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Response of seasonal vegetation development to climatic variations in eastern central Asia
Remote Sensing of Environment 87 (2003) 42 – 54 www.elsevier.com/locate/rse
Response of seasonal vegetation development to climatic variations in eastern central Asia Fangfang Yu a,b,*, Kevin P. Price a,b, James Ellis c, Peijun Shi d a
Kansas Applied Remote Sensing (KARS) Program, University of Kansas, 2335 Irving Hill Dr., Lawrence, KS 66045, USA b Department of Geography, University of Kansas, Lawrence, USA c Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, USA d Institute of Resource Sciences, Beijing Normal University, Beijing, China Received 5 October 2002; received in revised form 16 May 2003; accepted 1 June 2003
Abstract Meteorological records show that central Asia has experienced one of the strongest warming signals in the world over the last 30 years. The objective of this study was to examine the seasonal vegetation response to the recent climatic variation on the Mongolian steppes, the third largest grassland in the world. The onset date of green-up for central Asia was estimated using time-series analysis of advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) biweekly composite data collected between January 1982 and December 1991. Monthly precipitation and mean temperature data (1982 – 1990) were acquired from 19 meteorological stations throughout the grasslands of the eastern Mongolian steppes in China. Our results showed that while the taiga forest north of the Mongolian steppes (>50jN) experienced an earlier onset of green-up during the study period, a later onset was observed at the eastern and northern edges of the Gobi Desert (40jN – 50jN). Responses of different vegetation types to climatic variability appeared to vary with vegetation characteristics and spring soil moisture availability of specific sites. Plant stress caused by drought was the most significant contributor to later vegetation green-up as observed from satellite imagery over the desert steppe. Areas with greater seasonal soil moisture greened up earlier in the growing season. Our results suggested that water budget limitations determine the pattern of vegetation responses to atmospheric warming. D 2003 Elsevier Inc. All rights reserved. Keywords: Central Asia; Mongolian steppe; Vegetation
1. Introduction Hansen, Ruedy, Gloscoe, and Sato (1999) have shown over the past 30 years an unprecedented rate of global temperature increase. This warming signal is strongest over the subpolar land areas of Alaska, northwestern Canada, and northern Eurasia, and is especially notable in the winter and spring (Chapman & Walsh, 1993; Oechel et al., 2000). Ecosystem response in the higher northern latitudes to this warming trend include a reduction of annual snow cover and earlier melting of spring snow (Groisman, Karl, & Knight, 1994; Parkinson, Cavalieri, Gloersen, Zwally, & Comiso, 1999), an earlier draw-down of atmospheric CO2 in the spring (Keeling, Chin, & Whorf, 1996), advances in the timing of spring plant emergence (Menzel and Fabian, 1999; * Corresponding author. Kansas Applied Remote Sensing (KARS) Program, University of Kansas, 2335 Irving Hill Dr., Lawrence, KS 66045, USA. E-mail address: [email protected] (F. Yu). 0034-4257/03/$ - see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/S0034-4257(03)00144-5
Myneni, Tucker, Asrar, & Keeling, 1998; Myneni, Keeling, Tucker, Asrar, & Nemani, 1997; Schwartz, 1998; Zhou et al., 2001), and poleward migration of butterflies (Thomas & Lenon, 1999). The studies cited above focus in areas north of 45j latitude where temperature is normally the most important climatic factor limiting plant photosynthesis. The climate of northeast Asia (China, Russia, and Mongolia) has undergone significant changes over the last 30 years (1979 – 1997, + f 1.5 jC; Chase, Pielke, Knaff, Kittel, & Eastman, 2000). Recent findings (Chase et al., 2000; Mongolian Action Program, 2000) show that this region has one of the strongest warming signals on the earth. The center of the warming zone appears to lie just southeast of Lake Baikal, putting the drylands of northern China and Mongolia near the center of this hot spot. Changes in ecosystem dynamics for this region can directly affect land use, biodiversity, and human socioeconomics. Few studies, however, have investigated the effects and implications of global warming on the lower latitude grasslands of central Asia (35j –50jN) where both temperature and precipitation
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play important roles in limiting plant biological processes (Ellis, Price, Boone, Yu, Christensen, & Yu, 2001). Recent studies have explored the use of time-series remotely sensed measurements to investigate ecosystem response to climatic variation (Myneni et al., 1997, 1998; Tucker et al., 2001; Zhou et al., 2001). The normalized difference vegetation index (NDVI), which is the most commonly used remote sensing derived measurement, has been linked in numerous studies to such plant properties as leaf chlorophyll content, leaf area, leaf biomass, and net primary productivity (e.g., Goward, Waring, Dye, & Yang, 1994; Tieszen, Reed, Bliss, Wylie, & Dejong, 1997; Tucker, 1979; Tucker & Sellers, 1986). Analyses of the time-series NDVI data are therefore used as the evidence for a biotic response to climatic variation in the changing of growing season and greening patterns at high-latitudes (Lutch et al., 2002; Myneni et al., 1997; Zhou et al., 2001).
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The overall goal of this study is to determine how vegetation in central Asia is responding to recent changes in climatic conditions. More specifically, we wanted to (1) investigate whether vegetation phenological changes observed in the higher northern latitudes are also present in the lower mid-latitudes where such changes could impact a greater number of the world’s human population; (2) identify the predominant vegetation communities undergoing these changes; and (3) determine the climatic factors on the changing onset of green-up patterns.
2. Study area The Mongolian steppes is one of the world’s largest grasslands, extending across the nation of Mongolia and the Inner Mongolia Autonomous Region (IMAR) in northern China (Fig. 1). It is an arid to semiarid land with a strong
Fig. 1. The study area in central Asia. The land cover map of the central IMAR, China is after Series Resources Maps of Inner Mongolia Autonomous Region: Vegetation, IMNRS, 1991. The meteorological stations used for the statistical analysis are also shown in this figure (3 stations in forest and meadow steppe, 11 in the typical steppe, and 5 in the desert steppe).
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climate gradient. In the east (IMAR) and in the north (Mongolia), annual precipitation exceeds 400 mm and the annual rainfall coefficient of variance (CV) is less than 0.25 (Ellis, 1992). In the eastern IMAR, the steppes grade into what was formerly deciduous forest; presently, the land use is dominated by rainfed and irrigated agriculture. Along the northern edge of the steppes (Mongolia), there is an interface with coniferous forest (taiga biome). The southern and western parts of the steppes grade into the Gobi Desert where annual rainfall is less than 100 mm/year and the annual rainfall CV is greater than 0.40 (Ellis, 1992). When one moves from the forest and grassland to the Gobi Desert, mean annual temperature increases as annual precipitation decreases. The mean annual temperature in the study area ranges from less than 4 jC in the north forest area to over 8 jC in the central Gobi Desert (Shi et al., 1989). Between the desert and the forest, annual rainfall ranges from 100 to 400 mm/year. These zones are covered by a variety of types of grassland and shrubland. Three types of grassland (meadow steppe, typical steppe or dry steppe, and desert steppe) and desert were identified along this climate gradient. Meadow steppe is the most productive of all the grass steppes, with typical steppe the next productive. Plant biomass is least for the desert steppe and desert areas. The forest is composed primarily of deciduous trees, including Betula platyphylla, Populus davidiana, and Quercus spp. The dominant coniferous tree is Larix gmelinii. The dominant vegetation types for the meadow steppe include Filifolium sibiricum, Festuca ovina, and Stipa baicalensis, and at lower elevations, Aneurolepidium chinense. The dominant vegetation types of the typical steppes include Stipa grandis, Aneurolepidium Chinense, and Agropyron michnoi. The desert steppe is dominated by short grasses including Stipa krylovii, Stipa bungeana, and Thymus serpyllum (Ellis, 1992). Soil types associated with the three grasslands (meadow steppe, typical steppe, and desert steppe) in this region are chernozems, castanozems, and brown desert, respectively (Li et al., 1990). The Mongolian steppes has a strong continental climate characterized by short hot summers and long cold winters. In this region, most of the annual precipitation falls in summer, coinciding with peak temperatures. Winter and spring are typically windy and cold. Severe drought frequently occurs in spring and early summer, which can cause low productivity of vegetation across the study area (Zhang, 1992).
3. Data and methods 3.1. AVHRR NDVI data For this study, a 10-year (1982 – 1991) 15-day maximum value composite advanced very high resolution radiometer (AVHRR) NDVI data set with a spatial resolution of about 4.0 km was used. This data set was processed and archived
by the Global Inventory Mapping and Monitoring Study (GIMMS) group at NASA/Goddard Space Flight Center. The continental GIMMS NDVI data set was derived from Global Area Coverage (GAC) data, which were collected from 1982 to 1991 by three different on-board sensors; (1) National Oceanic and Atmospheric Administration (NOAA)-7 from January 1982 to February 1985, (2) NOAA-9 from February 1985 to October 1988, and (3) NOAA-11 from October 1988 to December 1991. These images were processed using methods described by Holben (1986). It is widely accepted that composite NDVI images can greatly reduce cloud and other atmospheric noise while retaining dynamic vegetation information. Prior to compositing, the data were preprocessed to eliminate the large viewing-angle and cloud-affected pixels. Detailed information on the processing of this NDVI data can be found in Los, Justice, and Tucker (1994). The 15-day composite data set was geometrically transformed to a Lambert Azimuthal Equal Area projection using ground control points and a nearest neighbor spectral resampling approach. During the georectification process, the pixels were resampled to 4.2 4.2 km. Since radiometric values among the three sensors varied due to differences in detector calibration and orbital drift, each NDVI value was adjusted based on offset coefficients derived through the analysis of NDVI values obtained over an invariant target in a hyper-arid area of the Gobi Desert (Tucker, Vanprate, & Newcomm, 1991 and Tucker, Newcomb, & Dregne, 1994; Myneni et al., 1998). A smoothing algorithm was also applied to the NDVI data set to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination (van Dijk, Callis, Sakamoto, & Decker, 1987). Subsequent remote sensing data processing for this study was based on the smoothed NDVI time-series data set. 3.2. Estimating the onset timing of vegetation green-up in Mongolian steppes Previous studies have described two ways of calculating onset of green-up using time-series NDVI datasets. The first method uses an NDVI ‘‘threshold’’ to identify the beginning of photosynthetic activity in the spring, and the second method identifies the time period when there is a ‘‘sudden increase’’ in NDVI. The NDVI threshold approach more specifically identifies the beginning of photosynthetic activity in the spring (e.g., Fischer, 1994; Lloyd, 1990; Markon, Fleming, & Binnian, 1995; Myneni et al., 1998; White, Thornton, & Running, 1997), while the sudden increase approach essentially identifies the time period when there is an abrupt increase in NDVI (e.g., Badhwar, 1984; Badhwar, Austin, & Carnes, 1982; Kaduk & Heimann, 1996; Reed et al., 1994). A limitation associated with the ‘‘threshold’’ approach is that various land cover types require the use of different thresholds (Reed et al., 1994). Since most land cover types are a mixture of plant types, determining the optimal
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threshold value for an area can be difficult, if not impossible. Changing solar zenith and azimuth viewing angles can influence the NDVI values, also complicating the use of the threshold approach. The most popular method for identifying a sudden change in NDVI uses a ‘‘moving average’’ approach by Reed et al. (1994). The ‘‘moving average’’ is used to estimate the departure from the actual smoothed NDVI data by running a moving average of NDVI values over a certain time interval. The smoothed and ‘‘moving average’’ timeseries NDVI data are then superimposed to identify the time-period when the two curves cross, which is defined as the time when onset of green-up occurs. The ‘‘moving average’’ approach also has a limitation that made it difficult to use in this study. This limitation stems from the fact that the biweekly GIMSS GAC NDVI values are not computed for higher latitude regions of the earth in winter periods (Los et al., 1994). During the data processing for GIMSS NDVI compositing, low surface brightness temperatures in the AVHRR thermal-infrared channel are used for cloud screening. Pixels with less than a certain brightness temperature in the AVHRR thermal-infrared channel are considered as cloud contaminated and replaced with zero values. An inherent problem with this cloud-screening algorithm is that much continental data from higher northern latitudes and arid regions at mid-latitudes are eliminated during the winter periods. Due to this problem, NDVI data for the areas above 45j and the central Gobi Desert for our study area were not available between late October and early March. The lack of data during the early spring and late winter made it impossible to calculate a moving average early in the year just prior to the green-up event. Due to the limitations of the two approaches described above, it was necessary to develop a new method for extracting estimates of onset of green-up (Yu, Price, Ellis, & Kastens, 2003). Our new approach for calculating onset of green-up considers the modality of the NDVI multitemporal curve and is therefore able to account for spring snowfall events that temporally decrease the NDVI values (Reed et al., 1994). The method also constrains the estimate of onset date to a logical time interval, which reduces gross miscalculations for sparsely vegetation areas that do not always experience a major onset of green-up event. We assumed that the onset date of green-up represents the period when vegetation growth begins to increase rapidly in the spring or early summer. To avoid nonvegetation NDVI increases caused by snow-melt events or occurrence of NDVI after a missing winter period, a set of thresholds was used to constrain the estimates of onset date (Fig. 2). First, if biweekly period t is the onset period, then Dht must be positive and represent the maximum change in the NDVI slope angle. The maximum change in NDVI slope angle corresponds to the maximum second derivative value approximately by the second difference over the smoothed NDVI curves (Fig. 2, step 1). Second, the NDVI value at the abrupt increase period should be above 0.05, the
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mean value for the Gobi desert at late March. Third, NDVI values after the estimated onset time should successively increase in the following two time-intervals (1 month). This increasing trend after the onset event is to ensure elimination of the influence of snow events (Fig. 2, step 3). Finally, the onset event must occur within a logical time interval, which in our study area was determined to be between late March and late August (Fig. 2, step 4). This time interval is in accordance with the temperature requirement for the onset event. Late March was selected as the beginning of this time interval because Kaduk and Heimann (1996) found that onset of vegetation growth begins after mean temperature reaches 5 jC, and work by Shi et al. (1989) shows that the 5 jC mean temperature normally occurs by late March within the Mongolian Plateau. Late August was selected as the end of the logical time interval because both precipitation and temperatures normally peak in summer and start to decrease dramatically in September (Shi et al., 1989), making it difficult for newly emerging plants to survive under the thereafter stressful conditions. A more detailed description of this methodology is provided by Yu et al. (in press). The Julian day of onset of green-up was then established as the day mid-way through the biweekly period in which the green-up event occurred. In sparsely vegetated areas such as the Gobi desert region, onset of green-up may not occur in years of low precipitation. If the green-up event could not be detected before late August within the time interval constraint, a randomly selected pseudo onset date in the fall season, Julian day 250 in early September, was assigned to the specific pixels at the particular year for further multiple linear regression analysis. 3.3. Climatic data Mean monthly temperature and total monthly precipitation from 1982 to 1990 were obtained from 19 meteorological stations located in the eastern Mongolian region (central IMAR) of China, 3 in the meadow steppe and the forest, 11 in the typical steppe, and 5 in the desert steppe (Fig. 1). We used the monthly mean temperature of 5 jC to define winter period (Kaduk & Heimann, 1996). As the result, the winter season in the desert steppe starts from November through March. In the arid and semi-arid region, although there is a correlation between mean rainfall and vegetation productivity over the growing season and the soil moisture is regarded as the determining factor in vegetation conditions, considerable uncertainty of the vegetation response to climate change still remains (Goward & Prince, 1995). This uncertainty is mainly due to our current limited understanding of the forcing/feedback surface – atmosphere interactions, which usually have complex temporal lag effects (Braswell, Schimel, Linder, & Moore, 1997; Tian et al., 2000; Zhou et al., 2001). For example, warming temperature, combined with changes in precipitation, can affect vegetation growth through influencing soil moisture and nutrient availability
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273175
Fig. 2. A flowchart showing the method for calculating onset dates from the time-series NDVI. Let yt 1 be the pixel NDVI in period t 1, yt the NDVI in period t, and so on. Dyt = yt yt 1; ht = arctg(Dyt); {h} is the collection of hs where s is the period within the specific modality.
(Kindermann, Wurth, Kohlmaier, & Badeck, 1996; Tian, Melillo, Kicklighter, McGuire, & Helfrich, 1999; Tian et al., 2000). Potter, Klooster, and Brooks (1999) found that, in the arid and semi-arid mid-latitude areas of the northern hemisphere, vegetation net primary production can be affected by temperatures preceding the current period by up to 1 year. Di, Rundquist, and Han (1994) found a 1 – 2-month lagperiod effect of precipitation on NDVI values derived during the growing season. To evaluate the lag effects of temperature and rainfall within our study area, we used seasonal climate data from previous winters through the mean onset dates, which were then used to analyze the relationships between climatic variability and onset data for different land cover types. Our estimate of the mean onset of green-up was in early May for the forests, early June for typical steppe and late July for desert steppe in central IMAR, respectively. The time interval used for these analyses therefore ranged from the previous October through
May for the forest ecosystem, from the previous October through June for the typical steppe, and from the previous November through July for the desert steppe. We use the term ‘‘preseason’’ to refer to the period before the mean onset date. 3.4. Statistical analysis A set of linear multiple regression models was used to evaluate the effects of climatic variation on the onset dates of green-up for the three different land cover types in central IMAR. Due to the coarse spatial resolution of AVHRR GAC data, misregistration between years may exist to generate noises that complicate the interpretation of year-to-year differences in onset date. To minimize the noise, we derived the estimates of onset date by using the median value of a 3 3-pixel window. We also assumed that the spatial variation of monthly climate data was relatively consistent
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within an area of about 10-km radium of each meteorological station, corresponding to the 3 3-pixel window size area (Yang, Wylie, Tieszen, & Reed, 1998). All the dependent variables in the regression models were the median onset date and the independent variables were the preseason monthly climate data for each meteorological station from 1983 to 1990. The general relation between onset date and climate was described as: n X Onset ¼ a þ ðbi1 Ti þ bi2 Pi Þ þ e;
ð1Þ
Table 1 Multiple regression analysis between the standardized onset date of greenup and intra-annual climate data (monthly precipitation and mean temperature of the months proceeding the average date of onset of greenup) at desert steppe and typical steppe (only months with significant results are listed) Time of the year Typical steppe
precipitation temperature
i¼0
where onset is the timing of onset of green-up in Julian days; T is the monthly mean temperature; P is the monthly precipitation; a, bi1, and bi2 are coefficients that are estimated statistically; e is the unexplained error from this model; n is the number of periods before the onset event, varying for different land cover types (e.g., n = 8 (October – May) for the forest ecosystem, n = 9 (Oct– June) for the typical steppe, and n = 9 (November – July) for the desert steppe).1 This relationship between onset and climate data is based on the grassland phenological physiology that onset of green-up in the grassland was strongly influenced by temperature and precipitation (French & Saucer, 1974; Pitt & Wikeem, 1990). One problem associated with regression models is multicollinearity among independent variables, which typically inflates the estimation of the standard errors for regression coefficients and hence renders them statistically insignificant. Variance inflation factor (VIF) analysis is used to test for multicollinearity among independent variables (Wesolowsky, 1976). A close examination of the VIF values revealed the existence of high multicollinearity. Most of the multicollinearity among variable is attributable to the strong correlation among monthly temperatures and the correlation between monthly temperature and precipitation. Multicollinearity, however, does not affect the significance tests of the R2 for the assessment of the whole model. Besides these three models designated as ‘‘the entire model’’ in Table 1, we also performed a series of regression analyses of onset date against preseason monthly climate factors (two in each model). The equation for these single monthly models is described as:
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Desert steppe
R2 precipitation temperature R2
March
May
June
0.40 0.00* 0.05 0.65 0.22*
0.15 0.27 0.67 0.00* 0.42*
0.22 0.05* 0.09 0.41 0.14* 0.49 0.00* 0.17 0.26 0.32*
b Sig. b Sig. b Sig. b Sig.
The entire model for Eq. (1)
0.54*
0.87*
The entire model stands for the regression between the onset dates and all the preseason climate variables (as described in Eq. (1)). b = standardized regression coefficient and R 2 = determination of coefficient. * P < 0.05.
steppe and the desert steppe, respectively) to the mean onset month (June and July for the typical and desert steppes, respectively) for each land cover type. These models were designed to determine which month(s)’s temperature and precipitation can best predict the green-up date. Since the general model for the forest ecosystem shows insignificant results, we decided exclude the forest ecosystem from the single month analyses. Significant results were reported in Table 1 along with the R2 statistics of the general models described with Eq. (1). Temporal autocorrelation of the 8-year data set was tested using the Durbin –Watson test (Durbin & Watson, 1971). Low to moderate Durbin – Watson values indicate non-significant temporal autocorrelation for the selected climate data in the models (Durbin –Watson ranging from 1.54 to 2.08). Since the meteorological stations are sparsely distributed across the study area (Fig. 1), there is no reason to suspect any spatial autocorrelation among the climate variables.
4. Results Onset ¼ a þ bk1 Tk þ bk2 Pk þ e
ð2Þ
where k ranges from month from the end of previous growing season (October and November for the typical 1 For the forest ecosystem, there were three meteorological stations, each with eight annual onset events, providing a total of 24 observations. There were also eight pairs of monthly climate factors, providing a total of 16 independent variables. For the typical steppe, there were 11 meteorological stations and 9 pairs of monthly climatic factors, giving 88 observations of dependent variables and 18 independent variables. For the desert steppe, there were 5 meteorological stations and 9 pairs of monthly climatic factors, which provided 40 onset observations and 18 independent variables.
4.1. General onset pattern in eastern central Asia The distribution of average onset dates is shown in Fig. 3. The date of onset of green-up varies considerably across the study area. This variation is linked to ecosystem (i.e., forest, meadow steppe, typical steppe, and desert steppe) distribution that is strongly influenced by geographic features and their impacts on climate. The average date of onset of green-up between 1982 and 1991 corresponds well with the temperature and precipitation gradients described earlier. The mixed forest in the Da Xingan Mountains and taiga
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Fig. 3. Averaged onset date of green-up in the central Asia from 1982 to 1991.
forest at the northern edge of the Mongolian steppes have the earliest dates of onset, usually in late April or early May. Onset of green-up becomes progressively later with increasing distance from the eastern border moving toward the Gobi Desert. In general, the typical steppe experiences green-up in late May to early June. The date of onset of green-up for the desert steppe normally occurs in late June to early July and is the most variable among the grassland ecosystems in this study area (Yu et al., in press). This spatial distribution of mean timing of onset of green-up in IMAR generally agrees with the pattern of onset of the rainy season in this region. For the meadow steppe, the typical steppe, and the desert steppe, the summer rainy season usually arrives in May, June and early July, respectively (Shi et al., 1989; Xue, 1996). Onset events were undetectable in the Gobi Desert over the 10-year study period. 4.2. Interannual variation of onset in the Mongolian steppes The change rate in onset dates of green-up, averaged over the 10-year study, is shown in Fig. 4. The region of advanced green-up patterns (negative slopes for the fitting line) covers much of the eastern steppe of Mongolia, extending south from the forest zone to near the northern edge of the Gobi Desert (Fig. 4a). This is the region on the Mongolia steppe, with precipitation ranging from about 200 mm per annum in the south to more than 400 mm near the ecotone of the forest zone (Ellis et al., 2001). Although most of the region did not experience significant change in onset date of green-up from 1982 to 1991, a significant earlier onset date can be observed in the taiga zone north to the Mongolian steppes (Fig. 4b). This earlier onset is consistent with the observation reported by Myneni et al. (1997, 1998) for high latitudes (>45jN).
A contiguous band of areas that are experiencing a later date of onset can be observed along the northern and eastern edges of the Gobi Desert at 40 –50jN latitude (Fig. 4a). In IMAR, the later green-up areas are associated with the desert steppe, the transition between the Gobi Desert and the typical grassland, and part of the typical steppe (Fig. 1). Mean annual rainfall in most of this delayed green-up zone is 100– 200 mm per annum. Another area showing later onset date in IMAR appears to be associated with the forest/ shrub ecosystem in the southern Da Xingan Mountains. Since we have no meteorological data for this forested region, our analyses of the climate-onset interaction are focused on the IMAR grasslands bounding the Gobi Desert only. Fig. 5 displays the average onset dates of green-up for the pixels experiencing a significant change ( P < 0.10) from 1982 to 1991. Onset dates in the desert steppe were highly variable with a significant late trend during this period. In the taiga forest, progressive early onset can be observed from 1982 to 1991 except for 1983 and 1988. Both of these two profiles indicate late onset dates in 1983 and the relatively late onset dates may be associated with the strong El-Nino effect in the 1982 – 1983 period (Myneni et al., 1998). Two reasons attribute to the possibly overestimated onset trends, about + 35 days for the taiga and about 25 days for the desert steppe in IMAR. First, the Mongolian steppes were subjected to one of the strongest warming signals, especially in the winter and early spring, over the last two decades (Chase et al., 2000; Mongolian Action Program, 2001). Dramatic changes in the onset of plant growth are expected as consequences of the warming trend at the beginning of growing season (Ellis et al., 2002). Zhou et al. (2001) reported the mean growing season for vegeta-
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Fig. 4. (a) Mean change rate of onset date of green-up in central Asia (1982 – 1991). (b) Areas with significant changes in onset date during 1982 – 1991. Warm colors stand for the areas experiencing later onset pattern during this period and cold colors for the earlier onset pattern.
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Fig. 5. Average onset date of green-up for the pixels in the Taiga forest and the desert steppe in IMAR that experienced a significant onset changes (a < 0.1) from 1982 to 1991.
tion at the northwest Asia increased about 18 days over the past two decades. Secondly, although the 15-day GAC NDVI composites do have the advantage of using a longer time period to reduce cloud and aerosol contamination, effects of bidirectional reflectance, and data volume (Holben, 1986), the temporal resolution is scarified (White et al., 1997). Since our method for estimating onset date uses the mid-date of the 15-day NDVI composite (Holben, 1986), we estimate the maximum expected error of the onset date to be 7 –8 days for each year. This error, when averaged over the area undergoing significant onset changes, may cause a bias toward a higher onset trend values. 4.3. Relationships between onset and climate data in central Imar, China 4.3.1. Meadow steppe No significant relationship between onset dates and the preseason climate could be found in the meadow steppe. An examination of the onset dates showed that there was little year-to-year variation in this spring phenology (Yu et al., in press). Several reasons may account for this consistent onset. First, the meadow steppe is mainly distributed in higher latitudes with low interannual variability in annual precipitation. The interannual variability in precipitation, calculated as the coefficient of variance (standard deviation/mean) was 0.14, 0.18, and 0.21 for meteorological stations 1, 2, and 3 (Fig. 1), respectively, indicating a relatively stable or equilibrium environment in this region (Ellis, 1994). Secondly, the coarse temporal resolution of the NDVI composite data (15-day interval for compositing) may somehow mask the subtle response of vegetation to the late winter and spring warming effects in the temperature-limited ecosystems such as forest. Therefore, we discuss only the onset-climate relation for the typical and desert steppes where interannual variation of onset dates is highly variable.
4.3.2. Typical steppe Monthly model results show that precipitation in May and June is the most import factor determining the onset date of green-up (R2 = 0.14 and 0.22, respectively; Table 1). Accumulation of precipitation in May and June can explain about 30% of the changes in observed onset dates in the typical steppe (Fig. 6). The entire monthly preseason climate (precipitation and temperature data from November to March) can explain over 50% of the interannual variations in the onset dates in the typical desert. Spring precipitation is negatively correlated with onset, indicating an early trend of onset for increased spring precipitation, or late trend for decreased spring precipitation. Similar results were also reported for the warm grasslands of southern U.S. Great Plains where grasslands remains dormant until the arrival of raining season in late spring (Kemp, 1983). The timing of onset of green-up in the IMAR typical steppe is therefore largely controlled by the intensity and timing of spring precipitation. Since most of the meteorological stations in the typical steppe of eastern IMAR showed no significant increase or decrease in precipitation patterns in May and June from 1982 to 1990, this may partially explain the non-significant changes in the onset date of this grassland in eastern IMAR as shown in Fig. 4b. 4.3.3. Desert steppe Two significant monthly climate variables entered into the seasonal model: March temperature and May precipitation. March temperature and May precipitation explained about 42% and 32% of the variance in the observed onset dates, respectively (Table 1). The entire monthly preseason climate (precipitation and temperature data from November to June) can explain about 87% of the interannual variation in the onset dates in the typical desert. The relative high correlation between March temperature and the onset date suggests that warming in winter or early spring may lead to a late onset of growing season in this region. Regression between onset dates and the mean winter
Fig. 6. The relationship between the onset dates and the spring precipitation (in May and June) for meteorological stations in the typical steppe in central IMAR.
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Fig. 7. Mean winter temperature (November – March) for three meteorological stations in the desert steppe, which experienced significant late trend of onset from 1982 to 1990.
and early spring temperatures (averaged from previous November through March) was then used to examine whether changes in winter temperature had caused the variability in onset date. A significant positive relationship was also found between the onset dates and the mean temperature from November to March ( P < 0.10, n = 24). Fig. 7 shows the mean winter temperature (November – March) and May precipitation for three meteorological station sites in the desert steppe. The early onset of 1985 seems to be associated with the relatively wet May that year. The winter mean temperatures for all three stations exhibit a clear warming winter pattern beginning in 1985, while May precipitation was highly variable during this period (precipitation CV in May = 1.33, 1.00, and 0.84 for stations 15, 17, and 18, respectively). We therefore believe that it is warming in winter and early spring, especially in March that caused the later onset in desert steppe during the study period (Fig. 4b).
5. Discussion Our observation of vegetation green-up pattern at the relatively wet area of taiga forest and part of the meadow steppes confirmed the advance in the beginning of growing season at high latitudes (>50jN) over the study period between 1982 and 1991 (Myneni et al., 1997, 1998). The change in green-up pattern in these areas thus suggests that temperature-limited, but not water-limited ecosystems in early spring, could experience an advance in the green-up timing due to the warming temperature. The variable responses of the typical steppe to the climate change may be seen as the results of the characteristics of specific sites, or the varying seasonal water budgets over space and time occurring in this vegetation type. For example, our analysis of the changing onset patterns of the comparable vegetation communities along the southwest– northeast trending boundary of Mongolia and IMAR that extend across the political (Fig. 4a and b) border show some
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major inconsistencies in green-up onset trends within vegetation types. Typical steppe has a trend of advanced and/or slightly delayed green-up on the Mongolia side and a broad mix of delayed green-up site, and sites with no change on the IMAR side. These different responses of seasonal vegetation to the climatic variations appear to be associated with the different levels of grazing intensity and land use management across the international boundary of Mongolia and IMAR, where stocking rates are much higher on IMAR side of the border (Sneath, 1998). Recent research shows that current policies in northern China promote privatization and fragmentation of rangeland in IMAR (Sneath, 1998). Overgrazing, or as a combined result of overgrazing and climate stress, in association with the small scale and pattern of livestock grazing has led to land degradation in the typical and meadow steppes of IMAR (Christensen, 2001; Ellis et al., 2002). It appears that the degraded land surface may delay the onset dates of green-up by influencing local atmospheric conditions through several possible land –atmosphere feedback processes. If human land use does influence the course of climate-induced vegetation change, then this cross-boundary situation provides an extraordinary opportunity to begin to dissect the intertwined effects of changes in atmospheric conditions and changes in land use on steppe ecosystems. Our climate and onset date analysis in the desert steppe show that where the vegetation is water-limited, onset dates of green-up are delayed by the warming trend. The positive regression coefficients between the winter temperature and onset dates also supports the finding from the model simulations by Mabutt (1989) and Greco, Moss, Viner, and Jenne (1994) that, in the desert areas, increases in temperature of 0.5 – 2.0 jC raise evapotranspiration by 0.2 – 2.0 mm/day, unless accompanied by increased rainfall. Analysis of the meteorological records shows that over last 60 years spring rainfall in the Mongolian steppes has declined by 17%, mostly in May (Natsagdorj, 2000; Baatarbileg et al., 2001). We assembled the available longterm winter climate records (1961 – 1995 for winter temperature and 1961 – 1990 for May rainfall) from three meteorological stations in the desert steppe of IMAR (Fig. 8). These stations showed a strong warming trend in the winter (November through March) over the period of 1961 – 1995, which is consistent with the report of winter and early spring warming in the high latitudes (Chapman & Walsh, 1993). The mean annual May rainfall from 1961 – 1990 decreased slightly with substantial yearto-year variation. This long-term climate change further confirms the effect of spring drought on the later onset dates of green-up observed in the desert steppes in the Mongolian Plateau. The result of the onset-climate relation in the Mongolian steppes exhibits the complex effects of temperature on vegetation growth. Increases in spring temperature may stimulate earlier vegetation photosynthesis activity, while at the same time increase the water stress in arid and semi-
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Fig. 8. Summed winter (November through March) temperature at three meteorological stations in the desert steppe. The dashed lines are for annual values; solid lines are for the annual values smoothed with 5-year running and fitted with regression line.
arid ecosystems. In most regions of the Mongolia steppe, the vast majority (85 –90%) of annual precipitation comes in a relatively short summer rainy season, June, July, and August (Dagvadorg, Batima, & Natsagdorj, 2001). About 10% of the annual precipitation falls as winter snow. This winter snow may be very important in the seasonal water budget because snow appears to stay on the ground until relatively late winter, providing soil moisture through the spring drought (Ellis et al., 2001). As a result, warming in winter and early spring stimulates earlier onset dates of green-up in wet areas while delays the occurrence of green-up in dry areas. Our analysis of this study supports the hypothesis that water budget limitations in arid and semiarid region is determining the patterns of vegetation responses to atmospheric warming (Neilson, 1995).
6. Conclusions The findings of this study show a linkage between onset date of green-up predicted using AVHRR NDVI GAC 15day maximum composites and climatic factors. Our findings also show that the onset dates of green-up vary considerably within and across the steppes of the Inner Mongolian study area. Unlike the findings of Myneni et al. (1997) that show an earlier date of onset at the global scale (>50jN), our findings show that the response of vegetation to climate change varies among different land cover types in central Asia. While some of the taiga forest (including the meadow steppe) north to the Mongolian steppe (>50jN) experiences an earlier onset of green-up during the study period, a significantly later onset is observed in the desert steppes
banding the eastern and northern edges of the Gobi Desert (40 – 50jN). Results from most of the typical steppe region, which falls between the meadow steppe and the desert steppe, are varied. Some typical steppe sites show a weak trend of delayed onset of green-up, other sites show no change and a few have an earlier green-up. For much of the Gobi Desert, onset of green-up was not detectable by NDVI analysis in most years. For the typical steppe, spring precipitation, especially in May and June, is the most important factor influencing the onset date of green-up, while for the desert steppe, the timing of green-up is more influenced by temperature, especially the temperature in late winter and early spring. In water-limited regions, changes in temperature can significantly alter seasonal water budgets that influence vegetation phenological patterns and photosynthetic activity. Our analysis of the regional green-up patterns along the international border suggests that the differential impact of heavy grazing in IMAR vs. relatively light grazing in Mongolia may modify the effects of atmospheric warming. We believe that the significantly delayed onset in the desert steppe is mainly caused by warming-induced drought stress over the study period. Future studies will concentrate on extending our remote sensing and meteorological data sets to the present time period to determine whether patterns observed during this study continue to persist.
Acknowledgements This study was supported by the United States National Science Foundation, Models and Methods for Integrated
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Assessment Program. We wish to express the appreciation to Dr. Li Zhenghai, Mr. Pai Hao, Dr. Wang Yanrong, and Mr. Zhou Wuguan for their assistance with this project. We are grateful to Dr. Hanqin Tian for the comments on the manuscript, Dr. Compton J. Tucker for providing the AVHRR GAC data, and Mr. Chenyang Xiao for help with the statistical analysis.
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Response of seasonal vegetation development to climatic variations in eastern central Asia Fangfang Yu a,b,*, Kevin P. Price a,b, James Ellis c, Peijun Shi d a
Kansas Applied Remote Sensing (KARS) Program, University of Kansas, 2335 Irving Hill Dr., Lawrence, KS 66045, USA b Department of Geography, University of Kansas, Lawrence, USA c Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, USA d Institute of Resource Sciences, Beijing Normal University, Beijing, China Received 5 October 2002; received in revised form 16 May 2003; accepted 1 June 2003
Abstract Meteorological records show that central Asia has experienced one of the strongest warming signals in the world over the last 30 years. The objective of this study was to examine the seasonal vegetation response to the recent climatic variation on the Mongolian steppes, the third largest grassland in the world. The onset date of green-up for central Asia was estimated using time-series analysis of advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) biweekly composite data collected between January 1982 and December 1991. Monthly precipitation and mean temperature data (1982 – 1990) were acquired from 19 meteorological stations throughout the grasslands of the eastern Mongolian steppes in China. Our results showed that while the taiga forest north of the Mongolian steppes (>50jN) experienced an earlier onset of green-up during the study period, a later onset was observed at the eastern and northern edges of the Gobi Desert (40jN – 50jN). Responses of different vegetation types to climatic variability appeared to vary with vegetation characteristics and spring soil moisture availability of specific sites. Plant stress caused by drought was the most significant contributor to later vegetation green-up as observed from satellite imagery over the desert steppe. Areas with greater seasonal soil moisture greened up earlier in the growing season. Our results suggested that water budget limitations determine the pattern of vegetation responses to atmospheric warming. D 2003 Elsevier Inc. All rights reserved. Keywords: Central Asia; Mongolian steppe; Vegetation
1. Introduction Hansen, Ruedy, Gloscoe, and Sato (1999) have shown over the past 30 years an unprecedented rate of global temperature increase. This warming signal is strongest over the subpolar land areas of Alaska, northwestern Canada, and northern Eurasia, and is especially notable in the winter and spring (Chapman & Walsh, 1993; Oechel et al., 2000). Ecosystem response in the higher northern latitudes to this warming trend include a reduction of annual snow cover and earlier melting of spring snow (Groisman, Karl, & Knight, 1994; Parkinson, Cavalieri, Gloersen, Zwally, & Comiso, 1999), an earlier draw-down of atmospheric CO2 in the spring (Keeling, Chin, & Whorf, 1996), advances in the timing of spring plant emergence (Menzel and Fabian, 1999; * Corresponding author. Kansas Applied Remote Sensing (KARS) Program, University of Kansas, 2335 Irving Hill Dr., Lawrence, KS 66045, USA. E-mail address: [email protected] (F. Yu). 0034-4257/03/$ - see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/S0034-4257(03)00144-5
Myneni, Tucker, Asrar, & Keeling, 1998; Myneni, Keeling, Tucker, Asrar, & Nemani, 1997; Schwartz, 1998; Zhou et al., 2001), and poleward migration of butterflies (Thomas & Lenon, 1999). The studies cited above focus in areas north of 45j latitude where temperature is normally the most important climatic factor limiting plant photosynthesis. The climate of northeast Asia (China, Russia, and Mongolia) has undergone significant changes over the last 30 years (1979 – 1997, + f 1.5 jC; Chase, Pielke, Knaff, Kittel, & Eastman, 2000). Recent findings (Chase et al., 2000; Mongolian Action Program, 2000) show that this region has one of the strongest warming signals on the earth. The center of the warming zone appears to lie just southeast of Lake Baikal, putting the drylands of northern China and Mongolia near the center of this hot spot. Changes in ecosystem dynamics for this region can directly affect land use, biodiversity, and human socioeconomics. Few studies, however, have investigated the effects and implications of global warming on the lower latitude grasslands of central Asia (35j –50jN) where both temperature and precipitation
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play important roles in limiting plant biological processes (Ellis, Price, Boone, Yu, Christensen, & Yu, 2001). Recent studies have explored the use of time-series remotely sensed measurements to investigate ecosystem response to climatic variation (Myneni et al., 1997, 1998; Tucker et al., 2001; Zhou et al., 2001). The normalized difference vegetation index (NDVI), which is the most commonly used remote sensing derived measurement, has been linked in numerous studies to such plant properties as leaf chlorophyll content, leaf area, leaf biomass, and net primary productivity (e.g., Goward, Waring, Dye, & Yang, 1994; Tieszen, Reed, Bliss, Wylie, & Dejong, 1997; Tucker, 1979; Tucker & Sellers, 1986). Analyses of the time-series NDVI data are therefore used as the evidence for a biotic response to climatic variation in the changing of growing season and greening patterns at high-latitudes (Lutch et al., 2002; Myneni et al., 1997; Zhou et al., 2001).
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The overall goal of this study is to determine how vegetation in central Asia is responding to recent changes in climatic conditions. More specifically, we wanted to (1) investigate whether vegetation phenological changes observed in the higher northern latitudes are also present in the lower mid-latitudes where such changes could impact a greater number of the world’s human population; (2) identify the predominant vegetation communities undergoing these changes; and (3) determine the climatic factors on the changing onset of green-up patterns.
2. Study area The Mongolian steppes is one of the world’s largest grasslands, extending across the nation of Mongolia and the Inner Mongolia Autonomous Region (IMAR) in northern China (Fig. 1). It is an arid to semiarid land with a strong
Fig. 1. The study area in central Asia. The land cover map of the central IMAR, China is after Series Resources Maps of Inner Mongolia Autonomous Region: Vegetation, IMNRS, 1991. The meteorological stations used for the statistical analysis are also shown in this figure (3 stations in forest and meadow steppe, 11 in the typical steppe, and 5 in the desert steppe).
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climate gradient. In the east (IMAR) and in the north (Mongolia), annual precipitation exceeds 400 mm and the annual rainfall coefficient of variance (CV) is less than 0.25 (Ellis, 1992). In the eastern IMAR, the steppes grade into what was formerly deciduous forest; presently, the land use is dominated by rainfed and irrigated agriculture. Along the northern edge of the steppes (Mongolia), there is an interface with coniferous forest (taiga biome). The southern and western parts of the steppes grade into the Gobi Desert where annual rainfall is less than 100 mm/year and the annual rainfall CV is greater than 0.40 (Ellis, 1992). When one moves from the forest and grassland to the Gobi Desert, mean annual temperature increases as annual precipitation decreases. The mean annual temperature in the study area ranges from less than 4 jC in the north forest area to over 8 jC in the central Gobi Desert (Shi et al., 1989). Between the desert and the forest, annual rainfall ranges from 100 to 400 mm/year. These zones are covered by a variety of types of grassland and shrubland. Three types of grassland (meadow steppe, typical steppe or dry steppe, and desert steppe) and desert were identified along this climate gradient. Meadow steppe is the most productive of all the grass steppes, with typical steppe the next productive. Plant biomass is least for the desert steppe and desert areas. The forest is composed primarily of deciduous trees, including Betula platyphylla, Populus davidiana, and Quercus spp. The dominant coniferous tree is Larix gmelinii. The dominant vegetation types for the meadow steppe include Filifolium sibiricum, Festuca ovina, and Stipa baicalensis, and at lower elevations, Aneurolepidium chinense. The dominant vegetation types of the typical steppes include Stipa grandis, Aneurolepidium Chinense, and Agropyron michnoi. The desert steppe is dominated by short grasses including Stipa krylovii, Stipa bungeana, and Thymus serpyllum (Ellis, 1992). Soil types associated with the three grasslands (meadow steppe, typical steppe, and desert steppe) in this region are chernozems, castanozems, and brown desert, respectively (Li et al., 1990). The Mongolian steppes has a strong continental climate characterized by short hot summers and long cold winters. In this region, most of the annual precipitation falls in summer, coinciding with peak temperatures. Winter and spring are typically windy and cold. Severe drought frequently occurs in spring and early summer, which can cause low productivity of vegetation across the study area (Zhang, 1992).
3. Data and methods 3.1. AVHRR NDVI data For this study, a 10-year (1982 – 1991) 15-day maximum value composite advanced very high resolution radiometer (AVHRR) NDVI data set with a spatial resolution of about 4.0 km was used. This data set was processed and archived
by the Global Inventory Mapping and Monitoring Study (GIMMS) group at NASA/Goddard Space Flight Center. The continental GIMMS NDVI data set was derived from Global Area Coverage (GAC) data, which were collected from 1982 to 1991 by three different on-board sensors; (1) National Oceanic and Atmospheric Administration (NOAA)-7 from January 1982 to February 1985, (2) NOAA-9 from February 1985 to October 1988, and (3) NOAA-11 from October 1988 to December 1991. These images were processed using methods described by Holben (1986). It is widely accepted that composite NDVI images can greatly reduce cloud and other atmospheric noise while retaining dynamic vegetation information. Prior to compositing, the data were preprocessed to eliminate the large viewing-angle and cloud-affected pixels. Detailed information on the processing of this NDVI data can be found in Los, Justice, and Tucker (1994). The 15-day composite data set was geometrically transformed to a Lambert Azimuthal Equal Area projection using ground control points and a nearest neighbor spectral resampling approach. During the georectification process, the pixels were resampled to 4.2 4.2 km. Since radiometric values among the three sensors varied due to differences in detector calibration and orbital drift, each NDVI value was adjusted based on offset coefficients derived through the analysis of NDVI values obtained over an invariant target in a hyper-arid area of the Gobi Desert (Tucker, Vanprate, & Newcomm, 1991 and Tucker, Newcomb, & Dregne, 1994; Myneni et al., 1998). A smoothing algorithm was also applied to the NDVI data set to minimize the effects of anomalous values caused by atmospheric haze and cloud contamination (van Dijk, Callis, Sakamoto, & Decker, 1987). Subsequent remote sensing data processing for this study was based on the smoothed NDVI time-series data set. 3.2. Estimating the onset timing of vegetation green-up in Mongolian steppes Previous studies have described two ways of calculating onset of green-up using time-series NDVI datasets. The first method uses an NDVI ‘‘threshold’’ to identify the beginning of photosynthetic activity in the spring, and the second method identifies the time period when there is a ‘‘sudden increase’’ in NDVI. The NDVI threshold approach more specifically identifies the beginning of photosynthetic activity in the spring (e.g., Fischer, 1994; Lloyd, 1990; Markon, Fleming, & Binnian, 1995; Myneni et al., 1998; White, Thornton, & Running, 1997), while the sudden increase approach essentially identifies the time period when there is an abrupt increase in NDVI (e.g., Badhwar, 1984; Badhwar, Austin, & Carnes, 1982; Kaduk & Heimann, 1996; Reed et al., 1994). A limitation associated with the ‘‘threshold’’ approach is that various land cover types require the use of different thresholds (Reed et al., 1994). Since most land cover types are a mixture of plant types, determining the optimal
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threshold value for an area can be difficult, if not impossible. Changing solar zenith and azimuth viewing angles can influence the NDVI values, also complicating the use of the threshold approach. The most popular method for identifying a sudden change in NDVI uses a ‘‘moving average’’ approach by Reed et al. (1994). The ‘‘moving average’’ is used to estimate the departure from the actual smoothed NDVI data by running a moving average of NDVI values over a certain time interval. The smoothed and ‘‘moving average’’ timeseries NDVI data are then superimposed to identify the time-period when the two curves cross, which is defined as the time when onset of green-up occurs. The ‘‘moving average’’ approach also has a limitation that made it difficult to use in this study. This limitation stems from the fact that the biweekly GIMSS GAC NDVI values are not computed for higher latitude regions of the earth in winter periods (Los et al., 1994). During the data processing for GIMSS NDVI compositing, low surface brightness temperatures in the AVHRR thermal-infrared channel are used for cloud screening. Pixels with less than a certain brightness temperature in the AVHRR thermal-infrared channel are considered as cloud contaminated and replaced with zero values. An inherent problem with this cloud-screening algorithm is that much continental data from higher northern latitudes and arid regions at mid-latitudes are eliminated during the winter periods. Due to this problem, NDVI data for the areas above 45j and the central Gobi Desert for our study area were not available between late October and early March. The lack of data during the early spring and late winter made it impossible to calculate a moving average early in the year just prior to the green-up event. Due to the limitations of the two approaches described above, it was necessary to develop a new method for extracting estimates of onset of green-up (Yu, Price, Ellis, & Kastens, 2003). Our new approach for calculating onset of green-up considers the modality of the NDVI multitemporal curve and is therefore able to account for spring snowfall events that temporally decrease the NDVI values (Reed et al., 1994). The method also constrains the estimate of onset date to a logical time interval, which reduces gross miscalculations for sparsely vegetation areas that do not always experience a major onset of green-up event. We assumed that the onset date of green-up represents the period when vegetation growth begins to increase rapidly in the spring or early summer. To avoid nonvegetation NDVI increases caused by snow-melt events or occurrence of NDVI after a missing winter period, a set of thresholds was used to constrain the estimates of onset date (Fig. 2). First, if biweekly period t is the onset period, then Dht must be positive and represent the maximum change in the NDVI slope angle. The maximum change in NDVI slope angle corresponds to the maximum second derivative value approximately by the second difference over the smoothed NDVI curves (Fig. 2, step 1). Second, the NDVI value at the abrupt increase period should be above 0.05, the
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mean value for the Gobi desert at late March. Third, NDVI values after the estimated onset time should successively increase in the following two time-intervals (1 month). This increasing trend after the onset event is to ensure elimination of the influence of snow events (Fig. 2, step 3). Finally, the onset event must occur within a logical time interval, which in our study area was determined to be between late March and late August (Fig. 2, step 4). This time interval is in accordance with the temperature requirement for the onset event. Late March was selected as the beginning of this time interval because Kaduk and Heimann (1996) found that onset of vegetation growth begins after mean temperature reaches 5 jC, and work by Shi et al. (1989) shows that the 5 jC mean temperature normally occurs by late March within the Mongolian Plateau. Late August was selected as the end of the logical time interval because both precipitation and temperatures normally peak in summer and start to decrease dramatically in September (Shi et al., 1989), making it difficult for newly emerging plants to survive under the thereafter stressful conditions. A more detailed description of this methodology is provided by Yu et al. (in press). The Julian day of onset of green-up was then established as the day mid-way through the biweekly period in which the green-up event occurred. In sparsely vegetated areas such as the Gobi desert region, onset of green-up may not occur in years of low precipitation. If the green-up event could not be detected before late August within the time interval constraint, a randomly selected pseudo onset date in the fall season, Julian day 250 in early September, was assigned to the specific pixels at the particular year for further multiple linear regression analysis. 3.3. Climatic data Mean monthly temperature and total monthly precipitation from 1982 to 1990 were obtained from 19 meteorological stations located in the eastern Mongolian region (central IMAR) of China, 3 in the meadow steppe and the forest, 11 in the typical steppe, and 5 in the desert steppe (Fig. 1). We used the monthly mean temperature of 5 jC to define winter period (Kaduk & Heimann, 1996). As the result, the winter season in the desert steppe starts from November through March. In the arid and semi-arid region, although there is a correlation between mean rainfall and vegetation productivity over the growing season and the soil moisture is regarded as the determining factor in vegetation conditions, considerable uncertainty of the vegetation response to climate change still remains (Goward & Prince, 1995). This uncertainty is mainly due to our current limited understanding of the forcing/feedback surface – atmosphere interactions, which usually have complex temporal lag effects (Braswell, Schimel, Linder, & Moore, 1997; Tian et al., 2000; Zhou et al., 2001). For example, warming temperature, combined with changes in precipitation, can affect vegetation growth through influencing soil moisture and nutrient availability
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273175
Fig. 2. A flowchart showing the method for calculating onset dates from the time-series NDVI. Let yt 1 be the pixel NDVI in period t 1, yt the NDVI in period t, and so on. Dyt = yt yt 1; ht = arctg(Dyt); {h} is the collection of hs where s is the period within the specific modality.
(Kindermann, Wurth, Kohlmaier, & Badeck, 1996; Tian, Melillo, Kicklighter, McGuire, & Helfrich, 1999; Tian et al., 2000). Potter, Klooster, and Brooks (1999) found that, in the arid and semi-arid mid-latitude areas of the northern hemisphere, vegetation net primary production can be affected by temperatures preceding the current period by up to 1 year. Di, Rundquist, and Han (1994) found a 1 – 2-month lagperiod effect of precipitation on NDVI values derived during the growing season. To evaluate the lag effects of temperature and rainfall within our study area, we used seasonal climate data from previous winters through the mean onset dates, which were then used to analyze the relationships between climatic variability and onset data for different land cover types. Our estimate of the mean onset of green-up was in early May for the forests, early June for typical steppe and late July for desert steppe in central IMAR, respectively. The time interval used for these analyses therefore ranged from the previous October through
May for the forest ecosystem, from the previous October through June for the typical steppe, and from the previous November through July for the desert steppe. We use the term ‘‘preseason’’ to refer to the period before the mean onset date. 3.4. Statistical analysis A set of linear multiple regression models was used to evaluate the effects of climatic variation on the onset dates of green-up for the three different land cover types in central IMAR. Due to the coarse spatial resolution of AVHRR GAC data, misregistration between years may exist to generate noises that complicate the interpretation of year-to-year differences in onset date. To minimize the noise, we derived the estimates of onset date by using the median value of a 3 3-pixel window. We also assumed that the spatial variation of monthly climate data was relatively consistent
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within an area of about 10-km radium of each meteorological station, corresponding to the 3 3-pixel window size area (Yang, Wylie, Tieszen, & Reed, 1998). All the dependent variables in the regression models were the median onset date and the independent variables were the preseason monthly climate data for each meteorological station from 1983 to 1990. The general relation between onset date and climate was described as: n X Onset ¼ a þ ðbi1 Ti þ bi2 Pi Þ þ e;
ð1Þ
Table 1 Multiple regression analysis between the standardized onset date of greenup and intra-annual climate data (monthly precipitation and mean temperature of the months proceeding the average date of onset of greenup) at desert steppe and typical steppe (only months with significant results are listed) Time of the year Typical steppe
precipitation temperature
i¼0
where onset is the timing of onset of green-up in Julian days; T is the monthly mean temperature; P is the monthly precipitation; a, bi1, and bi2 are coefficients that are estimated statistically; e is the unexplained error from this model; n is the number of periods before the onset event, varying for different land cover types (e.g., n = 8 (October – May) for the forest ecosystem, n = 9 (Oct– June) for the typical steppe, and n = 9 (November – July) for the desert steppe).1 This relationship between onset and climate data is based on the grassland phenological physiology that onset of green-up in the grassland was strongly influenced by temperature and precipitation (French & Saucer, 1974; Pitt & Wikeem, 1990). One problem associated with regression models is multicollinearity among independent variables, which typically inflates the estimation of the standard errors for regression coefficients and hence renders them statistically insignificant. Variance inflation factor (VIF) analysis is used to test for multicollinearity among independent variables (Wesolowsky, 1976). A close examination of the VIF values revealed the existence of high multicollinearity. Most of the multicollinearity among variable is attributable to the strong correlation among monthly temperatures and the correlation between monthly temperature and precipitation. Multicollinearity, however, does not affect the significance tests of the R2 for the assessment of the whole model. Besides these three models designated as ‘‘the entire model’’ in Table 1, we also performed a series of regression analyses of onset date against preseason monthly climate factors (two in each model). The equation for these single monthly models is described as:
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Desert steppe
R2 precipitation temperature R2
March
May
June
0.40 0.00* 0.05 0.65 0.22*
0.15 0.27 0.67 0.00* 0.42*
0.22 0.05* 0.09 0.41 0.14* 0.49 0.00* 0.17 0.26 0.32*
b Sig. b Sig. b Sig. b Sig.
The entire model for Eq. (1)
0.54*
0.87*
The entire model stands for the regression between the onset dates and all the preseason climate variables (as described in Eq. (1)). b = standardized regression coefficient and R 2 = determination of coefficient. * P < 0.05.
steppe and the desert steppe, respectively) to the mean onset month (June and July for the typical and desert steppes, respectively) for each land cover type. These models were designed to determine which month(s)’s temperature and precipitation can best predict the green-up date. Since the general model for the forest ecosystem shows insignificant results, we decided exclude the forest ecosystem from the single month analyses. Significant results were reported in Table 1 along with the R2 statistics of the general models described with Eq. (1). Temporal autocorrelation of the 8-year data set was tested using the Durbin –Watson test (Durbin & Watson, 1971). Low to moderate Durbin – Watson values indicate non-significant temporal autocorrelation for the selected climate data in the models (Durbin –Watson ranging from 1.54 to 2.08). Since the meteorological stations are sparsely distributed across the study area (Fig. 1), there is no reason to suspect any spatial autocorrelation among the climate variables.
4. Results Onset ¼ a þ bk1 Tk þ bk2 Pk þ e
ð2Þ
where k ranges from month from the end of previous growing season (October and November for the typical 1 For the forest ecosystem, there were three meteorological stations, each with eight annual onset events, providing a total of 24 observations. There were also eight pairs of monthly climate factors, providing a total of 16 independent variables. For the typical steppe, there were 11 meteorological stations and 9 pairs of monthly climatic factors, giving 88 observations of dependent variables and 18 independent variables. For the desert steppe, there were 5 meteorological stations and 9 pairs of monthly climatic factors, which provided 40 onset observations and 18 independent variables.
4.1. General onset pattern in eastern central Asia The distribution of average onset dates is shown in Fig. 3. The date of onset of green-up varies considerably across the study area. This variation is linked to ecosystem (i.e., forest, meadow steppe, typical steppe, and desert steppe) distribution that is strongly influenced by geographic features and their impacts on climate. The average date of onset of green-up between 1982 and 1991 corresponds well with the temperature and precipitation gradients described earlier. The mixed forest in the Da Xingan Mountains and taiga
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Fig. 3. Averaged onset date of green-up in the central Asia from 1982 to 1991.
forest at the northern edge of the Mongolian steppes have the earliest dates of onset, usually in late April or early May. Onset of green-up becomes progressively later with increasing distance from the eastern border moving toward the Gobi Desert. In general, the typical steppe experiences green-up in late May to early June. The date of onset of green-up for the desert steppe normally occurs in late June to early July and is the most variable among the grassland ecosystems in this study area (Yu et al., in press). This spatial distribution of mean timing of onset of green-up in IMAR generally agrees with the pattern of onset of the rainy season in this region. For the meadow steppe, the typical steppe, and the desert steppe, the summer rainy season usually arrives in May, June and early July, respectively (Shi et al., 1989; Xue, 1996). Onset events were undetectable in the Gobi Desert over the 10-year study period. 4.2. Interannual variation of onset in the Mongolian steppes The change rate in onset dates of green-up, averaged over the 10-year study, is shown in Fig. 4. The region of advanced green-up patterns (negative slopes for the fitting line) covers much of the eastern steppe of Mongolia, extending south from the forest zone to near the northern edge of the Gobi Desert (Fig. 4a). This is the region on the Mongolia steppe, with precipitation ranging from about 200 mm per annum in the south to more than 400 mm near the ecotone of the forest zone (Ellis et al., 2001). Although most of the region did not experience significant change in onset date of green-up from 1982 to 1991, a significant earlier onset date can be observed in the taiga zone north to the Mongolian steppes (Fig. 4b). This earlier onset is consistent with the observation reported by Myneni et al. (1997, 1998) for high latitudes (>45jN).
A contiguous band of areas that are experiencing a later date of onset can be observed along the northern and eastern edges of the Gobi Desert at 40 –50jN latitude (Fig. 4a). In IMAR, the later green-up areas are associated with the desert steppe, the transition between the Gobi Desert and the typical grassland, and part of the typical steppe (Fig. 1). Mean annual rainfall in most of this delayed green-up zone is 100– 200 mm per annum. Another area showing later onset date in IMAR appears to be associated with the forest/ shrub ecosystem in the southern Da Xingan Mountains. Since we have no meteorological data for this forested region, our analyses of the climate-onset interaction are focused on the IMAR grasslands bounding the Gobi Desert only. Fig. 5 displays the average onset dates of green-up for the pixels experiencing a significant change ( P < 0.10) from 1982 to 1991. Onset dates in the desert steppe were highly variable with a significant late trend during this period. In the taiga forest, progressive early onset can be observed from 1982 to 1991 except for 1983 and 1988. Both of these two profiles indicate late onset dates in 1983 and the relatively late onset dates may be associated with the strong El-Nino effect in the 1982 – 1983 period (Myneni et al., 1998). Two reasons attribute to the possibly overestimated onset trends, about + 35 days for the taiga and about 25 days for the desert steppe in IMAR. First, the Mongolian steppes were subjected to one of the strongest warming signals, especially in the winter and early spring, over the last two decades (Chase et al., 2000; Mongolian Action Program, 2001). Dramatic changes in the onset of plant growth are expected as consequences of the warming trend at the beginning of growing season (Ellis et al., 2002). Zhou et al. (2001) reported the mean growing season for vegeta-
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Fig. 4. (a) Mean change rate of onset date of green-up in central Asia (1982 – 1991). (b) Areas with significant changes in onset date during 1982 – 1991. Warm colors stand for the areas experiencing later onset pattern during this period and cold colors for the earlier onset pattern.
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Fig. 5. Average onset date of green-up for the pixels in the Taiga forest and the desert steppe in IMAR that experienced a significant onset changes (a < 0.1) from 1982 to 1991.
tion at the northwest Asia increased about 18 days over the past two decades. Secondly, although the 15-day GAC NDVI composites do have the advantage of using a longer time period to reduce cloud and aerosol contamination, effects of bidirectional reflectance, and data volume (Holben, 1986), the temporal resolution is scarified (White et al., 1997). Since our method for estimating onset date uses the mid-date of the 15-day NDVI composite (Holben, 1986), we estimate the maximum expected error of the onset date to be 7 –8 days for each year. This error, when averaged over the area undergoing significant onset changes, may cause a bias toward a higher onset trend values. 4.3. Relationships between onset and climate data in central Imar, China 4.3.1. Meadow steppe No significant relationship between onset dates and the preseason climate could be found in the meadow steppe. An examination of the onset dates showed that there was little year-to-year variation in this spring phenology (Yu et al., in press). Several reasons may account for this consistent onset. First, the meadow steppe is mainly distributed in higher latitudes with low interannual variability in annual precipitation. The interannual variability in precipitation, calculated as the coefficient of variance (standard deviation/mean) was 0.14, 0.18, and 0.21 for meteorological stations 1, 2, and 3 (Fig. 1), respectively, indicating a relatively stable or equilibrium environment in this region (Ellis, 1994). Secondly, the coarse temporal resolution of the NDVI composite data (15-day interval for compositing) may somehow mask the subtle response of vegetation to the late winter and spring warming effects in the temperature-limited ecosystems such as forest. Therefore, we discuss only the onset-climate relation for the typical and desert steppes where interannual variation of onset dates is highly variable.
4.3.2. Typical steppe Monthly model results show that precipitation in May and June is the most import factor determining the onset date of green-up (R2 = 0.14 and 0.22, respectively; Table 1). Accumulation of precipitation in May and June can explain about 30% of the changes in observed onset dates in the typical steppe (Fig. 6). The entire monthly preseason climate (precipitation and temperature data from November to March) can explain over 50% of the interannual variations in the onset dates in the typical desert. Spring precipitation is negatively correlated with onset, indicating an early trend of onset for increased spring precipitation, or late trend for decreased spring precipitation. Similar results were also reported for the warm grasslands of southern U.S. Great Plains where grasslands remains dormant until the arrival of raining season in late spring (Kemp, 1983). The timing of onset of green-up in the IMAR typical steppe is therefore largely controlled by the intensity and timing of spring precipitation. Since most of the meteorological stations in the typical steppe of eastern IMAR showed no significant increase or decrease in precipitation patterns in May and June from 1982 to 1990, this may partially explain the non-significant changes in the onset date of this grassland in eastern IMAR as shown in Fig. 4b. 4.3.3. Desert steppe Two significant monthly climate variables entered into the seasonal model: March temperature and May precipitation. March temperature and May precipitation explained about 42% and 32% of the variance in the observed onset dates, respectively (Table 1). The entire monthly preseason climate (precipitation and temperature data from November to June) can explain about 87% of the interannual variation in the onset dates in the typical desert. The relative high correlation between March temperature and the onset date suggests that warming in winter or early spring may lead to a late onset of growing season in this region. Regression between onset dates and the mean winter
Fig. 6. The relationship between the onset dates and the spring precipitation (in May and June) for meteorological stations in the typical steppe in central IMAR.
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Fig. 7. Mean winter temperature (November – March) for three meteorological stations in the desert steppe, which experienced significant late trend of onset from 1982 to 1990.
and early spring temperatures (averaged from previous November through March) was then used to examine whether changes in winter temperature had caused the variability in onset date. A significant positive relationship was also found between the onset dates and the mean temperature from November to March ( P < 0.10, n = 24). Fig. 7 shows the mean winter temperature (November – March) and May precipitation for three meteorological station sites in the desert steppe. The early onset of 1985 seems to be associated with the relatively wet May that year. The winter mean temperatures for all three stations exhibit a clear warming winter pattern beginning in 1985, while May precipitation was highly variable during this period (precipitation CV in May = 1.33, 1.00, and 0.84 for stations 15, 17, and 18, respectively). We therefore believe that it is warming in winter and early spring, especially in March that caused the later onset in desert steppe during the study period (Fig. 4b).
5. Discussion Our observation of vegetation green-up pattern at the relatively wet area of taiga forest and part of the meadow steppes confirmed the advance in the beginning of growing season at high latitudes (>50jN) over the study period between 1982 and 1991 (Myneni et al., 1997, 1998). The change in green-up pattern in these areas thus suggests that temperature-limited, but not water-limited ecosystems in early spring, could experience an advance in the green-up timing due to the warming temperature. The variable responses of the typical steppe to the climate change may be seen as the results of the characteristics of specific sites, or the varying seasonal water budgets over space and time occurring in this vegetation type. For example, our analysis of the changing onset patterns of the comparable vegetation communities along the southwest– northeast trending boundary of Mongolia and IMAR that extend across the political (Fig. 4a and b) border show some
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major inconsistencies in green-up onset trends within vegetation types. Typical steppe has a trend of advanced and/or slightly delayed green-up on the Mongolia side and a broad mix of delayed green-up site, and sites with no change on the IMAR side. These different responses of seasonal vegetation to the climatic variations appear to be associated with the different levels of grazing intensity and land use management across the international boundary of Mongolia and IMAR, where stocking rates are much higher on IMAR side of the border (Sneath, 1998). Recent research shows that current policies in northern China promote privatization and fragmentation of rangeland in IMAR (Sneath, 1998). Overgrazing, or as a combined result of overgrazing and climate stress, in association with the small scale and pattern of livestock grazing has led to land degradation in the typical and meadow steppes of IMAR (Christensen, 2001; Ellis et al., 2002). It appears that the degraded land surface may delay the onset dates of green-up by influencing local atmospheric conditions through several possible land –atmosphere feedback processes. If human land use does influence the course of climate-induced vegetation change, then this cross-boundary situation provides an extraordinary opportunity to begin to dissect the intertwined effects of changes in atmospheric conditions and changes in land use on steppe ecosystems. Our climate and onset date analysis in the desert steppe show that where the vegetation is water-limited, onset dates of green-up are delayed by the warming trend. The positive regression coefficients between the winter temperature and onset dates also supports the finding from the model simulations by Mabutt (1989) and Greco, Moss, Viner, and Jenne (1994) that, in the desert areas, increases in temperature of 0.5 – 2.0 jC raise evapotranspiration by 0.2 – 2.0 mm/day, unless accompanied by increased rainfall. Analysis of the meteorological records shows that over last 60 years spring rainfall in the Mongolian steppes has declined by 17%, mostly in May (Natsagdorj, 2000; Baatarbileg et al., 2001). We assembled the available longterm winter climate records (1961 – 1995 for winter temperature and 1961 – 1990 for May rainfall) from three meteorological stations in the desert steppe of IMAR (Fig. 8). These stations showed a strong warming trend in the winter (November through March) over the period of 1961 – 1995, which is consistent with the report of winter and early spring warming in the high latitudes (Chapman & Walsh, 1993). The mean annual May rainfall from 1961 – 1990 decreased slightly with substantial yearto-year variation. This long-term climate change further confirms the effect of spring drought on the later onset dates of green-up observed in the desert steppes in the Mongolian Plateau. The result of the onset-climate relation in the Mongolian steppes exhibits the complex effects of temperature on vegetation growth. Increases in spring temperature may stimulate earlier vegetation photosynthesis activity, while at the same time increase the water stress in arid and semi-
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Fig. 8. Summed winter (November through March) temperature at three meteorological stations in the desert steppe. The dashed lines are for annual values; solid lines are for the annual values smoothed with 5-year running and fitted with regression line.
arid ecosystems. In most regions of the Mongolia steppe, the vast majority (85 –90%) of annual precipitation comes in a relatively short summer rainy season, June, July, and August (Dagvadorg, Batima, & Natsagdorj, 2001). About 10% of the annual precipitation falls as winter snow. This winter snow may be very important in the seasonal water budget because snow appears to stay on the ground until relatively late winter, providing soil moisture through the spring drought (Ellis et al., 2001). As a result, warming in winter and early spring stimulates earlier onset dates of green-up in wet areas while delays the occurrence of green-up in dry areas. Our analysis of this study supports the hypothesis that water budget limitations in arid and semiarid region is determining the patterns of vegetation responses to atmospheric warming (Neilson, 1995).
6. Conclusions The findings of this study show a linkage between onset date of green-up predicted using AVHRR NDVI GAC 15day maximum composites and climatic factors. Our findings also show that the onset dates of green-up vary considerably within and across the steppes of the Inner Mongolian study area. Unlike the findings of Myneni et al. (1997) that show an earlier date of onset at the global scale (>50jN), our findings show that the response of vegetation to climate change varies among different land cover types in central Asia. While some of the taiga forest (including the meadow steppe) north to the Mongolian steppe (>50jN) experiences an earlier onset of green-up during the study period, a significantly later onset is observed in the desert steppes
banding the eastern and northern edges of the Gobi Desert (40 – 50jN). Results from most of the typical steppe region, which falls between the meadow steppe and the desert steppe, are varied. Some typical steppe sites show a weak trend of delayed onset of green-up, other sites show no change and a few have an earlier green-up. For much of the Gobi Desert, onset of green-up was not detectable by NDVI analysis in most years. For the typical steppe, spring precipitation, especially in May and June, is the most important factor influencing the onset date of green-up, while for the desert steppe, the timing of green-up is more influenced by temperature, especially the temperature in late winter and early spring. In water-limited regions, changes in temperature can significantly alter seasonal water budgets that influence vegetation phenological patterns and photosynthetic activity. Our analysis of the regional green-up patterns along the international border suggests that the differential impact of heavy grazing in IMAR vs. relatively light grazing in Mongolia may modify the effects of atmospheric warming. We believe that the significantly delayed onset in the desert steppe is mainly caused by warming-induced drought stress over the study period. Future studies will concentrate on extending our remote sensing and meteorological data sets to the present time period to determine whether patterns observed during this study continue to persist.
Acknowledgements This study was supported by the United States National Science Foundation, Models and Methods for Integrated
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Assessment Program. We wish to express the appreciation to Dr. Li Zhenghai, Mr. Pai Hao, Dr. Wang Yanrong, and Mr. Zhou Wuguan for their assistance with this project. We are grateful to Dr. Hanqin Tian for the comments on the manuscript, Dr. Compton J. Tucker for providing the AVHRR GAC data, and Mr. Chenyang Xiao for help with the statistical analysis.
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