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Study area
The Source Region of Yangtze River (SRYR for short, Latitude: 32° 25′ E and 35° 53′ E; Longitude: 89° 43′ E–97° 19′ E), located in the western Tibetan plateau, covers an area of 141,398 km2 (Fig. 10a). The elevation ranges from 6456 m in the West to 3512 m in the East, with an average of 4779 m. The SRYR belongs to transition zone from semi-arid to semi-humid alpine area. The annual temperature is − 2 to − 3 °C. Monthly mean temperature in the coldest month is − 13.0 °C and that in the warmest month is 9.7 °C. The annual temperature of the study area is 265 mm. The temperature decreases from southeast to northwest37. The aridity index is 3.67 in the SRYR, which means the climate is very dry. The vegetation types are mainly meadow (84,985 km2) and grassland (33,743 km2), which are 60.1% and 23.9% (Fig. 10b) of the study area respectively. We divided the SRYR into five sub-regions, including Tuotuo River Basin (I), Dam River Basin (II), Qumar River Basin (III), Middle Stream Region (IV) and Downstream Region (V).
Figure 10

The location of Source Region of Yangtze River (a) and vegetation types (b). Map was generated using ArcGIS 10.3 (http://www.esri.com/software/arcgis/arcgis-for-desktop).

Full size image

Datasets
The monthly NDVI data for SRYR was obtained from Resource and Environment Data Cloud Platform (RESDC, http://www.resdc.cn/). It was produced with Maximum Value Composite (MVC) approach based on the SPOT/VEGETATION NDVI data. The effects of cloud cover and non-vegetation were reduced. This dataset was at a spatial resolution of 1 km, covering the period 2000 to 2014.
The gridded meteorological data used are obtained from China Ground Precipitation 0.5° × 0.5° Grid Dataset V2.0 and China Ground Temperature 0.5° × 0.5° Grid Dataset V2.0. These datasets are provided by National Meteorological Information Center (NMIC, http://data.cma.cn/). A total of 102 grids in the SRYR and the surroundings during 2000–2014 are selected. The gridded data has been projected and resampled in order to ensure the same coordinate system and resolution with NDVI data. The NMIC also provides meteorological data of 9 meteorological stations within and around the study area, including parameters such as solar radiation, surface water, pressure, sunshine hours, wind speed and relative humidity. Grid data of the study area was interpolated by ANUSPLINE.
NPP simulation
In this study, the NPP were simulated by CASA (Carnegie–Ames–Stanford Approach) model. The CASA model is based on the plant growing mechanism38,39,40 which can be summarized by Eq. (1).

$$ NPPleft( {x,t} right) = APARleft( {x,t} right) times varepsilon left( {x,t} right) $$
(1)

where x and t are spatial location and time respectively, NPP is simulated value (gC m−2). APAR and ε represent absorbed photosynthetically active radiation and light use efficiency, which can be obtained by Eqs. (2) and (3).

$$ APARleft( {x,t} right) = fPARleft( {x,t} right) times SOLleft( {x,t} right) times R $$
(2)

$$ varepsilon left( {x,t} right) = Tleft( {x,t} right) times Wleft( {x,t} right) times varepsilon_{max } $$
(3)

where fPAR is the fraction of absorbed photosynthetically active radiation, SOL is the total solar radiation (MJ/m2), R is the fraction of solar active radiation that can be used by vegetation. T and W are temperature stress index and moisture stress factor, respectively. εmax is maximum light utilization efficiency. Further details of the above equations can be obtained from previous studies38,39,40.
The NPP calculated by CASA model can be considered as the actual NPP which is influenced by both climate change and human activities. It can be expressed as Eq. (4).

$$ NPP = PNPP – HNPP $$
(4)

where PNPP and HNPP represent potential NPP and human-induced NPP, respectively. PNPP is only determined by climate conditions and without interference from human activities. It can be calculated by Thornthwaite Memorial model41, using the follows formulas:

$$ PNPP = 3000left[ {1 – e^{{ – 0.0009695left( {v – 20} right)}} } right] $$
(5)

$$ v = frac{1.05N}{{sqrt {1 + left( {1.05{N mathord{left/ {vphantom {N L}} right. kern-nulldelimiterspace} L}} right)^{2} } }} $$
(6)

$$ L = 300 + 25t + 0.05t^{3} $$
(7)

where t, L, N and v are average annual temperature (°C), annual maximum evapotranspiration (mm), annual total precipitation (mm) and average annual actual evapotranspiration (mm).
According to Eq. (4), the HNPP can be represented by the difference between PNPP and NPP.
Statistical analysis
To identify the inter-annual trends of temperature (Tem.), precipitation (Pre.) and NPP, the linear regression method was adopted to eliminate the increase or decrease rate42, which can be calculated as follows:

$$ theta_{Slope} = frac{{n times sumnolimits_{i = 1}^{n} {(i times X_{i} ) – sumnolimits_{i = 1}^{n} {isumnolimits_{i = 1}^{n} {X_{i} } } } }}{{n times sumnolimits_{i = 1}^{n} {i^{2} – left( {sumnolimits_{i = 1}^{n} i } right)^{2} } }} $$
(8)

where θslope is the linear slope of the time series variable, which can be used to characterize the increase or decrease rate during a given study period; n is the number of years (here n = 15); Xi is the temperature, precipitation and NPP for the ith year (i = 1,2, … n).
A nonparametric test, Mann–Kendall (M–K) trend analysis43,44 was utilized to detect the break points of temperature, precipitation and NPP series in the SRYR. The test statistic UFi is calculated as follows:

$$ begin{array}{*{20}c} {UF_{i} = frac{{S_{i} – Eleft( {S_{i} } right)}}{{sqrt {Varleft( {S_{i} } right)} }}} & {left( {i = 1,2, ldots ,n} right)} \ end{array} $$
(9)

$$ begin{array}{*{20}c} {S_{k} = sumlimits_{i = 1}^{k} {r_{i} } } & {left( {k = 2,3, ldots ,n} right)} \ end{array} $$
(10)

$$ begin{array}{*{20}c} {ri = left{ {begin{array}{*{20}c} { + 1} & {x_{i} > x_{j} } \ 0 & {x_{i} le x_{j} } \ end{array} } right.} & {(j = 1,2, ldots ,i – 1)} \ end{array} $$
(11)

where xi is the variable with the sample of n. E(Sk) and variance Var(Sk) could be estimated as follows:

$$ Eleft( {S_{i} } right) = frac{{ileft( {i – 1} right)}}{4} $$
(12)

$$ Varleft( {S_{i} } right) = frac{{ileft( {i – 1} right)left( {2i + 5} right)}}{72} $$
(13)

Using the same equation but in the reverse data series (xn, xn − 1, …, x1), UFi could be calculated again. Defining UBi = UFi (i = n, n − 1, …, 1), we can get the curve of UFi and UBi. If the intersection of the UFi and UBi curves occurs within the confidence interval, it indicates a change point45.
To assess the effects of temperature and precipitation on NPP in the SRYR, correlation coefficient R was employed to analyze the correlation between two variables (NPP vs. Tem., NPP vs. Pre.), using the following formula:

$$ R_{XY} = frac{{sumnolimits_{i = 1}^{n} {left( {X_{i} – overline{X} } right)left( {Y_{i} – overline{Y} } right)} }}{{sqrt {sumnolimits_{i = 1}^{n} {left( {X_{i} – overline{X} } right)^{2} sqrt {sumnolimits_{i = 1}^{n} {left( {Y_{i} – overline{Y} } right)^{2} } } } } }} $$
(14)

where Y denotes the NPP and X denotes temperature or precipitation.
The results of the statistical analysis above can be got by MATLAB.
Identification of the relative roles of climate change and human activities in NPP
A positive PNPP slope indicates that vegetation growth is promoted by climate change, whereas a negative PNPP slope means that climate change reduced the vegetation NPP. A positive HNPP slope suggests that human activities have negative influence on vegetation growth and create ecological degradation, whereas a negative HNPP slope means that human activities contribute to vegetation growth46. Thus, the determinants for NPP change can be identified according to Table 2.
Table 2 The causes of actual NPPA change.
Full size table More

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