Stoch Environ Res Risk Assess DOI 10.1007/s00477-013-0690-5

ORIGINAL PAPER

Study of runoff response to land use change in the East River basin in South China Jun Niu • Bellie Sivakumar

Ó Springer-Verlag Berlin Heidelberg 2013

Abstract The East River in South China plays a key role in the socio-economic development in the region and surrounding areas. Adequate understanding of the hydrologic response to land use change is crucial to develop sustainable water resources management strategies in the region. The present study makes an attempt to evaluate the possible impacts of land use change on hydrologic response using a numerical model and corresponding available vegetation datasets. The variable infiltration capacity model is applied to simulate runoff responses to several land use scenarios within the basin (e.g., afforestation, deforestation, and reduction in farmland area) for the period 1952–2000. The results indicate that annual runoff is reduced by 3.5 % (32.3 mm) when 25 % of the current grassland area (including grasslands and wooded grasslands, with 46.8 % of total vegetation cover) is converted to forestland. Afforestation results in reduction in the monthly flow volume, peak flow, and low flow, but with significantly greater reduction in low flow for the basin. The simulated annual runoff increases by about 1.4 % (12.6 mm) in the deforestation scenario by changing forestland (including deciduous

J. Niu (&) Department of Civil Engineering, The University of Hong Kong, Hong Kong, China e-mail: [email protected]; [email protected] B. Sivakumar School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW, Australia e-mail: [email protected] B. Sivakumar Department of Land, Air and Water Resources, University of California, Davis, CA, USA e-mail: [email protected]

broadleaf, evergreen needleleaf, and broadleaf, with 15.6 % of total vegetation cover) to grassland area. Increase in seasonal runoff occurs mainly in autumn for converting cropland to bare soil. Keywords Runoff  Land use change  Scenario  VIC model  East River

1 Introduction The East River (Dongjiang in Chinese) is an eastern tributary of the Pearl River basin in South China (Fig. 1). It plays a major role in fulfilling various water demands in the region and surrounding areas. For example, about 80 % of the fresh water consumption in Hong Kong is supplied by the East River. As one of the fastest developing regions in China since the nation adopted the ‘open door and reform’ policy, the region has been experiencing water shortage in recent years, including in the local cities (e.g., Heyuan, Huizhou), cities in the Pearl River Delta (e.g., Dongguan, Shenzhen), and Hong Kong. This is particularly the case during drought periods (a frequent occurrence in the basin), when there is insufficient water in the river to meet the various demands in the region. The change in land uses, especially to accommodate the rapid economic growth and increasing urban population, has also exerted various hydrologic impacts in the basin. Numerous studies, including those in the East River basin, have demonstrated that land use change and the associated altered features in river flow severely affect channel sedimentation status, water environment conditions, and river ecosystem health (Lu 2004; Fok et al. 2009; Heathcote 2009; Zhang et al. 2011). Therefore, investigation of the influence of land use change on the runoff responses in the East River basin is

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vital for regional development strategies and undertaking proper water management measures. In regards to runoff response to land use change, a large number of studies have been conducted around the world. Sahin and Hall (1996) performed an extensive analysis on this subject, through application of fuzzy linear regression to data from as many as 145 experimental catchments around world. Their study revealed a 5 mm decrease in annual runoff for a 10 % increase of scrub cover, and an 18 mm increase for a 10 % reduction in the cover of deciduous hardwood. Sharda et al. (1998) showed that conversion of grasslands to bluegum plantation in the Nilgiris district of India reduced water yield by 16 % during the first 10-year period and 25.4 % during the second 10-year period. Huang et al. (2003) compared a paired watershed (one untreated and the other treated with tree planting) of areas of 0.87 and 1.15 km2, respectively, to study runoff response to afforestation in the watershed of the Loess Plateau in China. They estimated that runoff generation was reduced by 32 % as a result of 80 % afforestation. These studies were mainly based on experimental approach. However, since relevant information about vegetation cover change may not be accessible for many river basins (Huang and Cai 2007), especially for large-scale river basins, one practical way to obtain knowledge of runoff response to land use change for such basins is to utilize hydrologic models by setting different land use change scenarios. For instance, Wang et al. (2008) employed the Soil and Water Assessment Tool (SWAT), a distributed hydrologic model, to study the hydrologic response to different land use change scenarios for the Zamu River basin (catchment area 851 km2) in northwest China. Their study revealed that the runoff of the mountain reaches of the catchment increased for the increased grassland area and decreased forestland. Numerical models are increasingly used to make evaluations or predictions of land use or climate change impacts (Mishra et al. 2011). As a hypothesis of the real world’s functioning, codified in quantitative terms, different models may have different advantages on certain perspectives (Mishra and Desai 2005; Chen and Wu 2012). With the advancement of geophysical science, especially with the availability of significant spatial information from remote sensing and data from digital elevation models (DEMs), hydrologic modeling of large-scale river basins is now possible. For instance, the variable infiltration capacity (VIC) model (Liang et al. 1994), a large-scale land surface hydrologic model, has been applied for many regions from small catchments to continental level ones on a grid cell basis (Abdulla et al. 1996; Nijssen et al. 1997, 2001a, b). The area percentage of different vegetation types within each grid cell is considered in the model for representing sub-grid heterogeneity. This model feature allows

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us to study the hydrologic effects of land use change by setting different vegetation cover scenarios. As the runoff generation output is computed for each grid cell, it can be easily tailored to evaluate the runoff response for different sub-regions within the study area. In the present study, to give a general assessment of the runoff response to possible land use change (e.g. afforestation, deforestation, return the farmland to forestland) in the entire East River basin, the VIC model is employed to quantify the impact by setting corresponding vegetation cover scenarios. Given the great importance of the East River basin in the regional socio-economic development of the Pearl River Delta and Hong Kong, studies on the role and influence of hydrologic cycle on the local water resources have gained significant momentum. Lu (2004) identified an increasing trend in annual minimum discharge at the Boluo station for the period 1960–1987. Zhang et al. (2009) examined the scaling and persistence features of the observed daily streamflow series for four hydrologic stations. Jiang et al. (2007) analyzed the hydrologic cycle in the East River basin using several conceptual models. The terrestrial hydrologic processes for the whole East, North, and West River basin were simulated with VIC model in Niu and Chen (2008, 2010). Following up on these studies, the present study focuses on the hydrologic effects of vegetation cover change in the East River basin, to provide an overall assessment on runoff response to the possible land use changes. It is worth noting that the water resources in the East River basin have been heavily regulated due to the important water supply role in the region (Niu and Chen 2010; Wu and Chen 2012). The hydrologic effects due to land use changes in this study are evaluated based on the natural hydrologic responses. The VIC model is used to examine the possible hydrologic effects of several land use scenarios, related to afforestation, deforestation, and returning farmland to forestland, over the East River basin for the period of 1952–2000. The baseline land use information for the VIC model is obtained from the global datasets in Nijssen et al. (2001a). The rest of the paper is organized as follows: Sect. 2 provides relevant information about the study region. In Sect. 3, a brief description of the VIC model is presented. The evaluation of the VIC model and the quantification of the hydrologic effects are presented in Sect. 4. Section 5 gives conclusions.

2 Study region The East River basin in China lies between latitudes 22°340 and 25°120 N and longitudes 113°240 and 115°530 E (see Fig. 1). It originates in Xunwu County of Jiangxi Province (Pearl River Water Resource Commission 2005). The river flows from northeast to southwest and discharges into the

Stoch Environ Res Risk Assess

500

Pearl River basin

Km

Fig. 1 Location of the East River basin and runoff control stations

Pearl River Delta with an average gradient of 0.39 % (Jiang et al. 2007). The upper reach is named Xunwushui, which flows towards the southwest and joins the Anyuanshui River in Longchuan County. The upstream area is mountainous, and the river channel is shallow and narrow. The middle and downstream channels of the East River are 3000

Annual precipitation (mm/yr)

Fig. 2 Observed inter-annual variation in precipitation in the East River basin

used for navigation. The length of the mainstem is 523 km. The drainage area of the East River basin above the Boluo station is 25,325 km2, while the upstream area above the Longchuan station is 7,699 km2 (see Fig. 1 for station locations). The land surface consists of granites, sandstone, shale, limestone, and alluvium, under the Precambrian, Silurian, and Quaternary geological formations (Jiang et al. 2007). The soil types over the basin are mainly latosolic soil, red soil, and lime soil. The East River basin lies in the subtropical monsoonal climatic zone (Pearl River Hydraulic Research Institute 2007). The annual mean temperature ranges from 9.7 °C in the coldest month of January to 29 °C in the hottest month of July. The total annual precipitation over the basin for the period of 1952–2000 is shown in Fig. 2, and no significant trend is detected. The average annual precipitation is about 1800 mm, with 75 % of it falling in the wet season from April to September. The major rain-producing mechanism is frontal rain and tropical cyclones. The mean annual discharge is about 23.7 billion m3/year at the Boluo station and is about 6.3 billion m3/year at the Longchuan station (Fig. 1). The runoff in the wet season is about 80 % of the value in the whole year (Liang et al. 1993). To regulate the water resources in the basin, several reservoirs (e.g., Xinfengjiang, Fengshuba) were constructed for flood control, power generation, irrigation, navigation, and water supply. The Xinfengjiang reservoir is the largest one with 370 km2 of water surface area, and started to operate in October 1959. The drainage area of the Xinfengjiang sub-basin is about 5,740 km2, and the average annual streamflow is about 195.7 m3/s (Wu and Chen 2012). The Fengshuba reservoir started to operate in 1973. Its drainage area is about 5,150 km2, with an average annual streamflow of 130.6 m3/s. The population in the region has increased from 10.2 million in 1990 to 23.9 million in 2000 (an increase of about 134 %), and the corresponding Gross Domestic Product has improved from 37.5 billion Yuan to 403.7 billion Yuan (an increase of about 986 %) (Statistics

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Bureau of Guangdong Province 1991, 2001; Statistics Bureau of Jiangxi Province 1991, 2001). Chen and Wang (2010) indicated that, for the period 1980–2000, the grassland was reduced from 1,328 to 946 km2, the bare soil was increased from 43 to 239 km2, and the build-up area was increased from 38 to 770 km2. Currently, the land use change practices prevail in the lower reach with significant decrease in natural land use types (Ren et al. 2011).

3 Method and data 3.1 VIC model and forcing data The VIC model represents surface and subsurface hydrologic processes on a spatially distributed (grid cell) basis. The distinguishing features of the model, compared to other soil–vegetation–atmosphere transfer schemes, are the eponymous variable infiltration curve, which scales the maximum infiltration by a nonlinear function of fractional grid cell area to enable runoff calculations for sub-gridscale areas, and the parameterization of baseflow as a nonlinear recession from the lower soil moisture zone (Liang et al. 1994). The forcing data for the VIC model are daily precipitation, daily maximum temperature, and daily minimum temperature. These data for the East River basin are obtained from Feng et al. (2004). The data were originally gridded to 1° 9 1° grids for the period 1951–2000 from weather stations over Mainland China. The GTOPO30 DEM dataset with 1 km spatial resolution is used to delineate the basin area. The soil and vegetation data over the basin are extracted from global datasets in Nijssen et al. (2001a). The soil column in each grid cell is divided into three layers. The VIC model simulation provides daily time series of runoff for the period 1952–2000, as the year 1951 serves as the model spin-up time. For more details on the VIC model simulation for the whole Pearl River basin in South China and other relevant details, the reader is directed to Niu and Chen (2010). 3.2 Land use change scenarios Figure 3 (top) shows the vegetation cover proportion in the East River basin, which is derived from the grid cell datasets in Nijssen et al. (2001a). The main land use types are forestland, woodland, grassland, and cropland, combinedly occupying over 90 % of the total basin area. To understand the impacts of likely land use changes on runoff in the river basin, four vegetation cover change scenarios are considered: (1) Scenario A—it assumes that all the current grassland will be converted into forestland, while the remaining land uses remain constant; (2) Scenario B—

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it assumes that 25 % of the current grassland will be changed into forestland; (3) Scenario C—it assumes that the current forestland will be converted to grassland; and (4) Scenario D—it assumes that all the current cropland will be changed to the other vegetation types proportionally. Both Scenario A and Scencario B are related to afforestation; Scenario C is related to deforestation; and Scenario D is to return farmland to forestland. The percentages of the different vegetation types corresponding to these four scenarios are shown in Fig. 3. To quantify the hydrologic impact of land use change, the land use percentage (top one in Fig. 3) extracted from the global dataset in Nijssen et al. (2001a) is used as the baseline land use situation (hereafter denoted as ‘Normal’). Therefore, the runoff change quantified for each scenario in this study is basically the difference between the runoff for that particular (hypothesized) land use situation and the runoff for the baseline land use, under the same climatic condition during the period 1952–2000.

4 Results and discussion 4.1 Model validation The detailed VIC model application and validation for three tributaries (i.e., the East River, the North River, and the West River) in the Pearl River basin have been presented in Niu and Chen (2010). To facilitate presentation of the present analysis, the related validation results for the three stations (Boluo, Xinfengjiang, and Longchuan; see Fig. 1) are briefly described here. Table 1 lists the statistical results for three objective functions, namely relative bias (RB), relative root mean square error (RRMSE), and Nash–Sutcliffe efficiency coefficient (NSE), which provide a measure of the goodness of the fit of the modeled to the observed runoff. It is found that the NSE value for the Boluo station during the period 1954–1988 is relatively low, which is mainly due to the heavy reservoir operations (e.g., Xinfengjiang and Fengshuba). The values of statistical terms for the period 1954–1958 (i.e., before reservoir operation) are reasonable. Figure 4 shows a direct time series comparison of simulated and observed runoff for the upstream area at its control station, Longchuan, at the monthly timescale. The figure shows that the simulated runoff matches the observations well. The values of the statistics in Table 1 also indicate that the VIC model can properly simulate the runoff for the Longchuan and Xinfengjiang stations, according to the general performance rating guidelines (which recommend that the model performance at the monthly scale with NSE [ 0.50 and RB \ ±0.25 can be roughly evaluated as ‘satisfactory’) suggested by Moriasi et al. (2007).

Stoch Environ Res Risk Assess

Percentage (%)

50

1 Evergreen Needleleaf 2 Evergreen Broadleaf

37.2

40 31.3

3 Deciduous Broadleaf

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Fig. 3 Percentage of different types of vegetation cover in the East River basin (top) and vegetation cover percentages for four different scenarios: a Scenario A—all the grassland is converted to forest,

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b Scenario B—1/4th grassland is converted to forest, c Scenario C— high-density forest is converted to grassland, and d Scenario D—all cropland is converted to other vegetation types proportionally

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Fig. 4 Comparison of simulated and observed runoff at the Longchuan station in the East River basin

4.2 Extreme situation analysis To further examine the model performance on the vegetation cover change in the basin (Xu et al. 2010), two extreme situations are hypothesized. One is to suppose that the basin is completely covered by bare soil (denoted as

‘Extreme 1’), and the other is that the basin area is completely covered by evergreen forest (denoted as ‘Extreme 2’). Figure 5 shows the mean monthly values of simulated runoff, evapotranspiration, and soil water in the top soil layer for the baseline vegetation cover and for the two extreme situations under the climatic conditions during

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Stoch Environ Res Risk Assess Table 1 Runoff simulation results by VIC model at a monthly timestep for three gaging stations in the East River basin Station

Period

Mean (O) (mm/mon)

Mean (S) (mm/mon)

RBa

RRMSEb

NSEc 0.56

Boluo

1954–1988

77.35

77.17

-0.01

0.52

Boluo

1954–1958

65.62

63.27

-0.03

0.29

0.94

Xinfengjiang

1951–1958

76.03

71.77

-0.06

0.39

0.85

Longchuan

1952–1972

77.28 61.29 -0.21 0.41 Pn i¼1 ðQsi Qoi Þ RB relative bias, defined as RB ¼ P , with Qoi and Qsi the observed (O) and simulated (S) flow in month i n Q i¼1 oi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P RRMSE relative root-mean-square error, defined as RRMSE ¼ 1n ni¼1 ðQoi  Qsi Þ2 =Qo Pn ðQsi Qoi Þ2 NSE Nash–Sutcliffe efficiency coefficient, defined as NSE ¼ 1  P i¼1 2 n

0.82

a

b

c



i¼1 ðQoi Qo Þ

Table 2 shows the changes in mean annual runoff over the East River basin for the four land use scenarios considered in this study. The results indicate that the mean annual flow is reduced by 13.9 % of the Normal runoff under Scenario A, which corresponds to all the current grassland area (46.8 % of total vegetation cover) converted into forestland. The reduction is about 3.5 % if 25 % of the current grassland area is converted into forestland. These results indicate that the mean annual flow generally reduces with an increase in

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Unit : mm

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Precipitation Runoff (Normal) Runoff (Extreme 1) Evap ( Normal) Evap (Extreme 1) SW1 (Normal) SW1 (Extreme 1)

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4.3 Runoff response to different land use scenarios

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1952–2000. From Fig. 5a, it is observed that the runoff volume increases from the situation of Normal to that of Extreme 1. The canopy interception and transpiration from vegetation class are removed, and evapotranspiration decreases in terms of magnitude, especially in the wet season. As all the evaporation mainly comes from the top soil layer, soil moisture in the first layer decreases accordingly. The maximum runoff increase occurs in August, which coincides with the period of full tree canopy and maximum evaportranspiration. Differences in monthly runoff increase can be attributed to the fact that the vegetation cover is deciduous and that it has relatively smaller leaf area index in the dry season. For the Extreme 2 case, in which the basin area is completely covered by evergreen forest, the monthly runoff volume decreases for each month and the evapotranspiration increases significantly. Among others, transpiration from vegetation class also increases, and so a decrease in soil moisture in the top layer is still observed in the dry season (see Fig. 5b). In the wet season, rainfall is abundant and, since no evaporation from bare soil compared to the Normal situation, soil moisture in the top layer increases a little bit, especially from July to September. Under the combined control of rainfall, vegetation, and soil behavior, the maximum runoff reduction occurs in June, while the maximum value of the percentage runoff reduction is observed during December and January.

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Fig. 5 Simulation of hydrologic processes (including runoff, evapotranspiration, and soil moisture in the first soil layer) at monthly timestep for the period 1952–2000. a Comparison of the baseline vegetation cover (denoted as Normal) and completely covered by bare soil (denoted as Extreme 1), and b same with (a) but for the area completely covered by evergreen forest (denoted as Extreme 2)

the area of conversion of grassland into forestland, and the reduction is slightly more when the conversion area is on the higher side. This is mainly attributed to the fact that trees generally store more water and, hence, less runoff would be generated from forested catchment. The results also show that the mean annual runoff increases by 1.3 % under Scenario C, in which forestland (15.6 % of total vegetation cover) is converted to grassland. For Scenario D, in which all cropland (37.2 % of total vegetation cover) is converted to other types of vegetation cover proportionally (the proportion is according to the vegetation cover percentage of different types in the Normal situation), the mean annual runoff

Stoch Environ Res Risk Assess Table 2 Simulated runoff in the East River basin for different land use change scenarios Item

Normal

Mean annual runoff (mm)

931.77

Scenario A

Scenario B

Scenario C

Scenario D 924.34

802.55

899.43

944.35

Absolute change (mm)

-129.53

-32.34

12.58

-8.43

Percentage change (%)

-13.90

-3.47

1.35

-0.90

1000

Runoff (mm/mon)

Fig. 6 Simulation of monthly runoff for baseline land use cover, land use scenario A, and land use scenario B in the East River basin during the period 1990–2000

Normal

Scenario A

Scenario B

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decreases by 0.9 %. Figure 6 shows the monthly runoff response to the land use change, with a comparison of the runoff between Normal and the Scenarios A and B for the period of 1990–2000 (note that the logarithmic scale for the runoff value is used for better visualization). Scenarios A and B, both related to afforestation, are illustrated in the figure because their impacts on runoff are more obvious. The figure also shows that the runoff reduction happens in both dry and wet seasons. To further reveal the flow changes in magnitude, which are particularly important in the context of mitigation of extreme events or river ecosystem health, cumulative frequency distributions of monthly runoff volume, monthly peak flow, and monthly low flow for the period of 1952–2000 are shown in Fig. 7. Among these, the monthly peak/low flow is the maximum/minimum daily runoff value in each month, and the monthly runoff volume is averaged to daily scale for direct comparison with the daily peak/low flow. In general, it is observed that the runoff response due to afforestation is more obvious for low runoff value days than it is for high runoff value days. The cumulative frequency distribution of runoff volume for every month (Fig. 7a) shows that afforestation results in reduction in monthly runoff. This can be attributed to the facts that forest trees basically increase soil water storage capacity and that the stored water is likely to be returned to the atmosphere by transpiration. The cumulative distributions of peak flow (Fig. 7b) and low flow (Fig. 7c) for each month show that both peak flow and low flow decrease due to afforestation, but the reduction in magnitude for low flow is relatively more significant. It is relevant to note that the saturation-excess runoff is the dominant runoff generation mechanism in South China due to abundant rainfall (Zhao 1984), with high baseflow level

and low soil water deficit. On one hand, the high flow for both Scenario A and Scenario B normally occurs when the soil water deficit has been satisfied and rainfall is abundant and, hence, the peak flow differences (i.e., reduction here) due to afforestation are relatively small. On the other hand, trees increase soil water storage capacity and transportation and, during the episodes of prevailing low runoff, the soil water deficit tends to increase for Scenario A (i.e., more forestland) compared to Scenario B, especially in the dry season. Therefore, the low-flow reduction due to afforestation is relatively more significant when compared to the high-flow situation. In addition, Fig. 8 shows the sensitivity of runoff generation to the cropland decrease in the East River basin for the period 1952–2000. For this hypothesized situation, the reduced cropland area is preserved as bare soil with fixing other types of vegetation cover. The reduction is set to 2, 5, and 10 % of the total cropland area, respectively. The results indicate that annual runoff in the East River basin increases with a decrease in cropland cover. The annual runoff increases by about 0.6 % of the Normal runoff for a reduction of 10 % in cropland. The figure shows that a higher runoff increase appears in autumn (i.e., September–November). This is explained by the high evapotranspiration amount from leaf area during the crop growth period in South China.

5 Conclusions In this study, terrestrial hydrologic processes over the East River basin in South China for the period 1952–2000 are simulated by the VIC model using observed climate variables, available vegetation and soil global datasets. The simulated runoff at monthly timescale is validated based

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Fig. 8 Mean runoff proportion change for annual and four different seasons in the East River basin for the cropland proportion decrease (-2, -5, and -10 %)

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Fig. 7 Cumulative frequency distributions of runoff simulations for baseline land use cover, scenario A, and scenario B for the period 1952–2000. a Average daily runoff value for each month, b monthly peak flow, and c monthly low flow

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on observed runoff records at three gaging stations. The rationality of model performance in simulating the hydrologic cycles in response to land use change is also evaluated for two extreme vegetation cover conditions. In regards to afforestation in the East River basin, annual runoff is reduced by less than 3.5/13.9 % (32.3/ 129.5 mm) when 25/100 % current grassland area (46.8 % of total vegetation cover) is converted to forestland area. Afforestation reduces the monthly runoff volume, peak flow, and low flow, but the impact on low flow is far greater over the basin. In the deforestation scenario (Scenario D), the simulated annual runoff increases by about 1.4 % (i.e., 12.58 mm) with a reduction in forestland area (15.6 % of total vegetation cover) and an equally increased grassland area. When returning the farmland to forest, the annual runoff reduces by about 1 % when all the cropland is converted to the other types of vegetation cover proportionally. Monthly runoff increase occurs mainly in autumn when clearing cropland to bare soil in the basin. The present results indicate that model simulations with varying vegetation cover scenarios allow quantification of hydrologic effects of land use change for large-scale river basins and provide valuable information to better devise regional development strategies and water resources (including extreme events) management. However, largescale hydrologic modeling is still a tremendously challenging task, especially in obtaining physically meaningful parameters for representing spatial heterogeneity. The study by Niu and Chen (2010) indicated that one degree resolution is suitable for simulation of hydrologic processes at the monthly timestep over the whole East or West River basin. However, the present results seem to indicate that even this spatial resolution may be relatively coarse for studying the runoff generation response to land use change. Use of new soil and vegetation parameter datasets with higher spatial resolution for better outcomes is currently underway, details of which will be reported elsewhere.

Stoch Environ Res Risk Assess Acknowledgments This research was supported by the Hong Kong RGC GRF project (HKU 710910E) and PPR project (HKU 7022-PPR-2). The first author is grateful to Dr. Ji Chen for providing financial support to attend the 8th International Conference on HydroScience and Engineering in Nagoya, Japan in 2008, to present some early results of this work and also for some discussions immediately after that. The authors thank the anonymous reviewer for his/her valuable comments and suggestions on the paper.

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