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Temperature controls on the spatial pattern of tree phenology in China's temperate zone
Agricultural and Forest Meteorology 154–155 (2012) 195–202
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Temperature controls on the spatial pattern of tree phenology in China’s temperate zone Xiaoqiu Chen ∗ , Lin Xu College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, PR China
a r t i c l e
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Article history: Received 1 March 2011 Received in revised form 12 August 2011 Accepted 17 November 2011 Keywords: Phenological growing season Ulmus pumila Temperature Spatial modeling Spatial response Sensitivity
a b s t r a c t We used Ulmus pumila leaf unfolding and leaf fall data collected at 46 stations during the 1986–2005 period to construct and validate daily temperature-based spatial phenology models. These models allowed simulation of the 20-year mean and yearly spatial patterns of U. pumila growing season beginning and end dates. This work was undertaken to explore the ecological mechanisms driving tree phenology spatial patterns and examine tree phenology spatial responses to temperature across China’s temperate zone. The results show that spatial patterns of daily temperatures within the optimum spring and autumn length periods control spatial patterns of growing season beginning and end dates, respectively. Regarding 20-year mean growing season modeling, mean growing season beginning date correlates negatively with mean daily temperature within the optimum spring length period at the 46 stations. The mean spring spatial phenology model explained 90% of beginning date variance (P < 0.001) with a Root Mean Square Error (RMSE) of 4.6 days. In contrast, mean growing season end date correlates positively with mean daily temperature within the optimum autumn length period at the 46 stations. The mean autumn spatial phenology model explained 82% of end date variance (P < 0.001) with a RMSE of 5.6 days. On average, a spatial shift in mean spring and autumn daily temperatures by 1 ◦ C may induce a spatial shift in mean beginning and end dates by −3.1 days and 2.6 days, respectively. Similarly, a significant negative and positive correlation was detectable between beginning date and spring daily temperature and between end date and autumn daily temperature at the 46 stations for each year, respectively. In general, the explained variances for yearly spatial phenology models are less than those of mean spatial phenology models, whereas the RMSEs of yearly models are greater than those of mean models. On average, a spatial shift in spring and autumn daily temperatures by 1 ◦ C in a year may induce a spatial shift in beginning and end dates between −4.28 days and −2.75 days and between 2.17 days and 3.16 days in the year, respectively. Moreover, both mean and yearly spatial phenology models perform satisfactorily in predicting beginning and end dates of the U. pumila growing season at external stations. Further analysis showed that the negative spatial response of yearly beginning date to spring daily temperature was stronger in warmer years than in colder years. This finding suggests that climate warming in late winter and spring may enhance sensitivity of the growing season’s spatial response due to the relationship of beginning date to temperature. © 2011 Elsevier B.V. All rights reserved.
1. Introduction One important purpose of phenological observations is to discover more meaningful relationships between the meteorological variables and the associated biological responses (Newman and Beard, 1962). Simulating temporal and spatial relationships between occurrence dates of plant phenophases and climatic factors is crucial not only for predicting phenological responses to climate change but also for identifying the carbon-uptake period (Goulden et al., 1996; Black et al., 2000; White and Nemani, 2003;
∗ Corresponding author. Tel.: +86 10 62753976; fax: +86 10 62751187. E-mail address: [email protected] (X. Chen). 0168-1923/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2011.11.006
Barr et al., 2004; Churkina et al., 2005; Baldocchi, 2008) and examining the seasonal exchanges of water and energy between land surface and atmosphere (Wilson and Baldocchi, 2000; Kljun et al., 2007). The latter in turn affects the global carbon cycle and climate change (Sellers et al., 1997). So far, many studies have focused on time series simulation of plant phenology using different heat unit models (Robertson, 1968; Sarvas, 1974; Cannell and Smith, 1983; Hänninen, 1990; Hunter and Lechowicz, 1992; Kramer, 1994; Chuine et al., 1999) and statistical models (Harding et al., 1976; Chen, 1994; Chmielewski and Rötzer, 2001; Schwartz and Chen, 2002; Matsumoto et al., 2003; Menzel, 2003; Askeyev et al., 2010) at individual sites or in specific areas. In contrast, few studies have used spatial series for simulation of plant phenology (Chen et al., 2005). Traditional spatial series simulations
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of plant phenology are usually carried out by establishing multiple linear regression equation between multiyear mean occurrence dates of a plant phenophase (y) at individual sites and geo-location parameters, such as longitudes (x1 ), latitudes (x2 ) and altitudes (x3 ) of the utilized sites (Hopkins, 1918; Nakahara, 1948; Gong and Jian, 1983; Park-Ono et al., 1993; Rötzer and Chmielewski, 2001; Hense et al., 2002). This method has two major disadvantages: (1) geo-location parameters are not climatic factors, so that they neither explain the essential environmental causes nor detect the climatic differences driving plant phenology spatial variations; and (2) since geo-location parameters are constant at a given site, multiple linear regression equation cannot represent the interannual variation of plant phenological spatial pattern related to climate change. Because air temperature is the most important factor influencing spatial and temporal variations of plant phenology (Chen, 1994; Chmielewski and Rötzer, 2001; Schwartz and Chen, 2002; Menzel, 2003; Gordo and Sanz, 2010), the spatial series of multiyear mean monthly temperature within a length period (LP) of months at individual sites (replacing geo-locations) were used by Chen et al. (2005) for simulating the spatial pattern of growing season beginning date (BGS) and end date (EGS) derived from surface phenology and remote sensing data. Their results showed that spatial series of multiyear mean BGS correlates negatively (P < 0.001) with spatial series of multiyear mean temperature from March to May, whereas spatial series of multiyear mean EGS correlates positively (P < 0.001) with spatial series of multiyear mean temperature from August to October (at 87 stations in temperate eastern China from 1982 to 1993). However, the correlations between BGS and March–May temperature spatial series and between EGS and August–October temperature spatial series may not reflect the spatial relationship between plant phenology and temperature precisely enough because phenological events are not likely induced by the integral mean monthly temperature exactly, but by mean daily temperature within a certain LP of days. In order to simulate the spatial pattern of plant phenology more precisely, daily mean temperatures within a LP of days should be identified. Thus, the objectives of this study were to: (1) explore the ecological mechanism of tree phenology spatial pattern on the multiyear mean level by creating daily temperature-based multiyear mean spatial phenology models; (2) explore the ecological mechanism of tree phenology spatial patterns on the yearly level by creating daily temperature-based yearly spatial phenology models; and (3) examine climatic controls of tree phenology spatial responses to temperature.
cold, and salt tolerances, it is often adopted as forest plantation material in different landscapes across China’s temperate zone. Thus, it can serve as an “indicator species” for phenological studies according to the criteria proposed by Newman and Beard (1962). Recently, Ghelardini and Santini (2009) found a good relationship between bud burst date of some European elm species and winter–spring temperature. This suggests that phenology of the genus Ulmus can be a sensitive indicator of climate change. Therefore, selecting U. pumila as the sample species for carrying out daily temperature-based spatial modeling of tree phenology is appropriate and representative.
2. Materials and methods
2.3. Spatial phenology model
2.1. Study area and plant species
Our basic hypothesis for tree phenology time series modeling is that the year-to-year variation of a phenological event occurrence date at a station is mainly influenced by year-to-year variation of daily mean temperature within a particular LP of days during and before its occurrence at the station (Chen and Xu, 2011). To use this method for tree phenology spatial series modeling, we assume that the station-to-station variation of a phenological event occurrence date over an area is mainly influenced by station-to-station variation of daily mean temperature within a particular LP of days during and before its occurrence over the area. In order to determine the LP during which the station-to-station variation of daily mean temperature affects station-to-station variation of U. pumila BGS (EGS) most remarkably in a year, we first calculated the number of days between the earliest and latest date in BGS (EGS) spatial series across the 46 stations in the year, calling this the basic LP (bLP). Then, we computed the daily mean temperature spatial series of the 46 stations in the year during the bLP plus a moving LP (mLP) prior to the earliest date in BGS (EGS) spatial series by step length
The study area is located in China’s temperate zone, including warm, middle and cold temperate zones from south to north (China Meteorological Administration, 1978). The dominant vegetation types are deciduous conifer forest, deciduous broad-leaved and coniferous mixed forest, deciduous broad-leaved forest, temperate steppe, and temperate desert (Compilation Committee of the Vegetation of China, 1980). Because of the remarkable seasonality and spatial heterogeneity in thermal and moisture conditions, plant phenology displays extensive temporal and spatial differences. Thus, this area is suitable for exploring the ecological mechanism of tree phenology spatial patterns and examining climatic controls of tree phenology spatial responses to temperature. Ulmus pumila (Siberian Elm) is a local deciduous tree in China’s temperate zone that grows widely on plains, hills, low mountains, and sand hills below 2500 m (Chun and Huang, 1998). In addition, since U. pumila is an adaptable tree with moderate drought,
2.2. Phenological and climate data Phenological data for U. pumila were acquired from the China Meteorological Administration (Chen, 2009). Because leaf unfolding and leaf fall dates of a tree can represent BGS and EGS (Rötzer and Chmielewski, 2001), we defined the U. pumila growing season as the period between the beginning date of leaf unfolding (BGS) and the end date of leaf fall (EGS). Leaf unfolding beginning was identified when a few leaves are fully spreading in spring, whereas leaf fall end was determined when almost all leaves have fallen to the ground (China Meteorological Administration, 1993). Then, we chose 46 phenological stations for the spatial modeling. The least time series length of U. pumila BGS and EGS data was 16 years for each station during the period 1986–2005. To validate model performance in the spatial extrapolation, we also used U. pumila BGS and EGS data at 62 external stations with time series lengths less than 16 years during 1986 to 2005. The stations were mostly distributed across China’s temperate zone, except for desert and high mountain areas (Fig. 1). Air temperature data were acquired from the China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/), including daily mean air temperature data at 343 stations in China’s temperate zone from 1986 to 2005. Since several phenological stations were not located nearby meteorological stations, we used ANUSPLIN 4.2 (Hutchinson, 2002) and Digital Elevation Model (DEM) data derived from the United States Geological Survey to interpolate the daily mean air temperature into 8 km × 8 km grids over the study area. Thus, we obtained gridded daily mean air temperature data at the phenological stations without meteorological observations.
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Fig. 1. Spatial distribution of phenological stations.
of one day, namely, during bLP + 1 day, bLP + 2 days, bLP + 3 days, etc. Thus, the LP is defined as follows: LP = bLP + mLP
(1)
Further, we calculated correlation coefficients between BGS (EGS) spatial series and daily mean temperature spatial series during different spring (autumn) LPs (bLP + 1 day, bLP + 2 days, bLP + 3 days, etc.) at the 46 stations in the year. Finally, we obtained the optimum spring (autumn) LP with the largest correlation coefficient between BGS (EGS) spatial series and spring (autumn) daily mean temperature spatial series in the year. Fig. 2 shows an example for determining the optimum spring and autumn LP in 1986.
The curves illustrate variation of correlation coefficients between BGS (EGS) spatial series and daily mean temperature spatial series within different spring (autumn) LPs. The optimum LP with the largest correlation coefficient is 98 days for BGS (negative correlation) and 84 days for EGS (positive correlation). Fig. 3 shows the yearly optimum spring and autumn LPs (bLP + mLP) from 1986 to 2005 for the 46 stations. The above four step procedure for looking for the optimum LP in a year was also applied to the entire study period. In that case, the 20-year mean BGS (EGS) spatial series and 20-year mean spring (autumn) daily temperature spatial series at the 46 stations during the period 1986–2005 were used to calculate the 20-year mean optimum spring (autumn) LP.
Fig. 2. Schematic demonstration of determination of the optimum (A) spring length period (LP) and (B) autumn LP based on correlation coefficients between BGS (EGS) spatial series and spring (autumn) daily temperature spatial series within the different LPs (LP = bLP + mLP) at the 46 stations in 1986.
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between observed and predicted BGS and between observed and predicted EGS were 5.8 days and 4.8 days, respectively (Fig. 5). Comparing the model validations (Fig. 5) with the model simulations (Fig. 4), we found that RMSE of the model validation for BGS is larger (by 1.2 days) than that of the model simulation but RMSE of the model validation for EGS is smaller (by 0.8 days) than that of the model simulation. Thus, the mean spatial phenology models indicate a strong spatial extrapolation capability to multiyear mean BGS and EGS. 3.2. Spatial simulation and validation of yearly BGS and EGS
Fig. 3. Optimum spring LP [black bars: basic LP (bLP), gray bars: moving LP (mLP)] and autumn LP (black bars: bLP, gray bars: mLP) in each year.
To evaluate the model performance in the spatial simulation and extrapolation, we used Root Mean Square Error (RMSE) between predicted and observed BGS or EGS and explained variance (R2 ). The RMSE was calculated by the following formula:
RMSE =
n (Prei i=1
− Obsi )
2
n
(2)
where Obsi denotes the observed BGS or EGS at the station i; Prei denotes the predicted BGS or EGS at the station i; n is the number of stations. 3. Results 3.1. Spatial simulation and validation of 20-year mean BGS and EGS The mean spring spatial phenology model shows that mean U. pumila BGS correlates negatively with the mean daily temperature within the optimum spring LP (123 days) at the 46 stations over the period 1986–2005 (P < 0.001). That is, the higher the 20-year mean daily temperature within the optimum spring LP at a station, the earlier the 20-year mean BGS. Mean spring spatial phenology model explained 90% of the BGS variance (P < 0.001) and the RMSE for differences between observed and simulated BGS is 4.6 days. On average, a spatial shift in the 20-year mean daily temperature within the optimum spring LP by 1 ◦ C may induce a spatial shift in the 20-year mean BGS by −3.1 days (Fig. 4A). Other than BGS, mean U. pumila EGS correlates positively with the mean daily temperature within the optimum autumn LP (66 days) at the 46 stations over the period 1986–2005 (P < 0.001), namely, the higher the 20year mean daily temperature within the optimum autumn LP at a station, the later the 20-year mean EGS. Mean autumn spatial phenology model explained 82% of the EGS variance (P < 0.001) and the RMSE for differences between observed and simulated EGS is 5.6 days. On average, a spatial shift in the 20-year mean daily temperature within the optimum autumn LP by 1 ◦ C may cause a spatial shift in the 20-year mean EGS by 2.6 days (Fig. 4B). It is worth noting that the simulation accuracy (in RMSE) of 20-year mean BGS is higher than that of 20-year mean EGS. To validate the mean spatial phenology models, we substituted multiyear mean daily temperatures within the optimum spring and autumn LP at 16 external stations into the corresponding mean spring and autumn spatial phenology models (Fig. 4), respectively, and obtained predicted multiyear mean U. pumila BGS and EGS at the 16 stations. The results show that the RMSEs for differences
In order to explore the spatial relationship between BGS (EGS) and daily temperature during the optimum spring (autumn) LP in each year, we created yearly spatial phenology models from 1986 to 2005, resulting in 40 models (20 models for BGS and 20 models for EGS). Similarly, BGS correlates negatively with daily mean temperature within the optimum spring LP in each year at the 46 stations (P < 0.001), namely, the higher the daily mean temperature within the optimum spring LP in a specific year at a station, the earlier the BGS in the year at the station. The explained variances of yearly spring spatial phenology models to BGS are between 73% and 86% (P < 0.001) and the RMSEs of yearly BGS simulations are between 5.4 days and 9.8 days. Slopes of significant linear regression equations show that a spatial shift in daily mean temperature within the optimum spring LP by 1 ◦ C in a year may induce a spatial shift in BGS between −4.28 days (in 2002) and −2.75 days (in 1991) in the year. In contrast, EGS correlates positively with daily mean temperature within the optimum autumn LP in each year at the 46 stations (P < 0.001). That is, the higher the daily mean temperature within the optimum autumn LP in a specific year at a station, the later the EGS in the year at the station. The explained variances of yearly autumn spatial phenology models to EGS are between 49% and 77% (P < 0.001) and the RMSEs of yearly EGS simulations are between 7.4 days and 11.2 days. Slopes of significant linear regression equations indicate that a spatial shift in daily mean temperature within the optimum autumn LP by 1 ◦ C in a year may cause a spatial shift in EGS between 2.17 days (in 1991 and 1992) and 3.16 days (in 2005) in the year. We noted that the average simulation accuracy (in average RMSE) of yearly BGS is higher than that of yearly EGS (Table 1). The model validation was implemented by substituting daily mean temperatures within the optimum spring and autumn LP in each year at external stations into the corresponding yearly spring and autumn spatial phenology models, respectively. It should be noted that because phenological data are uneven among external stations, the number of external stations for model validation is different from year to year, namely, between 12 and 39 (Table 2). The total number of external stations for model validation is 62 over the 20-year period (Fig. 1). The results show that the average RMSEs of yearly BGS and EGS predictions were 7.5 days and 8.7 days, respectively (Table 2), which are close to the average RMSEs of yearly BGS and EGS simulations (Table 1). Thus, the yearly spatial phenology models also show a strong spatial extrapolation capability to yearly BGS and EGS. 3.3. Climatic controls of the spatial response of BGS and EGS to temperature As mentioned above, the spatial response of BGS and EGS to temperature (in the form of spatial regression slope, days ◦ C−1 ) shows obvious interannual variation (Table 1). This may be associated with the interannual variation of thermal condition at regional scales. In order to detect the relationship between spatial phenology response and regional temperature regime with regard to the interannual variation, we calculated the correlation
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Fig. 4. Spatial correlation and regression analyses (A) between 20-year mean daily temperature within the optimum spring LP and 20-year mean BGS and (B) between 20-year mean daily temperature within the optimum autumn LP and 20-year mean EGS at the 46 stations.
Fig. 5. External validation of mean spatial phenology models by the spatial extrapolation of mean (A) BGS and (B) EGS. Table 1 Spatial correlation and regression analyses between daily temperature within the optimum spring LP and BGS and between daily temperature within the optimum autumn LP and EGS in each year. Year
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average *
P < 0.001.
BGS simulation
EGS simulation 2
Number of stations
Slope (days ◦ C−1 )
R
RMSE (days)
Number of stations
Slope (days ◦ C−1 )
R2
RMSE (days)
35 40 41 40 45 46 46 46 46 46 46 45 46 46 46 46 46 46 46 45 –
−3.07 −3.06 −3.08 −3.36 −3.30 −2.75 −3.01 −3.12 −2.94 −3.25 −3.39 −3.43 −3.71 −2.93 −3.04 −3.43 −4.28 −3.71 −3.33 −2.85 –
0.78* 0.86* 0.81* 0.87* 0.79* 0.73* 0.80* 0.81* 0.79* 0.82* 0.76* 0.85* 0.79* 0.78* 0.79* 0.80* 0.79* 0.72* 0.83* 0.82* –
6.1 5.5 6.2 5.4 6.6 7.2 6.6 7.0 6.5 6.9 7.9 6.1 7.0 7.5 7.3 8.1 9.3 9.8 7.6 6.9 7.1
35 40 39 40 45 46 46 46 46 46 46 46 45 46 46 46 46 45 46 45 –
2.83 2.77 2.46 3.00 2.44 2.17 2.17 2.56 3.14 2.70 2.55 2.89 2.58 2.84 2.34 3.09 2.95 2.41 2.98 3.16 –
0.69* 0.54* 0.61* 0.69* 0.49* 0.58* 0.51* 0.59* 0.68* 0.50* 0.73* 0.66* 0.71* 0.73* 0.73* 0.70* 0.77* 0.67* 0.68* 0.74* –
7.7 9.8 8.0 8.2 9.9 8.7 8.9 9.8 9.0 11.2 7.4 9.2 7.9 8.2 7.6 8.4 7.6 8.0 8.5 7.8 8.6
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Table 2 External validation of yearly spatial phenology models by the spatial extrapolation of yearly BGS and EGS. Year
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average * **
BGS validation
EGS validation
Number of stations
r
RMSE (days)
Number of stations
r
RMSE (days)
30 32 32 35 39 38 37 33 18 20 17 16 14 15 12 12 12 13 12 13 –
0.86** 0.78** 0.87** 0.78** 0.76** 0.83** 0.90** 0.92** 0.90** 0.92** 0.90** 0.90** 0.91** 0.91** 0.90** 0.89** 0.89** 0.88** 0.80* 0.73* –
6.3 8.9 5.7 7.8 7.9 7.4 6.4 5.8 6.0 5.5 5.2 7.0 7.3 6.3 6.7 9.2 11.4 7.2 11.5 11.0 7.5
28 31 32 34 39 37 36 33 19 19 17 16 16 16 15 14 15 16 15 16 –
0.90** 0.70** 0.87** 0.83** 0.78** 0.87** 0.79** 0.83** 0.66* 0.72** 0.88** 0.84** 0.92** 0.86** 0.89** 0.93** 0.92** 0.64* 0.77** 0.81** –
8.6 9.4 8.5 11.0 11.1 8.7 8.5 8.5 11.9 9.0 7.7 6.9 5.0 7.3 6.0 7.7 6.3 11.9 10.2 10.6 8.7
P < 0.01. P < 0.001.
coefficients between BGS-temperature spatial regression slope (Table 1) and regional February–April mean temperature and between EGS-temperature spatial regression slope (Table 1) and regional September–November mean temperature from 1986 to 2005, respectively. We selected regional February–April and September–November mean temperatures as the independent variables because they can represent regional thermal status when BGS and EGS occurred over the study area, respectively, and are convenient for comparing regional thermal status among different years. The results show that a significantly negative correlation appeared between BGS-temperature spatial regression slope and regional February–April mean temperature (P < 0.01). In general, the negative spatial response of yearly BGS to daily mean temperature within the optimum spring LP was stronger in warmer years with higher regional February–April mean temperatures than in colder years with lower regional February–April mean temperatures (Fig. 6). In contrast, there was no significant correlation
Fig. 6. Correlation analysis between regional February–April mean temperature and spatial regression slope (days ◦ C−1 ) across China’s temperate zone from 1986 to 2005.
between EGS-temperature spatial regression slope and regional September–November mean temperature.
4. Discussion In contrast to models based exclusively on geo-locations (Hopkins, 1918; Nakahara, 1948; Gong and Jian, 1983; Park-Ono et al., 1993; Rötzer and Chmielewski, 2001; Hense et al., 2002), our new model offers a possible ecological mechanism to explain the spatial variation of multiyear mean occurrence dates of tree phenophases, namely, that the spatial pattern of multiyear mean daily temperature within the optimum LP controls the spatial pattern of multiyear mean occurrence dates of tree phenophases. This ecological mechanism can also explain the observed spatial patterns of occurrence dates of tree phenophases in individual years. That is, the spatial pattern of daily mean temperature within the optimum LP in a year controls the spatial pattern of occurrence dates of tree phenophases in the same year. The new model improves upon an earlier 3-month mean temperature based spatial phenology model (Chen et al., 2005) by calculating an optimal and year-specific LP of days to produce a more precise and yearly simulation of tree phenology spatial pattern. The precision of the LP is enhanced from monthly to daily. The daily temperature-based spatial phenology models provide robust tools for simulating and predicting U. pumila BGS and EGS across China’s temperate zone. Generally speaking, the spatial simulations of 20-year mean and yearly BGS are more accurate than the spatial simulations of 20-year mean and yearly EGS (Fig. 4, Table 1). This finding indicates that the spatial pattern of BGS was mainly determined by the spatial pattern of seasonal daily temperature but the spatial pattern of EGS may also be influenced by spatial patterns of other climatic factors, such as precipitation, photoperiod, wind speed and so on, which is consistent with results of time series modeling of plant phenology (Chen, 1994; Chmielewski and Rötzer, 2001; Matsumoto et al., 2003; Gordo and Sanz, 2010; Chen and Xu, 2011). More detailed studies on the mechanism of the spatial variation of autumn plant phenophases are required. Moreover, the spatial extrapolations of 20-year mean BGS and EGS are more accurate than the spatial extrapolations of yearly BGS and EGS (Fig. 5, Table 2). However, spatial extrapolation accuracies of BGS
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Fig. 7. Correlation analysis between regional February–April mean temperature and spatial standard deviation of BGS across China’s temperate zone from 1986 to 2005.
and EGS based on both 20-year mean and yearly models are close to spatial simulation accuracies of these models. Therefore, these models have a strong spatial extrapolation capability to U. pumila BGS and EGS. This kind of spatial extrapolation of tree phenophases may provide a more detailed phenological data set for surface validation of remote sensing phenology (Duchemin et al., 1999; Chen et al., 2000, 2001; Schwartz et al., 2002; Badeck et al., 2004; Studer et al., 2007; Liang et al., 2011) and regional climate modeling based ˜ et al., 2009). on land surface processes (Sellers et al., 1997; Penuelas Furthermore, the current study found that the negative spatial response of yearly U. pumila BGS to daily mean temperature within the optimum spring LP (days ◦ C−1 ) was stronger in warmer years with higher regional February–April mean temperatures than in colder years with lower regional February–April mean temperatures. Thus, we might expect that seasonal climate warming will enhance sensitivity of the spatial response of spring tree phenology to temperature. Further analysis displays that the spatial variability of BGS (in spatial standard deviation of BGS) was also larger in warmer years than in colder years (Fig. 7) but the spatial variability of daily mean temperature within the optimum spring LP (in spatial standard deviation of temperature) was not associated with year-to-year temperature variation. That is, the spatial shift range of daily mean temperature within the optimum spring LP remained approximately the same from year to year, whereas the spatial shift range of BGS fluctuated noticeably along with year-to-year temperature variation. Therefore, the interannual variation of the spatial response of BGS to temperature is associated mainly with spatial variability of BGS from year to year. Namely, climate warming will enhance sensitivity of the spatial response of BGS to temperature through increasing BGS spatial variability. Our finding that climate warming increases the spatial variability of spring tree phenology supports the result of Menzel et al. (2006). Because climate warming may significantly increase the spatial variability of spring tree phenology and consequently speed up the spatial response of spring tree phenology to temperature, the terrestrial ecosystem under global climate change scenarios will likely become more sensitive and uncertain than at present.
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spatial patterns and examining tree phenology spatial responses to temperature across China’s temperate zone. Our main conclusions are as follows: Spatial patterns of daily temperatures within the optimum spring and autumn LPs control spatial patterns of U. pumila BGS and EGS, respectively. With regard to 20-year mean U. pumila BGS and EGS modeling, there is a significantly negative correlation between mean BGS and mean daily temperature within the optimum spring LP at the 46 stations. The mean spring spatial phenology model explained 90% of the BGS variance (p < 0.001) and the RMSE of mean BGS simulation is 4.6 days. According to the linear regression equation, a spatial shift in mean daily temperature within the optimum spring LP by 1 ◦ C may induce a spatial shift in mean BGS by −3.1 days. Meanwhile, there is a significantly positive correlation between mean EGS and mean daily temperature within the optimum autumn LP at the 46 stations. The mean autumn spatial phenology model explained 82% of the EGS variance (p < 0.001) and the RMSE of mean EGS simulation is 5.6 days. In the light of the linear regression equation, a spatial shift in mean daily temperature within the optimum autumn LP by 1 ◦ C may cause a spatial shift in mean EGS by 2.6 days. The accuracy of mean BGS simulation is higher than that of mean EGS simulation. Generally speaking, the mean spatial phenology models perform satisfactorily in predicting mean U. pumila BGS and EGS at external stations. Considering yearly U. pumila BGS and EGS modeling, a consistent negative and positive correlation was detectable between BGS and daily mean temperature within the optimum spring LP and between EGS and daily mean temperature within the optimum autumn LP in each year at the 46 stations, respectively. The explained variances of yearly spring spatial phenology models to BGS are between 73% and 86% (p < 0.001) and the RMSEs of yearly BGS simulations are between 5.4 days and 9.8 days, whereas the explained variances of yearly autumn spatial phenology models to EGS are between 49% and 77% (p < 0.001) and the RMSEs of yearly EGS simulations are between 7.4 days and 11.2 days. Slopes of linear regression equations show that a spatial shift in daily mean temperature within the optimum spring LP by 1 ◦ C in a year may induce a spatial shift in BGS between −4.28 days and −2.75 days in the year, whereas a spatial shift in daily mean temperature within the optimum autumn LP by 1 ◦ C in a year may cause a spatial shift in EGS between 2.17 days and 3.16 days in the year. The average accuracy of yearly BGS simulations is also higher than that of yearly EGS simulations. Overall, the yearly spatial phenology models perform also satisfactorily in predicting yearly U. pumila BGS and EGS at external stations from 1986 to 2005. Furthermore, the negative spatial response of yearly BGS to daily mean temperature within the optimum spring LP was stronger in warmer years with higher regional February–April mean temperatures than in colder years with lower regional February–April mean temperatures. This suggests that climate warming in late winter and spring may enhance sensitivity of the spatial response of BGS to temperature. Acknowledgements The authors wish to thank the Meteorological Information Center of the China Meteorological Administration for providing phenological data. This research is funded by the National Natural Science Foundation of China under Grant Nos. 40871029 and 41071027.
5. Conclusions References In this study, daily temperature (within the optimum LP) based spatial phenology models of U. pumila BGS and EGS were created for exploring the ecological mechanisms driving tree phenology
Askeyev, O.V., Sparks, T.H., Askeyev, I.V., Tishin, D.V., Tryjanowski, P., 2010. East versus West: contrasts in phenological patterns? Global Ecol. Biogeogr. 19, 783–793.
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Temperature controls on the spatial pattern of tree phenology in China’s temperate zone Xiaoqiu Chen ∗ , Lin Xu College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, PR China
a r t i c l e
i n f o
Article history: Received 1 March 2011 Received in revised form 12 August 2011 Accepted 17 November 2011 Keywords: Phenological growing season Ulmus pumila Temperature Spatial modeling Spatial response Sensitivity
a b s t r a c t We used Ulmus pumila leaf unfolding and leaf fall data collected at 46 stations during the 1986–2005 period to construct and validate daily temperature-based spatial phenology models. These models allowed simulation of the 20-year mean and yearly spatial patterns of U. pumila growing season beginning and end dates. This work was undertaken to explore the ecological mechanisms driving tree phenology spatial patterns and examine tree phenology spatial responses to temperature across China’s temperate zone. The results show that spatial patterns of daily temperatures within the optimum spring and autumn length periods control spatial patterns of growing season beginning and end dates, respectively. Regarding 20-year mean growing season modeling, mean growing season beginning date correlates negatively with mean daily temperature within the optimum spring length period at the 46 stations. The mean spring spatial phenology model explained 90% of beginning date variance (P < 0.001) with a Root Mean Square Error (RMSE) of 4.6 days. In contrast, mean growing season end date correlates positively with mean daily temperature within the optimum autumn length period at the 46 stations. The mean autumn spatial phenology model explained 82% of end date variance (P < 0.001) with a RMSE of 5.6 days. On average, a spatial shift in mean spring and autumn daily temperatures by 1 ◦ C may induce a spatial shift in mean beginning and end dates by −3.1 days and 2.6 days, respectively. Similarly, a significant negative and positive correlation was detectable between beginning date and spring daily temperature and between end date and autumn daily temperature at the 46 stations for each year, respectively. In general, the explained variances for yearly spatial phenology models are less than those of mean spatial phenology models, whereas the RMSEs of yearly models are greater than those of mean models. On average, a spatial shift in spring and autumn daily temperatures by 1 ◦ C in a year may induce a spatial shift in beginning and end dates between −4.28 days and −2.75 days and between 2.17 days and 3.16 days in the year, respectively. Moreover, both mean and yearly spatial phenology models perform satisfactorily in predicting beginning and end dates of the U. pumila growing season at external stations. Further analysis showed that the negative spatial response of yearly beginning date to spring daily temperature was stronger in warmer years than in colder years. This finding suggests that climate warming in late winter and spring may enhance sensitivity of the growing season’s spatial response due to the relationship of beginning date to temperature. © 2011 Elsevier B.V. All rights reserved.
1. Introduction One important purpose of phenological observations is to discover more meaningful relationships between the meteorological variables and the associated biological responses (Newman and Beard, 1962). Simulating temporal and spatial relationships between occurrence dates of plant phenophases and climatic factors is crucial not only for predicting phenological responses to climate change but also for identifying the carbon-uptake period (Goulden et al., 1996; Black et al., 2000; White and Nemani, 2003;
∗ Corresponding author. Tel.: +86 10 62753976; fax: +86 10 62751187. E-mail address: [email protected] (X. Chen). 0168-1923/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2011.11.006
Barr et al., 2004; Churkina et al., 2005; Baldocchi, 2008) and examining the seasonal exchanges of water and energy between land surface and atmosphere (Wilson and Baldocchi, 2000; Kljun et al., 2007). The latter in turn affects the global carbon cycle and climate change (Sellers et al., 1997). So far, many studies have focused on time series simulation of plant phenology using different heat unit models (Robertson, 1968; Sarvas, 1974; Cannell and Smith, 1983; Hänninen, 1990; Hunter and Lechowicz, 1992; Kramer, 1994; Chuine et al., 1999) and statistical models (Harding et al., 1976; Chen, 1994; Chmielewski and Rötzer, 2001; Schwartz and Chen, 2002; Matsumoto et al., 2003; Menzel, 2003; Askeyev et al., 2010) at individual sites or in specific areas. In contrast, few studies have used spatial series for simulation of plant phenology (Chen et al., 2005). Traditional spatial series simulations
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of plant phenology are usually carried out by establishing multiple linear regression equation between multiyear mean occurrence dates of a plant phenophase (y) at individual sites and geo-location parameters, such as longitudes (x1 ), latitudes (x2 ) and altitudes (x3 ) of the utilized sites (Hopkins, 1918; Nakahara, 1948; Gong and Jian, 1983; Park-Ono et al., 1993; Rötzer and Chmielewski, 2001; Hense et al., 2002). This method has two major disadvantages: (1) geo-location parameters are not climatic factors, so that they neither explain the essential environmental causes nor detect the climatic differences driving plant phenology spatial variations; and (2) since geo-location parameters are constant at a given site, multiple linear regression equation cannot represent the interannual variation of plant phenological spatial pattern related to climate change. Because air temperature is the most important factor influencing spatial and temporal variations of plant phenology (Chen, 1994; Chmielewski and Rötzer, 2001; Schwartz and Chen, 2002; Menzel, 2003; Gordo and Sanz, 2010), the spatial series of multiyear mean monthly temperature within a length period (LP) of months at individual sites (replacing geo-locations) were used by Chen et al. (2005) for simulating the spatial pattern of growing season beginning date (BGS) and end date (EGS) derived from surface phenology and remote sensing data. Their results showed that spatial series of multiyear mean BGS correlates negatively (P < 0.001) with spatial series of multiyear mean temperature from March to May, whereas spatial series of multiyear mean EGS correlates positively (P < 0.001) with spatial series of multiyear mean temperature from August to October (at 87 stations in temperate eastern China from 1982 to 1993). However, the correlations between BGS and March–May temperature spatial series and between EGS and August–October temperature spatial series may not reflect the spatial relationship between plant phenology and temperature precisely enough because phenological events are not likely induced by the integral mean monthly temperature exactly, but by mean daily temperature within a certain LP of days. In order to simulate the spatial pattern of plant phenology more precisely, daily mean temperatures within a LP of days should be identified. Thus, the objectives of this study were to: (1) explore the ecological mechanism of tree phenology spatial pattern on the multiyear mean level by creating daily temperature-based multiyear mean spatial phenology models; (2) explore the ecological mechanism of tree phenology spatial patterns on the yearly level by creating daily temperature-based yearly spatial phenology models; and (3) examine climatic controls of tree phenology spatial responses to temperature.
cold, and salt tolerances, it is often adopted as forest plantation material in different landscapes across China’s temperate zone. Thus, it can serve as an “indicator species” for phenological studies according to the criteria proposed by Newman and Beard (1962). Recently, Ghelardini and Santini (2009) found a good relationship between bud burst date of some European elm species and winter–spring temperature. This suggests that phenology of the genus Ulmus can be a sensitive indicator of climate change. Therefore, selecting U. pumila as the sample species for carrying out daily temperature-based spatial modeling of tree phenology is appropriate and representative.
2. Materials and methods
2.3. Spatial phenology model
2.1. Study area and plant species
Our basic hypothesis for tree phenology time series modeling is that the year-to-year variation of a phenological event occurrence date at a station is mainly influenced by year-to-year variation of daily mean temperature within a particular LP of days during and before its occurrence at the station (Chen and Xu, 2011). To use this method for tree phenology spatial series modeling, we assume that the station-to-station variation of a phenological event occurrence date over an area is mainly influenced by station-to-station variation of daily mean temperature within a particular LP of days during and before its occurrence over the area. In order to determine the LP during which the station-to-station variation of daily mean temperature affects station-to-station variation of U. pumila BGS (EGS) most remarkably in a year, we first calculated the number of days between the earliest and latest date in BGS (EGS) spatial series across the 46 stations in the year, calling this the basic LP (bLP). Then, we computed the daily mean temperature spatial series of the 46 stations in the year during the bLP plus a moving LP (mLP) prior to the earliest date in BGS (EGS) spatial series by step length
The study area is located in China’s temperate zone, including warm, middle and cold temperate zones from south to north (China Meteorological Administration, 1978). The dominant vegetation types are deciduous conifer forest, deciduous broad-leaved and coniferous mixed forest, deciduous broad-leaved forest, temperate steppe, and temperate desert (Compilation Committee of the Vegetation of China, 1980). Because of the remarkable seasonality and spatial heterogeneity in thermal and moisture conditions, plant phenology displays extensive temporal and spatial differences. Thus, this area is suitable for exploring the ecological mechanism of tree phenology spatial patterns and examining climatic controls of tree phenology spatial responses to temperature. Ulmus pumila (Siberian Elm) is a local deciduous tree in China’s temperate zone that grows widely on plains, hills, low mountains, and sand hills below 2500 m (Chun and Huang, 1998). In addition, since U. pumila is an adaptable tree with moderate drought,
2.2. Phenological and climate data Phenological data for U. pumila were acquired from the China Meteorological Administration (Chen, 2009). Because leaf unfolding and leaf fall dates of a tree can represent BGS and EGS (Rötzer and Chmielewski, 2001), we defined the U. pumila growing season as the period between the beginning date of leaf unfolding (BGS) and the end date of leaf fall (EGS). Leaf unfolding beginning was identified when a few leaves are fully spreading in spring, whereas leaf fall end was determined when almost all leaves have fallen to the ground (China Meteorological Administration, 1993). Then, we chose 46 phenological stations for the spatial modeling. The least time series length of U. pumila BGS and EGS data was 16 years for each station during the period 1986–2005. To validate model performance in the spatial extrapolation, we also used U. pumila BGS and EGS data at 62 external stations with time series lengths less than 16 years during 1986 to 2005. The stations were mostly distributed across China’s temperate zone, except for desert and high mountain areas (Fig. 1). Air temperature data were acquired from the China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/), including daily mean air temperature data at 343 stations in China’s temperate zone from 1986 to 2005. Since several phenological stations were not located nearby meteorological stations, we used ANUSPLIN 4.2 (Hutchinson, 2002) and Digital Elevation Model (DEM) data derived from the United States Geological Survey to interpolate the daily mean air temperature into 8 km × 8 km grids over the study area. Thus, we obtained gridded daily mean air temperature data at the phenological stations without meteorological observations.
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Fig. 1. Spatial distribution of phenological stations.
of one day, namely, during bLP + 1 day, bLP + 2 days, bLP + 3 days, etc. Thus, the LP is defined as follows: LP = bLP + mLP
(1)
Further, we calculated correlation coefficients between BGS (EGS) spatial series and daily mean temperature spatial series during different spring (autumn) LPs (bLP + 1 day, bLP + 2 days, bLP + 3 days, etc.) at the 46 stations in the year. Finally, we obtained the optimum spring (autumn) LP with the largest correlation coefficient between BGS (EGS) spatial series and spring (autumn) daily mean temperature spatial series in the year. Fig. 2 shows an example for determining the optimum spring and autumn LP in 1986.
The curves illustrate variation of correlation coefficients between BGS (EGS) spatial series and daily mean temperature spatial series within different spring (autumn) LPs. The optimum LP with the largest correlation coefficient is 98 days for BGS (negative correlation) and 84 days for EGS (positive correlation). Fig. 3 shows the yearly optimum spring and autumn LPs (bLP + mLP) from 1986 to 2005 for the 46 stations. The above four step procedure for looking for the optimum LP in a year was also applied to the entire study period. In that case, the 20-year mean BGS (EGS) spatial series and 20-year mean spring (autumn) daily temperature spatial series at the 46 stations during the period 1986–2005 were used to calculate the 20-year mean optimum spring (autumn) LP.
Fig. 2. Schematic demonstration of determination of the optimum (A) spring length period (LP) and (B) autumn LP based on correlation coefficients between BGS (EGS) spatial series and spring (autumn) daily temperature spatial series within the different LPs (LP = bLP + mLP) at the 46 stations in 1986.
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between observed and predicted BGS and between observed and predicted EGS were 5.8 days and 4.8 days, respectively (Fig. 5). Comparing the model validations (Fig. 5) with the model simulations (Fig. 4), we found that RMSE of the model validation for BGS is larger (by 1.2 days) than that of the model simulation but RMSE of the model validation for EGS is smaller (by 0.8 days) than that of the model simulation. Thus, the mean spatial phenology models indicate a strong spatial extrapolation capability to multiyear mean BGS and EGS. 3.2. Spatial simulation and validation of yearly BGS and EGS
Fig. 3. Optimum spring LP [black bars: basic LP (bLP), gray bars: moving LP (mLP)] and autumn LP (black bars: bLP, gray bars: mLP) in each year.
To evaluate the model performance in the spatial simulation and extrapolation, we used Root Mean Square Error (RMSE) between predicted and observed BGS or EGS and explained variance (R2 ). The RMSE was calculated by the following formula:
RMSE =
n (Prei i=1
− Obsi )
2
n
(2)
where Obsi denotes the observed BGS or EGS at the station i; Prei denotes the predicted BGS or EGS at the station i; n is the number of stations. 3. Results 3.1. Spatial simulation and validation of 20-year mean BGS and EGS The mean spring spatial phenology model shows that mean U. pumila BGS correlates negatively with the mean daily temperature within the optimum spring LP (123 days) at the 46 stations over the period 1986–2005 (P < 0.001). That is, the higher the 20-year mean daily temperature within the optimum spring LP at a station, the earlier the 20-year mean BGS. Mean spring spatial phenology model explained 90% of the BGS variance (P < 0.001) and the RMSE for differences between observed and simulated BGS is 4.6 days. On average, a spatial shift in the 20-year mean daily temperature within the optimum spring LP by 1 ◦ C may induce a spatial shift in the 20-year mean BGS by −3.1 days (Fig. 4A). Other than BGS, mean U. pumila EGS correlates positively with the mean daily temperature within the optimum autumn LP (66 days) at the 46 stations over the period 1986–2005 (P < 0.001), namely, the higher the 20year mean daily temperature within the optimum autumn LP at a station, the later the 20-year mean EGS. Mean autumn spatial phenology model explained 82% of the EGS variance (P < 0.001) and the RMSE for differences between observed and simulated EGS is 5.6 days. On average, a spatial shift in the 20-year mean daily temperature within the optimum autumn LP by 1 ◦ C may cause a spatial shift in the 20-year mean EGS by 2.6 days (Fig. 4B). It is worth noting that the simulation accuracy (in RMSE) of 20-year mean BGS is higher than that of 20-year mean EGS. To validate the mean spatial phenology models, we substituted multiyear mean daily temperatures within the optimum spring and autumn LP at 16 external stations into the corresponding mean spring and autumn spatial phenology models (Fig. 4), respectively, and obtained predicted multiyear mean U. pumila BGS and EGS at the 16 stations. The results show that the RMSEs for differences
In order to explore the spatial relationship between BGS (EGS) and daily temperature during the optimum spring (autumn) LP in each year, we created yearly spatial phenology models from 1986 to 2005, resulting in 40 models (20 models for BGS and 20 models for EGS). Similarly, BGS correlates negatively with daily mean temperature within the optimum spring LP in each year at the 46 stations (P < 0.001), namely, the higher the daily mean temperature within the optimum spring LP in a specific year at a station, the earlier the BGS in the year at the station. The explained variances of yearly spring spatial phenology models to BGS are between 73% and 86% (P < 0.001) and the RMSEs of yearly BGS simulations are between 5.4 days and 9.8 days. Slopes of significant linear regression equations show that a spatial shift in daily mean temperature within the optimum spring LP by 1 ◦ C in a year may induce a spatial shift in BGS between −4.28 days (in 2002) and −2.75 days (in 1991) in the year. In contrast, EGS correlates positively with daily mean temperature within the optimum autumn LP in each year at the 46 stations (P < 0.001). That is, the higher the daily mean temperature within the optimum autumn LP in a specific year at a station, the later the EGS in the year at the station. The explained variances of yearly autumn spatial phenology models to EGS are between 49% and 77% (P < 0.001) and the RMSEs of yearly EGS simulations are between 7.4 days and 11.2 days. Slopes of significant linear regression equations indicate that a spatial shift in daily mean temperature within the optimum autumn LP by 1 ◦ C in a year may cause a spatial shift in EGS between 2.17 days (in 1991 and 1992) and 3.16 days (in 2005) in the year. We noted that the average simulation accuracy (in average RMSE) of yearly BGS is higher than that of yearly EGS (Table 1). The model validation was implemented by substituting daily mean temperatures within the optimum spring and autumn LP in each year at external stations into the corresponding yearly spring and autumn spatial phenology models, respectively. It should be noted that because phenological data are uneven among external stations, the number of external stations for model validation is different from year to year, namely, between 12 and 39 (Table 2). The total number of external stations for model validation is 62 over the 20-year period (Fig. 1). The results show that the average RMSEs of yearly BGS and EGS predictions were 7.5 days and 8.7 days, respectively (Table 2), which are close to the average RMSEs of yearly BGS and EGS simulations (Table 1). Thus, the yearly spatial phenology models also show a strong spatial extrapolation capability to yearly BGS and EGS. 3.3. Climatic controls of the spatial response of BGS and EGS to temperature As mentioned above, the spatial response of BGS and EGS to temperature (in the form of spatial regression slope, days ◦ C−1 ) shows obvious interannual variation (Table 1). This may be associated with the interannual variation of thermal condition at regional scales. In order to detect the relationship between spatial phenology response and regional temperature regime with regard to the interannual variation, we calculated the correlation
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Fig. 4. Spatial correlation and regression analyses (A) between 20-year mean daily temperature within the optimum spring LP and 20-year mean BGS and (B) between 20-year mean daily temperature within the optimum autumn LP and 20-year mean EGS at the 46 stations.
Fig. 5. External validation of mean spatial phenology models by the spatial extrapolation of mean (A) BGS and (B) EGS. Table 1 Spatial correlation and regression analyses between daily temperature within the optimum spring LP and BGS and between daily temperature within the optimum autumn LP and EGS in each year. Year
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average *
P < 0.001.
BGS simulation
EGS simulation 2
Number of stations
Slope (days ◦ C−1 )
R
RMSE (days)
Number of stations
Slope (days ◦ C−1 )
R2
RMSE (days)
35 40 41 40 45 46 46 46 46 46 46 45 46 46 46 46 46 46 46 45 –
−3.07 −3.06 −3.08 −3.36 −3.30 −2.75 −3.01 −3.12 −2.94 −3.25 −3.39 −3.43 −3.71 −2.93 −3.04 −3.43 −4.28 −3.71 −3.33 −2.85 –
0.78* 0.86* 0.81* 0.87* 0.79* 0.73* 0.80* 0.81* 0.79* 0.82* 0.76* 0.85* 0.79* 0.78* 0.79* 0.80* 0.79* 0.72* 0.83* 0.82* –
6.1 5.5 6.2 5.4 6.6 7.2 6.6 7.0 6.5 6.9 7.9 6.1 7.0 7.5 7.3 8.1 9.3 9.8 7.6 6.9 7.1
35 40 39 40 45 46 46 46 46 46 46 46 45 46 46 46 46 45 46 45 –
2.83 2.77 2.46 3.00 2.44 2.17 2.17 2.56 3.14 2.70 2.55 2.89 2.58 2.84 2.34 3.09 2.95 2.41 2.98 3.16 –
0.69* 0.54* 0.61* 0.69* 0.49* 0.58* 0.51* 0.59* 0.68* 0.50* 0.73* 0.66* 0.71* 0.73* 0.73* 0.70* 0.77* 0.67* 0.68* 0.74* –
7.7 9.8 8.0 8.2 9.9 8.7 8.9 9.8 9.0 11.2 7.4 9.2 7.9 8.2 7.6 8.4 7.6 8.0 8.5 7.8 8.6
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Table 2 External validation of yearly spatial phenology models by the spatial extrapolation of yearly BGS and EGS. Year
1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Average * **
BGS validation
EGS validation
Number of stations
r
RMSE (days)
Number of stations
r
RMSE (days)
30 32 32 35 39 38 37 33 18 20 17 16 14 15 12 12 12 13 12 13 –
0.86** 0.78** 0.87** 0.78** 0.76** 0.83** 0.90** 0.92** 0.90** 0.92** 0.90** 0.90** 0.91** 0.91** 0.90** 0.89** 0.89** 0.88** 0.80* 0.73* –
6.3 8.9 5.7 7.8 7.9 7.4 6.4 5.8 6.0 5.5 5.2 7.0 7.3 6.3 6.7 9.2 11.4 7.2 11.5 11.0 7.5
28 31 32 34 39 37 36 33 19 19 17 16 16 16 15 14 15 16 15 16 –
0.90** 0.70** 0.87** 0.83** 0.78** 0.87** 0.79** 0.83** 0.66* 0.72** 0.88** 0.84** 0.92** 0.86** 0.89** 0.93** 0.92** 0.64* 0.77** 0.81** –
8.6 9.4 8.5 11.0 11.1 8.7 8.5 8.5 11.9 9.0 7.7 6.9 5.0 7.3 6.0 7.7 6.3 11.9 10.2 10.6 8.7
P < 0.01. P < 0.001.
coefficients between BGS-temperature spatial regression slope (Table 1) and regional February–April mean temperature and between EGS-temperature spatial regression slope (Table 1) and regional September–November mean temperature from 1986 to 2005, respectively. We selected regional February–April and September–November mean temperatures as the independent variables because they can represent regional thermal status when BGS and EGS occurred over the study area, respectively, and are convenient for comparing regional thermal status among different years. The results show that a significantly negative correlation appeared between BGS-temperature spatial regression slope and regional February–April mean temperature (P < 0.01). In general, the negative spatial response of yearly BGS to daily mean temperature within the optimum spring LP was stronger in warmer years with higher regional February–April mean temperatures than in colder years with lower regional February–April mean temperatures (Fig. 6). In contrast, there was no significant correlation
Fig. 6. Correlation analysis between regional February–April mean temperature and spatial regression slope (days ◦ C−1 ) across China’s temperate zone from 1986 to 2005.
between EGS-temperature spatial regression slope and regional September–November mean temperature.
4. Discussion In contrast to models based exclusively on geo-locations (Hopkins, 1918; Nakahara, 1948; Gong and Jian, 1983; Park-Ono et al., 1993; Rötzer and Chmielewski, 2001; Hense et al., 2002), our new model offers a possible ecological mechanism to explain the spatial variation of multiyear mean occurrence dates of tree phenophases, namely, that the spatial pattern of multiyear mean daily temperature within the optimum LP controls the spatial pattern of multiyear mean occurrence dates of tree phenophases. This ecological mechanism can also explain the observed spatial patterns of occurrence dates of tree phenophases in individual years. That is, the spatial pattern of daily mean temperature within the optimum LP in a year controls the spatial pattern of occurrence dates of tree phenophases in the same year. The new model improves upon an earlier 3-month mean temperature based spatial phenology model (Chen et al., 2005) by calculating an optimal and year-specific LP of days to produce a more precise and yearly simulation of tree phenology spatial pattern. The precision of the LP is enhanced from monthly to daily. The daily temperature-based spatial phenology models provide robust tools for simulating and predicting U. pumila BGS and EGS across China’s temperate zone. Generally speaking, the spatial simulations of 20-year mean and yearly BGS are more accurate than the spatial simulations of 20-year mean and yearly EGS (Fig. 4, Table 1). This finding indicates that the spatial pattern of BGS was mainly determined by the spatial pattern of seasonal daily temperature but the spatial pattern of EGS may also be influenced by spatial patterns of other climatic factors, such as precipitation, photoperiod, wind speed and so on, which is consistent with results of time series modeling of plant phenology (Chen, 1994; Chmielewski and Rötzer, 2001; Matsumoto et al., 2003; Gordo and Sanz, 2010; Chen and Xu, 2011). More detailed studies on the mechanism of the spatial variation of autumn plant phenophases are required. Moreover, the spatial extrapolations of 20-year mean BGS and EGS are more accurate than the spatial extrapolations of yearly BGS and EGS (Fig. 5, Table 2). However, spatial extrapolation accuracies of BGS
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Fig. 7. Correlation analysis between regional February–April mean temperature and spatial standard deviation of BGS across China’s temperate zone from 1986 to 2005.
and EGS based on both 20-year mean and yearly models are close to spatial simulation accuracies of these models. Therefore, these models have a strong spatial extrapolation capability to U. pumila BGS and EGS. This kind of spatial extrapolation of tree phenophases may provide a more detailed phenological data set for surface validation of remote sensing phenology (Duchemin et al., 1999; Chen et al., 2000, 2001; Schwartz et al., 2002; Badeck et al., 2004; Studer et al., 2007; Liang et al., 2011) and regional climate modeling based ˜ et al., 2009). on land surface processes (Sellers et al., 1997; Penuelas Furthermore, the current study found that the negative spatial response of yearly U. pumila BGS to daily mean temperature within the optimum spring LP (days ◦ C−1 ) was stronger in warmer years with higher regional February–April mean temperatures than in colder years with lower regional February–April mean temperatures. Thus, we might expect that seasonal climate warming will enhance sensitivity of the spatial response of spring tree phenology to temperature. Further analysis displays that the spatial variability of BGS (in spatial standard deviation of BGS) was also larger in warmer years than in colder years (Fig. 7) but the spatial variability of daily mean temperature within the optimum spring LP (in spatial standard deviation of temperature) was not associated with year-to-year temperature variation. That is, the spatial shift range of daily mean temperature within the optimum spring LP remained approximately the same from year to year, whereas the spatial shift range of BGS fluctuated noticeably along with year-to-year temperature variation. Therefore, the interannual variation of the spatial response of BGS to temperature is associated mainly with spatial variability of BGS from year to year. Namely, climate warming will enhance sensitivity of the spatial response of BGS to temperature through increasing BGS spatial variability. Our finding that climate warming increases the spatial variability of spring tree phenology supports the result of Menzel et al. (2006). Because climate warming may significantly increase the spatial variability of spring tree phenology and consequently speed up the spatial response of spring tree phenology to temperature, the terrestrial ecosystem under global climate change scenarios will likely become more sensitive and uncertain than at present.
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spatial patterns and examining tree phenology spatial responses to temperature across China’s temperate zone. Our main conclusions are as follows: Spatial patterns of daily temperatures within the optimum spring and autumn LPs control spatial patterns of U. pumila BGS and EGS, respectively. With regard to 20-year mean U. pumila BGS and EGS modeling, there is a significantly negative correlation between mean BGS and mean daily temperature within the optimum spring LP at the 46 stations. The mean spring spatial phenology model explained 90% of the BGS variance (p < 0.001) and the RMSE of mean BGS simulation is 4.6 days. According to the linear regression equation, a spatial shift in mean daily temperature within the optimum spring LP by 1 ◦ C may induce a spatial shift in mean BGS by −3.1 days. Meanwhile, there is a significantly positive correlation between mean EGS and mean daily temperature within the optimum autumn LP at the 46 stations. The mean autumn spatial phenology model explained 82% of the EGS variance (p < 0.001) and the RMSE of mean EGS simulation is 5.6 days. In the light of the linear regression equation, a spatial shift in mean daily temperature within the optimum autumn LP by 1 ◦ C may cause a spatial shift in mean EGS by 2.6 days. The accuracy of mean BGS simulation is higher than that of mean EGS simulation. Generally speaking, the mean spatial phenology models perform satisfactorily in predicting mean U. pumila BGS and EGS at external stations. Considering yearly U. pumila BGS and EGS modeling, a consistent negative and positive correlation was detectable between BGS and daily mean temperature within the optimum spring LP and between EGS and daily mean temperature within the optimum autumn LP in each year at the 46 stations, respectively. The explained variances of yearly spring spatial phenology models to BGS are between 73% and 86% (p < 0.001) and the RMSEs of yearly BGS simulations are between 5.4 days and 9.8 days, whereas the explained variances of yearly autumn spatial phenology models to EGS are between 49% and 77% (p < 0.001) and the RMSEs of yearly EGS simulations are between 7.4 days and 11.2 days. Slopes of linear regression equations show that a spatial shift in daily mean temperature within the optimum spring LP by 1 ◦ C in a year may induce a spatial shift in BGS between −4.28 days and −2.75 days in the year, whereas a spatial shift in daily mean temperature within the optimum autumn LP by 1 ◦ C in a year may cause a spatial shift in EGS between 2.17 days and 3.16 days in the year. The average accuracy of yearly BGS simulations is also higher than that of yearly EGS simulations. Overall, the yearly spatial phenology models perform also satisfactorily in predicting yearly U. pumila BGS and EGS at external stations from 1986 to 2005. Furthermore, the negative spatial response of yearly BGS to daily mean temperature within the optimum spring LP was stronger in warmer years with higher regional February–April mean temperatures than in colder years with lower regional February–April mean temperatures. This suggests that climate warming in late winter and spring may enhance sensitivity of the spatial response of BGS to temperature. Acknowledgements The authors wish to thank the Meteorological Information Center of the China Meteorological Administration for providing phenological data. This research is funded by the National Natural Science Foundation of China under Grant Nos. 40871029 and 41071027.
5. Conclusions References In this study, daily temperature (within the optimum LP) based spatial phenology models of U. pumila BGS and EGS were created for exploring the ecological mechanisms driving tree phenology
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