How do climatic and non-climatic factors contribute to the dynamics of vegetation autumn phenology in the Yellow River Basin, China?

Ecological Indicators 112 (2020) 106112

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How do climatic and non-climatic factors contribute to the dynamics of vegetation autumn phenology in the Yellow River Basin, China?

T

Moxi Yuana, Lin Zhaoa,1, Aiwen Lina, , Qingjun Lia, Dunxian Sheb,c, Sai Qua ⁎

a

School of Resources and Environmental Sciences, Wuhan University, Wuhan 430079, PR China State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, PR China c Hubei Provincial Key Laboratory of Water System Science for Sponge City Construction, Wuhan University, Wuhan, 430072, PR China b

ARTICLE INFO

ABSTRACT

Keywords: Autumn phenology Climate change Non-climatic factors Quantitative assessment Yellow River Basin

Autumn phenology is considered to be an important indicator of ecosystem carbon cycle processes, yet, its driving factors are not well-understood. Using the Normalized Difference Vegetation Index (NDVI) data from 1982 to 2015, this study investigated the dynamics of the end date of vegetation growing season (EGS) in the Yellow River basin (YRB) and quantified the impacts of both climatic and non-climatic drivers on the EGS. Our results show that the EGS was delayed by 5.6 days in the earlier period (1982–1999), and it was further delayed by another 3.4 days in the later period (2000–2015). Based on partial derivatives method, during the entire period (1982–2015), the average contributions of daytime temperature (Tmax), nighttime temperature (Tmin), precipitation and solar radiation to EGS trends across the YRB were −0.64, 1.69, 0.52, and 0.30 days/10 yr, respectively. Tmin was the climatic factor that provided the highest explanation of the EGS trends. However, for the two subperiods, the dominant factor controlling EGS trends has changed from Tmin to precipitation. Nonclimatic factors were found to have a strong positive impact on the EGS after 2000 as a result of increased PM2.5 concentration in the previous winter. This study provides the first quantitative insights into spatiotemporal differences in autumn phenology within a large fluvial basin context.

1. Introduction Regular seasonal changes in vegetation adapted to surrounding environmental factors are referred to as vegetation phenology events (Garonna et al., 2016; Piao et al., 2019). Shifts in vegetation phenology are generally considered to be the consequence of climate change (Richardson et al., 2013). Such significant phenological changes affect water-carbon-heat exchanges between the soil–vegetationatmosphere system (Myneni et al., 1997; Jeong et al., 2011; Pearson et al., 2013), as well as the transpiration, photosynthesis and productivity of vegetation to profound impacts on the global carbon, water, and energy cycles (Thackeray et al., 2016; Zeng et al., 2017; Lang et al., 2019). An indepth study of the dynamics of vegetation phenology is thus of great importance for investigating ecosystem carbon–water interactions, variability (Wu et al., 2012) and provides an invaluable opportunity to develop predictive models of how vegetation phenology is likely to change over large areas in response to future global warming. Previous studies of phenology primarily centered on investigating linkages between climatic factors and variations in the spring green-up

onset date (SGS) (Melaas et al., 2018; Park et al., 2018). However, the end date of the vegetation growing season (EGS) has received much less attention owing to the interplay of both climatic and local environmental factors as well as the protracted nature of autumn events (Gallinat et al., 2015; Rossi et al., 2016). However, a growing number of studies have revealed that variations of the EGS in the Northern Hemisphere have a larger impact on the length of the growing season compared to the SGS (Garonna et al., 2014; Park et al., 2016). Additionally, the EGS can alter nutrient cycles (Maillard et al., 2015) as well as influence the ecosystem carbon balance by regulating the length of the photosynthetically active period (Fu et al., 2018). Therefore, improved knowledge of the dynamics of the EGS could not only expand our understanding of carbon cycles in the context of ongoing global climate change but also help to predict the potential impacts of feedback from the biosphere on the climate system. Instances of advanced, delayed, and unchanged EGS periods have been reported (Estiarte and Penuelas, 2015), but unlike the SGS such changes are not unanimous. This might be because the senescence of vegetation is a prolonged phenomenon and the rate of change among

Corresponding author. E-mail address: [email protected] (A. Lin). 1 Co-first author. ⁎

https://doi.org/10.1016/j.ecolind.2020.106112 Received 1 November 2019; Received in revised form 10 January 2020; Accepted 14 January 2020 1470-160X/ © 2020 Elsevier Ltd. All rights reserved.

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vegetation indices tends to be more gradual in autumn (Rozensten and Adamowski, 2017), which makes estimates of the EGS more challenging to constrain than the SGS. Although a few remote sensing studies have attempted to track the progression of the EGS throughout its development (e.g. Zhang and Goldberg, 2011), most studies reduced the EGS to a single end of season date (Piao et al., 2006; Zhu et al., 2012; Liu et al., 2016a,b). To date, the processes and drivers governing the EGS remain poorly understood (Che et al., 2014; Gallinat et al., 2015). Several previous studies reported that a shorter photoperiod in autumn was a critical driver for vegetation dormancy in many temperate and boreal regions (Vitasse and Basler, 2014; Gill et al., 2015; Ford et al., 2017). However, the warmer temperature has been demonstrated to be the dominant controlling factor affecting postponement of leaf senescence which, as much, benefits vegetation growth (Jeganathan et al., 2014; Xie et al., 2015), particularly in lower latitudes regions (Dragoni and Rahman, 2012). Furthermore, recent studies have shed new light on the regulative role imposed by the asymmetric effect of daily maximum temperature (Tmax, daytime) and daily minimum temperature (Tmin, nighttime) warming in the EGS (Wu et al., 2018; Yang et al., 2017) in the context of the larger diurnal temperature range and faster nighttime warming over the past decades (IPCC, 2014). Apart from temperature and photoperiod, other climatic factors such, precipitation and solar radiation has also been reported to contribute to autumn phenological events (Liu et al., 2016a, b). In addition to climate, non-climatic factors have also been reported to affect EGS in recent years. For example, Li et al. (2018a) showed that moderate grazing would delay the EGS, whereas overgrazing would result in the advancement of grass senescence on the Tibetan Plateau (TP). Zhou et al. (2016) revealed that the effects of urbanization could lead to EGS differences between urban and rural settings. The role of biological factors has also been shown to provide a partial explanation of the variations in EGS (Keenan and Richardson, 2015; Zu et al., 2018). These results showed that changes in EGS thus cannot simply be attributed to climate change alone (Craufurd and Wheeler, 2009). Although there has been an increasing interest in the responses of vegetation phenology to climate change or non-climatic factors, there has been little progress toward quantifying their respective contributions and assessing their variations over time. In this paper, we focus on the Yellow River Basin (YRB), the secondlargest river basin in China, which spans arid, semi-arid and semihumid climate zones (Wohlfart et al., 2016). Global warming and increasing anthropogenic activities during the past 50 years in this region have resulted in unprecedented impacts on the ecological environment (Sun et al., 2015). The phenology-climate relationship in this region is therefore likely to be complex, but quantifying the climatic and nonclimatic factors governing EGS dynamics would be highly beneficial. This paper, therefore, aims to: (1) investigate the spatial and temporal patterns of EGS in the YRB from 1982 to 2015; (2) to analyze the effects of climatic factors (Tmax, Tmin, precipitation, and solar radiation) on the shifts in EGS; (3) to quantify the relative contributions of climatic and non-climatic factors on the dynamics of the EGS; and (4) to conduct further attempt to investigate the possible non-climatic factors for the dynamics in EGS.

Fig. 1. The overview of the study area (a), wet and dry zones (b), temperature zones (c), and spatial distribution of natural vegetation with no change in land type throughout the entire study period.

season (Zhao et al., 2008). The average annual temperature ranges from 1℃ in the southwestern to 14℃ in the southeastern (Wohlfart et al., 2016). Due to the various landforms and uneven hydrothermal condition, the southeastern parts of the basin are dominated by forest, while the central and western parts are characterized by grassland (Fig. 1d). 2.2. Datasets Normalize Difference Vegetation Index (NDVI), is commonly considered as a proxy of vegetation greenness and photosynthetic activity (Myneni et al., 1997). The EGS over the YRB was estimated from the third generation Global Inventory Monitoring and Modeling Study (GIMMS NDVI3g) derived from Advanced Very High Resolution Radiometer (AVHRR) satellite imagery (https://ecocast.arc.nasa.gov/ data/pub/gimms/3g.v1). This dataset covered the period from 1982 to 2015, with a spatial resolution of 1/12°and 15-day time steps. The various negative effects and noise associated with the change of satellite sensors, orbital drift, atmospheric interference, and non-vegetation dynamics were eliminated (Pinzon and Tucker, 2014). The gridded daily maximum temperature (Tmax), daily minimum temperature (Tmin), precipitation (Pre) datasets from 1982 to 2015, with a resolution of 0.5° × 0.5°, were collected from China Meteorological Data Service Center, China Meteorological Administration (CMA). The gridded daily solar radiation (or termed as absorbed downward shortwave radiation) (Ssd) data with a resolution of 0.1°×0.1°, was provided by China Meteorological Forcing Dataset (Chen et al., 2011). The 1-km spatial resolution land use and land cover (LULC) maps in 1990, 2000 and 2010 derived from Landsat TM/ETM+, OLI, and HJ-1 sensors were adopt form Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (Liu et al., 2014). These landuse maps were classified into six first level of land use categories (cropland, grassland, forest, water-body, urban and built-up, and barren or sparse vegetation land) and twenty-five second levels. In this study, we used these maps to detect unchanged vegetation types within the study area. The PM2.5 (atmospheric particulate matter < 2.5 μm in diameter) data product was employed to explore the possible effects of haze on the EGS. The newly-released Modern-Era Retrospective Analysis and Research and Application, version 2 (MERRA-2) product developed by NASA Global Modeling and Assimilation Office can provide daily PM2.5 data from 1980 to 2017 with a spatial resolution of 0.05°×0.625° (http://disc.sci.gsfc.nasa.gov/mdisc/). This dataset is by far the most accurate PM2.5 remote-sensing dataset with the longest time span and it has been validated and can be effectively applied on a regional scale (He et al., 2019). In this study, all datasets were resampled to the same spatial resolution as NDVI data by using the bilinear method.

2. Materials and methods 2.1. Study area The YRB is located in North of China (95°53′E-119°5′E, 32°10′N41°50′N), spanning the Qinghai-Tibet Plateau, Inner Mongolia Plateau, Loess Plateau and the North China Plain, with a total area of approximately 750,000 km2 (Fig. 1a). Under the continental monsoon circulation system, the climate in the YRB transitions from semi-arid and arid to sub-humid and humid conditions (Fig. 1b, c). Mean annual precipitation is 476 mm, which is mainly concentrated in the summer 2

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2.3. Retrieval of the SGS and EGS from satellite data

2013; Piao et al., 2015). Therefore, according to Wu et al (2018), the following steps were to determine the length of the preseason for each climate factor. Taking the preseason length of Tmax as an example, for each pixel, we first calculated the partial correlation coefficients between mean Tmax and EGS during 0, 1, 2, … n months before EGS, by controlling the corresponding mean Tmin, the sum of precipitation and solar radiation. The maximum range (n) of the preseason is generally from June to multiyear averaged EGS. Finally, the time range with the highest correlation (the absolute maximum of partial correlation coefficient) was defined as the preseason for Tmax and used in the following analysis. Similarly, the preseason for other climatic factors was determined in the same way.

The Polyfit-Maximum method has been widely used to extract the SGS and EGS from regional to global scale (Piao et al., 2006; Cong et al., 2012; Fu et al., 2014a; Liu et al., 2016b; Liu et al., 2017; Wang et al., 2018) and was applied here. To reduce the impact of bare soils and sparsely vegetated grids on the NDVI trend prior to extraction, only pixels with the annual mean NDVI great than 0.1 during the 34 years were used for further analysis (Shen et al., 2016). Next, the average seasonal NDVI curves at 15-day intervals during the period of 1982–2015 were calculated. The following formula (Eq. (1)) was then used to determine the relative change for each pixel.

NDVIratio (t ) = (NDVIt + 1

NDVIt )/ NDVIt

(1)

2.4.3. Estimating the contribution of each driving factor to EGS variations The method based on partial derivatives has been widely used to evaluate the effects of climatic variables on evaporation or hydrological dynamics (Meng and Mo, 2012; Li et al., 2017a). It has been increasingly applied to vegetation analysis (Zhang et al., 2016; Yan et al., 2019). We thus apply this method to quantitatively estimate the contribution of climatic and non-climatic factors to the observed EGS trends in this study. First, we assume that the variations in EGS are the result of climate changes (Tmax, Tmin precipitation, and solar radiation) and non-climatic factors. The contribution of each factor to the interannual variation rate of the EGS is then estimated for each pixel using Eq. (4).

where NDVIratio is the relative change in NDVI, t is the time (temporal resolution of 15 days). We then first obtained the time (t) with the maximum NDVIratio , and then used the corresponding NDVIt as the NDVI threshold for the SGS. Likewise, the time (t) with the minimum NDVIratio was detected and then used in the corresponding threshold NDVIt + 1 at time (t + 1) as the NDVI threshold for the EGS. Finally, we used the corresponding NDVI threshold to determine the SGS and EGS from the NDVI time-series data, fitted using an inverted 6-degree polynomial function (Piao et al., 2006; Wang et al., 2017), as shown in Eq. (2).

NDVI(t ) = a + a1 t 1 + a2 t 2 + a3 t 3 + a4 t 4 + a5 t 5 + a6 t 6

(2)

where t is the number of Julian days. a1, a2, a3…, a6 are the fitted coefficients obtained from the least-squares regression. To rule out the influences of land-use change on our results, we only extracted the phenology information of natural vegetation type pixels that were unchanged in the LULC maps for the entire study period.

D EGS = Con _ climate + Con _ non = Con _tmax + Con _tmin + Con _pre + Con _rad + Con _ non =

n× DEGS =

i=1

i × EGSi n×

n i

i2

i×

i=1 n i

i=1

tmax

EGS tmin

×

tmin t

+

EGS pre

×

pre t

+

EGS rad

×

rad t

+ Con _ non

tmin

pre

rad

2.4.4. Impacts of possible non-climatic factors on the EGS Several previous studies reported that biological factors could influence the EGS since the plant life-cycle stage is dependent on the previous one (Keenan and Richardson, 2015; Zu et al., 2018; Yuan et al., 2019). Thus, in the present study, we calculated partial correlations between EGS and SGS/summer NDVI to investigate the influences of biological factors on the variations in the EGS. Additionally, given the increase in anthropogenic PM2.5 concentrations, we explore its potential role in driving the significant changes observed in the EGS post-2000. To exclude the influence of climate factors, partial correlation analysis was used. Here, the PM2.5 concentration dataset from previous autumn to current summer was used since it may be assumed that PM2.5 may influence the whole plant life cycle of the plant. In the present study, spring was defined from March to May, summer from June to August, autumn from September to November and winter from December to February.

EGSi

2

i

+

Tmax (days ℃-1), Tmin (days ℃-1), precipitation (days mm−1), and solar radiation (days (w/m2) -1), respectively, which are commonly computed as the linear slope coefficient between EGS and mean preseason climatic factors (Wang et al., 2014). tmax , tmin , pre and rad are the t t t t inter-annual variation rates of variation in Tmax, Tmin precipitation, and solar radiation, respectively. Although the magnitude of the change in EGS and climatic factors in the present study were estimated as the slope of a simple linear regression model from the time series of each variable, the latter can also effectively permit the detection and quantification of the contribution of both climatic and non-climatic factors to EGS changes (Zhang et al., 2016).

2.4.1. Exploring the interannual variation of the EGS Based on a pixel-level analysis, a simple linear least-squares regression was used to calculate the temporal trends in the EGS from 1982 through 2015 and the F test was used to determine the significance for each pixel. n

tmax t

where DEGS is the long term inter-annual variation rate of the EGS; Con _ climate , Con _non , Con _tmax , Con _tmin , Con _pre , Con _rad represent the contribution of climate change, non-climatic factors, Tmax, Tmin, precipitation, and solar radiation to the inter-annual EGS trends, respectively. Con _ climate is the sum ofCon _tmax , Con _tmin , Con _pre , Con _rad ; Con _non is approximately equal to the residual between DEGS and Con _ climate . Moreover, EGS , EGS , EGS and EGS represent the sensitivity of EGS to

Climate warming is remarkably changing the leaf phenological events (Fu et al., 2018; Chen et al., 2018; Zhao et al., 2018b), however, recent studies reported that the warming trend in the first decade of the 21st century has slowed down compared with the last two decades of the 20th century (Fyfe et al., 2016; Medhaug et al., 2017). This warming hiatus might affect the trends in phenological events. Meanwhile, the influences of intensive human disturbances on vegetation after the millennium have become more pronounced (Liu et al., 2018a). To this end, the 34 years (1982–2015) were divided into two subperiods: 1982–1999 and 2000–2015. The analysis of the EGS trend and the relationship between climatic and non-climatic factors was conducted separately for these two periods.

n

×

(4)

2.4. Statistical analysis

n

EGS tmax

(3)

where DEGS is the interannual variation rate of EGS, n is the years of monitored, and EGSi is the EGS in the ith year. A negative DEGS indicates an advancing trend, whereas a positive DEGS indicates a delaying trend. 2.4.2. Determination of the preseason of climatic factors Numerous studies have suggested that the climatic conditions in the months prior to phenological events (referred to preseason) are of great importance in influencing vegetation phenology (Phillimore et al., 3

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Fig. 3. Inter-annual variations in the mean of EGS for the entire basin.

Fig. 2. Spatial distribution of the mean EGS from 1982 to 2015 (a), and standard deviation (SD) of the EGS (b). Top-left insets show the histogram of the pixels classes.

3. Results 3.1. Spatial patterns of the EGS Fig. 2 shows the spatial pattern of the annual averaged EGS and its standard deviation (SD) from 1982 to 2015. The EGS primarily ranged from 275 to 305 days of the year (DOY) (early October to the end of October), characterized by distinct spatial heterogeneity. Pixels with earlier EGS (275–285 DOY) values (accounting for 3.2% of the variation) tended to be distributed in the central part of the upper reaches. Furthermore, 15.8% of the pixels located in the south and eastern part of the middle reaches of the YRB exhibited a delayed EGS (e.g., after DOY 295) (Fig. 2a). There were significant spatial differences in the SD of the EGS. A larger area (62.6% of the total basin) primarily in the western part of the basin had low SD (< 9 days) of the EGS from 1982 to 2015 (Fig. 2b). In the center of the basin, pixels with SD of EGS between 9 and 15 days accounted for 29.5%. Pixels with SD of EGS higher than 15 days were only 7.9% and clustered in a small segment of the central part of the upper reaches.

central and western parts of YRB during 1982–1999 changed into statistically significant positive trends during the period 2000–2015 (Fig. 4b, c). Meanwhile, the results show that pixels with the largest positive difference in trends (greater than 0.4 days/yr) between the two subperiods were concentrated in the central region of YRB (Fig. 4d), indicating increased EGS delay in these areas. Conversely, the EGS delay was more muted in the margins of southwest and south YRB (Fig. 4d).

3.2. Inter-annual variations of EGS

3.3. Impacts of climatic factors on the inter-annual changes in EGS

At the basin scale, the timing of the EGS was progressively delayed at a rate of 0.37 days/yr from 1982 to 2015 (p < 0.01). For the two subperiods (1982–1999 and 2000–2015), the EGS also showed a trend of increasing delay, but the magnitude of delay for the EGS in 2000–2015 was almost twice that exhibited in the period 1982–1999 (Fig. 3). We also characterized the spatial patterns in the EGS trends at the pixel scale for different periods (Fig. 4). From 1982 to 2015, a trend of significant delay was found for 54.7% of pixels, mostly concentrated in the central YRB (Fig. 4a). In contrast, only 12.5% of pixels exhibited the reverse trend. Furthermore, the EGS trends varied greatly between the two subperiods. For 1982–1999, around 17.8% and 0.2% of pixels were characterized by significant delaying and advancing trends, respectively (Fig. 4b). Yet, the EGS trends after 2000 were different compared to 1982–1999 (Fig. 4c). For example, insignificant positive trends in the

3.3.1. The sensitivity of EGS to various climatic factors The preseason length for the four climatic factors typically ranged from 0 to 3 months (Fig. A.1), consistent with previous results (Piao et al., 2015; Yang et al., 2017). Then the sensitivity of EGS to various preseason climatic factors in each period is shown in Fig. 5. From 1982 to 2015, the positive sensitivity of preseason Tmin dominated a large portion (80.6%) of pixels, primarily located to the west of the upper reaches and south of the middle reaches (exceed 6 days/℃) (Fig. 5a). This result indicates that the EGS is positively correlated with the preseason Tmin. By contrast, the EGS for 58.3% of all pixels were inversely correlated with preseason Tmax (Fig. 5b), the mean sensitivity of EGS to Tmax over the whole basin was −1.36 days/°C. These results indicate that the increase in preseason Tmin would generally cause a delayed EGS, whereas the increase of preseason Tmax would typically lead to an earlier EGS. The EGS was shown to be sensitive to

Fig. 4. (a–c) Spatial distribution of EGS trends for different periods. (a: 1982–2015, b: 1982–1999, c: 2000–2015). (d) Differences in EGS trends between 1982 and 1999 and 2000–2015. The top-left insets show the pixels with significant (p < 0.05) delaying (red) and advancing (green) EGS, whilst the top-right insets show the histogram of the pixel classes. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 5. Spatial distribution of the sensitivity of the EGS to the preseason Tmin (a, e, i), Tmax (b, f, j), cumulative precipitation (c, g, k), and solar radiation (d, h, l) over different periods. Top-left insets show the pixels with significantly (p < 0.05) negative (blue) and positive (red) sensitivities. The top-right inset shows the histogram of the pixel classes. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

precipitation in 77.1% of all pixels, with the highest values (i.e. greater than 1 day/10 mm) to the northwest of the upper reaches. This implies that for every 10 mm increase in preseason precipitation the EGS would be delay by at least a day. The EGS was found to be negatively correlated with solar radiation in 61.3% of all pixels, which are primarily located in central, northern and eastern parts (Fig. 5d). These negative sensitivities mostly ranged from −0.5 to 0 days/100 W∙m2 but were only significantly negative for approximately 8.9% of all pixels. Additionally, the sensitivity of EGS to various climatic factors over different periods has also been detected (Fig. 5e-l). For instance, the sensitivity of the EGS to precipitation and solar radiation remained constant over different time periods. However, there were minor transitions in the spatial pattern of sensitivity of the EGS to Tmin and Tmax, specifically, that the sensitivity changed from a negative to a positive value in parts of the western YRB. 3.3.2. Contributions of climatic factors to the EGS trends To quantify the influence of each climatic factor on EGS, the contributions of each climatic factor to the EGS trends over the two periods were compared at both the regional and pixel scales (Fig. 6). At the regional scale, temperature predominantly controlled the EGS trends from 1982 to 2015 (Fig. 6a), despite the opposite contribution exerted by Tmin (0.169 days/yr) and Tmax (-0.064 days/yr). Tmin (0.267 days/yr) was the dominant contributor to the EGS trend from 1982 to 1999. In contrast, from 2000 to 2015, precipitation (0.126 days/yr) became the leading factor responsible for the observed EGS trends. Temperature was found to dominate the EGS in forested areas, but precipitation was more significant in the grassland regions (Fig. 6b and c). Thus in the YRB, the EGS in the forested areas was more sensitive to temperature, whereas the EGS for grassland was sensitive to both temperature and precipitation. At the pixel scale, there was strong spatial heterogeneity in the contributions of climatic factors to the EGS trends between the different periods (Fig. 7). From 1982 to 2015, Tmin was the dominant climatic factor over 33.1% of the basin (greater than 0.2 days/yr), particularly

Fig 6. Contributions of four climatic factors to the EGS trends at the regional scale.

in the northwestern and western parts of upper reaches (Fig. 7a); while a higher negative contribution of Tmax to EGS was observed in the corresponding replace (Fig. 7b) highlight their contrasting impacts. The contribution of precipitation and solar radiation (Fig. 7c and d), mostly ranged from 0 to 0.1 days/yr, accounted for 59.0% and 53.5% of all 5

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Fig. 7. Spatial-temporal contributions of preseason climatic factors on the EGS trends in different periods across the YRB. (a), (e), (i) contribution of preseason Tmax to EGS; (b), (f), (j) contribution of preseason Tmin to EGS; (c), (g), (k) contribution of preseason cumulative precipitation to EGS; (d), (h), (l) contribution of preseason solar radiation sum to EGS.

pixels, respectively. Notably, the strongest positive contribution from precipitation and solar radiation was observed in the central parts of the YRB. The percentage of pixels with a negative contribution of precipitation to EGS accounting for 26.2% and were most abundant in the northwestern and western regions of the upper reaches. Solar radiation made a negative contribution to EGS in the western and central parts of YRB. For the two subperiods, the positive impacts of Tmin (greater than 0.2 days/yr) declined from 46.7% in 1982–1999 to 24.4% in 2000–2015 (Fig. 7e and i), while the areas with a positive contribution between Tmax and EGS had expanded to 49.7% after 2000 (Fig. 7f and j). Additionally, the positive contribution of precipitation to EGS increased from 45.6% in 1982–1999 to 66.4% in 2000–2015, and pixels with contribution greater than 0.1 days/yr transferred from the western to the central and northern areas (Fig. 7g and k). However, the percentage of positive contribution of solar radiation to EGS remained almost in the two subperiods, that is, 54.0% in 1982–1999 and 53.2% in 2000–2015. Nonetheless, the divergent influences of solar radiation on EGS in spatial distribution. For example, pixels with a positive contribution greater than 0.2 days/yr were mainly in the southern areas in 1982–1999, whereas this contribution was concentrated in western and the edge of eastern parts in 2000–2015 (Fig. 7h and l).

contribution to grassland regions than to the forest regions during the period 1982–1999, while the opposite situation occurred after 2000. Spatially, the contribution of non-climatic factors to the EGS trends varied notably at the pixel scale (Fig. 9). From 1982 to 2015, the areas with positive contributions were widely distributed throughout the YRB, accounting for 72.3% of the total number of pixels (Fig. 9a), whilst a negative contribution was observed in the southwestern regions. Comparing the two subperiods, the pixels exhibiting this negative contribution covered a wider area in 1982–1999 relative to 2000–2015 (Fig. 9b and c). In contrast, the proportion of pixels showing a positive contribution increased substantially from 1982 to 1999 to 2000–2015, especially in the central part of the YRB. The pixels with a higher positive contribution (greater than 0.4 days/yr) accounted for 36.5% of the variations in EGS from 2000 to 2015. 3.4.2. Relations between biological factors and EGS The partial correlation coefficients between EGS and SGS/summer NDVI in the two subperiods are shown in Fig. 10. In general, the spatial pattern of the relationship between EGS and SGS/summer NDVI in the two subperiods shared nearly the same sign, indicating that the impacts of biological factors on the EGS were relatively stable during the study period. Specifically, more than two-thirds of all pixels exhibited a positive correlation between the EGS and summer NDVI (Fig. 10a and b), primarily in the central regions of the YRB. In contrast, pixels with a negative correlation between the EGS and summer NDVI were concentrated in the southwestern. Notably, the EGS was positively correlated with the SGS in the southwestern areas but negatively correlated with the SGS in the central areas (Fig. 10c and d).

3.4. Impacts of non-climatic factors on the inter-annual changes in EGS 3.4.1. Contribution of non-climatic factors to EGS trends The contribution of non-climatic factors to EGS trends at the regional scale is shown in Fig. 8. Generally, the non-climatic factors played a positive role in the EGS trends for each period, but especially so from 2000 to 2015 (0.307 days/yr) compared to 1982–1999 (0.139 days/yr). Moreover, non-climatic factors had a greater positive

3.4.3. Relations between haze and EGS The partial correlation between PM2.5 and the EGS for different seasons at the regional (Fig. 11) and pixel scales (Fig. 12) attest to a significant differences in the effects imposed by PM2.5 concentration from the previous winter on the EGS before and after 2000, while for the other seasons, the influence of PM2.5 on the EGS was consistent. Moreover, the PM2.5 in the previous winter was found to have a significant positive partial correlation with EGS (r = 0.589, p < 0.05), which indicated that higher PM2.5 from the previous winter would lead to the postponement of the EGS in the YRB. Furthermore, it is shown that the winter PM2.5 significantly increased from 1998 (Fig. A.2), attesting to the role of non-climatic factors in delaying the EGS in 2000–2015 (Fig. 4c), which could be partly attributed to the increases in PM2.5 from the previous winter (Fig. 12h, Fig. A.2).

Fig. 8. Contribution of non-climatic factors to EGS at the regional scale. 6

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Fig. 9. Spatial-temporal contributions of non-climatic factors on the EGS trends.

4. Discussion 4.1. Variation of autumn phenology in YRB Our results from the long-term remote sensing data have revealed that the timing of the EGS has been progressively delayed across the YRB during the past three decades, which is in accordance with previous studies (Jeong et al., 2011; Yang et al., 2015; Garonna et al., 2016). This trend of a delayed EGS (0.37 days/yr) is however much larger than for the temperate regions of China (the average rate at 0.12 days/yr) over 1982–2011 (Liu et al., 2016a). These discrepancies might be attributed to the different length of periods, vegetation types and methods for detecting changes in the EGS. By separating the entire study period into two subperiods (1982–1999 and 2000–2015), we found that the magnitude of the EGS delaying trend was quite different. From 1982 to 1999, the trend of the delay EGS in our study (0.31 days/ yr) is also very similar to results from Piao et al. (2006) (0.37 days/yr) and Yang et al. (2015) (0.32 days/yr). From 2000 to 2015, the longer delay trend in the EGS (0.56 days/yr) is comparable to the study conducted by Sun et al. (2015) who showed that the EGS in Loess Plateau was delayed by 9.6 days from 2000 to 2010. Although our results were consistent with previous, it is worth mentioning that the accuracy of estimated EGS based on remote sensing is lower than that of field-based measurements. Nevertheless, previous studies have confirmed that the trends in remotely sensed EGS were still well detected (Zhu et al., 2012; Cong et al., 2012).

Fig. 11. Partial correlation between EGS and PM2.5 for different seasons. * denotes significance level p < 0.05.

with previous results based on field experiments and satellite data (Estrella and Menzel, 2006; Xie et al., 2015; Fu et al., 2018). Furthermore, the diurnal temperature was shown to have a contrasting effect on the EGS in the YRB. In other words, we observed that preseason Tmin had a positive contribution to EGS in both whole and two subperiods, whereas preseason Tmax made a negative contribution. This result is in line with the conclusions conducted by Wang et al. (2019), who took the middle and upper reaches of YRB as the research area. It also confirms the study of Wu et al. (2018), who has investigated the response of EGS to daytime and nighttime warming in the Northern Hemisphere. There are several possible reasons for this contrasting

4.2. Comparison between the impacts of different climatic factors on the EGS Temperature rather than shortening of the photo-period (Ford et al., 2017; Lang et al., 2019) played a significant role in regulating the dynamics of the EGS during the past three decades in the YRB and accords

Fig. 10. Spatial patterns of partial correlation coefficients between EGS and summer average NDVI for the two periods 1982–1999 (a) and 2000–2015 (b); (c) same as (a), but for correlation between EGS and SGS; (d) same as (b), but for correlation between EGS and SGS. The top-left insets show the pixels with significantly (p < 0.05) negative (blue) and positive (red) correlations. The top-right insets show the histogram of corresponding coefficients. Percentage of positive (P) and negative (N) correlations and corresponding significant correlations (P < 0.05, in parentheses) are provided. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 12. The spatial partial correlation between EGS and spring PM2.5 (a, e), and summer PM2.5 (b, f), and the previous autumn PM2.5(c, g), and the previous winter PM2.5 (d, h). Percentage of positive (P) and negative (N) correlations and corresponding significant correlations (P < 0.05, in parentheses) are provided.

effect. Firstly, colder nights and increased frost risk are usually considered to be among the most important conditions governing plant senescence (Hartmann et al., 2013). Therefore, higher preseason Tmin could increase the frost-free period (Alward et al., 1999) thereby lengthening the EGS. Second, leaf coloration in autumn is a direct consequence of chlorophyll loss during leaf senescence (Yang et al., 2017). Nighttime warming could stimulate plant photosynthesis by reducing leaf carbohydrate concentrations (Turnbull et al., 2002, 2004) which may help to slowdown chlorophyll degradation and thus postpone physiological processes of leaf coloration. Equally, daytime warming might weaken plant transpiration due to stomatal closure to prevent excessive water loss (Cochard et al., 2002). This would act to reduce photosynthesis (Souza et al., 2004) and result in the early termination of autumn phenology. Furthermore, increased daytime temperature would increase water stress by reducing soil moisture (Williams et al., 2013), which might increase the risk of chlorophyll degradation and plant mortality (Anderegg et al., 2013; Dreesen et al., 2014). Previous study that investigated the asymmetric warming effects on SGS in the YRB reported that warmer nighttime temperatures would advance the SGS (Yuan et al., 2020), then it might be expected that the length of the growing season in YRB would be notably longer, thereby enhancing vegetation productivity. In contrast to Liu et al. (2016b), our results have shown that decreased preseason cumulative solar radiation would delay the EGS and could be due to the yearly abundance of solar energy in the YRB.

Excessive solar radiation would increase surface evapotranspiration (Che et al., 2014), reducing moisture availability in the basin which in turn would inhibit vegetation growth. Meanwhile, higher solar radiation might also lead to chlorophyll degradation by inducing excessive excitation energy in chloroplasts (Juvany et al., 2013), and thus result in an advanced EGS in the autumn. Furthermore, our study has also shown that the contribution of each of the climatic factors varied in different ecotones. For example, Tmin was the dominant factor which had the strongest contribution to the EGS changes within forest regions, while the temperature in conjunction with precipitation contributed to the dynamics of EGS in grassland areas. This difference could largely be due to the diversity of plant physiological structures and their adaptations to environmental variations (Diez et al., 2012). Forests in the YRB are generally distributed in semi-humid areas. This in concert with deep root systems and water-saving adaptations reduces water-stress thereby sustaining forest growth (Zhao et al., 2018a). In contrast, grasslands in the YRB tend to be located in the arid, semi-arid and high-altitude zones (Fig. 1d). In light of the responses of forest to asymmetric warming of daytime and nighttime, we might speculate that the growing season of forest might longer if the nighttime warming trend continues to be faster than that of daytime warming (Solomon, 2007; Peng et al., 2013), correspondingly, the local carbon sink would increase thereby provide a positive feedback to regional climate. Additionally, this study also detected the trends of climatic factors (Fig. A.3) to further explain the changes in the contribution of climatic 8

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factors over different time periods. Enhanced precipitation likely accounts for the stronger contribution of precipitation to EGS in the arid northern part of the YRB from 2000 to 2015 relative to 1982–1999 (Figs. 6, 8 and A.3c). Increased precipitation in arid areas is known to prolong the growing season into the autumn due to the alleviation of water stress (Dreesen et al., 2014; Liu et al., 2016a; Zhang et al., 2020). Interestingly, the negative contribution of Tmax to the EGS weakened during the same period (Figs. 6 and 7), possibly ascribed to the warming trends of Tmax had slowed down in 2000–2015 (Fig. A.3b) and coupled with the increase in precipitation, which made the general climate condition favorable for vegetation. This could also be the possible explanation for the positive contribution of Tmin on EGS weakened after 2000.

ecological engineering and land use management scheme since 1999 (Liu et al., 2018a). Such changes are reported to have a series of potential impacts on the local climate, soil moisture, water consumption, which inevitably interfered with the growth of the plant and further influence the phenological events (Jia et al., 2015; Feng et al., 2016). Although the effects of land-use change on the EGS trend in this study were excluded by investigating vegetation that has not changed in land use types during the study period, how to quantify the effects of ecological engineering on vegetation phenology is still needs to be investigated. 5. Potential applications and limitations Different from previous studies focusing only on the impact of climate elements on vegetation phenology, a method widely used to quantitatively analyze the difference between climatic and non-climatic elements on vegetation dynamics is used in this paper to quantify the impact of these two factors on vegetation phenology. It deepens our understanding of the biological feedback mechanism of vegetation autumn phenology to climate change. On the other hand, it provides a theoretical basis for investigating the impacts of non-climatic factors. However, the limitations of this study should also be recognized. Theoretically, temperature, light, and water are the vital factors to plant photosynthesis (Liu et al., 2018b), but in the context of global warming, the negative influences of frequent extreme events, especially the drought events, on vegetation can not be ignored (Cui et al., 2018; Jiang et al., 1981; Liu et al., 2019b). Additionally, although the total contribution of non-climatic factors to the variations of EGS was quantified and the relations of a few possible non-climatic factors with EGS have been analyzed in this study, other less-predictable factors, such as ecological engineering, urban expansion, should also pay more attention to. Furthermore, how to quantitatively separate the effect of specific non-climatic factors is worthy of further effort.

4.3. The influence of non-climatic factors on the EGS In this study, the contribution of non-climatic factors to the EGS trends was quantified, the relationships between EGS and several possible non-climatic drivers were also further elaborated. It should be noted that the EGS in the southwest YRB was negatively correlated with summer NDVI, but positively correlated with SGS, indicating that an earlier SGS and vegetation peak growth in summer would jointly advance the EGS. This situation can be explained by the circadian rhythm and survival strategies of vegetation (McWatters and Devlin, 2011; Farre, 2012; Dong et al., 2014). The vegetation in southwest YRB has to cope with cold, arid climate conditions. Since vegetation needs to mobilize to full capacity to survive, thus the effects stemming from biological factors of summer NDVI and the SGS on the EGS were strengthened. This could also explain the negative contribution of non-climatic factors in the southwestern areas during the two subperiods. Additionally, a positive correlation between the EGS and summer NDVI was also observed in central areas of the YRB implying that the peak vegetation growth in summer could delay the EGS consistent with the results of Wolf et al. (2016). Yet, on the TP, Li et al. (2018a), demonstrated that summer vegetation activity increases at the cost of soil water overconsumption which then advances the EGS. However, several alternative studies suggested that although the increase in precipitation induced by vegetation greening was not statistically significant, the increased water amount was sufficient to offset enhanced evapotranspiration, resulting in only a minor impact on soil moisture depletion (Li et al., 2018b; Ye et al., 2019), especially in the YRB (Cao et al., 2019). Overall, although these biological factors have been shown to have influences on the variations of EGS, they could not the dominant factors that lead to the delay of EGS after 2000, as their impacts on the EGS has remained the same in both periods. There is also growing evidence to suggest that intensive anthropogenic activities in recent decades were responsible for frequent severe haze weather (Liu et al., 2015; Peng et al., 2016). Previous studies have indicated that haze weather reduces light capture, resulting in shorter sunshine duration and lower temperatures (Kaiser and Qian, 2002; Ren et al., 2019). The high concentration of PM2.5 of the previous winter would retard the heat accumulation of vegetation (Liu et al., 2019a), which might lead to a later SGS in the next year and thus indirectly lead a later EGS in the following autumn (Fu et al., 2014b; Wu et al., 2016). Our results also showed that the EGS is significantly positively correlated with PM2.5 of the previous winter particularly in the central and northeastern areas from 2000 to 2015. It therefore follows that the increase in PM2.5 concentration is the most parsimonious explanation for the enhanced positive contribution from non-climatic factors to the EGS after 2000, but in spite of that, current studies are increasingly emphasizing that the urban island heat or urban morphology caused by rapid urbanization both would result in a profound influence on vegetation behaviors (Zhou et al., 2016; Li et al., 2017b; Yao et al., 2019a–c) and thereby lead to a great impact on the discrepancies in phenological events. Not only that, there are dramatic changes in land-use change and species diversity in the YRB as a result of the implementation of

6. Conclusion This study has investigated the respective roles of climatic and nonclimatic factors governing autumn phenology in the YRB and their spatiotemporal variations. A notable trend of delayed EGS in the YRB from 1982 to 2015 was observed, but the magnitude of this delay intensified from 2000 to 2015. The overall basin-scale contribution by climatic and non-climatic factors to the EGS trends in 1982–2015 was 0.187 days/yr and 0.165 days/yr, respectively. In terms of climatic factors, temperature played a dominant role in driving the EGS trends from 1982 to 2015, but with contrasting effects between daytime and nighttime. Furthermore, the dominant factor controlling the EGS trends switched from Tmin (1982–1999) to precipitation (2000–2015). Solar radiation explained a relatively small proportion of the EGS trends, with an average contribution of 0.03 days/yr. Regarding non-climatic factors, their contribution to the EGS trends was greater in 2000–2015 (0.307 days/yr) relative to 1982–1999 (0.139 days/yr), and probably related to increased haze weather during this period. Spatial differences in the EGS trends and their drivers also arose due to the regional climatic differences, partly controlled by altitude. These conclusions suggest that climatic and nonclimatic factors co-regulate changes in autumn phenology in the YRB, but there are marked spatial differences due to local climate conditions, altitude and feedbacks with local ecosystems. Future studies should investigate the potential interactions between climatic and non-climatic factors, and also consider the possible impacts of changing CO2 as well as extreme weather events on autumn phenology. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 9

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