Vegetation green up under the influence of daily minimum temperature and urbanization in the Yellow River Basin, China

Ecological Indicators 108 (2020) 105760

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Vegetation green up under the influence of daily minimum temperature and urbanization in the Yellow River Basin, China

T

Moxi Yuana, Lunche Wangb, , Aiwen Lina, Zhengjia Liuc, Qingjun Lia, Sai Qua ⁎

a

School of Resources and Environmental Sciences, Wuhan University, Wuhan 430079, China Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China c Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China b

ARTICLE INFO

ABSTRACT

Keywords: Plant phenology Asymmetric warming Urban heat island

Climate conditions are the major driving factors of vegetation phenology. However, there is limited effort to monitor dynamics of vegetation phenology and its responses to climate change and urbanization. Using NDVI data from 1982 to 2015, this study investigated the spatiotemporal change of spring green-up date (GUD) across the Yellow River Basin (YRB) and estimated the possible effects of different climatic factors on it. Additionally, the urban-rural differences in GUD and its linkage to spring land surface temperature (LST) for YRB’s major cities over 2001–2015 were investigated. The results showed that the GUD significantly advanced at a rate of 0.42 days yr−1 and delayed spatially from southeast to northwest. The interannual variations in regionally averaged GUD were driven mainly by preseason min-temperature (Tmin). Spatially, the effect of preseason Tmin was strongest in the central and western region. The confounding effects of preseason maximum temperature (Tmax) and precipitation jointly affected the GUD, while insolation had a weak impact on GUD. Moreover, the sensitivity of GUD to preseason Tmin and precipitation weakened with an increasing mean annual preseason precipitation gradient, but the sensitivity to preseason Tmax was gradually enhanced. Furthermore, the difference in the GUD between urban and rural areas presented a significant logarithmic relationship with the distance away from the urban center, and it was strongly related to the regional LST. Our findings confirmed the importance of Tmin and urbanization in regulating changes in GUD and further suggested that LST should be considered to develop an improved model of GUD under future climate change and urban development.

1. Introduction

2015b). Most previous studies have focused on the effect of daily mean temperature on GUD, and only a few studies devoted to the potential impacts of daytime and nighttime warming on GUD (Piao et al., 2015; Shen et al., 2016). However, the temperature changes over the past 50 years have been characterized by greater warming of the daily nighttime temperature (Tmin) than the daily daytime temperature (Tmax) (IPCC, 2014). As a result of asymmetric warming, the carbon assimilation and depletion of plant are expected to be affected (Anderegg et al., 2015). On the one hand, daytime warming can improve plant carbon uptake by increasing the temperature closer its optimum for photosynthesis (Xia et al., 2014). On the other hand, plant nocturnal respiration may acclimate to higher nighttime temperatures (Atkin and Tjoelker, 2003) so that enhance leaf respiration to deplete foliar carbohydrates (Turnbull et al., 2002). By extension, the study of Peng et al. (2013) noted that if the nighttime warming trend continues to be faster than the daytime warming trend, many terrestrial ecosystem models that use only daily or monthly mean temperature data as

Vegetation phenology is regarded as a good indicator for understanding how terrestrial ecosystems respond to changes in climate and hydrological cycles (Chen et al., 2011a; Richardson et al., 2012). Variations in vegetation phenology caused by environmental changes have exerted great influences on ecological processes, such as energy exchange, the cycling of nutrients and water and terrestrial carbon sequestration (Richardson et al., 2013; Keenan et al., 2014; Buitenwerf et al., 2015). Accurate knowledge of dynamic vegetation phenology, therefore, can help to not only improve the understanding of vegetation responses to climate changes but also accurately predict future ecosystem dynamics. The literature is filled with phenological studies on temperate and boreal regions, providing conclusive evidence that the early spring green-up date (GUD) have been highly accelerated by warming during the last three decades (Cleland et al., 2012; Fu et al., 2014; Wang et al.,

⁎

Corresponding authors. E-mail address: [email protected] (L. Wang).

https://doi.org/10.1016/j.ecolind.2019.105760 Received 21 September 2018; Received in revised form 16 September 2019; Accepted 19 September 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.

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inputs may not capture the response of vegetation to asymmetric diurnal temperature changes (Shen et al., 2018). Therefore, to understand the mechanism of GUD response to temperature more deeply, the asymmetric diurnal and nocturnal effect needs to be considered. Increasingly studies have agreed that the shifts in GUD are not the function of temperature alone but involve a series of other factors. Precipitation serves a critical role in regulating vegetation growth and community structure in many areas (Shen et al., 2015a), particularly in arid and semiarid areas (Dubovyk et al., 2015; Zhou et al., 2016b). For example, He et al. (2015) observed a widespread delay in GUD of arid mountain regions caused by a drought and hydroclimatic extremes; Guan et al. (2014) identified precipitation as the major environmental factor controlling variability in GUD. Despite the precipitation has been shown to have a definitely influence on GUD, studies of Shen et al. (2015a) suggested that the GUD still had diverse responses to precipitation even in arid/semiarid regions. Recent studies suggested that it is not enough to explain the variations of GUD through temperature and precipitation (Fu et al., 2015b; Zhao et al., 2018a; Yuan et al., 2019), since solar radiation (either insolation sum, peak irradiance, solar intensity or photoperiod) plays a vital role in triggering GUD (Tylewicz et al., 2018), especially the combination of solar radiation and temperature can significantly alter the phenological period of plants (Flynn and Wolkovich, 2018). Consequently, it is still necessary to strengthen the understanding of the interactive effects among temperature, precipitation, solar radiation and other factors on GUD changes. In addition to the influences of climate-related factors, the effects of anthropogenic activities on vegetation phenology seem to be worsening. Urbanization, as the most obvious product of anthropogenic activities, creates temperature differences between urban and surrounding rural area as the natural land cover is replaced by impervious paving and building materials, which triggers the urban heat island (UHI) effect and ultimately has a great impact on the hydrological regime and local and regional climates (Imhoff et al., 2010; Yao et al., 2017). Recent studies that focused on the urban-rural differences in phenology believed that warming could advance the onset of flowering in cities of Europe and Asia (Jochner et al., 2013; Zhou et al., 2016a) and accelerate the date of leaf emergence in North American cities (Li et al., 2017). Concurrently, given the similarities between urban climate and global climate change, urbanized areas are regarded as good testing sites to simulate the effects of future climate change on plant phenology (Yao et al., 2019). Although prior studies briefly explained the mechanisms by which vegetation phenology responds to climatic factors, they are geographically confined to specific regions or a single vegetation type. For example, in Tibetan Plateau (TP) where dominated by alpine vegetation or in arctic tundra region with small seed banks, snowfall or snowmelt was believed to be the key factor affecting spring phenology (Chen et al., 2015; Bjorkman et al., 2015). Yet, the leaf emergence dates across North American temperate and boreal forests were highly correlated with spring temperature (Melaas et al., 2018). To the best of our knowledge, there is particularly lack of phenological studies on complete ecosystems at the basin scale. The Yellow river basin (YRB), directly supporting a population of 107 million people, is the second largest basin in China (Xu et al., 2018). In the past several decades, climate change in the context of global warming and intensive human activities have led to the deterioration of the ecological environment in the basin (Sun et al., 2015). In this context, the complex ecosystem within the basin may produce phenology/climate interactions that complex and unique to this area. Therefore, revealing spatial patterns of the vegetation growing season and its climatic controls can deepen the understanding of the process, function and pattern characteristics of vegetation dynamics at large basin scales and provide a decision-making reference for local sustainable development. In recent years, numerous studies have shown that the vegetation in

YRB showed positive sustainability improvement trend due to the implementation of ecological recovery projects (Jiang et al., 2015; Cao et al., 2018). Other than that, climate change is also a driver of vegetation dynamic. Sun et al. (2015) stated higher temperature had a positive effect on the growth of vegetation in less water-stressed areas. Zhao et al. (2018) observed that summer drought had the strongest effect on vegetation dynamics. Previous studies have provided an indepth understanding of the dynamic changes of vegetation coverage and its response to climatic or human factors in YRB but neglected the important function played by vegetation phenology. Extendedly, the long-term shifts in GUD within the basin and the complex spatial interactions between GUD and multiple climatic factors in this area have not been reported thoroughly. Moreover, how rapid urbanization affects the GUD in this basin is still unclear. Therefore, it is necessary to monitor the dynamics of vegetation spring phenology and understand the driving mechanism. The primary objectives of this study are to (1) explore how GUD change across the YRB from 1982 to 2015, (2) untangle which preseason climatic factors (refers to a period during which the average or integrated climatic conditions affect plant before the phenological event occurring or growing season) are the main controls on GUD dynamics and investigate the extent to which these factors can explain variations in GUD, (3) identify whether shifts in the relationship between GUD and preseason climate factors are related to the spatial pattern of preseason precipitation, and (4) compare the differences of GUD in urban and rural areas and explore their relationships with UHI effects. 2. Materials and methods 2.1. Study area The Yellow River Basin, located in the northern area of East Asian monsoon region, originating from Bayankala Mountain in Qinghai Province, China, and crossing the Qinghai-Tibet Plateau, the Inner Mongolia Plateau, the Loess Plateau, and the Huang-Huai-Hai Plains, drains a total area of approximately 750,000 km2 (Fig. 1). Affected by atmospheric circulation and monsoon circulation, the spatial-temporal climate change of the basin is obvious: the southeastern part of the region has a semi-humid climate, the middle part has a semiarid climate, and the northwestern part subordinate arid climate (Jiang et al., 2015). The average annual precipitation in the entire basin is 476 mm, with a decreasing trend from southeast to northwest and the rainfall is mainly concentrated from July to September. Within the study area, the natural vegetation composition varies as a result of spatial heterogeneity associated with the severely fluctuating topography, altitudinal gradient, and climate. The dominant vegetation in the YRB is grassland, mostly in the northern and western areas. 2.2. Retrieving GUD from satellite data Remote sensing data has been widely used to extract vegetation phenology, given its continuous time series, larger scale observation, and easy to access features (Liu et al., 2017). The satellite-based normalized difference vegetation index (NDVI), which can reflect the intensity of vegetation metabolism and seasonal or interannual variations, is commonly regarded as a proxy of vegetation greenness and photosynthetic activities (Myneni et al., 1997). It is thus widely applied to provide valuable insights into vegetation change. The Global Inventory Monitoring and Modeling Study third-generation (GIMMS NDVI3g) (https://ecocast.arc.nasa.gov/data/pub/ gimms/3g.v1), with a spatial resolution of 1/12° (~8 km) and 15 day time steps, was prepared by Advanced Very High Resolution Radiometer (AVHRR) instrument from the NOAA satellite series 7, 9, 11, 14, 16, and 17. The various negative effects from different sensors and physical conditions, such as volcanic aerosols, have been corrected 2

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Fig. 1. Location of the Yellow River Basin (A) and the spatial distribution of the main vegetation type (B), climate zones (C), multiyear average precipitation (D) and average temperature (E) within the study area.

function (Zhang et al., 2003):

(Tucker et al., 2005). For the purpose of investigating the vegetation seasonality, only pixels meeting the following two criteria were used for further analysis. (1): the multiyear average NDVI should be higher than 0.1 (Jeong et al., 2011b); (2): the annual maximum NDVI should occur within JulySeptember (Shen et al., 2014). Next, to reduce the impact of noise caused by long term cloud contamination or other poor atmospheric condition on GUD extraction, the Savitzky-Golay (SG) filter was employed to smooth the NDVI datasets. This step was implemented in TIMESAT software (Eklundh and Jonsson, 2010). In addition, cloud classifications and other auxiliary quality data can be used to assign fitting weights to the values in the time series during the smoothing process. Therefore, the GIMMS percentile information was used to determine the fitting weight of each pixel point for the NDVI dataset. We set the weight to 1 when the data point is an acceptable value, 0.5 when the data point is an interpolation value and 0.1 when the data point is a seasonal extraction value. Subsequently, the filtered time series from the start of a year to time of annual maximum NDVI was fitted by using a four-parameter logistic

y(t) =

c +d 1 + ea+bt

(1)

where t is the day of year (DOY), y(t) represents the NDVI value at time t; a and b are fitting parameters, c is the amplitude of NDVI variation, d is the initial background NDVI value, usually the minimum vegetation index value preceding the growing season. Further, GUD was inferred from the rate of change of curvature (RCC), and the GUD was defined as the time when RCC reached its first local maximum value (Zhang et al., 2003). A schematic diagram can be seen in Fig. S1.

K=

b2cz(1 z)(1+z)3 [((1+z) 4 + (bcz) 2)]3/2

RCC = b3cz

(2)

3z(1 z)(1+z)3 [2(1+z)3 + b2c2z] [(1 z) 4 + (bcz) 2]2.5

(1+z) 2 (1 + 2 z 5z2) [(1+z) 4 + (bcz) 2]1.5 (3)

where K represents the curvature; z = e 3

a+bt

; and RCC is the rate of

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change of K.

n 1 n

S=

2.3. Climate, land surface temperature and land cover datasets

2.4.2. Identifying the relationship between GUD and climatic factors According to previous studies (Fu et al., 2015a; Piao et al., 2015; Shen et al., 2016), the length of preseason duration was defined as the period before the multiyear averaged GUD date for which the correlation coefficients between GUD and climatic variables was highest (absolute value) during 1982–2015 for each pixel. Specifically, taking Tmax as an example. Firstly, for each pixel, the mean Tmax for each of 10 periods with durations ranging from 15 to 150 days (i.e., 15, 30, 45, …, 150) preceding GUDmean (34-year-average GUD) was calculated. Secondly, the partial correlation coefficient between the 34-year time series of GUD and the mean Tmax during each of these 10 periods was calculated, consequently, 10 coefficients corresponding to the 10 periods can be obtained. Then the duration of the preseason period with the highest partial correlation coefficient (absolute value) was finally selected as the preseason (a graphical example was given Fig. S2). Finally, the preseason Tmax at each pixel for each year was determined as the mean maximum temperature during the preseason period of the selected duration before the GUD of that pixel in that year. A fixed preseason period was not used in this study, due to the preseason of each climatic variables could vary among different areas (Jeong et al., 2011b). Therefore, the preseason for each of the other three climatic factors (Tmin, precipitation, and insolation) was determined separately in the same way. The phenological and climatic variables were all linearly detrended before the partial correlation analysis, to minimize the possible spurious results from seasonal and/or temporal autocorrelation of vegetation and climatic series (Wen et al., 2019). Furthermore, to assess the magnitude of the impact of preseason Tmax on GUD, we calculated the sensitivity of GUD to preseason Tmax as the coefficient in a multiple linear regression in which GUD was regressed against preseason Tmax, Tmin, precipitation, and insolation. The impacts of preseason Tmin, precipitation, and insolation on GUD were assessed in the same way. 2.4.3. Analyzing the effect of urbanization on GUD Due to the urbanization process of China entered the stage of rapid promotion in the late 1990s (Chen et al., 2013) and limited by the short time series of LST data, this study thus analyzed the effects of urbanization on GUD during the period of 2001–2015. The specific analysis is as follows: Firstly, cities with build-up areas smaller than 10 km2 were excluded due to their relatively slow urbanization speed (Zhang et al., 2004). Then, the built-up area from the CLUDs (in the years 2000, 2005, 2010 and 2015) were selected to obtain the intersection part and defined it as “old urban area” (OUA), the OUA was the common part of build-up areas over above 4 land cover maps. Subsequently, the union of the built-up areas of the CLUDs (in the years 2000, 2005, 2010 and 2015) were taken and defined it as “pooled urban area” (PUA), which means the PUA includes all built-up areas during the period of 2001–2015. Next, the OUA was subtracted from the PUA and defined the remainder as “recent urban area” (RUA). Furthermore a series of buffers extending 0–1 km, 1–2 km, 2–3 km, 3–5 km, 5–10 km, 10–15 km, 15–20 km, and 20–25 km from the edge of the PUA for each capital cities in the YRB were created (Fig. S3), and the 20–25 km buffer was defined as rural area according to Zhou et al (2016a).

2.4.1. Investigating trends in GUD Compared with the conventional linear statistical techniques, the Theil-Sen (TS) slope estimator not only can help diminish the influence of missing time series observations but also can remove the outliers in time series. This study thus applied the TS slope estimator to explore the magnitude of the trend in GUD (Eq. (4)).

xi

j

i

for i = 1, …, N

(5) th

where xi and xj are the i and j values of the series, respectively; n is the length of series, and where sign(x) = 1, 0, −1, if × is positive, zero or negative respectively. Positive (negative) values of S indicate a positive (negative) monotonic trend. The significance test process was outlined in the Supplementary material to this paper. In this analysis, the MK test was used to detect if a trend in GUD for 1982–2015 is statistically significance at the 95% confidence levels.

2.4. Data analysis

xj

xi ) th

The gridded daily maximum temperature (Tmax), minimum temperature (Tmin), precipitation and insolation (absorbed down-ward short wave radiation) data from 1982 to 2015, with a spatial resolution of 0.1°×0.1°, were obtained from the Data Assimilation and Modeling Center for Tibetan Multi-spheres (http://westdc.westgis.ac.cn/data/ 7a35329c-c53f-4267-aa07-e0037d913a21), Institute of Tibetan Plateau Research, Chinese Academy of Sciences (Chen et al., 2011b). MOD11A2_LST data (8-day composite) with a 1000 m spatial resolution were used to explore the temporal variations of the UHI (https://ladsweb.modaps.eosdis.nasa.gov/). The LST data from Aqua MODIS were monitored at 1:30 (nighttime) and 13:30 (daytime), thus, we assumed the means of the LST nighttime temperature and LST daytime temperature can represent the daily average temperature. As the GUD was mainly controlled by spring temperature (Wu and Liu, 2013; Wu et al., 2015), the mean temperature from March to May was defined as the spring temperature. The China’s Land Use/Cover Dataset (CLUDs) were derived from the Landsat TM/ETM+ and HJ-1A/1B images. Quality control procedures have been carried out for the datasets, and the overall accuracy was up to 90% (Liu et al., 2010). The CLUDs in the year 2000, 2005, 2010, and 2015, with a spatial resolution of 1000 m, were provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Science (RESDC) (http://www.resdc.cn/). A hierarchical classification system of 25 land cover classes was applied to the CLUDs, and then the 25 classes of land cover were further grouped into 6 aggregated classes of land cover: croplands, woodland, grasslands, water bodies, built-up areas and unused land (Liu et al., 2005), more detailed definitions of land cover types were given in Table S1. The spatial distribution of vegetation was obtained through a digitalized vegetation map of China at the scale 1:1,000,000 (Editorial Board of Vegetation Map of China, CAS, 2001), downloaded from the RESDC. The vegetation map of YRB was clipped from the vegetation map of China, and then, based on the classifications, merged similar land cover types into one category (more details see Table S2), finally there are nine main vegetation types on the YRB (Fig. 1B), including deciduous needle-leaf forests (DNF), deciduous broadleaf forests (DBF), evergreen needle-leaf forests (ENF), grassland, shrub, meadow, cultivated vegetation, desert, and swamp.

Q = median

sign (xj i=1 i+1

(4)

where Q refers to the Sen’s slope; x j and x i are the data values at times j and i (j ＞i), respectively; and median is calculated from all pairs of observations in the time series. In the case of Q ＞ 0, indicates an increasing trend; Q < 0 indicates a decreasing trend; Q = 0 means no change. To test the significance of the TS slope, the Mann-Kendall (MK) trend test was used. This method has been frequently employed to detect significant trends in hydrological and meteorological time series as it does not require the data to be distributed normally (Gocic and Trajkovic, 2013). MK trend test is based on the value of the MK statistic S, which for a time series x; is defined as Eq. (5): 4

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The GUD differences between urban or buffer zones and rural areas were calculated for each city and each year separately according to Eq. (6)

GUDi _r = GUDi

GUDr

(6)

where GUDi represents the GUD in urban areas, i can be the OUA or the RUA or the six buffer areas GUDr , represents the GUD in the rural area of c city; GUDi _r is the difference in the GUD between i (urban area) and rural area. A negative GUDi _r indicates the GUD in urban area was earlier than that in rural area, while the opposite means a later. Subsequently, in order to analyze the overall urban-rural GUD trend for each city, we first calculated the mean GUDi_r of each city from 2001 to 2015 using Eq. (7).

¯ i _r = GUD

n k=1

GUDi_rk n

(7)

¯ i _r present the multiyear average of GUDi _r , GUDi _rk is where GUD the GUDi _r in the kth year, n is the number of years (n = 15 in current work). Similarly, the spring LST differences between urban (OUA or PUA, or buffers) and rural areas, which represent the UHI intensities, and the overall urban-rural LST trend for each city or whole basin were calculated using the same method as that used to calculate phenology. Finally, a non-linear model was used to study the trend of GUD or spring LST differences from urban to rural areas, the significance of the variation tendency is determined using the F test to represent the confidence level of variation. Moreover, the relationship between GUD differences and spring (March to May) LST differences were examined by Spearman’ s rank correlation coefficient, since this method can accurately identify the covariant trend level for two random variables with a linear or nonlinear correlation (de Beurs et al., 2009) and this analysis was performed using SPSS 22.0 software.

Fig. 2. (A)Spatial distribution of the mean GUD during 1982-2015, and (B) standard deviation (SD) of the GUD.

3. Results 3.1. Changes in vegetation GUD 3.1.1. Spatial-temporal variations in GUD The GUD date was gradually later from southeast to northwest (Fig. 2A), with ranging from DOY 70 to 150 (mid-March to the end of May). The earliest GUD was in Guanzhong Plain, while regions with later GUD were found in the upper reaches. The standard deviations (SD) of GUD in almost half of the areas (50.5%) were generally within 9 days during 1982 to 2015, which mainly located in the southwestern and southern parts of YRB. The central regions with SD of GUD between 10 and 20 days comprised about 39.3%, and regions with largest SD higher than 20 days were only 10.2%, which located in the northern border of YRB (Fig. 2B). At the regional level, the mean GUD across the YRB showed a significantly advancing trend with a rate of 0.42 days year−1 (p < 0.01) (Fig. 3A). Over the region, 69.6% of pixels showed advancing GUD, mostly at the northeastern and central to southern areas, of which 57.5% were significant (Fig. 3B). The rate of significantly advancing trends was < −0.03 days year−1 and approximately 44.4% of the area with an advancing rate between −0.03 days year−1 and −0.4 days year−1. The other 30.3% of pixels showed delaying GUD, mostly > 0.03 days year−1, mainly located in the northern and the southwestern edge, about 30% were significant.

Fig. 3. (A) Inter-annual variations in the mean of GUD for the entire study area. (B) The spatial pattern of the temporal trend in GUD over 1982–2015. The topleft inset shows the pixels with significantly advancing (blue) and delaying (red) GUD. The top right inset shows the fraction of pixels in each the GUD trend intervals of which the values were indicated by the map legend.

3.1.2. GUD in relation to elevation gradient Altitude may have a certain influence on phenology due to the varying terrain in YRB, thus, the averaged GUD was first calculated for every 100 m altitudinal bin with a sample size of > 90 pixels and regression models were constructed to investigated how the GUD and its temporal trends vary across an increasing altitude gradient. F-test was used to examine the reliability of regression model. The GUD was later

logarithmically with increasing elevation (p < 0.05) (Fig. 4A), and there were discrepancies in the trend of GUD at different altitudes, for example, the advancing GUD trend at 350 ~ 850 m a.s.l. increased with altitude, reaching the lowest value of 0.285 (d/a·100 m−1) at 850 m a.s.l., after that, the advancing trend was weakened and the GUD 5

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Fig. 4. (A) Changes in mean GUD during 1982–2015 and (B) the trends of GUD along gradient in the Yellow River Basin. Error bars show standard deviation (SD) of pixels in each elevation bin.

started delayed at elevations higher than 3900 m a.s.l. (Fig. 4B). Such changes possibly relate to the combination of hydrothermal conditions and vegetation types at different altitude gradients. 3.2. Relationship between GUD and its potential drivers

Fig. 5. Spatial patterns of the partial correlation coefficient (RP) between GUD and the preseason (A) Tmax,(B) Tmin, (C) cumulative precipitation and (D) sum insolation. For the partial correlations between GUD and each of these indicators, the preseason durations for Tmax, Tmin, cumulative precipitation, and sum insolation respectively. RP = ± 0.30, ± 0.36, and ± 0.46 correspond to significance at P < 0.10, P < 0.05, P < 0.01, respectively. The inset graph in each panel shows the percentage of the pixels in each correlation coefficient category. Categories are defined by the colors in the scale bar on the bottom of the figure.

First, the spatial distribution of preseason length for the four climatic factors at the pixel scale was investigated. In brief, the preseason length for the four climatic factors almost ranged between 15 and 90 days (Fig. S4). Then, the partial correlation between GUD and the preseason climatic factors at regional scale showed that the mean GUD had a significant negative partial correlation with preseason Tmin, but there is no statistically significant relationship between mean GUD and preseason Tmax, precipitation and solar insolation (Table 1). Moreover, the sensitivity of GUD to preseason Tmin showed that an increase of 1 °C in the regionally averaged preseason Tmin would advance the average GUD by 3.21 days (p < 0.01).

Furthermore, the partial correlation and sensitivity of GUD to each various climatic factor exhibited almost similar spatial patterns (the relationship between GUD and climatic factors at the regional level see Fig. S5). Specifically, in 43.4% of all pixels, a positive partial

Table 1 Impacts of preseason Tmax, Tmin, precipitation and insolation on the GUD.

Partial coefficient between GUD and climate Sensitivity of GUD to climate

Tmax

Tmin

Precipitation

Solar Insolation

−0.34 −2.82 (day °C−1)

−0.53** −3.21** (day °C−1)

−0.29 −0.11 (day mm)−1)

−0.13 −0.02 (day (W m−2)−1)

Note. For partial correlations (or the sensitivities) between GUD and each of these indicators, the preseason durations for Tmin, Tmax, cumulative precipitation and sum insolation were used respectively. ** Significant at p < 0.01. 6

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correlations (Fig. 5C). Moreover, although there were about 65.3% of the pixels showed negative precipitation sensitivity values, the sensitivity was ranged from 0 to −0.2 days mm−1 in 53.5% of the pixels. A precipitation sensitivity value less than –0.2 days was found in only 11.8% of the YRB, which occurred mostly in the western part of the YRB (Fig. 6C). For the relationship between GUD and preseason insolation, 59.2% of the pixels had a negative correlation, with cluster in the southwest and central parts of the YRB were significantly negatively (Fig. 5D). In addition, the GUD of approximately 15.2% of the pixels in the northern region were significantly positively correlated with preseason insolation and had a positive sensitivity to it (Fig. 6D). 3.3. Variations in the sensitivity of GUD along the preseason precipitation gradient Since the YRB located in water-limited regions, we wondered whether the GUD sensitivity to preseason climatic factors might be related to the multiyear average cumulative preseason precipitation. Therefore, the variations in the GUD sensitivity to preseason climatic variables along the gradient of multiyear averaged cumulative preseason precipitation were analyzed (since the GUD was less sensitive than other variables to insolation, this variable was not included in the analysis). Fig. 7A shows that the sensitivity of GUD to Tmax increased from −0.94 days °C−1 in the areas with a cumulative precipitation between 20 and 30 mm to about −3.07 days °C−1 in the areas with a cumulative preseason precipitation between 90 and 100 mm, indicating that GUD was more sensitive to preseason Tmax in wetter areas than in drier areas. In contrast with preseason Tmax, the strongest sensitivity of GUD to Tmin was approximately −3.9 days °C−1 in the driest areas (receiving 20–30 mm cumulative preseason precipitation), and the lowest sensitivity of GUD to Tmin was about −0.37 days °C−1 in the wettest areas (receiving 90–100 mm cumulative preseason precipitation) (Fig. 7B). In other words, as the cumulative preseason precipitation increased, the sensitivity of GUD to Tmin generally decreased. On the other hand, the precipitation sensitivity of GUD generally decreased from –0.19 days mm−1 in the aridest areas to −0.05 days mm−1 in the areas with a long-term average cumulative preseason precipitation of 90 mm or more (Fig. 7C). Moreover, the sensitivity of different vegetation types to preseason climatic variables was also explored. Note that, the cropland was excluded in the following analysis due to its vulnerability to human activities. The averages of multiyear averaged cumulative preseason precipitation and GUD sensitivities to preseason climatic variables among the six major vegetation types were compared (Fig. 8). Briefly, GUD was more sensitive to preseason Tmax for vegetation types with higher multiyear average cumulative preseason precipitation. The sensitivity of ENF to preseason Tmax, with the highest long-term average cumulative preseason precipitation (47.2 mm), had the highest Tmax sensitivity (−1.72 days °C−1). The sensitivity of grassland, with the lowest long-term average cumulative preseason precipitation (21.2 mm), was −1.18 days °C−1. Compared with the sensitivity of GUD to Tmax, that of GUD to preseason Tmin showed the exact opposite pattern: the grassland in the driest area had the strongest Tmin sensitivity (−2.15 days °C−1), whereas the ENF in the wetter area was less sensitive to Tmin (−1.53 days °C−1). Regarding the sensitivity of GUD to preseason precipitation for different vegetation types, the responses of herbaceous vegetation types to preseason precipitation were stronger than that of woody vegetation types. Specifically, the sensitivities for grassland, shrub, and meadow were −0.18, −0.06, and −0.07 days mm−1 respectively, whereas the sensitivities for DNF, DBF, and ENF were between −0.01 and −0.08 days mm−1. In addition, for herbaceous vegetation types, the sensitivity of GUD to preseason precipitation gradually decreased as the cumulative preseason precipitation increased, while the trend for the GUD of the woody vegetation types was reversed.

Fig. 6. Spatial pattern of the sensitivities of the GUD to the preseason (A) Tmax, (B) Tmin, (C) cumulative precipitation and (D) sum insolation. For the partial sensitivities between GUD and each of these indicators, the preseason durations for Tmax, Tmin, cumulative precipitation, and sum insolation, respectively. The inset graph in each panel shows the percentage of the pixels in each sensitivity category. Categories are defined by the colors in the scale bar on the right side of the figure.

correlation between GUD and preseason Tmax was observed throughout the YRB with 7.3% being significant (Fig. 5A), of which the GUD sensitivity to preseason Tmax in the edge of southwestern and northeastern areas was ranged from 0 to 1 days °C−1 (Fig. 6A). The other 18.6% of the pixels, which located in the central and southwestern parts of YRB, had statistically significant negative partial correlations with preseason Tmax, and the negative sensitivity in these areas was higher than −1 days °C−1. Across the basin, GUD had negative partial correlations with preseason Tmin in 74.2% of the all pixels (Fig. 5B) (significant in 44.3% of the pixels), the strongest was mainly in the central part, and the magnitude of sensitivity in these areas was exceeded (was lower than) −2 days °C−1 (Fig. 6B). Positive partial correlations were found mainly in the northern part and southwestern edge of YRB and significantly positive correlations were found in only 8.3% of the pixels. Additionally, positive correlations between GUD and preseason precipitation were observed in 44.5% of the pixels, with spatial clusters at the southwestern edge of YRB, and 17.1% of all pixels showed significant positive correlations. Approximately 55.5% of all pixels showed negative partial correlations between GUD and preseason precipitation, but only 10.1% of all pixels had significant negative 7

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Fig. 7. Variations in sensitivity of GUD to preseason (A) Tmax, (B) Tmin, and (C) cumulative precipitation along the gradient of multiyear (1982 to 2015) average precipitation. Bars represent the proportion of the total number of pixels in each 10 mm precipitation category. Only intervals with more than 0.1% of the total number of pixels are shown.

Fig. 8. Comparisons of (A) multiyear (1982 to 2015) annual average cumulative precipitation, (B) sensitivities of GUD to preseason Tmax, Tmin and cumulative precipitation, among the several major vegetation types of the Yellow River Basin. *and ** indicate significance at p < 0.05 and p < 0.01 levels, respectively.

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Fig. 9. Trend of the GUD differences from urban areas toward rural areas. The error bars represent one standard deviation across years.

maximum R2 of 0.9288 (p < 0.01) along the urban–rural gradient (Fig. 10). To further investigate the influence of the UHI effect on urban–rural GUD differences, the Spearman correlation was used to analyze the relationship between GUD differences and spring LST differences. As we expected, the results showed that the GUD differences of all cities were stronger negatively correlated with the spring LST differences, and the correlation coefficient was −0.976 (p < 0.05). For each city, Hohhot presented the strongest negative correlations (Fig. 11). In addition, on average, one degree increased in spring LST triggered an advance of the GUD by 7.46 days (Fig. 12).

3.4. Relationship between GUD and land surface temperature along the urban–rural gradient In general, the urban areas had an earlier GUD than rural area, and the urban–rural GUD differences of all cities from 2001 to 2015 had a significant logarithm relationship with distance from the urban center (p < 0.01). As for each city, there was a spatial heterogeneity in GUD differences, for example, GUD differences ranging from −26.4 days in Taiyuan to −14.3 days in Xining (Fig. 9). Similar to the GUD, the urban–rural spring mean LST differences of all cities presented a monotonous downtrend, with a significant logarithmic trend and a

Fig. 10. Trend of the spring mean LST differences from urban areas toward rural areas. The error bars represent one standard deviation across years.

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explanation for the stronger negative correlation between Tmin and GUD is that the important water source of YRB in the winter and early spring mainly depend on the spring thaw (Sun et al., 2015), hence, the increase in the minimum daily temperature helps to increase water availability and promote the growth of vegetation. Additionally, this study further observed that the GUD in the northwestern areas of the YRB was significantly positively correlated with Tmax but negatively correlated with preseason precipitation. This case might be related to the relatively low long-term cumulative preseason precipitation that plants in the northeastern areas receive (Fig. S7), the limited water availability may not only inhibit photosynthetic activities but also aggravate respiration (Singh et al., 2017). Moreover, the increase in Tmax may exacerbate the water deficit pressure by increasing evaporation (Yu et al., 2003; Dorji et al., 2013), and this negative effect on soil moisture would be greater than that of nighttime warming (Peng et al., 2013). For plants in the southeastern or central areas of the YRB, however, a higher Tmax could induce an earlier GUD with the cooperation of enough preseason precipitation. This suggested that the interaction effects of preseason Tmax and precipitation on GUD might stronger than the individual effects of two variables on GUD. Consistent with previous studies (Wang et al., 2015a; Zhang et al., 2018), a weak correlation between the GUD and preseason insolation was observed on both regional and pixels scales. This limited effect could be linked to the relative long days and high solar radiation levels in the YRB. But we found the GUD in the northwestern areas of the YRB had a significant positive correlation with preseason insolation, which might be because the strong insolation is always accompanied by an increase in daytime temperature (De Boeck et al., 2010) and also reduces soil moisture (Peng et al., 2013; Estiarte and Peñuelas, 2015). Yet, to date, the mechanisms by which GUD responds to insolation remains unclear because insolation is the result of a combination of sunlight, photoperiod, and light intensity factors, and it is difficult to disentangle the effects of these three elements on GUD (Calle et al., 2010; Estiarte and Peñuelas, 2015). Furthermore, our results showed that the variability in the sensitivity of GUD to climate factors across vegetation types was closely related to the preseason precipitation gradient. Herbaceous plants, such as grassland, were observed to be highly sensitive to precipitation but insensitive to Tmax, while the forest biomes (DBF, ENF) were found more sensitive to temperature. This is mainly because soil moisture content of herbaceous plants is suboptimal due to less cumulative preseason precipitation (Shen et al., 2015a), simultaneously, daytime warming could lead to a negative impact on photosynthesis by further altering soil water availability in the root zone (Penuelas et al., 2004; Xu et al., 2013). Consequently, herbaceous plants would maximize the water benefit through its unique survival strategies (Shen et al., 2015a). In contrast, for forest biomes, large amounts of preseason precipitation and its deep root systems and water conservation make precipitation no longer the major constraint (Zhao et al., 2018b). We speculated that future daytime warming coupled with altered precipitation regime would result in significant differences in the spatial patterns of spring phenology between herbaceous plants and forests biomes, thus further studies on how the phenology of individual vegetation types in YRB responds to climate change are necessary. Although this study disentangled the influences of preseason climatic factors on the GUD, it is notable that there was no extremely clear spatial pattern of preseason length for climatic factors. This phenomenon was consistent with previous studies (Menzel et al., 2006; Piao et al., 2015), and it might relate to the physiological processes of plants and hydrothermal combinations (Yang et al., 2017b). Therefore, further studies are still needed to fully and precisely explore the physiological process underly the GUD responses to climate change.

Fig. 11. Spearman’s correlations between the GUD and spring mean LST differences in urban and rural areas. * Significant at the 0.05 level. ** Significant at the 0.01 level.

Fig. 12. The linear regression between the GUD and spring mean LST differences across urban and buffer areas averaged over all cities. The error bar represents one standard deviation.

4. Discussion 4.1. Responses of GUD to climatic factors In general, the findings in this study confirmed that among climatic variables, temperature played a dominant role in controlling the dynamic of GUD, whereas, the preseason Tmin had greater control over the GUD than did preseason Tmax. This result was in line with the result of Zhou et al. (2016b) and Shen et al (2016), who studied the GUD in a semi-arid region and TP, respectively. This noticeable impacts of increasing Tmin on the advancement of GUD were probably due to nighttime warming optimizes both leaf and root respiration of plants (Griffin et al., 2002), especially in temperate grassland regions (Wan et al., 2009), which resulting in more carbohydrate consumption and subsequently enhances daytime photosynthesis on the following day (Turnbull et al., 2004). For another, as the multiyear average annual preseason Tmin value of the YRB were mainly below −4 °C (Fig. S6), if vegetation in a continuous lowtemperature environment is not only susceptible to freezing damage (Piao et al., 2015), but its photosynthetic efficiency would be reduced (Hietz et al., 2011; Melillo et al., 2011), therefore night warming could help to reduce these risks. This may also help to explain why the sensitivity of GUD to preseason Tmin decreased with the increase in cumulative preseason precipitation: although vegetation in the areas with a relatively high cumulative preseason precipitation can adapt to freezing injury by reducing water contents and accumulating protective solutes (Kreyling, 2010), being sensitive to preseason Tmin may induce a higher risk of cell structure injury due to excessive water at a low temperature can intensify the freezing injury (Yang et al., 2017a). An alternative

4.2. Comparison with the Yangtze River basin Climate change, especially warming and shifting in precipitation patterns, had a profound influence on vegetation phenology (Shen 10

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et al., 2011), the effects of different hydrothermal combinations on phenological events could vary among regions characteristic with different climate features (Prieto et al., 2009; Shen et al., 2018). For example, in boreal and arctic tundra, temperature rising has been unequivocally regarded as a major cue for the advancement of spring phenology (Siebert and Ewert, 2012), research on TP, however, reported that a declining response of GUD to warming temperature was attributed to warming winter in the previous year cannot meet the chilling requirements (Zhang et al., 2018), and the precipitation exerted a stronger influence on GUD in drier areas (Zhang et al., 2015). In comparison, Zhao et al. (2016) observed that in Northeast China (NEC), spring temperature had the greatest impact on GUD, while precipitation only had a relative impact on the GUD of grassland. To understand in depth how spring phenology responds to climate change under different climatic conditions. The response characteristics of GUD to climate change in YRB and Yangtze river basin (YZRB) were compared. As expected, the comparison in this study demonstrated that the GUD response to climatic factors in the YZRB was quite different from that to climate factors in the YRB. More specifically, the advance of GUDYZRB was related to preseason Tmax rather than Tmin (Table S3 and Fig. S8A, B). There are two main reasons explaining why GUD responded differently to asymmetric warming in the two studied basins. First, the photosynthesis activity of vegetation in the YZRB is limited by mainly temperature rather than water, and increasing of Tmax can not only increase the activity of photosynthetic enzymes but also increase the mineralization and availability of soil nitrogen (Hietz et al., 2011). Secondly, warmer nights might lead to an increase in the nocturnal respiration rates of plants, and photosynthesis cannot compensate for the corresponding increase over time (Alward et al., 1999), which ultimately hinders the growth of vegetation. Moreover, although the effects of preseason precipitation and solar radiation on GUD of YRB and YZRB were both limited (Table 1, Table S3), there was still a difference between the two basins. Contrary to the YRB, the GUDYZRB in the YZRB showed a weak positive correlation with precipitation and solar radiation and (Fig. S8C, D), which means that higher preseason precipitation or excessive solar radiation in the YZRB will adversely affect vegetation growth. Climate warming in China is projected to warmer in northern and wetter in southern (Ding et al., 2007), hence the findings in this study indicated that the spatial pattern of spring phenology in the two basins may undergo significant changes, which would increase the uncertainty of global carbon cycle prediction. Furthermore, it also suggested that the necessity of establishing a precise regional phenological model to predict the response of vegetation to climate change.

differently to urban warming. In addition, pollutants caused by urbanization have an impact on energy flux (i.e, decrease in global radiation and increase in diffuse radiation), especially the light pollution at night, can affect the pollination process of the pollinator communities (Macgregor et al., 2015), which indirectly caused effects on the vegetation behavior (Falchi et al., 2011). 4.4. The potential influences of ecological restoration The Chinese government began to implement “The Grain to Green Program (GGP)” with the Loess Plateau as a pilot since 1999 to alleviate severe environmental degradation problems. Despite the initial success of GGP, the program has also caused a series of negative impacts. Because its overemphasis on trees and shrub planting in environmentally fragile areas without considering climate, soil, hydrology and landscape factors (Cao et al., 2010; Sun et al., 2015), which may have potential impacts on the vegetation phenology in this region. Soil moisture is considered an important source of water in the YRB, yet some studies have suggested that excessively pursuing the rapid growth of artificial forests and ignoring local soil moisture could lead to a decrease in soil moisture (Liu and Shao, 2014; Jia et al., 2015). The dry layer of soil, in turn, prevents precipitation from replenishing the groundwater and blocks the exchange of groundwater and soil moisture (Wang et al., 2012), thus aggravating the shortage of soil moisture and ultimately hindering the growth of plants. In addition, Feng et al. (2016) suggested that the impact of land management on evapotranspiration in the Loess Plateau far exceeded that of climatic and/or environmental changes, and the measurement of the water lost to evapotranspiration in human-plantations indicated that the average reduction in water yield in the semi-arid Loess Plateau is approximately 50 mm/yr. Besides the increase in evapotranspiration, the demands for water resources by agriculture, industry and rapid urbanization are rising. Obviously, the sustainable water resources of the YRB have reached their limits. More importantly, afforestation also could provide feedback on the local climate by altering the albedo and turbulent energy flux of the region. Previous studies have suggested that afforestation generally cools the surface in tropical areas but warms the surface in boreal lands and dry regions (Lee et al., 2011; Peng et al., 2014). Yet the effects of afforestation on the climate of temperate regions are more complex, Li et al. (2018) argued that considering the physical feedback of vegetation on precipitation when assessing the hydrological effects of afforestation, the increase in precipitation caused by afforestation is enough to offset the increase in evapotranspiration, which has a very weak effect on soil moisture. Therefore, in future research, it will be necessary to quantitatively analyze how ecological engineering affects vegetation phenological changes.

4.3. Responses of GUD to urbanization Our results proved that the GUD in urban centers was generally earlier than the GUD in rural areas, which was in line with previous studies (Zhang et al., 2004; Han and Xu, 2013). Moreover, there was a significant linear correlation between the GUD differences and LST differences for the cities with a significant GUD trend along the urban–rural gradients, in other words, there was a GUD advance of 7.46 days per 1 °C increase in LST in spring. The pattern between GUD and LST was consistent with the response of GUD to temperature changes in the mid-high latitudes of the northern hemisphere. This further proved the feasibility of using urban areas as a laboratory to study the effects of global warming on phenology. It also suggested that the effects of surface temperature on accumulated temperature should be considered in phenological modeling prediction. Notably, in this study, we simplified the effect of urbanization to UHI, but in fact, it is one aspect of urbanization, there are still many other variables might have had a great impact on the discrepancies in GUD. For instance, Li et al. (2017) suggested that the differences in the GUD between urban and rural areas were negatively correlated with the urban area size, and a tenfold increase in urban size could advance the GUD by 1.3 days. Jeong et al. (2011a) found that different vegetation types responded

4.5. Implications This study demonstrated that the GUD in YRB had a stronger response to preseason Tmin among multiple meteorological drivers, and the dominant role of Tmin in the advance trend of GUD indicated that the current ongoing climate change, in which nighttime warming is greater than daytime warming, would have greater impact on vegetation activity in YRB than those controlled by Tmax. Furthermore, the feedbacks of vegetation to climate were mediated by spring phenology (Richardson et al., 2013). For one thing, a higher vegetation greenness or activity in later spring or early summer caused by advanced GUD might reduce the land surface albedo. The reduction in surface albedo would have a positive feedback effect on temperature, which might cause surface warming, especially in the Arctic (Pearson et al., 2013). Conversely, the earlier GUD and an increase in spring vegetation growth had a positive influence on the evapotranspiration of forest biomes with higher canopy cover in YRB (Pei et al., 2017), thereby weakening the warming trend (Shen et al., 2015b). In general, the net 11

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effect of these two biophysical mechanisms of opposite sign in the surface energy budget ultimately determines how vegetation changes regulate local climate (Jeong et al., 2009), and this local climate, in turn, affects the timing of phenological events. Hence, the evidence provided in this study supports our doubts that changes in regional vegetation phenology together with asymmetry in daytime and nighttime could feedback larger-scale biosphere–atmosphere interactions in future climate change. In parallel, it creates great opportunities for a more realistic representation of vegetation phenology and growth in land surface models (Shen et al., 2016). Additionally, it was found that at a local scale, GUD in urban with higher temperature was earlier than that in rural with lower temperature. These divergences in vegetation onset between urban and rural areas might result in reproductive insolation, especially for the plants with a short flowering duration (Jochner and Menzel, 2015), also could block or restrict gene flow among meta-population and meta-community in urban–rural transects (Neil and Wu, 2006). Furthermore, the unsynchronized flowering period of allergenic plants caused by the difference in urban–rural temperature would lead to substantial disorders in terms of human health (Jochner and Menzel, 2015).

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Three decades of multi-dimensional change in global leaf phenology. Nat. Clim. Change 5 (4), 364. Calle, Z., Schlumpberger, B.O., Piedrahita, L., Leftin, A., Hammer, S.A., Tye, A., Borchert, R., 2010. Seasonal variation in daily insolation induces synchronous bud break and flowering in the tropics. Trees 24, 865–877. Cao, S., Wang, G., Chen, L., 2010. Questionable value of planting thirsty trees in dry regions. Nature 465 (7294), 31. Cao, Z., Li, Y.R., Liu, Y.S., Chen, Y.F., Wang, Y.S., 2018. When and where did the Loess Plateau turn “green”? Analysis of the tendency and breakpoints of the normalized difference vegetation index. Land Degrad. Dev. 29 (1), 162–175. Chen, I.C., Hill, J.K., Ohlemüller, R., Roy, D.B., Thomas, C.D., 2011a. Rapid range shifts of species associated with high levels of climate warming. Science 333 (6045), 1024–1026. Chen, M., Liu, W., Tao, X., 2013. Evolution and assessment on China's urbanization 1960–2010: under-urbanization or over-urbanization? 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5. Conclusions Given the importance of revealing long-term phenological change and its linkages with climate drivers in the context of ongoing global climate change, this study investigated the temporal and spatial variations of GUD in YRB from 1982 to 2015, identified the main factor affecting the dynamics of GUD and further explained how the diverse hydrothermal combinations of different climatic factors affect the GUD. Moreover, we also explored the shifts in GUD induced by urbanization, which could provide evidence on how GUD will respond to global warming. Our results showed the GUD across the YRB significantly advanced from 1982 to 2015. Spatially, the significant advancing of GUD was mainly in northeastern and central to southern. On the contrary, there were significantly delayed trends of GUD in the edge of northern and western. In general, the preseason Tmin played a dominant role in the advancement of GUD across the YRB, while the influence of preseason Tmax on the GUD was not as clear as that of preseason Tmin. This finding suggested the impacts of asymmetric warming should be considered separately in the spring phenology models of river ecosystem models for YRB. Although the effect of preseason precipitation on GUD was non-significant at the regional scale, the spatial pattern of GUD was greatly affected by the interaction effects of preseason Tmax and precipitation. Compared with temperature and precipitation, insolation had relatively minor impacts on GUD. The sensitivities of GUD to different climatic factors varied along the multiyear averaged preseason precipitation gradient at regional and vegetation types suggested that precipitation is of great importance in GUD in water-limited areas. Furthermore, the great differences of GUD between urban area and rural area were closely related to the UHI effect, we thus speculate that future continued nighttime warming and rapid urbanization may further promote the advancement of GUD. The findings of this study provide a good reference for studying how river ecosystem responses to global climate change. Further studies are still needed to investigate the seasonal response of GUD to daytime and night-time warming and quantify the effects of anthropogenic activities (such as ecological engineering) on GUD in YRB. Acknowledgments This work was financially supported by National Natural Science Foundation of China (Nos. 41975044, 41601044), the Special Fund for Basic Scientific Research of Central Colleges, China University of Geosciences, Wuhan (No.CUGL170401 and CUGCJ1704). We would like to thank China Meteorological Administration (CMA), China for providing the meteorological data. 12

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