cover change in a catchment on the Loess Plateau, China

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Original Articles

Spatial patterns of hydrological responses to land use/cover change in a catchment on the Loess Plateau, China ⁎

Rui Yana, Xiaoping Zhanga, , Shengjun Yanb, Jianjun Zhanga, Hao Chena a State Key Laboratory of Soil Erosion and Dry land Farming on the Loess Plateau, Northwest A & F University; Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi 712100, China b State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China

A R T I C L E I N F O

A B S T R A C T

Keywords: SWAT simulation Surface hydrological responses Temporal and spatial distribution Land use and cover changes Loess plateau

The Loess Plateau of China has been experiencing great land use and land cover changes under the “Grain for Green” program to control severe soil loss from human activities. Over the past 30 years, annual streamflow and sediment delivery have also reduced in most areas of Loess Plateau. In consequence, a physically based model of Soil and Water Assessment Tool (SWAT) has been employed to simulate the responses of surface hydrology to human activities in a typical catchment in the upper reaches of the Beiluo River on China’s Loess Plateau. As a result of using various sources of information, including remote sensing, it has been shown that farmland in the catchment decreased by 22.8% in 2000 and 35.0% in 2010 compared to the area in 1990. Meanwhile, forestland increased by 22.6% in 2000 and 119.8% in 2010. The area of shrubland increased by a factor of 3.3 in 2000 and 5.5 in 2010. The vegetation coverage greatly increased in the catchment during this period. Using the SWAT model, it was found that the average ET at the sub-basin scale increased by 7.4 mm in 2000 and 44.0 mm in 2010 as the vegetation coverage improved compared to that in 1990. Meanwhile, the soil water content decreased by 8.1 mm and 14.9 mm and the surface runoff decreased by 6.1 mm and 16.2 mm by these two years. The trends in the evapotranspiration, surface runoff and soil water content were closely associated with alterations in the land use and cover categories at the sub-basin scale. Generally, the higher the increasing rate of forest and grassland, the more that evapotranspiration transferred and the less surface runoff and soil water content that was generated. Spatially, the ET, surface runoff and soil water content showed the same changing gradient with land use and cover from the northern and northwestern to the southern and southeastern areas of the catchment during these periods. The scenarios simulation showed that the streamflow were more sensitive to variability in the precipitation than temperature. These results are expected to be helpful to the sustainable watershed management and provide useful information regarding land use planning and ecosystem construction strategies in the future on the Loess Plateau.

1. Introduction Land use changes and climate variability are two main driving force of hydrological processes in watersheds (Juckem et al., 2008). Understanding the responses of hydrological processes to land use changes and climate variability is of great benefit to the sustainable development of water resource management strategies (Cao et al., 2009; Narsimlu et al., 2013). Global climate change can significantly affect the distribution of water quantity and quality (USEPA, 2014). Precipitation is the source of streamflow, and any variation in precipitation can change the streamflow (Arnold et al., 1990; Tan et al., 2013). Rising temperatures generally increase actual evapotran-

⁎

spiration, which decreases runoff and soil moisture (Band et al., 1996; Stone et al., 2001; Jeppesen et al., 2009; Somura et al., 2009; Cai et al., 2009a). Land use changes can significantly affect hydrological processes, such as canopy interception, infiltration and evapotranspiration, which may eventually change the runoff volume, peak flow and flow routing time (Laurance, 2007; Bradshaw et al., 2007; Hurkmans et al., 2009; Cai et al., 2009b). The trend of streamflow and the temporal and spatial distribution of hydrological processes have recently seen increased research in the field of ecology and hydrology interaction because of increasingly evident consequences of land use/cover changes worldwide (Zhang et al., 2008b; Fu et al., 2011; Gao et al., 2012).

Corresponding author. E-mail address: [email protected] (X. Zhang).

http://dx.doi.org/10.1016/j.ecolind.2017.04.013 Received 17 November 2016; Received in revised form 16 February 2017; Accepted 6 April 2017 1470-160X/ © 2017 Elsevier Ltd. All rights reserved.

Please cite this article as: Yan, R., Ecological Indicators (2017), http://dx.doi.org/10.1016/j.ecolind.2017.04.013

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Yellow River (Chen et al., 2001). Thus, the vegetation cover on the Loess Plateau has been greatly improved (Zhang et al., 2011a,b). The Beiluo River, which is situated in the center of the Loess Plateau, was one of the most eroded areas in the region, especially in its upper reaches, with a mean annual soil loss of 15000 t per km2 per year before 1980. According to the research of Yan et al. (2016), the mean vegetation cover in the upper reaches of the river basin has improved from 12.4% to 51.2% from 1987 to 2014 because of continuous soil and water conservation implementation and vegetation restoration during this time. Lin et al. (2015) showed that the mean annual soil loss here decreased to 3000 t/km2.a after 1999. Zhang et al. (2016) showed that the mean annual streamflow in the upper reaches of the catchment exhibited statistically significant negative trends from the 1960s to 2011, with an average change rate of 0.3 mm/a. Liu et al. (2015) and Gao et al. (2015) showed that a series of soil and water conservation measurements and the slope land conservation program’s execution were regarded as the main driving force in the reduction of streamflow in the catchment. The previous researches in the catchment mainly investigated the trends of annual streamflow and flood events by using the time series approach. However, the research lacked knowledge regarding the spatial hydrological responses to land use and land cover changes. Thus, applying a physically based distributed assessment tool is necessary to understand the effects of land use and land cover changes on runoff on temporal and spatial scales. The results are expected to benefit catchment ecological construction and water resource management, especially in a water-limited ecosystem such as the Loess Plateau. This study investigates the land use and land cover changes in the catchment to (1) validate SWAT and check its suitability in the catchment, (2) quantify the effects of human activities and climate variability on runoff with the SWAT model, and (3) investigate the spatial responses of surface hydrology to land use changes and land cover improvement in the catchment.

Generally, three types of methods are used to assess the effects of land cover changes on streamflow. One method is the paired watershed approach. Paired watersheds must be geographically close and share similar hydrological regimes and soil types to efficiently eliminate the influence of precipitation. At least 30 years of hydrological and forest disturbance records are needed to meet the requirements of statistical methods (Lorup et al., 1998). However, the disadvantage is that finding strict paired catchments is difficult under severely controlled and disturbed land use types (Li et al., 2009). Another problem is that applying the results of paired catchments in a medium or large catchment is difficult because of the existing diversity of land use patterns. The second method is time series analysis in a catchment. Double mass curves can be used to eliminate the influence of precipitation (Jothityangkoon et al., 2001). Several periods can be identified along the time series. The relationship between climate factors and streamflow during the controlled period can be applied to the next period, and the contributions of climate variability and land cover changes can be identified (Leavesley, 1994). Time series analysis may be used to study the effects of environmental change on hydrological processes. However, this method does not consider physical mechanisms. The GIS based Soil and Water Assessment Tool (SWAT) was developed by Easton et al. (2008). SWAT is a distributed hydrological model with physical mechanisms and has been widely used to evaluate hydrological processes under changing environments (Young et al., 1989; Donigian et al., 1995; Arnold et al., 1990; Tripathi et al., 2004). The model and the related parameters can potentially reflect real land surface characteristics and provide a framework to conceptualize the relationships of water resources with climate and human activities (Jothityangkoon et al., 2001; Leavesley et al., 1994). Abundant research has demonstrated the ability of SWAT to simulate hydrological processes under changing environments (Arnold and Fohrer, 2005; Benaman and Shoemaker, 2005; Tripathi et al., 2005; Kou et al., 2007; Li et al., 2015). Arnold et al. (1998) applied SWAT in a large basin in America and showed that the land management could greatly affect runoff, sediment and nutrients. Mehdi et al. (2015) used the SWAT model to assess the effects of climate change and agricultural land use change on runoff in Vermont. Lam et al. (2012) assessed the spatial and temporal variations in water quality with the SWAT model in lowland areas of Northern Germany. Li et al. (2009) assessed the impacts of land use and climate changes on streamflow based on SWAT model in an agricultural watershed on the Loess Plateau in China. The Loess Plateau, which has an area of 624,000 km2, is located in the middle reaches of the Yellow River in North China. The region is an arid and semi-arid climate with an average annual temperature gradually increasing from the northwest to the southeast. The average annual precipitation ranges gradually increase from the northwest to the southeast. However, the mean annual potential evapotranspiration may exceed 3000 mm in some areas (Fu et al., 1994). Because of intense rainstorms during the flood season, highly dissected landscapes, low vegetation coverage and loose loessial soil lead to this being the most severely eroded area of Loess Plateau. Nearly 68% of the Loess Plateau is disturbed by soil erosion, approximately 40% of which is extremely severe, with the erosion exceeding 5000 t/km2 a (Fu et al., 1994). This severe soil erosion induced great nutrient loss and land degradation in this area (Fu et al., 2011). To control the severe soil erosion, local famers purposefully constructed a great number of terraces and sediment trap dams on the Loess Plateau before 1980. Afterwards, the central government issued regulations in 1982 and established laws in 1991 to conserve soil and water based on the expanding legal, standard and science system across the entire country (Wang, 2003). As a response to the severe flooding in southern China in 1998, a slope land conversion program was initiated in 1999 to replant forestland and grassland on farmland with a slope of over 25 ° to improve vegetation cover, enhance biodiversity and conserve natural resources in the Yangtze River and

2. Catchment description and data collection 2.1. Catchment description The Beiluo River basin (107°33′33″E–110°10′30″E, 34°39′55″ N–37°18′22″) is a tributary of the Wei River, a secondary tributary of the Yellow River. The upper reaches of the Beiluo River, which are controlled by the Wuqi gauging station, cover an area of 3408 km2, which constitutes 12.7% of the Beiluo River basin (Fig. 1). Two counties are located in the catchment: Wuqi and Dingbian. The catchment area that is covered by Wuqi comprises 76.94% of the total catchment area, while Dingbian comprises 23.06%. The area in Dingbian County approximates to the Mu Us Desert, which has high altitude, low temperature, little precipitation, and sparse vegetation cover. The catchment is a typical hilly-gully area of the Loess Plateau and has a heavily dissected landscape with gully densities from 2 to 3 km/ km2. This region belongs to a semi-arid climate zone with a mean annual precipitation of 418 mm (1963–2009) and approximately 71.8% of the precipitation occurring during the flood season from May to September (Chen et al., 2016). The mean soil depth ranges from 100 to 200 m. Loess soil, which is a kind of calcic cambisol according to international soil classifications, comprises 97.6% over the entire catchment. This soil is actually aeolian sediment that formed from the accumulation of wind-blown silt, which is typically 20–50 μm in size, has a clay content of 20% or less and has equal parts sand and silt that are loosely cemented by calcium carbonate. The soil in this catchment tends to be sandy, with 15% clay and 24% sand and silt, which approximates the Mu Us Desert, and the texture is uniform within the top 3m. The groundwater table is fairly deep, approximately 100–400 m because of the high gully density and thick soil layer (Chen et al., 2016). The vegetation is transitional from forest to prairie. The natural 2

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Fig. 1. Locations of the upper reaches of the Beiluo River basin (c) and the Loess Plateau (b) in China (a). Three weather stations in the study area (c) are marked as the gray round circles with a dark point inside. The hydrological station is shown as a dark triangle.

Zhang et al., 2016). Based on these datasets, the monthly and yearly runoff were used to calibrate and validate the SWAT model, and further to investigate the hydrological response to land use changes in the catchment.

forest has been totally removed, and the planted arbors include Prunus armeniaca, Populus simonii Carr, Populus X hopeiensis Hu & Chow, Robinia pseudoacacia L, Pyrus betulaefolia Bunge, and Platycladus orientalis. Small bushes consists of Hippophae rhamnoides Linn, Caragana korshinskii, and Caragana intermedia. The grass community includes Salsola collina, Artemisa scoparia, Lespedeza davurica, Artemisa vestita, and Bothriochloa ischaemum (Qin et al., 2010). According to the forest survey in Wuqi County in 2006, the four main afforested plants were Prunus armeniaca, Populus simonii Carr, Hippophae rhamnoides, and Caragana korshinskii in terms of their economic merit and their excellent ability to control soil loss and easily survive in local place. The area of thse four plants is nearly 90% forestland and shrubland, respectively.

3. Methodology 3.1. Brief description of SWAT SWAT is a watershed-scale, time-continuous distributed hydrological model that runs on a daily time step. In this study, SWAT (version 2012) was used within ArcGIS10.2. SWAT comprises several major submodels, including flow generation, stream routing, erosion/sedimentation, plant growth, and land management. The model partitions a basin into sub-basins, which have the following input information: climate, soil types and the properties, land use types, groundwater, ponds/ wetlands and the main channel. These sub-basins are further discretized into HRUs (Hydrological Response Units), which are divided according to unique land cover, soil and management combinations. Using the information described in Section 2 and combing the various land use studies into a form suitable for the SWAT model the time behavior of the HRUs was defined. In this catchment, 52 sub-basins were classified and 1180 HRUs were discretized based on the soil types and land use categories. Each HRU had an average area of 2.9 km2. Application of the SWAT model proceeds in three stages, first the model analyses the parameters for sensitivity to the target outputs, in this case catchment generation of water and sediment; The most sensitive parameters are then used to adjust the model to fit the time series of catchment response; Finally, the calibrated model is used to draw conclusions about the spatial aspects of the processes. The soilwater balance is achieved through simulating hydrological processes, including precipitation, infiltration, surface runoff, evapotranspiration (ET) and percolation on soil profiles at the HRUs scale. The following soil water balance equation was used in the model:

2.2. Data collection Daily precipitation, solar radiation, maximum and minimum temperature, humidity and wind speed data from three climate stations, namely Wuqi, Dingbian and Jingbian, within and around the study catchment during 1980–2012 were obtained from the information center of the China Meteorological Administration (CMA). The annual potential evapotranspiration was obtained from the daily climate data by the Penman-Monteith equation. Digital elevation model (DEM) data with 30 m spatial resolution were used in this study. A soil map (1:500,000) and land use maps (1:100,000) from 1990, 2000 and 2010 in the catchment were obtained from the National Earth System Science Data Sharing Infrastructure (http://loess.geodata.cn/). The vegetation Normalized Differential Vegetation Index (NDVI) in the catchment during the corresponding periods was obtained from previous work by the authors (Yan et al., 2016). The soil property data for the soil types in the area were obtained from the Chinese Soil Database’s website: http://vdb3.soil. csdb.cn/. The land use data were then reclassified according to the land use categories provided in SWAT. Daily river discharge data from Wuqi hydrological station from 1980 to 2012 were selected based on previous work to match the land use series (Liu et al., 2015; Lin et al., 2015; 3

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t

SWt = SWt −1 +

∑

(Ri − Qi − ETi − Pi − QRi )

ENS = 1 −

(1)

i =1

where SW is the soil water content; i is the time of the day for the simulation period; and R, Q, ET, P and QR are the daily scale precipitation, surface runoff, evapotranspiration, percolation and return flow, respectively. Based on the principle of SWAT, the modified SCS curve number method in the SWAT tool was used to estimate the surface runoff from daily rainfall. ET is a collective term that includes all processes by which water at the Earth’s surface is converted to water vapor. The potential evapotranspiration was estimated by using the Penman-Monteith equation (Monteith 1965). SWAT calculates the maximum amount of transpiration based on the potential evapotranspiration. The following equation was used to calculate the transpiration:

Et =

E0'·LAI 3.0

ΔRTOT = ROC − ROB

ΔRTOT = ΔRHUM + ΔRCLIM

1990 2000 2010

4.3 4.5 4.9

2.2 2.5 2.8

1.8 2 2.3

1 1.2 1.5

ΔRHUM = ROC − RRN

(6)

ΔRCLIM = RRN − ROB

(7)

where RRN is the mean annual estimated streamflow during the affected period without considering land cover changes. Therefore, the estimated contribution of human activities to the total change in the mean annual streamflow during the two periods,ΔRHUM, could be inferred as the difference. The proportion ofΔRHUM in the total change in the mean annual streamflow was estimated as the contribution from human activities, while the remainder could be regarded as the contribution from climate variability. 4. Results and discussion 4.1. Land use and land cover changes in the catchment The land use changes in the study catchment were first investigated from 1990 to 2000 and 2010, which are shown in Table 2. The results showed that grassland was the dominant land use type in the catchment, which comprised a percentage of greater than 50% during the three periods. The second prominent type was farmland, which comprised a percentage of around 30% during these periods. Smaller areas belonged to water bodies and settlements, which comprised percentages of less than 0.15%.

Table 1 LAI for land use categories in different period in the catchment. Grassland

(5)

whereΔRHUM is the change in the mean annual streamflow from human activities, and ΔRCLIM is the change in the mean annual streamflow from climate variability (Zhang et al., 2012). Then, the following relationships are obtained:

The performance of the calibrated SWAT model to predict the runoff was mainly evaluated in terms of the Nash-Sutcliffe efficiency (ENS) and the coefficient of determination (R2) in the linear fit. The ENS varies from negative infinity to 1. Generally, when the model is considered to be perfect, satisfactory and unsatisfactory, the corresponding ENS value is greater than 0.75, 0.36-0.75 and smaller than 0.36, respectively (Nash and Sutcliffe, 1970).

Shrub

(4)

where △RTOT indicates the total difference in the mean annual streamflow, ROB is the measured average annual streamflow during the baseline period, and ROC is the measured average annual streamflow during the affected period. Following Zhang et al. (2008a,b), the total change in the mean annual streamflow could be estimated as follows:

3.2. Evaluation of the SWAT model

Forest

(3)

In our study, the calibrated SWAT was used to evaluate the effects of human activities and climate variability on annual streamflow changes. Entire data series could be divided into three periods according to the land use and cover changes. The years from 1986 to 1995 were regarded as the baseline period, and it was assumed that there was no significant land cover changes occurred during this stage. After the cropland conversion program was executed in 1999 in the catchment, and the periods were classified into the 2nd period from 1996 to 2005 and the 3rd period from 2006 to 2012, which experienced slight and a notable land cover changes, respectively. For a given catchment, a difference in the mean annual streamflow in two periods could be obtained as follows:

where Et is the maximum daily transpiration (mm H2O), E0' is the potential evapotranspiration (mm H2O), and LAI is the leaf area index. In this study, the LAI of different land use types in 1990, 2000 and 2010 was determined from an empirical relationship between the LAI and NDVI derived by Zhang et al. (2008a).The results are shown in Table 1. These results show that the LAI of forest, shrub and grassland gradually increased from 1990 to 2000 and 2010. One reason was the increase in area from the implementation of soil and water conservation measures and the “Grain for Green” program during this period, and another reason was the vegetation coverage improvement in the catchment. Meanwhile, the LAI of cropland increased, which probably resulted from the farmland gradually moving from sloped land with low productivity to plains and dam land with relatively high productivity. A number of research projects have shown that the soil water content in the upper 2–3 m of the soil profile from the soil surface was very steady because of the thick soil layer and uniform soil properties in this location (Qin et al., 2010; Tang, 2004). Thus, the amount of water that could percolate out of the lowest soil layer to the vadose zone was very little. The characteristics of the soil properties greatly affect the return flow. The result of this research showed that the amount of water that percolates across the soil profile and recharges the groundwater and base flow was much little and could be ignored compared to the other water components, which is consistent with the findings of the above references. Thus, in the present study, the significant factors are surface hydrological responses to land use and land cover changes in the catchment.

Cropland

(Oi − O )2

3.3. Estimating the effects of human activities and climate change on runoff

(2)

Year

(Oi − Si )2

where Si, Oi and O are the simulated and observed runoff values and the mean observed runoff, respectively; and n is the number of runoff values, which indicates how well the plot of the observed values versus the simulated values is close to the 1:1 line and provides an overall indication of fitness. R2, which ranges from 0 to 1, describes a linear relationship between the simulated runoff and observed runoff, or the proportion of variation in the observed data as explained by the model simulation (Moriasi et al., 2007).

0 ≤ LAI ≤ 3.0

Et = E0' LAI > 3.0

∑i =1 n ∑i =1

4

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Table 2 Land use types and their changes by 1990, 2000–2010 in the catchment. Land use

1990

2000

2010

type

Area (km2)

Percentage (%)

Area(km2)

Percentage (%)

Area(km2)

Percentage (%)

Forestland Shrubland Grassland Farmland Water body Settlements

60.32 36.88 1837.08 1469.34 2.43 1.62

1.77 1.08 53.91 43.12 0.07 0.05

73.95 159.33 2033.91 1134 4.05 2.43

2.17 4.67 59.69 33.28 0.12 0.07

132.56 240.85 2070.36 955.8 3.24 4.86

3.89 7.06 60.76 28.05 0.1 0.14

However, the amount of forestland and grassland increased, while the amount of farmland decreased. The amount of forestland, shrubland and grassland increased by 22.6%, 332.0% and 10.7% from 1990 to 2000, respectively, while cropland decreased by 22.8%. From 2000–2010, the amount of forestland, shrubland and grassland continued to increase by 79.2%, 51.2% and 1.8%, respectively, whereas cropland decreased by 15.7%. Yan et al. (2016) employed Landsat TM image data from 1987 to 1995 and 2014 and found that the corresponding percentage of the average vegetation cover in the catchment was 15.86%, 19.20% and 38.81%, respectively, which showed consistent results with the investigation in Table 2. Land use and cover changes in the study area are the primary components of human activities. As a demonstration, Wuqi County was selected to abandon cultivation and prevent open grazing in 1997 in China. Before this action, the farmland comprised 77% of the county’s total area, 95% of which was sloping cropland with slopes that exceeded 25° (Wuqi Statistical Yearbook, 1999). The area that suffered from severe soil erosion comprised 97.4% of the entire county. Since this action, agricultural and livestock production in Wuqi County experienced a notable transformation under the prohibition of open grazing and abandoned cultivation for a large area of sloping land. By the end of 2007, an area of 12000 ha of cropland was abandoned in the county, the largest among all the counties that abandoned cropland, approximately half of which was afforested (Wuqi Statistical Yearbook, 1999).

Table 4 Model performance for the simulated runoff yields. Period

Calibration(1986–1990) Validation(1991–1995)

In this study, program SWAT-CUP was used to analyze the sensitivity of the parameters. The results from the analysis showed that the parameters CN2, ESCO, SOL_AWC, SOL_K and ALPHA_BF (defined in Table 3) were more sensitive to runoff than other parameters. Then, these parameters were adjusted by using the autocalibration extension of SWAT2012 to calibrate the model in this study. Table 3 also presented the final values of these parameters as following: The model was then calibrated with daily streamflow data from 1986 to 1990 and validated from 1991 to 1995. Land use data in 1990 were used in this section because of the stability of land use during this

Description

Range

Initial value

Adjusted/last value

1

CN2

Initial SCS CN II value

35–98

+3

2

ESCO

0–1

3

SOL_AWC

0–1

Initial

+0.03

4

SOL_K

0–1

Initial

+0.05

5

ALPHA_BF

Soil evaporation compensation factor Available water capacity soil saturated water conductivity Baseflow alpha factor [days]

Default/ initial 0.95

0–1

0.1293

0.0837

ENS

R

RE(%)

ENS

R2

RE(%)

0.43 0.47

0.71 0.82

14.38 11.41

0.66 0.72

0.76 0.89

12.1 10.86

4.3. Effects of climate variability and human activities 4.3.1. Estimation of contributions from human activity to streamflow reduction Following Eqs. (4)–(7), the effects of human activities on changes in the streamflow during 1996–2005 and 2006–2012 were estimated compared to those during the baseline period of 1986–1995, as shown in Table 5. Table 5 shows that the mean annual runoff was 32.55 mm during the baseline period of 1986–1995, while the mean annual runoff during the following period of 1996–2005 was 23.83 mm with a relative reduction of 26.80%. The SWAT modeled that the proportion of the reduction in the mean annual runoff of 8.72 mm that resulted from the human activities (ΔRHUM) was 4.93 mm. Therefore, the effect from human activities on the change in steamflow was 56.5% during 1996–2005, while the remaining 43.5% could be attributed to climate variability in the area. During the following period of 2006–2012, the mean annual runoff was only 17.86 mm, a relative reduction of 45.13% compared to the baseline period. The modelled runoff that could be attributed to human activities was 11.43 mm. Consequently, the effects of human activities on the change in runoff reached 77.8% during the period of 2006–2012. The climate variability induced an effect of

Table 3 Final values of the sensitivity parameters. NAME

Yearly 2

stage. Table 4 shows that ENS and R2 were 0.66 and 0.76 at the annual scale, and 0.43 and 0.71 at the monthly scale, respectively, for the calibration period of 1986–1990. The observed and simulated average annual runoff depths during the calibration period were 26.2 and 21.7 mm, respectively. For the validation period of 1991–1995, ENS and R2 were 0.72 and 0.89 at the annual scale and 0.47 and 0.82 at the monthly scale, respectively. The observed and simulated average annual runoff depths during this period were 38.3 and 36.5 mm, respectively, and the relative change was 4.7%. The mean annual streamflow during the validation period was more than that during the calibration period, which resulted from a flood event with a one-hundred-year return period that occurred in August, 1994, as illustrated in Fig. 2. Fig. 2 shows that the model sometimes underestimated the streamflow, especially for high flow. However, the observed and simulated runoff generally matched at both the monthly and annual scales (Table 4). All values of ENS were larger than 0.4, and the modelling performance was acceptable according to the criteria (Nash and Sutcliffe, 1970). Overall, the calibrated model is reliable and acceptable to simulate the runoff and hydrological responses to land use and cover changes in the catchment.

4.2. Calibration and validation of the model

NO

Monthly

0.8

5

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Fig. 2. Observed and simulated average monthly runoff for the study catchment.

approximately 22.2%. Table 5 illustrates that the effect of human activities on streamflow reduction became more significant during the following two periods, increasing from 56.5% to 77.8%, which is consistent with the aforementioned trends of the land use and cover changes in the catchment and is consistent with the research by Zhang et al. (2008a,b); Liu et al. (2015); Lin et al. (2015); Gao et al. (2015); and Zhang et al. (2016).

Table 6 The impacts of precipitation and climate change on runoff. Year

1990 2000 2010

4.3.2. Sensitivity of the streamflow to climate variability Under the background of global warming, it is important to investigate the sensitivity of streamflow to climate variability to achieve sustainable watershed management (Cai et al., 2011). In this catchment, two scenarios were created to check the sensitivity of streamflow to changes in both precipitation and temperature based on land use and cover status data and the corresponding climates from 1990, 2000 and 2010. Scenario 1 involved increasing the precipitation by 10% without any changes in temperature. Scenario 2 involved increasing the temperature by 1 °C without any changes in precipitation. Table 6 shows that the effects of increasing precipitation increased the streamflow, while rising temperatures decreased the streamflow. Generally, the effect of 10% higher precipitation on streamflow was more significant than that by a 1 °C temperature increase under the same land cover status. The simulation in scenario 1 showed that as the land cover improved from 1990 to 2000 and 2010, the extent of streamflow change from increasing precipitation deflated from nearly 30% to 20% because higher vegetation coverage in the catchment would result in less fluctuation in runoff generation from higher interception, evapotranspiration and infiltration during the hydrological cycle. The simulation in scenario 2 showed that the extent of streamflow change from rising temperatures inflated from nearly 3% to 9% over time because of the presence and growth of vegetation and its growth, during which more water would be consumed by plant

Original runoff depth (mm)

30.31 20.07 14.89

+10% Precipitation

+1° Temperature

Simulated

Change%

Simulated

Change%

39.32 24.61 17.57

29.7 24.44 19.27

29.43 18.99 13.6

−2.93 −5.39 −8.64

transpiration and thus decreasing the runoff generation (Liu et al., 2011). Precipitation and temperature in climate variability were two major factors to influence the hydrological processes (Howden et al., 2007). Fu and Charles (2007) pointed out that a 30% precipitation increase in the Yellow River Basin would result in a 45% increase in streamflow at mean temperature. Legesse et al. (2010) reported that the potential evapotranspiration would increase by 6.02% and the simulated streamflow decrease by 13% when the temperature increased by 1.5 °C in a PRMS model for the Meki River basin in the Main Ethiopian Rift. 4.4. Spatial patterns of hydrological responses to land cover changes The spatial distributions of the annual surface runoff, ET and soil water content based on 52 sub-basins were shown in Fig. 3 for 1990, 2000 and 2010. Fig. 3 shows that the surface runoff and soil water content generally decreased while the ET increased from 1990 to 2000 and 2010 in the catchment. The spatial distributions of the changing rates in the surface runoff, ET and soil water content during these periods are shown in Fig. 4. 4.4.1. Spatial pattern of surface runoff and its changing rate The spatial distribution of the annual surface runoff is illustrated in

Table 5 Effects of human activities and climate change on the reduction of the average annual runoff in the catchment. Periods

1986–1995 1996–2005 2006–2012

Observed

32.55 23.83 17.86

Modelled

N/A 28.74 29.29

Average annual changes in runoff (mm/a)

Effects on runoff (%)

ΔRTOT

ΔRCLIM

ΔRHUM

ηHUM

ηCLIM

N/A −8.72 −14.69

N/A −3.79 −3.26

N/A −4.93 −11.43

N/A 56.5 77.8

N/A 43.5 22.2

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Fig. 3. Spatial patterns of the annual surface runoff (a), evapotranspiration (b) and soil water content (c) at the sub-basin scale in different years.

widespread across the entire catchment. Compared to 2000, the average surface runoff in 2010 decreased by 42.7% with a maximum decreasing rate of 71.8% and a minimum of 9.7%. Generally, the annual surface runoff during these three years and the changing rates during these three periods presented a decreasing gradient from northwestern to the southeast and from north to south (Figs. 3 and 4a).

Fig. 3a. The results show that the average annual surface runoff in 52 sub-basins was 26.0 mm in 1990, with a maximum surface runoff of 67.7 mm and a minimum of 19.9 mm. The annual surface runoff in up to 94.1% of the total catchment was concentrated in the range of 21–30 mm and widespread throughout the catchment. In 2000, the annual surface runoff in 52 sub-basins decreased to 19.9 mm, with a maximum surface runoff of 32.8 mm and a minimum of 10.5 mm. Approximately the annual surface runoff of 83.8% of the total catchment was concentrated in the range of 16–25 mm. In 2010, the simulated average annual runoff continuously decreased to only 9.8 mm with a maximum of 23.4 mm and a minimum of 5.9 mm. And the surface runoff about 93.8% of the total catchment was concentrated in the range of 0–15 mm. The spatial distributions of the changes in the surface runoff are shown in Fig. 4a. Compared to 1990, the average surface runoff in all the sub-basins decreased by 24.7% in 2000 and the maximum decreasing rate was 68.1% which occurred in the southern area of the catchment, while the minimum decreasing rate was 2.7% which

4.4.2. Spatial pattern of ET and the changing rate The spatial distributions of the annual ET are shown in Fig. 3b. The results show that the average annual ET in the 52 sub-basins was 362.7, 370.1 and 406.7 mm in 1990, 2000 and 2010, respectively. The maximum and minimum ET during these three years were 372.4 and 338.5 mm, 386.3 and 342.0 mm, and 419.5 and 350.6 mm, respectively. In 1990, the annual ET in approximately 50.0% of the entire catchment was concentrated in the range of 350–360 mm, and these areas were mainly distributed from the center to the southeastern area of the catchment. In 2000, the annual ET in about 58.2% of the 7

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Fig. 4. Spatial variations of surface runoff, ET and soil water content at the sub-basin scale from 1990, 2000–2010.

County, which is close to the Mu Us Desert and the lower temperatures and precipitation inhibits vegetation restoration compared with other areas.

catchment was mainly concentrated in the range of 370–380 mm, and these areas were distributed in the same region with that in 1990 from the center to southeastern area of the catchment. In 2010, the annual ET in about 61.6% of the catchment was concentrated in the range of 400 ∼ 415 mm, and these areas were mainly distributed from the northwestern to southeastern areas of the catchment. The spatial patterns of the changing rates in the ET are shown in Fig. 4b. Compared to 1990, the average annual ET in the 52 sub-basins increased by 1.8% in 2000, with a maximum increasing rate of 8.1% and a minimum of 0.1%. The increasing rate of the ET was distributed from the northern and northwestern to the southern and southeastern areas of the catchment. Compared to 2000, the average annual ET in 52 sub-basins increased by 10.1% in 2010 with a maximum rate of 18.1% and a minimum of 2.5%. From 1990–2010, the increasing rate of the ET was generally concentrated in the southern and southwestern areas of the catchment. The spatial distribution and change patterns of ET were closely associated with the vegetation coverage (Li et al., 2012). The northern and northwestern areas of the catchment belong to Dingbian

4.4.3. Soil water content and the spatial pattern of the changing rate The spatial distribution of the annual soil water content is shown in Fig. 3c. The results show that the average annual soil water content in the 52 sub-basins was 41.8, 33.7 and 26.9 mm in 1990, 2000 and 2010, respectively. The maximum and the minimum soil water content were 80.4 and 35.0 mm, 64.6 and 23.1 mm, 62.4 and 12.6 mm, respectively. In 1990, the soil water content in about 63.1% of the catchment was concentrated in the range of 31–40 mm, and these areas were mainly distributed in the northern, central-northeastern and southwestern areas of the catchment. In 2000, the soil water content in about 77.4% of the total catchment was in the range of 26–35 mm. In 2010, that in about 63.4% of the total area was in the range of 21–30 mm. The spatial patterns of the changing rates for the soil water content are shown in Fig. 4c. Similar to the reduction in the surface runoff, the 8

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Fig. 5. Spatial variations in land-use types at the sub-basin scale between 1990 and 2010.

15.7% during the corresponding periods. SWAT simulated that the average surface runoff was 26.0 mm, 19.9 mm and 9.8 mm at the subbasin scale in the baseline period of 1986–1995, the 2nd period of 1996–2005 and the 3rd period of 2006–2012, respectively. The average ET was 362.7 mm, 370.1 mm and 406.7 mm during the corresponding periods. The soil water content was 41.8 mm, 33.7 mm and 26.9 mm during these three periods. The spatial pattern of the changes in the hydrological components followed a gradient from the north and northwest to the south and southeast in the catchment. The changes in the hydrological components were closely related to the temporal alteration in land use types, especially for forestland, shrubland, grassland and cropland during the studied periods. The spatial patterns showed that the higher the increasing rate of forestland and grassland in the sub-basin, the greater the decreasing rate of the surface runoff and soil water content, whereas the ET presented the opposite trend. The SWAT simulations indicated that the land use and cover changes were the main driving forces and were 56.5% in the period of 1996–2005 and 77.8% in the period of 2006–2012 in terms of the reduction in streamflow during these two periods. The simulation in scenarios illustrated that the effect of precipitation on streamflow was more significant than that of rising temperature under the same land cover. This study is useful to understand the eco-hydrological processes in catchments especially those China’s Loess Plateau, which is experiencing evident vegetation restoration. This study provides a tool for watershed sustainable management in the future to undertake studies of how spatially distributed changes in land use and precipitation may affect runoff and erosion.

average annual soil water content in the 52 sub-basins decreased by 19.8%, from 1990 to 2000, with a maximum changing rate of 36.5% and a minimum of 0.6%. From 2000–2010, the average annual soil water content continued to decrease and the change rate was 21.8% with a maximum of 57.3% and a minimum of 0.2%. Generally, the decreasing rate gradually increased from the northern and central areas to the southwestern and southeastern areas of the catchment from 1990 to 2010. The spatial patterns may be related to differences in the vegetation restoration in the catchment. More soil water was consumed as forests and shrubs were planted, so that the soil moisture decreased in local areas (Gates John et al., 2011). 4.4.4. Land use and cover changes at the sub-basin scale The land use and cover changes were investigated to determine the reason for the spatial changes in the surface runoff, ET and soil water content in the catchment, as shown in Fig. 5. Fig. 5 shows an obvious increase in forestland and grassland and a decrease in farmland in sub-basins of the catchment. The average increasing rate of forestland and shrubland was 4.8%. The sub-basins with increased forestland were mainly located in the eastern, southern and southwestern areas of the catchment. The average increasing rate for grassland was 10.7%, which was basically higher than that of forestland. The sub-basins with increased grassland were distributed from the northern and northwestern to the southern and southeastern areas of the catchment. This type of spatial pattern was generally consistent with those of the surface runoff and soil water content. The average decreasing rate of farmland was 15.9%, and the spatially changing trend was opposite to that of grassland. These results showed that the effects of the soil and water conservation measures that were implemented in the 1980s and the “Green for Grain” Program after 1999 significantly affected the hydrological behavior in the catchment (Chen et al., 2016; Zhang et al., 2016).

Acknowledgements This study was supported by the National Natural Science Foundation of China (Grant Nos. 41230852, 41440012 and 41101265), and Special-Funds of Scientific Research Programs of State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau (A314021403-C2).

5. Summary The physically based distributed model SWAT was used to examine the spatial hydrological responses to land use and cover changes over the past 30 years in a catchment on the Loess Plateau in China. The calibrated SWAT model was considered a reliable tool to estimate the surface hydrology components and their changes in the catchment as well as the sensitivities of the catchment water generation to changes in precipitation and land use. As a result of using various sources of information, including remote sensing, it has been shown that the forestland, shrubland and grassland significantly increased by 22.6%, 332.0% and 10.7% from 1990 to 2000 and continued to increase by 79.2%, 51.2% and 1.8%, respectively, from 2000 to 2010. Meanwhile, farmland decreased by 22.8% and

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