# Critical role of water conditions in the responses of autumn phenology of marsh wetlands to climate change on the Tibetan Plateau

Xiangjin Shen, Miaogen Shen, Chaoyang Wu, Josep Peñuelas, Philippe Ciais, Jiaqi Zhang, Chris Freeman, Paul Palmer, Binhui Liu, Mark Henderson, et al.

► To cite this version:

Xiangjin Shen, Miaogen Shen, Chaoyang Wu, Josep Peñuelas, Philippe Ciais, et al. Critical role of water conditions in the responses of autumn phenology of marsh wetlands to climate change on the Tibetan Plateau. Global Change Biology, 2024, 30 (1), pp.e17097. 10.1111/gcb.17097. hal-04574832

HAL Id: hal-04574832

https://hal.science/hal-04574832v1

Submitted on 27 Mar 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
This is the **accepted version** of the journal article:

Shen, Xiangjin; Shen, Miaogen; Wu, Chaoyang; [et al.]. «Critical role of water conditions in the responses of autumn phenology of marsh wetlands to climate change on the Tibetan Plateau». Global change biology, Vol. 30, issue 1 (Jan. 2023), art. e17097. DOI 10.1111/gcb.17097

This version is available at https://ddd.uab.cat/record/287436

under the terms of the © IN COPYRIGHT license
1 Critical role of water conditions in the responses of autumn
2 phenology of marsh wetlands to climate change on the Tibetan
3 Plateau

4 Xiangjin Shen¹, Miaogen Shen², Chaoyang Wu³, Josep Peñuelas⁴,⁵, Philippe Ciais⁶, Jiaqi

5 Zhang¹, Chris Freeman⁷, Paul I. Palmer⁸,⁹, Binhui Liu¹⁰, Mark Henderson¹¹, Zhaoliang

6 Song¹², Shaobo Sun¹², Xianguo Lu¹, Ming Jiang¹

7 ¹Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences,
Changchun, China.

9 ²State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of
Geographical Science, Beijing Normal University, Beijing, China.

11 ³Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.

13 ⁴CREAF, Cerdanyola del Vallès, Barcelona, Spain.

14 ⁵CSIC, Global Ecology Unit CREAF-CSIC- UAB, Barcelona, Spain.

15 ⁶Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France.

17 ⁷School of Natural Sciences, Bangor University, Bangor LL57 2UW, UK.

18 ⁸School of GeoSciences, University of Edinburgh, Edinburgh, UK.

19 ⁹National Centre for Earth Observation, University of Edinburgh, Edinburgh, UK.

20 ¹⁰College of Forestry, Northeast Forestry University, Harbin, China.

21 ¹¹Public Policy Program and Environmental Studies Program, Mills College, Oakland, California, USA.

23 ¹²Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, China.
25

26

27 Abstract:

28 The Tibetan Plateau, housing 20% of China's wetlands, plays a vital role in the regional
29 carbon cycle. Examining the phenological dynamics of wetland vegetation in response to
30 climate change is crucial for understanding its impact on the ecosystem. Despite this
31 importance, the specific effects of climate change on wetland vegetation phenology in
32 this region remain uncertain. In this study, we investigated the influence of climate change
33 on the end of the growing season (EOS) of marsh wetland vegetation across the Tibetan
34 Plateau. Utilizing satellite-derived Normalized Difference Vegetation Index (NDVI) data
35 and observational climate data, we observed a significant delay in the regionally averaged
36 EOS of marsh vegetation by 4.10 days/decade from 2001 to 2020. Warming preseason
37 temperatures were found to be the primary driver behind the delay in the EOS of marsh
38 vegetation, whereas preseason cumulative precipitation showed no significant impact.

39 Interestingly, the responses of EOS to climate change varied spatially across the plateau,
40 indicating a regulatory role for hydrological conditions in marsh phenology. In the humid
41 and cold central regions, preseason daytime warming significantly delayed the EOS.
42 However, areas with lower soil moisture exhibited a weaker or reversed delay effect,
43 suggesting complex interplays between temperature, soil moisture, and EOS. Notably, in
44 the arid southwestern regions of the plateau, increased preseason rainfall directly delayed
45 the EOS, while higher daytime temperatures advanced it. Our results emphasize the
critical role of hydrological conditions, specifically soil moisture, in shaping marsh EOS

responses in different regions. Our findings underscore the need to incorporate

hydrological factors into terrestrial ecosystem models, particularly in cold and dry regions,

for accurate predictions of marsh vegetation phenological responses to climate change.

This understanding is vital for informed conservation and management strategies in the

face of current and future climate challenges.

KEYWORDS Marsh vegetation, autumn phenology, climate change, water condition,

Tibetan Plateau

55

1 Introduction

Vegetation phenology refers to the seasonal timing of life cycle events in plants and
reflects the dynamic responses of terrestrial ecosystems to global climate change (Chen
et al., 2017; Peñuelas et al., 2001; Piao et al., 2007, 2008, 2019; Richardson et al., 2013;
Wu et al., 2022). Several studies have demonstrated that autumn phenology, signaling the
end of the growing season (EOS), reflects the vegetation's growing period better than
spring phenology and has a significant effect on carbon sequestration in terrestrial
ecosystems (Bao et al., 2020; Fu et al., 2018; Garonna et al., 2014; Zhu et al., 2012).

Global climate change has significantly changed the autumn phenology worldwide,
affecting the regional and global energy balance, water flux, and carbon budget (Che et
al., 2014; Estiarte et al., 2015; Kelsey et al., 2021; Richardson et al., 2013; Yang et al.,
2021). Although the variations in the autumn phenology of vegetation and its response to regional and global climate changes have been extensively analyzed, most studies have focused on grassland or forest ecosystems, with few investigations of wetland ecosystems (Coleman et al., 2022; Ge et al., 2015; Ma et al., 2022; Rice et al., 2018; Yang et al., 2015). Due to the unique environmental conditions in wetlands, climate change may have different effects on the autumn phenology of vegetation compared to other ecosystems (Keppeler et al., 2021; Ma et al., 2022; Molino et al., 2022; Shen et al., 2022). Such differences must be considered if we are to better understand the responses of the global carbon cycle and ecosystem vegetation to climatic variation in the context of global climate change.

As the highest plateau in the world, the Tibetan Plateau is highly sensitive to climate change. And yet the roles of temperature or precipitation in determining the vegetation phenology of the Tibetan Plateau appear contradictory; Some studies assert that temperature plays a dominant role in determining vegetation phenology (Yu et al., 2010), while others argue that precipitation is critical to the vegetation phenology of the region (Shen et al., 2014, 2015a). Known as the “water tower of Asia”, the Tibetan Plateau features a large area of marsh wetlands with relatively high water content (Che et al., 2014; Shen et al., 2011; Shen et al., 2021a) that provide an ideal opportunity for clarifying the dominant effects of temperature and precipitation on the phenology of the vegetation of the Tibetan Plateau.

Recent studies have analyzed the effects of climate change on autumn phenology in
various ecosystems of the region (Shen et al., 2022b), reported the phenology of grassland vegetation as positively correlated with precipitation and negatively correlated with daytime maximum temperature. Increased precipitation can enhance the water use efficiency of grassland vegetation, potentially delaying the EOS (Shen et al., 2015b; Wu et al., 2018). However, an increase in daytime maximum temperature also promotes evaporation, reducing water use efficiency and consequently advancing the EOS. Clearly, an improved understanding of the influence of climate variation on the autumn phenology of marsh vegetation in this region can greatly improve our knowledge of the relationships between the vegetation of ecosystems and climate change.

This study utilizes data from 2001 to 2020, incorporating the Normalized Difference Vegetation Index (NDVI) and observational climate data. The aim is to explore spatiotemporal variations in the end of the growing season (EOS) and vegetation responses to climatic variations in the marshes of the Tibetan Plateau. The objective is to enhance our understanding and predictive capabilities regarding phenological changes in marsh vegetation. By illuminating the intricate relationship between vegetation and climate change in this ecologically significant area, our study's findings can offer valuable new insights for ecological management and conservation efforts.

# 2 Material and method

## 2.1 Study region

The Tibetan Plateau is located in southwestern China at an altitude of 3,000–5,000 m
(average altitude > 4,000 m) and is characterized by a semi-arid and cold climate (Figure 1) (Shen et al., 2022). The annual precipitation exceeds 1,000mm in the southeastern region and is < 100mm in the northwestern region (Cheng et al., 2021; Gao et al., 2013; Piao et al., 2006a). The average temperature in the northwestern and southeastern areas is approximately -6°C and 20°C, respectively (Qin et al., 2022).

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>
FIGURE 1 Spatiotemporal change of the end of the growing season (EOS) in the marshes of the Tibetan Plateau from 2001 to 2020. (a) Distribution of marshes at different altitudes on the Tibetan Plateau. (b) Spatial patterns of regionally averaged EOS. (c) Temporal trends in EOS. (d) Temporal variations of regionally averaged EOS. The inset histograms at the bottom of (b) and (c) describe the frequency distributions of the average EOS and EOS trend. - and + in C show the negative (advancing) and positive (delaying) trend, respectively; * and ** indicate the trend is significant (P < 0.05) and extremely significant (P < 0.01), respectively. The error bar and bold black line in D show standard error and linear trend of the regionally averaged EOS, respectively.

As the highest plateau in the world, the Tibetan Plateau is extremely sensitive to climate change (Dong et al., 2012; Zhang et al., 2013). Changes in vegetation phenology in this region serve as crucial indicators of global climate change (Chen et al., 2015). Large wetland areas characterized by marshes are distributed on the Tibetan Plateau and are important for the ecological security of the region and the major river systems that originate there, including the Huanghe (Yellow), Changjiang (Yangzi), and Brahmaputra rivers. The main species of marsh plants distributed on the Tibetan Plateau are *Phragmites australis*, *Blymus sinocompressus*, *Carex pseudosupina*, and *Kobresia littledalei* (Shen et al., 2021a).

## 2.2 Data

Satellite-derived NDVI data covering the 2001–2020 period were obtained from the
MOD13Q1 NDVI dataset, with temporal and spatial resolutions of 16 d and 250 m, respectively (Shen et al., 2023). This dataset was provided by the Earth Science Data Systems of the National Aeronautics and Space Administration.

The distribution of marshes in the study area was obtained from the wetland distribution datasets for China for years 2000 and 2015 at a resolution of 30 m × 30 m (Mao et al., 2020). These digital maps were available from the National Earth System Science Data Center. The accuracy of datasets had been verified through field observations, and the producer's and user's accuracies were over 95% and 98%, respectively (Mao et al., 2020). We used the marsh distribution data for two specific years (2000 and 2015) to extract the unchanged marsh distribution (Shen et al., 2023).

The soil moisture data used in this study were extracted from a 1-km daily soil moisture dataset of in situ measurements conducted in China from 2000 to 2020 (Li et al., 2022). These dataset was produced using spatially dense in situ observations and machine learning, and was obtained from the National Tibetan Plateau Scientific Data Center.

The Climate Change Research Center of the Chinese Academy of Sciences provided the daily gridded climate data from more than 2400 meteorological stations distributed across China. These included daily precipitation as well as the minimum, maximum, and mean temperatures with a spatial resolution of 1 km from 2001 to 2020. Marsh distribution, soil moisture, and climatic data were resampled at a resolution of 250 m × 250 m to maintain consistency with the spatial resolution of the NDVI data (Shen et al., 2021b).
157

## 2.3 Methods

Considering that snow cover decreases the NDVI value, consequently affecting the accuracy of satellite-derived phenology data, we replaced the snow-contaminated NDVI values with the median value of the uncontaminated winter NDVI values between November and the following March for each pixel (Shen et al., 2014, 2015). This preprocessing of data has been validated and included in numerous previous studies (Ganguly et al., 2010; Shen et al., 2015a; Wang et al., 2021). Based on previous research (Cong et al., 2013; Liu et al., 2016; Ma et al., 2021; Piao et al., 2006b; Shen et al., 2016; Su et al., 2022), we used the poly fit-maximum approach to extract phenological information (Piao et al., 2006b). In this method, the growth curve of the growing season is fitted with a daily temporal resolution in order to remove abnormal NDVI values (Piao et al., 2006b, 2011), and the EOS date is set to correspond to the time of the largest decrease in NDVI at the end of the growth period. The polyfit-maximum method has been widely used to extract vegetation phenology owing to its excellent performance (Fu et al., 2014; Ma et al., 2022; Piao et al., 2006b, 2011; Shen et al., 2018, 2019, 2023; Su et al., 2022) and consists in a number of steps.

First, we calculated the annual and multiyear average rates of NDVI variation to obtain the corresponding day of the year (DOY) for the vegetation's EOS (Piao et al., 2006b). The following equation was used to calculate the rate of NDVI variation:

$$NDVI_{ratio}(t) = \frac{NDVI(t+1)-NDVI(t)}{NDVI(t)} \quad (1)$$
where t is time (temporal resolution of 16 days), NDVI<sub>ratio</sub>(t) is the rate of NDVI change corresponding to period t, NDVI(t) is the NDVI value for period t, and NDVI(t+1) is the NDVI value for period t+1. We detected the time t with the minimum NDVI<sub>ratio</sub> and used the corresponding NDVI(t+1) at time (t+1) as the NDVI threshold for the EOS.

Then, we used the maximum value method of multivariate fitting to construct a
unary sixth-degree polynomial function (Piao et al., 2006b) and fitted the annual and
multiyear average daily NDVI fitting curve by pixel. The formula used was:

$$
185 \qquad \text{NDVI} = a + a_1 x^1 + a_2 x^2 + a_3 x^3 + a_4 x^4 + a_5 x^5 + a_6 x^6 \qquad (2)
$$

where x is the day of each year (DOY); and a₁, a₂, a₃, ... a₆ are the regression coefficients determined by least-squares regression.

Finally, we substituted the DOY into the fitting curve of the multiyear average daily
NDVI to obtain the NDVI threshold corresponding to the EOS (Piao et al., 2006b). We
applied the NDVI threshold values to the daily NDVI fitting curve of each year to obtain
the corresponding EOS values for marsh vegetation in that year.

To analyze the EOS trend and climatic variables on the Tibetan Plateau from 2001
to 2020, we performed a linear regression analysis using the following equation (Piao et
al., 2011):

$$
195 \qquad \theta_{\text{slope}} = \frac{(n \times \sum_{i=1}^{n} i \times x_i) - (\sum_{i=1}^{n} i \times \sum_{i=1}^{n} x_i)}{n \times \sum_{i=1}^{n} i^2 - (\sum_{i=1}^{n} i)^2} \qquad (3)
$$

where *n* is the number of years analyzed (i.e., 20 years for this study); *θ*<sub>slope</sub> indicates
the trend (of the EOS or climatic variables) for each pixel; and *x*<sub>*i*</sub> is the climate variable
(or EOS) during the *i* year. A negative *θ*<sub>slope</sub> implies that the temporal variation shows an
advancing (i.e., decreasing) trend, whereas a positive $\theta_{slope}$ implies a delaying (i.e.,
increasing) trend.

To investigate the seasonal changes in the EOS in response to climate variations, we
analyzed the simple correlation coefficients between monthly and seasonal climate
factors and the EOS in previous winter (November to February of the following year),
spring (March to May), summer (June to August), and autumn (September to October).
In addition, we carried out a partial correlation analysis to further examine correlations
between climate variables and the EOS. Through this analysis, it was possible to
determine the relationship between two parameters after removing the influence of other
factors (Peng et al., 2013; Shen et al., 2016).

The partial correlation coefficient between the time series of the EOS and daytime
maximum temperature (or nighttime minimum temperature) was calculated to assess the
effect of maximum temperature (or minimum temperature) on the EOS, with precipitation
and minimum temperature (or maximum temperature) as the controlling variables. In line
with previous studies, the duration of the preseason was calculated for the maximum
temperature (or minimum temperature) based on the period preceding the long-term
average date of the EOS. When the maximum temperature (or minimum temperature) had
the highest absolute value for the partial correlation coefficient with the EOS, this period
is referred to as the preseason for the maximum temperature (Shen et al., 2016; Wu et al.,
2018) (or the minimum temperature). In this study, an interval of 10 days was adopted to
determine the duration of the preseason period and smooth out potential extreme values
220 (Shen et al., 2015a).

221 The effect of preseason cumulative precipitation on the EOS was similarly analyzed,
222 and preseason precipitation was determined by setting the maximum and minimum
223 temperatures as the controlling variables (Shen et al., 2016). We did not constrain the
224 duration of preseason precipitation to be equal to that of preseason temperature. In
225 addition, to further analyze the influences of climatic variations on the EOS, we compared
226 the partial correlation coefficients between the EOS and preseason precipitation and
227 between the EOS and maximum and minimum temperatures at different levels of soil
228 moisture.

229

230 **3 Results**

231 3.1 Spatial and temporal variations in the EOS

232 The multiyear mean EOS on the Tibetan Plateau occurred primarily between 260th and
233 300th day of year (DOY), with a regional average of 277th DOY (October 4, or October
234 3 in leap years) (Figure 1b). The EOS was later in the low altitude (below 4,000 m) humid
235 areas of the eastern Tibetan Plateau and earlier in the high-altitude (above 4,000 m)
236 central areas (Figure 1a,b).

237 The regionally averaged EOS from 2001 to 2020 across the Tibetan Plateau
238 exhibited a significant delay of 4.10 days per decade (P < 0.05) (Figure 1d). The
239 percentage of pixels showing a trend of delayed EOS (68.5%, with a significant
240 proportion of 36.2%, P < 0.05) was higher than that showing a trend of advanced EOS
241 (31.5%, with a significant proportion of 6.4%, *P* < 0.05). The former trend was more
242 evident in the northern and western (high altitude permafrost areas) of the Tibetan Plateau,
243 while the latter was concentrated in the central regions (Figure 1a,c).

244
245
3.2 Relationships between the EOS and climate variables

246 We first analyzed partial correlations between seasonal climate variables and the EOS
247 without using pre-seasons. In autumn, the EOS showed a significant positive correlation
248 with temperatures (*P* < 0.05) but a weak negative correlation with precipitation (*P* > 0.05)
249 across the Tibetan Plateau (Figure S1). The partial correlations between EOS and climatic
250 variables in other seasons were not statistically significant (*P* > 0.05).

251 We further analyzed the partial correlations between EOS and preseason climatic
252 variables. Across the Tibetan Plateau, the regionally averaged EOS showed a weak
253 negative correlation with preseason cumulative precipitation but a significant positive
254 correlation with preseason maximum and minimum temperatures (Figure 2). Spatially,
255 the proportion of pixels displaying a negative correlation between EOS and preseason
256 cumulative precipitation was approximately 52.1%, and that with a significant negative
257 relationship was approximately 7.6% (Figure 3a). The proportions of pixels showing a
258 positive relationship between EOS and preseason maximum and minimum temperatures
259 were approximately 65.2% and 89.4%, respectively, and the proportions of pixels with a
260 significant (*P* < 0.05) positive relationship were 19.5% and 34.0%, respectively (Figure
261 3c,e).
<figure><img src="image_6.png" /></figure>

FIGURE 2 Partial correlation coefficients between preseason climatic factors and EOS of marsh vegetation on the Tibetan Plateau. \*P < 0.05; \*\*P < 0.01. Correlations lacking an asterisk are non-significant (P > 0.05).
<figure><img src="image_7.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>(c) EOS and preseason maximum temperature</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(d)</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>(e) EOS and preseason minimum temperature</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(f)</figcaption></figure>

<figure><img src="image_12.png" /></figure>

FIGURE 3 Relationship between EOS and preseason climate variables for the marshes on the Tibetan Plateau from 2001 to 2020. Spatial patterns of partial correlation coefficients between EOS and preseason cumulative precipitation (a), maximum
temperature (c), and minimum temperature (e). The changes in partial correlation coefficients between EOS and preseason cumulative precipitation (b), maximum temperature (d), and minimum temperature (f) along the spatial gradient of long-term preseason soil moisture on the Tibetan Plateau from 2001 to 2020. The inset histograms at the bottom of left figures (a, c, e) display the frequency distributions of partial correlation coefficients. - and + show the negative and positive correlation, respectively; * and ** indicate the correlation is significant (P < 0.05) and extremely significant (P < 0.01), respectively. The body lines in the right figures (b, d, f) indicate the linear fit for the partial correlation coefficients, and the shading represents the 95% confidence band of the fits.

The partial correlations between EOS and preseason climatic variables indicated spatial heterogeneity across the Tibetan Plateau. In the southwestern region, the EOS showed a positive and negative partial correlation with preseason cumulative precipitation and preseason maximum temperature, respectively; however, in the central area, it exhibited a significant negative and positive partial correlation with these two parameters, respectively (Figure 3a,c). The partial relationships between EOS and preseason minimum temperature were positive in most of the study area, and the positive relationships were extremely significant (P < 0.01) in the western and eastern regions (Figure 3e).

Subsequently, we examined the partial correlations between EOS and preseason climatic variables under different soil moisture levels in the study region (Figure 3). The
results showed that as the preseason soil moisture increased, the positive relationships between EOS and preseason cumulative precipitation gradually weakened, whereas those between EOS and preseason maximum and minimum temperatures became gradually stronger (Figures 3 and 4).

<figure><img src="image_13.png" /><figcaption>FIGURE 4 Impact of climate change on marsh EOS in different regions of the marshes on the Tibetan Plateau. (a) Conceptual diagrams showing the effects of climate change on the EOS. (b) Spatial distribution of long-term average preseason soil moisture (m³/m³). (c) The long-term average preseason temperature (°C) for marsh vegetation on the Tibetan Plateau. (d) long-term average preseason soil moisture (m³/m³) and preseason temperature (°C) in different regions of the Tibetan Plateau. “+” and “-” indicate that the</figcaption></figure>
302 climatic variable had a significant positive and negative effect on the EOS, respectively
303 (*P* < 0.05). “++” indicates an extremely significant positive effect (*P* < 0.01). In the
304 southwestern Tibetan Plateau (circled in blue), increased preseason precipitation can
305 significantly delay the EOS, while a higher preseason maximum temperature will advance
306 it. In contrast, in the central Tibetan Plateau (circled in red), a higher preseason maximum
307 temperature significantly delayed the EOS, while an increased preseason precipitation
308 advanced it. In the eastern Tibetan Plateau (circled in green), a higher preseason minimum
309 temperature significantly delayed the EOS.

310

311 **4 Discussions**

312 4.1 Autumn phenology of marsh vegetation on the Tibetan Plateau

313 The long-term average EOS for marsh vegetation occurred later in the eastern Tibetan
314 Plateau than in the central and southwestern regions (Figure 1b). This aligns with
315 observations that the eastern area has a lower altitude and a warmer climate (Shen et al.,
316 2021b), thus allowing the growing season to continue for longer. This result is consistent
317 with the findings of (Liu et al., 2021), which showed that the combined EOS for all
318 vegetation types occurred earlier in the central Tibetan Plateau and later in the eastern
319 regions. From 2001 to 2020, the average EOS was delayed by 4.10 days per decade across
320 the plateau (Figure 1c), a trend consistent with the results of Shen et al. (Shen et al., 2022),
321 which showed a delay of 8.2 days in the EOS for vegetation on the plateau over the same
322 period.
323

4.2 Climatic effects on the regionally averaged EOS for marsh vegetation

324

The regionally averaged EOS exhibited significant positive partial correlations with
preseason daytime maximum and nighttime minimum temperatures but a weak negative
correlation with preseason cumulative precipitation across the Tibetan Plateau. The
phenology of marsh vegetation on the plateau observed in this study differed from that of
grasslands reported in previous studies (Dorji et al., 2013; Shen et al., 2022; Yang et al.,
2021).

325

326

327

328

329

330

Previous studies have shown that in the grassland vegetation of the Tibetan Plateau,
increased precipitation significantly enhances water use efficiency, delaying the EOS. In
these relatively arid systems, the higher maximum temperatures increase evaporation and
reduce water use efficiency, thus advancing the EOS (Dorji et al., 2013; Yang et al., 2021).

In contrast, the marshes examined in the present study contained far more water
(Ganjurjav et al., 2022; Shen et al., 2022), making it less likely that preseason
precipitation would affect the regionally averaged EOS for their vegetation, and explain
how the abundance of water allowed the EOS to be delayed by the increased preseason
temperatures on the Tibetan Plateau.

331

332

333

334

335

336

337

338

339

Our results indicate that preseason temperature is a key factor affecting the EOS, and
an increase in this parameter significantly delayed the EOS on the Tibetan Plateau (Figure
3). It is known that increased preseason maximum temperature can promote
photosynthesis by enhancing the daytime photosynthetic activity of enzymes (Piao et al.,
2007; Turnbull et al., 2022). This increased photosynthesis, together with raised nighttime

340

341

342

343
minimum temperatures that reduce frost and low-temperature constraints (Shen et al., 2016), would further delay the EOS on the plateau.

## 4.3 Elucidating spatial variations in the effects of climatic change on the EOS

We identified 3 characteristic areas within the Tibetan Plateau, which exhibited differing primary drivers of the changes in EOS; south western, central and eastern.

In the relatively arid areas of the southwestern Tibetan Plateau, the EOS exhibited a significant positive partial correlation with preseason cumulative precipitation but a significant negative correlation with preseason maximum temperature (Figure 3a, c). This indicated that, in this region, higher preseason precipitation significantly delayed the EOS although this was constrained by preseason maximum temperatures tending to significantly advance the EOS. As the climate in this southwestern region is dry and the preseason soil moisture is low (Figure 4), an increase in preseason precipitation can alleviate water stress, enhancing water use efficiency (Liu et al., 2016; Munné-Bosch et al., 2004) and delaying the EOS. At the same time, an increase in maximum temperature would increase hydrological losses through evaporation and reduce the amount of available water (Kelsey et al., 2021; Shen et al., 2016), inhibiting the growth of marsh vegetation. These dynamics fit well with our results, indicating that the EOS was primarily affected by precipitation in the southwestern arid regions of the study area. For the first time, our results indicate that, even in marsh ecosystems with their relatively high water contents, available water may be insufficient for vegetation growth in the drier
365 regions of the Tibetan Plateau.

366 In the central Tibetan Plateau, a higher preseason temperature delayed the EOS,
367 while increased preseason precipitation advanced it (Figure 3). In the humid and cold
368 areas of the central Tibetan Plateau, soil moisture and temperature are higher and lower,
369 respectively, than those in the southwestern region (Cong et al., 2017) (Figure 4). In light
370 of these conditions, our results indicate that water was not the key factor affecting the
371 growth of marsh vegetation in the central Plateau, although temperature remained a
372 limiting factor.

373 An increase in maximum temperature can decelerate the process of chlorophyll
374 degradation (Shi et al., 2017) and retard the progression of leaf senescence (Estiarte et al.,
375 2017). In addition, high preseason nighttime temperatures reduce the occurrence of frost
376 damage (Shen et al., 2022). By calculating the number of the frost days, we confirmed
377 that warming preseason nighttime temperatures had the most notable negative effect on
378 the frost days in the central Tibetan Plateau, the coldest region of the study area (Figure
379 S2). This could account for the role of increasing minimum temperature in delaying the
380 EOS in the central Tibetan Plateau. In contrast, increased preseason precipitation could
381 retard the growth of marsh vegetation due to the accompanying cooling effect, which
382 would advance the EOS in the already cold and humid areas of the central Tibetan Plateau.

383 In the low altitude humid regions in the east and high-altitude cold permafrost regions
384 in the west, the EOS showed a significant positive correlation with preseason minimum
385 temperature (Figure 3), indicating that an increase in this parameter delayed the EOS in
the marshes distributed in these regions of the Tibetan Plateau. However, the mechanisms through which minimum temperature affects the EOS may differ between these two areas.

On one hand, increased nighttime temperatures cause a greater loss of organic matter due to enhanced respiration, but on the other hand it can also stimulate accumulation of more organic matter via the overcompensation effect (Shen et al., 2021b, 2022), a phenomenon through which the vegetation recovers and exceeds its original state by promoting photosynthesis the day after the enhanced respiration (Peng et al., 2013). It has been reported that the occurrence of this effect is favored by the presence of sufficient water and nutrients (Peng et al., 2013; Shen et al., 2022).

In cold high-altitude regions, the physiological processes causing vegetation senescence are typically determined by low temperatures during cold nights (Tang et al., 2016). In the high-altitude cold permafrost regions of the western Tibetan Plateau, nighttime minimum temperature is generally low (Nan et al., 2005), and an increase in preseason minimum temperature would delay the EOS by alleviating frost damage and retarding vegetation senescence (Cong et al., 2017). In contrast, in the low altitude areas of the eastern Tibetan Plateau, the climate is relatively warm and humid (Figure 4) and marsh vegetation has access to sufficient water (Shen et al., 2022). Therefore, although organic matter may be depleted through the respiration of marsh vegetation due to nighttime warming, increased temperatures can also promote photosynthesis, leading to accumulation of more organic matter the following day via the over compensation effect (Belsky et al., 1986; Shen et al., 2021b, 2022). This would contribute to delaying the EOS
407 in the eastern region as a consequence of the increased preseason minimum temperatures.

408 By building a multi-variable regression for each pixel, we showed spatially which
409 preseason variable is the most important for affecting the EOS. The results confirmed that
410 preseason cumulative precipitation and minimum temperature played a crucial role in the
411 relatively arid southwestern and humid eastern Tibetan Plateau, respectively (Figure S3).

412 By comparing the partial correlation coefficients between EOS and climatic factors in
413 different regions, we confirmed that as annual mean temperature increased, the delaying
414 effects of higher preseason maximum and minimum temperatures on the EOS gradually
415 weakened, while the delaying effect of increased preseason precipitation became
416 gradually stronger (Figure 5). Based on our results, we propose that the effects of climate
417 variations on the EOS differ depending on the hydrological constraints on soil moisture
418 in the marshes within the Tibetan Plateau. As such, we further compared the partial
419 correlations between EOS and climatic factors for preseason soil moisture gradients of
420 0.05 m3/m3. The results showed that as soil moisture increased, the delaying effect of
421 increased preseason precipitation on the EOS gradually weakened (Figure 3), while the
422 delaying effects of higher preseason maximum and minimum temperatures became
423 gradually stronger (Figure 3). This finding supports the proposal that increased
424 precipitation and warming temperatures significantly delayed the EOS for marsh
425 vegetation in the arid southwestern Tibetan Plateau and the humid central and eastern
426 areas, respectively. In contrast, increasing daytime temperatures and precipitation
427 advanced the EOS in the arid southwestern and humid central areas due to the reduced
428 soil moisture and cooling effect, respectively (Figure 3).

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

429

430 FIGURE 5 The changes in partial correlation coefficients between EOS and preseason
431 cumulative precipitation, maximum temperature, and minimum temperature along the
432 spatial gradient of long-term annual mean temperature on the Tibetan Plateau from 2001
433 to 2020. The body lines in the figures indicate the linear fit for the partial correlation
coefficients, and the shading represents the 95% confidence band of the fits.

435

436 4.4 Attribution of temporal changes in the EOS

437 To further explain the temporal and spatial variations in the EOS, we calculated the rates
438 at which precipitation and maximum and minimum temperatures varied on the plateau
439 from 2001 to 2020 (Figure 6 and Table 1). The preseason cumulative precipitation and
440 maximum and minimum temperatures exhibited increasing trends (0.52 mm/a, 0.02 °C/a,
441 and 0.05 °C/a, respectively), but only the increase in the minimum temperature was
442 significant (*P* < 0.05). Because the EOS showed a significant positive relationship with
443 preseason minimum temperature, the increase in this parameter may partly account for
444 the delayed EOS on the Tibetan Plateau (Figure 1 and Table 1).
<figure><img src="image_17.png" /><figcaption>(b) Trend of preseason maximum temperature (°C/a)</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>(c) Trend of preseason minimum temperature (°C/a)</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>FIGURE 6 Preseason climate change in the marshes of the Tibetan Plateau. Spatial patterns of temporal trends in preseason cumulative precipitation (a), maximum</figcaption></figure>
temperature (b), and minimum temperature (c) in the marshes of the Tibetan Plateau from 2001 to 2020. The inset histograms at the bottom of figures display the frequency distributions of the trends. - and + show the negative and positive trend, respectively; * and ** indicate the variation trend is significant (P < 0.05) and extremely significant (P < 0.01), respectively.

Table 1 Temporal trends of preseason and seasonal precipitation (mm/a), maximum temperature (°C/a), and minimum temperature (°C/a) in marshes of the Tibetan Plateau from 2001 to 2020.

<table><thead><tr><th></th><th>Precipitation</th><th>maximum<br>temperature</th><th>minimum<br>temperature</th></tr></thead><tbody><tr><td>Preseason</td><td>0.52*</td><td>-0.02</td><td>0.05*</td></tr><tr><td>Spring</td><td>0.22</td><td>0.01</td><td>0.05**</td></tr><tr><td>Summer</td><td>0.31</td><td>0.05*</td><td>0.06*</td></tr><tr><td>Autumn</td><td>0.14</td><td>0.05</td><td>0.08**</td></tr><tr><td>Winter</td><td>0.79</td><td>-0.03</td><td>0.03</td></tr></tbody></table>

In the high-altitude arid area of the southwestern Tibetan Plateau, the EOS was positively correlated with preseason cumulative precipitation (Figure 3). As preseason cumulative precipitation showed significant increasing trends in the high-altitude arid area of the southwestern Tibetan Plateau (Figure 6), the increase in preseason precipitation may partly account for the delayed EOS in this region (Figure 1). In the low

<footnote>**P < 0.01; *P < 0.05.</footnote>
462 altitude humid areas in the east and high-altitude cold permafrost areas in the west, the
463 EOS was positively correlated with preseason minimum temperature (Figure 3).

464 Therefore, we deduce that the extremely significant increases of preseason minimum
465 temperature may contribute to the delayed EOS in these regions (Figures 1 and 6). In the
466 northeastern area of the plateau, the EOS appeared to occur earlier throughout the study
467 period (Figure 1), possibly due to the rise in preseason minimum temperature (Figures 3
468 and 6).

469

470 **5 Conclusions**

471 Our study uncovers several crucial findings regarding the end of the growing season (EOS)
472 in marsh vegetation across the Tibetan Plateau. Firstly, we observed a significant delay in
473 the EOS by 4.10 days/decade during the study period. Secondly, while average preseason
474 cumulative precipitation did not notably impact the regionally averaged EOS, warmer
475 preseason temperatures led to a significant delay in the average EOS of marsh vegetation.

476 Notably, the delaying effect of higher nighttime temperatures on the regionally averaged
477 EOS was more pronounced than that of daytime temperatures. This asymmetric response
478 to diurnal temperature variations can be attributed to the widespread delaying effect of
479 nighttime warming and the spatially diverse relationship between EOS and daytime
480 temperature, influenced by water conditions. Furthermore, our evidence indicates that
481 hydrological factors influencing soil water content play a regulatory role in the impact of
482 climate change on the EOS. As soil moisture decreased, the delaying effect of increasing
preseason maximum temperatures gradually weakened and even reversed, while the delaying effect of increased preseason precipitation was strengthened. In the humid, cold regions of the central Tibetan Plateau, higher preseason maximum temperatures significantly delayed the EOS, whereas increased precipitation advanced it, potentially due to a cooling effect. In the low altitude, humid regions in the east, higher minimum temperatures delayed the EOS, possibly due to an overcompensation effect. In the arid southwestern area, increased precipitation directly and significantly delayed the EOS, whereas higher daytime temperatures advanced it, likely due to limited water availability.

These findings suggest that the EOS in these regions is constrained by water conditions, even within marsh ecosystems. Overall, our study highlights the asymmetric influences of daytime and nighttime temperatures on the EOS of marsh vegetation, particularly in the context of global diurnal asymmetric warming (stronger warming during nighttime than during daytime). It underscores the importance of considering water conditions in EOS simulations conducted by terrestrial ecosystem models in cold and dry regions worldwide.

498

Acknowledgements

We gratefully acknowledge the National Natural Science Foundation of China (42230516), Key Research Program of Frontier Sciences, Chinese Academy of Sciences (ZDBS-LY-7019), and the Spanish Government grant TED2021-132627 B-I00, funded by MCIN, AEI/10.13039/501100011033 European Union Next Generation EU/PRTR.

504
505 **Conflict of interest statement**

The authors declare no conflict of interest.

507

508 **Data availability statement**

The MOD13Q1 NDVI dataset was provided by the Earth Science Data Systems of the

510 National Aeronautics and Space Administration
511 (https://ladsweb.modaps.eosdis.nasa.gov). The distribution of marshes was obtained from
512 the wetland distribution datasets for China (http://www.geodata.cn). The climate data was
513 available from the National Meteorological Information Center of China
514 (http://data.cma.cn/data). The soil moisture data were obtained from the National Tibetan
515 Plateau Scientific Data Center (https://data.tpdc.ac.cn).

516

517 **References**

518 Bao, G., Tuya, A., Bayarsaikhan, S., Dorjsuren, A., Mandakh, U., Bao, Y., Li, C., &
519 Vanchindorj, B. (2020). Variations and climate constraints of terrestrial net primary
520 productivity over Mongolia. *Quaternary International*, 537, 112-125.
521 https://doi.org/10.1016/j.quaint.2019.06.017

522 Belsky, A. J. (1986). Does herbivory benefit plants? A review of the evidence. *The American Naturalist*, 127, 870-892. https://doi.org/10.1086/284531

524 Che, M., Chen, B., Innes, J. L., Wang, G., Dou, X., Zhou, T., Zhang, H., Yan, J., Xu, G.,
525 & Zhao, H. (2014). Spatial and temporal variations in the end date of the vegetation
growing season throughout the Qinghai-Tibetan Plateau from 1982 to 2011.

*Agricultural and Forest Meteorology*, 189, 81-90.

https://doi.org/10.1016/j.agrformet.2014.01.004

Chen, X. (2017). Plant phenology of natural landscape dynamics. In Spatiotemporal
processes of plant phenology. Springer, Berlin, Heidelberg.

Chen, X., An, S., Inouye, D. W., & Schwartz, M. D. (2015). Temperature and snowfall
trigger alpine vegetation green-up on the world's roof. *Global Change Biology*, 21,
3635-3646. https://doi.org/10.1111/gcb.12954

Cheng, M., Jin, J., & Jiang, H. (2021). Strong impacts of autumn phenology on grassland
ecosystem water use efficiency on the Tibetan Plateau. *Ecological Indicators*, 126,
107682. https://doi.org/10.1016/j.ecolind.2021.107682

Coleman, D. J., Schuerch, M., Temmerman, S., Guntenspergen, G., Smith, C. G., &
Kirwan, M. L. (2022). Reconciling models and measurements of marsh vulnerability
to sea level rise. *Limnology and Oceanography Letters*, 7, 140-149.
https://doi.org/10.1002/lol2.10230

Cong, N., Shen, M., & Piao, S. (2017). Spatial variations in responses of vegetation
autumn phenology to climate change on the Tibetan Plateau. *Journal of Plant
Ecology*, 10, 744-752. https://doi.org/10.1093/jpe/rtw084

Cong, N., Wang, T., Nan, H., Ma, Y., Wang, X., Myneni, R. B., & Piao, S. (2013). Changes
in satellite-derived spring vegetation green-up date and its linkage to climate in
China from 1982 to 2010: a multimethod analysis. *Global Change Biology*, 19, 881-
547 891. https://doi.org/10.1111/gcb.12077

548 Dong, M., Jiang, Y., Zheng, C., & Zhang, D. (2012). Trends in the thermal growing season throughout the Tibetan Plateau during 1960-2009. *Agricultural and Forest Meteorology, 166*, 201-206. https://doi.org/10.1016/j.agrformet.2012.07.013

551 Dorji, T., Totland, Ø., Moe, S. R., Hopping, K. A., Pan, J., & Klein, J. A. (2013). Plant functional traits mediate reproductive phenology and success in response to experimental warming and snow addition in Tibet. *Global Change Biology, 19*, 459-472. https://doi.org/10.1111/gcb.12059

555 Estiarte, M., & Peñuelas, J. (2015). Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency. *Global change biology, 21*, 1005-1017. https://doi.org/10.1111/gcb.12804

558 Fu, Y. H., Piao, S., Delpierre, N., Hao, F., Hänninen, H., Liu, Y., Sun, W., Janssens, I. A., & Campioli, M. (2018). Larger temperature response of autumn leaf senescence than spring leaf - out phenology. *Global Change Biology, 24*(5), 2159-2168. https://doi.org/10.1111/gcb.14021

562 Fu, Y. H., Piao, S., Op de Beeck, M., Cong, N., Zhao, H., Zhang, Y., Menzel, A., & Janssens, I. A. (2014). Recent spring phenology shifts in western Central Europe based on multiscale observations. *Global Ecology and Biogeography, 23*, 1255-1263. https://doi.org/10.1111/geb.12210

566 Ganguly, S., Friedl, M. A., Tan, B., Zhang, X., & Verma, M. (2010). Land surface phenology from MODIS: Characterization of the Collection 5 global land cover
568 dynamics product. Remote Sensing of Environment, 114, 1805-1816.

569 https://doi.org/10.1016/j.rse.2010.04.005

570 Ganjurjav, H., Gornish, E. S., Hu, G., Schwartz, M. W., Wan, Y., Li, Y., & Gao, Q. (2020).

571 Warming and precipitation addition interact to affect plant spring phenology in

572 alpine meadows on the central Qinghai-Tibetan Plateau. *Agricultural and Forest*

573 *Meteorology*, 287, 107943. https://doi.org/10.1016/j.agrformet.2020.107943

574 Gao, Y. C., & Liu, M. (2013). Evaluation of high-resolution satellite precipitation

575 products using rain gauge observations over the Tibetan Plateau. *Hydrology and*

576 *Earth System Sciences*, 17, 837-849. https://doi.org/10.5194/hess-17-837-2013

577 Garonna, I., De Jong, R., De Wit, A. J., Mücher, C. A., Schmid, B., & Schaepman, M. E.

578 (2014). Strong contribution of autumn phenology to changes in satellite - derived

579 growing season length estimates across Europe (1982-2011). *Global Change*

580 *Biology*, 20, 3457-3470. https://doi.org/10.1111/gcb.12625

581 Ge, Q., Wang, H., Rutishauser, T., & Dai, J. (2015). Phenological response to climate

582 change in China: a meta - analysis. *Global change biology*, 21(1), 265-274.

583 https://doi.org/10.1111/gcb.12648

584 Kelsey, K. C., Pedersen, S. H., Leffler, A. J., Sexton, J. O., Feng, M., & Welker, J. M.

585 (2021). Winter snow and spring temperature have differential effects on vegetation

586 phenology and productivity across Arctic plant communities. *Global Change*

587 *Biology*, 27, 1572-1586. https://doi.org/10.1111/gcb.15505

588 Keppeler, F. W., Olin, J. A., López - Duarte, P. C., Polito, M. J., Hooper - Bùi, L. M.,
Taylor, S. S., Rabalais, N. N., Fodrie F. J., Roberts, B. J., Turner, R. R., Matin, C. W., & Jensen, O. P. (2021). Body size, trophic position, and the coupling of different energy pathways across a saltmarsh landscape. *Limnology and Oceanography Letters, 6*, 360-368. https://doi.org/10.1002/lo12.10212

Li, Q., Shi, G., Shangguan, W., Nourani, V., Li, J., Li, L., Huang, F., Zhang, Y., Wang, C., Wang, D., Qiu, J., Lu, X., & Dai, Y. (2022). A 1 km daily soil moisture dataset over China using in situ measurement and machine learning, *Earth Syst. Sci. Data, 14*, 5267-5286. https://doi.org/10.5194/essd-14-5267-2022

Liu, Q., Fu, Y. H., Zeng, Z., Huang, M., Li, X., & Piao, S. (2016). Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China. *Global Change Biology, 22*, 644-655. https://doi.org/10.1111/gcb.13081

Liu, X., Chen, Y., Li, Z., Li, Y., Zhang, Q., & Zan, M. (2021). Driving Forces of the Changes in Vegetation Phenology in the Qinghai-Tibet Plateau. *Remote Sensing, 13*, 4952. https://doi.org/10.3390/rs13234952

Ma, R., Shen, X., Zhang, J., Xia, C., Liu, Y., Wu, L., Wang, Y., Jiang, M., & Lu, X. (2022). Variation of vegetation autumn phenology and its climatic drivers in temperate grasslands of China. *International Journal of Applied Earth Observation and Geoinformation, 114*, 103064. https://doi.org/10.1016/j.jag.2022.103064

Mao, D., Wang, Z., Du, B., Li, L., Tian, Y., Jia, M., Zeng, Y., Song, K., Jiang, M., & Wang, Y. (2020). National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images. *ISPRS Journal of
610 *Photogrammetry and Remote Sensing*, 164, 11-25.

611 https://doi.org/10.1016/j.isprsjprs.2020.03.020

612 Molino, G. D., Carr, J. A., Ganju, N. K., & Kirwan, M. L. (2022). Variability in marsh

613 migration potential determined by topographic rather than anthropogenic constraints

614 in the Chesapeake Bay region. *Limnology and Oceanography Letters*, 7, 321-331.

615 https://doi.org/10.1002/lol2.10262

616 Munné-Bosch, S., & Alegre, L. (2004). Die and let live: leaf senescence contributes to

617 plant survival under drought stress. *Functional Plant Biology*, 31, 203-216.

618 https://doi.org/10.1071/FP03236

619 Nan, Z., Li, S., & Cheng, G. (2005). Prediction of permafrost distribution on the Qinghai-

620 Tibet Plateau in the next 50 and 100 years. *Science in China Series D: Earth Sciences*,

621 48, 797-804. https://doi.org/10.1360/03yd0258

622 Peng, S., Piao, S., Ciais, P., Myneni, R. B., Chen, A., Chevallier, F., Dolman, A. J.,

623 Janssens, I. A., Penuelas, J., Zhang, G., Vicca, S., Wan, S., Wang, S., & Zeng, H.

624 (2013). Asymmetric effects of daytime and night-time warming on Northern

625 Hemisphere vegetation. *Nature*, 501, 88-92. https://doi.org/10.1038/nature12434

626 Peñuelas, J., Filella, I. (2001). Responses to a warming world. *Science*, 294(5543): 793-

627 795. https://doi.org/10.1126/science.10668.

628 Piao, S., Ciais, P., Friedlingstein, P., Peylin, P., Reichstein, M., Luyssaert, S., Margolis,

629 H., Fang, J., Barr, A., Chen, A., Grelle, A., Hollinger, D. Y., Laurila, T., Lindroth, A.,

630 Richardson, A. D., & Vesala, T. (2008). Net carbon dioxide losses of northern
631 ecosystems in response to autumn warming. *Nature*, **451**, 49-52.

632 https://doi.org/10.1038/nature06444

633 Piao, S., Cui, M., Chen, A., Wang, X., Ciais, P., Liu, J., & Tang, Y. (2011). Altitude and

634 temperature dependence of change in the spring vegetation green-up date from 1982

635 to 2006 in the Qinghai-Xizang Plateau. *Agricultural and Forest Meteorology*, **151**,

636 1599-1608. https://doi.org/10.1016/j.agrformet.2011.06.016

637 Piao, S., Fang, J., & He, J. (2006a). Variations in vegetation net primary production in the

638 Qinghai-Xizang Plateau, China, from 1982 to 1999. *Climatic Change*, **74**, 253-267.

639 https://doi.org/10.1007/s10584-005-6339-8

640 Piao, S., Fang, J., Zhou, L., Ciais, P., & Zhu, B. (2006b). Variations in satellite-derived

641 phenology in China's temperate vegetation. *Global Change Biology*, **12**, 672-685.

642 https://doi.org/10.1111/j.1365-2486.2006.01123.x

643 Piao, S., Friedlingstein, P., Ciais, P., Viovy, N., & Demarty, J. (2007). Growing season

644 extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over

645 the past 2 decades. *Global Biogeochemical Cycles*, **21**.

646 https://doi.org/10.1029/2006GB002888

647 Piao, S., Liu, Q., Chen, A., Janssens, I. A., Fu, Y., Dai, J., Liu, L., Lian, X., Shen, M., &

648 Zhu, X. (2019). Plant phenology and global climate change: Current progresses and

649 challenges. *Global Change Biology*, **25**, 1922-1940.

650 https://doi.org/10.1111/gcb.14619

651 Qin, G., Adu, B., Li, C., & Wu, J. (2022). Diverse Responses of Phenology in Multi-
652 Grassland to Environmental Factors on Qinghai-Tibetan Plateau in China.

653 *Theoretical and Applied Climatology*, 148, 931-942.

654 https://doi.org/10.1007/s00704-022-03963-3

655 Rice, K. E., Montgomery, R. A., Stefanski, A., Rich, R. L., & Reich, P. B. (2018).

656 Experimental warming advances phenology of groundlayer plants at the boreal -

657 temperate forest ecotone. *American journal of botany*, 105, 851-861.

658 https://doi.org/10.1002/ajb2.1091

659 Richardson, A. D., Keenan, T. F., Migliavacca, M., Ryu, Y., Sonnentag, O., & Toomey,

660 M. (2013). Climate change, phenology, and phenological control of vegetation

661 feedbacks to the climate system. *Agricultural and Forest Meteorology*, 169, 156-173.

662 https://doi.org/10.1016/j.agrformet.2012.09.012

663 Shen, M., Piao, S., Chen, X., An, S., Fu, Y. H., Wang, S., Cong, N., & Janssens, I. A.

664 (2016). Strong impacts of daily minimum temperature on the green-up date and

665 summer greenness of the Tibetan Plateau. *Global Change Biology*, 22, 3057-3066.

666 https://doi.org/10.1111/gcb.13301

667 Shen, M., Piao, S., Cong, N., Zhang, G., & Jassens, I. A. (2015a). Precipitation impacts

668 on vegetation spring phenology on the Tibetan Plateau. *Global Change Biology*, 21,

669 3647-3656. https://doi.org/10.1111/gcb.12961

670 Shen, M., Piao, S., Dorji, T., Liu, Q., Cong, N., Chen, X., An, S., Wang, S., Wang, T., &

671 Zhang, G. (2015b). Plant phenological responses to climate change on the Tibetan

672 Plateau: research status and challenges. *National Science Review*, 2, 454-467.
673 https://doi.org/10.1093/nsr/nwv058

674 Shen, M., Tang, Y., Chen, J., Zhu, X., & Zheng, Y. (2011). Influences of temperature and
675 precipitation before the growing season on spring phenology in grasslands of the
676 central and eastern Qinghai-Tibetan Plateau. *Agricultural and Forest Meteorology,*
677 **151**, 1711-1722. https://doi.org/10.1016/j.agrformet.2011.07.003

678 Shen, M., Wang, S., Jiang, N., Sun, J., Cao, R., Ling, X., Fang, B., Zhang, L., Zhang, L.,
679 Xu, X., Lv, W., Li, B., Sun, Q., Meng, F., Jiang, Y., Dorji, T., Fu, Y., Iier, A., Vitasse,
680 Y., Steltzer, H., Ji, Z., Zhao, W., Piao, S., & Fu, B. (2022). Plant phenology changes
681 and drivers on the Qinghai-Tibetan Plateau. *Nature Reviews Earth & Environment,*
682 **1-19**. https://doi.org/10.1038/s43017-022-00317-5

683 Shen, M., Zhang, G., Cong, N., Wang, S., Kong, W., & Piao, S. (2014). Increasing
684 altitudinal gradient of spring vegetation phenology during the last decade on the
685 Qinghai-Tibetan Plateau. *Agricultural and Forest Meteorology*, **189**, 71-80.
686 https://doi.org/10.1016/j.agrformet.2014.01.003

687 Shen, X., Jiang, M., & Lu, X. (2023). Diverse impacts of day and night temperature on
688 spring phenology in freshwater marshes of the Tibetan Plateau. *Limnology and
689 *Oceanography Letters*, 1-7. https://doi.org/10.1002/lol2.10285

690 Shen, X., Jiang, M., Lu, X., Liu, X., Liu, B., Zhang, J., Wang, X., Tong, S., Lei, G., Wang,
691 S., Tong, C., Fan, H., Tian, K., Wang, X., Hu, Y., Xie, Y., Ma, M., Zhang, S., Cao,
692 C., & Wang, Z. (2021a). Aboveground biomass and its spatial distribution pattern of
693 herbaceous marsh vegetation in China. *Science China Earth Sciences*, **64**, 1115-1125.
694 https://doi.org/10.1007/s11430-020-9778-7

695 Shen, X., Liu, B., Henderson, M., Wang, L., Wu, Z., Wu, H., Jiang, M., & Lu, X. (2018).

696 Asymmetric effects of daytime and nighttime warming on spring phenology in the

697 temperate grasslands of China. *Agricultural and Forest Meteorology*, 259, 240-249.

698 https://doi.org/10.1016/j.agrformet.2018.05.006

699 Shen, X., Liu, B., Jiang, M., Wang, Y., Wang, L., Zhang, J., & Lu, X. (2021b).

700 Spatiotemporal change of marsh vegetation and its response to climate change in

701 China from 2000 to 2019. *Journal of Geophysical Research: Biogeosciences*, 126,

702 e2020JG006154. https://doi.org/10.1029/2020JG006154

703 Shen, X., Liu, B., Xue, Z., Jiang, M., Lu, X., & Zhang, Q. (2019). Spatiotemporal

704 variation in vegetation spring phenology and its response to climate change in

705 freshwater marshes of Northeast China. *Science of the Total Environment*, 666, 1169-

706 1177. https://doi.org/10.1016/j.scitotenv.2019.02.265

707 Shen, X., Liu, Y., Zhang, J., Wang, Y., Ma, R., Liu, B., Lu, X., & Jiang, M. (2022).

708 Asymmetric impacts of diurnal warming on vegetation carbon sequestration of

709 marshes in the Qinghai Tibet Plateau. *Global Biogeochemical Cycles*, 36,

710 e2022GB007396. https://doi.org/10.1029/2022GB007396

711 Shi, C., Sun, G., Zhang, H., Xiao, B., Ze, B., Zhang, N., & Wu, N. (2014). Effects of

712 warming on chlorophyll degradation and carbohydrate accumulation of alpine

713 herbaceous species during plant senescence on the Tibetan Plateau. *PLoS One*, 9,

714 e107874. https://doi.org/10.1371/journal.pone.0107874
715 Su, M., Huang, X., Xu, Z., Zhu, W., & Lin, Z. (2022). A Decrease in the Daily Maximum Temperature during Global Warming Hiatus Causes a Delay in Spring Phenology in the China-DPRK-Russia Cross-Border Area. *Remote Sensing, 14*, 1462.
716
717
718 https://doi.org/10.3390/rs14061462

719 Tang, J., Körner, C., Muraoka, H., Piao, S., Shen, M., Thackeray, S. J., & Yang, X. (2016). Emerging opportunities and challenges in phenology: a review. *Ecosphere*, 7, e01436. https://doi.org/10.1002/ecs2.1436

722 Turnbull, M. H., Murthy, R., & Griffin, K. L. (2002). The relative impacts of daytime and night-time warming on photosynthetic capacity in Populus deltoides. *Plant, Cell & Environment*, 25, 1729-1737. https://doi.org/10.1046/j.1365-3040.2002.00947.x

725 Wang, Y., Shen, X., Jiang, M., Tong, S., & Lu, X. (2021). Spatiotemporal change of aboveground biomass and its response to climate change in marshes of the Tibetan Plateau. *International Journal of Applied Earth Observation and Geoinformation*, 102, 1023-1075. https://doi.org/10.1016/j.jag.2021.102385

729 Wu, C., Peng, J., Ciais, P., Peñuelas, J., Wang, H., Beguería, S., Andrew Black, T., Jassal, R. S., Zhang, X., Yuan, W., Liang, E., Wang, X., Hua, H., Liu, R., Ju, W., Fu, Y., & Ge, Q. (2022). Increased drought effects on the phenology of autumn leaf senescence. *Nature Climate Change*, 12(10), 943-949. https://doi.org/10.1038/s41558-022-01464-9

734 Wu, C., Wang, X., Wang, H., Ciais, P., Peñuelas, J., Myneni, R. B., Desai, A. R., Gough, C. M., Gonsamo, A., Black, A. T., Jassal, R. S., Ju, W., Yuan, W., Fu, Y., Shen, M.,
736 li, S., Liu, R., Chen, J., & Ge, Q. (2018). Contrasting responses of autumn-leaf senescence to daytime and night-time warming. *Nature Climate Change*, 8, 1092-1096. https://doi.org/10.1038/s41558-018-0346-z

739 Yang, Y., Guan, H., Shen, M., Liang, W., & Jiang, L. (2015). Changes in autumn vegetation dormancy onset date and the climate controls across temperate ecosystems in China from 1982 to 2010. *Global Change Biology*, 21, 652-665. https://doi.org/10.1111/gcb.12778

743 Yang, Y., Qi, N., Zhao, J., Meng, N., Lu, Z., Wang, X., Kang, L., Li, R., Ma, J., & Zheng, H. (2021). Detecting the Turning Points of Grassland Autumn Phenology on the Qinghai-Tibetan Plateau: Spatial Heterogeneity and Controls. *Remote Sensing*, 13, 4797. https://doi.org/10.3390/rs13234797

747 Yu, H., Luedeling, E., & Xu, J. (2010). Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. *Proceedings of the National Academy of Sciences*, 107(51), 22151-22156. https://doi.org/10.1073/pnas.1012490107

750 Zhang, G., Zhang, Y., Dong, J., & Xiao, X. (2013). Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. *Proceedings of the National Academy of Sciences*, 110, 4309-4314. https://doi.org/10.1073/pnas.1210423110

753 Zhu, W., Tian, H., Xu, X., Pan, Y., Chen, G., & Lin, W. (2012). Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982-2006. *Global Ecology and Biogeography*, 21, 260-271. https://doi.org/10.1111/j.1466-8238.2011.00675.x