sign in sign up
<<
Home , Baseflow, Surface runoff, Water storage

Article Quantitative Assessment of and Base Flow Response to Multiple Factors in Pengchongjian Small Watershed

Lei Ouyang 1,2 , Shiyu Liu 1,2,*, Jingping Ye 1,2, Zheng Liu 1,2, Fei Sheng 1,2, Rong Wang 1 and Zhihong Lu 1 1 School of Land Resources and Environment, Jiangxi Agricultural University, Nanchang 330045, ; [email protected] (L.O.); [email protected] (J.Y.); [email protected] (Z.L.); [email protected] (F.S.); [email protected] (R.W.); [email protected] (Z.L.) 2 Key Laboratory of Poyang Watershed Agricultural Resources and Ecology of Jiangxi Province, Nanchang 330045, China * Correspondence: [email protected]; Tel.: +86-158-7061-1238

 Received: 22 July 2018; Accepted: 6 September 2018; Published: 10 September 2018 

Abstract: Quantifying the impacts of multiple factors on surface runoff and base flow is essential for understanding the mechanism of hydrological response and local resources management as as preventing floods and . Despite previous studies on quantitative impacts of multiple factors on runoff, there is still a need for assessment of the influence of these factors on both surface runoff and base flow in different temporal scales at the watershed level. The main objective of this paper was to quantify the influence of precipitation variation, (ET) and restoration on surface runoff and base flow using empirical statistics and slope change ratio of cumulative quantities (SCRCQ) methods in Pengchongjian small watershed (116◦25048”–116◦2707” E, 29◦31044”–29◦32056” N, 2.9 km2), China. The results indicated that, the contribution rates of precipitation variation, ET and vegetation restoration to surface runoff were 42.1%, 28.5%, 29.4% in ; 45.0%, 37.1%, 17.9% in summer; 30.1%, 29.4%, 40.5% in autumn; 16.7%, 35.1%, 48.2% in winter; and 35.0%, 38.7%, 26.3% in annual scale, respectively. For base flow they were 33.1%, 41.9%, 25.0% in spring; 39.3%, 51.9%, 8.8% in summer; 40.2%, 38.2%, 21.6% in autumn; 24.3%, 39.4%, 36.3% in winter; and 24.4%, 47.9%, 27.7% in annual scale, respectively. Overall, climatic factors, including precipitation and ET change, affect surface runoff generation the most, while ET affects the dynamic change of annual base flowthe most. This study highlights the importance of optimizing forest management to protect the water resource.

Keywords: precipitation variation; vegetation restoration; evapotranspiration (ET); surface runoff; base flow; empirical statistics; slope change ratio of cumulative quantities (SCRCQ)

1. Introduction At present, runoff is generally divided into two parts: surface runoff and base flow [1–3]. During a rainfall process in a basin, the flow duration curve formed at the outlet of a watershed consists of surface runoff and base flow (Figure1)[ 4]. There are many quantitative studies on the effects of precipitation variation and vegetation restoration on surface runoff [5–11]. Distinct hydrological effects based on different time scales were reported [12]. For instance, Chaplot investigated water and resources response to changes in precipitation and air in Iowa (USA), pointing to a considerable effect of precipitation changes with a 170% runoff increase as precipitation increases by 40% [11], while Zhang and Nearing predicted that decreasing precipitation by 7–14% would result in greater runoff (67–82%) [13]. The above two results showed the opposite effects on runoff caused by

Forests 2018, 9, 553; doi:10.3390/f9090553 www.mdpi.com/journal/forests Forests 2018, 9, x FOR PEER REVIEW 2 of 16

by precipitation, which is mainly due to different topography, forest change, soil condition, temperature as well as other factors like NO3–N loads [9]. Zhang conducted research over Poyang Lake basin and found that contributions of climate change and human activities to changes were 73.2% and 26.8%, respectively. However, human‐induced and climate‐induced influences on streamflow were different in different basins [14]. Similarly, Gu demonstrated that human activities accounted for a low portion (5.5%) in the runoff inputs in Poyang Lake [15]. Interestingly, however, the decrease of streamflow in the Fuhe River (sub‐catchment of Poyang Lake) in 2000s was mainly affected by human activities (156.9%), rather than climate change [6]. More recently, Rogger et al. reviewed research gaps in the field of land‐use change impacts on at the catchment scale [16]. They proposed that land‐use change impacts on floods are poorly understood at the catchment scale at present, then suggested possible ways forward for addressing these gaps. Despite numerous works, most research focused on calculating surface runoff in annual scale Forests 2018, 9, 553 2 of 16 [8,17,18], but little attention was paid to the seasonal scale. However, a study on the effects of precipitation and evapotranspiration (ET) variation, vegetation restoration on the seasonal surface precipitation,runoff is of whichgreat issignificance mainly due for to differentrationally topography, allocating forestwater change, resources soil condition,yearly, especially temperature for aswatersheds well as other with factors seasonal like NOdroughts3–N loads. Thus, [9]. it Zhang is still conducted necessary research to further over study Poyang the Lake influences basin and of foundprecipitation that contributions and ET variation, of climate vegetation change and restoration human activitieson seasonal to streamflow surface runoff. changes were 73.2% and 26.8%,Base respectively. flow is a relatively However, stable human-induced part of streamflow. and climate-induced Base flow studies influences are onnot streamflowonly helpful were for differentreasonable in differentregulation river of water basins quantity [14]. Similarly, and high Gu‐efficiency demonstrated utilization that human of water activities resources, accounted but also for abeneficial low portion for (5.5%)hydrological in the runoffanalysis, inputs evaluation in Poyang and Lake the management [15]. Interestingly, of water however, resources the decrease[19,20]. At of streamflowpresent, studies in the Fuheon base River flow (sub-catchment mainly focus of Poyangon regression Lake) in models, 2000s was improvement mainly affected of bybase human flow activitiesseparation (156.9%), methods rather and influencing than climate factors. change The [6 ].studies More of recently, base flow Rogger in China et al. began reviewed in the research 1960s, gapsand mainly in the fielddealt ofwith land-use qualitative change analysis impacts of onbase floods flow atevolution the catchment principles scale and [16 its]. Theydriving proposed factors, thatbase land-useflow separation change impactsand estimation. on floods Nonetheless, are poorly understood quantitative at studies the catchment on the scalecontribution at present, rates then of suggesteddifferent factors possible to base ways flow forward are rarely for addressing reported. these gaps. Despite numerous works, most research focusedConsequently, on calculating with surface the support runoff of in the annual National scale Natural [8,17,18 ],Science but little Foundation attention of was China, paid taking to the seasonalPengchongjian scale. However,small watershed a study onwith the rich effects precipitat of precipitationion as a study and evapotranspiration area, we quantitatively (ET)variation, assessed vegetationthe impacts restoration of multiple on factors the seasonal on both surfacesurface runoff isand of base great flow significance (Figure 2). for This rationally study could allocating help waterto understand resources how yearly, precipitation especially variation, for watersheds ET and with vegetation seasonal restoration droughts. affect Thus, runoff, it is still and necessary provide toscientific further foundations study the influences for the ofsustainable precipitation utiliz andation ET variation,of water vegetationresources restorationand the evaluation on seasonal of surfacehydrological runoff. benefits for natural forest protection and afforestation projects.

Figure 1. Process of water inputs, storage, and losses in watershed.

Base flow is a relatively stable part of streamflow. Base flow studies are not only helpful for reasonable regulation of water quantity and high-efficiency utilization of , but also beneficialForests 2018, for9, x; hydrologicaldoi: FOR PEER REVIEW analysis, evaluation and the management of waterwww.mdpi.com/jou resources [19rnal/forests,20]. At present, studies on base flow mainly focus on regression models, improvement of base flow separation methods and influencing factors. The studies of base flow in China began in the 1960s, and mainly dealt with qualitative analysis of base flow evolution principles and its driving factors, base flow separation and estimation. Nonetheless, quantitative studies on the contribution rates of different factors to base flow are rarely reported. Consequently, with the support of the National Natural Science Foundation of China, taking Pengchongjian small watershed with rich precipitation as a study area, we quantitatively assessed the impacts of multiple factors on both surface runoff and base flow (Figure2). This study could help to understand how precipitation variation, ET and vegetation restoration affect runoff, and provide scientific foundations for the sustainable utilization of water resources and the evaluation of hydrological benefits for natural forest protection and afforestation projects. Forests 2018, 9, 553 3 of 16 Forests 2018, 9, x FOR PEER REVIEW 3 of 16

Figure 2. Graphical structure of this paper.

2. Study Area and Methods

2.1. Study Area ◦ 0 ◦ 0 ◦ 0 ◦ 0 Pengchongjian small small watershed watershed is located is located between between 116 25 48”–116116°25′4827″7”–116°27 E and′ 297″ 31E 44”–29and 29°3132 56”′44″ N– 2 in29°32 Duchang′56″ N County,in Duchang Jiujiang County, City, Jiangxi Jiujiang Province, City, Jiangxi covering Province, a catchment covering area ofa 2.90catchment km (Figure area 3of). It2.90 is highkm2 (Figure in the north-west 3). It is high and in low the in north the south-east,‐west and whose low in altitude the south ranges‐east, from whose 80 to altitude 560 meters, ranges belonging from 80 to subtropicalto 560 meters, humid belonging monsoon climateto subtropical zone with humid average monsoon annual precipitation climate zone of 1560 with mm. average Major exposure annual strataprecipitation here are epimetamorphicof 1560 mm. Major rock, graniteexposure and limestone.strata here Moreover, are epimetamorphic the watershed isrock, rich ingranite vegetation and types,limestone. with standMoreover, types ofthe 70% watershed Cunninghamia is rich lanceolata in vegetation (Lamb.) Hook., types, 20% withQuercus stand acutissima types Carruth.of 70% andCunninghamia 2% Phyllostachys lanceolata heterocycla (Lamb.)(Carr.) Hook., Mitford 20% cv. Pubescens. Quercus acutissima The watershed Carruth is completely. and 2% closed Phyllostachys as there areheterocycla no inhabitants, (Carr.) waterMitford conservancy cv. Pubescens. facilities The or soilwatershed and water is conservationcompletely projects.closed as Yet there the China are no fir forestinhabitants, suffered water from conservancy faci in earlylities 1980s,or soil afterand whichwater theconservation secondary projects. forest has Yet been the recovering, China fir whichforest providessuffered anfrom ideal deforestation study area for in this early study. 1980s, According afterto which the investigating the secondary data offorest forest has resources been byrecovering, Wushan forestwhich provides in Duchang an ideal County, study area forest for coverage this study. increased According from to 80% the in investigating 1985 to 98% indata 2016, of × 4 3 × 4 3 andforest forest resources stocking by volume Wushan rose forest from farm 1.2 in10 Duchanm in 1985g County, to 2.5 forest10 m coveragein 2016. increased In 1981, a from hydrological 80% in station1985 to was98% built in 2016, up at and the forest outlet stocking of the watershed volume byrose Jiangxi from Province1.2 × 104 m Bureau,3 in 1985 and to the 2.5 precipitation× 104 m3 in 2016. and surfaceIn 1981, runoff a hydrological have been station continuously was built monitored up at the till outlet now. of the watershed by Jiangxi Province Bureau, and the precipitation and surface runoff have been continuously monitored till now.

Forests 2018, 9, x; doi: FOR PEER REVIEW www.mdpi.com/journal/forests Forests 2018, 9, 553 4 of 16 Forests 2018, 9, x FOR PEER REVIEW 4 of 16

FigureFigure 3. 3.Location Location map map and and observation observation sites sites in in Pengchongjian Pengchongjian small small watershed. watershed.

2.2.2.2. Methods Methods

2.2.1.2.2.1. Data Data DataData used used in in this this paper paper include include daily daily precipitation, precipitation, surface surface runoff runoff and and daily daily mean mean temperature temperature atat Pengchongjian Pengchongjian hydrological hydrological stationstation fromfrom 1983 1983 to to 2014, 2014, which which was was supplied supplied by by Jiangxi Jiangxi Province Province HydrologyHydrology Bureau. Bureau. BaseBase flow flow were were separated separated from from daily daily surface surface runoff runoff datasets datasets using using the the Kalinlin Kalinlin improvingimproving method method [21 [21].]. Seasonal Seasonal and and annual annual precipitation, precipitation, surface surface runoff runoff and and base base flow flow during during these these 3030 years years were were then then calculated calculated based based on daily on statistical daily statistical data for subsequentdata for subsequent trend analysis. trend Meanwhile, analysis. basedMeanwhile, on the waterbased balance on the equationwater balance in a totally equation closed in smalla totally watershed, closed small mean ETwatershed, is basically mean equal ET to is meanbasically precipitation equal to mean minus precipitation mean surface minus runoff. mean surface runoff. 2.2.2. Trend Analysis 2.2.2. Trend Analysis The Mann–Kendall test (MK) is a suitable method to determine the variation trend of hydrological The Mann–Kendall test (MK) is a suitable method to determine the variation trend of time series [22,23], which has been widely applied for trend and inflection analysis of precipitation hydrological time series [22,23], which has been widely applied for trend and inflection analysis of and runoff [24,25]. Thus, in this paper, we found a consistent inflection point of the year 2003 both for precipitation and runoff [24,25]. Thus, in this paper, we found a consistent inflection point of the year precipitation and surface runoff. The research period was the divided into baseline period (1983–2003) 2003 both for precipitation and surface runoff. The research period was the divided into baseline and changing period (2004–2014), which lays a solid foundation for separating the impacts of multiple period (1983–2003) and changing period (2004–2014), which lays a solid foundation for separating factors on surface runoff and base flow in different time scales. The calculation procedures of an MK the impacts of multiple factors on surface runoff and base flow in different time scales. The calculation trend test are given as follows. procedures of an MK trend test are given as follows. For a time series {r1, r2,..., rn−1, rn}, the statistic index UFm is calculated as, For a time series {r1, r2, …rn − 1, rn}, the statistic index UFm is calculated as,

S −E(S()) UF= m= m ((m == 1, 2, …n) ) UFm p ( ) m 1, 2, . . . n (1) Var(Sm) wherewhereS mSmis is deemed deemed as as cumulative cumulative number number of ofr i r>i >r j r(1j (1≤ ≤i ≤i ≤j ),j), E( E(SSmm)) isis averaged averagedS Smm,, and and Var( Var(SmSm)) representsrepresents variance variance of ofS Smm.. TheThe changechange ofof UFUFmm reflectsreflects the variation in hyro hyro-climate‐climate variables. UF UFmm >> 0 0(<0) (<0) means means the the variables variables show show anan increasing increasing (a decreasing) (a decreasing) trend. trend. If |UF If |UFm| > 1.64,m| > 1.96, 1.64, 2.58, 1.96, the 2.58, change the changetrend is trend significant is significant at p > 0.1, at p 0.05> 0.1, and 0.05 0.01, and respectively. 0.01, respectively. Contrarily, Contrarily, the value the calculated value calculated with inverse with inverseseries ( seriesrn, rn − 1 (, r…n,rr2n, −r1), is... termed, r2, r1 )as is UB termedm. If the as exact UBm .intersection If the exact point intersection of the two point lines of located the two within lines the critical limit line and the trend is significance, the point is deemed as the possible change point [22].

Forests 2018, 9, x; doi: FOR PEER REVIEW www.mdpi.com/journal/forests Forests 2018, 9, 553 5 of 16 located within the critical limit line and the trend is significance, the point is deemed as the possible change point [22].

2.2.3. Separating the Effect of ET on Surface Runoff and Base Flow The slope change ratio of cumulative quantities (SCRCQ) method was proposed to quantitatively assess the factors impacting the streamflow in the Huangfuchuan River Basin, a first-level of the middle reaches of the Yellow River. It is believed that this method can be applied to the quantitative assessment of river runoff changes and its influencing factors in arid and semi-arid regions [26]. Subsequently, similar research on Songhua River basin [27], Miyun watershed [28] and some areas of the southern humid areas such as the Yinjiang River watershed [29], Dongting Lake catchment [30], and Ning Jiang River basin [31] had been carried out in China. The cumulative quantities can eliminate the effects of inter-annual fluctuations to some extent, with a high correlation coefficient between year and cumulative quantities, which creates conditions for quantifying analysis [26]. Therefore, using the method of SCRCQ, we calculated the contribution rates of ET to surface runoff and base flow in seasonal and annual scale. Calculation equations are as follows:

(K2 − K1)/K1 CET = −100 × (2) (K4 − K3)/K3 where K (mm·year−1) is the slope of the linear relationship between year and cumulative ET in baseline period (labeled by 1) and changing period (labeled by 2), cumulative surface runoff or base flow in baseline period (labeled by 3) and changing period (labeled by 4). CET is the relative contribution rate of ET to surface runoff or base flow. According to the equation in a relatively closed watershed, the change in storage is likely to be small, which is negligible in annual scale [26,28]. Thus, the contribution rates of precipitation variation and vegetation restoration to surface runoff and base flow is equal to 1 − CET.

2.2.4. Excluding the Contribution Rates of Precipitation Variation and Vegetation Restoration The formation of surface runoff is mainly affected by precipitation and underlying surface conditions. For the same small watershed with no inhabitants and water and soil conservancy projects, underlying surface change can be mainly regarded as vegetation restoration. In this paper, empirical statistics was used to quantitatively assess the two factors (precipitation variation and vegetation restoration) that have significant influence on surface runoff as well as base flow. Firstly, regression analyses were conducted between observed precipitation, surface runoff and base flow in baseline period when vegetation was less changed, after which a linear Equation (1) could be built. Then, substituting observed mean precipitation in the changing period, when vegetation changes apparently in the equation, a value that represents surface runoff (base flow) produced in the condition of vegetation in baseline period can be obtained which is called simulated surface runoff (base flow). The value of simulated surface runoff (base flow) minus observed mean surface runoff (base flow) in the changing period represents surface runoff (base flow) variation caused by vegetation restoration. Similarly, the value of observed mean surface runoff (base flow) in the baseline period minus simulated surface runoff (base flow) represents surface runoff (base flow) variation caused by precipitation variation. In this way, the impacts of precipitation variation and vegetation restoration on surface runoff and base flow can be quantitatively distinguished. Calculation equations of the contribution rates of precipitation variation and vegetation restoration are listed as follows:

Q = aP + b (3) where Q is surface runoff (base flow) [mm], P is precipitation [mm], a and b are parameters.

0 ∆Qv = Qsim − Q obs, ∆QP = Qobs − Qsim (4) Forests 2018, 9, 553 6 of 16

0 ∆Qtotal = ∆Qv + ∆QP = Qobs − Q obs (5)

∆QP ∆Qv Cp = × (1 − CET)% and Cv = × (1 − CET)% (6) ∆Qtotal ∆Qtotal 0 where Qobs and Q obs are observed mean surface runoff (base flow) (mm) in baseline period and 0 changing period respectively; Qsim = aPobs + b, which means simulated surface runoff (base flow), 0 where Pobs is observed mean precipitation (mm) in the changing period; ∆Qtotal is total change in observed mean surface runoff (base flow) comparing baseline period to changing period; ∆Qv and ∆QP are surface runoff (base flow) changes caused by vegetation restoration and precipitation variation, respectively.

3. Results and Discussion

3.1. Seasonal Distribution of Precipitation, Evapotranspiration (ET), Surface Runoff and Base Flow Based on daily precipitation, runoff data and base flow separation results of Pengchongjian hydrological station in 1983–2014, the monthly, seasonal (spring is March to May, summer is June to August, autumn is September to November, winter is December to the following February) and annual precipitation, ET, surface runoff and base flow were obtained. In this condition, it was possible to investigate the annual and inter-annual distribution of precipitation, ET and runoff, and the impacts of various factors on surface runoff and base flow in small watershed. Annual mean precipitation, ET, surface runoff and base flow from 1983 to 2014 were 1560.3, 811.9, 743.7 and 353.2 mm, respectively (Table1). In spring and summer, both parameters except ET accounted for a large proportion in whole year, while a small ratio in autumn and winter. However, the seasonal distribution of ET was relatively even.

Table 1. Seasonal distribution of precipitation, ET, surface runoff and base flow in Pengchongjian small watershed from 1983 to 2014.

Parameters Spring Summer Autumn Winter Year Precipitation/mm 564.6 589.7 199.3 206.7 1560.3 Annual proportion/% 36.2% 37.8% 12.8% 13.2% — ET/mm 218.0 272.2 168.8 153.0 811.9 Annual proportion/% 26.9% 33.5% 20.8% 18.8% — Surface runoff/mm 354.8 300.9 34.3 53.7 743.7 Annual proportion/% 47.7% 40.5% 4.6% 7.2% — Base flow/mm 159.9 131.2 19.1 43.0 353.2 Annual proportion/% 45.3% 37.1% 5.4% 12.2% —

3.2. Variations of Precipitation and Surface Runoff According to the MK test, the annual variation trend of precipitation (P) in Pengchongjian small watershed during 1983–2014 was as follows: (i) precipitation generally showed a fluctuated downward trend (Figure4a). For example, it declined significantly in 1983–1986, and reached the bottom in 1986 (p > 0.05), while in 1987–2003, it showed a fluctuated rise, reaching the peak in 1998. However, from 2004 to 2009, precipitation began to decline, at the beginning of 2010 a peak (2010) and a (2011) appeared, then gradually it increased. (ii) According to the intersection point of UF and UB curves, the t test method was applied. It was also determined that the inflection point of the small watershed was 2003 (Figure4b). Forests 2018, 9, 553 7 of 16 Forests 2018, 9, x FOR PEER REVIEW 7 of 16 Forests 2018, 9, x FOR PEER REVIEW 7 of 16 2400 2400 2.5 (a) 2.5 (b) UF (a) (b) UF 1.5 UB 2000 1.5 UB95% Confidence level 2000 95% Confidence level 0.5 1600 0.5 1600 -0.5 -0.5MK value 1200 MK value 1200 P = -3.8996y + 1624.7 -1.5 P = -3.8996y + 1624.7 -1.5 Annual precipitation (mm) precipitation Annual Annual precipitation (mm) precipitation Annual 800 -2.5 800 -2.5 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013

Figure 4. Inter‐annual variation of observed precipitation (a) and its Mann–Kendall (MK) test results FigureFigure 4.4. Inter-annualInter‐annual variationvariation ofof observedobserved precipitationprecipitation ((aa)) andand itsits Mann–KendallMann–Kendall (MK)(MK) testtest resultsresults (b) in Pengchongjian small watershed. in 1983–2014. ((bb)) inin PengchongjianPengchongjian small small watershed. watershed. inin 1983–2014.1983–2014. According to the MK test, annual variation trend of surface runoff (SR) in 1983–2014 was as AccordingAccording toto thethe MKMK test,test, annualannual variationvariation trendtrend ofof surfacesurface runoffrunoff (SR)(SR) inin 1983–20141983–2014 waswas asas follows: (i) surface runoff showed a fluctuated downward trend (Figure 5a). It decreased significantly follows:follows: (i)(i) surfacesurface runoffrunoff showedshowed aa fluctuatedfluctuated downwarddownward trendtrend (Figure(Figure5 5a).a). It It decreased decreased significantly significantly in 1983–1986 (p > 0.05), then increased gradually in 1987–2003 and reached the maximum value in inin 1983–19861983–1986 ( p(p> > 0.05), 0.05), then then increased increased gradually gradually in 1987–2003in 1987–2003 and and reached reached the maximum the maximum value value in 1999; in 1999; since 2004, surface runoff was relatively small except for 2010. (ii) UF and UB curves have since1999; 2004,since surface2004, surface runoff wasrunoff relatively was relatively small except small forexcept 2010. for (ii) 2010. UF and (ii) UBUF curvesand UB have curves multiple have multiple intersections (Figure 5b). In order to remove the invalid mutation point, the t test method intersectionsmultiple intersections (Figure5b). (Figure In order 5b). to In remove order to the remove invalid the mutation invalid point, mutation the tpoint,test method the t test was method used. was used. Furthermore, surface runoff and precipitation shared the same inflection point of year 2003. Furthermore,was used. Furthermore, surface runoff surface and runoff precipitation and precipitatio shared then shared same inflectionthe same inflection point of year point 2003. of year In other2003. In other words, surface runoff change was directly related to precipitation. words,In other surface words, runoff surface change runoff was change directly was related directly to related precipitation. to precipitation.

1900 2.5 1900 (a) 2.5 (b) 1600 (a) (b) UF 1600 1.5 UF 1.5 UB 1300 UB 1300 0.5 1000 0.5 1000 -0.5

700 MK value -0.5

700 MK value -1.5 400 -1.5 400 SR = -8.5693y + 889.77 Annual surface runoff (mm) Annual surface runoff SR = -8.5693y + 889.77 Annual surface runoff (mm) Annual surface runoff 100 -2.5 100 -2.5 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013

1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013

Figure 5. Inter-annual variation of observed surface runoff (a) and its MK results (b) in Pengchongjian Figure 5. Inter‐annual variation of observed surface runoff (a) and its MK results (b) in Pengchongjian smallFigure watershed 5. Inter‐annual in 1983–2014. variation of observed surface runoff (a) and its MK results (b) in Pengchongjian small watershed in 1983–2014. small watershed in 1983–2014. On the basis of the trend and inflection point analysis of precipitation and surface runoff, the On the basis of the trend and inflection point analysis of precipitation and surface runoff, the researchOn periodsthe basis were of the divided trend intoand 1983–2003inflection point and 2004–2014. analysis of The precipitation average annual and surface precipitation runoff, and the research periods were divided into 1983–2003 and 2004–2014. The average annual precipitation and surfaceresearch runoff periods in were 1983–2003 divided were into 1608.2 1983–2003 and 831.9 and 2004–2014. mm, while The in 2004–2014,average annual they precipitation were 1468.9 andand surface runoff in 1983–2003 were 1608.2 and 831.9 mm, while in 2004–2014, they were 1468.9 and 588.9surface mm. runoff Compared in 1983–2003 with thewere baseline 1608.2 period,and 831.9 precipitation mm, while andin 2004–2014, surface runoff they inwere the 1468.9 changing and 588.9 mm. Compared with the baseline period, precipitation and surface runoff in the changing period588.9 mm. decreased Compared by 8.7% with and the 29.2%, baseline respectively, period, precipitation with mean annual and surface reduction runoff of 12.7 in andthe 22.1changing mm. period decreased by 8.7% and 29.2%, respectively, with mean annual reduction of 12.7 and 22.1 mm. Theperiod decreasing decreased proportion by 8.7% and of surface29.2%, respectively, runoff was larger with mean than thatannual of precipitation.reduction of 12.7 That and was 22.1 to mm. say, The decreasing proportion of surface runoff was larger than that of precipitation. That was to say, besidesThe decreasing precipitation, proportion ET and of vegetation surface runoff factors was also larger influenced than that surface of precipitation. runoff. How toThat quantitatively was to say, besides precipitation, ET and vegetation factors also influenced surface runoff. How to quantitatively distinguishbesides precipitation, the contribution ET and rates vegetation of various factors factors also on influenced surface runoff surface in a runoff small watershed. How to quantitatively is a scientific distinguish the contribution rates of various factors on surface runoff in a small watershed is a problemdistinguish that the needs contribution to be further rates investigated. of various factors on surface runoff in a small watershed is a scientific problem that needs to be further investigated. scientific problem that needs to be further investigated. 3.3. Contribution Rates of ET to Surface Runoff and Base Flow 3.3. Contribution Rates of ET to Surface Runoff and Base Flow 3.3. Contribution Rates of ET to Surface Runoff and Base Flow 3.3.1. Variation in Temperature and ET 3.3.1. Variation in Temperature and ET 3.3.1.Annual Variation mean in Temperature temperature and from ET 1983–2014 overall presented an upward trend, with a rate of 0.0349Annual◦C·year mean−1 (Figure temperature6a). It reached from 1983–2014 its highest overall level of presented 17.9 ◦C inan year upward 2013, trend, while with in year a rate 1984, of Annual mean temperature from 1983–2014 overall presented an upward trend, with a rate of its0.0349 historical °C·year low−1 (Figure level was 6a). 15.9It reached◦C. Compared its highest to level the of baseline 17.9 °C periodin year (1983–2003),2013, while in annual year 1984, mean its 0.0349 °C·year−1 (Figure 6a). It reached its highest level of 17.9 °C in year 2013, while in year 1984, its historical low level was 15.9 °C. Compared to the baseline period (1983–2003), annual mean historical low level was 15.9 °C. Compared to the baseline period (1983–2003), annual mean temperature in the changing period (2004–2014) increased by 1 °C. The annual variation of ET is temperature in the changing period (2004–2014) increased by 1 °C. The annual variation of ET is Forests 2018, 9, x; doi: FOR PEER REVIEW www.mdpi.com/journal/forests Forests 2018, 9, x; doi: FOR PEER REVIEW www.mdpi.com/journal/forests Forests 2018, 9, 553 8 of 16

Forests 2018, 9, x FOR PEER REVIEW 8 of 16 temperature in the changing period (2004–2014) increased by 1 ◦C. The annual variation of ET is shownshown inin Figure Figure6 b.6b. The The fluctuation fluctuation since since the the early early 21st 21st century century was was more more remarkable remarkable than than that that in in thethe 1980s,1980s, andand showedshowed anan upward upward trend trend not not as as remarkable remarkable as as that that as as temperature. temperature. However,However, thethe risingrising temperature temperature caused caused the the ET ET to goto up,go up, which whic wouldh would probably probably affect affect the process the process of the of water the cycle,water directlycycle, directly leading leading to a decrease to a decrease in surface in surface runoff runoff as well as as well base as flow.base flow. Thus, Thus, we firstly we firstly calculated calculated the contributionthe contribution rate ofrate ET of to ET surface to surface runoff runoff and baseand base flow flow decrease decrease by SCRCQ. by SCRCQ.

18 1200 (a) (b) 17.5 ET = 4.6692y - 8519.4 1000

17 800 16.5 T = 0.0349y - 52.967 ET (mm) 600 Temperature Temperature (℃) 16

15.5 400 1980 1985 1990 1995 2000 2005 2010 2015 1980 1985 1990 1995 2000 2005 2010 2015

Figure 6. Variation trend of annual mean temperature (a) and ET (b) from 1983 to 2014. Figure 6. Variation trend of annual mean temperature (a) and ET (b) from 1983 to 2014. 3.3.2. Impacts of ET on Surface Runoff and Base Flow 3.3.2. Impacts of ET on Surface Runoff and Base Flow The linear relationships between year and cumulative ET, surface runoff and base flow are shown in FiguresThe 7linear and8 .relationships The calculation between results year of the and contribution cumulative rates ET, ofsurface ET to runoff surface and runoff base and flow base are flowshown are in shown Figures in Table7 and2 8.. AnnualThe calculation average results ET during of the the contribution baseline period rates and of ET changing to surface period runoff were and 773.4base andflow 860.9 are shown mm, while in Table annual 2. Annual mean surfaceaverage runoffET during were the 831.9 baseline and 588.9 period mm, and and changing average period base flowwere were 773.4 384.3 and and860.9 293.7 mm, mm, while respectively. annual mean Compared surface runoff with thewere baseline 831.9 and period, 588.9 ET, mm, surface and average runoff andbase base flow flow were in changing384.3 and period 293.7 increasedmm, respectively. by 87.5, 243 Compared and 90.6 mm,with withthe baseline corresponding period, change ET, surface rates ofrunoff 11.3%, and−29.2% base flow and − in23.6%, changing respectively. period increased This somewhat by 87.5, reflected 243 and the 90.6 fact mm, that with surface corresponding runoff and basechange flow rates declined of 11.3%, synchronously −29.2% and with −23.6%, the increaserespectively. of ET. This In annual somewhat scale, reflected the contribution the fact that rates surface of ET torunoff the decrease and base of flow surface declined runoff synchronously and base flow with were the 38.7% increase and 47.9%,of ET. In which annual was scale, basically the contribution consistent withrates the of ET conclusion to the decrease drawn of in surface the Yinjiang runoff basin and [ba29se], whileflow were quite 38.7% distinct and from 47.9%, that which (less thanwas basically 10%) in theconsistent Yellow Riverwith the basin conclusion [32]. This drawn fully illustrated in the Yinjiang that under basin the [29], subtropical while quite humid distinct monsoon from that climate, (less ETthan could 10%) affect in the surface Yellow runoff River and basin base [32]. flow This to a greaterfully illustrated extent, similar that under to the the conclusion subtropical drawn humid by Chaplotmonsoon [11 climate,]. On a seasonalET could scale, affect the surface contribution runoff and rates base of ET flow to the to decreasea greater ofextent, surface similar runoff to was the betweenconclusion 28%~40%, drawn by while Chaplot that to [11]. base On flow a seasonal reduction scale, was the between contribution 38%~52%. rates We of ET could to the deem decrease that the of sensitivitysurface runoff of response was between of base 28%~40%, flow to ET while is higher that thanto base that flow of surface reduction runoff, was asbetween depicted 38%~52%. by Lin [33 We]. Moreover,could deem the that contribution the sensitivity rate ofof ETresponse to base of flow base in flo summerw to ET was is higher obviously than higher that of than surface that runoff, in other as seasons,depicted which by Lin may [33]. be Moreover, due to more the precipitation, contribution higher rate of temperature ET to base and flow higher in summer ET in summer was obviously caused byhigher forest than restoration. that in other On the seasons, other hand,which the may variation be due ofto averagemore precipitation, ET, surface runoffhigher andtemperature base flow and in winterhigher were ET in both summer 2~3 times caused that by of otherforest seasons restoration. compared On the to theother baseline hand, periodthe variation (Table2 ).of Meanwhile,average ET, thesurface contribution runoff and rates base of ET flow to surfacein winter runoff were and both base 2~3 flow times in winterthat of were other quite seasons close compared to that in otherto the seasons,baseline indicatingperiod (Table that 2). vegetation Meanwhile, accounted the contribution for a larger rates part of in ET water to surface yield whenrunoff encountering and base flow the in drywinter season were with quite less close precipitation. to that in other seasons, indicating that vegetation accounted for a larger part in waterIn short, yield ET when impact encountering on surface the runoff dry andseason base with flow less could precipitation. not be negligible whether in annual scale orIn short, seasonal ET scale,impact especially on surface in runoff winter. and The base response flow could of dry not season be negligible runoff to whether forest change in annual is mainlyscale or determined seasonal scale, by soil especially conditions, in winter. tree species The andresponse vegetation of dry regeneration season runoff after to disturbances,forest change as is wellmainly as topography determined [ 34by], soil or rather,conditio waterns, tree consumption species and (ET) vegetation and subsequent regeneration changes after in disturbances, soil infiltration as andwell water as topography storage capacity. [34], or rather, water consumption (ET) and subsequent changes in soil and capacity.

Forests 2018, 9, x; doi: FOR PEER REVIEW www.mdpi.com/journal/forests Forests 2018, 9, x FOR PEER REVIEW 9 of 16

Table 2. Slopes of the relations between year and cumulative quantities of ET (K1,2), surface runoff

(K3,4), base flow (K’3,4) and contribution rates of ET to surface runoff and base flow decrease.

Slope (mm·year−1) Spring Summer Autumn Winter Whole Year

K1 213.6 260.2 152.7 146.9 773.4 K2 229.7 289.1 165.5 176.6 860.9 ∆KET 0.075 0.111 0.084 0.202 0.113 K3 381.1 353.9 26.2 62.8 831.9

K4 280.9 248.1 18.7 26.7 588.9 ∆KSR −0.263 −0.299 −0.286 −0.575 −0.292 K’3 166.5 150.1 18.2 49.5 384.3 K’4 136.7 118 14.2 24.1 293.7 ∆KB −0.179 −0.214 −0.220 −0.513 −0.236 CET to surface runoff (%) 28.5 37.1 29.4 35.1 38.7 CET to base flow (%) 41.9 51.9 38.2 39.4 47.9

Forests 2018,Note:9, 553 ∆KET, ∆KSR, ∆KB is the variation rate of slopes of ET, surface runoff and base flow compared to 9 of 16 baseline period, respectively.

15000 12000 (a) Surface runoff ET (b) Surface runoff ET 12500 SR = 280.91y - 554495 SR = 248.11y - 490164 R² = 0.9808 9000 R² = 0.9835 10000 SR = 381.09y - 755158 SR = 353.91y - 701884 R² = 0.997 R² = 0.9658 7500 6000

5000 ET = 289.06y - 573485 R² = 0.9984 ET = 229.74y - 455836 3000

Cumulative Cumulative quantities (mm) 2500 R² = 0.9943 Cumulative Cumulative quantities (mm) ET= 260.24y - 515791 ET = 213.64y - 423638 R² = 0.9972 R² = 0.9968 0 0 1980 1990 2000 2010 2020 1980 1990 2000 2010 2020

6000 6000 (c) Surface runoff ET (d) Surface runoff ET 5000 ET = 176.63y - 350861 ET= 152.67y - 302259 R² = 0.9943 4000 4000 R² = 0.9962 ET = 146.88y - 291189 3000 ET = 165.49y - 327941 R² = 0.9958 R² = 0.9912 SR = 18.701y - 36665 SR = 26.733y - 52231 2000 R² = 0.8428 2000 R² = 0.9815 SR = 26.204y - 51668 1000 R² = 0.8109 Cumulative Cumulative quantities (mm) Cumulative Cumulative quantities (mm) SR= 62.807y - 124582 R² = 0.9819 Forests 20180 , 9, x FOR PEER REVIEW 0 10 of 16 1980 1990 2000 2010 2020 1980 1990 2000 2010 2020 30000 (e) Surface runoff ET 25000 SR = 588.85y -1E+06 R² = 0.9938 20000

SR = 831.93y -2E+06 15000 R² = 0.9952 ET = 860.93y -2E+06 R² = 0.9983 10000 ET = 773.42y -2E+06

Cumulative Cumulative quantities (mm) 5000 R² = 0.9995

0 1980 1990 2000 2010 2020

Figure 7. Relationships between year and cumulative surface runoff, ET in spring (a), summer (b), autumnForestsFigure 2018 (c,), 9, winter7.x; doi:Relationships FOR (d) PEER and REVIEW wholebetween year year (e and) during cumulative two periods surface inrunoff, Pengchongjian ET in springwww.mdpi.com/jou small (a), summer watershed.rnal/forests (b), autumn (c), winter (d) and whole year (e) during two periods in Pengchongjian small watershed. Table 2. Slopes of the relations between year and cumulative quantities of ET (K1,2), surface runoff (K , 8000), base flow (K’ , ) and contribution rates of ET to10000 surface runoff and base flow decrease. 3 4 (a) Base3 4 flow ET (b) Base flow ET Slope (mm·year−1)ET = 229.74Springy - 455836 Summer8000 AutumnET = Winter 289.06y - 573485 Whole Year 6000 R² = 0.9943 R² = 0.9984 K1 213.6 260.2 152.7 146.9 773.4 K2ET = 213.64y - 423638 229.7 289.16000 165.5 176.6 860.9 4000 ∆KET R² = 0.9968 0.075 0.111 0.084ET = 260.24y - 515791 0.202 0.113 R² = 0.9972 K3 381.1B = 136.65y - 270117 353.94000 26.2 62.8 831.9 K4 280.9R² = 0.9876 248.1 18.7 26.7 588.9 2000 ∆KSR −0.263 −0.299 −0.286 −0.575 B = 118.04y−-0.292 233586 ’ B = 166.49y - 329932 2000 R² = 0.98 Cumulative Cumulative quantities (mm) K 166.5 150.1 Cumulative quantities (mm) 18.2 49.5 384.3 3 R² = 0.9959 B = 150.12y - 297779 K’ 136.7 118 14.2 24.1 293.7 0 4 R² = 0.9676 ∆K −0.179 −0.214 0 −0.220 −0.513 −0.236 1980B 1990 2000 2010 2020 1980 1990 2000 2010 2020 CET to surface runoff (%) 28.5 37.1 29.4 35.1 38.7 CET to base flow (%) 41.9 51.9 38.2 39.4 47.9 6000 6000 Note: ∆KET, ∆(c)KSR, ∆KBaseB is theflow variationET rate of slopes of ET, surface runoff(d) andBase base flow flow comparedET to baseline period, respectively. ET = 176.63y - 350861 R² = 0.9943 4000 4000 ET = 152.67y - 302259 R² = 0.9962 ET = 146.88y - 291189 ET = 165.49y - 327941 R² = 0.9958 R² = 0.9912 2000 2000 B = 49.504y - 98197 B = 14.217y - 27969 R² = 0.9823 B = 18.224y - 35949 R² = 0.9834 Cumulative Cumulative quantities (mm) Cumulative Cumulative quantities (mm) R² = 0.8191 B = 24.101y - 47257 R² = 0.9929 0 0 1980 1990 2000 2010 2020 1980 1990 2000 2010 2020

30000 (e) Base flow ET

ET = 860.93y -2E+06 20000 R² = 0.9983

ET = 773.42y -2E+06 10000 R² = 0.9995 B = 293.74y - 580337 R² = 0.9955 B = 384.34y - 761858 Cumulative Cumulative quantities (mm) 0 R² = 0.9966 1980 1990 2000 2010 2020

Figure 8. Relationships between year and cumulative base flow, ET in spring (a), summer (b), autumn (c), winter (d) and whole year (e) during two periods in Pengchongjian small watershed.

Forests 2018, 9, x; doi: FOR PEER REVIEW www.mdpi.com/journal/forests Forests 2018, 9, x FOR PEER REVIEW 10 of 16

30000 (e) Surface runoff ET 25000 SR = 588.85y -1E+06 R² = 0.9938 20000

SR = 831.93y -2E+06 15000 R² = 0.9952 ET = 860.93y -2E+06 R² = 0.9983 10000 ET = 773.42y -2E+06

Cumulative Cumulative quantities (mm) 5000 R² = 0.9995

0 1980 1990 2000 2010 2020

ForestsFigure2018, 9 ,7. 553 Relationships between year and cumulative surface runoff, ET in spring (a), summer (b10), of 16 autumn (c), winter (d) and whole year (e) during two periods in Pengchongjian small watershed.

8000 10000 (a) Base flow ET (b) Base flow ET ET = 229.74y - 455836 8000 ET = 289.06y - 573485 6000 R² = 0.9943 R² = 0.9984

ET = 213.64y - 423638 6000 4000 R² = 0.9968 ET = 260.24y - 515791 R² = 0.9972 B = 136.65y - 270117 4000 R² = 0.9876 2000 B = 118.04y - 233586 B = 166.49y - 329932 2000 R² = 0.98 Cumulative Cumulative quantities (mm) R² = 0.9959 Cumulative quantities (mm) B = 150.12y - 297779 0 0 R² = 0.9676 1980 1990 2000 2010 2020 1980 1990 2000 2010 2020

6000 6000 (c) Base flow ET (d) Base flow ET ET = 176.63y - 350861 R² = 0.9943 4000 4000 ET = 152.67y - 302259 R² = 0.9962 ET = 146.88y - 291189 ET = 165.49y - 327941 R² = 0.9958 R² = 0.9912 2000 2000 B = 49.504y - 98197 B = 14.217y - 27969 R² = 0.9823 B = 18.224y - 35949 R² = 0.9834 Cumulative Cumulative quantities (mm) Cumulative Cumulative quantities (mm) R² = 0.8191 B = 24.101y - 47257 R² = 0.9929 0 0 1980 1990 2000 2010 2020 1980 1990 2000 2010 2020

30000 (e) Base flow ET

ET = 860.93y -2E+06 20000 R² = 0.9983

ET = 773.42y -2E+06 10000 R² = 0.9995 B = 293.74y - 580337 R² = 0.9955 B = 384.34y - 761858 Cumulative Cumulative quantities (mm) 0 R² = 0.9966 1980 1990 2000 2010 2020

Figure 8. Relationships between year and cumulative base flow, ET in spring (a), summer (b), autumn Figure 8. Relationships between year and cumulative base flow, ET in spring (a), summer (b), autumn (c), winter (d) and whole year (e) during two periods in Pengchongjian small watershed. (c), winter (d) and whole year (e) during two periods in Pengchongjian small watershed. 3.4. Contribution Rates of Precipitation Variation and Vegetation Restoration to Surface Runoff Decrease Forests 2018, 9, x; doi: FOR PEER REVIEW www.mdpi.com/journal/forests The regression analysis between mean surface runoff and precipitation in seasonal and whole year scale during baseline period were carried out, and the linear equations were shown in Figure9. The mean annual and seasonal precipitation during changing period were then substituted into the corresponding equations, and the simulated mean surface runoffs were obtained. The calculation results of contribution rates of precipitation variation and vegetation restoration to surface runoff are shown in Table3. Forests 2018, 9, x FOR PEER REVIEW 11 of 16

3.4. Contribution Rates of Precipitation Variation and Vegetation Restoration to Surface Runoff Decrease The regression analysis between mean surface runoff and precipitation in seasonal and whole year scale during baseline period were carried out, and the linear equations were shown in Figure 9. The mean annual and seasonal precipitation during changing period were then substituted into the corresponding equations, and the simulated mean surface runoffs were obtained. The calculation results of contribution rates of precipitation variation and vegetation restoration to surface runoff are Forestsshown2018 in, 9Table, 553 3. 11 of 16

700 1200

600 1000 SR= 0.9961P - 259.47 500 SR = 0.9809P - 193.01 R² = 0.8803 800 R² = 0.9155 400 600 300 400 200 Surface runoff (mm) Surface runoff Surface runoff (mm) Surface runoff 100 200 Spring Summer 0 0 200 300 400 500 600 700 800 900 300 450 600 750 900 1050 1200 1350 1500 Precipitation (mm) Precipitation (mm) 100 150

2.5064 80 SR = 0.0001P2.2579 SR = 8E-05P R² = 0.6105 R² = 0.7835 100 60

40 50

20 (mm) Surface runoff Surface runoff (mm) Surface runoff Autumn Winter 0 0 100 150 200 250 300 350 400 450 500 50 100 150 200 250 300 Precipitation (mm) Precipitation (mm) 1800 1600 1400 SR = 0.9953P - 768.8 1200 R² = 0.8588 1000 800

Surface runoff (mm) Surface runoff 600 400 Whole year 200 1000 1200 1400 1600 1800 2000 2200 2400 Precipitation (mm) FigureFigure 9.9. RelationshipRelationship betweenbetween observedobserved surfacesurface runoffrunoff andand precipitationprecipitation inin seasonalseasonal andand annualannual scalescale duringduring baselinebaseline period.period.

Forests 2018, 9, x; doi: FOR PEER REVIEW www.mdpi.com/journal/forests Forests 2018, 9, 553 12 of 16

Table 3. Calculating results of contribution rates of precipitation variation and vegetation restoration to surface runoff decrease.

Impact of Precipitation Impact of Vegetation Simulated Mean Observed Mean Variation Restoration Time Scale Surface Runoff (mm) Surface Runoff (mm) Variation Contribution Variation Contribution (mm) (%) (mm) (%) Spring 322.1 280.9 −59.0 42.1 −41.2 29.4 Summer 278.2 248.1 −75.7 45.0 −30.1 17.9 Autumn 23.0 18.7 −3.2 30.1 −4.3 40.5 Winter 53.5 26.7 −9.3 16.7 −26.8 48.2 Year 693.2 588.9 −138.7 35.0 −104.3 26.3 Note: The observed mean surface runoff during baseline period is 381.1, 353.9, 26.2, 62.8 and 831.9 mm in spring, summer, autumn, winter and whole year respectively.

Compared to the baseline period, average surface runoff in spring, summer, autumn, winter and the whole year decreased by 100.2, 105.8, 7.5, 36.1 and 243 mm, respectively. The surface runoff decrease caused by precipitation variation was 59.0, 75.7, 3.2, 9.3 and 138.7 mm, respectively, while by vegetation restoration was 41.2, 30.1, 4.3, 26.8 and 104.3 mm. The contribution rates of precipitation variation and vegetation restoration to surface runoff were 42.1%, 29.4% in spring, 45.0%, 17.9% in summer, 30.1%, 40.5% in autumn, 16.7%, 48.2% in winter, and 35.0%, 26.3% in annual scale, respectively (Table3). Obviously, in seasonal scale, attribution of surface runoff reduction to precipitation in spring and summer was larger than that in autumn and winter, and vice versus for attribution to vegetation restoration. Herein the contribution rate of precipitation in summer reached the maximum, which may be related to much more precipitation, heavier rainfall intensity, and more rainfall days. For this reason, on the contrary, vegetation restoration counted less (only 44% of that in autumn) than precipitation for surface runoff decease in summer (Table3). The water diminishing effect of vegetation in autumn and winter was significantly strengthened, which is likely because of less precipitation in autumn and winter, and vegetation enriched the understory and forest , and enhanced soil infiltration and water storage [34], hence reducing surface runoff. On an annual scale, in contrast to the conclusion that vegetation change induced by human activity dominated surface runoff generation in arid areas of northern China [25,26,31,35,36], we found that for Pengchongjian small watershed, the contribution of precipitation (35%) to surface runoff was nearly close to that of ET (38.7%), followed by vegetation restoration (26.3%). The reason why the results varied, on the one hand, is probably due to distinct climatic conditions, topography, vegetation structure and type. On the other hand, human activity there is relatively slight with no local residents or water conservancy facilities and soil and projects. In general, climatic factors (including precipitation variation and elevated ET caused by rising temperature) are the major drivers for annual surface runoff, which is in accordance to similar results in Poyang Lake basin, China [15,16,37].

3.5. Contribution Rates of Precipitation Variation and Vegetation Restoration to Base Flow Decrease The regression analysis between mean base flow and precipitation in seasonal and whole year scale during the baseline period were carried out, and the linear equations were shown in Figure 10. The mean annual and seasonal precipitation during the changing period were then substituted into the corresponding equations, and the simulated mean base flow was obtained. The calculation results of contribution rates of precipitation variation and vegetation restoration to base flow are shown in Table4. ForestsForests2018 2018, ,9 9,, 553 x FOR PEER REVIEW 1313 ofof 16 16

300 400

B = 0.3558P - 37.294 350 250 R² = 0.7987 B = 0.3452P - 62.469 300 R² = 0.8858 200 250

150 200 (mm) Baseflow (mm) 150 100 100 Spring Summer 50 50 200 300 400 500 600 700 800 900 300 400 500 600 700 800 900 10001100120013001400 Precipitation (mm) Precipitation (mm) 60 120

50 100

80 40 2.2782 B = 7E-05P B = 6E-05P2.5026 R² = 0.621 30 60 R² = 0.7918

20 40 Baseflow (mm) Baseflow(mm) 10 20 Autumn Winter 0 0 0 100 200 300 400 500 50 100 150 200 250 300 350 Precipitation (mm) Precipitation (mm) 800 700 600 B = 0.3115P - 115.79 R² = 0.6667 500 400 300 Baseflow (mm) 200 Whole year 100 1000 1200 1400 1600 1800 2000 2200 2400 Precipitation (mm) FigureFigure 10.10.Relationship Relationship betweenbetween observedobserved basebase flowflow andand precipitationprecipitation inin seasonalseasonal andand annualannual scalescale duringduring baselinebaseline period.period.

Table 4. Calculated results of contribution rates of precipitation variation and vegetation restoration to Table 4. Calculated results of contribution rates of precipitation variation and vegetation restoration base flow decrease. to base flow decrease. Impact of Precipitation Impact of Vegetation Simulated Mean Observed Mean Impact of PrecipitationVariation ImpactRestoration of Vegetation Time Scale Simulated Observed Time Base Flow (mm) Base Flow (mm) Variation Restoration Mean Base Mean Base Variation Contribution Variation Contribution Scale Variation(mm) Contribution(%) Variation(mm) Contribution(%) Flow (mm) Flow (mm) Spring 149.5 136.7 (mm)− 17.0(%) 33.1 (mm)−12.8 25.0(%) − − SpringSummer 149.5 123.9 136.7 118.0 −17.0 26.233.1 39.3 −12.85.9 8.825.0 Autumn 15.6 14.2 −2.6 40.2 −1.4 21.6 SummerWinter 123.9 39.3 118.0 24.1 −26.2− 10.239.3 24.3 −−5.915.2 36.38.8 AutumnYear 15.6 341.8 14.2 293.7 −2.6 −42.540.2 24.4 −−1.448.1 27.721.6 Note:Winter The observed39.3 mean base flow during 24.1 baseline period−10.2 is 166.5, 150.1,24.3 18.2, 49.5− and15.2 384.3 mm in36.3 spring, summer,Year autumn, winter341.8 and whole year 293.7 respectively. −42.5 24.4 −48.1 27.7 Note: The observed mean base flow during baseline period is 166.5, 150.1, 18.2, 49.5 and 384.3 mm in Comparedspring, summer, to the autumn, baseline winter period, andaverage whole year base respectively. flow in spring, summer, autumn, winter and the whole year decreased by 29.8, 32.1, 4.0, 25.4 and 90.6 mm, respectively. Base flow decrease caused by precipitationCompared to variation the baseline was period, 17.0, 26.2, average 2.6, 10.2 base and flow 42.5 in spring, mm, respectively, summer, autumn, while bywinter vegetation and the whole year decreased by 29.8, 32.1, 4.0, 25.4 and 90.6 mm, respectively. Base flow decrease caused by

Forests 2018, 9, x; doi: FOR PEER REVIEW www.mdpi.com/journal/forests Forests 2018, 9, 553 14 of 16 restoration it was 12.8, 5.9, 1.4, 15.2 and 48.1 mm. The contribution rates of precipitation variation and vegetation restoration to base flow were 33.1%, 25.0% in spring, 39.3%, 8.8% in summer, 40.2%, 21.6% in autumn, 24.3%, 36.3% in winter and 24.4%, 27.7% on an annual scale, respectively (Table4). It could be seen that, as with surface runoff, the contribution rate of each factor to the reduction of base flow also showed obvious seasonality. The contribution rate of precipitation variation was the lowest in winter while almost the same in other seasons. The contribution rate of vegetation restoration, however, reached the minimum in summer (only 20%~40% of that in other seasons). Moreover, base flow reduction due to vegetation restoration obviously escalated in winter, which was consistent with the conclusion drawn on surface runoff. On an annual scale, the contribution rate of vegetation restoration to base flow reduction was slightly larger than that of precipitation, but ET still dominated. We know that the increase of vegetation coverage and the improvement of stand quality directly led to the increase of ET and thus base flow decrease. So, we assumed that forest may be a main factor with a negative impact on base flow, as mentioned by Huang [38] and Zhang [39]. Anyhow, ET has an important influence on the dynamic change of base flow. Meanwhile, vegetation plays an irreplaceable role in regulating water flow in dry seasons and improving hydrological ecology in watersheds.

4. Conclusions In this paper, we quantitively calculated the contribution rates of precipitation variation, vegetation restoration and ET to surface runoff and base flow. The results indicated that each factor played a role in reducing surface runoff and base flow, and their contribution rates varied from season to season. For spring and summer, the contribution rate of precipitation was equal to that of ET, which was slightly larger than vegetation restoration; for autumn and winter; however, the effect of vegetation restoration on water yield was obviously stronger than that in spring and summer. On an annual scale, climatic factors including precipitation and ET change dominated surface runoff generation. Meanwhile, the sensitivity of base flow response to ET was higher than surface runoff, so that ET dominated the dynamic change of annual base flow. As presented in this study, ET change is closely associated with vegetation restoration, which fully illustrates that the protection of natural forest and the optimization of forest operation and management are important guarantees for ecological water circulation and the sustainable utilization of forest resources.

Author Contributions: S.L., R.W. and Z.L. (Zhihong Lu) designed this study. Z.L. (Zheng Liu), J.Y. and F.S. collected data and performed the data analysis. L.O. wrote this paper. Funding: This research was funded by the Chinese Natural Science Foundation Program grant number (No. 31460222). Acknowledgments: The authors are grateful for the support from Jiangxi Hydrological Bureau and the local government in Duchang County, Jiujiang. We also appreciate the reviewers for their helpful comments in polishing this article. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Smakhtin, V.U. Low flow : A. review. J. Hydrol. 2001, 240, 147–186. [CrossRef] 2. Ficklin, D.L.; Robeson, S.M.; Knouft, J.H. Impacts of recent climate change on trends in baseflow and stormflow in United States watersheds. Geophys. Res. Lett. 2016, 43, 5079–5088. [CrossRef] 3. Guzmán, P.; Batelaan, O.; Huysmans, M.; Wyseure, G. Comparative analysis of baseflow characteristics of two Andean catchments, Ecuador. Hydrol. Process. 2015, 29, 3051–3064. [CrossRef] 4. Ni, Y.Q.; Zhang, W.H.; Guo, S.L. Discussion on the separation methods. Hydrology 2005, 25, 11–23. (In Chinese) 5. Githui, F.; Gitau, W.; Mutua, F.; Bauwens, W. Climate change impact on SWAT simulated streamflow in western Kenya. Int. J. Climatol. 2009, 29, 1823–1834. [CrossRef] Forests 2018, 9, 553 15 of 16

6. Ye, X.C.; Zhang, Q.; Liu, J.; Li, X.H.; Xu, C.Y. Distinguishing the relative impacts of climate change and human activities on variation of streamflow in the Poyang Lake catchment, China. J. Hydrol. 2013, 494, 83–95. [CrossRef] 7. Kuk-Hyun Ahn, V.M. Quantifying the relative impact of climate and human activities on streamflow. J. Hydrol. 2014, 515, 257–266. 8. Röderstein, M.; Perdomo, L.; Villamil, C.; Hauffe, T.; Schnetter, M. Long-term vegetation changes in a tropical coastal lagoon system after interventions in the hydrological conditions. Aquat. Bot. 2014, 113, 19–31. [CrossRef] 9. Chaplot, V.; Saleh, A.; Jaynes, D.B. Effect of the accuracy of spatial rainfall information on the modeling of

water, , and NO3–N loads at the watershed level. J. Hydrol. 2005, 312, 223–234. [CrossRef] 10. Alvarenga, L.A.; de Mello, C.R.; Colombo, A.; Cuartas, L.A.; Bowling, L.C. Assessment of land cover change on the hydrology of a Brazilian headwater watershed using the Distributed Hydrology-Soil-Vegetation Model. Catena 2016, 143, 7–17. [CrossRef]

11. Chaplot, V. Water and soil resources response to rising levels of atmospheric CO2 concentration and to changes in precipitation and air temperature. J. Hydrol. 2007, 337, 159–171. [CrossRef] 12. Zeng, S.D.; Xia, J.; Du, H. Separating the effects of climate change and human activities on runoff over different time scales in the Zhang River basin. Stoch. Environ. Res. Risk Assess. 2014, 28, 401–413. [CrossRef] 13. Zhang, X.C.; Nearing, M.A. Impact of climate change on soil , runoff, and wheat productivity in central Oklahoma. Catena 2005, 61, 185–195. [CrossRef] 14. Zhang, Q.; Liu, J.Y.; Singh, V.P.; Gu, X.H.; Chen, X.H. Evaluation of impacts of climate change and human activities on streamflow in the Poyang Lake basin, China. Hydrol. Process. 2016, 30, 2562–2576. [CrossRef] 15. Gu, C.J.; Mu, X.M.; Gao, P.; Zhao, G.J.; Sun, W.Y.; Li, P.F. Effects of climate change and human activities on runoff and sediment inputs of the largest freshwater lake in China, Poyang Lake. Hydrol. Sci. J. 2017, 62, 2313–2330. [CrossRef] 16. Rogger, M.; Agnoletti, M.; Alaoui, A.; Bathurst, J.C.; Bodner, G.; Borga, M.; Chaplot, V.; Gallart, F.; Glatzel, G.; Hall, J.; et al. change impacts on floods at the catchment scale: Challenges and opportunities for future research. Water Resour. Res. 2017, 53, 5209–5219. [CrossRef][PubMed] 17. Jiang, S.H.; Ren, L.L.; Yong, B.; Singh, V.P.; Yang, X.L.; Yuan, F. Quantifying the effects of climate variability and human activities on runoff from the Laohahe basin in northern China using three different methods. Hydrol. Process. 2011, 25, 2492–2505. [CrossRef] 18. Xia, J.; Zeng, S.; Du, H.; Zhan, C.S. Quantifying the effects of climate change and human activities on runoff in the water source area of Beijing, China. Hydrol. Sci. J. 2014, 59, 1794–1807. [CrossRef] 19. Koskelo, A.I.; Fisher, T.R.; Utz, R.M.; Jordan, T.E. A new precipitation-based method of baseflow separation and event identification for small watersheds (<50 km2). J. Hydrol. 2012, 450, 267–278. [CrossRef] 20. Rumsey, C.A.; Miller, M.P.; Susong, D.D.; Tillman, F.D.; Anning, D.W. Regional scale estimates of baseflow and factors influencing baseflow in the Upper Basin. J. Hydrol. Reg. Stud. 2015, 4, 91–107. [CrossRef] 21. Ding, Z.L.; Hu, K.D.; Fang, Y.Y. Analysis and discussion of dividing up ground water by the kalinlin improving Method. JiangXi Hydraul. Sci. Technol. 2003, 29, 211–215. (In Chinese) 22. Chattopadhyay, G.; Chakraborthy, P.; Chattopadhyay, S. Mann-Kendall trend analysis of tropospheric and its modeling using ARIMA. Theor. Appl. Climatol. 2012, 110, 321–328. [CrossRef] 23. Rahman, M.A.; Yunsheng, L.; Sultana, N. Analysis and prediction of rainfall trends over Bangladesh using Mann-Kendall, Spearman’s rho tests and ARIMA model. Meteorol. Atmos. Phys. 2017, 129, 409–424. [CrossRef] 24. Nourani, V.; Danandeh Mehr, A.; Azad, N. Trend analysis of hydroclimatological variables in Urmia lake basin using hybrid wavelet Mann–Kendall and ¸Sentests. Environ. Earth. Sci. 2018, 77, 207. [CrossRef] 25. Kamruzzaman, M.; Rahman, A.T.M.S.; Ahmed, M.S.; Kabir, M.E.; Mazumder, Q.H.; Rahman, M.S.; Jahan, C.S. Spatio-temporal analysis of climatic variables in the western part of Bangladesh. Environ. Dev. Sustain. 2018, 20, 89–108. [CrossRef] 26. Wang, S.J.; Yan, Y.J.; Yan, M.; Zhao, X.K. Quantitative estimation of the impact of precipitation and human activities on runoff change of the Huangfuchuan River Basin. J. Geogr. Sci. 2012, 22, 906–918. [CrossRef] 27. Wang, S.J.; Wang, Y.J.; Ran, L.S.; Su, T. Climatic and anthropogenic impacts on runoff changes in the Songhua River basin over the last 56years (1955–2010), Northeastern China. Catena 2015, 127, 258–269. [CrossRef] Forests 2018, 9, 553 16 of 16

28. Pang, S.J.; Wang, X.Y. The characteristics and attribution of runoff change in the Miyun reservoir watershed. J. Arid Land Resour. Environ. 2016, 30, 144–148. (In Chinese) 29. Wu, L.H.; Wang, S.J.; Bai, X.Y.; Luo, W.J.; Tian, Y.C.; Zeng, C.; Luo, G.J.; He, S.Y. Quantitative assessment of the impacts of climate change and human activities on runoff change in a typical karst watershed, SW China. Sci. Total Environ. 2017, 601, 1449–1465. [CrossRef][PubMed] 30. Cheng, J.X.; Xu, L.G.; Jiang, J.H.; Tan, Z.Q.; Yu, Q.W.; Fan, H.X. The research of runoff responses to climate change and human activities in the Dongting Lake catchment. J. Agro-Environ. Sci. 2016, 35, 2146–2153. (In Chinese) 31. Yang, C.X.; Zhang, Z.D.; Zhu, R.X.; Wan, L.W.; Ye, C.; Zhang, J. Response of runoff to climate change and human activities in the river basin of southern humid area-A case study of Ning Jiang. Res. Soil Water Conserv. 2017, 24, 113–119. (In Chinese) 32. Wang, S.J.; Yan, M.; Yan, Y.X.; Shi, C.X.; He, L. Contributions of climate change and human activities to the changes in runoff increment in different sections of the Yellow River. Quat. Int. 2012, 282, 66–77. [CrossRef] 33. Lin, P.R.; Rajib, M.A.; Yang, Z.L.; Somos-Valenzuela, M.; Merwade, V.; Maidment, D.R.; Wang, Y.; Chen, L. Spatiotemporal evaluation of simulated evapotranspiration and streamflow over Texas using the WRF-Hydro-RAPID Modeling Framework. J. Am. Water Resour. Assoc. 2018, 54, 40–54. [CrossRef] 34. Hou, Y.P.; Zhang, M.F.; Meng, Z.Z.; Liu, S.R.; Sun, P.S.; Yang, T.L. Assessing the impact of forest change and climate variability on dry season runoff by an improved single watershed approach: A comparative study in two large watersheds, China. Forests 2018, 9, 46. [CrossRef] 35. Wu, J.W.; Miao, C.Y.; Zhang, X.M.; Yang, T.T.; Duan, Q.Y. Detecting the quantitative hydrological response to changes in climate and human activities. Sci. Total Environ. 2017, 586, 328–337. [CrossRef][PubMed] 36. Xu, X.Y.; Yang, D.W.; Yang, H.B.; Lei, H.M. Attribution analysis based on the Budyko hypothesis for detecting the dominant cause of runoff decline in Haihe basin. J. Hydrol. 2014, 510, 530–540. [CrossRef] 37. Sun, S.L.; Chen, H.S.; Ju, W.M.; Song, J.; Zhang, H.; Sun, J.; Fang, Y.J. Effects of climate change on annual streamflow using climate elasticity in Poyang Lake Basin, China. Theor. Appl. Climatol. 2013, 112, 169–183. [CrossRef] 38. Huang, X.; Shi, Z.; Fang, N.; Li, X. Influences of Land Use Change on Baseflow in Mountainous Watersheds. Forests 2016, 7, 16. [CrossRef] 39. Zhang, Y.K.; Schilling, K.E. Increasing streamflow and baseflow in Mississippi River since the 1940s: Effect of land use change. J. Hydrol. 2006, 324, 412–422. [CrossRef]

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).