Factors affecting forest growth and possible effects of climate change in the Taihang Mountains, northern China YONGHUI YANG1*, MASATAKA WATANABE2, FADONG LI1, JIQUN ZHANG3, WANJUN ZHANG1 and JIANWEN ZHAI4 1 Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, No. 286, Huaizhong Road, Shijiazhuang 050021, China 2 National Institute for Environmental Studies, Onogawa, 16-2, Tsukuba, Ibaraki 305-8506, Japan 3 Water Resources Management Center, Ministry of Water Resources, 100053, Beijing, China 4 Forestry Department of Hebei Province, Shijiazhuang, Hebei 050081, China * Corresponding author. E-mail:
[email protected]
Summary To estimate the possible effects of site factors and climate change on forest growth in the Taihang Mountains, northern China, we assessed the factors influencing forest growth by using forest inventory data from 712 forest sample plots. Meteorological data from 77 meteorological stations in the region were used to estimate temperature and precipitation at each site from elevation and longitude. Analyses showed that temperature, aspect, precipitation and soil thickness all significantly influenced forest growing stock (FGS), i.e. stem volume. When temperature rose, FGS was reduced, possibly because increasing temperature increased evapotranspiration. Precipitation had a positive effect on FGS. The effect of aspect on FGS was perfectly expressed as a cosine function, with southwest- and south-facing slopes having the lowest FGS and north-facing slopes having the highest. We developed multifactorial regression models to predict changes in FGS in the Taihang Mountains. Temperature, forest age, forest cover, soil thickness, precipitation and aspect were well related to FGS. The effects of a temperature decrease and a precipitation increase on FGS would be 2.5–8 per cent per degree centigrade and 10 per cent per 100 mm, respectively. The combination of temperature increase and precipitation changes under future climate change is likely to result in a decrease of FGS, though this does not take account the effect of increasing CO2. We also used multifactorial regression models to analyse the effects of site factors on FGS of Pinus tabulaeformis Carr. and Robinia pseudoacacia L., two major species used in afforestation in the Taihang Mountains. Although site factors had similar effects on FGS, diameter at breast height and tree height of both species, prediction accuracy (regression coefficient) was improved greatly when we treated the species separately.
© Institute of Chartered Foresters, 2006. All rights reserved. For Permissions, please email:
[email protected]
Forestry, Vol. 79, No. 1, 2006. doi:10.1093/forestry/cpi062 Advance Access publication date 1 December 2005
136
FORESTRY
Introduction Understanding the effects of site conditions and climatic factors on forest growth is important for the development of forest cover and forest management (Worrell and Malcolm, 1990a, b). Given the rapid rise of temperature (Houghton et al., 2001) and the possible average increase in precipitation of about 3.4 per cent globally per 1°C temperature rise that we face (Allen and Ingram, 2002), it is critical that we take climatic factors into consideration in the assessment of forest growth and prediction (Bonan et al., 1990; Tyler et al., 1995; Raich et al., 1997), especially in areas where climatic change is likely to reduce forest growth and cover. The Taihang Mountains run from south-west to north-east in northern China (34° 35′–40° 19′ N, 110° 15′–116° 27′ E; Figure 1), and cover a region of 108 000 km2 (Yang et al., 1993),
including part of Beijing City and Shanxi, Hebei and Henan Provinces. To the east of the Taihang Mountains, lie Beijing and one of China’s largest agricultural production areas, the North China Plain (NCP). Improvement of the environmental conditions in the Taihang Mountains will be beneficial both to the environment of Beijing and to the water resources of the NCP downstream. Since 1950, intensive afforestation has been carried out in the region. Recently, the Taihang Mountains were selected as one of the five major afforestation regions by the Chinese central government. According to the scheme of the China State Department (1997), which runs to the year 2050, 35 600 km2 of forest is to be planted in the region, increasing the regional forest cover by >30 per cent. Low and unevenly distributed annual precipitation varying from 400 to 650 mm is the major factor limiting successful afforestation (Yang
Figure 1. Location of the Taihang Mountains in China and distribution of sample sites.
FACTORS AFFECTING FOREST GROWTH
et al., 1993). With nearly 70 per cent of the annual precipitation taking place from July to the end of September, drought in late spring and early summer always limits the survival of young forest and plant growth (Su, 1996; Zhang et al., 1996). Even with the huge efforts at both governmental and local levels, forest cover in 1990 was only 15.3 per cent (Yang et al., 1993). In such a region, an increase of temperature under climate change might increase evapotranspiration and reduce soil moisture, further limiting plant growth (Yang et al., 2003) and decreasing forest productivity. In preparation for the afforestation of the Taihang Mountains, site classification was carried out systematically in the late 1980s to early 1990s. Forest growth, elevation, soil types, slope aspect, soil thickness and soil organic matter content were investigated and analysed (Yang et al., 1993). The effects of temperature and precipitation on forest growth were not assessed, possibly because of difficulties in estimating climatic factors in forest sample plots. However, the importance of climatic factors in influencing forest growth was indirectly expressed by elevation and aspect. Traditionally, elevation is an important factor in forest site classification in both China and other countries (Mayhead, 1973; Zhan, 1989; Gu et al., 1993), as it affects temperature. Worrell and Malcolm (1990a) studied the influence of temperature on Sitka Spruce (Picea sitchensis (Bong.) Carr.) in northern Britain by focusing on elevation, and found a strong correlation between temperature and forest productivity. In the Taihang Mountains, the large variation in annual average temperature, which ranges from −4.1°C at its highest summit to 14.9°C at its southern end, gives us the possibility of analysing the effects of climatic factors on forest productivity under future afforestation and of understanding the effects of future climate change on forest growth in the Taihang Mountains.
Material and data preparation In preparation for the major afforestation scheme, and as part of the national forest inventory, many forest plots were sampled from 1983 to 1990. In this study we use only data from
137
planted forests. The distribution of sample plots is shown in Figure 1. Because Global Positioning System data were not available at the end of the 1980s, the plot location is accurate only to county level. Since the purpose of the forest survey was to find out factors influencing forest growth, sample plots were selected from planted pure forest stands of different species and different age without former pest and disease damage and distributed in as many site conditions as possible. The number of sample plots in each county can be found in Figure 1. Our study encompassed 712 sample plots, of which 333 are Pinus tabulaeformis Carr. stands, 114 are Robinia pseudoacacia L. and the others are covered by mainly Platycladus orientalis (L.) Franco, Larix principis Mayr., Pinus bungeana Zucc., Quercus variabilis Blume and also other species in minor amounts. The size of each sample plot was 20 m × 20 m. In each plot, surveyors recorded elevation, aspect, microtopography, soil thickness, other soil physical and chemical information, tree species, diameter at breast height (d.b.h.), tree height and location. Soil thickness was defined as depth to parent materials or to weathered rock. Forest growing stock (FGS) was calculated from d.b.h. and tree height in accordance with the national standard equations for different tree species published by the Ministry of Forestry, as FGS is an important indicator of forest growth rate and productivity. Basic information on the 712 sample plots is shown in Table 1. Some factors, such as soil types and texture, were difficult to digitize numerically and were not used in the statistical analysis. To facilitate the statistical analysis, aspect was expressed as a cosine function while temperature and precipitation at each plot were estimated. Preparation of aspect data Aspect was formerly classified as south-facing, (i.e. south-east, south and south-west), northern (i.e. north to north-east) and semi-shadowed (east, west and north-west) (Yang et al., 1993). However, Worrell and Malcolm (1990a) used angle and Han et al. (1998) used a trigonometric function for analysing the effect of aspect. Here we used a cos(θ − 45) function to represent aspect in our analyses.
138
FORESTRY
Table 1: Mean and range of forest growth characteristics and site factors influencing forest growth in 712 forest sample plots Variable Forest growing stock (m3 ha−1) d.b.h. (cm) Tree height (m) Forest age (year) Forest cover (%) Elevation (m) Soil depth (cm)
Mean
Minimum value
Maximum value
50.06 9.4 6.8 22.8 66 1065 50.3
1.67 4.0 2.3 8 40 60 10
218.3 18.5 16.2 45 100 2330 142
Preparation of temperature data from sample plots
(1981) and Su (1996) for the Taihang Mountains, to estimate precipitation:
Since the sample plots did not have climatic data, we gathered 30-year average meteorological data from 77 meteorological stations in the Taihang Mountains and used them to estimate temperature at each sample plot from the following equation: T = 15.4 − 0.628 × (L − 34.7) − 0.522 × (E/100); r = 0.95
(1)
where T is annual average air temperature, L is latitude in degrees (34.7° N is the lowest latitude in the Taihang Mountains) and E is elevation in metres above sea level. Temperature was strongly correlated with latitude and elevation. Since latitude differences between sample plots and their nearest meteorological stations are low (750.0 mm (n = 83). Differences in FGS, d.b.h. and tree height among the four precipitation groups were analysed by one-way ANOVA. The differences were statistically significant (P ⭐ 0.01; Figure 3). Pairwise multiple comparison of the four precipitation groups showed that, aside from the pair of the two low precipitation groups, FGS was significantly different in other groups (P ⭐ 0.05).
8 7 6 5 4 3 2 1 0 9.011.9
12.014.4
Temperature (ºC)
Figure 2. Temperature-related changes in FGS (± SE), d.b.h. (d.b.h. ± SE) and tree height (H ± SE) in sample plots.
80
70
70
60
60
Forest growing stock (m3 ha-1)
Forest growing stock (m3 ha-1)
FACTORS AFFECTING FOREST GROWTH
50 40 30 20 10
50 40 30 20 10
0 478549.9
550649.9
650749.9
0
750-
NE
E
SE
S
SW
W
NW
NE
E
SE
S
SW
W
NW
NE
E
SE
N
12
12 10
10
8
8
dbh (cm)
dbh (cm)
141
6
6
4
4
2
2
0 478549.9
550649.9
650749.9
0
750-
N
8
8 7
7
6
Tree height (m)
Tree height (m)
6 5 4 3 2
5 4 3 2
1 1
0 478549.9
550649.9
650749.9
750-
Precipitation (mm)
Figure 3. Precipitation related changes in FGS (± SE), d.b.h. (d.b.h. ± SE) and tree height (H ± SE) in sample plots.
0 S
SW
W
NW
N
Aspects
Figure 4. The effect of aspect on FGS (± SE), d.b.h. (± SE) and tree height (H ± SE).
FORESTRY
Effect of aspect on FGS By influencing solar radiation and temperature, two factors related to evapotranspiration and photosynthesis, aspect is important in forest growth. One-way ANOVA showed a significant effect of aspect on FGS, d.b.h. and tree height (P ⭐ 0.01; Figure 4). Pairwise multiple comparison showed that the south-facing slopes had a significantly lower FGS than the other slope directions, and north-facing slopes had a significantly higher FGS than the others. Tree and d.b.h. height followed a similar trend (P ⭐ 0.01). In the northfacing slopes d.b.h. was significantly higher than in the south-facing slopes. In the east-facing slopes d.b.h. was also significantly lower than in northand north-east-facing slopes. Tree height in the north-facing slopes was significantly higher than in the other slopes. These results suggest that south-facing slopes are not good for the growth of forests in the Taihang Mountains.
90 80
Forest growing stock (m3 ha-1)
The effects of precipitation on d.b.h. were nearly the same except for the pair of 550.0–649.9 and 650.0–749.9 groups. Similar to the effect of temperature on tree height, precipitation did not affect tree height significantly.
70 60 50 40 30 20 10 0 0-39
40-59
60-79
80-
0-39
40-59
60-79
80-
12 10 8
dbh (cm)
142
6 4 2
Influence of soil depth on FGS 0
9 8 7
Tree height (m)
The average soil depth of all forest sample plots was 50.3 cm (range 10–142 cm) (Table 1). Poor soil depth is an important factor limiting forest growth (Zhang et al., 1996). We divided soil depth into four groups: 10–39 (n = 198), 40–59 (n = 196), 60–79 (n = 288) and >80 cm (n = 42) to analyse the effect of soil depth on FGS, d.b.h. and tree height by one-way ANOVA. Results suggested a significant effect of soil depth on all groups (P ⭐ 0.01). Figure 5 shows that deeper soil is positive for forest growth. Pairwise multiple comparison showed that FGS was significantly different in all groups (P ⭐ 0.05). Tree and d.b.h. height were also significantly different, except d.b.h. between 40–59 and 60–79 cm, and tree height between 60–80 cm and >80 cm.
6 5 4 3 2 1 0 0-39
Estimating forest growth by multifactorial analysis Stepwise regression analysis was used to find out the contributions of the different factors to the
40-59
60-79
80-
Soil depth (cm)
Figure 5. Effects of soil depth on FGS, d.b.h. and tree height in the Taihang Mountains. Error bars show SE.
FACTORS AFFECTING FOREST GROWTH
143
Table 5: Models for predicting forest growing stock, diameter of breast height, tree height from site factors Equation no. 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Model
Ra
V = 82.66 − 3.98T V = 53.86 − 3.86T + 1.22A V = −12.21 − 3.18T + 1.42A + 84.29Fc V = −23.87 − 3.21T + 1.37A + 75.57Fc + 0.38Sd V = −11.91 − 8.29T + 0.36T 2 + 1.46A + 74.77Fc + 0.38Sd V = −43.48 − 9.56T + 0.51T 2 + 1.56A + 68.95Fc + 0.39Sd + 0.05P V = −42.47 − 9.25T + 0.49T 2 + 1.52A + 66.33Fc + 0.37Sd + 0.05P − 3.12 cos(θ − 45) d.b.h. = 6.31 + 0.14A d.b.h. = 8.28 + 0.13A − 0.23T d.b.h. = 6.87 + 0.13A − 0.23T + 0.03Sd d.b.h. = 2.81 + 0.14A − 0.14T + 0.03Sd + 0.005P H = 6.64 + 0.72 cos(θ − 45) H = 5.51 + 0.02Sd + 0.64 cos(θ − 45) H = 6.44 + 0.02Sd − 0.11T + 0.57 cos(θ − 45) H = 8.17 + 0.02Sd − 0.76T + 0.05T2 + 0.47 cos(θ − 45) H = 9.22 + 0.03Sd − 0.78T + 0.05T2 − 1.48Fc + 0.46 cos(θ − 45)
0.40 0.49 0.58 0.61 0.63 0.63 0.64 0.34 0.43 0.47 0.49 0.24 0.31 0.35 0.44 0.45
Ra is the adjusted regression coefficient. All models are significant (P < 0.01), and all independent variables and constants are significant (P < 0.01). V is forest growing stock (in m3 ha-1), d.b.h. is diameter of breast height (in cm), tree height H (in m), site temperature T (in °C), soil depth (Sd in cm), aspect (θ), forest age A (in years), precipitation P (in mm) and forest cover Fc (in %). n = 712 for all models.
growth of FGS in the Taihang Mountains. The significant regression equations (P ⭐ 0.01) are listed in Table 5. Table 5 suggests that the contribution of factors to FGS can be listed as: temperature > forest age > forest cover > soil thickness > precipitation > aspect. Similar to the results of ANOVA, the effect of temperature on forest growth is negative while that of precipitation is positive. Given an average FGS for 712 sample plots of 50.05 m3 ha−1, equations suggest that the effects of a 1°C temperature decrease and 100 mm precipitation increase on FGS could increase FGS by 3.21–3.98 and 5 m3 ha−1, accounting for about 6.4–8.0 per cent and 10 per cent, respectively. Naturally, regression analysis shows that forest age is the most important factor influencing d.b.h. growth, while aspect and soil thickness stimulates tree height growth the most. While the average tree height for the 712 sample plots was 6.8 m, the difference of tree height on north-facing slopes could be >0.9 m higher than the southfacing slope. Temperature plays the second important role in influencing d.b.h. and the third important role in influencing tree height. The negative effect
of 1°C temperature increase on d.b.h. and tree height would be about 2.5 and 1.6 per cent. Equation (13) suggests the effect of precipitation on d.b.h., when average annual precipitation increases by 100 m and average d.b.h. can become 0.5 cm higher, accounting for ~5.3 per cent. Estimating forest growth of different species Even the best regression equation in Table 5, an adjusted regression coefficient of 0.64 (R2 = 0.41), suggests a large variance in forest growth in the Taihang Mountains. This could be caused by the large sample number with large variation in tree species, soil type, topography, and so on. To improve the accuracy of prediction, we tried to analyse the effect of factors on forest growth of different species. The best equations selected from stepwise regression analysis are listed in Table 6. Because of the limited sample numbers, only P. tabulaeformis and R. pseudoacacia were analysed. Table 6 shows greatly improved regression coefficients for FGS: from r = 0.64 for all species to 0.69 for R. pseudoacacia and 0.72 for P. tabulaeformis. The improvement in d.b.h. estimation
144
FORESTRY
Table 6: Best equations for estimating FGS, d.b.h. and tree height for two main species, Pinus tabulaeformis (n = 333) and Robinia pseudoacacia (n = 114) Species P. tabulaeformis P. tabulaeformis P. tabulaeformis R. pseudoacacia R. pseudoacacia R. pseudoacacia
Model
Ra
V = −119.06 − 12.72T + 0.81T 2 + 2.93A + 71.04Fc + 0.21Sd + 0.12P + 8.50 cos(θ − 45) d.b.h. = 4.19 + 0.22A − 0.77T + 0.03Sd − 4.10Fc + 0.05T 2 − 0.006P H = 3.43 + 0.004P + 0.02Sd − 1.07T + 0.07T 2 + 0.11A + 0.70 cos(θ − 45) V = −243.28 + 43.03T − 1.98T 2 + 1.50A + 0.56Sd d.b.h. = −24.05 + 0.13A + 5.60T + 0.05Sd − 3.79Fc − 0.25T 2 H = 8.21 − 3.17Fc − 0.02Sd − 0.57 cos(θ − 45)
0.72 0.58 0.68 0.69 0.72 0.43
Ra is the adjusted regression coefficient.
was even higher: from r = 0.49 for all species to r = 0.58 for P. tabulaeformis and r = 0.72 for R. pseudoacacia. Estimation of tree height was also improved for P. tabulaeformis. To show the validation of the regression analysis, we plotted out the original value of FGS in comparison with the predicted value for all species (using equation (9) in Table 5), P. tabulaeformis and R. pseudoacacia (equations for two species in Table 6) in Figure 6. The sample data and predicted data correspond nicely.
Discussion This is the first study of the effects of temperature and precipitation in combination with traditional forest site factors on forest growth in the Taihang Mountains. In an area where intensive afforestation is planned to improve the local environment, this study might provide useful information on possible changes to forests under future climate change since both temperature and precipitation are major factors influencing forest growth. It is, however, necessary to confirm the result and clarify the reason for the negative effect of temperature on forest growth. Effect of temperature on FGS Results both from correlation analyses and from multifactorial regressions show the negative effect of temperature on forest growth, indicating a positive effect of elevation on forest growth. This is different from the study of Luo et al. (2004), whose study showed that China’s net primary productivity (NPP) generally decreased with increasing elevation. However, historical forest site classifications
in the Taihang Mountains gave similar indications to our study. The latest forest site classification in the Taihang Mountains for the preparation of the afforestation scheme (Yang et al., 1993) divided the whole region into four subregions based on location and elevation. The subregion with elevation above 1500 m had the highest forest growth, similar to our analysis. The mountains in Shanxi Province with elevation generally higher than 800 m were treated as another subregion. The two subregions with low elevation, Hebei province in the north and Henan Province in the south, show the difference in latitude and changes in temperature resulting from latitude as suggested by equation (1). Li et al. (2002) classified sites in the Taihang Mountains in Shanxi Province only. They treated elevation change as the top factor influencing forest growth and afforestation. Previously, in the same area, Liu et al. (1991) classified sites and treated differences in both latitude and elevation as the top factors influencing growth of P. tabulaeformis. In those classifications, the negative effect of temperature on forest growth was indirectly indicated by the positive relationship between forest growth and elevation increase. The negative effect was possibly caused by drier conditions under a warmer climate. By model simulation, Bonan et al. (1990) showed that the effect of climate change on forest is not only a simple response to temperature rise but also a response to increased evapotranspiration demands accompanying global warming. The life-zone classification map of Holdridge (1967) shows the negative influence of temperature rise on ecosystem change when precipitation is around 500 mm, which is similar to the precipitation range of the Taihang Mountains. Tang
FACTORS AFFECTING FOREST GROWTH 140
(a)
120 100 80 60 40 20 0 0
Predicted forest growing stock (m3 ha−1)
140
50
100
150
200
250
(b)
120 100
145
decrease of forest cover in China. Similarly, many studies showed that NPP of ecosystems is related to evapotranspiration (Woodward, 1988; Chong et al., 1993). In the Taihang Mountains, through the use of the Water Vegetation Energy and Solute modelling model, Yang et al. (2003) predicted the possible decrease of plant productivity under warmer conditions owing to increased limitation of soil moisture. All this evidence suggests that the negative effect of temperature rise on forest growth is caused by the drier situation under warmer conditions in the Taihang Mountains. Effect of precipitation on forest growth
80 60 40 20 0 0 140
50
100
150
200
250
(c)
120 100 80 60 40 20 0 0
50
100
150
200
250
Measured forest growing stock (m3 ha−1) Figure 6. Comparison of measured forest growing stock with predicted forest growing stock (FGS) of all species (a) Pinus tabulaeformis (b) and Robina pseudoacacia (c).
et al. (1998) studied the effect of temperature on vegetation types in north-east China and showed the possibility of decreases in wet forest by 38.1 per cent and of moist forest by 13.6 per cent, and in an increase of desert shrubs by 26.3 per cent, suggesting a drier condition under higher temperature. Zhou and Zhang (1996) studied the climate–vegetation relationship and concluded that temperature rise will possibly result in a
Both principal factor analysis and the correlation between FGS and precipitation suggested positive effect of precipitation on forest growth. This is natural since drought is the most important factor limiting forest growth in the Taihang Mountains (Yun, 1989). However, precipitation has large spatial variation. Even in a very small area, precipitation can differ with aspect (Johansson and Chen, 2003). Liu and Cai (1996) analysed data from five meteorological stations in a small valley in the Taihang Mountains in 1987–1989. Although the distance among five stations was
