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Attribution analysis of different driving forces on vegetation and streamflow variation in the Jialing River Basin, China

  • Qingsong Li , Huishan Gao , Shan Chai , Zongyu He and Guangxing Ji EMAIL logo
Published/Copyright: December 14, 2022
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Open Geosciences
From the journal Open Geosciences Volume 14 Issue 1

Abstract

As an important tributary of the Yangtze River basin, the Jialing River Basin (JRB) has a variable topography and fragile ecological environment. Under the influence of climate warming and human activities, the runoff and vegetation cover of the area are undergoing significant changes. For evaluating the impact of different factors on vegetation and streamflow variation in the JRB, the univariate linear regression method was employed to analyze the variation trend of streamflow and vegetation, and a heuristic segmentation algorithm was applied for identifying the mutation year of streamflow and NDVI time-series data in the JRB. Then, the influence degree of different factors on streamflow variation in the JRB was calculated by the Budyko equation and elastic coefficient method. Finally, the contribution rates of different factors to vegetation variation in the JRB were calculated by the multiple linear regression method. The results indicated that the mutation year of streamflow and NDVI time series data were 1985 and 2006, respectively. The contribution rates of climate factors and human activities to streamflow change in the JRB were 42.7 and 57.3%, respectively. The contribution rates to vegetation change were 28.89 and 71.11%, respectively. In general, human activities are the main driving force leading to runoff and vegetation changes in the JRB. This study can provide a theoretical basis for the ecological environment protection and water resources management of the JRB.

Keywords: streamflow variation; vegetation variation; attribution analysis; Budyko equation; Jialing River Basin

1 Introduction

As the main body of the terrestrial ecosystem, vegetation is a participant in radiation balance and the water cycle process, which can effectively regulate the ecological environment [1]. As one of the important links of the water cycle, runoff has an important impact on landform formation, soil development, and plant growth. As the largest tributary of the Yangtze River Basin, Jialing River Basin (JRB) is an important water source in the upper reaches of the Yangtze River. The region plays an important role not only in the hydrological cycle and ecological balance of the Yangtze River Basin [2,3], but also in the social and economic development of the basin. In the past, under the comprehensive influence of human activities, the ecosystem of JRB has been damaged and soil erosion has been serious. The ecological environment has become very fragile. In recent years, under the joint influence of large-scale ecological restoration projects and climate change, the vegetation coverage and runoff of JRB have changed significantly [4,5]. For this reason, monitoring the change of vegetation coverage and runoff and analyzing the influencing factors of vegetation coverage and runoff change in JRB not only have practical significance for the prevention and control of soil and water loss in the JRB but also have a far-reaching impact on the sustainable development of the ecological environment in the Yangtze River Basin.

Some scholars have carried out a lot of research on streamflow and vegetation variation [6,7,8,9]. First, some scholars have just started to analyze the characteristics and trends of changes in runoff and vegetation; Tang [10] analyzed the trends of annual runoff of major rivers in the Tarim Basin by the rank correlation method in recent years, and the study showed that there were three types: rising, falling, and stable. Zhang et al. [11] used various statistical methods to find that the interannual variation of runoff in Miyun Reservoir throughout the year and during the flood period from 1919 to 1989 has the characteristics of continuity, periodicity, and partial depletion in most years. Huang [12] used wavelet transform to examine the variation pattern of annual runoff at Beibei station and found that the annual runoff was de-creasing, with a variation period of about 9.8 a. Mou et al. [13] analyzed the variation characteristics of annual runoff of the JRB (1954–2012) by Morlet wavelet, and the results demonstrated that the annual run-off of JRB has the 44-year scale cycle and the 14-year scale cycle in the study period. Pao and Fang [14] analyzed the dynamic changes in vegetation cover in China from 1982 to 1999 based on remote sensing and GIS technology using NOAA-AVHRR data. The results showed that the areas with decreasing NDVI were mainly distributed in the northwestern region and the Qinghai-Tibet Plateau, and the areas with increasing NDVI mainly occurred in the eastern region; the decreasing trend of NDVI in the northwestern arid region was obvious. The areas with the most obvious decreasing trend of vegetation cover were in the Pearl River Delta and Yangtze River Delta regions in China. Yang et al. [15] compared the vegetation changes of the grassland steppe in 1984 and 2001 in Duolun County, the results showed that the proportion of potentially desertified land was larger in 2001.

Then, scholars began to explore the factors affecting runoff and vegetation change. Li and Li [16] used the time series comparison method to find that the intra-annual distribution of runoff in the upper Chaobai River was found to be extremely unbalanced with large inter-annual variation; the annual runoff showed a significant decrease in general, and they concluded that precipitation and temperature changes were not the main influencing factors for the significant decrease in the annual runoff, but mainly due to the influence of human activities such as water use, land use, reservoir construction, and inter-basin water transfer. Fan et al. [17] analyze the variation characteristics of annual runoff in the JRB using the non-parametric Mann–Kendall trend test algorithm, and the results demonstrated that there was a significant decreasing trend in the annual runoff, and both climate change and human activities have an impact on runoff. Li et al. [18] made use of Spearman correlation analysis and Mann–Kendall trend test algorithm to analyze the runoff variation characteristics of the Beibei hydrological station from 1954 to 2017. It was found that the runoff in the JRB showed a slight downward trend, and the main driving factor affecting runoff reduction is precipitation. Liu et al. [19] analyzed the temporal and spatial variation characteristics of vegetation cover in the Jialing River from 1982 to 2006. The results illustrated that vegetation cover was in a slight upward trend, and it developed in a favorable direction driven by climate change and human activities. Wang et al. [20] discussed the seasonal variation characteristics of NDVI and its relationship with meteorological factors in the JRB from 1982 to 2006. Zhang et al. [21] analyzed the vegetation change in JRB from 2000 to 2019, and the results illustrated that the fluctuation of vegetation cover change in the northern part of the basin was greater than that in the southern region, and human activities had two-sided effects on vegetation cover change. The results illustrated that the temperature in the basin was the main factor affecting vegetation growth. In recent years, some scholars have started to study how to quantify the contribution of each driver to runoff and vegetation; however, fewer studies have been conducted on the JRB.

Taken together, these scholars have mostly described qualitatively the effects of each driver on runoff and vegetation. There is an urgent need to quantify the contribution of each driver of runoff and vegetation in the JRB. This article uses hydrometeorological and NDVI data to quantify the contribution of different factors to streamflow variation and vegetation variation in the JRB through the following two steps: (1) identification of abrupt years of runoff and NDVI using B–G segmentation algorithm. (2) The Budyko hypothesis and linear regression were used to quantify the contribution of different factors to streamflow variation and vegetation variation. This study is helpful to understand the erosion situation in JBR and provides technical support for ecological protection and water resources management in the JRB area.

2 Data and methods

2.1 Study area

The JRB is located between 102° 36′E-109° 02′ E and 29° 18′ N-34° 29′ N. It covers an area of 159,812 km2 and is shown in (Figure 1). It originates from the north foot mountains of the Qinling Mountains in China. With a total length of 1,120 km, it is the tributary with the largest area and sediment transport capacity of the Yangtze River Basin [22]. The terrain of the JRB is complex and diverse. As a whole, the terrain changes in a “ladder” shape from northwest to southeast. The valleys from the upstream to the middle reaches are narrow, and the valleys from the middle reaches to the downstream are wide, with a fan-shaped distribution. The drop across the basin is more than 5,000 m high [23], the basin contains rich hydropower resources and is an important hydropower utilization basin in China. Most of the region has a subtropical monsoon climate with an average precipitation of 912.8 mm, with precipitation mainly concentrated in May–October. From the perspective of spatial and temporal distribution, the upper reaches of the winter are cold and snowy, and the precipitation is relatively small. The middle and lower reaches of the winter are soft and foggy, and the precipitation is more than upstream. Due to the extremely uneven distribution of precipitation, droughts and floods occur frequently in the JRB.

Figure 1 Location of the study area.
Figure 1

Location of the study area.

2.2 Data

The streamflow data of Beibei station from 1961 to 2016 are obtained from the Chinese Hydrological Yearbook.

The meteorological station's data in the JRB from 1961 to 2016 are obtained from China Meteorological Administration (http://data.cma.cn/). We first check the data integrity of these meteorological stations. The missing part of the data is obtained by linear interpolation of the data of the adjacent two years [24]. Then, the potential evaporation of each station can be calculated by the Penman–Monteith formula. Finally, the Kriging interpolation method in ArcGIS software is applied to interpolate precipitation and potential evapotranspiration data.

NDVI data during 1982–2015 are downloaded from the National Aeronautics and Space Administration (https://ecocast.arc.nasa.gov/data/pub/gimms/). Its spatial resolution is 8 km × 8 km, and its temporal resolution is 15 days. The preprocessing of NDVI data includes format and coordinate conversion, data cropping, and NDVI truth calculation [25]. NDVI truth value is obtained by multiplying the initial pixel value of NDVI data by 0.0001 [26], and the range is between −1 and 1.

3 Research methods

3.1 Mutation analysis methods

Heuristic segmentation algorithm was proposed in 2001 in the study of electrocardiogram sequence data [27] and is also known as Bernaola–Galavan (B–G) segmentation algorithm. Compared with traditional methods, this method can effectively detect the mutation characteristics of nonlinear and non-stationary time series data. Based on the T-test, the non-stationary sequence data are divided into multiple stationary subsequences. Each stationary subsequence has different mean values and represents different physical backgrounds [28,29].

For a nonlinear time series data x(t) (t = 1, 2, …, N), the calculation process of the B–G segmentation algorithm is as follows:

  1. Calculate the average value of the two segments to the left and right of the i point, denoted as U L and U R, respectively (i = 2,3,…, N − 1).

  2. The T-test is used to measure the significance of the difference between U L and U R and is recorded as T(i).

    (1) T ( i ) = ( U L − U R ) / S D ,

    (2) S D = [ ( S L 2 + S R 2 ) / ( N L + N R − 2 ) ] 1 / 2 ( 1 / N L + 1 / N R ) 1 / 2 .

    In the formulas, S D is the joint variance, S L and S R, N L and N R are the standard deviation and sample number of the left and right segments of the segmentation point, respectively.

  3. Calculating the significance probability P(T m) corresponding to the maximum value (T m) of T-test statistics by the Monte Carlo simulation [18].

    (3) P ( T m ) = P r o b ( T ≤ T m ) ,

    (4) P ( T m ) ≈ [ ( 1 − I v / ( V + T m 2 ) ( δ v , δ ) ) ] γ ,

    γ = 4.19 In N − 11.54, δ = 0.40, N is the sample of the time series x(t), v = N − 2, I x (a,b) is the incomplete β function. P 0 is a threshold set in this study, and its value range is [0.5–0.95]. If P(T max) ≥ P 0, x(t) is divided into two sequences; otherwise, it is not divided.

  4. Repeating steps (1)–(3), respectively, for the two newly sequences to detect all mutation points. In addition, in order to ensure the effectiveness of statistics, if the length of the subsequence is less than or equal to l 0, the subsequence will not be segmented.

In this study, to ensure the accuracy of the B–G segmentation algorithm, the results of the M–K mutation test are used for verification. M–K mutation test is a non-parametric test method to test the mutation characteristics of samples and is a commonly used mutation detection method due to its advantages of no interference from a few outliers and simple calculation [30].

3.1.1 Budyko hypothesis

Budyko equation represents the nonlinear functional coupling relationship between water and heat. Combined with the elastic coefficient method, it is often applied to estimate the impact of climate change and human activities on runoff. The sensitivity coefficients of runoff to climate factors and the underlying surface can be calculated by partial derivatives [31,32,33,34,35]. The expression of the Fu-type Budyko equation is [36]:

(5) F ( φ ) = 1 + φ − ( 1 + φ ω ) 1 / ω ,

where φ is the drying index (ET 0/P). The impact of climate change on runoff depth variation can be mastered by analyzing the changes of P and ET 0. ω is an integral constant with the obvious indigenous region, representing the underlying surface characteristics of the basin [37,38].

(6) ε P = 1 + φ F ′ ( φ ) 1 − F ( φ ) ,

(7) ε E T 0 = − 1 + φ F ′ ( φ ) 1 − F ( φ ) ,

(8) ε ω = − 1 + ω F ′ ( ω ) 1 − F ( φ ) ,

(9) F ′ ( φ ) = 1 − φ ω − 1 ( 1 + φ ω ) 1 / ω − 1 ,

(10) F ′ ( ω ) = ( 1 + φ ω ) 1 / ω ln ( 1 + φ ω ) ω 2 − φ ω ( 1 + φ ω ) 1 / ω − 1 l n ( φ ) ω ,

where ε P, ε E T 0 , and ε ω are the elastic coefficients of streamflow to precipitation, potential evapotranspiration, and underlying surface parameters, respectively.

Based on the mutation analysis result of runoff time series data, the multi-year average precipitation, potential evaporation, underlying surface characteristic parameter in the reference period (T 1), and the disturbance period (T 2) are recorded by P 1 and P 2, ET 01 and ET 02, ω 1 and ω 2. Then, the ΔP, ΔET 0, and Δω change values can be obtained from T 1 to T 2.

(11) Δ P = P 2 − P 1 ,

(12) Δ E T 0 = E T 02 − E T 01 ,

(13) Δ ω = ω 2 − ω 1 ,

(14) Δ R P = ε P R P Δ P ,

(15) Δ R E T 0 = ε E T 0 R E T 0 Δ E T 0 ,

(16) Δ R ω = ε ω R ω Δ ω ,

(17) Δ R = Δ R P + Δ R E T 0 + Δ R ω ,

where ΔR P, Δ R E T 0 , and ΔR ω represent the changes in runoff from the base period to the change period due to changes in mean annual precipitation, potential evapotranspiration, and subsurface characteristics parameters, respectively.

(18) η R P = Δ R P / Δ R × 1 00 %,

(19) η R E T 0 = Δ R E T 0 / Δ R × 1 00 %,

(20) η R hum = Δ R ω / Δ R × 1 00 %,

η R P , η R E T 0 , and η R hum respectively represent the contribution rates of precipitation, potential evaporation, and anthropic factors on streamflow.

3.1.2 Attribution analysis of vegetation change

According to the result of NDVI mutation analysis, the difference between the average NDVI in the reference period (T 1) and the disturbance period (T 2) can be calculated ( Δ NDVI ¯ ), while Δ NDVI ¯ can be attributed to the influence of natural factors ( Δ NDVI ¯ C ) and human actions ( Δ NDVI ¯ hum ).

(21) Δ NDVI ¯ = NDVI ¯ T 2 − NDVI ¯ T 1 ,

(22) Δ NDVI ¯ = Δ NDVI ¯ C + Δ NDVI ¯ hum .

The relationship between NDVI and precipitation and potential evapotranspiration in the reference period (T 1) is calculated [39,40]. Then, the NDVI value under the influence of climate factors in the disturbance period (T 2) can be calculated. NDVI values for the T 2 period are only under the influence of climatic factors ( NDVI T 2 , s ) Finally, the NDVI variation caused by human action ( Δ NDVI ¯ hum ) and climatic factors ( Δ NDVI ¯ C ) can be obtained.

(23) NDVI T 1 = a ∗ P T 1 + b ∗ E T 0 T 1 + c ,

(24) NDVI T 2 , s = a ∗ P T 2 + b ∗ E T 0 T 2 + c ,

(25) Δ NDVI ¯ hum = NDVI ¯ T 2 − NDVI ¯ T 2 , s ,

(26) Δ NDVI ¯ C = NDVI ¯ T 2 , s − NDVI ¯ T 1 ,

(27) η NDVI hum = Δ NDVI ¯ hum / Δ NDVI ¯ ,

(28) η NDVI ¯ C = Δ NDVI ¯ C / Δ NDVI ¯ ,

η NDVI hum and η NDVI C respectively represent the contributions of human action to climate variation.

4 Results analysis

4.1 Trends analysis of runoff depth, NDVI, and climatic factors

The trends of runoff depth and NDVI in JRB were analyzed by univariate linear regression method. Figure 2a shows the overall fluctuating downward trend of runoff depth from 1961 to 2016, which is generally consistent with the results of existing studies [41,42]. It decreased by an average of 2.0383 mm per year. Its maximum and minimum values were 683.02 and 199.79 mm, which appeared in 1983 and 1997, respectively. The variation trend of NDVI in the JRB from 1982 to 2015 is shown in Figure 2b. During the study period, the maximum and minimum values of NDVI in the JRB appeared in 2015 and 1984, which were 0.64 and 0.54, respectively. NDVI in the JRB showed a fluctuating upward trend from 1982 to 2015, with an average annual growth trend of 0.0016/annum, which is also basically the same as the studies of Liu et al. [19] and Hu et al. [43].

Figure 2 Interannual variation trend of runoff depth (a) and NDVI (b) in JRB.
Figure 2

Interannual variation trend of runoff depth (a) and NDVI (b) in JRB.

Figure 3 shows the variation trend of annual precipitation and annual potential evapotranspiration in the JRB. It can be found from Figure 3a, the annual average precipitation in the JRB from 1961 to 2016 reduced slowly with an average annual decrease of 0.7934 mm. The maximum precipitation reached 1132.22 mm in 1981 and minimum precipitation reached 625.14 mm in 1997. It can be found from Figure 3b, the annual average potential evapotranspiration in the JRB was fluctuating with increasing-decreasing-increasing, with the slope of 0.3856 mm/annum. The maximum and minimum potential evapotranspiration appeared in 2013 and 1989, and the potential evapotranspiration was 960.22 and 783.43 mm, respectively. A comprehensive analysis of the hydrometeorological characteristics of the JRB shows that precipitation has been declining, runoff depth has been declining significantly, and potential evapotranspiration has been increasing over the past half-century, which shows that climate change is one of the main factors affecting runoff. Although potential evapotranspiration is on an upward trend and precipitation and runoff are on a downward trend, NDVI still maintains an increasing trend. This may imply that human activities are the main driving factor for vegetation change in the watershed. However, the changing dynamics of runoff and vegetation require further attribution analysis studies.

Figure 3 Interannual variation trend of (a) precipitation and (b) potential evapotranspiration in JRB.
Figure 3

Interannual variation trend of (a) precipitation and (b) potential evapotranspiration in JRB.

4.2 Mutation analysis of runoff depth and NDVI

According to B–G segmentation algorithm Equations (1–3), all mutation points of runoff depth were detected as shown in (Figure 4). It is worth noting that P 0 is a threshold value set in this study, and its value range is [0.5–0.95]. In this study, the value of P 0 is 0.62, and in order to ensure the validity of the statistics, this study defines the value of subseries as not less than 25, i.e. l 0 ≥ 25. If the length of the subsequence is too short and the amount of data is too small, there is too much error in testing the mutation points.The result of the B–G segmentation algorithm reveals the mutation year of the annual runoff time series data is around 1985 at Beibei Hydrological Station. According to Formula (4), the probability at the maximum value of T-test statistics (P Tm) was calculated. If P Tm > P 0, the mutation is considered to be significant. The calculation results show that the probability of aboriginality at the maximum value of T-test statistics (1985) is 0.62667 > 0.62, which proved the reliability of the result that NDVI time-series data mutated in 1985.

Figure 4 Result of B–G segmentation algorithm for runoff depth in JRB during 1961–2016.
Figure 4

Result of B–G segmentation algorithm for runoff depth in JRB during 1961–2016.

The B–G segmentation algorithm was used to detect the mutation year of NDVI time-series data (Figure 5). In this article, the parameter P 0 of NDVI in the B–G segmentation algorithm is set to 0.80, and l 0 is set to 25. The result of B–G segmentation algorithm reveals that the mutation year of NDVI time-series data is around 2006 (Figure 5), and the maximum value of the T-test statistics is about 5.31. The probability of aboriginality at the maximum value of T-test statistics (2006) is 0.80373 > 0.80, which proved the reliability of the result that NDVI time-series data mutated in 2006.

Figure 5 B–G segmentation algorithm result of NDVI data in JRB from 1982 to 2015.
Figure 5

B–G segmentation algorithm result of NDVI data in JRB from 1982 to 2015.

In this article, the B–G segmentation algorithm is used to test for runoff and NDVI mutation points and the Mann–Kendall nonparametric test is not used, mainly because the Mann–Kendall nonparametric test is not disturbed by the sample sum and distribution type though, However, it is highly likely that there will be multiple mutation points during the testing process, and these mutation points need to be tested，and in this article using the B–G partitioning method in the test process only appears to appear a mutation point, can better identify the mutation point, is conducive to determine the mutation point year, so choose the B–G partitioning method for testing, but in order to test the accuracy of the results, can use Mann–Kendall nonparametric test for mutual corroboration.

The JRB runoff depth mutation points from 1961-2016 were examined by Mann–Kendall mutation test, and from Figure 6, it can be found that several mutation points appear, and these mutation points were verified, and one of them was not significant and appeared around 2010, and there is a mutation point that is similar to the mutation point appearing in the runoff depth in the B–G segmentation method, which in turn can corroborate the accuracy of the mutation year in the B–G segmentation; The results of the NDVI Mann–Kendall mutation test were calculated for the years 1982–2015 and in (Figure 7) show that the NDVI mutation point was found to occur in 2005. It did not fall within the 0.05 level threshold, In the Mann–Kendall mutation test it is not considered a mutation point, it can indicate a trend, However, using the B–G partitioning method in this study, the NDVI mutation point was significant and appeared in 2006.

Figure 6 Mann–Kendall mutation test result of runoff depth in the JRB from 1961 to 2016.
Figure 6

Mann–Kendall mutation test result of runoff depth in the JRB from 1961 to 2016.

Figure 7 Mann–Kendall mutation test result of NDVI in the JRB 1982 to 2015.
Figure 7

Mann–Kendall mutation test result of NDVI in the JRB 1982 to 2015.

4.3 Quantitative evaluation of different factors on streamflow and vegetation variation

According to the B–G segmentation algorithm results of runoff time series data in the Beibei hydrological station. The study period was divided into the reference period (1961–1985) and the disturbance period (1986–2015), and the characteristic values of meteorological and hydrological variables of JRB in different periods were obtained Table 1. Compared with the reference period, the potential evapotranspiration, underlying surface parameters and aridity index in the disturbance period increased by 0.33 mm, 0.17 and 0.39, respectively. The runoff depth, precipitation, and runoff coefficient decreased by 80.41 mm, 46.74 mm, and 0.07, respectively.

Table 1

Characteristic values of meteorological and hydrological variables in JRB

Hydrological station Period ET 0 (mm) R (mm) P (mm) ω R/P ET 0 /P
Beibei 1961–1985 870.05 461.52 905.93 1.73 0.51 1.89
1986–2015 870.38 381.11 859.19 1.90 0.44 2.28
Δ 0.33 −80.41 −46.74 0.17 −0.07 0.39

The elastic coefficients of precipitation, potential evapotranspiration, and underlying surface parameters on runoff in different periods of JRB were calculated and shown in Table 2. The elastic coefficients of precipitation, potential evapotranspiration and underlying surface parameters on runoff depth were 1.58, −0.58 and −1.21, respectively, indicating that 1% increase of the precipitation result in 1.58% increase of the runoff depth; 1% increase of potential evapotranspiration result in 0.58% decrease of runoff depth; 1% increase of underlying surface parameters result in 1.21% decrease of runoff depth. Compared to the reference period, the runoff depth change value caused by precipitation, potential evapotranspiration and underlying surface conditions were −35.08, −0.09 and −47.20 mm, respectively. The contributions of precipitation, potential evapotranspiration, and human activities to streamflow change in the JRB were 42.59, 0.11, and 57.3%, respectively.

Table 2

Attribution analysis of streamflow change in JRB

Hydrological station ε P ε E T 0 ε ω ΔR P Δ R E T 0 Δ R ω η R P η R E T 0 η R H
Beibei 1.58 −0.58 −1.21 −35.08 −0.09 −47.20 42.59% 0.11% 57.30%

According to the mutation analysis results of NDVI data in JRB, 1982–2015 was separated to T 1 (1982–2006) and T 2 (2007–2015). Many scholars have shown that there is a close correlation between NDVI and climatic factors. This article uses the multiple linear regression method to quantify the impact of different factors on vegetation change and is shown in Table 3. The contribution rates of climate and human activities to vegetation change were 28.89 and 71.11%, respectively. The result showed that human activities were the main factor resulting in the rapid growth of vegetation in the JRB.

Table 3

Attribution analysis of vegetation change in JRB

Fitting equation Δ NDVI ¯ NDVI ¯ T 1 NDVI ¯ T 2 , S NDVI ¯ T 2 η NDVI ¯ C (%) η NDVI ¯ H (%)
NDVI = 8.8926 × 10−4 P + 1.8840 × 10−3 ET 0 + 0.3841 (R 2 = 0.83) 0.0318 0.5820 0.5912 0.6138 28.89 71.11

In this article, the main driver affecting the changes in runoff and NDVI is human activity, which is mainly due to the Chinese government’s major ecological restoration projects such as the construction of the Yangtze River and other key protective forest systems, the protection of natural forest resources, and the return of farmland to forests [44], and is also largely consistent with the time period of the Chinese government’s work on soil and water conservation in the JRB [45].

5 Discussions and conclusions

5.1 Discussions

As an important tributary of the Yangtze River, the JRB is an important protective barrier to China’s ecological environment. The increasing trend of drought in JRB in recent years is obvious [46], and it has a certain impact on the runoff of the JRB. Saft et al. [47] showed that prolonged drought can lead to significant changes in the rainfall-runoff relationship, and such changes will lead to large uncertainties in the attribution analysis of runoff changes. Applying the non-parametric Bootstrap resampling method, Guo [48] found that the uncertainty of the Budyko parameter changed with the increase of the basin drought index, showing a rapid increase, and then slowly decreased.

The implementation of a series of soil and water conservation measures in the JRB (especially the reforestation and grass restoration project in 1999) has significantly changed the rainfall–runoff relationship. The underlying surface parameters (ω) in the Budyko equation reflect the combined effect of soil properties, topographic factors, and vegetation cover. Soil properties and topography are relatively stable parameters, while vegetation factors become the main factors affecting ω. Both NDVI and ω in JRB showed a significant growth trend (p < 0.01), with a strong synergistic change trend (Figures 2b and 8), which indicates that vegetation restoration has an important effect on ω. In addition, most studies directly attribute the change of vegetation cover to the water and soil conservation measures, ignoring the impact of climate change on the vegetation in the basin. Subsequently, we will try to analyze the contribution of vegetation restoration caused by human activities and vegetation restoration caused by climate change to runoff change, respectively.

Figure 8 Interannual variation trend of underlying surface parameters (ω) in JRB.
Figure 8

Interannual variation trend of underlying surface parameters (ω) in JRB.

In addition, this research exists some indeterminacy in the runoff and vegetation change attribution study. (1) Meteorological observations (especially precipitation) are often affected by instrument and observation field conditions [49] (2) The Penman–Monteith formula does not consider the impact of elevated CO2 concentration on the physiological characteristics of vegetation (such as stomatal conductance and vegetation structure characteristics), which causes some deviations of potential evapotranspiration estimation [50]. (3) This study is based on the assumption that climate change and human activities are relatively independent, and ignores the interaction and feedback between various factors, such as how human activities affect climate through changes in production and lifestyle and land use [51], how does climate change affect vegetation growth, and previous studies often attribute all vegetation growth factors to the underlying surface changes, which are considered to be the embodiment of the indirect effects of human activities [52].

5.2 Conclusions

This article uses the hydrological and meteorological data of Beibei Hydrological Station from 1961 to 2016 and NDVI data from 1982 to 2015. Firstly, the trend analysis method was used to analyze the variation trend of runoff depth and vegetation in the JRB, and then the B–G segmentation algorithm was used to identify the mutation years of runoff depth and NDVI time series data. Then, the contribution of different driving forces on runoff variation in the JRB was calculated by the Budyko equation and elastic coefficient method. Finally, the multiple linear regression method was used to calculate the contribution of different driving forces on vegetation change in the JRB. The results showed that (1) the runoff depth and precipitation of the JRB showed a decreasing trend, while NDVI and potential evapotranspiration showed an increasing trend. (2) The mutation year of runoff depth and NDVI time series data were 1985 and 2006, respectively. (3) The contributions of precipitation, potential evapotranspiration, and human activities to streamflow change in the JRB were 42.59, 0.11, and 57.3%, respectively. (4) Climate factors and human activities contributed 28.89 and 71.11% to the vegetation change in the Jialing River, respectively. (5) The contribution of human activities to streamflow reduction and NDVI increase is far greater than that of climate change. Therefore, human activities are the primary factor for streamflow changes and vegetation changes in JRB, and climate change is a secondary factor.

  1. Funding information: This research was funded by the National Key R&D Program of China (2021YFD1700900) and the special fund for top talents in Henan Agricultural University (30501031).

  2. Author contributions: Q. L. and G. J.: conceptualization; Q. L., H. G. and S. C.: methodology; H. G., Z. H., and G. J.: software; H. G. and S. C. and G. J.: validation; Q. L. and H. G.: formal analysis; Q. L. and G. J.: data curation; Q. L. and H. G.: writing – original draft preparation; Q. L. and G. J.: writing – review and editing; Q. L. and G. J.: funding acquisition.

  3. Conflict of interest: We declare no conflict of interest.

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Received: 2022-03-17
Revised: 2022-11-13
Accepted: 2022-11-22
Published Online: 2022-12-14

© 2022 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/geo-2022-0443
Keywords for this article
streamflow variation; vegetation variation; attribution analysis; Budyko equation; Jialing River Basin
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Study on observation system of seismic forward prospecting in tunnel: A case on tailrace tunnel of Wudongde hydropower station
  3. The behaviour of stress variation in sandy soil
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  60. Soil wind erosion resistance analysis for soft rock and sand compound soil: A case study for the Mu Us Sandy Land, China
  61. Investigation into the pore structures and CH4 adsorption capacities of clay minerals in coal reservoirs in the Yangquan Mining District, North China
  62. Overview of eco-environmental impact of Xiaolangdi Water Conservancy Hub on the Yellow River
  63. Response of extreme precipitation to climatic warming in the Weihe river basin, China and its mechanism
  64. Analysis of land use change on urban landscape patterns in Northwest China: A case study of Xi’an city
  65. Optimization of interpolation parameters based on statistical experiment
  66. Late Cretaceous adakitic intrusive rocks in the Laimailang area, Gangdese batholith: Implications for the Neo-Tethyan Ocean subduction
  67. Tectonic evolution of the Eocene–Oligocene Lushi Basin in the eastern Qinling belt, Central China: Insights from paleomagnetic constraints
  68. Geographic and cartographic inconsistency factors among different cropland classification datasets: A field validation case in Cambodia
  69. Distribution of large- and medium-scale loess landslides induced by the Haiyuan Earthquake in 1920 based on field investigation and interpretation of satellite images
  70. Numerical simulation of impact and entrainment behaviors of debris flow by using SPH–DEM–FEM coupling method
  71. Study on the evaluation method and application of logging irreducible water saturation in tight sandstone reservoirs
  72. Geochemical characteristics and genesis of natural gas in the Upper Triassic Xujiahe Formation in the Sichuan Basin
  73. Wehrlite xenoliths and petrogenetic implications, Hosséré Do Guessa volcano, Adamawa plateau, Cameroon
  74. Changes in landscape pattern and ecological service value as land use evolves in the Manas River Basin
  75. Spatial structure-preserving and conflict-avoiding methods for point settlement selection
  76. Fission characteristics of heavy metal intrusion into rocks based on hydrolysis
  77. Sequence stratigraphic filling model of the Cretaceous in the western Tabei Uplift, Tarim Basin, NW China
  78. Fractal analysis of structural characteristics and prospecting of the Luanchuan polymetallic mining district, China
  79. Spatial and temporal variations of vegetation coverage and their driving factors following gully control and land consolidation in Loess Plateau, China
  80. Assessing the tourist potential of cultural–historical spatial units of Serbia using comparative application of AHP and mathematical method
  81. Urban black and odorous water body mapping from Gaofen-2 images
  82. Geochronology and geochemistry of Early Cretaceous granitic plutons in northern Great Xing’an Range, NE China, and implications for geodynamic setting
  83. Spatial planning concept for flood prevention in the Kedurus River watershed
  84. Geophysical exploration and geological appraisal of the Siah Diq porphyry Cu–Au prospect: A recent discovery in the Chagai volcano magmatic arc, SW Pakistan
  85. Possibility of using the DInSAR method in the development of vertical crustal movements with Sentinel-1 data
  86. Using modified inverse distance weight and principal component analysis for spatial interpolation of foundation settlement based on geodetic observations
  87. Geochemical properties and heavy metal contents of carbonaceous rocks in the Pliocene siliciclastic rock sequence from southeastern Denizli-Turkey
  88. Study on water regime assessment and prediction of stream flow based on an improved RVA
  89. A new method to explore the abnormal space of urban hidden dangers under epidemic outbreak and its prevention and control: A case study of Jinan City
  90. Milankovitch cycles and the astronomical time scale of the Zhujiang Formation in the Baiyun Sag, Pearl River Mouth Basin, China
  91. Shear strength and meso-pore characteristic of saturated compacted loess
  92. Key point extraction method for spatial objects in high-resolution remote sensing images based on multi-hot cross-entropy loss
  93. Identifying driving factors of the runoff coefficient based on the geographic detector model in the upper reaches of Huaihe River Basin
  94. Study on rainfall early warning model for Xiangmi Lake slope based on unsaturated soil mechanics
  95. Extraction of mineralized indicator minerals using ensemble learning model optimized by SSA based on hyperspectral image
  96. Lithofacies discrimination using seismic anisotropic attributes from logging data in Muglad Basin, South Sudan
  97. Three-dimensional modeling of loose layers based on stratum development law
  98. Occurrence, sources, and potential risk of polycyclic aromatic hydrocarbons in southern Xinjiang, China
  99. Attribution analysis of different driving forces on vegetation and streamflow variation in the Jialing River Basin, China
  100. Slope characteristics of urban construction land and its correlation with ground slope in China
  101. Limitations of the Yang’s breaking wave force formula and its improvement under a wider range of breaker conditions
  102. The spatial-temporal pattern evolution and influencing factors of county-scale tourism efficiency in Xinjiang, China
  103. Evaluation and analysis of observed soil temperature data over Northwest China
  104. Agriculture and aquaculture land-use change prediction in five central coastal provinces of Vietnam using ANN, SVR, and SARIMA models
  105. Leaf color attributes of urban colored-leaf plants
  106. Application of statistical and machine learning techniques for landslide susceptibility mapping in the Himalayan road corridors
  107. Sediment provenance in the Northern South China Sea since the Late Miocene
  108. Drones applications for smart cities: Monitoring palm trees and street lights
  109. Double rupture event in the Tianshan Mountains: A case study of the 2021 Mw 5.3 Baicheng earthquake, NW China
  110. Review Article
  111. Mobile phone indoor scene features recognition localization method based on semantic constraint of building map location anchor
  112. Technical Note
  113. Experimental analysis on creep mechanics of unsaturated soil based on empirical model
  114. Rapid Communications
  115. A protocol for canopy cover monitoring on forest restoration projects using low-cost drones
  116. Landscape tree species recognition using RedEdge-MX: Suitability analysis of two different texture extraction forms under MLC and RF supervision
  117. Special Issue: Geoethics 2022 - Part I
  118. Geomorphological and hydrological heritage of Mt. Stara Planina in SE Serbia: From river protection initiative to potential geotouristic destination
  119. Geotourism and geoethics as support for rural development in the Knjaževac municipality, Serbia
  120. Modeling spa destination choice for leveraging hydrogeothermal potentials in Serbia
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