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.
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Forestry, Vol. 79, No. 1, 2006. doi:10.1093/forestry/cpi062
Advance Access publication date 1 December 2005
136
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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.
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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 (<0.25°)
and we do not have the exact latitudes of the
sample plots, we ignored the influence of latitude
on temperature. From equation (1), a value of
−0.52°C per 100 m elevation rise from the local
meteorological station was used to calculate
the temperature at the sample plots. The estimated annual average air temperatures at the
712 sample plots ranged from −0.1 to 14.4°C.
Preparation of precipitation data from
sample plots
Statistical analysis of precipitation data from the
77 meteorological stations did not suggest a good
relationship between location, elevation and precipitation. However, Guo (1981) reported that
precipitation increased with elevation in the
Taihang Mountains. We used the equation of
Guo (1994), a similar equation to the study of Shi
P = 519.23 + 151.62E − 43.26E2
(2)
where P is precipitation in millimetres, E is elevation in metres divided by 1000 and 519.23 mm is
the precipitation at the foothill of the Taihang
Mountains.
The equation neglected the variation of precipitation in space; for instance, precipitation is
high in the south and north of the Taihang
Mountains but low in the middle. To improve
the accuracy of estimation, we did not directly
use equation (2). Instead, we calculated separately the precipitation at the sample plot and its
corresponding meteorological station from their
elevations using equation (2) and calculated
the precipitation change resulting from the difference in elevation between the two. Then,
precipitation at the meteorological station and
precipitation change caused by elevation change
from the meteorological station to the sample
plot were used to calculate precipitation at each
sample plot.
Data analysis
All analyses including principal components
analyses, analysis of variance (ANOVA), regression and correlation were done with the software
package SPSS 10.0. Principal component analysis
(PCA) is probably the most widely used technique
in factor analysis and factor reduction (Harman,
1970). We used it to select major factors from
temperature and precipitation, site aspect and
soil thickness, forest cover and age. Spearman’s
method was used in regression and correlation
FACTORS AFFECTING FOREST GROWTH
139
Table 2: Total variance explained by different components
Initial eigenvalues
Component
Total
% of Variance
Cumulative %
Extracted component
1
2
3
4
5
6
1.839
1.197
1.065
0.819
0.707
0.373
30.647
19.944
17.751
13.652
11.782
6.224
30.647
50.591
68.342
81.994
93.776
100
*
*
*
*Showed the components extracted as principal components.
analysis. ANOVA was used to find out the significant differences between different groups, for
instance when temperature and soil depth were
artificially divided into different categories. For
multifactorial regression analysis, stepwise regression was used.
Results
Determining major factors influencing forest
growth
Principal factor analysis showed that the first
three principal components explain about
68.3 per cent of variance (Table 2). The contributions of each factor to the three principal components are listed in Table 3. The most important
component, which contributes about 30.6 per
cent of the total variance, is influenced mainly by
precipitation on the positive side and temperature on the negative side. The negative relationship between temperature and precipitation in
Taihang Mountains confirms this. Forest cover
could be a third factor selected in the first component. The second component is controlled
mainly by soil thickness and aspect. The third
component reveals mainly the effect of forest
age. Thus, the principal factor analysis suggests
that temperature, precipitation, aspect, soil thickness, forest age and forest cover should be
selected.
To further test the importance of the different
factors in influencing forest growth, we separately analysed the correlations between FGS,
Table 3: Eigenvectors of different principal
components corresponding to different factors
Component
Factors
Aspects
Soil thickness
Forest age
Forest cover
Precipitation
Temperature
1
2
3
0.353
0.176
−0.093
0.599
0.811
−0.811
0.654
0.658
0.465
0.088
−0.310
0.129
−0.017
0.365
−0.772
0.446
−0.154
0.335
which is the most valuable indicator of forest
growth, and each site factor. The correlation
coefficients are listed in Table 4. The results are
in agreement with the results of PCA. The major
factors influencing forest growth are temperature, soil thickness, precipitation and aspect.
Although forest cover and age show a lower contribution in PCA, their effect on FGS is high
because both factors influence FGS directly.
Influence of temperature on FGS
To understand the influence of temperature on
forest growth, the sample plots were divided into
five groups according to annual average temperature: −0.1 to 2.9°C (n = 80), 3.0–5.9°C (n = 50),
6.0–8.9°C (n = 266), 9.0–11.9°C (n = 277) and
12–14.4°C (n = 39). Differences in FGS, d.b.h.
and tree height among the five temperature groups
were analysed by one-way ANOVA. The differences were statistically significant (P ⭐ 0.01;
Figure 2). Pairwise multiple comparison of the
140
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Table 4: Correlation between FGS and some major
factors influencing forest growth
FGS and temperature
FGS and aspect
FGS and soil thickness
FGS and forest age
FGS and forest cover
FGS and precipitation
Correlation
coefficient
Significance
−0.356
0.210
0.289
0.273
0.369
0.279
P < 0.01
P < 0.01
P < 0.01
P < 0.01
P < 0.01
P < 0.01
90
Forest growing stock
(m3 ha-1)
Correlating
factors
100
80
70
60
50
40
30
20
10
0
-0.12.9
3.05.9
6.08.9
9.011.9
12.014.4
-0.12.9
3.05.9
6.08.9
9.011.9
12.014.4
-0.12.9
3.05.9
6.08.9
14
12
10
dbh (cm)
five temperature groups showed that the FGS
value in the two coldest groups were significantly
higher than those in the three warmest groups
(P ⭐ 0.05). Values of FGS were not significantly
different within the two sets. The effects of temperature on d.b.h. were nearly the same except
in the warmest group, in which d.b.h. was significantly lower than in any other group. Tree
height did not exactly decrease with increasing temperature, although pairwise multiple comparison
showed significant differences in most paired
groups, except between 3.0–5.9°C and 11.9–
15.0°C. These results suggest that temperature is
a major factor influencing forest growth. Generally, low temperature is more favourable for
forest growth in the Taihang Mountains. FGS
growth at sites above 6°C almost doubled the
growth of FGS at sites below 6°C. Above 6°C,
FGS dropped at a rate of about 2.5 per cent.
8
6
4
2
0
10
9
Influence of precipitation on FGS
Tree height (m)
Precipitation is one of the most important factors directly influencing FGS, d.b.h. and tree
height in semi-arid and semi-humid regions. To
understand how strong the effect can be, the
sample plots were divided into four precipitation groups according to average annual precipitation: 478–549.9 (n = 89), 550.0–649.9 (n =
275), 650.0–749.9 (n = 265) and >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).
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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
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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 <1000 m and
the elevation difference was <200 m, the average
annual total precipitation varied between 507
and 605 mm. Microtopography also influences
precipitation. Su (1996) noted more rain events
in the Taihang Mountains in the evening and
more in the valley during the night.
Different equations and methods have been
developed for the spatial estimation of precipitation in the Taihang Mountains. Those equations
mostly consider the variation of precipitation
caused by elevation change. The spatial change
was often ignored. Considering the large spatial
changes in precipitation in the Taihang Mountains, estimating precipitation only from elevation is inexact. So we tried to calculate the
precipitation in the sample plots from the precipitation at the nearest meteorological station
and differences resulting from elevation changes.
The results showed a significant effect of precipitation on FGS. However, the accuracy of
precipitation estimation may still need further
improvement.
146
FORESTRY
Influence of aspect change on forest growth
through drought
Aspect is always an important factor in forest
site classification and site index estimation.
Changes of aspect have different effects on forest
growth owing to its influence on solar radiation,
air temperature, wind speed, and so on. Worrell
and Malcolm (1990b) studied factors influencing
forest growth in northern Britain and found that
south-west-facing slopes have the lowest productivity, owing mainly to the strong winds. In the
Taihang Mountains, drought is the most serious
problem for south-facing slopes. In earlier forest
classifications (Lu et al., 1991; Yang et al., 1993;
Liu et al. 1996; Li et al., 2002), south-facing
aspects were recognized as unfavourable for
forest growth, suggesting a more serious limitation by drought on south-facing slopes.
Effect of climate change on FGS
By 2100, further increases of greenhouse gases in
the atmosphere are expected to increase the global
average surface temperature by about 1.4–5.8°C
(Houghton et al., 2001), Along with the increase
of temperature, the possible average increase in
precipitation will be about 3.4 per cent globally
per 1°C (Allen and Ingram, 2002).
Our study suggested the negative effect of temperature and the positive effect of precipitation
on FGS, d.b.h. and tree height. The results give
some important indication of changes in forest
growth and NPP as well, since favourable conditions for the growth of FGS should also be favourable for vegetation growth. According to our
study results, the effects of temperature decrease
and precipitation increase on FGS could cause an
increase of FGS by 2.5–8.0 per cent per degree
centigrade and 10 per cent per 100 mm, respectively. Thus, the combination of increasing temperature by 3°C and precipitation by 10 per cent
(around 60 mm), a likely scenario of climate
change, may decrease FGS, though this does not
take account of the effect of increasing CO2.
Acknowledgements
We would like to acknowledge the support of the Key
Innovation Project (KZCX3-SW-446) and ‘One Hundred Talent Program’ from the Chinese Academy of
Sciences and the Asia Pacific Environmental Innovation
Strategy Project (APEIS) from the Ministry of the Environment, Japan.
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Received 18 May 2004