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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 2 3 4 5

*

School of Geography Sciences, South China Normal University, Guangzhou 510631, China Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48823, USA; [email protected] The Bureau of Land and Resources Huangshi, Huangshi 435000, China; [email protected] Institute for Geospatial Research and Education, Eastern Michigan University, Ypsilanti, MI 48197, USA; [email protected] 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].

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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,


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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 Sustainability 10, x FOR PEER REVIEW accounting for approximately 82.5% of the annual precipitation, 3 of 21 the period from2018, March to September, ◦ 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 an undulating terrain tipped toward the east. The land cover types in this area watershed in South China, having an undulating terrain tipped toward the east. The land include mountains (32%), areas (40%), terraces and areas small(40%), plainsterraces (28%). and Thesmall vegetation type in the cover types in this areahilly include mountains (32%), hilly plains (28%). river The basin is South Asian tropical monsoon evergreen broad-leaved forest; it is dominated by artificial vegetation type in the river basin is South Asian tropical monsoon evergreen broad-leaved forest; forests followed by by primary bushes undergrowth. The location of the river is presented it is dominated artificial forestsand followed by primary bushes and map undergrowth. Thebasin location map of the1.river basin is presented in Figure 1. in Figure

Figure ofthe theriver riverbasin. basin. Figure1.1.Location Location map map of

2.2. Data Sources 2.2. Data Sources Spatial data:data: Digital Elevation Model (DEM) of the of River sourced from thefrom International Spatial Digital Elevation Model (DEM) the Basin River are Basin are sourced the Scientific Data Service Platform of the Chinese of Sciences, and haveand a spatial resolution International Scientific Data Service PlatformAcademy of the Chinese Academy of they Sciences, they have a resolution of 30 m. Landsat TM and images in 1990 2013 are initially and corrected and processed of 30spatial m. Landsat TM images in 1990 2013 are and initially corrected processed with ENVI with ENVI 5.1 and subsequently visually interpreted classification by supervised using classification using well5.1 and subsequently visually interpreted by supervised well-established visual established visual interpretation symbols and standards. Field survey and sampling are conducted interpretation symbols and standards. Field survey and sampling are conducted in the River Basin to in the River Basin to improve interpretation precision. The kappa coefficient after interpretation is improve interpretation precision. The kappa coefficient after interpretation is 0.86, indicating improved 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 main land-use types in the River Basin are as follows: forestland, grassland, cultivated land, cultivated land, and construction land. and construction land. Hydrometeorological data: The hydrometeorological data for this study are obtained from the Hydrometeorological The hydrometeorological data for this study are obtained national meteorological data: stations and the Hydrology Yearbook of Guangdong Province. The from the national meteorological and thetwo Hydrology Yearbook Guangdong meteorological data of the stations River Basin from meteorological stations,ofnamely, Wuhua Province. and Longchuan, from 1961 include and annually stations, average temperature and and The meteorological data of to the2013 River Basin the frommonthly two meteorological namely, Wuhua precipitation. The monthly runoff data in Hezikou hydrological station from 1981 to 2013 was also Longchuan, from 1961 to 2013 include the monthly and annually average temperature and precipitation. obtained.runoff data in Hezikou hydrological station from 1981 to 2013 was also obtained. The monthly 2.3. Research Methods 2.3. Research Methods Mann–Kendall 2.3.1.2.3.1. Mann–Kendall TestTest 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 method used by many scholars climate changes detailed principle has andbeen calculation method given to inanalyze a previous study [38].[33–37], For a with timethe series principle For a time series Xt = ( x1 , x2 , · · · , xn ), (x1 ,calculation X t =and x2 , , xn ) method given in a previous study x[38]. k and x j first, then given to S, the , we determine we determine the magnitude of xk andthex j magnitude first, then ofgiven to S, the statistics of trend test are as statistics trend testThe are as Equations (1)only and (2). The teston statistics only depends on the ranks ofnot the their Equations (1)ofand (2). test statistics depends the ranks of the observations, 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


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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 j − xk > 0   sgn x j − xk = 0, i f x j − xk = 0    −1, i f x − x < 0 j

var(S) =

k

n(n − 1)(2n + 5) 18

   

(1)

   (2)

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

W f ( a, b) = | a|−1/2 ∑ f (k )eict e−t

2 /2

(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: Var ( a) =

Z ∞ −∞

2 W f ( 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)

(2)

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. 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)


(3)

(4)

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∑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. 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 (Figure 2), 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 (Figure 3), 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 (Figure 3), 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 (Figure 4), 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

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roughlyexperienced experiencedanan“up–down–up–down–up–down” “up–down–up–down–up–down”trend. trend.This Thistrend trendcan canbebedivided dividedinto intosix six roughly phases: (1)from from 1981 to1985, 1985, the annual runoff wason thehigh highside; side;(2) (2)from from1986 1996, the annual phases: (1)(1) from 1981 toto1985, the annual runoff was the high side; (2) from 1986toto1996, 1996, the annual phases: 1981 the annual runoff was ononthe the annual runoff was on the low side; (3) from 1997 to 2001, the annual runoff was on the high side; (4) from runoff was on the low side; (3) from 1997 to 2001, the annual runoff was on the high side; (4) from 2002 runoff was on the low side; (3) from 1997 to 2001, the annual runoff was on the high side; (4) from 2002 to 2004, the annual runoff was on the low side; (5) from 2005 to 2008, the annual runoff was to 2002 2004,tothe annual runoffrunoff was on theonlow (5) from 20052005 to 2008, thethe annual runoff was the 2004, the annual was theside; low side; (5) from to 2008, annual runoff wasononon the high side; and (6) since 2009, the annual runoff has been on the low side. An analysis of the results high since 2009,2009, the annual runoff has has been on on thethe low side. An analysis by theside; highand side;(6) and (6) since the annual runoff been low side. An analysisofofthe theresults results bythe thedecade decade (Figure 4) shows that the highest annual runoff was in the 1980s, whereas the lowest theby decade (Figure 4) shows that the highest annual runoff was in the 1980s, whereas the lowest was (Figure 4) shows that the highest annual runoff was in the 1980s, whereas the lowest in was in the 2010s. The annual runoff the 2000s washigh the high side,whereas whereas that inthe the1990s 1990son was in the 2010s. The annual ininthe 2000s ononthe high side, was thewas 2010s. The annual runoff inrunoff the 2000s was onwas the side, whereas that inthat thein1990s was the on the low side. onside. the low side. low

(a)(a)

(b) (b) Figure2.2.(a)(a)Interannual Interannualchange changeand and(b) (b)Cumulative Cumulative departurecurves curves of theaverage average temperatureinin Figure Figure 2. (a) Interannual change and (b) Cumulative departure departure curvesofofthe the averagetemperature temperature in the river basin from 1961 to 2013. river basin from 1961toto2013. 2013. thethe river basin from 1961 2,900 2,900

Annual rainfall/mm Annual rainfall/mm

2,500 2,500

Annual average rainfall y = 1.1395x + 1420.8 Annual average rainfall y = 1.1395x + 1420.8 R²= 0.003 5-year moving average R²= 0.003 5-year moving average Linear (Annual average rainfall) Linear (Annual average rainfall)

2,100 2,100 1,700 1,700 1,300 1,300 900 900 500 500 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.

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Cumulative departure/mm Cumulative departure/mm

800 800 600 600 400 400 200 200 0 0 -200 -200 -400 -400 -600 -600 -800 -800 -1000 -1000 -1200 -1200 1961 1961

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1969 1969

1977 1977

1985 1985

1993 1993

2001 2001

2009 2009

Year Year

(b)(b) Figure (a) Interannual change and (b)(b) Cumulative departure curves ofof the average precipitation ininin Figure 3. (a) Interannual change and Cumulative departure curves of the average precipitation Figure 3. 3. (a) Interannual change and (b) Cumulative departure curves the average precipitation the river basin from 1961 to 2013. the river basin from 1961 to 2013. the river basin from 1961 to 2013.

(a)(a)

(b)(b) Figure (a)(a) Interannual change and (b)(b) Cumulative departure curves ofof the runoff. Figure 4. Interannual change and Cumulative departure curves of the runoff. Figure 4.4.(a) Interannual change Cumulative departure curves runoff.

3.1.2. Discontinuities ininTemperature, Precipitation, and Runoff 3.1.2. Discontinuities Precipitation, 3.1.2. Discontinuities inTemperature, Temperature, Precipitation, andRunoff Runoff The M–K test discontinuity results for the temperature sequences theriver river basin (Figure 5a) The M–K test discontinuity results for basin (Figure 5a) The M–K test discontinuity results for thetemperature temperaturesequences sequencesininthe river basin (Figure 5a) show that most test curve from 1961 and during show that most ofthe theUF UFstatistics statistics ofthe theM–K M–K test curve from 1961 to1996 1996 arenegative, negative, and during show that most of of the UF statistics of of the M–K test curve from 1961 to to 1996 areare negative, and during that that the exhibits a adownward the UF dodo not thatperiod, period, theannual annualtemperature temperature exhibits downward trend.However, However, the UFstatistics statistics not period, the annual temperature exhibits a downward trend.trend. However, the UF statistics do not exceed


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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 (Figure 5b) 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 within the confidence interval, which are are particularly concentrated inin the within the confidence interval, which particularly concentrated the1990s 1990sand andthe the2000s. 2000s. Within Within the 53the years, the precipitation hashas notnot shown anyany distinct discontinuity, 53 years, the precipitation shown distinct discontinuity,but butthe theprecipitation precipitation in in the the 1990s1990s and that in the 2000s are extremely volatile. and that in the 2000s are extremely volatile. The M–K test discontinuity results of the runoff in the basin (Figure 5c) show thatthat the The M–K test discontinuity results ofannual the annual runoff in river the river basin (Figure 5c) show runoff is volatile general.inThe UF and curves have several crossing points within the confidence the runoff isinvolatile general. TheUB UF and UB curves have several crossing points within the interval, and these points are concentrated in the periods from 1981 to 1985 and fromand 1996 to 2008. confidence interval, and these points are concentrated in the periods from 1981 to 1985 from 1996 Thus,towithin the 53 years, the runoff does not have any significant discontinuity, but the runoff from 2008. Thus, within 53 years, the runoff does not have any significant discontinuity, but the runoff to 1985 andtothat from 1996 toconsiderably. 2008 fluctuate considerably. 1981 to 1985from and 1981 that from 1996 2008 fluctuate a temperature UF curve

6

UB curve

threshold (0.05)

M-K statistics

5 4 3 2 1 0 -1 -2 -3 1961

1969

1977

1985

1993

2001

2009

Year

M-K statistics

b precipitation UF curve UB curve threshold (0.05) 2.5 2 1.5 1 0.5 0 -0.5 -1 -1.5 -2 -2.5 1961 1966 1971 1976 1981 1986 1991 1996 2001 2006 2011 Year Figure 5. Cont.

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UF curve UF curve

UB curve UB curve

threshold (0.05) threshold (0.05)

M-K statistics M-K statistics

c runoff c runoff 2.5 2.5 2 2 1.5 1.5 1 1 0.5 0.5 0 0 -0.5 -0.5 -1 -1 -1.5 -1.5 -2 -2 -2.5 -2.5 1981 1981

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1986 1986

1991 1991

1996 1996

2001 2001

2006 2006

2011 2011

Year Year Figure 5. Mann–Kendall (M–K) curves of the temperature, precipitation, Figure 5. Mann–Kendall (M–K) curves of the temperature, precipitation,and andrunoff runoffininthe theriver river 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 (Figure 6) shows that the average annual The wavelet variance change curve of the time series (Figure 6) shows that the average annual The wavelet variancepeak change curve the time series (Figure 6) shows that the average annual of temperature has a distinct on the 28aof scale, indicating that temperature has a distinct peak on the 28a scale, indicating thatthe thestrongest strongestperiodical periodical oscillation oscillation of temperature has a distinct peak on the 28a scale, indicating that the strongest periodical oscillation of the average annual temperature is on thethe 28a28a scale, which is is the the average annual temperature is on scale, which thefirst firstprimary primaryperiod period of of the the average average the average annualcurve. temperature is on the 28a scale, which is the first primary period of the average annual temperature annual temperature curve. annual temperature curve. As shown in the 2D of the wavelet coefficient is As shown in the isogram 2D isogram of the wavelet coefficient(Figure (Figure6), 6),the thestrength strength of of the the signal signal is As shown in the 2D isogram of the wavelet coefficient (Figure 6), the strength of the signal is expressed by the wavelet coefficient. In the isogram, the positive values are enclosed by solid lines, expressed by the wavelet coefficient. In the isogram, the positive values are enclosed solid lines, expressed by the wavelet coefficient. In the isogram, the positive values are enclosed by solid lines, and they indicate the temperatures on on thethe high side. ByBy contrast, by and they indicate the temperatures high side. contrast,the thenegative negativevalues values are are enclosed by and they indicate the temperatures on the high side. By contrast, the negative values are enclosed by dashed lines,lines, and and theythey indicate thethe temperatures onon thethe low side. dashed indicate temperatures low side.The Theisogram isogramshows shows that that the phase phase dashed lines, and they indicate the temperatures on the low side. The isogram shows that the phase structure corresponding to the average annual temperatureon onthe the28a 28ascale scalehas has aa strong strong periodicity, structure corresponding to the average annual temperature periodicity, structure corresponding to the average annual temperature on the 28a scale has a strong periodicity, and positive/negative the positive/negative phase fluctuates on this scale.On Onthis thisscale, scale,the thetemperature temperature has three and the phase fluctuates on this scale. and the positive/negative phase fluctuates on this scale. On this scale, the temperature has three quasi-periodic oscillations (QPOs) with alternating high and lowvalues values atthe thepositive positive phase phase of of each quasi-periodic oscillations high and low each quasi-periodic oscillations(QPOs) (QPOs)with withalternating alternating high and low values atatthe positive phase of each of the periods from 1961 to 1963, from 1974 to 1984, from 1993 to 2002, and from 2009 to 2013. In these of of thethe periods from from 1993 1993to to2002, 2002,and andfrom from2009 2009 2013. these periods from1961 1961toto1963, 1963,from from1974 1974 to to 1984, 1984, from toto 2013. In In these periods, the temperatures were on the high side, whereas thetemperatures temperaturesininthe theremaining remaining years years periods, the temperatures were on the high side, whereas the periods, the temperatures were on the high side, whereas the temperatures in the remaining years were onlow the side. low side. were onon the were the low side.

(a)

(a)

Figure 6. Cont.


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(b) (b) Figure Time–frequencydistribution distribution of of the the real and (b)(b) Wavelet Figure 6. 6. (a)(a) Time–frequency real part part of of the thewavelet waveletcoefficient coefficient and Wavelet Figure 6. change (a) Time–frequency distribution of the real part of the wavelet coefficient and (b) Wavelet variance curve of temperature. variance change curve of temperature. variance change curve of temperature.

The wavelet variance change theseries time (Figure series 7) (Figure shows that the annual The wavelet variance change curvecurve of theoftime shows7)that the annual precipitation The wavelet varianceperiodic. change The curve of theperiods time series (Figure 7) shows that the annual precipitation is apparently primary of the annual precipitation include is apparently periodic. The primary periods of the annual precipitation include 7a, 9a, 13a, 7a, and9a, 28a, precipitation is apparently periodic. The primary periods of the annual precipitation include 7a, 9a, 13a, and 28a, among which 28a is the first (primary) period. As shown in Figure 7, the among which 28a is the first (primary) period. As shown in Figure 7, the positive/negative phase 13a, and 28a, among which 28a is the first period. As shown in is Figure 7, the positive/negative phase time oscillation on evident, the 28a(primary) scale is theis most evident, and 28a the center of time oscillation on the 28a scale is the most and 28a the center of oscillation. On this positive/negative phase time oscillation on has the 28a scale is the most evident, dry and and 28a is theseasons centerscale, of oscillation. On this scale, the precipitation three QPOs with alternating wet at theoscillation. precipitation this has three QPOs with alternating dryQPOs and wet at each of thewet positive phases theappearing precipitation three withseasons alternating seasons at each of the On positivescale, phases fromhas 1961 to 1965, from 1975 to 1984, dry andand from 1994 to 2001. appearing from 1961 to 1965, from 1975from to 1984, and fromfrom 1994 1975 to 2001. The and precipitation was2001. on the each of the positive phases 1961 to 1965, to 1984, The precipitation was on theappearing high side in these periods but exhibit negative phasesfrom from1994 1966 to to 1974, high side in these periods but exhibit negative 1966 to 1974, from 1985 to 1993, and from The the 2002 high side in these phases periodsfrom but exhibit negative phases from 1966 to 1974, fromprecipitation 1985 to 1993,was andon from to 2011. 2002 to 2011. from 1985 to 1993, and from 2002 to 2011.

(a) (a)

Figure 7. Cont.


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(b) (b) Figure Time–frequencydistribution distributionof of the the real real part (b)(b) thethe wavelet Figure 7. 7. (a)(a) Time–frequency part of ofthe thewavelet waveletcoefficient coefficientand and wavelet Figure 7. change (a) Time–frequency distribution of the real part of the wavelet coefficient and (b) the wavelet variance curveofofprecipitation. precipitation. variance change curve variance change curve of precipitation.

shownininFigure Figure7,7,the theprecipitation precipitation is is on on the the low thethe 53 53 years, AsAs shown low side. side. Furthermore, Furthermore,within within years, As shown intime Figure 7, the precipitation is onscale. the low side.scale, Furthermore, within has the six 53 years, relatively strong oscillations occur on the 13a On this the precipitation QPOs relatively strong time oscillations occur on the 13a scale. On this scale, the precipitation has six QPOs relatively strongand time oscillations occurwavelet on the 13a scale. On this scale, the has(Figure six QPOs alternating seasons. variance change curve ofprecipitation the time series 8) alternating drydry and wetwet seasons. TheThe wavelet variance change curve of the time series (Figure 8) shows alternating dry annual and wet seasons. The wavelet variance change curve of the basin time exhibits series (Figure 8) shows that the runoff is prominently periodic. The runoff in the river primary that the annual runoff is prominently periodic. The runoff in the river basin exhibits primary periods shows the11a, annual is prominently The runoff in the river basin exhibits primary periodsthat 7a, and runoff 19a, among 19aperiodic. represents first primary of 7a, 11a, of and 19a, among which 19awhich represents the first the primary period. period. periods of 7a, 11a, and 19a, among which 19a represents the first primary period.

(a) (a)

Figure 8. Cont.

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(b) Figure 8. (a) Time–frequency distribution of the ofwavelet the wavelet coefficient and (b)wavelet The Figure 8. (a) Time–frequency distribution of the real real part part of the coefficient and (b) The wavelet variance change curve of runoff. variance change curve of runoff.

shown Figure8,8,the thepositive/negative positive/negative phase scale is is thethe most AsAs shown inin Figure phasetime timeoscillation oscillationononthe the11a 11a scale most obvious, and 11a thecenter centerofofoscillation. oscillation. On On this this scale, alternating obvious, and 11a is isthe scale, the therunoff runoffhas hasfour fourQPOs QPOswith with alternating and dry seasons.The The positive positive phases to to 1986, from 1991 to 1993, fromfrom 1998 1998 to wetwet and dry seasons. phasesappear appearfrom from1983 1983 1986, from 1991 to 1993, 2002, and from 2006 to 2008, indicating that the runoff is on the high side during these periods. By to 2002, and from 2006 to 2008, indicating that the runoff is on the high side during these periods. phases appear from 19871987 to 1990, fromfrom 1994 1994 to 1997, from 2003 2005,toand Bycontrast, contrast,the thenegative negative phases appear from to 1990, to 1997, fromto2003 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 (Table 1) 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 (Figure 9) exceeds 85%. Based on Table 1, 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 land area of the river basin inover 1990 andThe 2013, followed by land. However, theirperiods proportions proportions are declining time. proportions of cultivated the grassland areas in the two are are2.33% declining over time. The proportions of the grassland areas in the two periods are 2.33% and 7.57%, and 7.57%, respectively, and they expand more rapidly. Compared with the grassland area in respectively, they expand rapidly. Comparedofwith the grassland area in 1990,inthat 2013 1990, that inand 2013 increases bymore 224.5%. The proportions the land areas for construction 1990inand increases by 224.5%. The proportions of the land areas for construction in 1990 and 2013 account 2013 account for 2.32% and 9.18%, respectively. From 1990 to 2013, the land area for constructionfor 2.32% and 9.18%, respectively. From to 2013, the land area forchange. construction increases by 295.46% increases by 295.46% and exhibits the1990 highest interannual dynamic By contrast, the cultivated and exhibits the highest change. By contrast, the cultivated land area decreases land area decreases byinterannual 40% within dynamic the 23 years and exhibits a discernible downward trend. Both the by 40% within the 23watershed years andareas exhibits discernible downward trend. Both the forestland watershed forestland and are areduced, albeit not significantly. The forestland area and decreases by 6.67%, and the watershed area decreasesThe byforestland 12.44%. Therefore, the grain-for-green have areas are reduced, albeit not significantly. area decreases by 6.67%, andpolicies the watershed played an active role inTherefore, regainingthe forests and grasslands. However, owing theserole policies and area decreases by 12.44%. grain-for-green policies have played anto active in regaining urbanization, the cultivated land is reduced substantially, whereas the construction land exhibits forests and grasslands. However, owing to these policies and urbanization, the cultivated landa is remarkable upward whereas trend. The in the land in thea river basin show that trend. both the areas of reduced substantially, thechanges construction land use exhibits remarkable upward The changes grassland andinthe land have whereas the areas and of cultivated land, forestland, in the land use theconstruction river basin show that increased, both the areas of grassland the construction land have and watershed have decreased. increased, whereas the 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. Year 2 2 Area/km Area/km

Year

1990 1990 2013 2013

RateRate of change of change in From 1990 From 1990 to 2013 in the area/% the area/% to 2013

Cultivated

Forestland

Grassland

Watershed

313.19 313.19 187.9 187.9

1417.2 1417.2 1322.62 1322.62

42.73 42.73 138.66 138.66

17.2 17.2 15.06 15.06

−−40 40

−6.67 −6.67

224.5 224.5

−12.44 −12.44

Cultivated LandLand

Forestland

Grassland

Watershed

Construction Construction Land Land 42.51 42.51 168.11 168.11 295.46 295.46

Figure 9. Land use RiverBasin Basininin 1990 and 2013. Figure 9. Land usetypes typesin inthe the Wuhua Wuhua River 1990 and 2013.

The transition matrices in in Tables the probabilities probabilities transitions between different The transition matrices Tables2 2and and33show show the ofof transitions between different land-use types. Thesematrices matrices are are designed designed to changes in the structure and and land-use types. These to characterize characterizethethe changes in the structure characteristics of the different land-usetypes typesand and the the directions transitions to distinguish characteristics of the different land-use directionsofofland-use land-use transitions to distinguish the changing primary secondaryland landtypes types clearly clearly and and explain the reasons for for the changing primary andand secondary andtotoanalyze analyze and explain the reasons the land-use transitions. As shown in Tables 2 and 3, the land-use transitions in the river basin are as the land-use transitions. As shown in Tables 2 and 3, the land-use transitions in the river basin 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, cultivated land from to construction and forestland to increase construction matrix of land-use transition 1990 to 2013land, in Table 2 shows that the in the land. area The matrix of land-use transition from 1990 to 2013 in Table 2 shows that the increase of cultivated land is mainly contributed by the forestland. The reduced part of cultivated landin the 2 and mainly area of cultivated land is mainly contributed by the forestland. The reduced part of cultivated involved a total area of 210 km transfer to the forestland, construction land, and land 2 2 involved a totalThe area106.85 of 210km kmincrease and mainly transfer to the forestland, construction land, and grassland. in the area of forestland mainly comes from cultivated land.grassland. The 2 increase reduced of forestland cultivated land, grassland, andland. construction land. part The 106.85 kmpart in the mainly area oftransferred forestland to mainly comes from cultivated The reduced An increase of 124.77 km2 in the area of grassland is from cultivated land and forestland, the of of forestland mainly transferred to cultivated land, grassland, and construction land. Anand increase reduced part of grassland mainly transferred to the forest land. The increase in the area of the 2 124.77 km in the area of grassland is from cultivated land and forestland, and the reduced 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; The in theactively area oftransformed the construction land is primarily contributed by the cultivated land thus,increase they are more into other land-use types to a large extent. 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.


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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 2 Table 2. Matrix of the land-use transition in the river basin from 1990 to 2013 (1) (unit: km ). Grassland 9.80 41.21 5.04 83.56 2.42 138.03 Watershed 0.79 2.47 Land 0.68 Land 4.21 8.29 From 1990 to 2013 Grassland Cultivated Construction Forestland Watershed 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 Total in 1990 42.73 313.19 41.51 1416.46 17.2 1832 Forestland 21.82 106.85 1.96 1145.84 1.58 1315.66 Grassland Watershed Table Construction land3. Total in 1990

9.80 0.79 Matrix 4.66of 42.73

the

41.21 2.47 land-use 68.31transition 313.19

in

5.04 0.68 the river 24.73 41.51

basin

83.56 4.21 from 1990 83.77 1416.46

2.42 138.03 8.29 15.94 to 2013 %). 2.11(2) (unit: 167.87 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 Grassland 4.2 31.31Land 0.75 60.55Watershed 3.2 From 1990 to 2013 Grassland Cultivated Construction Land Forestland Watershed 4.94 15.5 52 Cultivated land 2.22 51.15 1.56 1.13 43.73 26.41 1.34 Forestland 1.99 9.39 0.12 3.78 88.29 47.06 0.2 Construction land 2.62 45.15 1.35

3.3.

Grassland 4.2 Watershed 4.94 Construction land Changes 2.62and Impacts of Climate

31.31 15.5 Human45.15 Activities

on

0.75 1.13 3.78 Runoff

60.55 26.41 47.06

3.2 52 1.35

Departure percentage

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 The As runoff, temperature, curvesinare plotted their departure percentages Basin. shown in Figureand 10, precipitation the precipitation the river based basin on fluctuates more violently than totemperature. examine the relationships between changes and and the runoff in thealternating River Basin.positive As shown From 1981 to 2013, the bothclimate precipitation runoff exhibit and innegative Figure 10, the precipitation in the river basin fluctuates morecorrespondence violently than temperature. From 1981 departures, their overall change trends have good remarkable consistency, toand 2013,their bothdeparture precipitation and runoffbarely exhibit alternating positive the andchange negativetrends departures, their overall percentages change. By contrast, of temperature and change have correspondence. good correspondence consistency, and their departuredetermines percentages runofftrends have poor Thus, remarkable compared with temperature, precipitation the barely change. By contrast, the change trends of temperature and runoff have poor correspondence. runoff. Thus, compared temperature, the runoff.impacts of climatic factors, and The runoffwith in the river basinprecipitation is under the determines direct and significant in the basin is underThe thetrend directanalysis and significant of climatic factors, and it it isThe alsorunoff affected by river human activities. can onlyimpacts qualitatively explain the impacts is of also affected by human activities. The (changes trend analysis can only qualitatively explain impacts climatic factors and human activities in land-use/cover type) on the runoff.the Thus, in this ofsection, climaticthe factors and human activities (changes in land-use/cover type) onhuman the runoff. Thus, LR equation is used to estimate the impacts of precipitation and activities onin the this section, the LR equation is used to estimate the impacts of precipitation and human activities on runoff. the runoff. The interannual changes in the 33a precipitation and runoff in the river basin are compared in Figure The annual precipitation is precipitation positively associated with runoff, the runoff The 11. interannual changes in the 33a and runoff in the the annual river basin are and compared in Figure 11. The precipitation is positively associated the annual runoff, andathe runoff confirms that annual the precipitation is positively associated withwith the runoff, demonstrating correlation coefficient r =precipitation 0.885 (Figure is 12). confirms thatofthe positively associated with the runoff, demonstrating a correlation coefficient of r = 0.885 (Figure 12). 120% 100% 80% 60% 40% 20% 0% -20% -40% -60% -80%

Departure percentage of runoff Departure percentage of precipitation

1981 1985 1989 1993 1997 2001 2005 2009 2013 Year

(a) Figure 10. Cont.


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Departure percentage of temperature Departure percentage runoff Departure percentage ofof temperature

100% 100% 50%

Departure percentage Departurepercentage

Departure percentage

100%

Departure percentage of temperature Departure percentage of runoff Departure percentage of runoff

50% 50%

0%

0% 0%

-50%

-50% -50%

-100%

-100% -100%

-150%

-150% -150%1981 1981 1981

1986 1986 1986

1991 1991 1991

1996 2001 2001 2006 2006 20112011 1996 1996 2001 2006 2011 Year Year Year

(b) (b) (b) Figure 10. Departurecurves curvesof of (a) annual runoff and precipitation andand (b)and annual runoff and Figure 10. 10. Departure runoff and precipitation (b) annual runoff and Figure 10. Departure curves of(a) (a)annual annual runoff and precipitation (b) annual runoff Figure Departure curves of (a) annual runoff and precipitation and (b) annual runoff and temperature. and temperature. temperature. temperature.

runoff

15 15

precipitation precipitation

2500 precipitation 2500 2500 2000 2000

15

2000

10 10

1500 1500

10

1500

1000 1000

5 5

500 1000 500

5 0 0

0

3000 30003000

precipitation (mm) precipitation(mm)

runoff meter cubicmeter (billioncubic runoff(billion

runoff (billion cubic meter

runoff runoff

1981 1981

1986 1986

1991 1991

1996 1996

2001 2001

2006 2006

0 500 0 2011 Year 2011 Year 0

precipitation (mm)

20

2020

1981 1986 runoff 1991 and 1996 2001 changes 2006 2011 Year Figure 11. Interannual runoff and precipitation inin thethe river basin. Figure 11.11. Interannual precipitation changes river basin. Figure Interannual runoff and precipitation changes in the river basin.

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

Figure 12. Regression Regression analysison onthe theaverage average annual annual runoff with respect to precipitation. precipitation. Figure 12. 12. Regression analysis annualrunoff runoff with respect to precipitation. Figure analysis on the average with respect to

The points in the plotted precipitation–runoff precipitation–runoff DMC DMC are observed, observed, and the the curve iscurve divided into The The points in in the plotted DMC are observed, and theis isinto divided points the plotted precipitation–runoff are and curve divided the reference period and the change period by the cutoff point. Subsequently, the impacts of of into the period andthe the change period the cutoff point. Subsequently, the impacts the reference reference period and change byby the cutoff point. Subsequently, the impacts of Figure 12. Regression analysis onperiod the average annual runoff with respect to precipitation. precipitation and human activities on the runoff in the River Basin are analyzed. As shown in Figure precipitation and and human activities onon thethe runoff Asshown shown Figure 13, precipitation human activities runoffininthe theRiver RiverBasin Basin are are analyzed. analyzed. As inin Figure 13, the the precipitation–runoff precipitation–runoff DMC DMC initially initially deviated deviated from from its its trajectory trajectory in in 1986. 1986. Therefore, Therefore, the the 13, the precipitation–runoff DMC initially deviated from its trajectory in 1986. reference The points in the plotted precipitation–runoff DMC are observed, and Therefore, the curve isthe divided into reference period is determined to be the period from 1981 to 1985. Based on the annual precipitation period isto determined to befrom the period period from 1981Based to 1985. Based on the precipitation annual precipitation period is determined be the period 1981 to 1985. on the annual and runoff the reference reference period and the change by the cutoff point. Subsequently, the impacts of and runoff runoff from from 1981 1981 to to 1985, 1985, the the following following expression expression is is established established by by regression regression analysis analysis where where PP and from 1981 to 1985, the following expression is established by regression analysis where P is cumulative precipitation andsequences human activities on the runoff in the River Basin are analyzed. As shown in Figure is cumulative cumulative and R R is is cumulative cumulative runoff sequences: is sequences and runoff sequences: sequences and R is cumulative DMC runoff initially sequences: 13, the precipitation–runoff 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 R = 0.0066P − 1.229 (6)P and runoff from 1981 to 1985, the following expression is established by regression analysis where is cumulative sequences and R is cumulative runoff sequences:

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 m 3, 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 3 runoff2018, reduction Sustainability 10, 3405in the River Basin within the change period is 0.183 billion m , of which precipitation 16 of 21 accounts for 14.2% and human activities contribute 85.8%.

Figure 13.13. Precipitation-runoff accumulation curve. Figure Precipitation-runoff double double accumulation 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 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 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%.


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

1981–1985 1986–1989 1990–1999 2000–2009 2010–2013 1986–2013

Precipitation

Runoff Per Ten Thousand Tons

Impact of Precipitation

Impact of Human Activities

(mm)

Actual Value

Calculated Value

Total Reduction

Quantity Affected/ Hundred Million m3

Contribution Factor

Quantity Affected/ Hundred Million m3

Contribution Factor

1586.3 1336.1 1428.2 1487.8 1478.9 1443.6

10.29 7.14 8.31 8.64 7.44 8.46

7.59 8.68 9.25 8.13 8.72

3.15 1.98 1.65 2.85 1.83

0.45 0.37 0.61 0.69 0.26

14.3% 18.7% 37% 24.2% 14.2%

2.7 1.61 1.04 2.16 1.57

85.7% 81.3% 63% 75.8% 85.8%


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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


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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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