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Article

The Relative Effects of Climate Change and Phenological Change on Net Primary Productivity Vary with Grassland Types on the Tibetan Plateau

by
Jianyu Xiao
1,2,
Zhishu Wang
1,2,
Wei Sun
1,
Shaowei Li
1,
Fusong Han
1,3,
Shaolin Huang
1,2 and
Chengqun Yu
1,*
1
Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modelling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
College of Urban and Environment Sciences, Hunan University of Technology, Zhuzhou 412007, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(15), 3733; https://doi.org/10.3390/rs15153733
Submission received: 9 June 2023 / Revised: 13 July 2023 / Accepted: 25 July 2023 / Published: 27 July 2023
(This article belongs to the Section Ecological Remote Sensing)
Browse Figures
Versions Notes

Abstract

:
Quantifying the impact of climate change and vegetation phenology on ecosystem productivity in the alpine grasslands of the Tibetan Plateau (TP) is essential for assessing carbon balance dynamics at regional and global scales. However, the relative contributions of climate change and phenological change to vegetation productivity across various grassland types remain indistinguishable. This study examined the effects of climate change and phenological change on net primary productivity (NPP) in the alpine meadow and alpine steppe ecosystems of the TP from 2001 to 2020. The results revealed that (1) NPP exhibited a positive correlation with vegetation phenology, particularly with an extended growing season length and an earlier start of the growing season. Among the phenological variables studied, changes in the start of the growing season had the strongest influence on NPP variations in both alpine meadows and alpine steppes. (2) NPP displayed a positive correlation with annual precipitation and annual temperature, with changes in annual precipitation playing a dominant role in shaping NPP variations in alpine steppes. (3) NPP showed a negative correlation with annual radiation, and the impact of radiation changes on NPP variations was comparable to that of precipitation or temperature in both alpine meadows and alpine steppes. (4) Climate change exerted a stronger impact on NPP than phenological change in alpine steppes, while NPP was jointly affected by climate change and phenological change in alpine meadows. Our findings indicated that the relative effects of climate change and phenological change on vegetation productivity vary across different grassland types on the TP.

1. Introduction

Net primary productivity (NPP) quantifies the amount of carbon fixed by plants as biomass, reflecting the capacity for supply and energy flow in terrestrial ecosystems [1,2]. As a fundamental component of carbon cycling, NPP has been the focus of extensive ecological research aimed at unraveling its drivers [3]. Climate change, particularly changes in temperature and precipitation, has been proven to be a critical abiotic factor that significantly affects NPP [4]. Warming and water availability directly impact plant photosynthesis by altering photosynthetic rates and water use efficiency, as well as indirectly affecting plant growth by affecting nutrient availability and species composition [5,6]. Solar radiation also plays an essential role in regulating carbon uptake by influencing the availability and interception of photosynthetically active radiation [7,8]. Identifying the primary factor among these climatic variables and quantifying its contributions to vegetation productivity is essential for predicting how vegetation will respond to current and future climate conditions [9]. To achieve this goal, numerous studies have investigated the effects of climate change on NPP variations at different spatial and temporal scales [8,9,10]. However, there are still some ambiguities. Firstly, on the one hand, quantifying the complex interactions between climate factors in controlling interannual variability of vegetation productivity is challenging [6,11]. On the other hand, climate change itself (e.g., warming magnitude, precipitation change magnitude) can also modify the reactions of the vegetation to climate change [12]. Secondly, climate factors are influenced by environmental conditions, such as biome type, location, and water use efficiency [13,14,15]. Consequently, more quantitative assessments are needed to gain a deeper insight into the response regularity and related driving mechanisms of NPP variations to climate change.
Vegetation phenology, including the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (GSL), plays a crucial role in determining the timing and duration of canopy photosynthetic activity, and drives ecosystem carbon sequestration [2,16]. Over the past decades, both satellite and ground-based evidence has shown significant shifts in plant phenology in response to the global surface mean temperature increase, especially in most regions of the Northern Hemisphere [17]. These changes in vegetation phenology have implications for ecosystem structure and functions, as they directly impact surface energy exchange, water cycling, and terrestrial carbon cycling [18]. An increasing number of studies have been carried out to examine the impact of phenological change on ecosystem productivity [19,20,21]. Such studies are valuable for protecting and managing ecosystems under the background of global change and promoting sustainable human development [8,22]. However, there are still two unresolved issues. Firstly, while it is widely acknowledged that changes in vegetation phenology can influence plant production, the relationship between phenological changes, such as prolonged GSL and advanced SOS, and vegetation productivity remains a subject of ongoing debate [20,22,23]. Secondly, although it is recognized that climate change has and will continue to alter the impacts of plant phenology on ecosystem productivity [24,25], there is still debate about the relative contributions of climate and vegetation phenology to changes in vegetation productivity [26,27,28]. Hence, it is necessary to comprehensively study the effects of phenological variability on NPP and its interaction with climate change.
The Tibetan Plateau (TP), known as the Earth’s third pole, exerts a significant influence on global climate system due to its unique geographical features and atmospheric circulation [29,30]. In recent decades, the TP has experienced unprecedented warming, resulting in various effects on its delicate ecosystems, including fluctuations in ecosystem productivity [19,31,32]. Alpine grassland, as the dominant vegetation type in the TP, represents a substantial portion of the carbon pool within the alpine ecosystems [21,33]. The response of alpine grassland ecosystems to global change is a key scientific issue in the field of global change ecology, with profound implications for the conservation and management of alpine ecosystems [12,33]. Although several studies have examined the effects of climate change and vegetation phenology on alpine grassland ecosystems in the TP, ambiguities still persist [2,31]. Firstly, most studies have focused on the impacts of climate warming and/or precipitation change on alpine grassland ecosystems, with limited investigation into the potential effects of radiation changes on the alpine grassland ecosystems [6,10,34]. Secondly, few studies have specifically examined the relative contributions of climate change and phenological change to alpine grassland ecosystem productivity at transect and single-site scales [19,35]. It is still unclear whether climate change or phenological change predominates NPP variations in alpine grassland ecosystems in the TP. Moreover, previous studies have primarily concentrated on a single type of grassland [35], with limited comparisons made regarding the relative contributions of climate change and phenological change to NPP variations across different grassland types, such as alpine meadows and alpine steppes.
Therefore, this study examined the response of NPP to climate change and vegetation phenology in alpine meadow and alpine steppe regions on the TP between 2001 and 2020, utilizing remote sensing data. The specific objectives of this study are to (1) analyze the relationships between NPP and climatic variables (i.e., annual precipitation (PRE), annual temperature (TEM) and annual solar radiation (RAD)) as well as phenological variables (i.e., SOS, EOS, and GSL); and (2) compare the relative effects of climate and phenological variables on NPP in alpine meadows and alpine steppes, respectively.

2. Materials and Methods

2.1. Study Area

The TP is located in the southeastern part of China’s mainland, covering an area of about 2.5 million km2, which accounts for approximately 26.80% of China’s total land area [36]. As the world’s highest plateau, it boasts an average altitude exceeding 4000 m, gradually decreasing in terrain from southeast to northwest (Figure 1a). The TP features a typical plateau mountain and subtropical monsoon climate, characterized by diverse thermal and moisture gradients from southeast to northwest due to the influence of monsoon winds from the Indian Ocean and high elevation. Annual temperatures on the plateau range from −15 to 10 °C, while precipitation distribution varies significantly. The northwest regions receive less than 50 mm of annual precipitation, whereas the southeast regions receive over 1000 mm. The distribution of vegetation on the plateau is influenced by the spatial pattern of climate types, with the primary vegetation types consisting of alpine steppes, alpine meadows, and forests. Alpine steppes and alpine meadows together account for approximately 55.30% of the total area (Figure 1b).

2.2. Dataset

The meteorological data for PRE, TEM, and RAD from the meteorological stations in the TP and its surrounding areas between 2001 and 2020 were obtained from the China Meteorological Data Sharing Network (http://data.cma.cn/ (accessed on 8 November 2022)). Raster meteorological data with a spatial resolution of 1 km were generated by interpolating the meteorological data using the ANUSPLINE method. The MOD17A3HGF NPP data, which have been subjected to rigorous technical processes ensuring high accuracy and widely used by numerous scholars, were acquired from Earth Science Data Systems (http://earthdata.nasa.gov/ (accessed on 8 November 2022)). To facilitate further analysis, the NPP data was resampled using the nearest neighbor method, resulting in a resolution of 1 km. The vegetation phenology data, including information on the SOS, EOS, and GSL from 2001 to 2020 with a spatial resolution of 1 km, were obtained from the Qinghai-Tibet Plateau Vegetation Phenology dataset provided by the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/ (accessed on 18 December 2022)) [37]. The vegetation cover characteristics data of TP were extracted from a digitized China vegetation map, which was made available by the Resources and Environment Science and Data Centre (https://www.resdc.cn (accessed on 20 December 2022)).

2.3. Statistical Analysis

Climate factors included PRE, TEM, and RAD, while phenological factors included SOS, EOS, and GSL. Firstly, the multi-year mean values of NPP (NPP_mean), PRE (PRE_mean), TEM (TEM_mean), RAD (RAD_mean), SOS (SOS_mean), EOS (EOS_mean), and GSL (GSL_mean) were calculated for each pixel during the period between 2001 and 2020, respectively. Subsequently, the linear change rates of NPP, PRE, TEM, RAD, SOS, EOS, and GSL were computed for each pixel using sens.slope function (Sen’s slope, trend package), respectively. These linear changes were labeled as NPP_slope, PRE_slope, TEM_slope, RAD_slope, SOS_slope, EOS_slope, and GSL_slope. The advantage of Sen’s trend estimation lies in its resilience to missing data or outliers in individual data series, ensuring accurate trend results. The formula is as follows:
Slope = Median x j − x i j − i , ∀ 1 ≤ i ≤ j ≤ n
where xi and xj represent the values at times i and j, respectively, and n represents the length of the time series data (i.e., 20 years from 2001 to 2020 in this study). Slope > 0 indicates an upward trend, and Slope < 0 indicates a downward trend.
Next, simple linear regressions were performed to analyze the relationships between NPP_mean and PRE_mean, TEM_mean, RAD_mean, SOS_mean, EOS_mean, and GSL_mean for each pixel in both alpine meadows and alpine steppes. The effect (R2) of each variable on NPP can be defined as follows:
R 2 = ∑ i = 1 n y i 2 − ∑ i = 1 n ( y i − Y i ) 2 ∑ i = 1 n y i 2
where yi is the actual NPP value, and Yi is the fitted NPP value.
Finally, the “vegan” package in R software (R Core Development Team, R Foundation for Statistical Computing, Vienna, Austria), was utilized for variation partitioning analysis, aiming to quantitatively assess the contributions of climate and vegetation phenology to NPP variations in alpine meadow and alpine steppe regions. Specifically, the variation in NPP_mean was partitioned using climate factors (i.e., PRE_mean, TEM_mean, and RAD_mean) and phenological factors (i.e., SOS_mean, EOS_mean, and GSL_mean), while the varpart function was employed for this partitioning. Similarly, the variation in NPP_slope was partitioned using climate change variables (i.e., PRE_slope, TEM_slope, and RAD_slope) and phenological change variables (i.e., SOS_slope, EOS_slope, and GSL_slope).

3. Results

3.1. Climate Change

The change trends of climate variables in alpine grasslands of the TP from 2001 to 2020 were provided in Table 1. The PRE ranged from 344.40 to 479.17 mm and displayed a non-significant increasing trend of 2.25 mm yr−1. The TEM ranged from −2.36 to −0.98 °C and exhibited a significant increasing trend of 0.028 °C yr−1 (p < 0.05). However, the RAD ranged from 5.15 × 103 to 5.48 × 103 MJ m−2 and showed a significant decreasing trend of −7.44 MJ m−2 yr−1 (p < 0.05). The PRE_mean and TEM_mean showed a gradual decrease from east to west (Figure 2a,b), with an increasing trend observed in 82.37% and 81.23% of the regions (slope > 0, Figure 2d,e), respectively, especially in the northwest and northeast of the plateau. The RAD_mean decreased gradually from west to east (Figure 2c), with 77.83% of the study area exhibiting a downward trend (slope < 0, Figure 2f), primarily concentrated in the northern plateau. The temporal variations characteristics of the climate variables were depicted in Figure A1. The PRE_mean and TEM_mean were significantly higher in the alpine meadow than that in the alpine steppe (p < 0.001), and there was a noticeable increase in both variables over time. The RAD_mean in the alpine meadow was significantly lower than that in the alpine steppe (p < 0.001), and these values gradually decreased from 2001 to 2020.

3.2. Phenological Change and Net Primary Productivity Change

The change trends of phenological variables in alpine grasslands on the TP from 2001 to 2020 were listed in Table 1. The SOS exhibited a significant decreasing trend by −0.34 day yr−1 (p < 0.05), while the GSL displayed a significant increasing trend of 0.46 day yr−1 (p < 0.05). However, the EOS showed an insignificant increasing trend of 0.12 day yr−1. The SOS_mean gradually decreased from west to east in the TP, with a decreasing trend observed in 75.55% of the regions, especially in the east of the plateau (Figure 3b,f). Furthermore, the GSL_mean increased gradually from west to east, with 72.93% of grids showing an increased trend (Figure 3d,h). The temporal variations characteristics of the phenological variables were depicted in Figure A2. The SOS_mean in the alpine meadow was significantly lower than that in the alpine steppe (p < 0.001), and it was observed to gradually decrease over time. The EOS_mean and GSL_mean in the alpine meadow were significantly larger than that in the alpine steppe (p < 0.001), and these values gradually increased during the study period.
The NPP in study area ranged from 136.10 to 162.91 g C m−2 and displayed a significant increasing trend of 1.02 g C m−2 yr−1 (p < 0.05, Table 1). The NPP_mean gradually increased from west to east, with an increasing trend observed in the most regions (80.87%) of grassland on the plateau (Figure 3a,e). Moreover, the NPP_mean in the alpine meadow was significantly higher than that in the alpine steppe (p < 0.001), and it was observed to gradually decrease over time (Figure A2).

3.3. Effects of Climate Change and Phenological Change on Net Primary Productivity

In the alpine meadow, NPP_mean showed significant positive correlations with PRE_mean (R2 = 0.40, p < 0.001, Figure 4a), TEM_mean (R2 = 0.25, p < 0.001, Figure 4b), EOS_mean (R2 = 0.55, p < 0.001, Figure 5b), and GSL_mean (R2 = 0.61, p < 0.001, Figure 5c). Conversely, there were significant decreases in NPP_mean with increasing RAD_mean (R2 = 0.41, p < 0.001, Figure 4c) and SOS_mean (R2 = 0.52, p < 0.001, Figure 5a). In the alpine steppe, high NPP_mean values were observed in regions with high PRE_mean (R2 = 0.21, p < 0.001, Figure 4d), TEM_mean (R2 = 0.29, p < 0.001, Figure 4e), EOS_mean (R2 = 0.22, p < 0.001, Figure 5e), and GSL_mean (R2 = 0.33, p < 0.001, Figure 5f). Additionally, NPP_mean exhibited a significant negative correlation with RAD_mean (R2 = 0.25, p < 0.001, Figure 4f) and SOS_mean (R2 = 0.23, p < 0.001, Figure 5d).
The varpart analysis showed that NPP_mean variations in the alpine meadow were primarily explained by the combined effects of climate and phenological variables (Figure 6a). In contrast, climate variables had a stronger influence on NPP_mean variations in alpine steppes compared to phenological variables (Figure 6d). TEM_mean had the largest exclusive effects on NPP_mean in both alpine meadows and alpine steppes, and RAD_mean had a comparable impact to PRE_mean and TEM_mean on NPP_mean in these ecosystems (Figure 6b,e). Moreover, GSL_mean accounted for a greater proportion of NPP_mean variations than SOS_mean and EOS_mean in both alpine meadows and alpine steppes (Figure 6c,f).
The variations in NPP_slope in alpine meadows were equally contributed to by both climate change and phenological change, while NPP in alpine steppes was predominantly influenced by climate change (Figure 7a,d). Moreover, in both alpine meadow and alpine steppe ecosystems, the variations in NPP_slope were primarily affected by PRE_slope and RAD_slope, rather than TEM_slope (Figure 7b,e). Specifically, PRE_slope had the largest exclusive effects on NPP_slope in the alpine steppe (Figure 7e). Regarding phenological changes, the impact of SOS_slope on the variations in NPP_slope was more pronounced compared to EOS_slope and GSL_slope in both alpine meadow and alpine steppe ecosystems (Figure 7c,f).

4. Discussion

4.1. Effects of Climate Changes on Net Primary Productivity

Our findings implied that TEM_mean had strongest exclusive effects on NPP_mean than PRE_mean in both alpine meadow and alpine steppe (Figure 6b,e). This finding indicates that temperature significantly impacts the spatial distribution of NPP, which consistent with previous studies [32,38,39]. Interestingly, contrary to studies suggesting that warming exacerbates plant water stress and reduces ecological system output [27,40], our results have shown that increased temperature promotes NPP in both alpine meadow and steppe ecosystems (Figure 4b,e). We speculate that several reasons may account for this phenomenon. Firstly, the low mean annual temperatures in alpine meadows and alpine steppes limit plant growth due to their adverse effects on plant physiology [36,41]. Warming alleviates the low-temperature stress of alpine plant species, stimulates photosynthesis enzyme activity, increases CO2-induced photosynthetic rate and water use efficiency, and promotes plant growth by improving nutrient availability and uptake [3,42,43]. Secondly, warming accelerates the melting of snow and glaciers on the mountaintop, effectively supplementing shallow soil moisture, which promotes the growth of alpine grassland vegetation [29,44]. Lastly, warming generally extends the growing season, resulting in increased plant biomass accumulation [16,45].
Our findings implied that the effect of solar radiation on NPP variations was at least equivalent to that of precipitation or temperature in both alpine meadows and alpine steppes (Figure 7b,e). This finding highlights the importance of considering changes in solar radiation when studying the effects of climate change on ecosystem productivity. Similarly, some studies have also demonstrated that solar radiation played a dominant role in carbon use efficiency, soil pH, forage nutritional quality, and ecosystem productivity of grasslands on the TP [7,12,46]. The radiation showed a significant downward trend during the study period (Table 1) which, in turn, promoted the NPP of alpine grasslands on the TP (Figure 4c,f). The strong effect of the reduction in solar radiation on NPP may be attributed to one or more of the following mechanisms. Firstly, reduced solar radiation can promote the growth of herbaceous plants with shallow root systems by decreasing surface soil evaporation and increasing soil water availability [47]. Secondly, the decrease in solar radiation is closely associated with an increase in cloud cover and precipitation, further alleviating hydraulic stress on plants [48]. Thirdly, the reduction in solar radiation can mitigate the negative impact on NPP resulting from soil anaerobic conditions caused by excessive water content, by slowing down the melting rate of snow cover and permafrost [49,50].
Our findings implied that PRE_slope dominated the variations in NPP_slope in the alpine steppe, indicating that changes in precipitation were the main climatic drivers of NPP change in the alpine steppe (Figure 7e). The alpine steppe, particularly in the northern and western regions of the TP, experiences relatively low precipitation (Figure A1) that fails to meet the ecological water demand of plants [14,51]. Climate warming may further intensify evaporation, exacerbating the limited availability of soil water [15,50]. Therefore, the NPP of alpine steppe exhibits a strong positive response to increased precipitation owing to the enhanced available water supply (Figure 4d), as supported by satellite observations [9,52] and in situ manipulated experiments [10,53]. Conversely, the majority of alpine meadows are located in the southeastern regions of the TP, affected by warm and wet airflow from the East Asian and Indian monsoons, resulting in relatively abundant precipitation (Figure A1). In these areas, short-term precipitation deficiencies are unlikely to limit plant growth [9,52]. However, this explanation remains contentious, and future manipulative experiments are necessary to investigate the effects of precipitation on alpine grasslands.

4.2. Effects of Phenological Changes on Net Primary Productivity

Our findings implied that the GSL plays a significant role in shaping the spatial distribution of NPP (Figure 6c,f), which was consistent with some previous research conducted on the TP [2,23,35]. Our study further demonstrated that the extension of GSL promotes NPP increase in alpine grasslands (Figure 5c,f). Likewise, NPP increased with GSL across a suite of high elevation grasslands in the Mongolian Plateau and North America [20,54]. We propose that the strong positive effect of GSL on NPP can be explained by multiple mechanisms. Firstly, the extension of GSL can increase the available time for plant photosynthesis to absorb carbon dioxide, resulting in increased biomass production and accumulation in terrestrial ecosystems [21,25]. Secondly, the elevated biomass results in a larger leaf area, further enhancing the light interception and the photosynthetic potential of vegetation canopy [55,56]. Thirdly, warming-induced prolongation of GSL may accelerate nitrogen mineralization rates, leading to an increase in leaf nitrogen concentration and enhanced photosynthetic activity of plants [34,57]. Lastly, the positive effects of plant species richness on ecosystem functions are more pronounced with longer GSL, which may also result in higher NPP [58].
Our findings implied that changes in SOS were the primary phenological drivers of NPP change in alpine grasslands (Figure 7c,f). Similar patterns have been observed in previous studies, such as in the northern TP [44] and Yunnan-Guizhou Plateau [59], where the contribution of SOS to vegetation productivity changes overweighs that of EOS and GSL. Xu et al. [60] further confirmed that the aboveground net primary productivity of the grasslands in northern China was more sensitive to the rate of SOS advancement than to GSL changes. We speculate that this phenomenon can be attributed to the following mechanisms. Firstly, the advancement of SOS can accelerate plant canopy growth and development, extending carbon uptake period and enhancing vegetation productivity [60,61]. Secondly, an earlier onset of SOS may contribute to the delay of EOS, which can affect net carbon uptake capacity of ecosystem [17,18,24]. Thirdly, previous studies have shown a negative correlation between the advancement of SOS and spring precipitation on the TP, indicating that an earlier SOS is associated with higher soil water availability, which is conducive to promoting plant growth [62]. Fourthly, the photosynthetic absorption of CO2 by plants in the TP depends on water from snowmelt, and an advanced SOS is closely linked to warming temperatures, resulting in early snowmelt and higher plant productivity [35,63].

4.3. Different Contributions of Climate and Phenological Changes to Variations in Net Primary Productivity of Alpine Meadows and Alpine Steppes

Our findings implied that climate change had a stronger impact on the NPP than phenological change in alpine steppes, while the NPP was jointly affected by both climate change and phenological change in alpine meadows (Figure 6 and Figure 7). This finding highlights the significant importance of vegetation phenology on the NPP changes in alpine meadows compared to alpine steppes. The reasons for this finding may be one or more of the following. Firstly, vegetation productivity in alpine steppes is more susceptible to climate change due to the combined limitations of low temperature and drought on plant growth, whereas alpine meadows are primarily restricted by low temperature [64,65]. Secondly, the alpine steppe exhibits a less complex ecological network and weaker community stability, which leads to lower resistance to climate change when compared to the alpine meadow [66,67]. For example, an experimental study conducted in the TP revealed that warming had no significant effect on plant community composition and species diversity in the alpine meadow. However, it did lead to a significant decrease in the coverage of graminoids and forbs in the alpine steppe, resulting in a reduction in community productivity [40]. Thirdly, alpine meadows exhibit a higher carbon sequestration capacity attributed to their larger leaf area and higher plant biomass [38,39]. This, in turn, implies that their productivity is more vulnerable to phenological prolongation or shortening [68]. Fourthly, this study suggests that the smaller phenological changes observed in the alpine steppe compared to the alpine meadow (Figure A2) may have a negligible impact on NPP. Similarly, a previous study, based on field phenological observation data from 1990 to 2006, found that the leaf-out dates of plants belonging to Gramineae and Cyperaceae families significantly advanced in alpine meadows but not in alpine steppes [69]. Fifthly, alpine meadows have a more diverse plant community compared to alpine steppes, which can also contribute to their increased sensitivity to phenological changes [45,58,66]. The diverse plant community in alpine meadows includes a mix of early and late regreening species, and shifts in the timing of these species’ growth can affect the overall ecosystem productivity.

4.4. Limitations and Prospects

Although this study examined the effects of climate change and phenological change on NPP in the alpine meadow and alpine steppe ecosystems of the TP, there were some limitations. Firstly, a potential constraint of this study lay in the uncertainty associated with satellite remote sensing data, as the accuracy of the NPP data used could have been affected by inherent limitations of satellite-based measurements. Secondly, the topography and harsh climate of the TP resulted in a spatial bias in the distribution of meteorological stations, with most stations concentrated in the eastern part of the plateau and a lack of representation in the western region. This spatial imbalance limited the representativeness of the meteorological data employed in this study. Moreover, vegetation growth in the alpine grasslands of the TP was influenced by multiple environmental factors beyond climate and vegetation phenology, highlighting the need for future research to investigate the interactions and combined effects of these additional factors. Additionally, the influence of human activities on grassland vegetation could not be overlooked, and future studies should consider integrating human-related variables and quantifying their effects on grassland productivity to obtain a more holistic understanding of the underlying processes driving vegetation dynamics. Lastly, the results of our study were not verified by field measurements, emphasizing the importance of conducting field measurements and validation campaigns across the TP to collect on-site data that can verify the accuracy of NPP and vegetation phenology data derived from remote sensing.

5. Conclusions

In this study, we investigated the relative effects of climate and phenological variables on net primary productivity (NPP) in alpine meadow and alpine steppe ecosystems on the Tibetan Plateau. Our findings suggest that NPP changes in alpine meadows are mainly influenced by the combined effects of climate and phenological change. In contrast, variations in NPP of alpine steppes are predominantly driven by precipitation changes, exhibiting stronger responses to climate change compared to phenological changes. Importantly, we emphasize the significant impact of solar radiation changes on the NPP of alpine grasslands, which should be given equal attention alongside warming and precipitation changes. These findings offer new insights into the complex relationship between phenology, climate, and NPP in alpine ecosystems, contributing to a better understanding of vegetation productivity response to global warming.

Author Contributions

Conceptualization, C.Y.; methodology, J.X.; software, J.X. and Z.W.; validation, W.S., S.L. and F.H.; formal analysis, J.X.; investigation, J.X. and Z.W.; resources, C.Y.; data curation, J.X. and S.H.; writing—original draft preparation, J.X.; writing—review and editing, C.Y., J.X., Z.W., W.S. and S.L.; visualization, J.X. and Z.W.; supervision, C.Y., F.H. and S.H.; project administration, W.S., S.L. and F.H.; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Pilot Project of Chinese Academy of Sciences [XDA26050501], the China National Key Scientific Research Project [2021YFD1000303], and the Tibet Autonomous Region Science and Technology Project [XZ202301YD0012C; XZ202101ZD0003N; XZ202202YD0005C].

Data Availability Statement

Correspondence and requests for materials should be addressed to C.Y.

Acknowledgments

We thank the editors and reviewers for their valuable comments.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

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Figure A1. Comparisons of the multi-year mean values and linear slopes of climate variables (PRE, TEM, and RAD) in the alpine meadow and alpine steppe.
Figure A1. Comparisons of the multi-year mean values and linear slopes of climate variables (PRE, TEM, and RAD) in the alpine meadow and alpine steppe.
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Figure A2. Comparisons of the multi-year mean values and linear slopes of phenological variables (SOS, EOS, and GSL) in the alpine meadow and alpine steppe.
Figure A2. Comparisons of the multi-year mean values and linear slopes of phenological variables (SOS, EOS, and GSL) in the alpine meadow and alpine steppe.
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Figure 1. Spatial distribution of elevation (a) and grassland types (b) in the study area.
Figure 1. Spatial distribution of elevation (a) and grassland types (b) in the study area.
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Figure 2. Spatial patterns of multi-year mean values (a–c) and the linear slopes (d–f) of climate variables (annual total precipitation (PRE), annual average temperature (TEM), and annual mean total radiation (RAD)).
Figure 2. Spatial patterns of multi-year mean values (a–c) and the linear slopes (d–f) of climate variables (annual total precipitation (PRE), annual average temperature (TEM), and annual mean total radiation (RAD)).
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Figure 3. Spatial patterns of multi-year mean values (a–d) and the linear slopes (e–h) of net primary productivity (NPP) and phenological variables (the start of the growing season (SOS), the end of the growing season (EOS), and the growing season length (GSL)).
Figure 3. Spatial patterns of multi-year mean values (a–d) and the linear slopes (e–h) of net primary productivity (NPP) and phenological variables (the start of the growing season (SOS), the end of the growing season (EOS), and the growing season length (GSL)).
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Figure 4. Relationships between NPP and climate variables (PRE, TEM, RAD) in the alpine meadow (a–c) and alpine steppe (d–f).
Figure 4. Relationships between NPP and climate variables (PRE, TEM, RAD) in the alpine meadow (a–c) and alpine steppe (d–f).
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Figure 5. Relationships between NPP and phenological variables (SOS, EOS, GSL) in the alpine meadow (a–c) and alpine steppe (d–f).
Figure 5. Relationships between NPP and phenological variables (SOS, EOS, GSL) in the alpine meadow (a–c) and alpine steppe (d–f).
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Figure 6. Relative contributions of climate variables (PRE, TEM, and RAD) and phenological variables (SOS, EOS, and GSL) to NPP in the alpine meadow (a–c) and alpine steppe (d–f).
Figure 6. Relative contributions of climate variables (PRE, TEM, and RAD) and phenological variables (SOS, EOS, and GSL) to NPP in the alpine meadow (a–c) and alpine steppe (d–f).
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Figure 7. Relative contributions of climate change (linear slopes of PRE, TEM, and RAD) and phenological change (linear slopes of SOS, EOS, and GSL) to the linear slope of NPP in the alpine meadow (a–c) and alpine steppe (d–f).
Figure 7. Relative contributions of climate change (linear slopes of PRE, TEM, and RAD) and phenological change (linear slopes of SOS, EOS, and GSL) to the linear slope of NPP in the alpine meadow (a–c) and alpine steppe (d–f).
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Table
Table 1. Multi-year mean values and temporal trends of climate variables, NPP and vegetation phenology during 2001–2020 in the grassland of TP. Trends with an asterisk (*) are significant at a level of 0.05.
Table 1. Multi-year mean values and temporal trends of climate variables, NPP and vegetation phenology during 2001–2020 in the grassland of TP. Trends with an asterisk (*) are significant at a level of 0.05.
PRE
(mm)		TEM
(°C)		RAD
(MJ m−2)		NPP
(gC m−2)		SOS
(DOY)		EOS
(DOY)		GSL
(Days)
Mean407.64−1.785353.83149.96147.13281.51134.34
SD34.980.3797.169.213.992.404.63
Min344.40−2.365148.66136.09138.92275.81127.34
Max479.17−0.985482.76162.91153.75286.39147.37
Trend (yr−1)2.250.028 *−7.44 *1.02 *−0.34 *0.120.46 *
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Xiao, J.; Wang, Z.; Sun, W.; Li, S.; Han, F.; Huang, S.; Yu, C. The Relative Effects of Climate Change and Phenological Change on Net Primary Productivity Vary with Grassland Types on the Tibetan Plateau. Remote Sens. 2023, 15, 3733. https://doi.org/10.3390/rs15153733

AMA Style

Xiao J, Wang Z, Sun W, Li S, Han F, Huang S, Yu C. The Relative Effects of Climate Change and Phenological Change on Net Primary Productivity Vary with Grassland Types on the Tibetan Plateau. Remote Sensing. 2023; 15(15):3733. https://doi.org/10.3390/rs15153733

Chicago/Turabian Style

Xiao, Jianyu, Zhishu Wang, Wei Sun, Shaowei Li, Fusong Han, Shaolin Huang, and Chengqun Yu. 2023. "The Relative Effects of Climate Change and Phenological Change on Net Primary Productivity Vary with Grassland Types on the Tibetan Plateau" Remote Sensing 15, no. 15: 3733. https://doi.org/10.3390/rs15153733

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