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 Academyof Sciences and Ministry of Water Resources, Yangling, Shaanxi 712100, Chinab 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
Keywords:SWAT simulationSurface hydrological responsesTemporal and spatial distributionLand use and cover changesLoess plateau
A B S T R A C T
The Loess Plateau of China has been experiencing great land use and land cover changes under the “Grain forGreen” program to control severe soil loss from human activities. Over the past 30 years, annual streamflow andsediment delivery have also reduced in most areas of Loess Plateau. In consequence, a physically based model ofSoil and Water Assessment Tool (SWAT) has been employed to simulate the responses of surface hydrology tohuman 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 farmlandin 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 in2000 and 5.5 in 2010. The vegetation coverage greatly increased in the catchment during this period. Using theSWAT model, it was found that the average ET at the sub-basin scale increased by 7.4 mm in 2000 and 44.0 mmin 2010 as the vegetation coverage improved compared to that in 1990. Meanwhile, the soil water contentdecreased 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 withalterations in the land use and cover categories at the sub-basin scale. Generally, the higher the increasing rate offorest and grassland, the more that evapotranspiration transferred and the less surface runoff and soil watercontent that was generated. Spatially, the ET, surface runoff and soil water content showed the same changinggradient with land use and cover from the northern and northwestern to the southern and southeastern areas ofthe catchment during these periods. The scenarios simulation showed that the streamflow were more sensitive tovariability in the precipitation than temperature.
These results are expected to be helpful to the sustainable watershed management and provide usefulinformation regarding land use planning and ecosystem construction strategies in the future on the LoessPlateau.
1. Introduction
Land use changes and climate variability are two main driving forceof hydrological processes in watersheds (Juckem et al., 2008).Understanding the responses of hydrological processes to land usechanges and climate variability is of great benefit to the sustainabledevelopment of water resource management strategies (Cao et al.,2009; Narsimlu et al., 2013). Global climate change can significantlyaffect the distribution of water quantity and quality (USEPA, 2014).Precipitation is the source of streamflow, and any variation inprecipitation can change the streamflow (Arnold et al., 1990; Tanet 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 pro-cesses, such as canopy interception, infiltration and evapotranspiration,which may eventually change the runoff volume, peak flow and flowrouting time (Laurance, 2007; Bradshaw et al., 2007; Hurkmans et al.,2009; Cai et al., 2009b). The trend of streamflow and the temporal andspatial distribution of hydrological processes have recently seenincreased research in the field of ecology and hydrology interactionbecause of increasingly evident consequences of land use/coverchanges worldwide (Zhang et al., 2008b; Fu et al., 2011; Gao et al.,2012).
http://dx.doi.org/10.1016/j.ecolind.2017.04.013Received 17 November 2016; Received in revised form 16 February 2017; Accepted 6 April 2017
Generally, three types of methods are used to assess the effects ofland cover changes on streamflow. One method is the paired watershedapproach. Paired watersheds must be geographically close and sharesimilar hydrological regimes and soil types to efficiently eliminate theinfluence of precipitation. At least 30 years of hydrological and forestdisturbance records are needed to meet the requirements of statisticalmethods (Lorup et al., 1998). However, the disadvantage is that findingstrict paired catchments is difficult under severely controlled anddisturbed land use types (Li et al., 2009). Another problem is thatapplying the results of paired catchments in a medium or largecatchment is difficult because of the existing diversity of land usepatterns.
The second method is time series analysis in a catchment. Doublemass curves can be used to eliminate the influence of precipitation(Jothityangkoon et al., 2001). Several periods can be identified alongthe time series. The relationship between climate factors and stream-flow during the controlled period can be applied to the next period, andthe contributions of climate variability and land cover changes can beidentified (Leavesley, 1994). Time series analysis may be used to studythe effects of environmental change on hydrological processes. How-ever, this method does not consider physical mechanisms.
The GIS based Soil and Water Assessment Tool (SWAT) wasdeveloped by Easton et al. (2008). SWAT is a distributed hydrologicalmodel with physical mechanisms and has been widely used to evaluatehydrological 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 landsurface characteristics and provide a framework to conceptualize therelationships of water resources with climate and human activities(Jothityangkoon et al., 2001; Leavesley et al., 1994). Abundantresearch has demonstrated the ability of SWAT to simulate hydrologicalprocesses 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 inAmerica and showed that the land management could greatly affectrunoff, sediment and nutrients. Mehdi et al. (2015) used the SWATmodel to assess the effects of climate change and agricultural land usechange on runoff in Vermont. Lam et al. (2012) assessed the spatial andtemporal variations in water quality with the SWAT model in lowlandareas of Northern Germany. Li et al. (2009) assessed the impacts of landuse and climate changes on streamflow based on SWAT model in anagricultural watershed on the Loess Plateau in China.
The Loess Plateau, which has an area of 624,000 km2, is located inthe middle reaches of the Yellow River in North China. The region is anarid and semi-arid climate with an average annual temperaturegradually increasing from the northwest to the southeast. The averageannual precipitation ranges gradually increase from the northwest tothe southeast. However, the mean annual potential evapotranspirationmay exceed 3000 mm in some areas (Fu et al., 1994). Because of intenserainstorms during the flood season, highly dissected landscapes, lowvegetation coverage and loose loessial soil lead to this being the mostseverely eroded area of Loess Plateau. Nearly 68% of the Loess Plateauis disturbed by soil erosion, approximately 40% of which is extremelysevere, with the erosion exceeding 5000 t/km2 a (Fu et al., 1994). Thissevere soil erosion induced great nutrient loss and land degradation inthis area (Fu et al., 2011).
To control the severe soil erosion, local famers purposefullyconstructed a great number of terraces and sediment trap dams onthe Loess Plateau before 1980. Afterwards, the central governmentissued regulations in 1982 and established laws in 1991 to conserve soiland water based on the expanding legal, standard and science systemacross the entire country (Wang, 2003). As a response to the severeflooding in southern China in 1998, a slope land conversion programwas initiated in 1999 to replant forestland and grassland on farmlandwith a slope of over 25 ° to improve vegetation cover, enhancebiodiversity and conserve natural resources in the Yangtze River and
Yellow River (Chen et al., 2001). Thus, the vegetation cover on theLoess Plateau has been greatly improved (Zhang et al., 2011a,b).
The Beiluo River, which is situated in the center of the LoessPlateau, was one of the most eroded areas in the region, especially in itsupper reaches, with a mean annual soil loss of 15000 t per km2 per yearbefore 1980. According to the research of Yan et al. (2016), the meanvegetation cover in the upper reaches of the river basin has improvedfrom 12.4% to 51.2% from 1987 to 2014 because of continuous soil andwater conservation implementation and vegetation restoration duringthis time. Lin et al. (2015) showed that the mean annual soil loss heredecreased to 3000 t/km2.a after 1999. Zhang et al. (2016) showed thatthe mean annual streamflow in the upper reaches of the catchmentexhibited statistically significant negative trends from the 1960s to2011, with an average change rate of 0.3 mm/a. Liu et al. (2015) andGao et al. (2015) showed that a series of soil and water conservationmeasurements and the slope land conservation program’s executionwere regarded as the main driving force in the reduction of streamflowin the catchment.
The previous researches in the catchment mainly investigated thetrends of annual streamflow and flood events by using the time seriesapproach. However, the research lacked knowledge regarding thespatial hydrological responses to land use and land cover changes.Thus, applying a physically based distributed assessment tool isnecessary to understand the effects of land use and land cover changeson runoff on temporal and spatial scales. The results are expected tobenefit catchment ecological construction and water resource manage-ment, especially in a water-limited ecosystem such as the Loess Plateau.
This study investigates the land use and land cover changes in thecatchment to (1) validate SWAT and check its suitability in thecatchment, (2) quantify the effects of human activities and climatevariability on runoff with the SWAT model, and (3) investigate thespatial responses of surface hydrology to land use changes and landcover improvement in the catchment.
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 ofthe Yellow River. The upper reaches of the Beiluo River, which arecontrolled by the Wuqi gauging station, cover an area of 3408 km2,which constitutes 12.7% of the Beiluo River basin (Fig. 1). Two countiesare located in the catchment: Wuqi and Dingbian. The catchment areathat is covered by Wuqi comprises 76.94% of the total catchment area,while Dingbian comprises 23.06%. The area in Dingbian Countyapproximates to the Mu Us Desert, which has high altitude, lowtemperature, little precipitation, and sparse vegetation cover.
The catchment is a typical hilly-gully area of the Loess Plateau andhas a heavily dissected landscape with gully densities from 2 to 3 km/km2. This region belongs to a semi-arid climate zone with a meanannual precipitation of 418 mm (1963–2009) and approximately 71.8%of the precipitation occurring during the flood season from May toSeptember (Chen et al., 2016). The mean soil depth ranges from 100 to200 m. Loess soil, which is a kind of calcic cambisol according tointernational soil classifications, comprises 97.6% over the entirecatchment. This soil is actually aeolian sediment that formed fromthe accumulation of wind-blown silt, which is typically 20–50 μm insize, has a clay content of 20% or less and has equal parts sand and siltthat are loosely cemented by calcium carbonate. The soil in thiscatchment tends to be sandy, with 15% clay and 24% sand and silt,which approximates the Mu Us Desert, and the texture is uniformwithin the top 3m. The groundwater table is fairly deep, approximately100–400 m because of the high gully density and thick soil layer (Chenet al., 2016).
The vegetation is transitional from forest to prairie. The natural
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forest has been totally removed, and the planted arbors include Prunusarmeniaca, Populus simonii Carr, Populus X hopeiensis Hu & Chow,Robinia pseudoacacia L, Pyrus betulaefolia Bunge, and Platycladusorientalis. Small bushes consists of Hippophae rhamnoides Linn,Caragana korshinskii, and Caragana intermedia. The grass communityincludes 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 fourmain afforested plants were Prunus armeniaca, Populus simonii Carr,Hippophae rhamnoides, and Caragana korshinskii in terms of theireconomic merit and their excellent ability to control soil loss and easilysurvive in local place. The area of thse four plants is nearly 90%forestland and shrubland, respectively.
2.2. Data collection
Daily precipitation, solar radiation, maximum and minimum tem-perature, humidity and wind speed data from three climate stations,namely Wuqi, Dingbian and Jingbian, within and around the studycatchment during 1980–2012 were obtained from the informationcenter of the China Meteorological Administration (CMA). The annualpotential evapotranspiration was obtained from the daily climate databy the Penman-Monteith equation.
Digital elevation model (DEM) data with 30 m spatial resolutionwere 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 obtainedfrom the National Earth System Science Data Sharing Infrastructure(http://loess.geodata.cn/). The vegetation Normalized DifferentialVegetation Index (NDVI) in the catchment during the correspondingperiods was obtained from previous work by the authors (Yan et al.,2016). The soil property data for the soil types in the area wereobtained from the Chinese Soil Database’s website: http://vdb3.soil.csdb.cn/. The land use data were then reclassified according to the landuse categories provided in SWAT. Daily river discharge data from Wuqihydrological station from 1980 to 2012 were selected based on previouswork to match the land use series (Liu et al., 2015; Lin et al., 2015;
Zhang et al., 2016). Based on these datasets, the monthly and yearlyrunoff were used to calibrate and validate the SWAT model, and furtherto investigate the hydrological response to land use changes in thecatchment.
3. Methodology
3.1. Brief description of SWAT
SWAT is a watershed-scale, time-continuous distributed hydrologi-cal model that runs on a daily time step. In this study, SWAT (version2012) was used within ArcGIS10.2. SWAT comprises several major sub-models, including flow generation, stream routing, erosion/sedimenta-tion, plant growth, and land management. The model partitions a basininto 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 discretizedinto HRUs (Hydrological Response Units), which are divided accordingto unique land cover, soil and management combinations. Using theinformation described in Section 2 and combing the various land usestudies into a form suitable for the SWAT model the time behavior ofthe HRUs was defined. In this catchment, 52 sub-basins were classifiedand 1180 HRUs were discretized based on the soil types and land usecategories. Each HRU had an average area of 2.9 km2.
Application of the SWAT model proceeds in three stages, first themodel analyses the parameters for sensitivity to the target outputs, inthis case catchment generation of water and sediment; The mostsensitive parameters are then used to adjust the model to fit the timeseries of catchment response; Finally, the calibrated model is used todraw conclusions about the spatial aspects of the processes. The soil-water balance is achieved through simulating hydrological processes,including precipitation, infiltration, surface runoff, evapotranspiration(ET) and percolation on soil profiles at the HRUs scale. The followingsoil water balance equation was used in the model:
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 circleswith a dark point inside. The hydrological station is shown as a dark triangle.
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where SW is the soil water content; i is the time of the day for thesimulation period; and R, Q, ET, P and QR are the daily scaleprecipitation, surface runoff, evapotranspiration, percolation and re-turn flow, respectively. Based on the principle of SWAT, the modifiedSCS curve number method in the SWAT tool was used to estimate thesurface runoff from daily rainfall. ET is a collective term that includesall processes by which water at the Earth’s surface is converted to watervapor. The potential evapotranspiration was estimated by using thePenman-Monteith equation (Monteith 1965). SWAT calculates themaximum amount of transpiration based on the potential evapotran-spiration. The following equation was used to calculate the transpira-tion:
E LAI= 0 ≤ ≤ 3.0tE LAI·
3.00'
E E LAI= > 3.0t 0' (2)
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 LAIand NDVI derived by Zhang et al. (2008a).The results are shown inTable 1. These results show that the LAI of forest, shrub and grasslandgradually increased from 1990 to 2000 and 2010. One reason was theincrease in area from the implementation of soil and water conservationmeasures and the “Grain for Green” program during this period, andanother reason was the vegetation coverage improvement in thecatchment. Meanwhile, the LAI of cropland increased, which probablyresulted from the farmland gradually moving from sloped land with lowproductivity to plains and dam land with relatively high productivity.
A number of research projects have shown that the soil watercontent in the upper 2–3 m of the soil profile from the soil surface wasvery steady because of the thick soil layer and uniform soil properties inthis location (Qin et al., 2010; Tang, 2004). Thus, the amount of waterthat could percolate out of the lowest soil layer to the vadose zone wasvery little. The characteristics of the soil properties greatly affect thereturn flow. The result of this research showed that the amount of waterthat percolates across the soil profile and recharges the groundwaterand base flow was much little and could be ignored compared to theother water components, which is consistent with the findings of theabove references. Thus, in the present study, the significant factors aresurface hydrological responses to land use and land cover changes inthe catchment.
3.2. Evaluation of the SWAT model
The performance of the calibrated SWAT model to predict the runoffwas mainly evaluated in terms of the Nash-Sutcliffe efficiency (ENS)and the coefficient of determination (R2) in the linear fit. The ENSvaries from negative infinity to 1. Generally, when the model isconsidered to be perfect, satisfactory and unsatisfactory, the corre-sponding ENS value is greater than 0.75, 0.36-0.75 and smaller than0.36, respectively (Nash and Sutcliffe, 1970).
∑∑
ENSO S
O O= 1 −
( − )
( − )i
ni i
i
ni
=12
=12
(3)
where Si, Oi andO are the simulated and observed runoff values and themean observed runoff, respectively; and n is the number of runoffvalues, which indicates how well the plot of the observed values versusthe simulated values is close to the 1:1 line and provides an overallindication of fitness.
R2, which ranges from 0 to 1, describes a linear relationshipbetween the simulated runoff and observed runoff, or the proportionof variation in the observed data as explained by the model simulation(Moriasi et al., 2007).
3.3. Estimating the effects of human activities and climate change on runoff
In our study, the calibrated SWAT was used to evaluate the effects ofhuman activities and climate variability on annual streamflow changes.Entire data series could be divided into three periods according to theland use and cover changes. The years from 1986 to 1995 wereregarded as the baseline period, and it was assumed that there wasno significant land cover changes occurred during this stage. After thecropland conversion program was executed in 1999 in the catchment,and the periods were classified into the 2nd period from 1996 to 2005and the 3rd period from 2006 to 2012, which experienced slight and anotable land cover changes, respectively.
For a given catchment, a difference in the mean annual streamflowin two periods could be obtained as follows:
R R RΔ = −TOT OC OB (4)
where △RTOT indicates the total difference in the mean annual stream-flow, ROB is the measured average annual streamflow during thebaseline period, and ROC is the measured average annual streamflowduring the affected period.
Following Zhang et al. (2008a,b), the total change in the meanannual streamflow could be estimated as follows:
ΔRTOT = ΔRHUM + ΔRCLIM (5)
whereΔRHUM is the change in the mean annual streamflow from humanactivities, and ΔRCLIM is the change in the mean annual streamflowfrom climate variability (Zhang et al., 2012). Then, the followingrelationships are obtained:
R R RΔ = −HUM OC RN (6)
R R RΔ = −CLIM RN OB (7)
where RRN is the mean annual estimated streamflow during the affectedperiod without considering land cover changes. Therefore, the esti-mated contribution of human activities to the total change in the meanannual streamflow during the two periods,ΔRHUM, could be inferred asthe difference. The proportion ofΔRHUM in the total change in the meanannual streamflow was estimated as the contribution from humanactivities, while the remainder could be regarded as the contributionfrom 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 investigatedfrom 1990 to 2000 and 2010, which are shown in Table 2. The resultsshowed that grassland was the dominant land use type in the catch-ment, which comprised a percentage of greater than 50% during thethree periods. The second prominent type was farmland, whichcomprised a percentage of around 30% during these periods. Smallerareas belonged to water bodies and settlements, which comprisedpercentages of less than 0.15%.
Table 1LAI for land use categories in different period in the catchment.
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However, the amount of forestland and grassland increased, whilethe amount of farmland decreased. The amount of forestland, shrublandand grassland increased by 22.6%, 332.0% and 10.7% from 1990 to2000, respectively, while cropland decreased by 22.8%. From2000–2010, the amount of forestland, shrubland and grassland con-tinued to increase by 79.2%, 51.2% and 1.8%, respectively, whereascropland decreased by 15.7%. Yan et al. (2016) employed Landsat TMimage data from 1987 to 1995 and 2014 and found that the corre-sponding percentage of the average vegetation cover in the catchmentwas 15.86%, 19.20% and 38.81%, respectively, which showed consis-tent results with the investigation in Table 2.
Land use and cover changes in the study area are the primarycomponents of human activities. As a demonstration, Wuqi County wasselected to abandon cultivation and prevent open grazing in 1997 inChina. Before this action, the farmland comprised 77% of the county’stotal area, 95% of which was sloping cropland with slopes thatexceeded 25° (Wuqi Statistical Yearbook, 1999). The area that sufferedfrom severe soil erosion comprised 97.4% of the entire county. Sincethis action, agricultural and livestock production in Wuqi Countyexperienced a notable transformation under the prohibition of opengrazing and abandoned cultivation for a large area of sloping land. Bythe end of 2007, an area of 12000 ha of cropland was abandoned in thecounty, the largest among all the counties that abandoned cropland,approximately half of which was afforested (Wuqi Statistical Yearbook,1999).
4.2. Calibration and validation of the model
In this study, program SWAT-CUP was used to analyze thesensitivity of the parameters. The results from the analysis showedthat the parameters CN2, ESCO, SOL_AWC, SOL_K and ALPHA_BF(defined in Table 3) were more sensitive to runoff than otherparameters. Then, these parameters were adjusted by using the auto-calibration 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 from1986 to 1990 and validated from 1991 to 1995. Land use data in 1990were used in this section because of the stability of land use during this
stage. Table 4 shows that ENS and R2 were 0.66 and 0.76 at the annualscale, and 0.43 and 0.71 at the monthly scale, respectively, for thecalibration period of 1986–1990. The observed and simulated averageannual runoff depths during the calibration period were 26.2 and21.7 mm, respectively. For the validation period of 1991–1995, ENSand R2 were 0.72 and 0.89 at the annual scale and 0.47 and 0.82 at themonthly scale, respectively. The observed and simulated averageannual 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 morethan that during the calibration period, which resulted from a floodevent 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 stream-flow, especially for high flow. However, the observed and simulatedrunoff generally matched at both the monthly and annual scales(Table 4). All values of ENS were larger than 0.4, and the modellingperformance was acceptable according to the criteria (Nash andSutcliffe, 1970). Overall, the calibrated model is reliable and acceptableto simulate the runoff and hydrological responses to land use and coverchanges in the catchment.
4.3. Effects of climate variability and human activities
4.3.1. Estimation of contributions from human activity to streamflowreduction
Following Eqs. (4)–(7), the effects of human activities on changes inthe streamflow during 1996–2005 and 2006–2012 were estimatedcompared to those during the baseline period of 1986–1995, as shownin Table 5.
Table 5 shows that the mean annual runoff was 32.55 mm duringthe baseline period of 1986–1995, while the mean annual runoff duringthe following period of 1996–2005 was 23.83 mm with a relativereduction of 26.80%. The SWAT modeled that the proportion of thereduction in the mean annual runoff of 8.72 mm that resulted from thehuman activities (ΔRHUM) was 4.93 mm. Therefore, the effect fromhuman activities on the change in steamflow was 56.5% during1996–2005, while the remaining 43.5% could be attributed to climatevariability in the area. During the following period of 2006–2012, themean annual runoff was only 17.86 mm, a relative reduction of 45.13%compared to the baseline period. The modelled runoff that could beattributed to human activities was 11.43 mm. Consequently, the effectsof human activities on the change in runoff reached 77.8% during theperiod of 2006–2012. The climate variability induced an effect of
Table 2Land 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 (%)
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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 afore-mentioned trends of the land use and cover changes in the catchmentand 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).
4.3.2. Sensitivity of the streamflow to climate variabilityUnder the background of global warming, it is important to
investigate the sensitivity of streamflow to climate variability toachieve sustainable watershed management (Cai et al., 2011). In thiscatchment, two scenarios were created to check the sensitivity ofstreamflow to changes in both precipitation and temperature basedon land use and cover status data and the corresponding climates from1990, 2000 and 2010. Scenario 1 involved increasing the precipitationby 10% without any changes in temperature. Scenario 2 involvedincreasing the temperature by 1 °C without any changes in precipita-tion.
Table 6 shows that the effects of increasing precipitation increasedthe streamflow, while rising temperatures decreased the streamflow.Generally, the effect of 10% higher precipitation on streamflow wasmore significant than that by a 1 °C temperature increase under thesame land cover status. The simulation in scenario 1 showed that as theland cover improved from 1990 to 2000 and 2010, the extent ofstreamflow change from increasing precipitation deflated from nearly30% to 20% because higher vegetation coverage in the catchmentwould result in less fluctuation in runoff generation from higherinterception, evapotranspiration and infiltration during the hydrologi-cal cycle. The simulation in scenario 2 showed that the extent ofstreamflow change from rising temperatures inflated from nearly 3% to9% over time because of the presence and growth of vegetation and itsgrowth, during which more water would be consumed by plant
transpiration and thus decreasing the runoff generation (Liu et al.,2011).
Precipitation and temperature in climate variability were two majorfactors to influence the hydrological processes (Howden et al., 2007).Fu and Charles (2007) pointed out that a 30% precipitation increase inthe Yellow River Basin would result in a 45% increase in streamflow atmean temperature. Legesse et al. (2010) reported that the potentialevapotranspiration would increase by 6.02% and the simulated stream-flow decrease by 13% when the temperature increased by 1.5 °C in aPRMS 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 soilwater 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 watercontent generally decreased while the ET increased from 1990 to 2000and 2010 in the catchment. The spatial distributions of the changingrates in the surface runoff, ET and soil water content during theseperiods are shown in Fig. 4.
4.4.1. Spatial pattern of surface runoff and its changing rateThe spatial distribution of the annual surface runoff is illustrated in
Fig. 2. Observed and simulated average monthly runoff for the study catchment.
Table 5Effects of human activities and climate change on the reduction of the average annual runoff in the catchment.
Periods Observed Modelled Average annual changes in runoff (mm/a) Effects on runoff (%)
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Fig. 3a. The results show that the average annual surface runoff in 52sub-basins was 26.0 mm in 1990, with a maximum surface runoff of67.7 mm and a minimum of 19.9 mm. The annual surface runoff in upto 94.1% of the total catchment was concentrated in the range of21–30 mm and widespread throughout the catchment. In 2000, theannual surface runoff in 52 sub-basins decreased to 19.9 mm, with amaximum surface runoff of 32.8 mm and a minimum of 10.5 mm.Approximately the annual surface runoff of 83.8% of the total catch-ment was concentrated in the range of 16–25 mm. In 2010, thesimulated average annual runoff continuously decreased to only9.8 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 wasconcentrated in the range of 0–15 mm.
The spatial distributions of the changes in the surface runoff areshown in Fig. 4a. Compared to 1990, the average surface runoff in allthe sub-basins decreased by 24.7% in 2000 and the maximum decreas-ing rate was 68.1% which occurred in the southern area of thecatchment, while the minimum decreasing rate was 2.7% which
widespread across the entire catchment. Compared to 2000, theaverage surface runoff in 2010 decreased by 42.7% with a maximumdecreasing rate of 71.8% and a minimum of 9.7%. Generally, theannual surface runoff during these three years and the changing ratesduring these three periods presented a decreasing gradient fromnorthwestern to the southeast and from north to south (Figs. 3 and 4a).
4.4.2. Spatial pattern of ET and the changing rateThe 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. Themaximum and minimum ET during these three years were 372.4 and338.5 mm, 386.3 and 342.0 mm, and 419.5 and 350.6 mm, respec-tively.
In 1990, the annual ET in approximately 50.0% of the entirecatchment was concentrated in the range of 350–360 mm, and theseareas were mainly distributed from the center to the southeastern areaof the catchment. In 2000, the annual ET in about 58.2% of the
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.
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catchment was mainly concentrated in the range of 370–380 mm, andthese areas were distributed in the same region with that in 1990 fromthe center to southeastern area of the catchment. In 2010, the annualET in about 61.6% of the catchment was concentrated in the range of400 ∼ 415 mm, and these areas were mainly distributed from thenorthwestern to southeastern areas of the catchment.
The spatial patterns of the changing rates in the ET are shown inFig. 4b. Compared to 1990, the average annual ET in the 52 sub-basinsincreased 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 distributedfrom the northern and northwestern to the southern and southeasternareas of the catchment. Compared to 2000, the average annual ET in 52sub-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 ETwas generally concentrated in the southern and southwestern areas ofthe catchment. The spatial distribution and change patterns of ET wereclosely associated with the vegetation coverage (Li et al., 2012). Thenorthern and northwestern areas of the catchment belong to Dingbian
County, which is close to the Mu Us Desert and the lower temperaturesand precipitation inhibits vegetation restoration compared with otherareas.
4.4.3. Soil water content and the spatial pattern of the changing rateThe spatial distribution of the annual soil water content is shown in
Fig. 3c. The results show that the average annual soil water content inthe 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 were80.4 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 wasconcentrated in the range of 31–40 mm, and these areas were mainlydistributed in the northern, central-northeastern and southwesternareas of the catchment. In 2000, the soil water content in about77.4% 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 contentare shown in Fig. 4c. Similar to the reduction in the surface runoff, the
Fig. 4. Spatial variations of surface runoff, ET and soil water content at the sub-basin scale from 1990, 2000–2010.
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average annual soil water content in the 52 sub-basins decreased by19.8%, from 1990 to 2000, with a maximum changing rate of 36.5%and a minimum of 0.6%. From 2000–2010, the average annual soilwater content continued to decrease and the change rate was 21.8%with a maximum of 57.3% and a minimum of 0.2%. Generally, thedecreasing rate gradually increased from the northern and central areasto the southwestern and southeastern areas of the catchment from 1990to 2010. The spatial patterns may be related to differences in thevegetation restoration in the catchment. More soil water was consumedas forests and shrubs were planted, so that the soil moisture decreasedin local areas (Gates John et al., 2011).
4.4.4. Land use and cover changes at the sub-basin scaleThe land use and cover changes were investigated to determine the
reason for the spatial changes in the surface runoff, ET and soil watercontent in the catchment, as shown in Fig. 5.
Fig. 5 shows an obvious increase in forestland and grassland and adecrease in farmland in sub-basins of the catchment. The averageincreasing rate of forestland and shrubland was 4.8%. The sub-basinswith increased forestland were mainly located in the eastern, southernand southwestern areas of the catchment. The average increasing ratefor grassland was 10.7%, which was basically higher than that offorestland. The sub-basins with increased grassland were distributedfrom the northern and northwestern to the southern and southeasternareas of the catchment. This type of spatial pattern was generallyconsistent with those of the surface runoff and soil water content. Theaverage decreasing rate of farmland was 15.9%, and the spatiallychanging trend was opposite to that of grassland. These results showedthat the effects of the soil and water conservation measures that wereimplemented in the 1980s and the “Green for Grain” Program after1999 significantly affected the hydrological behavior in the catchment(Chen et al., 2016; Zhang et al., 2016).
5. Summary
The physically based distributed model SWAT was used to examinethe spatial hydrological responses to land use and cover changes overthe past 30 years in a catchment on the Loess Plateau in China. Thecalibrated SWAT model was considered a reliable tool to estimate thesurface hydrology components and their changes in the catchment aswell as the sensitivities of the catchment water generation to changes inprecipitation and land use.
As a result of using various sources of information, including remotesensing, it has been shown that the forestland, shrubland and grasslandsignificantly increased by 22.6%, 332.0% and 10.7% from 1990 to 2000and continued to increase by 79.2%, 51.2% and 1.8%, respectively,from 2000 to 2010. Meanwhile, farmland decreased by 22.8% and
15.7% during the corresponding periods. SWAT simulated that theaverage surface runoff was 26.0 mm, 19.9 mm and 9.8 mm at the sub-basin scale in the baseline period of 1986–1995, the 2nd period of1996–2005 and the 3rd period of 2006–2012, respectively. The averageET was 362.7 mm, 370.1 mm and 406.7 mm during the correspondingperiods. The soil water content was 41.8 mm, 33.7 mm and 26.9 mmduring these three periods. The spatial pattern of the changes in thehydrological components followed a gradient from the north andnorthwest to the south and southeast in the catchment.
The changes in the hydrological components were closely related tothe temporal alteration in land use types, especially for forestland,shrubland, grassland and cropland during the studied periods. Thespatial patterns showed that the higher the increasing rate of forestlandand grassland in the sub-basin, the greater the decreasing rate of thesurface runoff and soil water content, whereas the ET presented theopposite trend. The SWAT simulations indicated that the land use andcover changes were the main driving forces and were 56.5% in theperiod of 1996–2005 and 77.8% in the period of 2006–2012 in terms ofthe reduction in streamflow during these two periods. The simulation inscenarios illustrated that the effect of precipitation on streamflow wasmore significant than that of rising temperature under the same landcover.
This study is useful to understand the eco-hydrological processes incatchments especially those China’s Loess Plateau, which is experien-cing evident vegetation restoration. This study provides a tool forwatershed sustainable management in the future to undertake studiesof how spatially distributed changes in land use and precipitation mayaffect runoff and erosion.
Acknowledgements
This study was supported by the National Natural ScienceFoundation of China (Grant Nos. 41230852, 41440012 and41101265), and Special-Funds of Scientific Research Programs ofState Key Laboratory of Soil Erosion and Dryland Farming on theLoess Plateau (A314021403-C2).
References
Arnold, J.G., Fohrer, N., 2005. SWAT2000: current capabilities and researchopportunities in applied watershed modeling. Hydrol. Processes 19, 563–572.
Arnold, J.G., Williams, J.R., Nicks, A.D., 1990. SWRRB: A Basin Scale Simulation Modelfor Soil and Water Resources Management. Texas A &M Press, College Station.
Band, L., Mackay, D., Creed, I., Semkin, R., Jeffries, D., 1996. Ecosystem processes at thewatershed scale: sensitivity to potential climate change. Limnol. Ocenogr. 41 (5),928–938.
Benaman, J., Shoemaker, C.A., 2005. An analysis of high-flow sediment event data forevaluating model performance. Hydrol. Processes 19, 605–620.
Bradshaw, C.J.A., Sodhi, N.S., Peh, K.S.H., Brook, B.W., 2007. Global evidence that
Fig. 5. Spatial variations in land-use types at the sub-basin scale between 1990 and 2010.
R. Yan et al. Ecological Indicators xxx (xxxx) xxx–xxx
deforestation amplifies flood risk and severity in the developing world. Glob. ChangeBiol. 13, 1–17.
Cai, Y.P., Huang, G.H., Yang, Z.F., 2009a. Identification of optimal strategies for energymanagement systems planning under multiple uncertainties. Appl. Energy 86 (4),480–495.
Cai, Y.P., Huang, G.H., Yang, Z.F., 2009b. Community-scale renewable energy systemsplanning under uncertainty-An interval chance-constrained programming approach.Renewable Sustain. Energy Rev. 13 (4), 721–735.
Cai, Y.P., Huang, G.H., Tan, Q., 2011. An integrated approach for climate-change impactanalysis and adaptation planning under multi-level uncertainties. Part II. Case study.Renewable Sustain. Energy Rev. 15 (6), 3051–3073.
Cao, W., Bowden, W.B., Davie, T., 2009. Modelling impacts of land cover change oncritical water resources in the Motueka River catchment, New Zealand. Water Resour.Manage. 23 (1), 137–151.
Chen, L., Wang, J., Fu, B.J., Qiu, Y., 2001. Land-use change in a small catchment ofnorthern Loess Plateau China. Agric. Ecosyst. Environ. 86, 163–172.
Chen, N., Ma, T.Y., Zhang, X.P., 2016. Response of soil erosion processes to land coverchanges in the Loess Plateau of China: A case study on the Beiluo River basin. Catena136, 118–127.
Donigian, A.S., Bicknell, B.R., Imhoff, J.C., 1995. Hydrological simulationprogram—Fortran (HSPF), chap. 12. In: Singh, V.P. (Ed.), Computer Models ofWatershed Hydrology. Water Resources Publications, Colorado, USA, pp. 395–442.
Easton, Z.M., Fuka, D.R., Walter, M.T., Cowan, D.M., Schneiderman, E.M., Steenhuis, T.S.,2008. Re-conceptualizing the soil and water assessment tool (SWAT) model to predictrunoff from variable source areas. J. Hydrol. 348, 279–291.
Fu, G.B., Charles, S.P., 2007. A two-parameter climate elasticity of streamflow index toassess climate change effects on annual streamflow. Water Resour. Res. 43, W11419.http://dx.doi.org/10.1029/2007WR005890.
Fu, B.J., Gulinck, H., Masum, M.Z., 1994. Loess erosion in relation to land-use changes inthe Ganspoel catchment, central Belgium. Land Degrad. Rehabil. 52, 261–270.
Fu, B.J., Liu, Y., He, C.S., Wu, B.F., 2011. Assessing the soil erosion control service ofecosystems change in the Loess Plateau of China. Ecol. Complexity 8 (4), 284–293.
Gao, Z.L., Zhang, L., Zhang, X.P., Cheng, L., Potter, N., Cai, W.J., 2015. Long-termstreamflow trends in the middle reaches of the Yellow River Basin: detecting driversof change. Hydrol. Process. http://dx.doi.org/10.1002/hyp.10704.
Gates John, B., Scanlon Bridget, R., Mu, X.M., Zhang, L., 2011. Impacts of soilconservation on groundwater recharge in the semi-arid Loess Plateau, China. Hydrol.J. 19, 865–875.
Howden, S.M., Soussana, J.F., Tubiello, F.N., Chetri, N., Dunlop, M., Meinke, H., 2007.Adapting agriculture to climate change. Proc. Natl. Acad. Sci. U. S. A. 104,1961–1969.
Hurkmans, R.T., Terink, W., Uijlenhoet, R., Moors, E.J., Troch, P.A., Verburg, P.H., 2009.Effects of land use changes on streamflow generation in the Rhine basin. WaterResour. Res. 45, 1–15.
Jeppesen, E., Kronvang, B., Meerhoff, M., et al., 2009. Climate change effects on runoff,catchment phosphorus loading and lake ecological state, and potential adaptations. J.Environ. Qual. 38, 1930–1941.
Jothityangkoon, C., Sivapalan, M., Farmer, D.L., 2001. Process controls of water balancevariability in a large semi-arid catchment: downward approach to hydrological modeldevelopment. J. Hydrol. 254, 174–198.
Kou, X.J., Ge, J.P., Wang, Y., Zhang, C.J., 2007. Validation of the weather generatorCLIGEN with daily precipitation data from the Loess Plateau, China. J. Hydrol. 347,347–357.
Lam, Q.D., Schmalz, B., Fohrer, N., 2012. Assessing the spatial and temporal variations ofwater quality in lowland areas, Northern Germany. J. Hydrol. 438, 137–147.
Laurance, W.F., 2007. Forests and floods. Nature 449, 409–410.Leavesley, G.H., 1994. Modeling the effects of climate change on water resources – a
review. Clim. Change 28, 159–177.Li, Z., Liu, W.Z., Zhang, X.C., Zheng, F.L., 2009. Impacts of land use change and climate
variability on hydrology in an agricultural catchment on the Loess Plateau of China.J. Hydrol. 377, 35–42.
Li, Z., Zheng, F.L., Liu, W.Z., 2012. Spatiotemporal characteristics of referenceevapotranspiration during 1961–2009 and its projected changes during 2011–2099on the Loess Plateau of China. Agric. For. Meteorol. 154–155, 147–155.
Li, L.C., Zhang, L.P., Xia, J., 2015. Implications of modelled climate and land coverchanges on runoff in the middle route of the south to north water transfer project inChina. Water Resour. Manage. 29, 2563–2579.
Lin, P.F., Zhang, X.P., Liu, E.J., 2015. Responses of the water and sediment behavior to
vegetation restoration in tipical catchments on the loess plateau. J. Soil WaterConserv. 29 (1), 1–5 (in Chinese).
Liu, Y., Huang, G.H., Cai, Y.P., 2011. An inexact mix-integer two-stage linearprogramming model for supporting the management of a low-carbon energy systemin China. Energies 4 (10), 1657–1686.
Liu, E.J., Zhang, X.P., Xie, M.L., 2015. Hydrologic responses to vegetation restoration andtheir driving forces in a catchment in the Loess hilly-gully area: a case study in theupper Beiluo River. Acta Ecol. Sin. 35, 623–629 (in Chinese).
Lorup, J.K., Refsgaard, J.C., Mazvimavi, D., 1998. Assessing the effect of land use changeon catchment runoff by combined use of statistical tests and hydrological modelling:case studies from Zimbabwe. J. Hydrol. 205 (3–4), 147–163.
Mehdi, B., Lehner, B., Gombault, C., 2015. Simulated impacts of climate change andagricultural land use change on surface water quality with and without adaptationmanagement strategies. Agric. Ecosyst. Environ. 213, 47–60.
Monteith, J.L., 1965. Evaporation and the environment. The State and Movement ofWater I Living Organisms XIXth.
Moriasi, D.N., Arnold, J.G., Bingner, R.L., Harmel, R.D., Veith, T.L., 2007. Modelevaluation guidelines for systematic quantification of accuracy in watershedsimulations. Trans ASABE 50 (3), 885–900.
Narsimlu, B., Gosain, A.K., Chahar, B.R., 2013. Assessment of future climate changeimpacts on water resources of upper sind river basin, India using SWAT model. WaterResour. Manage. 27 (10), 3647–3662.
Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models partI—a discussion of principles. J. Hydrol. 10 (3), 282–290.
Qin, W., Zhu, Q.K., Liu, G.Q., Zhang, Y., 2010. Regulation effects of runoff and sedimentof ecological conservation in the upper reaches of Beiluo River. Shuili Xuebao 41(11), 1325–1330.
Somura, H., Arnold, J., Hoffman, D., Takeda, I., Mori, Y., Luzio, M.D., 2009. Impacts ofclimate change on the Hii River basin and salinity in Lake Shinji: a case study usingthe SWAT model and a regression curve. Hydrol. Process. 23, 1887–1900.
Stone, M., Hotchkiss, R., Hubbard, C., Fontaine, T., Arnold, J., 2001. Impacts of climatechange on Missouri River Basin water yield. J. Am. Water Resour. Assoc. 37 (5),1119–1128.
Tan, Q., Huang, G.H., Cai, Y.P., 2013. Multi-source multi-sector sustainable water supplyunder multiple uncertainties: an inexact fuzzy-stochastic quadratic programmingapproach. Water Resour. Manage. 27 (2), 451–473.
Tang, K., 2004. Soil and Water Conservation in China. Science China Press.Tripathi, M.P., Panda, R.K., Raghuwanshi, N.S., Singh, R., 2004. Hydrological modeling
of a small watershed using generated rainfall in the soil and water assessment toolmodel. Hydrol. Processes 18, 1811–1821.
Tripathi, M.P., Panda, R.K., Raghuwanshi, N.S., 2005. Development of effectivemanagement plan for critical subwatersheds using SWAT model. Hydrol. Processes19, 809–826.
USEPA, 2014. Climate Change Site (Online). http://www.epa.gov/climatechange/.Wang, L.X., 2003. On the concept and category of the soil and water conservation science
in China. Sci. Soil Water Conserv. 1 (2), 108–110.Wuqi Statistical Yearbook, 1999. 1999 Wuqi Social and Economic Statistical Year-book.
Shaanxi Provincial Bureau of Statistics, Shaanxi, China.Yan, R., Zhang, X.P., Yan, S.J., Zhao, W.H., 2016. The vegetation restoration in the Beiluo
River Basin from 1995 to 2014 and their distribution on the landform. J.Northeastern Univ. (Natural Science) 37 (11), 1598–1603 (in Chinese).
Young, R.A., Onstad, C.A., Bosch, D.D., 1989. AGNPS: a nonpoint-source pollution modelfor evaluating agricultural watersheds. J. Soil Water Conserv. 44 (2), 168–173.
Zhang, J., Zhang, Q., Li, D.L., 2008a. MODIS retrieval of different vegetation LAI and itsapplication to the east of Qinghai-Xizang plateau. Plateau Meteorol. 27 (4), 905–909.
Zhang, X.P., Zhang, L., Zhao, J., Rustomji, P., Hairsine, P., 2008b. Responses ofstreamflow to changes in climate and land use/cover in the Loess Plateau, China.Water Resour. Res. 44 (W00A07).
Zhang, B.Q., Wu, P.T., Zhang, X.N., 2011a. Detecting and analysis of spatial and temporalvariation of vegetation cover in the Loess Plateau during 1982–2009. Trans. CSAE 27(4), 287–293 (in Chinese).
Zhang, J.J., Li, H.M., Xu, J.J., 2011b. Soil moisture dynamics of water and soilconservation forest on the Loess Plateau. Acta Ecol. Sin. 31 (23), 7056–7066.
Zhang, A.J., Zhang, C., Fu, G.B., 2012. Assessments of impacts of climate change andhuman activities on runoff with SWAT for the huifa river basin, Northeast China.Water Resour. Manage. 26, 2199–2217.
