Impacts of Climate Change and Human Activities on the Surface Runoff in the Wuhua River Basin

sustainability

Article Impacts of Climate Change and Human Activities on the Surface Runoff in the Wuhua River Basin

Zhengdong Zhang 1,*, Luwen Wan 2 , Caiwen Dong 3, Yichun Xie 4 , Chuanxun Yang 5, Ji Yang 5 and Yong Li 5

1 School of Geography Sciences, South China Normal University, Guangzhou 510631, China 2 Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48823, USA; [email protected] 3 The Bureau of Land and Resources Huangshi, Huangshi 435000, China; [email protected] 4 Institute for Geospatial Research and Education, Eastern Michigan University, Ypsilanti, MI 48197, USA; [email protected] 5 Guangzhou Institute of Geography, Guangzhou 510070, China; [email protected] (C.Y.); [email protected] (J.Y.); [email protected] (Y.L.) * Correspondence: [email protected]; Tel.: +86-138-0241-1132

Received: 19 July 2018; Accepted: 18 September 2018; Published: 25 September 2018

Abstract: The impacts of climate change and human activities on the surface runoff in the Wuhua River Basin (hereinafter referred to as the river basin) are explored using the Mann–Kendall trend test, wavelet analysis, and double-mass curve. In this study, all the temperature and precipitation data from two meteorological stations, namely, Wuhua and Longchuan, the measured monthly runoff data in Hezikou Hydrological Station from 1961 to 2013, and the land-cover type data in 1990 and 2013 are used. This study yields valuable results. First, over the past 53 years, the temperature in the river basin rose substantially, without obvious changes in the average annual precipitation. From 1981 to 2013, the annual runoff ﬂuctuated and declined, and this result is essentially in agreement with the time-series characteristics of precipitation. Second, both temperature and precipitation had evidently regular changes on the 28a scale, and the annual runoff changed on the 19a scale. Third, forestland was the predominant land use type in the Wuhua river basin, followed by cultivated land. Major transitions mainly occurred in both land-use types, which were partially transformed into grassland and construction land. From 1990 to 2013, cultivated land was the most active land-use type in the transitions, and construction land was the most stable type. Finally, human activities had always been a decisive factor on the runoff reduction in the river basin, accounting for 85.8%. The runoff in the river basin suffered most heavily from human activities in the 1980s and 1990s, but thereafter, the impact of these activities diminished to a certain extent. This may be because of the implementation of water loss and soil erosion control policies.

Keywords: runoff; land-use change; climate change; human activities; Wuhua River

1. Introduction Climate change and human activity are two important factors of the changes in stream runoff [1]. In recent years, stream runoff reductions in different regions under the impacts of climate changes and human activities exhibit varying degrees; however, the contributions of these factors to the runoff reduction in a speciﬁc time period moderately differ [2]. Climate changes have impacted the ecosystem and hydrological cycle signiﬁcantly and irreversibly [3]. In the mid-latitudes, climate changes induced by temperature and precipitation lead to water resource reduction. For nearly a decade, the greenhouse effects on hydrological variables have been extensively investigated in the world [4–9].

Sustainability 2018, 10, 3405; doi:10.3390/su10103405 www.mdpi.com/journal/sustainability Sustainability 2018, 10, 3405 2 of 21

The results of existing studies show that the impacts of precipitation on runoff far outweigh those of temperature. With constant precipitation, the impacts of temperature on runoff also increase with rising temperature, and with the temperature unchanged, precipitation exerts a remarkable impact on runoff [10,11]. Most studies on the impacts of climate changes on runoff and sediment have been conducted along the middle and upper reaches of the Yellow River [12,13], and these studies show that both reduced precipitation and increased temperature contribute considerably to runoff reduction [14–17]. Land cover under the impacts of land use and human activities also cause changes in runoff. With the technological and social development, human intervention in nature is gradually increasing, causing significant changes in river basin complex [18,19]. Different human activities can produce various hydrological effects. Many studies have been conducted by Chinese and foreign scholars on topics such as the impacts of hydrological process on conservation of water and soil, farmland irrigation, deforestation, urbanization, land-use/cover, and the impacts of human life water on runoff [20–23], and indicate that human activities are important driving factors for significant changes in runoff in the basin due to human interventions especially soil and water conservation measures. In the past 50 years, global climate changes and human activities have had profound impacts on global hydrology and water resources. In the basin scale, the research methods that can quantitatively distinguish the hydrological effects of climate changes and human activities are mainly hydrological model simulation methods [24–26] and quantitative evaluation methods [27–30], such as the climate coefficient method, sensitivity analysis method, and the precipitation-runoff double accumulation curve method [31]. The former has a good physical basis, but the parameter sensitivity has certain uncertainty. Runoff is affected by climate change and human activities. Therefore, it is necessary to combine the two to study the impact on water resources. However, most of the research only focuses on one of the drivers of climate change and human activities. There are fewer studies on the effects of the two driving factors on runoff and how to distinguish the effects of the two on it. Also, the hydrological impacts of climate changes and human activities have been mainly investigated on a large scale, but those on a river basin complex have been rarely studied on a mesoscale, probably due to the difficulty of data acquisition. The Wuhua River Basin is mainly located in Wuhua County, which has the largest area of barren hills and the most serious soil erosion in Guangdong Province, located in Southeast of China. For the past decades, the impacts of climate changes and human activities in the river have intensified, changing the underlying surface of the river basin. Under the same precipitation condition, the runoff in the river basin is declining, and the sediment decreases substantially. It has brought great constraints to agricultural production and people’s lives. Here we analyze the impacts of climate changes and human activities on the surface runoff in the river basin by using the Mann–Kendall (M–K) trend test, and wavelet analysis to analyze the change trends and separate the influences that arose from these two driving factors on runoff with a double-mass curve (DMC). The results can provide theories and methods for further research on the hydrological impacts of climate changes and human activities and decision support for local ecological construction, especially mesoscale watersheds in hot and humid areas.

2. Materials and Methods

2.1. Study Area The Wuhua River is the primary tributary of the Mei River in the upper reaches of the Han River. It is located in northeastern Guangdong Province, and it rises at Yajizhai in the northeast of Huilong Town, Longchuan County, Guangdong Province. Its main recharge source is precipitation. In the upper reaches, it runs from north to south; it ﬂows into Wuhua County after brooks join in Longchuan County, mingles in the Mei River in Shuizhai. Its total length is 105 km, and the drainage basin area is 1832 km2. The slope of its riverbed is 0.99%. With a catchment area of 1031 km2, Hezikou Hydrological Station is the most important control station in the river basin. To the south of the Wuling Mountains, Sustainability 2018, 10, 3405 3 of 21 the river basin is affected by warm–humid ocean currents, and it belongs to a typical humid subtropical monsoon climate zone. It has an average annual precipitation of 1518 mm mainly concentrated in the periodSustainability from 2018 March, 10, x FOR toSeptember, PEER REVIEW accounting for approximately 82.5% of the annual precipitation,3 of 21 and its average annual temperature is 21.1 ◦C. The river basin is a typical mesoscale watershed in the annual precipitation, and its average annual temperature is 21.1 °C. The river basin is a typical Southmesoscale China, having watershed an undulatingin South China, terrain having tipped an undulating toward theterrain east. tipped The landtoward cover the east. types The in land this area includecover mountains types in this (32%), area hilly include areas mountains (40%), terraces (32%), hilly and areas small (40%), plains terraces (28%). and The small vegetation plains (28%). type in the riverThe basin vegetation is South type Asian in the tropical river basin monsoon is South evergreen Asian tropical broad-leaved monsoon forest; evergreen it is broad dominated-leaved byforest; artiﬁcial forestsit is followed dominated by by primary artificial bushes forests and followed undergrowth. by primary The bushes location and mapundergrowth. of the river The basin location is presentedmap in Figureof the1. river basin is presented in Figure 1.

FigureFigure 1. 1.Location Location mapmap of of the the river river basin basin..

2.2. Data2.2. Data Sources Sources SpatialSpatial data: data: Digital Digital Elevation Elevation Model Model (DEM) (DEM) of the of River the Basin River are Basin sourced are sourced from the from International the ScientificInternational Data Service Scientific Platform Data Service of theChinese Platform Academy of the Chinese of Sciences, Academy and of theySciences, have and a spatial they have resolution a of 30spatial m. Landsat resolution TM of 30 images m. Landsat in 1990 TM andimages 2013 in 1990 are and initially 2013 are corrected initially andcorrected processed and processed with ENVI 5.1 andwith subsequently ENVI 5.1 and visually subsequently interpreted visually by supervised interpreted classificationby supervised using classification well-established using well visual- interpretationestablished symbols visual interpretation and standards. symbols Field and survey standards. and sampling Field survey are and conducted sampling in are the conducted River Basin to improvein the interpretation River Basin to precision. improve interpretation The kappa coefficient precision. after The interpretationkappa coefficient is 0.86,after indicatinginterpretation improved is 0.86, indicating improved classification accuracy. On the basis of the national land classification and classification accuracy. On the basis of the national land classification and the local conditions, the local conditions, the main land-use types in the River Basin are as follows: forestland, grassland, the maincultivated land-use land, typesand construction in the River land. Basin are as follows: forestland, grassland, cultivated land, and constructionHydrometeorological land. data: The hydrometeorological data for this study are obtained from the Hydrometeorologicalnational meteorological data: stations The and hydrometeorological the Hydrology Yearbook data for of Guangdongthis study are Province. obtained The from the nationalmeteorological meteorological data of the stationsRiver Basin and from the two Hydrology meteorological Yearbook stations, of namely, Guangdong Wuhua Province. and The meteorologicalLongchuan, from data 1961 of tothe 2013 River include Basin thefrom monthly two meteorological and annually stations, average temperaturenamely, Wuhua and and Longchuan,precipitation. from 1961The monthly to 2013 includerunoff data the monthlyin Hezikou and hydrological annually average station from temperature 1981 to 2013 and was precipitation. also The monthlyobtained.runoff data in Hezikou hydrological station from 1981 to 2013 was also obtained.

2.3. Research2.3. Research Methods Methods

2.3.1.2.3.1. Mann–Kendall Mann–Kendall Test Test The M–K test [32] is commonly used in the fields of meteorology and climatology to determine The M–K test [32] is commonly used in the ﬁelds of meteorology and climatology to determine the change trends of the hydrological variables as well as test and identify the climate jump points. the change trends of the hydrological variables as well as test and identify the climate jump points. This method has been used by many scholars to analyze climate changes [33–37], with the detailed This methodprinciple has and been calculation used by many method scholars given to in analyze a previous climate study changes [38].[ 33For–37 ], a with time the series detailed principleX = and(x , calculationx ,, x ) method given in a previous studyx [38]. For ax time series Xt = (x1, x2, ··· , xn), t 1 2 n , we determine the magnitude of k and j first, then given to S, the we determine the magnitude of xk and xj ﬁrst, then given to S, the statistics of trend test are as Equationsstatistics (1) of and trend (2). test The are as test Equation statisticss (1) only and (2). depends The test on statistics the ranks only depends of the observations, on the ranks of not the their observations, not their actual values, resulting in a distribution-free test statistics. The variance of S actual values, resulting in a distribution-free test statistics. The variance of S could be calculated by Sustainability 2018, 10, 3405 4 of 21

Equation (2), where n is the number of observations. UF and UB are the statistical characteristic values of the sequence and reverse sequence of data for mutation test, respectively.   +1, i f x − x > 0  j k    sgn xj − xk = 0, i f xj − xk = 0 (1)    −1, i f xj − xk < 0 

n(n − 1)(2n + 5) var(S) = (2) 18

2.3.2. Wavelet Analysis Morlet wavelet is a complex-valued wavelet commonly used to analyze the hydrological changes in a long time series [39]. It has the special advantages of signal processing to the time-frequency analysis because it gives the scale of sequence variation, also show the time position of variation. In the current study, the Morlet wavelet is used to analyze the runoff, precipitation, and temperature periodicities in the River Basin and their jump points due to the mean value of this function is 0 and it has good symmetry in time-frequency. The coefficient of variation of the Morlet wavelet function is as follows (3): N −1/2 ict −t2/2 Wf (a, b) = |a| ∑ f (k)e e (3) K=1 where Wf(a,b) represents the wavelet transform coefficient, a represents the scaling factor, which indicates the period length of the wavelet, b represents the time shift factor, t represents the time variables, and c is set to 6. The squared value of the wavelet coefficient is integrated in Domain b to obtain the wavelet variance as follows:

Z ∞ 2 Var(a) = Wf (a, b) db (4) −∞

The change in the wavelet variance with the scale α is described by a wavelet variogram, which reveals the energy distribution of signal ﬂuctuation with scale α [40]. The wavelet variogram can be used to determine the relative signal disturbance intensity of each time scale and the main existing time scale, that is, the primary period.

2.3.3. Double-Mass Curve DMC is widely used in hydrological research. A regression analysis of the correlation between two variables is conducted using a rectangular coordinate system; thus, the relationship curve between these two variables is plotted. In the analysis of the DMC of precipitation–runoff, the cumulative precipitation is taken as the reference variable. The precipitation naturally changes within a limited period of time. The precipitation is affected less by human activities, while runoff is under the combined impacts of precipitation and human activities. Therefore, the impact of human activities can be distinguished using this method [41]. The speciﬁc procedure is as follows:

(1) Precipitation sequences and runoff sequences are cumulatively calculated. The cumulative curve of precipitation ∑P–runoff ∑Q is plotted to determine the year in which human activities began to affect the runoff. The curve can be divided into two periods, namely, the reference period and the change period, and the dividing time point is the cutoff point. (2) The ∑P and ∑Q data for the change period are analyzed based on the corresponding data for the reference period by using LR. Consequently, the ﬁtting equation for the cumulative precipitation and the runoff in the period, where a is the slope and b is the ∑Q-intercept:

∑ Q = a∑ P + b (5) Sustainability 2018, 10, 3405 5 of 21

(3) ∑P in the change period is substituted into the Equation (3) to obtain the simulated cumulative runoff depth ∑Q0 in the change period. If ∑Q0 is assumed to be the same as the underlying surface conditions in the reference period, then the runoff is unaffected by human activities. (4) The calculation is inversed based on the superposition principle of cumulative values. With the simulated value ∑Q0, the simulated runoff depth Q0 is calculated inversely, and the difference between the measured runoff in the change period and the averaged value indicates the impact of human activities on the runoff in the River Basin.

3. Results

3.1. Analysis on the Trends, Discontinuities, and Periodicities of the Temperature, Precipitation, and Runoff

3.1.1. Analysis on the Change Trends of Temperature, Precipitation, and Runoff in 1961–2013 The average annual temperature in the River Basin is evidently increasing from 1961 to 2013 (Figure2), and the interannual rate of change is 0.166 ◦C/10a. The maximum value of the average annual temperature (22.48 ◦C) is recorded in 1998, and the minimum value (20.67 ◦C) is recorded in 1984. An analysis of the five-year moving average curve shows that the average annual temperature from 1998 to 2002 is the highest, reaching up to 22.1 ◦C, whereas that from 1967 to 1971 is the lowest, reaching only 21.01 ◦C. The cumulative departure curve of the average annual temperature apparently changes. The changes in the average annual temperature from 1961 to 2013 experiences can be divided into two stages: stable fluctuation and rapid increase. The average annual temperature from 1961 to 1998 is on the low side, whereas that from 1998 to 2013 is on the high side. Evident from the interdecadal variations of the annual temperature, the average temperature in the 1970s is lowest. The average temperature in the 1960s is roughly equivalent to that in the 1980s, but the temperature rises slowly in the 1980s. Since the 1990s, the temperature has rapidly increased. The temperature rise has accelerated in the 21st century, increasing by 0.76 ◦C on average compared with the 1960s. The average annual precipitation in the river basin is 1451 mm. As shown in the linear trend of the annual precipitation (Figure3), the precipitation in the River Basin from 1961 to 2013 shows a slowly rising tendency. The interannual rate of change is 11.4 mm/10a. The maximum value of the annual precipitation in 53 years appears in 2006, reaching up to 2431 mm. The minimum value is 938 mm, which is recorded in 1991. The ratio of these extreme values is 2.59. As shown in the five-year moving average curve, the average annual precipitation within five years reaches the maximum value of 1655 mm from 2006 to 2010 and the minimum value of 1238 mm from 1962 to 1966. As shown in the cumulative departure histogram (Figure3), the changes in the annual precipitation roughly follow the pattern “down–up–down–up–down–up,” exhibiting relatively apparent phase change characteristics. The cumulative departure curve of the annual precipitation shows a declining trend from 1961 to 1971, from 1984 to 1996, and from 2002 to 2005, and the annual precipitation is generally low in these periods. The cumulative departure curve shows a fluctuating upward trend from 1972 to 1983, from 1997 to 2001, and from 2006 to 2009, and the precipitation is mainly increasing in these periods. However, the changes in the annual precipitation from 2010 to 2013 are not significant. The interdecadal variation trend of the precipitation is as follows: the precipitation is on the low side in the 1960s, but it is on the high side in the 1970s. Subsequently, it continues to increase in the 1980s, it drops in the 1990s, and it is on the high side again in 2000. As shown in the linear trend of the annual runoff (Figure4), the runoff in the River Basin is more volatile. Overall, it is mainly significantly reducing. The rate of change is −0.324 billion m3/10a. The maximum value of the annual runoff is recorded in 1983, reaching up to 1.655 billion m3, whereas the minimum value of 0.355 billion m3 is reached in 1991. An analysis of the five-year moving average curve shows that the maximum average annual runoff appears from 2005 to 2009, reaching up to 10.35 billion m3, whereas the minimum is 0.657 billion m3 from 2008 to 2012. As shown in the cumulative departure curve, during the nearly 33 years, the annual runoff in the river basin has roughly experienced an “up–down–up–down–up–down” trend. This trend can be divided into six Sustainability 2018, 10, x FOR PEER REVIEW 6 of 21 SustainabilitySustainability2018 2018, 10, ,10 3405, x FOR PEER REVIEW 6 of6 21 of 21

roughlyroughly experienced experienced an an “up “up–down–down–up–up–down–down–u–pu–pdown”–down” trend. trend. This This trend trend can can be be divided divided into into six six phases:phases:phases: (1) (1) from(1) from from 1981 1981 1981 to to 1985,to 1985, 1985, the the the annual annual annual runoffrunoff runoff waswas was on on the the high high high side; side; side; (2) (2)(2) from fromfrom 1986 1986 to to 1996, 1996, 1996, the the the annual annual annual runoffrunoffrunoff was was was on on the on the lowthe low low side; side; side; (3) (3) from (3) from from 1997 1997 1997 to 2001,to to 2001, 2001, the the annualthe annual annual runoff runoff runoff was was was on on the on the highthe high high side; side; side; (4) (4) from (4) from from 2002 to2 2004,0022002 to theto 2004, 2004, annual the the annual runoffannual runoff wasrunoff on was was the on on low the the side;low low side; (5) side; from (5) (5) from 2005from 2005 to2005 2008, to to 2008, 2008, the the annualthe annual annual runoff runoff runoff was was was on on on the highthethe side; high high andside; side; (6) and and since (6) (6) since 2009, since 2009, the2009, annual the the annual annual runoff runoff runoff has has been has been been on on the on the lowthe low low side. side. side. An An An analysis analysis analysis of of theof the the results results results by theby decadeby the the decade decade (Figure (Figure (Figure4) shows 4) 4) shows thatshows the that that highest the the highest highest annual an an runoffnualnual runoff wasrunoff in was thewas in 1980s,in the the 1980s, whereas1980s, whereas whereas the lowest the the lowest lowest was in thewas 2010s.was in in the Thethe 2010s. 2010s. annual The The runoff annual annual in runoff therunoff 2000s in in the the was 2000s 2000s on was the was highon on the the side, high high whereas side, side, whereas whereas that in that that the in 1990sin the the 1990s was1990s onwas was the on the low side. lowon side. the low side.

(a()a )

(b()b ) Figure 2. (a) Interannual change and (b) Cumulative departure curves of the average temperature in FigureFigure 2. 2.(a )(a Interannual) Interannual change change and and ((bb)) CumulativeCumulative departure curves curves of of the the average average temperature temperature in in the river basin from 1961 to 2013. thethe river river basin basin from from 1961 1961 to to 2013. 2013.

2,900 2,900 Annual average rainfall y = 1.1395x + 1420.8 Annual average rainfall y = 1.1395x + 1420.8 5-year moving average R² = 0.003 2,500 5-year moving average R² = 0.003 2,500 Linear (Annual average rainfall) Linear (Annual average rainfall) 2,100 2,100 1,700 1,700 1,300

1,300 Annual rainfall/mm Annual Annual rainfall/mm Annual 900 900 500 500 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 Year Year

(a()a ) Figure 3. Cont. Sustainability 2018, 10, 3405 7 of 21 SustainabilitySustainability 2018 2018, 10, 10, x, FORx FOR PEER PEER REVIEW REVIEW 7 7of of21 21

800800 600600 400400 200200 0 0 -200-200 -400-400 -600-600

-800-800 Cumulative departure/mm Cumulative -1000departure/mm Cumulative -1000 -1200-1200 19611961 19691969 19771977 19851985 19931993 20012001 20092009

YearYear

(b()b )

FigureFigureFigure 3. 3.(a 3.()a Interannual()a Interannual) Interannual change change change andand and ((bb )()b CumulativeCumulative) Cumulative departure departure departure curves curves curves of ofof the thethe average averageaverage precipitation precipitation in in in thethe riverthe river river basin basin basin from from from 1961 1961 1961 to to 2013.to 2013. 2013.

(a()a )

(b()b )

FigureFigureFigure 4. 4.( a4.()a () Interannuala Interannual) Interannual changechange change and and ( b ()b Cumulative) Cumulative departure departure departure curves curvescurves of of the the runoff. runoff.

3.1.2.3.1.2.3.1.2. Discontinuities Discontinuities Discontinuities in in Temperature,in Temperature, Temperature, Precipitation,Precipitation, Precipitation, and and Runoff Runoff TheTheThe M–K M M–K– test Ktest test discontinuity discontinuity discontinuity resultsresults results forfor for the the temperature temperature sequences sequencessequences in in the the river river basin basin basin (Figure ( (FigureFigure 5a) 5a)5 a) showshowshow that that that most most most of of the of the UFthe UF statisticsUF statistics statistics of of the of the M–Kthe M M– testK– Ktest test curve curve curve from from from 1961 1961 1961 to to 1996 to 1996 1996 are are negative,are negative, negative, and and and during during during that period,thatthat period, theperiod, annual the the annual temperatureannual temperature temperature exhibits exhibits exhibits a downward a adownward downward trend. trend. However,trend. However, However, the UF the the statistics UF UF statistics statistics do not do do exceed not not Sustainability 2018, 10, 3405 8 of 21

Sustainability 2018, 10, x FOR PEER REVIEW 8 of 21 the threshold (Y = −1.96) at a significance level 0.05; thus, the downward trend is indistinct. The UF exceed the threshold (Y = −1.96) at a significance level 0.05; thus, the downward trend is indistinct. statistics from 1997 to 2013 are positive, and exceeds the threshold (Y = 1.96) at a significance level 0.05, The UF statistics from 1997 to 2013 are positive, and exceeds the threshold (Y = 1.96) at a significance thus, the temperature in the River Basin has exhibited an obvious upward trend in recent years and a level 0.05, thus, the temperature in the River Basin has exhibited an obvious upward trend in recent jump point in 1997. years and a jump point in 1997. The M–K test discontinuity results of the annual precipitation in the River Basin (Figure5b) show The M–K test discontinuity results of the annual precipitation in the River Basin (Figure 5b) show that the precipitation generally fluctuates, and the UF and UB curves have several crossing points that the precipitation generally fluctuates, and the UF and UB curves have several crossing points withinwithin the confidence the confidence interval, interval, which which are are particularly particularly concentrated concentrated in in the the 1990s 1990s and and the the 2000s.2000s. Within the 53the years, 53 years, the precipitationthe precipitation has has not not shown shown any any distinct distinct discontinuity, discontinuity, but but the the precipitation precipitation inin thethe 1990s1990s and thatand inthat the in 2000sthe 2000s are extremelyare extremely volatile. volatile. The M–KThe M test–K discontinuitytest discontinuity results results of the of annualthe annu runoffal runoff in the in riverthe river basin basin (Figure (Figure5c) show 5c) show that that the runoffthe is volatilerunoff is in volatile general. in The general. UF and The UB UF curves and UB have curves several have crossing several points crossing within points the confidence within the interval,confidence and these interval, points and are these concentrated points are concentrated in the periods in the from periods 1981 tofrom 1985 1981 and to 1985 from and 1996 from to 2008.1996 Thus,to within 2008. theThus, 53 within years, the runoff53 years, does the not runoff have does any not significant have any discontinuity, significant discontinuity, but the runoff but from the 1981 torunoff 1985 from and 1981 that fromto 1985 1996 and to that 2008 from fluctuate 1996 to considerably. 2008 fluctuate considerably.

a temperature 6 UF curve UB curve threshold (0.05) 5 4 3

K K statistics -

M 1 0 -1 -2 -3 1961 1969 1977 1985 1993 2001 2009

Year

b precipitation 2.5 UF curve UB curve threshold (0.05) 2 1.5 1 0.5

K K statistics 0 -

M -0.5 -1 -1.5 -2 -2.5 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011

Year

Figure 5. Cont. Sustainability 2018, 10, 3405 9 of 21 Sustainability 2018, 10, x FOR PEER REVIEW 9 of 21 Sustainability 2018, 10, x FOR PEER REVIEW 9 of 21 c runoff c runoff UF curve UB curve threshold (0.05) 2.5 UF curve UB curve threshold (0.05) 2.5 2 2 1.5 1.5 1 1 0.5 0.5

K K statistics 0 -

M -0.5 M -0.5 -1 -1 -1.5 -1.5 -2 -2 -2.5 -2.5 1981 1986 1991 1996 2001 2006 2011 1981 1986 1991 1996 2001 2006 2011 Year Year

FigureFigure 5. Mann–Kendall 5. Mann–Kendall (M–K) (M– curvesK) curves of theof the temperature, temperature, precipitation, precipitation, and and runoff runoff in in the the riverriver basin.basin. Figure 5. Mann–Kendall (M–K) curves of the temperature, precipitation, and runoff in the river basin. 3.1.3. Periodicities of the Changes in Temperature, Precipitation, and Runoff 3.1.3. Periodicities of the Changes in Temperature, Precipitation, and Runoff 3.1.3. Periodicities of the Changes in Temperature, Precipitation, and Runoff The wavelet variance change curve of the time series (Figure6) shows that the average annual The wavelet variance change curve of the time series (Figure 6) shows that the average annual The wavelet variance change curve of the time series (Figure 6) shows that the average annual temperaturetemperature has ahas distinct a distinct peak peak on theon the 28a 28a scale, scale, indicating indicating that that the the strongest strongest periodical periodical oscillationoscillation of temperature has a distinct peak on the 28a scale, indicating that the strongest periodical oscillation of the averagethe average annual annual temperature temperature is on is on the the 28a 28a scale, scale, which which is is the the first first primary primary period period ofof thethe averageaverage the average annual temperature is on the 28a scale, which is the first primary period of the average annualannual temperature temperature curve. curve. annual temperature curve. As shownAs shown in the in 2Dthe isogram2D isogram of theof the wavelet wavelet coefficient coefficient (Figure (Figure6), 6), the the strength strength of of the the signal signal isis As shown in the 2D isogram of the wavelet coefficient (Figure 6), the strength of the signal is expressedexpressed by the by waveletthe wavelet coefficient. coefficient. In theIn the isogram, isogram, the the positive positive values values are are enclosedenclosed by solid lines, lines, expressed by the wavelet coefficient. In the isogram, the positive values are enclosed by solid lines, and theyand they indicate indicate the temperaturesthe temperatures on on the the high high side. side. By By contrast, contrast, the the negative negative valuesvalues areare enclosed by by and they indicate the temperatures on the high side. By contrast, the negative values are enclosed by dasheddashed lines, lines, and and they they indicate indicate the the temperatures temperatures on on the the low low side. side. The The isogram isogramshows shows thatthat the phase dashed lines, and they indicate the temperatures on the low side. The isogram shows that the phase structure corresponding to the average annual temperature on the 28a scale has a strong periodicity, structurestructure corresponding corresponding to to the the average average annualannual temperature on on the the 28a 28a scale scale has has a strong a strong periodicity, periodicity, and the positive/negative phase fluctuates on this scale. On this scale, the temperature has three andand the the positive/negative positive/negative phase phase fluctuates fluctuates on this this scale. scale. On On this this scale, scale, the the temperature temperature ha hass three three quasi-periodic oscillations (QPOs) with alternating high and low values at the positive phase of each quasi-periodicquasi-periodic oscillations oscillations (QPOs) (QPOs) with with alternatingalternating high and and low low values values at at the the positive positive phase phase of ofeach each of the periods from 1961 to 1963, from 1974 to 1984, from 1993 to 2002, and from 2009 to 2013. In these ofof the the periods periods from from 1961 1961 to to 1963, 1963, from from 19741974 toto 1984, from 1993 1993 to to 2002, 2002, and and from from 2009 2009 to to2013. 2013. In Inthese these periods, the temperatures were on the high side, whereas the temperatures in the remaining years periods,periods, the the temperatures temperatures were were onon thethe highhigh side,side, whereas the the temperatures temperatures in in the the remaining remaining years years were on the low side. werewere on on the the low low side. side.

(a) (a)

Figure 6. Cont. Sustainability 2018, 10, 3405 10 of 21 Sustainability 2018, 10, x FOR PEER REVIEW 10 of 21 Sustainability 2018, 10, x FOR PEER REVIEW 10 of 21

(b) (b) FigureFigure 6. 6.(a )(a Time–frequency) Time–frequency distribution distribution ofof the real part part of of the the wavelet wavelet coefficient coefﬁcient and and (b ()b Wavelet) Wavelet Figure 6. (a) Time–frequency distribution of the real part of the wavelet coefficient and (b) Wavelet variancevariance change change curve curve of of temperature. temperature. variance change curve of temperature. TheThe wavelet wavelet variance variance change change curve curve of the of time the series time (Figure series 7 ()Figure shows 7) that shows the annual that the precipitation annual The wavelet variance change curve of the time series (Figure 7) shows that the annual is apparentlyprecipitation periodic. is apparently The primaryperiodic. periodsThe primary of the periods annual of precipitation the annual precipitation include 7a, include 9a, 13a, 7a, and 9a, 28a, precipitation is apparently periodic. The primary periods of the annual precipitation include 7a, 9a, among13a, whichand 28a, 28a among is the ﬁrst which (primary) 28a is period. the first As (primary) shownin period. Figure 7 As, the shown positive/negative in Figure 7, phase the 13a,positive/negative and 28a, among phase time which oscillation 28a is theon the first 28a (primary) scale is the period. most evident, As shown and 28a in isFigure the center 7, the of timepositive/negative oscillation on thephase 28a time scale oscillation is the most on evident,the 28a scale and is 28a the is most the center evident, of and oscillation. 28a is the On center this scale,of theoscillation. precipitation On this has threescale, QPOsthe precipitation with alternating has three dry QPOs and wetwith seasons alternating at each dry of and the wet positive seasons phases at oscillation.each of the Onpositive this scale, phases the appearing precipitation from has 1961 three to 19 QPOs65, from with 1975 alternating to 1984, dryand and from wet 1994 seasons to 2001. at appearing from 1961 to 1965, from 1975 to 1984, and from 1994 to 2001. The precipitation was on the eachThe precipitation of the positive was phases on the appearing high side infrom these 1961 periods to 19 65,but from exhibit 1975 negative to 1984, phases and from from 19941966 toto 2001.1974, high side in these periods but exhibit negative phases from 1966 to 1974, from 1985 to 1993, and from Thefrom precipitation 1985 to 1993, was and on from the 2002high toside 2011. in these periods but exhibit negative phases from 1966 to 1974, 2002from to 2011.1985 to 1993, and from 2002 to 2011.

(a) (a)

Figure 7. Cont. Sustainability 2018, 10, 3405 11 of 21 Sustainability 2018, 10, x FOR PEER REVIEW 11 of 21 Sustainability 2018, 10, x FOR PEER REVIEW 11 of 21

(b) (b) FigureFigure 7. 7.(a ()a Time–frequency) Time–frequency distribution distribution ofof thethe real part of of the the wavelet wavelet coefficient coefﬁcient and and (b ()b the) the wavelet wavelet Figure 7. (a) Time–frequency distribution of the real part of the wavelet coefficient and (b) the wavelet variancevariance change change curve curve of of precipitation. precipitation. variance change curve of precipitation. AsAs shown shown in in Figure Figure7, 7 the, the precipitation precipitation is is on on thethe lowlow side.side. Furthermore, Furthermore, within within the the 53 53 years, years, As shown in Figure 7, the precipitation is on the low side. Furthermore, within the 53 years, relativelyrelatively strong strong time time oscillations oscillations occur occur on on thethe 13a13a scale. On On this this scale, scale, the the precipitation precipitation has has six six QPOs QPOs relatively strong time oscillations occur on the 13a scale. On this scale, the precipitation has six QPOs alternatingalternating dry dry and and wet wet seasons. seasons. The The wavelet wavelet variance variance change change curve curve of theof timethe ti seriesme series (Figure (Figure8) shows 8) alternating dry and wet seasons. The wavelet variance change curve of the time series (Figure 8) thatshows the annual that the runoff annual is runoff prominently is prominently periodic. periodic. The runoff The inrunoff the riverin the basin river exhibitsbasin exhibits primary primary periods shows that the annual runoff is prominently periodic. The runoff in the river basin exhibits primary of 7a,periods 11a, of and 7a, 19a, 11a, among and 19a, which among 19a which represents 19a represents the ﬁrst the primary first primary period. period. periods of 7a, 11a, and 19a, among which 19a represents the first primary period.

(a) (a)

Figure 8. Cont. Sustainability 2018, 10, 3405 12 of 21 Sustainability 2018, 10, x FOR PEER REVIEW 12 of 21

(b)

FigureFigure 8. ( 8.a) ( Time–frequencya) Time–frequency distribution distribution of the of thereal real part part of the of wavelet the wavelet coefﬁcient coefficient and (b and) The (b wavelet) The variancewavelet change variance curve change of runoff. curve of runoff.

AsAs shown shown in in Figure Figure8, 8, the the positive/negative positive/negative phasephase time oscillation oscillation on on the the 11a 11a scale scale is isthe the most most obvious,obvious, and and 11a 11a is is the the center center of of oscillation. oscillation. OnOn this scale, the the runoff runoff has has four four QPOs QPOs with with alternating alternating wetwet and and dry dry seasons. seasons. TheThe positivepositive phases appear appear from from 1983 1983 to to1986, 1986, from from 1991 1991 to 1993, to 1993, from from 1998 1998to to2002, and and from from 2006 2006 to to 2008, 2008, indicating indicating that that the therunoff runoff is on is the on high the high side sideduring during these theseperiods. periods. By Bycontrast, contrast, the the negative negative phases phases appear appear from from 1987 1987 to 1990, to 1990, from from1994 1994to 1997, to 1997,from 2003 from to 2003 2005, to and 2005, from 2009 to 2012, indicating that the runoff is on the low side during these periods. Furthermore, a and from 2009 to 2012, indicating that the runoff is on the low side during these periods. Furthermore, relatively strong time oscillation is evident in 19a, in which the runoff exhibits three QPOs with a relatively strong time oscillation is evident in 19a, in which the runoff exhibits three QPOs with alternating wet and dry seasons. alternating wet and dry seasons. 3.2. Relationship Between Land Use and Runoff 3.2. Relationship Between Land Use and Runoff The accuracy of the land-use classification (Table 1) based on the statistical results of the Landsat The accuracy of the land-use classiﬁcation (Table1) based on the statistical results of the Landsat TM images of the river basin in 1990 and 2013 (Figure 9) exceeds 85%. Based on Table 1, forestland is TM images of the river basin in 1990 and 2013 (Figure9) exceeds 85%. Based on Table1, forestland is the the predominant land use type in the study area, respectively accounting for 77.32% and 72.18 % of predominant land use type in the study area, respectively accounting for 77.32% and 72.18 % of the total the total land area of the river basin in 1990 and 2013, followed by cultivated land. However, their landproportions area of the are river declining basin inover 1990 time. and The 2013, proportions followed byof cultivatedthe grassland land. areasHowever, in the two their periods proportions are are2.33% declining and 7.57%, over time. respectively, The proportions and they of expand the grassland more rapidly. areas inCompared the two periods with the are grassland 2.33% and area 7.57%, in respectively,1990, that in and 2013 they increases expand by more 224.5%. rapidly. The proportions Compared of with the land the grasslandareas for construction area in 1990, in that 1990 in and 2013 increases2013 account by 224.5%. for 2.32% The proportionsand 9.18%, respectively. of the land areasFrom for1990 construction to 2013, the inland 1990 area and for 2013 construction account for 2.32%increases and 9.18%, by 295.46% respectively. and exhibits From the 1990 highest to 2013, interannual the land dynamic area for change. construction By contra increasesst, the cultivated by 295.46% andland exhibits area decreases the highest by interannual 40% within dynamicthe 23 years change. and exhibits By contrast, a discernible the cultivated downward land areatrend. decreases Both the by 40%forestland within the and 23 watershed years and areas exhibits are areduced, discernible albeit downward not significantly. trend. BothThe forestland the forestland area anddecreases watershed by areas6.67%, are reduced, and the watershed albeit not signiﬁcantly. area decreases The by forestland 12.44%. Therefore, area decreases the grain by- 6.67%,for-green and policies the watershed have areaplayed decreases an active by 12.44%. role in Therefore, regaining the forests grain-for-green and grasslands. policies However, have played owing an to active these role policies in regaining and forestsurbanization, and grasslands. the cultivated However, land is owing reduced to thesesubstantially, policies whereas and urbanization, the construction the cultivated land exhibits land a is reducedremarkable substantially, upward whereastrend. The the changes construction in the land land use exhibits in the a river remarkable basin show upward that trend.both the The areas changes of in thegrassland land use and in the the construction river basin showland have that increased, both the areas whereas of grassland the areas andof cultivated the construction land, forestland, land have increased,and watershed whereas have the decreased. areas of cultivated land, forestland, and watershed have decreased.

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Table 1. Land-use structure in the river basin from 1990 to 2013 and its change trend. Table 1. Land-use structure in the river basin from 1990 to 2013 and its change trend. Cultivated Construction Year Forestland Grassland Watershed Year CultivatedLand Land Forestland Grassland Watershed ConstructionLand Land 19901990 313.19313.19 1417.2 1417.2 42.73 42.73 17.2 17.2 42.51 42.51 Area/kmArea/km2 2 20132013 187.9187.9 1322.62 1322.62 138.66 138.66 15.06 15.06 168.11 168.11 RateRate of change of change in From 1990 From 1990 to 2013 −−4040 −6.67−6.67 224.5 224.5 −12.44−12.44 295.46 295.46 in the area/%the area/% to 2013

FigureFigure 9. Land9. Land use use types types in inthe the WuhuaWuhua River River Basin Basin inin 1990 1990 and and 2013. 2013.

The transitionThe transition matrices matrices in in Tables Tables2 and2 and3 show3 show the the probabilitiesprobabilities of of transitions transitions between between different different land-useland- types.use types. These These matrices matrices are are designed designed to to characterize characterize the the changes changes in the in the structure structure and and characteristicscharacteristics of the of the different different land-use land-use types types andand the direction directionss of of land land-use-use transitions transitions to distinguish to distinguish the changingthe changing primary primary and and secondary secondary land land typestypes clearly and and to to analyze analyze and and explain explain the reasons the reasons for for the land-usethe land-use transitions. transitions. As As shown shown inin Tables 2 and 3,3, the the land land-use-use transitions transitions in the in river the riverbasin basinare as are follows: forestland to cultivated land, cultivated land to forestland, cultivated land to grassland, as follows: forestland to cultivated land, cultivated land to forestland, cultivated land to grassland, forestland to grassland, cultivated land to construction land, and forestland to construction land. forestlandThe to grassland,matrix of land cultivated-use transition land from to construction 1990 to 2013 land,in Table and 2 shows forestland that the to increase construction in the land.area Theof cultivated matrix of land land-use is mainly transition contributed from by 1990 the toforestland. 2013 in TheTable reduced2 shows part that of the cultivated increase land in the area ofinvolved cultivated a total land area is mainly of 210 km contributed2 and mainly by thetransfer forestland. to the forestland, The reduced construction part of cultivated land, and land involvedgrassland. a total The area 106.85 of 210 km km2 increase2 and mainly in the area transfer of forestland to the forestland, mainly comes construction from cultivated land, andland. grassland. The The 106.85reduced km part2 increase of forestland in the mainly area of transfer forestlandred to mainly cultivated comes land, from grassland, cultivated and land. construction The reduced land. part of forestlandAn increase mainly of 124.77 transferred km2 in the to cultivatedarea of grassland land, grassland,is from cultivated and construction land and forestland, land. An and increase the of 124.77reduced km2 in part the of area grassland of grassland mainly is transfer from cultivatedred to the forestland and land. forestland, The increase and in the the reduced area of the part of watershed is primarily from the forestland and the cultivated land. grassland mainly transferred to the forest land. The increase in the area of the watershed is primarily The increase in the area of the construction land is primarily contributed by the cultivated land from the forestland and the cultivated land. and the forestland. On the 23a scale, the cultivated land and forestland each have a large coverage; Thethus, increase they are more in the actively area of transformed the construction into other land land is primarily-use types to contributed a large extent. by the cultivated land and the forestland. On the 23a scale, the cultivated land and forestland each have a large coverage; thus, they are more actively transformed into other land-use types to a large extent. Sustainability 2018, 10, x FOR PEER REVIEW 14 of 21

Table 2. Matrix of the land-use transition in the river basin from 1990 to 2013 (1) (unit: km2). Sustainability 2018, 10, 3405 14 of 21 From 1990 to 2013 Grassland Cultivated Land Construction Land Forestland Watershed Total in 2013 Cultivated land 4.13 94.85 9.90 81.09 2.68 185.44 Forestland 21.82 106.85 1.96 1145.84 1.58 1315.66 Table 2. Matrix of the land-use transition in the river basin from 1990 to 2013 (1) (unit: km2). Grassland 9.80 41.21 5.04 83.56 2.42 138.03 FromWatershed 1990 to 2013 Grassland0.79 Cultivated2.47 Land Construction0.68 Land Forestland4.21 Watershed8.29 Total in15.94 2013 Construction land 4.66 68.31 24.73 83.77 2.11 167.87 Cultivated land 4.13 94.85 9.90 81.09 2.68 185.44 TotalForestland in 1990 21.8242.73 106.85313.19 1.9641.51 1145.841416.46 1.5817.2 1315.661832 Grassland 9.80 41.21 5.04 83.56 2.42 138.03 Watershed 0.79 2.47 0.68 4.21 8.29 15.94 ConstructionTable land 3. Matrix 4.66 of the land-use 68.31 transition in the 24.73 river basin from 83.77 1990 to 2013 2.11 (2) (unit: 167.87%). Total in 1990 42.73 313.19 41.51 1416.46 17.2 1832 From 1990 to 2013 Grassland Cultivated Land Construction Land Forestland Watershed Cultivated land 2.22 51.15 1.56 43.73 1.34 Table 3. Matrix of the land-use transition in the river basin from 1990 to 2013 (2) (unit: %). Forestland 1.99 9.39 0.12 88.29 0.2 GrasslandFrom 1990 to 2013 Grassland4.2 Cultivated31.31 Land Construction0.75 Land Forestland60.55Watershed 3.2 WatershedCultivated land 4.94 2.22 51.1515.5 1.561.13 43.7326.41 1.34 52 ConstructionForestland land 2.62 1.99 45.15 9.39 0.123.78 88.2947.06 0.2 1.35 Grassland 4.2 31.31 0.75 60.55 3.2 Watershed 4.94 15.5 1.13 26.41 52 3.3. ImpactsConstruction of Climate land Changes 2.62 and Human 45.15 Activities on Runoff 3.78 47.06 1.35 The runoff, temperature, and precipitation curves are plotted based on their departure 3.3. Impacts of Climate Changes and Human Activities on Runoff percentages to examine the relationships between the climate changes and the runoff in the River Basin.The As runoff, shown temperature, in Figure and10, precipitation the precipitation curves in are the plotted river based basin onfluct theiruates departure more violently percentages than totemperature. examine the relationships From 1981 to between 2013, theboth climate precipitation changes and and the runoff runoff exhibit in the alternating River Basin. positive As shown and innegative Figure 10 departures,, the precipitation their overall in the riverchange basin trends fluctuates have good more correspondence violently than temperature. remarkable consistency, From 1981 toand 2013, their both departure precipitation percentages and runoff barel exhibity change. alternating By contrast, positive the and change negative trends departures, of temperature their overall and changerunoff trends have poor have correspondence. good correspondence Thus, remarkablecompared with consistency, temperature, and their precipitation departure determines percentages the barelyrunoff. change. By contrast, the change trends of temperature and runoff have poor correspondence. Thus, comparedThe runoff with in the temperature, river basin precipitationis under the determinesdirect and significant the runoff. impacts of climatic factors, and it isThe also runoff affected in the by riverhuman basin activities. is under The the trend direct analysis and significant can only impacts qualitatively of climatic explain factors, the impacts and it isof also climatic affected factors by human and human activities. activities The (changes trend analysis in land can-use/cover only qualitatively type) on the explain runoff. the Thus, impacts in this ofsection, climatic the factors LR equation and human is used activities to estimate (changes the impacts in land-use/cover of precipitation type) and on human the runoff. activities Thus, on inthe thisrunoff. section, the LR equation is used to estimate the impacts of precipitation and human activities on the runoff.The interannual changes in the 33a precipitation and runoff in the river basin are compared in FigureThe 11. interannual The annual changes precipitation in the 33a is precipitationpositively associated and runoff with in the the annual river basin runoff, are and compared the runoff in Figureconfirms 11. Thethat annualthe precipitation precipitation is positively is positively associated associated with with the runoff, the annual demonstrating runoff, and a the correlation runoff confirmscoefficient that of the r = precipitation0.885 (Figure is12). positively associated with the runoff, demonstrating a correlation coefficient of r = 0.885 (Figure 12).

120% Departure percentage of runoff 100% Departure percentage of precipitation 80% 60% 40% 20% 0% -20% -40% Departure percentage Departure -60% -80% 1981 1985 1989 1993 1997 2001 2005 2009 2013 Year

(a)

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100% Departure percentage of temperature

100% DepartureDeparture percentage percentage of oftemperature runoff 50%100% Departure percentage of temperature Departure percentage of runoff 50% Departure percentage of runoff 50% 0% 0% 0% -50% -50% -50% -100% Departure percentage Departure -100%

Departure percentage Departure -100% Departure percentage Departure -150% -150% -150% 19811981 19861986 1991 19961996 20012001 20062006 20112011 1981 1986 1991 1996 2001 2006 2011 Year Year Year

((b(bb)) )

FigureFigureFigureFigure 10. 10. 10. 10.DepartureDeparture DepartureDeparture curves curves curves of of of of ( ( (aaa ()))a ) annual annual annual runoff runoff runoff runoff and and and and precipitation precipitation precipitation precipitation and and and ( (bb) and) annual (annualb) ( annualb) runoff runoff annual runoff and and runoff and andtemperature. temperature.temperature.temperature.

20 3000 20 3000 20 runoff precipitation 3000 runoff precipitation 2500 runoff precipitation2500 ) 15 2500) 15 ) 2000 mm 15 2000 mm

2000 mm 10 1500 10 1500 10 10001500 1000 5 5 ( precipitation 500 1000( precipitation 500

5meter cubic (billion runoff

runoff (billion cubic meter cubic (billion runoff precipitation ( precipitation 0 0 500 0 0 runoff (billion cubic meter cubic (billion runoff 1981 1986 1991 1996 2001 2006 2011 1981 1986 1991 1996 2001 2006 2011 Year 0 Year 0 1981 1986 1991 1996 2001 2006 2011 FigureFigureFigure 11. 11.11.Interannual InterannualInterannual runoff runoffrunoff and andand precipitation precipitation changeschanges changes inin inthethe the riverYearriver river basin.basin. basin.

Figure 11. Interannual runoff and precipitation changes in the river basin.

FigureFigureFigure 12. 12.Regression12. RegressionRegression analysis analysisanalysis on onon the thethe average averageaverage annualannual runoffrunoff runoff withwith with respectrespect respect toto precipreci to precipitation.pitation.pitation.

TheTheThe points pointspoints in inin the thethe plotted plottedplotted precipitation–runoffprecipitationprecipitation––runoffrunoff DMCDMC DMC areare observed, areobserved, observed, andand thethe and curvecurve the isis curve divideddivided is intointo divided the reference period and the change period by the cutoff point. Subsequently, the impacts of into the referenceFigure 12. period period Regression and and the theanalysis change change on periodthe period average by by the annual the cutoff cutoff runoff point. point. with Subsequently, respect Subsequently, to preci thepitation. impacts the impacts of of precipitationprecipitationprecipitation and andand human humanhuman activities activitiesactivities on onon the thethe runoff runoffrunoff in inin the thethe River RiverRiver Basin BasinBasin areareare analyzed.analyzed. AsAs As shownshown shown inin in FigureFigure Figure 13, 13, the precipitation–runoff DMC initially deviated from its trajectory in 1986. Therefore, the the precipitation–runoff13,The the points precipitation in the plotted– DMCrunoff initiallyprecipitation DMC initially deviated– runoff deviated from DMC its from trajectoryare its observed, trajectory in 1986. and in 1986. Therefore,the curve Therefore, is the divided reference the into reference period is determined to be the period from 1981 to 1985. Based on the annual precipitation periodthe reference reference is determined period period is to determined and be the the period changeto be fromthe periodperiod 1981 tofrom by 1985. the1981 Based cutoff to 1985. on point. Based the annual on Subsequently, the precipitationannual precipitation the and impacts runoff of and runoff from 1981 to 1985, the following expression is established by regression analysis where P fromprecipitationand 1981 runoff to 1985, and from thehuman 1981 following to activities1985, expressionthe following on the runoff is expression established in the is River byestablished regression Basin byare regression analysis analyzed. where analysis As shown P iswhere cumulative in P Figure isis cumulativecumulative sequencessequences andand RR isis cumulativecumulative runoffrunoff sequences:sequences: sequences13, the precipitation and R is cumulative–runoff DMC runoff initially sequences: deviated from its trajectory in 1986. Therefore, the reference period is determined to be the period from 1981 to 1985. Based on the annual precipitation and runoff from 1981 to 1985, the followingR = 0.0066expressionP − 1.229 is established by regression analysis where (6) P is cumulative sequences and R is cumulative runoff sequences: Sustainability 2018, 10, x FOR PEER REVIEW 16 of 21

RP=−0.00661.229 (6) By plotting the precipitation–runoff curve in the reference period, a linear correlation equation is obtained by regression analysis to predict the natural runoff in the change period. The total change in the runoff is obtained by subtracting the measured average runoff in the change period from that in the reference period, and this change includes the impact of human activities and the consequent precipitation changes. The precipitation changes are obtained by subtracting the calculated value of the change period from the measured value of the same period, and the impact of human activities refer to the runoff changes in the change period after the precipitation changes are deducted. As shown in Table 4, within the change period, the impact of human activities on runoff reduction is the most notable, accounting for approximately 85.8% of the reduction. Under the combined impacts of precipitation and human activities, the average annual runoff reductions in the River Basin in the late 1980s, 1990s, 2000s, and from 2010 to 2013 are 0.315, 0.198, 0.165, and 0.285 billion m3, respectively. The percentages of human activities in each of these periods are 85.7%, 81.3%, 63%, and 75.8%, respectively. Under the combined impacts of precipitation and human activities, the average annual Sustainabilityrunoff2018 reduction, 10, 3405 in the River Basin within the change period is 0.183 billion m3, of which precipitation16 of 21 accounts for 14.2% and human activities contribute 85.8%.

FigureFigure 13. 13.Precipitation-runoff Precipitation-runoff double accumulation accumulation curve. curve.

By plotting the precipitation–runoff curve in the reference period, a linear correlation equation is obtained by regression analysis to predict the natural runoff in the change period. The total change in the runoff is obtained by subtracting the measured average runoff in the change period from that in the reference period, and this change includes the impact of human activities and the consequent precipitation changes. The precipitation changes are obtained by subtracting the calculated value of the change period from the measured value of the same period, and the impact of human activities refer to the runoff changes in the change period after the precipitation changes are deducted. As shown in Table4, within the change period, the impact of human activities on runoff reduction is the most notable, accounting for approximately 85.8% of the reduction. Under the combined impacts of precipitation and human activities, the average annual runoff reductions in the River Basin in the late 1980s, 1990s, 2000s, and from 2010 to 2013 are 0.315, 0.198, 0.165, and 0.285 billion m3, respectively. The percentages of human activities in each of these periods are 85.7%, 81.3%, 63%, and 75.8%, respectively. Under the combined impacts of precipitation and human activities, the average annual runoff reduction in the River Basin within the change period is 0.183 billion m3, of which precipitation accounts for 14.2% and human activities contribute 85.8%. Sustainability 2018, 10, 3405 17 of 21

Table 4. Impacts of precipitation and human activities on the runoff in the river basin.

Year Precipitation Runoff Per Ten Thousand Tons Impact of Precipitation Impact of Human Activities Actual Calculated Total Quantity Affected/ Contribution Quantity Affected/ Contribution (mm) Value Value Reduction Hundred Million m3 Factor Hundred Million m3 Factor 1981–1985 1586.3 10.29 1986–1989 1336.1 7.14 7.59 3.15 0.45 14.3% 2.7 85.7% 1990–1999 1428.2 8.31 8.68 1.98 0.37 18.7% 1.61 81.3% 2000–2009 1487.8 8.64 9.25 1.65 0.61 37% 1.04 63% 2010–2013 1478.9 7.44 8.13 2.85 0.69 24.2% 2.16 75.8% 1986–2013 1443.6 8.46 8.72 1.83 0.26 14.2% 1.57 85.8% Sustainability 2018, 10, 3405 18 of 21

4. Discussion There are several important characteristics in the climate change, and the spatiotemporal variation characteristics of land use and their contributions to runoff reduction in Wuhua River Basin. We found that the distribution of precipitation in the Wuhua River Basin is uneven during the year, mainly concentrated in April to September, accounting for about 75% of the total annual precipitation. The monthly precipitation mainly occurs in May and June. This might be because the Wuhua River is located in the south of the Wuling Mountains, which is affected by the warm and humid air currents of the ocean and it belongs to the typical subtropical monsoon humid climate, also is the transition zone between the southern subtropical zone and the mid-subtropical climate zone. The results of land use analysis show that significant land use changes have occurred in the Wuhua River Basin, mainly represented by the two-way conversion of other land types and forest land and cultivated land. Changes in land use and cover are influenced by natural factors and human activities, and population is one of the most dynamic drivers of land use change. In the past 60 years, the population of the Wuhua River Basin has experienced rapid changes. The total population has increased from 450,000 in 1949 to 1.33 million in 2015, with a net increase of 880,000 over 60 years. On the one hand, the demand for agricultural products indirectly affects the changes in the spatial distribution of land use, and on the other hand, the growth in demand for residential land and infrastructure land affects the land use pattern. The effects of human activities is the main factor leading to the reduction of runoff, consistent with the results of Luanhe River [42], Baiyangdian Lake [2] and Chaobai River [25],which are located in Northern China. Also, like most of rivers in China, the impacts of human activities is mainly to reduce annual runoff [43]. However, previous studies have also suggested that the influences of human activities and climate change on runoff are basically equal [28,29], or the synthetica effects of climatic factors is the root cause of runoff change [11]. So, there are significant regional differences in the contribution rate of climate change and human activities to the change of surface runoff between different basins, and in the analysis of river basin water resources, the respective effects of climate change and human activities should be taken into account and considered comprehensively, in order to propose sustainable river basins countermeasures for the coordinated development of water resources. The analysis and interpretation of the study would have been more useful if more climatic and hydrolic monitoring stations had been available, which would allow for the visualization of long-term change trends in temperature, precipitation and runoff over time. Furthermore, empirical statistical methods have lower requirements on parameters and the results have certain credibility while hydrological model has more requirements on data, and its results are more scientific. How to couple the two methods to improve the accuracy of the contributions of climate change and human activities to the change of surface runoff is the focus of future research.

5. Conclusions In this study, we analyzed the change trends, discontinuities and periodicities of the temperature, precipitation and runoff in the river basin, also discriminated the impacts of climate change and human activities on surface runoff by using a variety of mathematical statistics methods. The conclusions drawn are as follows: Firstly, the average annual temperature in the River Basin from 1961 to 2013 shows a noticeable rising trend, with a jump point appearing in 1997 and average annual precipitation shows a slowly rising tendency within the 53 years, while the runoff within the survey period exhibits a pronounced downward trend, and the discontinuity in the runoff begins to reduce from 1986. Secondly, the temperature and precipitation in the river basin has a clear 28a change period while annual runoff changes is on the 19a primary period. The runoff in the river basin is positively correlated with precipitation but negatively correlated with average annual temperature. The runoff has high temporal consistency with precipitation, both of them are mainly concentrated in the period from April to September. Thirdly, the main land-use types in the river basin are forestland, which cumulatively Sustainability 2018, 10, 3405 19 of 21 account for about 80% of the total area. The areas of grassland and construction land increase sharply by 224.5% and 295.5%, respectively. Lastly, the quantitative analysis shows that the River Basin is under the combined impacts of precipitation and human activities, human activities contribute to the runoff by 85.8%, and the contribution factor is signiﬁcantly greater than that of precipitation, indicating human activities are driving factors for the runoff reduction in the river basin. Compared with the 57.8% reduction of sediment transport caused by human activities [44], it suggests the conservation of water and soil in the river basin plays an important role in the prevention and control of water loss and soil erosion.

Author Contributions: Z.Z. and C.D. came up with the idea and designed the study; C.D. performed some analyses and wrote the draft with L.W., L.W. and C.Y. collected the data and partial analyses, Y.X., Y.L. and J.Y. gave some comments and helped in polishing the paper. Funding: This research was supported by the National Natural Science Foundation of China (Grant No. 41471147), Natural Science Foundation of Guangdong Province (Grant No. 2015A030313811), GDAS’ Special Project of Science and Technology Development (2017GDASCX-0101), Guangdong Innovative and Entrepreneurial Research Team Program (2016ZT06D336), Guangdong Innovative and Entrepreneurial Research Team Program (2016ZT06D336) and GDAS’ Special Project of Science and Technology Development (No. 2017GDASCX-0101, No. 2017GDASCX-0601, 2018GDASCX-0403). We are grateful to International Scientific Data Service Platform of the Chinese Academy of Sciences, Guangdong Hydrological Bureau and China Meteorological Data Sharing Service System for their help in providing data. Conflicts of Interest: The authors declare no conflicts of interest.

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