Seasonally and spatially varied controls of climatic factors on net primary productivity in alpine grasslands on the Tibetan Plateau

Journal Pre-proof Seasonally and spatially varied controls of climatic factors on net primary productivity in alpine grasslands on the Tibetan Plateau Zhoutao Zheng, Wenquan Zhu, Yangjian Zhang PII:

S2351-9894(19)30409-3

DOI:

https://doi.org/10.1016/j.gecco.2019.e00814

Reference:

GECCO 814

To appear in:

Global Ecology and Conservation

Received Date: 17 July 2019 Revised Date:

11 October 2019

Accepted Date: 11 October 2019

Please cite this article as: Zheng, Z., Zhu, W., Zhang, Y., Seasonally and spatially varied controls of climatic factors on net primary productivity in alpine grasslands on the Tibetan Plateau, Global Ecology and Conservation (2019), doi: https://doi.org/10.1016/j.gecco.2019.e00814. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

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Title: Seasonally and spatially varied controls of climatic factors on net primary productivity

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in alpine grasslands on the Tibetan Plateau

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Zhoutao Zhenga, Wenquan Zhub, Yangjian Zhanga,c,d,*

4 5

a

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Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101,

7

China

8

b

Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

9

c

CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences,

Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic

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Beijing 100101, China

11

d

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100190, China

College of Resources and Environment, University of Chinese Academy of Sciences, Beijing

13 14

*

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E-mail address: [email protected] (Y. Zhang).

Corresponding Author. Tel.: +86 10 64889703.

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Abstract: Vegetation net primary productivity (NPP) is a core parameter regulating carbon

18

cycles of terrestrial ecosystem, which also has close relations with climates. The alpine

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ecosystems on the Tibetan Plateau (TP) are highly sensitive to climate changes. However,

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systematic analyses on the seasonal and annual responses of NPP to climatic factors in

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different grassland types on the TP are still lacking. In this study, the spatial and temporal

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patterns of NPP and their responses to temperature, precipitation and solar radiation during

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2001–2015 at seasonal and annual temporal scales were investigated based on outputs of an

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improved Carnegie–Ames–Stanford Approach (CASA) model. The improved CASA model

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showed solid performances in simulating NPP in reference to field observations (R2 = 0.79, P <

26

0.001), resulting in mean error (ME) of -16.68, root mean square error (RMSE) of 87.59 g

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C·m-2·yr-1, and mean relative error (MRE) of -4.29%, respectively. The annual NPP displayed

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different altitude dependences between the regions below and above 3500 m, which could be

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attributed to the altitude associated precipitation variations. The temporal trends of the seasonal

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and annual NPP exhibited high spatial heterogeneity. For the entire alpine grasslands, solar

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radiation exerted stronger influences on annual NPP than temperature and precipitation did.

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The responses of NPP to climatic factors also varied among grassland types and seasons. For

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alpine meadow, solar radiation and temperature were the dominant climatic factors in

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controlling the NPP variability in spring and summer, respectively, while the effect of

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precipitation was weak in all seasons. On the contrary, precipitation played a more crucial role

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in influencing NPP than temperature and solar radiation in both summer and autumn for alpine

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steppe. Our results shed further lights on the mechanism underlying the responses of alpine

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ecosystem to climate changes. The improved understanding can provide guidelines for alpine

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grassland management.

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Keywords: net primary productivity; altitude dependence; climatic effects; seasonal

41

variations; alpine grasslands

42 43

1. Introduction

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Vegetation is one key component of the global terrestrial ecosystem, and plays a crucial

45

role in mediating global carbon cycle (Ahlstrom et al., 2015; Wu et al., 2018). Its response to

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climate change has received considerable attentions in the past decades. Net primary

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productivity (NPP), the net amount of carbon fixed by vegetation in a given period, is a

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convenient indicator to characterize vegetation dynamics and their responses to climate

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changes (Gao et al., 2009; Taylor et al., 2017). It also represents carbon assimilation by

50

ecosystems (Fang et al., 2003; Feng et al., 2013; Chen et al., 2018). The dynamics in NPP are

51

strongly regulated by climatic variations, such as temperature, precipitation and solar radiation

52

(Schloss et al., 1999; Nemani et al., 2003). With the rapid developments of remote sensing and

53

modeling techniques, NPP studies have shifted from the traditional site scale to regional or

54

global scale. For example, a set of models based on light use efficiency (LUE) and remote

55

sensing data have been developed to simulate NPP, such as CASA (Carnegie–Ames–Stanford

56

Approach) (Potter et al., 1993), GLO-PEM (Global Production Efficiency Model) (Prince and

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Goward, 1995) and C-Fix (Veroustraete et al., 2002).

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The Tibetan Plateau, known as the ‘third pole’ of the Earth, is the highest plateau in the

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world, with an average elevation higher than 4000 m. The dominant vegetation in the TP is

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alpine grasslands, which mainly consists of alpine meadow and steppe. As an important

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ecological barrier in China, the alpine grasslands play an essential role in carbon sequestration,

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climatic regulation, water and soil conservation and biodiversity maintenance (Yao et al.,

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2012). However, due to the frigid and dry environments, vegetation growth in the TP is

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extremely sensitive to climate change (Zhang et al., 2019). The mechanism underlying how its

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vegetation responds to climate changes can provide pre-warning for other ecosystems in the

66

world. Till now, some related studies have reported the spatio-temporal patterns of vegetation

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growth or NPP and their responses to climatic driving factors on the TP (Piao et al., 2006; Gao

68

et al., 2013; Zhang et al., 2014; Xu et al., 2016; Shen et al., 2016; Cong et al., 2017; Luo et al.,

69

2018; An et al., 2018). However, these studies were mainly conducted at an annual scale. Their

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seasonal variations and spatial heterogeneities among grassland types were primarily

71

neglected. Besides, the altitude dependent NPP pattern and its causing factors have neither

72

been fully explored. Vegetation dynamics are usually more relevant to climatic factors during a

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certain season as opposed to throughout an entire year (Zhang et al., 2018). Seasonal

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correlations can further tighten the linkages between vegetation and climatic factors. To

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improve our understanding, responses of NPP to climatic changes are entailed to be

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investigated by seasons and grassland types. It has been reported that climatic factors

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dominating vegetation growth differed among seasons and biomes (Piao et al., 2011; Kong et

78

al., 2017; Zhang et al., 2018). So, it is imperative to reveal how vegetation on the TP responds

79

to climatic factors at a finer temporal and spatial scale. Only with these information, our

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capability in predicting their future status can be strengthened.

81

Among the set of ecosystem productivity models, the CASA model is one of the most

82

commonly utilized. It has been extensively applied to simulate NPP at regional (Feng et al.,

83

2016), continental (Hicke et al., 2002) and global scales (Potter et al., 2012). However, the

84

original CASA model was subject to some deficiencies. For example, the maximum LUE

85

(εmax) was set to be 0.389 g C·MJ-1 for all vegetation types, which in fact varies with vegetation

86

types and environmental conditions (Zhu et al., 2006). Besides, the fraction of

87

photosynthetically active radiation (FPAR) was estimated only based on the linear relationship

88

between FPAR and simple ratio (SR) in the original CASA model, which did not properly

89

represent the actual relationship between FPAR and vegetation. Those limitations undermine

90

its accuracies in simulating NPP on the TP. In this study, we improved the CASA model by

91

optimizing the two key parameters. The improved model was applied to explore the

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spatio-temporal NPP dynamics and the associated driving factors on the TP. Specifically, we

93

aimed to (1) examine the NPP variations at annual and seasonal scales for different grassland

94

types; (2) reveal the different responses of NPP to climatic factors (temperature, precipitation

95

and solar radiation) at the annual and seasonal temporal scales and the spatial scale of grassland

96

types.

97 98

2. Materials and Methods

99

2.1. Datasets

100

The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized

101

Difference Vegetation Index (NDVI) product (16-day, 250 m, MOD13Q1, Collection 6)

102

covering 2001–2015 was acquired from National Aeronautics and Space Administration

103

(NASA)

104

(https://ladsweb.modaps.eosdis.nasa.gov/). To minimize the effects of cloud and Nadir

105

Bidirectional Reflectance Distribution Function (BRDF), the NDVI composite was performed

106

by the Constrained View Angle-Maximum Value Composite (CV-MVC) algorithm. The

107

MODIS Collection 6 is the latest version of the MODIS product, which has been calibrated to

108

remove the effects of sensor degradation in the Collection 5 (Zhang et al., 2017). We conducted

109

further processing to remove snow and cloud contaminations. For each pixel, a time series of

110

annual minimum uncontaminated NDVI during 2001-2015 was generated. The median value

111

in the minimum NDVI time series was then extracted as the background NDVI value, which

112

was later used to replace the smaller values and snow flagged values (Zhang et al., 2007; Wang

113

et al., 2015). Moreover, the Savitzky-Golay filter was used to reconstruct the NDVI time series

114

to remove the remaining cloud contamination (Chen et al., 2004). Finally, the monthly NDVI

115

data during 2001-2015 were generated using the Maximum Value Composite method and

116

utilized in CASA model to calculate monthly NPP. To focus on the alpine grasslands and

117

exclude pixels with low vegetation coverage, grassland pixels were selected according to the

118

criteria set in the previous related study (Zheng and Zhu, 2017).

Earth

Observing

System

Data

and

Information

System

119

The field NPP data was inferred from the observed grassland biomass of seven agricultural

120

meteorological stations (Gande, Haiyan, Henan, Maqu, Qumarleb, Zoige, Xinghai) maintained

121

by the China Meteorological Administration (CMA) for the period spanning 2001-2012

122

(Figure 1). The stations of Gande, Haiyan, Henan and Zoige are located in the alpine meadow

123

while the other stations are located in the alpine steppe (Chen et al., 2015). The biomass

124

observation was carried out in fenced natural pastures with an area of 100 m × 100 m at each

125

station since 1981. According to the observation criteria ( China Meteorological

126

Administration, 1993), four random plots (1 m × 1 m) were selected to measure the

127

aboveground dry biomass in each month during the growth period. For grasslands, the NPP can

128

be estimated by peak biomass (Meyer et al., 2015). The belowground biomass was estimated

129

according to the ratio of belowground to aboveground net production, with value of 3.09, 3.44

130

and 3.01 for alpine meadow, alpine shrub meadow and alpine swamp meadow in Haibei region

131

of Qinghai, respectively (Li, 2006). Unfortunately, that data was not reported for alpine steppe.

132

Then we took a mean value of 3.18 as the constant ratio of belowground to aboveground

133

biomass. Lastly, we converted the total biomass [g·m-2] to NPP [g C·m-2] with a factor of 0.45

134

(Fang et al., 2007). The CMA also provides monthly meteorological data of 172 stations on the

135

TP and surrounding areas during 2001-2015, including mean temperature, total precipitation,

136

percentage of sunshine and total solar radiation. Due to the absence of solar radiation data for

137

most stations, we used the Angstrom–Prescott model to calculate the monthly total solar

138

radiation based on the percentage of sunshine (Angstrom, 1924; Prescott, 1940), which is

139

expressed as: =

×( +

× )

(1)

140

where Q and Q0 are the global radiation and the extraterrestrial solar radiation on a horizontal

141

surface, respectively; S is the percentage of sunshine; a and b are empirically determined

142

regression constants. After calculation, a and b were set to be 0.2088 and 0.5787 over the TP,

143

respectively.

144 145

Figure 1. Spatial distribution of (a) grassland types and net primary productivity (NPP) sampling sites, (b)

146

digital elevation model (DEM), (c) mean annual temperature, (d) total annual precipitation, and (e) total

147

annual solar radiation on the Tibetan Plateau.

148 149

Lastly, all the meteorological data was interpolated into 250 m × 250 m raster grids using

150

ANUSPLIN 4.3 software (Hutchinson, 2004). The distribution of the major biomes (alpine

151

meadow and steppe) on the TP was obtained from the China Vegetation Map with a scale of

152

1:1000000 (Editorial Board of Vegetation Map of China, 2007) (Figure 1).

153 154

2.2 Simulation of NPP

155

In this study, we simulated NPP from 2001 to 2015 by employing an improved CASA

156

model. In CASA model, NPP [g C·m-2] is estimated from the absorbed photosynthetically

157

active radiation (APAR) [MJ·m-2] and the actual LUE (ε) [g C·MJ-1] as follow (Potter et al.,

158

1993; Field et al., 1995): =

×

(2)

159

APAR is determined by the solar radiation (SOL) [MJ·m-2] and the fraction of

160

photosynthetically active radiation (FPAR):

=

×

(3)

× 0.5

161

where the coefficient of 0.5 is the fraction of effective solar radiation (wavelength range of

162

0.4-0.7 µm) used by vegetation (i.e., the ratio of photosynthetically active radiation to solar

163

radiation) (Piao et al., 2005). FPAR can be expressed based on the linear relationship between

164

FPAR and NDVI as well as simple ratio (SR). However, it was found that FPAR derived from

165

NDVI tended to overestimate while that derived from SR tended to underestimate (Zhu et al.,

166

2006). Then we took their mean as follows: =

#$

(

−

)×( !" −

( =

(

−

!"

)×( (

!"

!"

−

− )

)

− )

)

+

(4)

+

(5)

= (1 +

)/(1 −

)

(6)

=(

+

#$ )/2

(7)

167

where FPARmax and FPARmin are constants with values of 0.950 and 0.001, respectively; and

168

NDVImax and NDVImin represent the 95% and 5% of NDVI for different vegetation types.

169 170

The LUE is influenced by the external environmental factors of temperature and moisture. Specifically, the actual LUE (ε) [g C·MJ-1] is calculated as follows (Yu et al., 2009): = T)* × +), × -) ×

(8)

!"

+)* = 0.8 + 0.02 × +/01 − 0.005 × +/01 , +), = 1.1814/(1 + 3

.,×4567 8* 84

)/(1 + 3

.9×(84567 )8* :4

(9) )

-) = 0.5 + 0.5 × (;/;0 )

(10) (11)

171

where Tε1 and Tε2 are the temperature stress coefficients representing the restriction of low and

172

high temperature on LUE; Topt [°C] is the optimum temperature for vegetation growth, and

173

defined as the mean temperature in the month of maximum NDVI; T [°C] is the monthly mean

174

temperature; Wε is the water stress coefficient; E [mm] and Ep [mm] are the actual and potential

175

evapotranspiration; εmax [g C·MJ-1] is the maximum LUE under ideal conditions.

176

In the original CASA model, εmax was set as a constant value of 0.389 g C·MJ-1 for all

177

vegetation types (Potter et al., 1993). However, it actually varies significantly with vegetation

178

types, spatial resolution and data sources (Wang et al., 2010). Considering the difficulties in

179

measuring εmax directly, we optimized εmax for the alpine grasslands based on the particle

180

swarm optimizer (PSO) (Li et al., 2012). The PSO method is a population based stochastic

181

optimization technique, which is initialized with a population of random solutions and then

182

optima is searched by updating generation (Eberhart and Kennedy, 1995) until the objective

183

function meets the convergence conditions. In this study, the object function was defined as the

184

root mean square error (RMSE) between observed and simulated NPP. The optimum εmax was

185

obtained when the object function reached the minimum value through PSO algorithm. To

186

assess the robustness of the adjusted CASA model, a two-fold even-odd cross validation was

187

used (Peng and Gitelson, 2011; Vitasse et al., 2018). The observed NPP data in odd-numbered

188

years were used as training samples (n = 42), while those in even-numbered years (n = 41) were

189

used as validation. Based on the series of calculation, the value of εmax was reset to be 0.4812 g

190

C·MJ-1 for the alpine grasslands.

191 192

2.3 Statistical analysis

193

To assess the accuracies of simulated NPP, we directly compared them with ground-based

194

observations to derive the correlation coefficient (r) and p-values. The mean error (ME), root

195

mean square error (RMSE) and mean relative error (MRE) were also calculated (Jia et al.,

196

2012). To characterize the spatial pattern, the average and standard deviation of annual NPP in

197

each altitude bin of 100 m were calculated. A simple linear regression model was constructed

198

to detect the temporal trends in NPP, with time as the independent variable and NPP as the

199

dependent

200

(September-November) and the entire year (including winter) during 2001-2015. To reveal the

variable

for

spring

(March-May),

summer

(June-August),

autumn

201

dominant climatic factors on NPP variability over 2001-2015, partial correlation analyses

202

between NPP and climatic factors (temperature, precipitation and solar radiation) were

203

performed at the annual and seasonal temporal scales. The partial correlation coefficient

204

between NPP and each climatic factor was calculated with the other two climatic factors being

205

set as control variables. The statistical significances of the regression and partial correlation

206

coefficients were examined using the T test. The p-values less than 0.05 were considered

207

significant.

208 209

3. Results

210

3.1. Ground validation of NPP

211

The observed NPP data of even-numbered years was used to validate the simulated NPP

212

by modified CASA model (Figure 2). A significant and positive correlation was observed

213

between the field-observed NPP and simulated NPP, with a high R2 of 0.79 (P < 0.001). In

214

addition, ME and RMSE between the two NPP datasets was -16.68 and 87.59 g C·m-2·yr-1,

215

respectively, while MRE was -4.29%. They further indicated the estimation accuracy of the

216

modified CASA model was reliable.

217 218

Figure 2. Comparison of CASA-simulated and observed net primary productivity (NPP).

219 220

3.2 Temporal variations in NPP

221

The annual NPP increased significantly from 2001 to 2015 (P < 0.05), with an annual

222

rising rate of 1.25 g C·m-2·yr-1 (0.54% yr-1) (Figure 3a). The annual NPP reached the highest

223

value in 2010 (245.39 g C·m-2·yr-1) while the lowest value was recorded in 2003 (223.17 g

224

C·m-2·yr-1). Obvious differences were observed among the trends of each season (Figure 3b-d).

225

Spring NPP exhibited a significantly increasing trend of 0.32 g C·m-2·yr-2 (1.12% yr-1) (P <

226

0.05), while NPP showed insignificant trends in summer and autumn because of large

227

interannual variations.

228 229

Figure 3. Temporal variations in (a) annual net primary productivity (NPP), (b) spring NPP, (c) summer

230

NPP, and (d) autumn NPP in the alpine grasslands on the Tibetan Plateau during 2001-2015.

231 232

The annual NPP increased significantly for both alpine meadow and steppe at a rate of

233

1.38 g C·m-2· yr-2 (or 0.53% yr-1) and 0.67 g C·m-2·yr-2 (or 0.52% yr-1), respectively (Figures S1

234

and S2). In addition, the trends in NPP were positive in all seasons for alpine meadow as well

235

as alpine steppe, but significant trend was only observed in spring for alpine meadow at a rate

236

of 0.37 g C·m-2· yr-2 (or 1.20% yr-1).

237 238

3.3 Spatial patterns of NPP

239

During 2001-2015, the average annual NPP decreased gradually from southeast to

240

northwest on the TP, with an overall mean value of 232.25 g C·m-2·yr-1 (Figure 4). Moreover,

241

the variations in annual NPP showed strong dependence on altitude (Figure 5a). Between 3500

242

and 5500 m, the annual NPP was significantly and negatively correlated with altitude (P <

243

0.001). Overall, the annual NPP decreased by 17.9 g C·m-2· yr-1 per 100 m rising of altitude.

244

However, the annual NPP increased with rising altitude below 3500 m by 24.8 g C·m-2·yr-1 per

245

100 m. The spatial pattern with altitude below and above 3500 m could be seen in Figure S3.

246

The average annual NPP was 260.87 g C·m-2·yr-1 for the alpine meadow, more than twice

247

higher than that of the alpine steppe (127.88 g C·m-2·yr-1). The pattern of annual NPP along

248

altitude for alpine meadow was similar to that for the entire grasslands (Figure 5b). However,

249

the annual NPP of the alpine steppe displayed a different distribution pattern along altitude

250

from alpine meadow (Figure 5c). Overall, the annual NPP decreased with rising altitude by 8.3

251

g C·m-2· yr-1 per 100 m in the altitude ranges of 3500-5500 m, but no obvious altitude

252

dependence could be observed below 3500 m. The spatial distribution of average annual NPP

253

for alpine meadow and steppe in different altitude zones below and above 3500 m could be

254

seen in Figure S3.

255 256

Figure 4. Spatial variations in average annual net primary productivity during 2001-2015.

257 258

Figure 5. Variations in average annual net primary productivity (NPP) during 2001-2015 along altitude

259

gradient in the Tibetan Plateau for (a) all grasslands, (b) alpine meadow, and (c) alpine steppe. Error bars

260

show standard deviation (SD) of NPP at each altitude bin.

261 262

3.4 Spatial patterns of NPP trends

263

The trends in annual NPP showed considerable heterogeneity over the TP (Figure 6a).

264

Significant increasing interannual trends (P < 0.05) were observed over 24.87% of the entire

265

grasslands during 2001-2015, which were mainly located in the eastern and central TP. In

266

addition, the trends in annual NPP were relatively stronger in the northeastern part of the

267

plateau. Meanwhile, significant decreasing interannual trends (P < 0.05) accounted for only

268

2.74% of the grasslands, which were mainly distributed in the southwestern TP.

269

Seasonally, the areas with increasing interannual trend of NPP shrank from spring to

270

autumn, accounting for 19.32%, 16.12% and 6.81% of the entire grasslands in spring, summer

271

and autumn, respectively (Figure 6b-d). The area proportions with decreasing trends were quite

272

small for all the three seasons, with a fraction value of 0.83%, 2.28% and 1.62% for spring,

273

summer and autumn, respectively.

274 275

Figure 6. Spatial distribution of significant interannual trends (P < 0.05) in (a) annual net primary

276

productivity (NPP), (b) spring NPP, (c) summer NPP, and (d) autumn NPP during 2001-2015.

277 278

3.5 Spatial patterns of correlations between NPP and climate

279

The correlations between NPP and temperature showed apparent annual and seasonal

280

variations (Figure 7a). The annual NPP was positively correlated with temperature over

281

70.16% of the alpine grasslands (with significant correlation in 11.26% of pixels), primarily

282

distributed in the central and eastern TP (Table 1, Figure 7a). For separate season, significantly

283

positive correlation (P < 0.05) between NPP and temperature was mostly observed in spring

284

(32.85%) and autumn (20.51%) (Table 1, Figure 7b-d). By contrast, area with significantly

285

positive correlation (P < 0.05) between NPP and temperature was relatively small in summer,

286

accounting for 8.64% of the study area (Table 1, Figure 7c). The negative correlation between

287

NPP and summer temperature was mainly observed in the southwestern part of the TP, with

288

3.04% of the alpine grasslands being significant (P < 0.05) (Table 1).

289

Compared with temperature, the annual NPP was significantly (P < 0.05) and positively

290

correlated with precipitation over more areas (15.16% of pixels) (Table 1, Figure 7e), mainly

291

observed in the southwestern and northern TP. The significantly positive correlation between

292

NPP and precipitation was more prevalent in spring (16.27%) and autumn (16.19%) than in

293

summer (11.32%) (Table 1, Figure 7f-h), which were mainly found in the southwestern part of

294

the TP for spring and autumn. Pixels with significantly negative correlation only accounted for

295

1.76%, 0.68% and 0.52% of the entire grasslands for spring, summer and autumn, respectively

296

(P < 0.05) (Table 1).

297

The correlation between NPP and solar radiation also showed a strong spatial and temporal

298

heterogeneity (Figure 7i-l). The annual NPP correlated positively with solar radiation mostly in

299

the eastern and northeastern TP, with 19.68% of pixels being significant (P < 0.05) (Table 1,

300

Figure 7i). Meanwhile, only 0.41% of pixels possessed significantly negative correlations (P <

301

0.05). Positive correlation between solar radiation and NPP were prevalent in the central and

302

eastern TP for all seasons (Figure 7j-l). Significantly positive correlations (P < 0.05) accounted

303

for a higher proportion in summer (23.39%) than in spring (8.91%) and autumn (9.51%) (Table

304

1). On the other hand, significantly negative correlations (P < 0.05) were rare for all seasons,

305

with area proportions less than 1% (Table 1).

306 307

Figure 7. Spatial patterns of partial correlations between net primary productivity (NPP) and climatic factors

308

during 2001-2015. (a) annual NPP and annual temperature; (b) spring NPP and spring temperature; (c)

309

summer NPP and summer temperature; (d) autumn NPP and autumn temperature; (e) annual NPP and

310

annual precipitation; (f) spring NPP and spring precipitation; (g) summer NPP and summer precipitation; (h)

311

autumn NPP and autumn precipitation; (i) annual NPP and annual solar radiation; (j) spring NPP and spring

312

solar radiation; (k) summer NPP and summer solar radiation; (l) autumn NPP and autumn solar radiation.

313 314

Table 1. Pixel percentage (%) of the partial correlations between net primary productivity (NPP) and

315

climatic factors in the alpine grasslands on the Tibetan Plateau. Annual

Spring

Summer

Autumn

Climatic factor

Temperature

Precipitation

Positive

Negative

Positive

Negative

Positive

Negative

Positive

Negative

70.16

29.84

90.93

9.07

64.43

35.57

82.59

17.41

(11.26)

(1.58)

(32.85)

(0.16)

(8.64)

(3.04)

(20.51)

(0.49)

77.59

22.41

69.80

30.20

73.83

26.17

76.59

23.41

Solar radiation

316

(15.16)

(0.77)

(16.27)

(1.76)

(11.32)

(0.68)

(16.19)

(0.52)

79.71

20.29

72.20

27.80

79.21

20.79

71.87

28.13

(19.68)

(0.41)

(8.91)

(0.53)

(23.39)

(0.86)

(9.51)

(0.92)

Note: value in the parenthesis was the pixel percentage of the significant (P < 0.05) partial correlation.

317 318

3.6 Climatic controls on NPP in different grassland types

319

At a regional scale, solar radiation showed the highest partial correlation with annual NPP

320

(R = 0.76, P < 0.01), followed by precipitation (R = 0.59, P < 0.05) and temperature (R = 0.44,

321

P > 0.05) (Table 2). The seasonal responses of NPP to climatic factors were varied. Though

322

temperature had relatively weaker influences on NPP than other climatic factors at an annual

323

scale, it was the dominant climatic element on spring NPP (R = 0.81, P < 0.001). In addition,

324

NPP was significantly and positively correlated with solar radiation in summer (R = 0.69, P <

325

0.01). However, the partial correlations between NPP and all the three analyzed climatic

326

factors were insignificant in autumn.

327

Moreover, alpine meadow and steppe responded differently to the same climatic factor

328

(Table 2). Overall, NPP was affected more by solar radiation and temperature than by

329

precipitation in alpine meadow while precipitation and temperature played relatively more

330

important roles in mediating NPP than solar radiation in alpine steppe at a seasonal scale.

331

Significantly positive partial correlations were observed between NPP and temperature in

332

spring for alpine meadow (R = 0.82, P < 0.001). Meanwhile, NPP was correlated significantly

333

with solar radiation in summer (R = 0.73, P < 0.01). Besides, NPP also showed the stronger

334

partial correlation with temperature (R = 0.40) than with precipitation (R = 0.11) and solar

335

radiation (R = 0.08) in autumn. By contrast, the partial correlation between NPP and

336

precipitation was weak in all seasons. However, for alpine steppe, NPP showed higher partial

337

correlations with precipitation than with temperature and solar radiation in both summer and

338

autumn. But in spring, temperature was the dominant climatic factor influencing NPP, as

339

exhibited by its high partial correlation with spring NPP (R = 0.76, P < 0.001).

340 341

Table 2. Partial correlation coefficients between net primary productivity and climatic factors in the alpine

342

grasslands on the Tibetan Plateau. Grassland type

Season

Temperature

Precipitation

Solar radiation

All grasslands

Annual

0.44

0.59*

0.76**

Spring

0.81***

0.44

0.27

Summer

0.41

0.34

0.69**

Autumn

0.42

0.20

0.13

Annual

0.62*

0.63*

0.81***

Spring

0.82***

0.36

0.31

Summer

0.52

0.35

0.73**

Autumn

0.40

0.11

0.08

Annual

0.34

0.59*

0.12

Spring

0.76***

0.38

0.24

Summer

0.18

0.54

0.37

Autumn

0.50

0.55*

0.10

Alpine meadow

Alpine steppe

343

Note: *, P < 0.05; **, P < 0.01; ***, P < 0.001.

344 345

4. Discussion

346

4.1 Comparison of validations with other studies

347

Ecosystem productivity simulation on the TP is severely constrained by lacking model

348

parameter optimization and field validation. The primary reason lies that there have been not

349

enough field data. In this study, we improved CASA model for alpine grasslands of the TP by

350

optimizing model parameters of εmax and FPAR. Compared with other model studies, our

351

improved model exhibited higher or comparable accuracies of R2 (Mao et al., 2015; Wang et

352

al., 2017; Luo et al., 2018). Furthermore, RMSE used in this study for model accuracy

353

assessment corroborated the validation process (Luo et al., 2018). The improved model paved

354

the way for exploring ecosystem responses to climatic changes on the TP.

355 356

4.2 Spatial patterns of NPP

357

This study revealed that the annual NPP over the TP exhibited a weakened trend from

358

southeast to northwest in parallel with decreased temperature and precipitation, which is in

359

accord with previous studies (Piao et al., 2006; Gao et al., 2013; Zhang et al., 2014). NPP also

360

exhibited strong dependence on altitude on the TP in this study, similar to spring phenology in

361

this area (Piao et al., 2011; Shen et al., 2014). With rising altitude, NPP strengthened below

362

3500 m but weakened above 3500 m, which was consistent with the result of Wang et al

363

(2017). This distribution pattern could also be observed for NDVI in the TP (An et al., 2018).

364

However, alpine meadow and steppe showed different relationships between NPP and altitude.

365

NPP increased significantly with altitude from 2700 m to 3500 m for alpine meadow while

366

showed no evident dependence on altitude for alpine steppe. To sort out the reasons causing

367

this discrepancy, we further investigated the spatially explicit relationships between altitude

368

and climatic factors in the regions within the two altitude ranges (i.e., below and above 3500

369

m) (Figure 8). In the zone below 3500 m of the entire grasslands, temperature and solar

370

radiation decreased while precipitation increased with rising altitude. By contrast, precipitation

371

and NPP followed similar trends along altitude, resulting in their strong positive correlations

372

between precipitation and NPP (R = 0.97, P < 0.001) for the entire grasslands (Figure 8g). In

373

the altitude zone above 3500 m, both temperature and precipitation decreased with rising

374

altitude, also being consistent with weakened NPP (Figure 8a,d,g). However, solar radiation

375

changed inversely. Similar phenomenon was also found for alpine meadow and steppe. In

376

summary, NPP showed consistent variation along altitude with precipitation below 3500 m,

377

which indicated that the dependence of NPP on altitude below 3500 m might be dominated by

378

precipitation. Above 3500 m, similar altitude distributions could be observed between NPP and

379

temperature as well as precipitation. It revealed the dependence of NPP on altitude above 3500

380

m might be controlled by temperature and precipitation.

381 382

Figure 8. Variations in annual net primary productivity (NPP) and climatic factors (mean annual

383

temperature, total annual precipitation and total annual solar radiation) along rising altitude in the alpine

384

grasslands on the Tibetan Plateau. Rbelow indicated the spatial correlation coefficient between climatic factor

385

and net primary productivity (NPP) in the altitude zone below 3500 m while Rabove indicated the spatial

386

correlation coefficient between climatic factor and NPP in the altitude zones above 3500 m.

387

indicated P < 0.01 and P < 0.001, respectively.

388

**

and

***

389

4.3 Temporal variations in NPP

390

Our analysis revealed an overall increasing trend in annual NPP over the TP alpine

391

grasslands during the past 15 years, which is in accord with previous studies (Piao et al., 2006;

392

Zhang et al., 2014; Xu et al., 2016). But the increasing magnitude (0.54% yr-1) differs to some

393

extent from other studies, for example, being greater than the result (0.46% yr-1) of Zhang et al

394

(2014) but smaller than the results (0.66% yr-1 and 1.27% yr-1) of two other studies (Piao et al.,

395

2006; Xu et al., 2016). These magnitude differences might be caused by the discrepant study

396

domain, study period and data source. However, a study reported an overall decreasing NPP

397

trend over the entire TP (including forest) during 2001-2015 based on EVI data (Luo et al.,

398

2018). Such discrepancy might result from the differences of data being utilized and

399

ecosystems targeted. The growing NPP trend as revealed in this study is coherent with that of

400

the grasslands of China (Liang et al., 2015; Zhang et al., 2016). Under the background of

401

weakened productivity for the grasslands in Europe and North America (Gang et al., 2015), the

402

alpine grasslands on the TP has been playing a strengthened role in global carbon

403

sequestration. Seasonally, significantly increasing NPP trend was only observed in spring,

404

which might be caused by advanced spring phenology under rising spring temperature. Then

405

more plant biomass could be provided for grazing livestock in spring.

406 407

4.4 Climatic controls on NPP

408

Climatic factors of temperature, precipitation and solar radiation were thought to be the

409

major elements regulating grassland growth (Nemani et al., 2003; Wang et al., 2017). Our

410

study indicated that solar radiation exerted relatively stronger impacts on annual NPP than

411

temperature and precipitation for the entire alpine grasslands on the TP, which agrees with the

412

previous study (Piao et al., 2006). Some studies revealed weak influences of precipitation on

413

annual NPP (Gao et al., 2013; Xu et al., 2016), but we found significant impacts of

414

precipitation on annual NPP by using partial correlation analysis. This finding is consistent

415

with that of a previous study, which showed that precipitation was an important climatic factor

416

affecting vegetation growth in the entire TP (Sun et al., 2013). Besides, effects of climatic

417

factors on NPP varied with seasons. Temperature showed higher positive partial correlations

418

with NPP in spring than in summer and autumn for the entire grasslands. Solar radiation

419

correlated with NPP more strongly in summer than in spring and autumn.

420

Moreover, the relationships between NPP and climatic factors differed with grassland

421

types. NPP of the alpine meadow showed a response pattern similar to that of the entire

422

grasslands, with temperature and solar radiation being the main factors at seasonal scales.

423

Environmental factors control carbon uptake of ecosystem directly or indirectly through

424

affecting plant physiological activities (Stoy et al., 2014; Xia et al., 2015). Seasonal variation

425

in physiological activities might contribute much to the seasonal variability of NPP (Medvigy

426

et al., 2013). The alpine meadow, which is mainly distributed in the eastern TP, receives

427

relatively adequate precipitation for vegetation growth. In spring, temperature was the

428

dominant factor controlling NPP. It might be due to that increasing spring temperature could

429

stimulate photosynthetic enzyme activities from the cold environment of winter and ignite

430

vegetation growth through its impacts on nutrient availability and uptake (Jarvis and Linder,

431

2000; Cristiano et al., 2014; Shen et al., 2018). In summer, temperature became increasingly

432

suitable for vegetation growth, then enhanced solar radiation could promote photosynthesis by

433

extending sunshine duration. This explained why solar radiation was the dominant climatic

434

factor in influencing NPP in summer. However, the relationship between NPP and each

435

climatic factor became relatively weaker in autumn. It might be caused by decreased

436

photosynthetic capacity associated with depleted soil nutrient during the later growing season.

437

The soil nutrient availability shrinks with the vegetation growth.

438

For the alpine steppe, precipitation exerted strong positive effects on NPP in summer and

439

autumn, while influences of solar radiation were marginal. The response differences between

440

alpine meadow and steppe were masked when they were lumped together in the analysis. The

441

alpine steppe, especially the southwestern part of the TP, is distributed in regions of dry

442

climates. Soil water plays a primarily regulating role in the water-limited regions (Liu et al.,

443

2015; Jiang et al., 2017). Enhanced precipitation leads to wetter soil environments which, in

444

turn, would strengthen the availability of nutrients (Guo et al., 2018). In parts of alpine steppe

445

of the southwestern TP, negative correlations between NPP and summer temperature were

446

observed. A previous study indicated that NDVI would decline with rising temperature in that

447

regions if precipitation did not increase (Cong et al., 2017). Therefore, to deepen our

448

understanding on the mechanism underlying the climate-vegetation relationship on the TP,

449

interactions among temperature, precipitation and solar radiation need to be taken into

450

consideration in the future studies.

451

In this study, we aimed to reveal the dominant climatic factors on NPP of different

452

seasons. It should be noted that anthropogenic activities such as grazing, can also influence

453

NPP greatly (Chen et al., 2014; Huang et al., 2016). However, plenty of uncertainties still

454

exist in quantifying the influences of climate changes and anthropogenic activities on

455

grassland productivity. The underlying mechanisms also need to be further explored. Besides,

456

some uncertainties also exited in simulation of NPP in this study, including conversion of

457

NPP from aboveground biomass, different scales between remote sensing data and ground

458

observations, and lack of ground observations in the western TP. In addition, this study

459

calculated the maximum LUE for the entire alpine grasslands, instead of treating alpine

460

meadow and steppe separately, which was the same as previous studies (Piao et al., 2006; Gao

461

et al., 2013; Luo et al., 2018). Extracting the maximum LUE for each grassland type separately

462

can further increase model simulation accuracies of NPP, but it requires collecting more

463

field-observed data, especially in the central and western TP. All these aspects entail to be paid

464

mounting attentions in the future studies to constrain the simulation uncertainties.

465 466

5. Conclusions

467

In this study, we calibrated the CASA model with field observed data and simulated NPP

468

of the alpine grasslands on the TP during 2001-2015. The spatio-temporal variations in NPP

469

and the dominant climatic factors in controlling NPP were explored at seasonal and annual

470

scales. Main conclusions were reached as follows:

471 472

(1) The direction of NPP dependence on altitude differed between regions below and above 3500 m, as determined primarily by the altitude dependent pattern of precipitation.

473

(2) For the entire alpine grasslands, annual NPP was more responsive to solar radiation

474

than to temperature and precipitation, and the regulating magnitude from climatic factors also

475

varied with seasons.

476

(3) The relationships between NPP and climatic factors differed between alpine meadow

477

and steppe. In alpine meadow, solar radiation and temperature were the dominant factors

478

controlling NPP variability in spring and summer, respectively. By contrast, precipitation

479

played a relatively more important role in mediating NPP in both summer and autumn for

480

alpine steppe.

481 482

Acknowledgement

483

This research was funded by the strategic priority research program of the Chinese

484

Academy of Sciences (XDA20050102), the National Natural Science Foundation of China

485

(41571195, 41725003 and 41771047) and the National Key Research & Development

486

Program of China (2018YFA0606101, 2017YFA0604802).

487

488

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Appendix A. Supplementary data

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Figure S1. Temporal variations in (a) annual net primary productivity (NPP), (b) spring NPP,

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(c) summer NPP, and (d) autumn NPP in the alpine meadow on the Tibetan Plateau during

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2001-2015.

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Figure S2. Temporal variations in (a) annual net primary productivity (NPP), (b) spring NPP,

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(c) summer NPP, and (d) autumn NPP in the alpine steppe on the Tibetan Plateau during

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2001-2015.

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Figure S3. Spatial variations in average annual net primary productivity during 2001-2015 for

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all grasslands with altitude (a) below 3500 m, (b) above 3500 m, alpine meadow with altitude

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(c) below 3500 m, (d) above 3500 m, alpine steppe with altitude (e) below 3500 m, and (f)

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above 3500 m on the Tibetan Plateau.

Highlights: The CASA model was improved to simulate NPP in the alpine grasslands. Altitude dependence of NPP was primarily associated with precipitation variations. Responses of NPP to climatic factors varied with grassland types and seasons. Solar radiation and temperature controlled seasonal NPP changes for alpine meadow. Precipitation played a more important role in mediating NPP for alpine steppe.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: