Carbon flux phenology and net ecosystem productivity simulated by a bioclimatic index in an alpine steppe-meadow on the Tibetan Plateau

Ecological Modelling 394 (2019) 66–75

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Carbon flux phenology and net ecosystem productivity simulated by a bioclimatic index in an alpine steppe-meadow on the Tibetan Plateau

T

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Xi Chaia,b, Peili Shia,b, , Minghua Songa, Ning Zonga, Yongtao Hea,b, Guangshai Zhaoa,b, Xianzhou Zhanga,c a

Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China b College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China c China National Forestry Economics and Development Research Center, Beijing, 100714, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Carbon flux phenology Growing season index NDVI Net ecosystem productivity Alpine meadow Tibetan Plateau

Plant phenology is one of the main controls of variation in net ecosystem productivity (NEP). Accurate representation of vegetation phenology is important for predicting ecosystem carbon budget. Although using satellite observation to determine vegetation phenology is becoming a mature option, there are still uncertainties in its application at site scales. Our purpose is to establish a more robust phenological index to accurately predict carbon uptake phenology, which detailed results can complement the shortcomings of MODIS NDVI-derived phenology. Here we used a growing season index (GSI) phenology model to simulate carbon flux phenology (CFP) including the start of carbon uptake (CUstart), the end of carbon uptake (CUend) and the length of carbon uptake period (CUP) in an alpine meadow ecosystem on the Tibetan Plateau and to compare the results with those modeled from MODIS NDVI. We also further analyzed the main environmental factors in controlling CFP. The results indicated that the GSI model made substantially more precise prediction for CUstart, CUend and CUP (with higher correlation R2 > 0.90) than that of the MODIS derived phenology. The GSI model was also superior to NDVI in predicting both seasonal and annual variations of net ecosystem productivity (NEP). Moreover, CUP played an important role in regulating ecosystem carbon balance in the study site because NEP was significantly positive correlated with the period of annual carbon uptake. NEP would increase by 1.63 g C m−2 year-1 if one CUP-day was extended. Further, CUP was influenced by variation in CUstart. Previously overlooked water variability (soil water content and VPD) played a significant role in controlling CUP and CUstart. In addition, temperature could enhance water stress to delay CUstart and shorten CUP. It is indicated that decrease in carbon uptake could be induced by accelerative water stress in the face of global warming in the alpine meadow. These results suggest that CFP is more sensitive to not only temperature but also water condition, and a combination of soil water and temperature could be a useful way to enhance the estimation of CFP in future ecosystem model.

1. Introduction Vegetation phenology is the timing and length of growing season of plant species and the relationships with biotic and abiotic forces (Lieth, 1975). The phenology of vegetation can serve as an independent measure and powerful indicator of long-term biological impacts of climate change on terrestrial ecosystems due to its sensitivity to year-toyear climatic variability (Menzel and Fabian, 1999; Peng et al., 2017; Richardson et al., 2013). Furthermore, variation in phenology of vegetation may also influence ecosystem carbon cycle (Baldocchi and

Wilson, 2001; Piao et al., 2008; Richardson et al., 2010) by regulating seasonal and annual variations of carbon fluxes and their relationships with climate change (Du et al., 2017; Richardson et al., 2009; Wang et al., 2011; White and Nemani, 2003). Previous studies showed that poor interpretation of phenology led to inaccurate estimates of plant productivity and carbon sequestration (Shi et al., 2006b; Wu et al., 2017). So far, most of phenology studies have been conducted in forest ecosystems. However, knowledge from grasslands, especially from alpine meadow ecosystem is limited. Therefore, accurate prediction of variation in plant phenology of alpine meadows is imperative to better

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Corresponding author at: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (IGSNRR, CAS), A 11, Datun Road. Chaoyang District, Beijing, 100101, China. E-mail addresses: [email protected] (X. Chai), [email protected] (P. Shi), [email protected] (M. Song), [email protected] (N. Zong), [email protected] (Y. He), [email protected] (G. Zhao), [email protected] (X. Zhang). https://doi.org/10.1016/j.ecolmodel.2018.12.024 Received 10 February 2018; Received in revised form 22 December 2018; Accepted 28 December 2018 Available online 22 January 2019 0304-3800/ © 2018 Elsevier B.V. All rights reserved.

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extended the growing season by enhanced soil moisture in the shallowrooting grass dominated grassland (Ma et al., 2007). Phonological change might not always be caused by the vegetation response to single climatic factor. Sometimes multiple factors control phenology concurrently or at different time of the year (Jolly et al., 2005). However, little is known about the interaction between climatic factors and its affect plant phenology in the alpine ecosystem on the Tibetan Plateau. Tibetan Plateau, with an area of 1.2 million km2, is largely covered by alpine meadows and alpine steppes which are extremely fragile and particularly sensitive to climate change (Piao et al., 2011; Zheng et al., 2001). Low temperature is likely to be a main factor limiting plant growth. While water availability is greatly variable due to substantial regional precipitation variation and uneven seasonal distribution caused by difference in monsoon intensity. As a consequence, plant growth in spring may be delayed and constrained by slow increase of soil water till the summer, when temperature and precipitation are more suitable for fast plant growth (Ganjurjav et al., 2016a). We hypothesize that temperature and moisture interact to co-limit plant growth and phenology in the semiarid alpine grasslands on the Tibetan Plateau. To test this hypothesis, we modified the growing season index (GSI) model (Jolly et al., 2005) based on temperature and soil moisture to predict inter-annual variability of net ecosystem productivity (NEP) and the main transitions of NEE-derived CFP in a semiarid alpine steppe-meadow from 2004 to 2009. We have tested the accuracy of the GSI model in predicting the seasonal dynamic of GPP (see Chai et al., 2017a). In this study we use NDVI-derived phenology as a reference to compare with growing season phenological index in order to test the accurancy of GSI modelling because of unavailable field observation of phenology. The main objectives of this study are: 1) to predict CFP including the start of carbon uptake period (CUstar), the end of carbon uptake period (CUend), and the carbon uptake period (CUP), and the seasonal and annual variability of NEP using the GSI model; 2) to compare the results from the GSI model with those from MODIS derived phenology; 3) to examine the key climatic factors in controlling CFP. The results of our study can provide an evaluation of the performance of these data source for estimating CFP in the alpine grasslands.

understand carbon exchange in the context of global change. Growing season transition describes the dates during distinct change in leaf color observed from remote sensing or ground measurements, or the dates between bud-break in spring and leaf senescence in autumn. Significant uncertainty exists in exploring phenology due to variations in thresholds arising from site-specific factors, which may not be robust for global applications (Zhao et al., 2017). Although it is a mature option to use remote sensing image for determining vegetation phenology, there are still several impeding problems. For example, image resolution, atmospheric interference in the satellite reflectance, NDVI insensitivity and noise (Huete et al., 2002, 1994) make difficulties to extract the exact dates of the start of growing season (SOS) and end of season (EOS) from satellite time series (Gonsamo et al., 2012a; Michaela et al., 2009; Schwartz and Hanes, 2010). And these observations cannot predict changes in phonological events and its influence on carbon fluxes in response to future climate change (Ma et al., 2012). An alternative method is to detect the patterns of CO2 flux phenology (CFP) with continuous measurements of CO2 exchange from eddy-covariance (EC) technique. Net CO2 uptake phenology is estimated by the dates when daily net ecosystem exchange of CO2 (NEE) switches from positive to negative in spring and from negative to positive in autumn (Wu et al., 2013). Although the EC technique has been proven to be a good proxy in estimating CFP at site scale (Gonsamo et al., 2013), it can only provide very limited CO2 flux over footprints with restricted-size and varied shape. Moreover, it is difficult to capture the carbon flux phenology to a larger scale using limited sites and observations, especially in high-altitude areas, such as on the Tibetan Plateau (Chai et al., 2017a). This limitation could constrain the application of C flux phenology. Therefore, alternative models are needed to accurately predict and capture vegetation phenology at temporal and spatial scales. The climatic signals are important controls on carbon cycle during net C uptake period generally corresponding to dates between the beginning and end of growing season in grassland ecosystems (Grant et al., 2012). In high latitude and high altitude ecosystems, two critical factors regulating the length of the growing season are the timing of snowmelt and snow cover thickness (Berdanier and Klein, 2011; Dorji et al., 2013; Inouye et al., 2003). However, the timing of snow melt cannot always be used to indicate the start of plant growth in alpine meadow (Totland and Alatalo, 2002), as snow does not cover the landscape during the whole dormant season in some arid, semiarid, or monsoon-dominated ecosystems, likely in some areas on the Tibetan Plateau (Shen, 2011; Wang et al., 2008). So the temperature of soil thaw and subsequent soil water availability are the main environmental cues to indicate the starting time of alpine plants. Many Studies frequently focus on the role of temperature and recent climate warming trends in affecting phenology for alpine plants (Jin et al., 2016; Shen et al., 2016; Wohlfahrt et al., 2013) because low temperature limit plant cell development and hence the formation of new plant tissue (Körner, 2007), along with the somewhat related date of thaw. However, other factors can play a role including photoperiod (Keller and Körner, 2003), changes in water condition (Shen et al., 2015), nitrogen deposition, and interactions among these factors (Ganjurjav et al., 2016b; Smith et al., 2012), which may vary in different alpine ecosystems. Water availability is one of the critical environmental factors that regulate vegetation activities in many areas such as arid and semiarid grasslands (Ji and Peters, 2003; Pennington and Collins, 2007), and can therefore affect plant phenology (Shen et al., 2011; Yu et al., 2003). For example, in alpine grasslands of the central Himalaya, soil water availability is essential for growth initiation of some species (Pangtey et al., 1990). In addition, changes in precipitation in arid and semiarid ecosystems may have more profound impact on phenology and C exchange than changes in temperature because spring precipitation could increase soil water content, which may facilitate plant development and growth (Shen, 2011; Shen et al., 2015). Previous study showed that increase of 1 mm precipitation was associated with increase of 2 g C m−2 gross primary productivity (GPP) during April and May, presumably owing to

2. Materials and methods 2.1. Site description The study site is located at Damxung Alpine Meadow Research Station, one of the ChinaFlux sites (91°05′E, 30°51′N, 4333 m a.s.l), in the south-face slope of Nyainqentanglha Mountains, northern Tibetan Plateau. The site belongs to alpine continental monsoon climate, characterized by strong solar radiation, low air temperature and large daily temperature difference, and variable seasonal precipitation and soil moisture. Long-term (1963 - 2013) annual mean air temperature is 1.8 ℃ and annual mean precipitation is 476 mm with 80% in June to August (Chai et al., 2017b). The annual average sunlight is 2880 h, and the amount of sun radiation is 7528 MJ m−2, of which photosynthetically active radiation (PAR) is 3213.3 MJ m−2. Annual evaporation is 1725.7 mm and average wetness coefficient is 0.28 (Shi et al., 2006a). The growing season usually starts in May and ends in September. The vegetation type is an alpine steppe-meadow, with dominant species of Kobresia pygeama, Stipa capillacea, Carex montiseverestii, accompanied by K. capillofolis, Anaphalis xylorhiza, Potentilla bifurca Linn (Shi et al., 2006a). The soil is classified as meadow soil, and detailed information on soil properties can be found in Zong et al. (2014). 2.2. Eddy Covariance (EC) measurements and gap-filling CO2 flux was measured consecutively with the EC technique in the alpine meadow ecosystem at Damxung from summer in 2003 on. The description, data acquisition and gap-filling measurements of EC system 67

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2.3. Phenological model

were introduced in Chai et al., (2017a, b). Here we briefly describe the EC system and data processing as follows. The EC system is installed at 2.2 m above the gorund, which composed of open-path infrared gas analyzers (model LI-7500, LICOR, Lincoln, NE, USA) and three-dimensional sonic anemometers (Model CSAT3, Campbell Scientific, Logan, UT, USA). Digital output of fluctuations in three wind components, sensible heat, water vapor, and CO2 density can be provided at a rate of 10 Hz, and the signals are recorded by a CR5000 datalogger (Model CR5000, Campbell Scientific) and then block-averaged over 30 min intervals archiving. Standard meteorological and soil parameters measurements were conducted at the same site. For example, photosynthetically active radiation (PAR, μmolm−2s-1) was observed at 1.2 m using a quantum meter (L1190SB, LI-COR, Lincoln, USA), air temperature (Ta, ℃) and relative humidity (RH, %) were measured at 1.1 and 2.2 mm above the ground with a humidity and temperature probe (Model HMP45C, Vaisala, Helsinki, Finland), precipitation (PPT, mm) is measured using a rain gauge (Model 52203, RM Young Company, Michigan, USA) at 0.5 m above the ground. Vapor pressure deficit (VPD) is defined as the deficit between pressure exerted by the relative humidity and temperature present in the air currently and the pressure at saturation. Soil temperature (Ts, ℃) and soil water content (SWC, m3 m-3) are also measured by the thermcouples sensor (107-L, Campbell Scientific, Logan, USA) and the time-domain reflectometry (Model CS615-L, Campbell Scientific, Logan, USA) at different depths below the ground, respectively. Soil heat flux was measured at 0.05 m below the ground with heat flux plate (HFP01, Hukseflux, Delft, The Netherlands). All meteorological data were recorded as 30 min averages with a 10 Hz data-logger (Model CR23X, Campbell Scientific, Logan, USA). The CO2 flux data were discontinuous due to extreme weather, power supply and equipment failure. Therefore, gap-filling of lost data is an important prerequisite to controlling data quality and ensuring data reliability. The method of data preprocessing and gap-filling include spike removal corrections, etc. The data in rainy and extremely cloudy days and data from nighttime with friction velocity (u*) < 0.15 m s−1 were screened from analysis, as well as negative nighttime NEE were also discarded. Missing and rejected data during the study years (2004–2009) were less than 5% of the total in each year. The nighttime and non-growing season missing and rejecting data were filled using the exponential function with nighttime flux of u* > 0.15 and Ts corresponding to missing data as Eq. (1): Fc,

nighttime

= R10 Q10 ((Ts -Tref)/10)

Q10 = exp (10b1)

2.3.1. Growing season index (GSI) model The growing season index (GSI) model, originally developed by Jolly et al. (2005), combines a set of climatic variables into a daily metric to predict vegetation phenology. The original GSI model considers three key environmental variables on plant growth and canopy development, i.e. photoperiod, temperature and moisture. We chose minimum air temperature (Tmin) and soil water content (SWC) at 5 cm depth to indicate temperature and moisture conditions in our study site. The reason why we chose these parameters is that low temperature and soil water availability are main controllers to limit alpine plants growth and photosynthesis (Chai et al., 2017a). The interaction between SWC and Tmin often defines the length of photosynthetic season as the days below the thresholds of those in which plants do not produce leave or photosynthesize (Chapin et al., 2002). Light is not included in the model because it is not a limiting factor of plant growth in the Tibetan plateau (Churkina and Running, 1998; Chai et al., 2017a, 2017b). Each variable was set a threshold value to indicate the relative canopy development assumed varying from inactive (0) to unconstrained (1). The minimum and maximum threshold of Tmin (Tmmin and Tmmax) were set as -1℃ and 5℃ respectively by Jolly et al. (2005), but in present study Tmmin was adjusted slightly from -2℃ to -1℃ due to grasses and herbs in alpine environment being subject to frost damage near ice point. Meanwhile, we set the maximum and minimum SWC threshold (SWCmin and SWCmax) as 0.05 m3 m−3 and 0.15 m3 m−3, respectively. We found the thresholds of SWC are liable to change in different ecosystems reviewing literatures (Fu et al., 2006; Hao et al., 2008). According to their results, we selected several thresholds, ranging from 0.05 to 0.15 m3 m−3 to try the minimum and maximum. Multiplying the temperature and water stress indices (iTmin and iSWC in this study) forms a combined model to calculate daily growing season index (iGSI). The iGSI is continuous but normalized between 0 (inactive) and 1 (unconstrained), and then calculated as the 21-day moving averages of iGSI for all years to avoid reaction to short-term environmental changes and interference from abnormal data, so as to guarantee the authenticity of seasonal dynamic variations in GSI and NEP (Chai et al., 2017a). We chose the 21-day moving average based on not only literature but also the result of the test from 3 to 25-day moving average. Because of its flexibility, it can be used to ecosystems of other areas. We just need to change limited factors in order to meet the requirements of the ecosystems. In other ecosystems of different climate zones, key environmental factors can be selected such as radiation, photoperiod, VPD, nutrient availability and so on if they are limiting factors in specific ecosystems. A threshold value GSI = 0.5 was chosen to trigger the start of the growing season in the spring and the end of the growing season in the fall (Jolly et al., 2005). Alternative threshold values can be selected. We found, however, that anomalies in low or high threshold values were larger than that of 0.5 threshold values.

(1) (2)

−2 -1

Where Fc, nighttime (mgCO2 m s ) is carbon fluxes of u* > 0.15 m s-1 during nighttime, i.e. ecosystem respiration (Re) in the nighttime. R10 is ecosystem respiration rate at reference temperature (Tref) 10℃. Q10 is sensitivity coefficient of respiration, i.e. the increasing multiple of respiration rate as soil temperature (Ts) increased by 10 ℃. b1 is coefficient (Hoff, 1898). The daytime missing and rejected flux can be simulated by the Michaelis-Menten equation (Eq. (3)) (Flanagan, 2002; Xu and Baldocchi, 2004) which is used to describe the relationship between NEE and PAR. Fc

day

=Pmax *α*PAR/(αPAR + Pmax)+R

2.3.2. Remote sensing-based phenology data sources MODIS normalized difference vegetation index (NDVI) dataset (MOD13Q1) was derived from NASA Terra satellites from 2004 to 2009, including NDVI data and quality control data with spatial resolution of 250 m. The NDVI data were based on the maximum synthesis method in 16 days, and were processed with the geometric correction and atmospheric correction. We extracted pixels which values of quality control data were 0 or 1 in order to ensure data quality. We used ratio threshold method (RTM) to extract phenological information (Yu et al., 2010), and analyze changes in CUstart, CUend, and CUP for natural steppe-meadow in Damxung Station from 2004 to 2009. The NDVI-derived phenological information was used as a reference to test the accuracy of the GSI modeled phenology in present study. For the RTM, the NDVI ratio is the difference between the NDVI value at a certain time and minimum NDVI value for a specific time

(3)

Where Fc, daytime is carbon fluxes of u* > 0.15 during daytime. Pmax (mgCO2 m−2 s-1) is the ecosystem assimilation at “saturating” light. α (mgCO2 μmol photon-1) is ecosystem apparent quantum yield; PAR (μmol m−2 s-1) is photosynthetically active radiation. R (mg CO2 m−2 s1 ) is daytime ecosystem respiration (Ruimy et al., 1995; Shi et al., 2006a,b).

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(Deng, 2002). If two series have similar curves and close distance, the correlation is strong, and vice versa (Hui and Bifeng, 2009; Ling and Qi, 2011). Grey relational degree model has lower demand on the number and distribution of the samples than regression analysis and it is often used to quantitative comparison analysis of development and changing system, so it is suitable for comparing development and changing seasonal trend between observed NEP, GSI and NDVI. Partial least square (PLS) regression analysis was conducted to investigate the major drivers of the inter-annual variation of CFP. Typical outputs from the PLS regression analysis are the Variable-Importancein-the-Projection (VIP) statistic and model coefficients for each independent variable. Higher VIP scores exceeding 1.0 can be considered as the most informative drivers for the dependent variables (Ishtiaq and Abdul-Aziz, 2015; Kuhn, 2013). In this analysis, we only showed model coefficients for important variables with VIP > 1.0. The regression coefficients of the optimal PLS can represent the direction and strength of the effect of a given independent variable on the dependent variable (Yu et al., 2012).The PLS analysis was carried out using SIMCA software V. 11.5 (UMETRICS, Umeå, Sweden). Because NDVI data are the 16-day average values, we use corresponding 16-day averages of NEPscalar and GSI for grey correlation analysis and seasonal dynamic comparison in order to keep time in phase.

span, normalized by the total range of NDVI values during maximum NDVI value and minimum NDVI value in each year. In this study, we selected a NDVI ratio threshold of 0.2 to indicate the CUstart, and a drop of the NDVI ratio below 0.6 to mark the CUend, as determined by Yu et al. (2010) who used ground observations and modeled growing season parameters on the Qinghai-Tibetan Plateau. Based on the RTM method, the phenological metrics were derived from NDVI time series by using TIMESAT v.3.1 (Jönsson and Eklundh, 2004), a software package to best fit temporal dynamics of vegetation. Asymmetric Gaussian smoothing filter was selected because it exhibited superior performance in the preservation of the vegetation temporal dynamics on Qinghai-Tibetan Plateau (Song et al., 2011). 2.3.3. Determining carbon flux phenology from NEE data The carbon flux phenology was retrieved based on the method developed by Baldocchi et al. (2005) and Zhu et al. (2013). The start of carbon uptake (CUstart) was identified as the days when daily NEE experienced transitions from its winter or non-growing season respiration phase to spring/summer or growing season assimilation phase, similarly the end of carbon uptake (CUend) was from its summer/autumn or growing season assimilation phase to winter or non-growing season respiration phase (Baldocchi et al., 2005). We extracted carbon uptake phenology by using subset of NEE data from these springtime sourcesink and autumn sink-source transition periods to fit a linear regression equation between the daily NEE and the day of year (DOY), respectively. Specifically, we followed three steps: First, the original daily NEE data were smoothed with a 21-day running average width. Next, based on the smoothed daily NEE, two continuous 10-day width windows were selected to predict CUstart and CUend. One is the first 5 elements greater than 0 and the last 5 elements less than 0 was selected in spring /summer period, the other is the first 5 elements less than 0 and the last 5 elements greater than 0. Finally, the smoothed daily NEE in the two selected windows was linearly correlated with DOY and then the day of CUstart and CUend at the zero intersection were calculated (Zhu et al., 2013). The time duration between the respective start and end days with negative NEE is calculated as the CUP in each year.

3. Results 3.1. Seasonal variations in NDVI, GSI and NEPscalar Two important facts were shown from the seasonal dynamics of NEPscalar, NDVI and GSI during the period studied from 2004 to 2009. First, although NDVI tends to follow the time dynamics of NEPscalar, the start and the end of measurable CUP cannot be truly captured. Second, compared to NDVI, GSI followed the time dynamics of NEPscalar more closely and thus tracked vegetation CUP accurately (Fig. 1). We proved this observation through grey correlation analysis. Grey correlation coefficients between NEPscalar and GSI were higher than those between NEPscalar and NDVI from 2004 to 2009 (Fig. 1), and the 6-year mean grey correlation coefficients between NEPscalar and GSI and between NEPscalar and NDVI were 0.65 and 0.55, respectively. Additionally, annual NEP was correlated well with GSI rather than with NDVI, with GSI explaining 13% more than NDVI for the variation of annual NEP (Fig. 2). It implied that the GSI model were able to better predict the seasonal and annual variation of NEP than those of NDVI.

2.4. Standardizing NEP Net ecosystem productivity (NEP = -NEE) is the balance between carbon uptake through photosynthesis and carbon release through respiration and decomposition. In order to guarantee the stability and uniformity of data, NEP was turned into scalar,

NEPscalar = (NEP − NEPmin )/(NEPmax − NEPmin )

(4)

3.2. Comparison of simulated CFP from the NDVI and the GSI model

where NEPscalar is the standardized NEP ranging from 0 to 1. NEP is the daily NEP, NEPmax the annual maximum daily NEP, and NEPmin the annual minimum daily NEP.

We compared the CUstart, CUend and CUP derived from observation and simulation using the NDVI and the GSI models (Fig. 3). A large annual variation presented in the observed CUstart from 2004 to 2009. The measured CUstart ranged from 153 to 211 DOY, and mean DOY was 174 ± 26, with a small difference less than 20 days except for 2007 and 2009. The observed CUstart was about 27 and 38 days later than the average CUstart in the dry year of 2007 and 2009, respectively. There was a shorter mean interval less than 6 days between the observed CUstart and predicted CUstart with GSI compared with that of NDVI. The CUstart predicted with NDVI usually occurred earlier than observed results, with an average interval of 9 days. There was a small annual variation in the observed CUend during 2004 and 2009. The observed CUend ranged from 270 to 290, with an average of 279 ± 7 in the DOY. The predicted CUend with NDVI was usually latter than the observed CUend except for the year of 2005 and 2008. GSI had a better performance for the prediction of CUend compared with that of NDVI. The annual observed CUP ranged from 61 to 138 days, with an average of 101 ± 29 days. A large annual variation spanned from the drier year in 2009 and the wettest year in 2008. The shortest CUP

2.5. Statistical analyses Regression coefficient of determination was calculated in order to evaluate the goodness of fit between observed and predicted CUstart, CUend as well as CUP. Accuracy of regression model was validated by root mean square error (RMSE) and bias was calculated as Eq. (6).

RMSE =

∑ (Oi − Mi )2

BIAS = ∑ (Oi − Mi ) N

N

(5) (6)

Where Oi and Mi represent the observed and simulated carbon flux phenology respectively, and N indicates the total number of estimates at the study site. Higher R2, lower RMSE and bias indicate better model performance. The grey correlation analysis was used to weight correlative degree of similarity or difference according to the developing of the elements 69

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Fig. 1. Seasonal change of normalized difference vegetation index (NDVI), growing season index (GSI) and standardized net ecosystem productivity (NEPscalar) and grey correlation coefficients between NEPscalar and GSI (GSIgcc), between NEPscalar and NDVI (NDVIgcc) at 16-day intervals in the alpine steppe-meadow from 2004 to 2009. Red solid circles are NDVI, black solid squares are GSI and blue solid triangles are NEPscalar. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

overlapped with the observation, but the simulated values by NDVI had much larger difference as compared with the observation (Fig.3c).

3.3. The dependence of annual CO2 fluxes on CUP Fig. 4 showed the relationships between annual CO2 fluxes and CUP. Annual NEP was significantly correlated with the observed CUP, marginally correlative with the GSI simulated CUP, but not related to the NDVI-derived CUP (Fig. 4a). Longer CUP helped enhance annual carbon accumulation with increase of 1.63 g C m−2 if one day longer of the CUP. Annual GPP and CUP simulated by the GSI model and observed by NEE showed positive but non-significant correlations, while there was no trend in simulated results from NDVI (Fig. 4b). All CUP had no significant effect on annual ecosystem respiration (Re) (Fig. 4c). The annual Re/GPP ratios was marginally and negatively correlated with observed and predicted CUP from GSI (Fig. 4d).

Fig. 2. The dependence of annual net ecosystem productivity (ANEP) on yearly GSI sums and annual maximum NDVI from 2004 to 2009. Lines represent the linear regression, and the red is GSI and the black is NDVI, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

3.4. Factors controlling CUP CUP showed a strong dependence on CUstart (Fig. 5). Based on the VIP and standardized model coefficients of PLS regression, we found that environmental factors showed different importance in affecting the observed CUP and CUstart (Fig. 6). The VIP values of mean annual soil water content (MASWC) and mean annual vapor pressure deficit (MAVPD) exceeded 1.0 (the threshold for variable importance), indicating that both are the most important factors to control the observed CUP, with model coefficients of 0.190 and -0.193, respectively (Fig.6a, c). Meanwhile, for MASWC, MAVPD, mean annual air temperature (MATa) and mean annual 5 cm soil temperature (MATs) the VIP values exceeded 1.0 and model coefficients are -0.16, 0.17, 0.15 and 0.15, respectively, significantly affected variation of the CUstart (Fig. 6b, d). Both the CUP and the CUstart were limited by water condition.

occurred in 2009 for both the observation and the simulation from GSI, with values of 61 and 48 days. The longest CUP occurred in 2008 for both the observed and simulated values from GSI, with values of 138 and 128 days. The average CUPs predicted with GSI and NDVI were 95 ± 28 and 113 ± 24 days. The predicted CUP from NDVI was much longer than the observed one, mainly caused by the early prediction of CUstart and the late prediction of CUend. One of our objectives was to compare CUstart, CUend and CUP from remote sensing data and the GSI model (Fig. 3). The overall results indicated that CUstart, CUend and CUP from GSI were more close to the observed data by EC (with reduced RMSE and bias, and higher correlation) than those of NDVI. The simulated CUP by GSI almost 70

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climate change could alter vegetation development and phenology processes in alpine ecosystems (Kato et al., 2006; Shen et al., 2016). While changed phenology can in return feedback to carbon and water fluxes through the controlling of leaf area index (Piao et al., 2011). Therefore, accurately predicting plant phenology requires better phenology models. In this study, we evaluated the feasibility of a GSI model by coupling environmental factors, i.e. daily Tmin and SWC into a daily metric for the development and refinement models of CFP (CUstart, CUend and CUP) and NEP models in an alpine steppe-meadow, and compared to the performance of satellite remote sensing data as a reference. Our results showed that the GSI model made considerably more accurate predictions of CUstart, CUend and CUP than the MODIS NDVI-derived phenology. The GSI model had lower RMSE, bias and higher linear correlation coefficient than those of the NDVI model. Additionally, the GSI model reflected better predicted NEP than NDVI in the target alpine steppe-meadow. We proved that the GSI model could effectively predict carbon flux phenology, gross primary production, and carbon budget in studied ecosystem on the Tibetan Plateau. Meteorological factors are important candidate explanatory variables in estimating carbon flux phenology dates (Wu et al., 2013; Zhu et al., 2013). Combining optimal climate drivers within phenological models can obviously improve the estimation robustness as the models are applied in a wide spatial and temporal range (Gonsamo et al., 2012b; Richardson et al., 2012). So the key question is how to select the optimal explanatory climate drivers. Solar radiation is intense in alpine steppe-meadow on the Tibetan Plateau, while annual precipitation and annual mean temperature are relatively low. The low temperature and frequent drought could be the most severe stress on plant growth, especially in semi-arid or arid alpine ecosystems (Kato et al., 2006; Luan et al., 2016; Pullens et al., 2016; Wertin et al., 2015). We combined minimum temperature and soil moisture which are the most important factors to control vegetation growth in the alpine ecosystems on the Tibetan Plateau (Chapin et al., 2002) into the GSI model to predict seasonal and annual dynamics of NEP and carbon flux phenology. Meanwhile, a threshold GSI value was defined based on the study of Jolly et al., (2005), as plant canopy can or cannot develop above or below the value. This cutoff value represents at least half of the days were sufficient to maintain vegetation carbon uptake from atmosphere. The GSI model is so flexible that different environmental factors that control phenology regionally such as light, temperature and moisture as well as threshold value can be included. For example, in forest, grassland or crop ecosystems, daily temperature, VPD and photoperiod can be combined into the GSI model (Jolly et al., 2005; Ma et al., 2012; Xin et al., 2015). Large discrepancies exist in estimation of plant phenology using the NDVI and the GSI model. For example, the time of leaf onset or senescence does not always match CUstart or CUend for years under different precipitation in the alpine meadow ecosystem. Because carbon flux phenology captured by CUstart dates based on plant leaves is large enough to ensure that photosynthetic rate is higher than respiration, and the time of CUstart general later than that of leaf onset. Similarly, the CUend dates based on ecosystem respiration becomes larger than photosynthesis, but plant growth may continue, and senescence and dormancy processes should be later than CUend (Zhu et al., 2013). In addition, alpine ecosystem may become a net carbon source for a few days due to drought events (Chai et al., 2017b), which does not imply the end of growing season. As a result, NDVI cannot accurately estimate carbon flux phenology at a site scale. Instead, a site-based water and thermal integrated GSI index can do it because of high frequence of real-time data avaibility from flux stations. So GSI provides more detailed informations, which just complement the shortcomings of the NDVI approach be used at single site. We show that the GSI model can be well applied at site scale. However, there is a shortage for the GSI model to be applied at the broader scales because environmental data such as Ta, SWC, VPD are

Fig. 3. Comparing the start of carbon uptake (CUstart, a), the end of carbon uptake (CUend, b) and the carbon uptake period (CUP, c) with phenology estimated from the GSI and the NDVI model. Statistics for linear regression and coefficients of determination are also given. The lines are linear regression, with solid lines for significant correlation, dash lines for non-significant correlation, and no line for no trend.

4. Discussions 4.1. The performance of the GSI model Substantial efforts have been devoted to improving the accuracy of many widely used land surface models including phenology model. However, previous studies demonstrated that phenology was one field which the existing model had poor performance in predicting plant phenology (Chen et al., 2016). There is broad consensus that future 71

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Fig. 4. Comparison of the relationship between the predicted and observed carbon uptake period (CUP) and annual CO2 fluxes (NEP, GPP, Re and Re/GPP ratio) during the year of 2004 and 2009. The lines are linear regression, with solid lines for significant correlation, dash lines for non-significant correlation, but no line for no trend.

4.2. Carbon sequestration mediated by CUP The driving of CUP on carbon sequestration has been studied in many grassland and forest ecosystems. Evidence showed that the carbon exchange in land-atmosphere system is sensitive to phenology (Barr et al., 2004; Grant et al., 2012; Kang et al., 2016; White and Nemani, 2003). Our resluts also suggested that inter-annual variations in CO2 fluxes were controlled by annual CUP. Annual NEP would increase by 1.63 g C m−2 year-1 if one CUP-day were extended. The increased amplitudes in alpine meadow differed from those in other ecosystems (Richardson et al., 2013). In general, the increased NEP values in forests were higher than that in grasslands. For example, the NEP increased by 5.7 and 5.8 g C m-2 year-1 per extended CUP-day in a temperate broadleaf and a deciduous broadleaf forest, respectively (Baldocchi and Wilson, 2001; Churkina et al., 2005). And the NEP increased by 2.0 and 4.0 gC m-2 year-1 per extended growing season day for a savanna and an open grassland, respectively (Ma et al., 2007). The increase of carbon sequestration per extended CUP-day in the alpine steppe-meadow, by contrast, is much lower than those of abovementioned grassland ecosystems (Bao et al., 2014) perhaps due to low productivity limited by low temperature and frequent fluctuation of water availability.

Fig. 5. Relationship between the carbon uptake period (CUP) and the start of carbon uptake (CUstart) from 2004 to 2009.

available in sites but difficult to gain at large scales. So GSI has its disavanatage to be applied and scale up at broader scales compared with MODIS NDVI method. But if given more hygrothermal data provided by increasing the number of flux tower sites, accurate carbon flux phenology will be expected to be simulated by the GSI model at larger scales.

4.3. Effects of water condition on CUP Temperature is the key determinant in vegetation phenology development (Cleland et al., 2006; Piao et al., 2008, 2011). Meanwhile, 72

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Fig. 6. The major environmental controlling factors of the observed carbon uptake period (CUP) and the start of carbon uptake (CUstart) using partial least squares (PLS) regression model. In the figure, MAVPD represents mean annual vapor pressure deficit; MASWC, mean annual soil water content; MATs, mean annual soil temperature; MAP, mean annual precipitation; MATa, mean annual air temperature; and MAPAR, mean annual photosynthetically active radiation. a and b are VIP values of environmental factors for CUP and CUstart, red dashed line represents VIP = 1.0, the threshold of variable importance. c and d are PLS model coefficients of the variables that VIP > 1.0 for CUP and CUstart, in which red bars represent negative relationship and blue bars represent positive relationship. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

2015).

other environmental factors may also modulate vegetation phenology. Water availability is an important limiting factor to plant growth in the arid/semiarid areas, and the variability in phenology could be closely related to soil water conditions (Shen et al., 2015). PLS regression model analysis showed significant relationship of CUP with MASWC and MAVPD (Fig. 5b), and it can be interpreted by the relationships between CUP and CUstart and between CUstart and MASWC, MAVPD. The CUstart strongly controlled the length of CUP, and earlier CUstart could lengthen CUP in the alpine meadow (Fig. 5). This important date would be affected strongly by MASWC, MAVPD, and it could be aggravated by higher temperature (MATa or MATs) (Fig. 6). Because increasing temperature can cause water stress in shallow soil layers by enhancing evapotranspiration, and therefore suppresses the growth and development of shallow-rooted plants (Shen et al., 2015; Yu et al., 2003). This can explain the unexpected positive relationship between temperature and CUstart in our study site. In other words, a better water condition would result in an earlier beginning of the CUP, and the prolonged duration of the CUP could lead to increase of the annual carbon sequestration. This result confirms that opinion that water availability often, but not always, constrained the initiation of spring CUstart when soil moisture is low even though temperature is sufficiently high (Ganjurjav et al., 2016b). Ganjurjav et al. (2016b) and Dorji et al. (2013) observed similar results and suggested that water availability limited spring phenology on the Qinghai-Tibetan Plateau. Moreover, Shen et al. (2015) also found that spring greening was more sensitive to inter-annual variations in preseason precipitation in arid areas than in wetter areas. In addition, a study showed that plant production increased and phenology occurred earlier in wet years than in dry years in grassland ecosystem of Californian (Swarbreck et al.,

5. Conclusions Growing season index (GSI) model and MODIS phenology model were used to predict carbon flux phenology including CUstart, CUend and CUP based on 6-year continuous flux measurements at an alpine steppemeadow on the Tibetan Plateau. Results showed that the GSI model performed better than NDVI derived phenology to accurately predict the CUstart, CUend and CUP at the single site, especially in wet year and drought year. Its detailed results can complement the shortcomings of the NDVI approach be used at single site. Additionally, significant positive relationship was found between annual CUP and NEP, and NEP would increase by 1.63 gCm−2 year-1 if one CUP-day was extended. The length of CUP was determined by water condition (SWC and VPD), which mainly affected the CUstart, due to significant correlation of CUstart with the CUP. Meanwhile, higher temperature (MATa and MATs) can lead to delay in CUstart, and further lead to decline in CUP especially in dry year. It suggests that global warming does not always promote plant growth and vegetation phenology in the semiarid alpine steppemeadow, because it also can accelerate drought and shorten CUP, and thus decrease carbon sequestration.

Acknowledgements This work was funded by the National Natural Science Foundation of China (Nos. 31870406, 41661144045), and State Key Research and Development Program (No. 2016YFC0502001). 73

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