.be/disaster_profiles/index.html). These events have affected more than

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1.2 billion people, caused damaged around USD$ 403 billion, and resulted in about 82,000

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deaths. Therefore, evaluation of future water resources under climate change is important to develop better water management systems and climate adaptation strategies for achieving the

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United Nations’ Sustainable Development Goals (SDGs).

Incorporation of future climate projections from General Circulation Models (GCMs) in simulations of a hydrological basin is regarded as one of the most reliable methods to evaluate water resources changes (Xu et al., 1999). Generally, an ensemble of various GCMs from different groups around the world could provide a better water resources assessment than just a single GCM (Pierce et al., 2009). For example, Sellami et al. (2016) used an ensemble of four GCMs to investigate changes of water balance components in the Chiba and Thau catchments located in Tunisia and France, respectively. Elsewhere, Tan et al. (2014) evaluated future streamflow of the Johor River Basin, Malaysia using an ensemble of six

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GCMs selected from 18 GCMs. They concluded that the application of an ensemble of

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GCMs could reduce GCM structure uncertainty in hydro-climatic studies.

The IPCC has released four new greenhouse gas scenarios for the fifth Assessment

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Report (AR5) in 2014, known as Representative Concentration Pathways (RCPs) 2.5, 4.5, 6 and 8.5. These scenarios were named based on their possible range of radiative forcing values

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(Wm-2) by the end of the 21st century compared to the pre-industrial values (van Vuuren et al., 2011). Many hydro-climatic studies have been carried out using the GCMs and RCP

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scenarios as future climate scenarios (e.g. Tan et al., 2014, Zhang et al., 2016). Ouyang et al. (2015) found a decrease of future streamflow in the Huangnizhuang Catchment in China,

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using an ensemble of six GCMs under three RCP 2.6, 4.5 and 8.5 scenarios. Ho et al. (2016)

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used the climate projections under the RCP 4.5 to study streamflow changes of the Tocantins-

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Araguaia (Brazil), and found large declines of streamflow in the annual and dry season periods scenario for the period 2071-2100.

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The Soil and Water Assessment Tool (SWAT) model has been widely applied in evaluating the impacts of climate change on water resources (Gassman et al., 2007; Krysanova and White, 2015). The model is freely available and can simulate water resources changes under different environmental conditions and management practices. However, a direct application of the SWAT model is still a challenging issue in tropical regions due to diversity of soil types, tree species and climate systems (Krysanova and Mike 2015). Yesuf et al. (2016) evaluated the capability of the SWAT model in monthly streamflow simulations in a small tropical watershed (~1.14 km2) in Ethiopia. Similarly, the SWAT model’s capability assessment was also conducted by Fukunaga et al. (2015) in the upper Itapemirim River

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Basin, Brazil. Both studies showed that the SWAT model can adequately simulate streamflow in tropical regions, but more research is required to improve the model. To date,

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only limited applications of the SWAT model in Malaysia were reported (e.g. Memarian et

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al., 2014; Tan et al., 2014, 2015a).

The main objective of this study is to evaluate the future changes of water resources

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in the Kelantan River Basin (KRB) under climate change impacts using an ensemble of five GCMs and the SWAT model. The notable aspects of this study are: (1) to assess the

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capability of the SWAT model in the streamflow simulation in the KRB, and (2) to identify the future rainfall and temperature changes in the early-21st century (2015-2044) and mid-21st

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century (2045-2074) against the baseline period (1975-2004) under the RCP 2.6, 4.5 and 8.5

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scenarios.

There is little work done in the assessment of future climate changes and their impacts on water resources in the KRB using the Coupled Model Intercomparison Project Phase 5

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(CMIP5) GCMs. Yet, the findings from this study could be used by water managers to develop a comprehensive water resources management plan in the KRB. In addition, the projected water cycle components could also be used as input data for other applications such as dam development planning, flood management, modelling ecological changes and aquatic modelling.

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Materials and Methods

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Study Area

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The KRB (12,134km2) is one of the major basins in Malaysia (Figure 1a). It is occupies more than 80% of the Kelantan state in north-eastern Peninsular Malaysia. The N to 6°N and longitudes

E to

E. The

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basin lies between latitudes 4°

elevation varies from 8 to 2174m above mean sea level, with mountains in the west and

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southwest regions. The Kelantan River is about 248km long and originates from the Titiwangsa and Tahan mountain ranges. In 2015, the population of the Kelantan state is about

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1.7 million.

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The KRB is a tropical basin, with mean annual rainfall more than 2500mm and mean annual temperature about 27.5°C (Figure 1c). The KRB is dominated by tropical rainforest, followed by rubber and oil palm plantations. Granite soil is found in the mountainous ranges

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that located in the eastern and western regions. Elsewhere, peat and silt soil types are mainly found in the northern region. The mean annual streamflow at the Jambatan Guillemard station (outlet of the KRB) is about 500 m3s-1.

In Kelantan, surface water and groundwater are the main freshwater source for domestic use and irrigation purposes. However, this region is frequently affected by monsoon floods during the wet season (November to January) and droughts in the dry season (March to May). These water-related disasters are expected to be more severe in the future, as Tan et al. (2016) reported that the maximum 1-day (Rx1d) and 5-day (Rx5d) precipitation amount

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indices had increased significantly from 1985 to 2014. For instance, a massive flood occurred in 2004 resulted in the evacuation of more than 10,000 people, caused losses of about

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USD$ 370 million and 12 deaths.

SWAT Model

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The SWAT model is a physically-based, spatially semi-distributed and continuous

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hydrological model developed by the United Stated Department of Agriculture (USDA) and Texas A&M University (Arnold et al., 1998). It was developed to analyse the impacts of land management practices on quantity and quality of water resources in un-gauged river basins.

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The model can be used to study future hydro-climatic changes by modifying the climate

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parameters based on future climate projections.

In the SWAT model, a basin is divided into various sub-basins, which are then further

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divided into the hydrologic response units (HRUs) that are comprised of unique land use, slope and soil characteristics. Initial simulation of hydrological cycle occurs at the HRU level, and excess discharge is then aggregated across the HRUs. At each HRU, the SWAT model calculates the hydrology cycle using the water balance equation as follows:

t

SWt  SWo   ( Rday  Qsurf  Ea  wseep  Qgw ) i 1

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Where SWt is the final soil water content (mm H2O), SWo is the initial soil water

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content on day i (mm H2O), t is the time (days), Rday is the amount of precipitation on day i

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(mm H2O), Qsurf is the amount of surface runoff on day i (mm H2O), Ea is the amount of

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evapotranspiration on day i (mm H2O), wseep is the amount of water entering the vadose zone from the soil profile on day i (mm H2O), and Qgw is the amount of return flow on day i (mm

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H2O). More theoretical information of the SWAT model is described in Neitsch et al. (2011).

SWAT Model Input Data

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Figure 2 shows a schematic diagram of the research framework. The main inputs for the SWAT model are digital elevation model (DEM), land use, soil, rainfall and temperature

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data. The Shuttle Radar Topography Mission (SRTM) DEM with 90m resolution, which had a better performance in SWAT modelling in Malaysia was used (Tan et al., 2015b). The

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DEM data is downloaded from http://srtm.csi.cgiar.org/. The land use map (1990) and soil map (2002) were obtained from the Ministry of Agriculture and Agro-based Industry Malaysia (MOA). Soil properties information (e.g. soil texture and depth) was extracted from a soil report prepared by Paramananthan (2000).

Daily climate data from 1974 to 2004, including rainfall, maximum and minimum temperature were collected from twenty-eight climate stations managed by the Malaysian Meteorological Department (MMD) and the Department of Irrigation and Drainage Malaysia (DID). Only two climate stations located in the downstream region (Figure 1) contain longterm maximum and minimum temperature data. Missing climate data were filled with climate

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data of the nearest station. Monthly observed streamflow data at the Jambatan Guillemard (outlet of the KRB) was used to calibrate and validate the SWAT model. The river network

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data was digitized from the topography map prepared by the Department of Survey and

delineation in the low land region.

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Model Setup, Calibration and Validation

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Mapping Malaysia (JUPEM), in order to improve river network extraction and basin

In this study, the ArcSWAT 2012, an interface of the SWAT model (version 2012) in the ArcGIS 10.2 system, was used to develop the SWAT model for the KRB. The ArcSWAT

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2012 can manage and handle multiple spatial data sets easily. There are six main steps in SWAT modelling: (1) basin delineation and river network extraction; (2) HRU definition; (3)

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validation.

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climate station formation; (4) parameter sensitivity analysis; (5) calibration; and (6)

The basin is delineated into 36 sub-basins, with the digitized river network merged into the DEM using the “burn-in” method. The sub-basins were further divided into 177 HRUs. The HRUs definition threshold values of the land use, soil and slope were set as 20%, 10% and 20%, respectively, which are recommended by Winchell et al. (2013). These percentage values could ignore minor land uses, soils and slopes in each sub-basin for controlling the number of HRUs (Tan et al., 2015b). This could reduce model errors in HRUs aggregation. Moreover, large threshold values (e.g. 20%) are generally less sensitive in the water quantity simulations (e.g. streamflow and water yield) compared to the water quality simulations (Her et al., 2015). The first year (1974) was considered as a warm up period to

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initiate the hydrological parameters. In this study, the Hargreaves method was selected for the evapotranspiration (ET) computation, as this method requires only precipitation and

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Moreover, surface runoff (SURQ) and streamflow (SF) routing were

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temperature data.

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measured using the curve number and variable storage methods, respectively.

Model parameter sensitivity analysis, calibration and validation were performed with

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the SWAT-CUP tool (http://swat.tamu.edu/software/swat-cup/). The global sensitivity analysis method was applied to evaluate the most important parameters for monthly

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streamflow simulations in the KRB. Then, the SWAT model was calibrated using the sequential uncertainty fitting algorithm (SUFI-2), with 500 different parameters combinations

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(one iteration) for the period 1975-1989. The SUFI-2 was selected due to its capability in

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analysing many parameters in the model runs. In an iteration, the SUFI-2 measures the

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goodness of fit and the 95% prediction uncertainty (95PPU) between simulated and observed streamflow (Abbaspour et al., 2015). In addition, new parameters ranges were produced which can be used in the next iteration, to re-calibrate the model until the best parameters

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ranges were obtained. These best parameters ranges were then applied to validate the monthly streamflow from 1990 to 1999.

The SUFI-2 allows application of numerous objective functions to evaluate the quality of the SWAT simulations. Five objective functions including the Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2), adjusted R2 (bR2), sum of the squares of residuals (SSQR) and percentage bias (PB) (Abbaspour, 2015) were used to calibrate the SWAT model from 1975 to 1989 with the same parameters ranges, in order to identify the most suitable objective function in this study. The mean square error (MSE) was used to

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identify the optimal simulations, where the value closer to zero is better. Finally, the NSE with the best performance, was then selected as the optimal objective function in the SWAT

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calibration and validation. The NSE is one of the most widely applied objective functions in

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hydrological modelling (Willmott et al., 2015). The NSE values range from -∞ to , with a

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negative NSE value being regarded as unacceptable performance. Moreover, collinearity between simulated and observed streamflow was analysed using the R2, and its value varies

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from -1 to 1 (ideal). The performance of the SWAT model can be characterised as good and

Future Climate Scenarios

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satisfactory with NSE values from 0.65-0.75 and 0.5-0.65, respectively (Moriasi et al. 2007).

The climate projections from five CMIP5 GCMs that showed good performance in

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historical rainfall simulation in Malaysia region (Siew et al., 2014; Tan et al., 2014) were used in this study. These GCMs were produced by various international institutions including

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(1) the National Center for Atmospheric Research, United States (CESM1-CAM5); (2) Centre National de Recherches Meteorologiques, France (CNRM-CM5); (3) Atmosphere and Ocean Research Institute (The University of Tokyo), Japan (MIROC-ESM); (4) Meteorological Research Institute, Japan (MRI-CGCM3); and (5) Norwegian Climate Centre, Norway (NorESM1-M).

Five GCM outputs (daily rainfall, minimum and maximum temperature) under the RCP 2.6, 4.5 and 8.5 scenarios were utilized to project the early-21st (2015-2044) and mid21st (2045-2074) centuries climate scenarios of the basin. The RCP 2.6 scenario indicates a very low forcing level, with a peak of 3.1 Wm-2 in the mid-21st century, subsequently

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declining to 2.6 Wm-2 by the end of the 21st century (van Vuuren et al., 2011). Greenhouse gas emissions should be reduced significantly to match the RCP 2.6 scenario. While, forcing

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level of the RCP 4.5 scenario is stabilized with 4.5 Wm-2 before 2100 by applying a range of strategies and technologies to reduce the emission of greenhouse gases. In contrast, the RCP

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8.5 scenario is a very high greenhouse gases emission scenario, with a rising radiative forcing

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pathway leading to 8.5 Wm-2 by 2100.

Generally, the spatial resolution of raw GCMs is too coarse (~200km) to study

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regional or local hydro-climatic processes, therefore downscaling must be conducted before applying future projections of GCMs into the calibrated SWAT. Two main approaches to

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downscale GCMs to finer scale are (1) dynamic downscaling - nesting of a regional climate

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model within a GCM; and (2) statistical downscaling - developing empirical relationship of

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interested variables between GCMs and observed data, and applying these relationships to downscale the GCM. The popular change factor approach (Ouyang et al., 2015; Basheer et al.,

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2016) under statistical downscaling method was used in this study.

Thirty-seven climate projections under various GCMs, RCP scenarios and study periods are listed in Table 1. Climate change impacts on water cycle components (streamflow, evapotranspiration, surface runoff and water yield) were assessed based on annual and monthly changes between each climate scenario (ID 1-36) compared to the baseline scenarios (ID 37). Besides that, climate projections of a mean ensemble of five GCMs are known as “Ensemble_ ”. Many researchers have applied the climate projection of an ensemble of three to six GCMs in their hydro-climatic studies, to obtain reliable future projections (e.g. Tan et al., 2014; Ouyang et al., 2015; Sellami et al., 2016; Zhang et al., 2016).

3.1

Parameter Sensitivity Analysis

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Results

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Table 2 ranks the parameters for the SWAT model in the KRB in terms of sensitivity.

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The results show that the most sensitive parameters were groundwater ‘revap’ coefficient (GW_REVAP), followed by the channel effective hydraulic conductivity (CH_K2), baseflow

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alpha factor (ALPHA_BF), initial SCS CN II value (CN2), groundwater delay (GW_DELAY), soil evaporation compensation factor (ESCO), threshold water depth in the

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shallow aquifer for flow (GWQMN), manning’s value for main channel (CH_N2), available

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water capacity (SOL_AWC), surface runoff lag time (SURLAG), threshold depth of water in

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the shallow aquifer for “revap” to occur (REVAPMN) and deep aquifer percolation faction (RCHRG_DP). Generally, the most sensitivity parameters of the basin are groundwaterrelated parameters (GW_REVAP, ALPHA_BF and GW_DELAY) and surface runoff

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parameter (CN2), which are also found to be sensitive in other studies in Malaysia (Memarian et al., 2014; Tan et al., 2014). This could be attributed to the intensive groundwater-stream water interactions in the basin due to the shallow groundwater.

3.2

Calibration and Validation

Figure 3 presents the calibration and validation results using monthly streamflow at the Jambatan Guillemard station for the periods 1975-1989 and 1990-1999, respectively. The initial and final parameters ranges as well as a set of the best parameters are listed in Table 2.

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Statistical analysis of the calibration (NSE=0.75; R2=0.8) and validation (NSE=0.63; R2=0.64) indicates the model performed well in monthly streamflow simulation. The SWAT model

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underestimated the peak flows during the periods of 1975-1985 and 1991-1997, but overestimated the peak flows for the 1988-1990 and 1998-1999 periods . Similarly, the

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SWAT model was also unable to match the peak flows in southern Peninsular Malaysia (Tan

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et al., 2014). This could be due to sparse distribution of the rainfall stations.

Incompleteness of the observed hydro-climatic data could also influence the model

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simulation results. For example, only a climate station located within the basin contains temperature data, but it was excluded from this study due to an inappropriate operation period.

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Although there are several streamflow stations in the basin, however most of them had

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missing data and suffer from a short operation period. The model also appears to

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underestimate the evapotranspiration processes of the basin, which is commonly observed in applications of the SWAT model in tropical regions (Nyeko, 2014; Krysanova and White, 2015). To circumvent these problems, the GW_REVAP parameter that controls the water

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movement from shallow aquifer into unsaturated zone was modified by increasing its default value, to allow more water for evapotranspiration process in the basin. Besides that, the SWAT model also overestimated the baseflow and underestimated the peak flow.

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recommended by Abbaspour et al. (2015), the CN2 parameter was increased from the original value, to increase the surface runoff and reduce the infiltration of the basin.

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3.3

Future Precipitation & Temperature Changes

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In this section, the Haiwan Machang and Gua Musang stations were used to represent climate changes of the lower and upper basins, respectively (Figure 1a). Future changes in

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annual rainfall and maximum temperature at the Haiwan Machang and Gua Musang stations (Figure 1a) are shown in Figure 4. For the Ensemble_5 under the three RCP scenarios, the

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annual rainfall and maximum temperature could increase from 1.2 to 3.6% (2.5 to 8.7%) and 0.6 to 1oC (0.8 to 2.1oC) for the 2015-2044 (2045-2074) period, respectively. The greatest

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annual rainfall (8.7%) and maximum temperature (2.1oC) changes are observed at the Gua Musang station during the 2045-2075 period under the RCP 8.5 scenario, showing that

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climate changes in the upper basin are greater compared to the lower basin during the mid-

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21st century.

Figure 5 shows the monthly changes in rainfall and maximum temperature of the KRB from the Ensemble_5 for the periods of 2015-2044 and 2045-2074 under the three RCP

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scenarios. We observed that the monthly rainfall increases dramatically in January and December, ranging from 20.2 to 47% and 40.4 to 71.3%, respectively. Moreover, monthly rainfall is projected to decrease in April (-2.7 to -22%), June (2.9 to -7.5%) and July (3.2 to 9.2%). The results indicate monthly rainfall may increase during the wet season, and decrease during the dry season. These findings are consistent with the historical annual and monthly rainfall trend analysis of the basin reported by Adnan and Atkinson (2011).

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Under the three RCP scenarios, the average maximum temperature for the early-21st and mid-21st century have a rising trend in all months, and varies from 0.1 to 2.2 oC and 0.3

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to 2.3 oC at the Haiwan Machang and Gua Musang stations, respectively. The monthly maximum temperature changes are higher in April, May and June, which could lead to water-

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scarcity problem in the KRB. In addition, high temperature and low rainfall during these months might reduce productivity of paddy, oil palm and rubber plantations within and

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surrounding of the basin (Paterson et al., 2015).

Impact of Climate Change on Streamflow

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Table 3 presents the results of the SWAT-simulated annual streamflow changes under thirty-six climate scenarios as listed in Table 1. The annual streamflow at the Jambatan

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Guillemard station is projected to increase by 14.6 to 27.2% (91.3 to 170.3 m3s-1) for the 2015-2044 and 2045-2075 periods under the three RCP scenarios, which could be attributed

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by an increase in annual rainfall and temperature over the basin. Our results show that the increases under the RCP8.5 scenario is larger compared to the RCP 2.6 and 4.5 scenarios.

The future monthly streamflow changes of the Ensemble_5 at the Jambatan Guillemard station for the 2015-2044 and 2045-2074 periods under the three RCP scenarios are shown in Figure 6(a). We observe that the monthly streamflow tends to increase largely from November to February, with the largest increase in December by 115.1% under the RCP 8.5 scenario during the 2015-2044 period. Similarly, Shaaban et al. (2011) reported increment of average and maximum monthly streamflow at the same station for the future periods of 2025-2034 and 2041-2050. The monthly streamflow is projected to decrease from June to

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October during the 2015-2044 period, varying from -1.1 to -9.1%. The results imply that the

Impact of Climate Change on Evapotranspiration

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KRB will experience less freshwater supply during the dry season due to climate changes.

The average annual evapotranspiration over the entire basin are expected to increase

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by 0.3-2.7 % (0.7-6.5 mm) for the 2015-2044 and 2045-2074 periods, as shown in Table 3.

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The highest average annual evapotranspiration (2.7%) was found during the 2045-2074 period for the RCP 8.5 scenario. Figure 6(b) presents the monthly evapotranspiration will increase during the wet season (September to February) by 0.6-3% and 1.4-5.4% in the 2015-

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2044 and 2045-2074 periods, respectively. In contrast, decreases of monthly precipitation in April will cause reduction of monthly evapotranspiration from -1.1 to -5%. Overall,

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evapotranspiration changes are less significant compared to other water cycle components,

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which is consistent with Al-Mukhtar et al. (2014).

Similarly, Mishra and Lilhare (2016) reported that increase in rainfall and temperature will lead to an increase in evapotranspiration and vice-versa. Therefore, rainfall also plays an important role that causes a significant change in evapotranspiration. As future land use changes are not considered in this study, we expect that the evapotranspiration changes of the basin will be amplified, if unbridled expansion of rubber and oil palm plantations continues to occur in the future. This is because evapotranspiration rates are directly influenced by deforestation (Bosch and Hewlett 1982; Hirano et al. 2015). For example, Giambelluca et al. (2016) demonstrated that higher evapotranspiration rates occur in rubber plantations in Thailand and Cambodia compared to tropical forest regions.

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Impact of Climate Change on Surface Runoff

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The response of the basin to Ensemble_5 climate scenarios in terms of both annual and monthly surface runoff is shown in Table 3 and Figure 6(c), respectively. Our results show that the increase in the average annual rainfall and temperature causes a significant

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increase of annual surface runoff ranging from 41.5 to 90.2% (176.6 to 384.2 mm) in the

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2015-2044 and 2045-2074 periods. The greatest surface runoff change (90.2%) was observed under the RCP 8.5 scenario during the period 2015-2044. Human activities such as deforestation, urbanization, agriculture expansion and industrialization would increase

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surface runoff. Therefore, the surface runoff changes of the basin will be higher in the near

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future.

On the monthly scale, the ensemble mean surface runoff of the basin is projected to

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increase dramatically in November (23.2 to 191.4%), December (89.1 to 285.3%) and January (48 to 98.2%) under the three RCP scenarios. As shown in Figure 6(c), the increment of surface runoff in December and January is larger during the 2015-2044 period than the 2045-2074 period. Larger increases in surface runoff could be attributed to changes of rainfall during the northeast monsoon season. Suhaila et al. (2010) reported that the magnitude and frequency of total rainfall and extreme rainfall events increased significantly during northeast monsoon season in Peninsular Malaysia. Besides that, Adnan and Atkinson (2011) also found an increasing rainfall trend during the northeast monsoon over Kelantan, and a decreasing trend in the southwest monsoon. Surface runoff is likely to rise further, if there are more extreme rainfall events (Trenberth, 2011).

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Impact of Climate Change on Water Yield

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Water yield is also considered in this study, as it can be represented water resources availability of a basin (Sun et al., 2006). Table 3 shows annual water yield of the basin is expected to rise across the three RCP scenarios, ranging from 14.2 to 26.5% (238.7 to 444.4

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mm), indicating water resources are sufficient to support population and environment of the

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KRB until the mid-21st century. However, an effective and appropriate water resources management system should be put in place in the basin, in order to prevent decline of water

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resources in term of quantity and quality, especially during the dry season.

Figure 6(d) displays the monthly water yield changes over the basin under the three

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RCP scenarios. During the 2015-2044 period, the large increases in water yield can be found in December (83.5 to 118.5%) and January (30.4 to 36.2%), while, the decreases were

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recorded in August (-5.4 to -8%), September (-3.9 to -7.9%) and October (-6.5 to -8.9%). By the 2045-2074 period, monthly water yield is projected to increase across all the months by 4.2 to 42.5% under the RCP 4.5 scenario, showing that application of greenhouse gases reduction strategies and technologies could help to increase water yield of the basin during the dry season. These findings show water yield of the KRB will be increased significantly during the wet season and slightly decrease during the dry season. Therefore, more efficient irrigation systems for oil palm and rubber plantations as well as paddy crops should be developed and implemented to minimize losses during both drought and flood seasons.

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4

Discussion

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Monsoon floods are the main disasters in Kelantan, frequently occurring during the

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northeast monsoon season (November to January). For example, one of the worst floods in

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the recent decades occurred between 14th December 2014 and 3nd January 2015, causing the deaths of more than 21 people and devastated the states Kelantan, Terengganu, Pahang and

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Perak, with damage to infrastructure estimated at more than USD$ 560 million. The basin is also affected by severe drought events, especially during the El Niño period. For example, the

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2014 drought affected more than 8,000 paddy farmers in Kelantan, and caused about USD$ 22 million crop losses. These disasters caused massive impacts on agriculture,

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aquaculture, freshwater supply and industrial sectors in the KRB. As such, a systematic

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assessment of future hydro-climatic study is essential for this basin.

The SWAT model performed satisfactorily in simulating streamflow in the KRB, with validation outputs similar as a study conducted in southern Peninsular Malaysia (Tan et al.,

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2014). In this study, the accuracy of streamflow simulations in other sub-basins was not fully studied, as only observed streamflow from a stream gauge was considered for calibration and validation purposes due to incompleteness of streamflow data. In addition, the SWAT model tends to overestimate low flow conditions of the Kelantan River. This could be due to the plant growth and land use models within the SWAT model were developed for temperate regions, and these should be improved for application of SWAT model in tropical regions (Strauch and Volk, 2013).

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SWAT calibration outputs are largely influenced by different objective functions (Willmott et al., 2015), as well as on different hydrological components simulation. There

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are, however, some drawbacks with these objective functions. For example, the NSE and R2 are more sensitive on high flow than low flow (Legates and McCabe, 1999), resulting in the

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model calibration producing better performance in the wet period compared to the dry period (Zhang et al., 2015). Despite this, assessment of various objective functions on model

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calibration has received less attention, particularly the low flow component (Pushpalatha et al., 2012). Hence, selection of an appropriate objective function remains a challenging task to

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modellers.

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The relationships between atmosphere and water cycle in the basin are unable to be

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fully captured, as there are large uncertainties in GCMs structure and RCP emission scenarios.

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These uncertainties in future precipitation and temperature values could be propagated into streamflow modelling, which influence future water cycle simulations. For example, different climate scenarios as projected by different GCMs (ID 1-30) lead to varying annual

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streamflow changes (-4.9 to 111.2%), indicating high uncertainty may occur in the CMIP5 GCM projections which is similar to findings of other studies (Kingston et al., 2011; Tan et al., 2014). In addition, projection of future precipitation is more complex and difficult than temperature due to their high spatial and temporal variability.

Wilby et al. (2012) found the change factor downscaling approach to be useful for application of multiple GCMs in long-term hydro-climatic assessment due to its simplicity and low computation cost. However, this approach could also lead to uncertainty in hydroclimatic studies because it does not address local topography, micro-climate system, future

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precipitation occurrence and distribution. In addition, it is less accurate in predicting shortterm extreme precipitation events (Ouyang et al., 2015). Therefore, impacts of different

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downscaling approaches on future hydro-climatic studies should be further explored, to better

Conclusion

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5

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quantify the future environment changes.

To develop better global, regional and local climate adaptation strategies, a robust

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assessment of future hydro-climatic changes in a specific region is necessary. This study provides a comprehensive framework and up-to-date assessment of climate change impacts

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on water resources of the Kelantan River Basin (KRB), Malaysia. Future climate changes of

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the basin were projected from thirty-seven climate scenarios from five GCMs under the RCP

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2.6, 4.5 and 8.5 scenarios for the periods of 2015-2044 and 2045-2074. These climate scenarios were then used as inputs into a calibrated SWAT model, and the relative changes of

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future water cycle components were compared to the baseline period (1975-2004) condition.

The SWAT model performed well in monthly streamflow simulation in the KRB. The GW_REVAP parameter is found to be the most sensitive parameter during the calibration process, indicating that the SWAT model might be underestimating evapotranspiration process in tropical regions. Moreover, SWAT model tends to overestimate low flows and underestimate peak flows in this tropical basin. Therefore, more studies on SWAT model modification for application in tropical regions should be conducted.

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The future annual rainfall and temperature are projected to increase for all time periods under the three RCP scenarios, which results in similar trends for annual streamflow,

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evapotranspiration, surface runoff and water yield. The basin is most sensitive towards the change in annual surface runoff (up to 90.2%), followed by streamflow, water yield and

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evapotranspiration. On the monthly scale, significant increases of surface runoff and streamflow were observed in November, December and January. Decreases of monthly water

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yield between June and October would lead to water scarcity problems in the future. Hence, climate adaptation strategies such as reforestation, rainwater harvesting, water reuse and

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recycling, and employment of effective irrigation should be introduced, to reduce climate

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change impacts on water resources in KRB.

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Future studies should be evaluated with other dynamically downscaling climate

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scenarios such as the Coordinated Regional Climate Downscaling Experiment (CORDEX) Southeast Asia. In addition, extreme events assessment of future hydro-climatic conditions is essential in future, to develop better flood and drought adaptation strategies. Lastly,

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application of the SWAT model is still relatively limited in Malaysia, and more hydroclimatic studies should be performed in other regions.

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Acknowledgment

Malaysia

under

the

Transdisciplinary

Research Grant

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Teknologi

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This research was supported by the Ministry of Higher Education Malaysia and Universiti Scheme

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(R.J130000.7809.4L835). The authors would like to thank to the MMD, DID and DOA for supplying hydro-climatic data, land use and soil maps for this study. The authors also wish to

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thank the CMIP5 project for providing future climate data. The authors acknowledge the 2016 Summer Institute for Disaster and Risk Research fellowship for the opportunity to work

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for two-weeks in the Beijing Normal University, China. Special thanks to the reviewers for

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their helpful comments and suggestions.

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List of Figures

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Figure 1: (a) Kelantan River Basin (KRB), (b) Peninsular Malaysia, and (c) mean monthly rainfall and temperature at the Haiwan Machang station from 1975 to 2004 (The station with

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black dotted is the Gua Musang station).

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Figure 2: Schematic diagram of this study.

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Figure 3: Comparison of observed and simulated monthly streamflow during calibration and

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validation.

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Figure 4: Annual rainfall and maximum temperature changes at the (a) Haiwan Machang and

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(b) Gua Musang stations under three RCP scenarios for the 2015-2044 and 2045-2074 periods.

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Figure 5: Relative changes in monthly rainfall and maximum temperature at the (a) Haiwan Machang and (b) Gua Musang stations under the RCP scenarios for the 2015-2044 and 20452074 periods.

Figure 6: Relative changes of monthly (a) streamflow, (b) evapotranspiration, (c) surface runoff and (d) water yield for the periods of 2015-2044 and 2045-2074 under the three RCP scenarios.

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List of tables

ID

Model

Scenario

Period

1

CESM1-CAM5

RCP 2.6

2015-2044

2

CESM1-CAM5

RCP 2.6

2045-2074

CESM2.6_45-74

3

CESM1-CAM5

RCP 4.5

2015-2044

CESM4.5_15-44

4

CESM1-CAM5

RCP 4.5

5

CESM1-CAM5

RCP 8.5

6

CESM1-CAM5

RCP 8.5

2045-2074

CESM8.5_45-74

7

CNRM-CM5

RCP 2.6

2015-2044

CNRM2.6_15-44

8

CNRM-CM5

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Table 1: Thirty-seven climate change scenarios applied in this study.

RCP 2.6

2045-2074

CNRM2.6_45-74

9

CNRM-CM5

RCP 4.5

2015-2044

CNRM4.5_15-44

10

CNRM-CM5

RCP 4.5

2045-2074

CNRM4.5_45-74

11

CNRM-CM5

RCP 8.5

2015-2044

CNRM8.5_15-44

12

CNRM-CM5

RCP 8.5

2045-2074

CNRM8.5_45-74

13

MIROC-ESM

RCP 2.6

2015-2044

MIROC2.6_15-44

14

MIROC-ESM

RCP 2.6

2045-2074

MIROC2.6_45-74

15

MIROC-ESM

RCP 4.5

2015-2044

MIROC4.5_15-44

16

MIROC-ESM

RCP 4.5

2045-2074

MIROC4.5_45-74

17

MIROC-ESM

RCP 8.5

2015-2044

MIROC8.5_15-44

18

MIROC-ESM

RCP 8.5

2045-2074

MIROC8.5_45-74

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CESM2.6_15-44

2045-2074

CESM4.5_45-74

2015-2044

CESM8.5_15-44

D

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Symbol

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MRI-CGCM

RCP 2.6

2015-2044

MRI2.6_15-44

20

MRI-CGCM

RCP 2.6

2045-2074

MRI2.6_45-74

21

MRI-CGCM

RCP 4.5

2015-2044

MRI4.5_15-44

22

MRI-CGCM

RCP 4.5

2045-2074

MRI4.5_45-74

23

MRI-CGCM

RCP 8.5

2015-2044

MRI8.5_15-44

24

MRI-CGCM

RCP 8.5

2045-2074

25

Nor-ESM1M

RCP 2.6

2015-2044

26

Nor-ESM1M

RCP 2.6

2045-2074

Nor2.6_45-74

27

Nor-ESM1M

RCP 4.5

2015-2044

Nor4.5_15-44

28

Nor-ESM1M

RCP 4.5

2045-2074

Nor4.5_45-74

29

Nor-ESM1M

RCP 8.5

2015-2044

Nor8.5_15-44

30

Nor-ESM1M

RCP 8.5

2045-2074

Nor8.5_45-74

31

Ensemble

RCP 2.6

2015-2044

ENS2.6_15-44

32

Ensemble

RCP 2.6

2045-2074

ENS2.6_45-74

33

Ensemble

RCP 4.5

2015-2044

ENS4.5_15-44

34

Ensemble

RCP 4.5

2045-2074

ENS4.5_45-74

35

Ensemble

RCP 8.5

2015-2044

ENS8.5_15-44

36

Ensemble

RCP 8.5

2045-2074

ENS8.5_45-74

37

Observed dataset

Baseline

1975-2004

Baseline

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MRI8.5_45-74 Nor2.6_15-44

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19

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Fitted

Min

Max

value

0.02

0.4

0.1

Min

Max

V__GW_REVAP.gw

0.38

2

V__CH_K2.rte

0

500

0

3

V__ALPHA_BF.gw

0

1

0.1

4

R__CN2.mgt

-0.5

0.5

5

V__GW_DELAY.gw

0

500

6

V__ESCO.hru

0

7

V__GWQMN.gw

0

8

V__CH_N2.rte

0

0.2

0.29

9

R__SOL_AWC.sol

0

0.5

0

0.5

0.30

10

V__SURLAG.bsn

0.05

24

8

19

14.79

11

V__REVAPMN.gw

0

500

70

320

112.75

12

V__RCHRG_DP.gw

0

1

0.4

0.6

0.53

7.28

0.5

0.34

0

0.35

0.14

0

130

15.21

0.9

0.40

0.3

0.3

1

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1

0.4

D

5000 2200 4000 2723.80

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0.4

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Initial range Final range

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Table 2: Calibrated parameters range for the SWAT model (1: most sensitive)

* V__: the parameter value is replaced with the given value, R__: the parameter value is multiplied with the (1 + given value).

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Table 3: Changes of annual climate and water cycle components for the periods of 2015-2044 and 2045-2074. (SF = streamflow; ET = evapotranspiration; SURQ = surface runoff; WYLD

Symbol

SF

ET

%

m3s-1

%

SURQ

mm

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ID

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= water yield).

WYLD

%

mm

%

mm

CESM2.6_15-44

3.3

20.8

0.0

-0.1

19.5

82.8

3.2

54.2

2

CESM2.6_45-74

5.8

36.5

1.6

3.9

20.0

85.3

5.7

96.5

3

CESM4.5_15-44

5.9

36.7

0.7

1.6

25.8

109.8

5.7

95.9

4

CESM4.5_45-74

9.8

61.3

2.3

5.6

27.4

116.5

9.7

162.3

5

CESM8.5_15-44

8.9

3.8

28.0

119.4

8.7

145.8

6

CESM8.5_45-74

7

CNRM2.6_15-44

8

D

1.6

4.2

26.2

2.3

5.6

20.7

88.2

4.2

70.8

4.3

27.2

0.5

1.2

10.6

45.3

4.3

71.6

CNRM2.6_45-74

3.1

19.4

0.8

2.0

10.8

46.2

3.1

51.4

9

CNRM4.5_15-44

0.9

5.6

0.7

1.7

5.9

25.0

0.9

15.2

10

CNRM4.5_45-74

5.2

32.8

1.8

4.3

12.2

52.1

5.2

87.3

11

AC

MA

1

CNRM8.5_15-44

1.9

11.9

0.7

1.7

7.4

31.5

1.9

31.8

12

CNRM8.5_45-74

6.7

41.9

1.7

4.1

35.5

151.2

6.6

110.9

13 MIROC2.6_15-44

1.8

11.6

1.4

3.4

3.5

14.8

1.9

31.4

14 MIROC2.6_45-74

4.2

26.1

1.7

4.2

7.5

31.9

4.2

69.9

15 MIROC4.5_15-44

3.2

20.2

1.7

4.1

6.6

27.9

3.2

54.2

16 MIROC4.5_45-74

10.8

67.6

4.1

10.1

15.6

66.4

10.8

180.6

17 MIROC8.5_15-44

3.6

22.4

2.3

5.6

6.4

27.1

3.6

60.6

CE P

TE

55.4

27.2

169.9

6.0

14.6

43.3

184.4

26.8

449.9

19

MRI2.6_15-44

0.5

3.4

0.2

0.6

3.9

16.5

0.6

9.4

20

MRI2.6_45-74

-4.9

-30.6 -1.4 -3.3

0.7

2.8

T

-4.8

-80.5

21

MRI4.5_15-44

-3.6

-22.4 -0.9 -2.2

-0.2

-0.6

-58.8

22

MRI4.5_45-74

7.6

47.7

1.5

3.6

15.5

IP

-3.5

65.8

7.5

126.3

23

MRI8.5_15-44

10.6

66.5

-1.0 -2.5

48.1

204.8

10.3

173.0

24

MRI8.5_45-74

27.6

172.4

0.1

91.0

387.3

26.9

451.4

25

Nor2.6_15-44

75.4

471.6 -0.7 -1.7

252.5

1075.0

73.1

1228.2

26

Nor2.6_45-74

64.7

404.9

0.9

2.2

195.2

831.0

62.9

1056.5

27

Nor4.5_15-44

84.4

527.7

0.6

1.5

266.7

1135.7

81.8

1374.2

28

Nor4.5_45-74

49.9

312.2

2.9

7.1

136.7

582.1

48.6

816.6

29

Nor8.5_15-44

111.2 695.4

0.4

1.0

361.3

1538.2

107.8

1810.6

30

Nor8.5_45-74

58.8

367.9

3.3

8.0

165.5

704.5

57.3

962.6

31

ENS2.6_15-44

17.1

106.9

0.3

0.7

58.0

246.9

16.6

279.0

32

ENS2.6_45-74

14.6

91.3

0.7

1.8

46.8

199.4

14.2

238.7

33

ENS4.5_15-44

18.2

113.5

0.6

1.3

61.0

259.5

17.6

296.1

34

ENS4.5_45-74

16.7

104.3

2.5

6.1

41.5

176.6

16.4

274.6

35

ENS8.5_15-44

27.2

170.3

0.8

1.9

90.2

384.2

26.5

444.4

36

ENS8.5_45-74

24.9

155.7

2.7

6.5

71.2

303.1

24.4

409.1

0.3

NU

MA

D

TE

CE P

SC R

18 MIROC8.5_45-74

AC

ACCEPTED MANUSCRIPT

AC

CE P

TE

D

MA

NU

SC R

IP

T

ACCEPTED MANUSCRIPT

Graphical abstract

ACCEPTED MANUSCRIPT

Highlights

Future hydro-climatic changes in the Kelantan River Basin are investigated.

-

The SWAT model performed well in a tropical basin.

-

Annual climate, temperature and water cycle components are projected to increase in

SC R

future.

Rainfall, Streamflow and surface runoff will increase significantly from November to

NU

-

January.

MA

Water yield is expected to decrease between June and October during the period

CE P

TE

D

2015-2045.

AC

-

IP

T

-