**The Classification Indices-Based Model for NPP According to the Integrated Orderly Classification System of Grassland and Its Application**

Huilong Lin

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57297

## **1. Introduction**

Grassland accounts for 25% of the world's land area and has global significance for climatecarbon feedback [1]. As one of the most widespread ecosystem types, grassland makes a large contribution to carbon sequestration and plays a significant role in regional climates and the global carbon cycle [2]. Grassland net primary productivity (NPP) refers to carbon accumula‐ tion (photosynthesis less respiration) in grassland per unit of time, and can be used as an indicator of the capacity of the grassland ecosystem to accumulate carbon and to support grazing animals. Studies of the NPP have contributed significantly to our understanding of the pattern, process and dynamics of grassland ecosystems. Hence, the accurate estimation of the NPP of grasslands is very important not only for scientifically guiding grassland manage‐ ment but also for understanding the global carbon cycle. Reliable long-term data on grassland NPP are urgently needed to estimate carbon fluxes. Due to their time- and labor-intensive nature, ground measurements of grassland NPP are very limited in temporal and spatial coverage. The precision evaluation of NPP estimated in regional or global scales is always very difficult. Direct measurement of grassland NPP is tedious and not practical for large areas, so it is therefore appropriate to use computer models, calibrated with existing data, to study the spatial and temporal variations of grassland NPP [3].

A number of different methods of modelling the dynamics of grassland NPP have been reported. In climate-vegetation models, empirical relationships are developed, allowing NPP to be estimated as a function of climate variables such as temperature, precipitation, and evaporation [4-12]. Process models for estimating NPP simulate a series of plant physiological processes and their response to changes in environmental factors, including photosynthesis,

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autotrophic respiration and transpiration [13, 14]. In spite of this, the body of knowledge of basic biological mechanisms to simulate grassland NPP is still limited. Furthermore, process models cannot be used in developing countries because long-term data required for the parameters of the models is not available. Algorithms for estimating NPP by remote sensing may be poorly parameterized if the 'calibration' estimates of NPP are based upon an inappro‐ priate method. In this respect, climate-vegetation models have been drawing much attention and have been widely applied internationally, owing to their simplicity [4]. These models have been shown to yield 'reasonable estimates' of regional or global NPP distribution [4, 9, 14-16].

Vegetative types and their distribution pattern can be related to certain climatic types in a series of mathematical forms. Thus, the climate can be used to predict vegetative types and their distribution, or the reverse [17]. A growing number of research efforts have demonstrated the importance of climate-vegetation interaction in understanding climate sensitivity and climate change [18]. However, current climate-vegetation classification models mostly simulate the equilibrium state of vegetation types, and do not include NPP [19]. Furthermore, at present there is no vegetation-climate model based on a classification system including the NPP function. Therefore, development of climate-vegetation classification models coupled with NPP is urgently needed in order to evaluate the possible impacts of global change on terrestrial vegetation types [19]. So the focus of future research should be to master the link between NPP and grassland class, and to develop pertinent NPP models under a consistent classification system.

## **2. The classification indices-based model according to the Integrated Orderly Classification System of Grassland (IOCSG)**

A grassland classification system, named the Integrated Orderly Classification System of Grassland (IOCSG), uses the factors Growing Degree Days (GDD), MAP (mean annual precipitation) and the moisture index (K value) to classify grassland diversity [17]. The theory behind the IOCSG has been developed in the last 60 years since it was first put forward in the 1950s, and it has achieved widespread use in China [17, 20]. Liang et al. [20] compared the change in potential vegetation distributions from 1911 to 2000 between the IOCSG, the Holdridge Life Zone [5] and BIOME4 [21]. Their results show that the IOCSG has the advantage of being simple and operational in its simulation of grassland classes, making it by far the best grassland classification system.

The Classification Indices-based Model, dubbed the Holdridge life-zone system [5], and the IOCSG [17] were originally built using eco-physiological features and a regional evapotrans‐ piration model with the elimination of the common variable RDI (radioactive dryness index) by the chain rule. It results in a value for NPP (Mg DM ha-1 yr-1) as a function of GDD and the moisture index (K value). Its ecological base is the IOCSG [17, 20, 22-25]. The method of integrating the classification indices of IOCSG to estimate the NPP is of the form:

$$\text{NPP} = \frac{0.1 \text{GDD} \cdot L \text{ }^2 \text{(K)} \cdot \text{e}^{-\sqrt{13.55 + 3.17 K^{-1} - 0.16 K^{-2} + 0.0032 K^{-3}}}{\text{[} \text{(} K $^6 + L \text{ }^2$  (K)) \cdot \text{(} K $^5 + L \text{ (} K) K^2$ ]}} \cdot \text{(} \text{K)} \cdot \text{(} \text{K)} \cdot \text{m}^2 \text{(} \text{K)} \cdot \text{m} \text{(} \text{K)} \cdot \text{m} \text{(} \text{(} \text{K)} \cdot \text{m} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{K)} \text{(} \text{(} \text{($$

Where GDD is defined as the mean of positive unit-period temperatures with the substitution of zero for all unit-period values below 0 °C. The K value is the ratio of MAP to GDD, which provides an index of biological humidity conditions.

*K* =*MAP* / 0.1*GDD*, *L* (*K*)=0.58802*K* <sup>3</sup> + 0.50698*K* <sup>2</sup> −0.0257081*K* + 0.0005163874

The Classification Indices-based Model not only has the advantage of being simple, opera‐ tional, and especially compatible with the IOCSG used to derive NPP from its class, it also responds to dynamic vegetation-climate relationships.

## **3. Model validation and model inter-comparison**

autotrophic respiration and transpiration [13, 14]. In spite of this, the body of knowledge of basic biological mechanisms to simulate grassland NPP is still limited. Furthermore, process models cannot be used in developing countries because long-term data required for the parameters of the models is not available. Algorithms for estimating NPP by remote sensing may be poorly parameterized if the 'calibration' estimates of NPP are based upon an inappro‐ priate method. In this respect, climate-vegetation models have been drawing much attention and have been widely applied internationally, owing to their simplicity [4]. These models have been shown to yield 'reasonable estimates' of regional or global NPP distribution [4, 9, 14-16].

Vegetative types and their distribution pattern can be related to certain climatic types in a series of mathematical forms. Thus, the climate can be used to predict vegetative types and their distribution, or the reverse [17]. A growing number of research efforts have demonstrated the importance of climate-vegetation interaction in understanding climate sensitivity and climate change [18]. However, current climate-vegetation classification models mostly simulate the equilibrium state of vegetation types, and do not include NPP [19]. Furthermore, at present there is no vegetation-climate model based on a classification system including the NPP function. Therefore, development of climate-vegetation classification models coupled with NPP is urgently needed in order to evaluate the possible impacts of global change on terrestrial vegetation types [19]. So the focus of future research should be to master the link between NPP and grassland class, and to develop pertinent NPP models under a consistent

**2. The classification indices-based model according to the Integrated**

A grassland classification system, named the Integrated Orderly Classification System of Grassland (IOCSG), uses the factors Growing Degree Days (GDD), MAP (mean annual precipitation) and the moisture index (K value) to classify grassland diversity [17]. The theory behind the IOCSG has been developed in the last 60 years since it was first put forward in the 1950s, and it has achieved widespread use in China [17, 20]. Liang et al. [20] compared the change in potential vegetation distributions from 1911 to 2000 between the IOCSG, the Holdridge Life Zone [5] and BIOME4 [21]. Their results show that the IOCSG has the advantage of being simple and operational in its simulation of grassland classes, making it by far the best

The Classification Indices-based Model, dubbed the Holdridge life-zone system [5], and the IOCSG [17] were originally built using eco-physiological features and a regional evapotrans‐ piration model with the elimination of the common variable RDI (radioactive dryness index) by the chain rule. It results in a value for NPP (Mg DM ha-1 yr-1) as a function of GDD and the moisture index (K value). Its ecological base is the IOCSG [17, 20, 22-25]. The method of

integrating the classification indices of IOCSG to estimate the NPP is of the form:

**Orderly Classification System of Grassland (IOCSG)**

classification system.

172 CO2 Sequestration and Valorization

grassland classification system.

Model inter-comparison has been used as an alternative method for indirect validation and identification of model weaknesses and inconsistencies where ground observations are lacking [26]. However, a simultaneous comparison of these climate-vegetation models to estimate grassland NPP, using a consistent classification system, has not been attempted on a regional, China-wide or global scale.

The following three performance indicators were used as the model evaluation and perform‐ ance criteria: (1) the mean bias error (MBE) [24,27,28]; (2) the coefficient of variation of the rootmean-square error (CVRMSE), which is defined as the RMSE normalized to the mean of the observed values; and (3) the forecast efficiency, E [29]. The prediction is considered excellent with the CVRMSE < 10%, good if 10-20%, fair if 20-30%, poor if >30% [30]. CVRMSE is the relative difference between the simulated and observed data, while the Nash-Sutcliffe model efficiency statistic (E) [29], regarded as a measure of the overall fit between observed and predicted values, is the primary criterion.

For the purpose of comparison with model simulations, the observed or predicted NPP was expressed in g C m-2 per year, where 1 g carbon is equivalent to 2.2 g oven-dried organic matter [31-34].

#### **3.1. Evaluation of seven methods to predict grassland net primary productivity along an altitudinal gradient in the Alxa Rangeland, Western Inner Mongolia, China**

China's grasslands are mainly distributed in arid and semi-arid areas of West China [35]. The main grassland classes within China are Temperate meadow-steppe, Steppe and Desert-steppe [35, 36]. Helan Mountain (elevation 3556 m), located in an ecologically vulnerable area of the northwestern part of China, further enriches the diversity of vertical grassland classes. The rangeland classes change from the high to low elevation: Alpine meadow, Steppe, Desert steppe, Steppe desert, and Desert. Thus, Helan Mountain provides an ideal target system to validate the Classification Indices-based Model and test IOCSG theory, which can be done from plots to estimate landscape-scale effects, something that so far has proven to be a challenge to ecologists worldwide [37].

This study continues our efforts to develop a robust estimation capability of NPP for grassland and, specifically, verifies the grassland NPP estimates from the climate-vegetation models along an altitudinal gradient on Helan Mountain and in the surrounding desert rangeland. The specific research objectives are to: 1) compare field-measured NPP based on field-observed data among different grassland classes along an altitudinal gradient in the Helan Mountain range and the surrounding desert; 2) compare and evaluate NPP estimated from the Miami Model [6], Thornthwaite Memorial Model [38], Schuur Model [15], Chikugo Model [8], Beijing Model [12], Synthetic Model [10,11,39], and the Classification Indices-based Model [22,24,25,27,28] with NPP derived from measurements at eight sites in the region; and 3) evaluate the applicability and reliability of the seven models among these grassland classes.

#### *3.1.1. Study area and methods*

#### *3.1.1.1. Study area and site descriptions*

The study area is located along the northern slopes of the Helan Mountain and the surrounding Alxa Desert (long. 105°32′ to 105°51′, lat. 38°39′ to 39°19′; alt. 3556 m to1360 m a.s.l.), in western Inner Mongolia (Figure 1). Eight study sites were selected following annual rainfall gradient variations with respect to grassland classes at three altitudes that represent the full range of grassland vegetation, environmental, terrain and soil conditions (Figure 1). Based on the historical meteorological data among the eight study sites, GDD and the moisture index (K value) were calculated. According to the IOCSG, the grasslands at the eight sites were classified into five classes: Alpine meadow (site 1), Cold temperate-humid montane meadow (site 2), Cool temperate-sub-humid meadow steppe (site 3), Cool temperate-semiarid temperate typical steppe (site 4) and Cool temperate-arid temperate zonal semi-desert (sites 5-8), respectively (Figure 1).

#### *3.1.1.2. Plots-based NPP collection*

The total observed NPP dataset was the sum of above- and below-ground NPP, following standard methods [3,40,41], and it was based on averages of five duplicated plots at each sampling site from April to October once every month for three consecutive years (2003-2005). One hundred and twenty plot-based NPP sets from the eight study sites were obtained [25].

#### *3.1.2. Results*

#### *3.1.2.1. Descriptive statistics of observed NPP*

The gradient distribution of grassland classes varied in different NPP values among the study sites. With all data pooled together, the observed NPP values of the Cool temperatearid temperate zonal semi-desert has an extreme degree of variation, ranging from 72.60 to 242.02 g C m-2 per year, with a coefficient of variation of 28.38%. The mean NPP of the

from plots to estimate landscape-scale effects, something that so far has proven to be a

This study continues our efforts to develop a robust estimation capability of NPP for grassland and, specifically, verifies the grassland NPP estimates from the climate-vegetation models along an altitudinal gradient on Helan Mountain and in the surrounding desert rangeland. The specific research objectives are to: 1) compare field-measured NPP based on field-observed data among different grassland classes along an altitudinal gradient in the Helan Mountain range and the surrounding desert; 2) compare and evaluate NPP estimated from the Miami Model [6], Thornthwaite Memorial Model [38], Schuur Model [15], Chikugo Model [8], Beijing Model [12], Synthetic Model [10,11,39], and the Classification Indices-based Model [22,24,25,27,28] with NPP derived from measurements at eight sites in the region; and 3) evaluate the applicability and reliability of the seven models among these grassland classes.

The study area is located along the northern slopes of the Helan Mountain and the surrounding Alxa Desert (long. 105°32′ to 105°51′, lat. 38°39′ to 39°19′; alt. 3556 m to1360 m a.s.l.), in western Inner Mongolia (Figure 1). Eight study sites were selected following annual rainfall gradient variations with respect to grassland classes at three altitudes that represent the full range of grassland vegetation, environmental, terrain and soil conditions (Figure 1). Based on the historical meteorological data among the eight study sites, GDD and the moisture index (K value) were calculated. According to the IOCSG, the grasslands at the eight sites were classified into five classes: Alpine meadow (site 1), Cold temperate-humid montane meadow (site 2), Cool temperate-sub-humid meadow steppe (site 3), Cool temperate-semiarid temperate typical steppe (site 4) and Cool temperate-arid temperate zonal semi-desert (sites 5-8),

The total observed NPP dataset was the sum of above- and below-ground NPP, following standard methods [3,40,41], and it was based on averages of five duplicated plots at each sampling site from April to October once every month for three consecutive years (2003-2005). One hundred and twenty plot-based NPP sets from the eight study sites were obtained [25].

The gradient distribution of grassland classes varied in different NPP values among the study sites. With all data pooled together, the observed NPP values of the Cool temperatearid temperate zonal semi-desert has an extreme degree of variation, ranging from 72.60 to 242.02 g C m-2 per year, with a coefficient of variation of 28.38%. The mean NPP of the

challenge to ecologists worldwide [37].

174 CO2 Sequestration and Valorization

*3.1.1. Study area and methods*

respectively (Figure 1).

*3.1.2. Results*

*3.1.1.2. Plots-based NPP collection*

*3.1.2.1. Descriptive statistics of observed NPP*

*3.1.1.1. Study area and site descriptions*

The Classification Indices-Based Model for NPP According to the Integrated Orderly Classification System of... http://dx.doi.org/10.5772/57297 175

**Figure 1.** Location map of study area and study sites. Dark circles represent the study sites in elevation. Arabic numer‐ als denote the study site

Cool temperate-arid temperate zonal semi-desert was the lowest (133.43 g C m-2 per year), followed by that of Cool temperate-semiarid temperate typical steppe (160.57 g C m-2 per year), Cool temperate-subhumid meadow steppe (171.94 g C m-2 per year) and Alpine meadow (185.88 g C m-2 per year). The mean NPP of Cold temperate-humid montane meadow was the largest (192.54 g C m-2 per year).

#### *3.1.2.2. Comparison of the seven models in terms model performance values*

The performance of each of the seven models compared and evaluated in this study is also shown graphically by plotting predicted NPP values as a function of measured values, as presented in Figure 2. The biases that each model introduces are reflected at the relative positions of the 1:1 line. Data points above the 1:1 line are over-predicted while those under the 1:1 line are under-predicted. It can be seen that the Thornthwaite Memorial Model, Beijing Model and Miami Model over-predicted grassland NPP while the Synthetic Model and Schuur Model slightly under-predicted those quantities. Considering the slopes and intercepts for the regression of predicted NPP by the Chikugo Model and Classification Indices-based Model versus observed NPP, the regression of the Chikugo Model has a slope (0.9878) approximately equal to that (0.9873) of the Classification Indices-based Model, but the intercept (11.13) of the regression line of the Classification Indices-based Model is much closer to zero than that (17.13) of the regression line of the Chikugo Model.

**Figure 2.** Predicted net primary productivity (NPP) versus Observed NPP from seven different models compared in this study. Each data point represents average of five replicates. Arabic numeral denotes the study site No..

Accordingly, the high values of E (0.67 and 0.87) in predicting NPP using the Chikugo Model and the Classification Indices-based Model indicate that these models outperform the others. The fitted linear regressions (intercepts are forced to zero) between the observed NPP and the methods are: the predicted NPP by Chikugo Model=1.09×Observed NPP, the predicted NPP by Classification Indices-based Model=1.05×Observed NPP, respectively. The results indicate that predicted NPP by the Classification Indices-based Model has a closer correlation with the observed NPP compared to the predicted NPP by the Chikugo Model. With E value of -0.27, -0.45, -1.79 and -2.61 for predicting NPP, respectively, the Synthetic Model, Schuur Model, Miami Model and Beijing Model ranked third, fourth, fifth and sixth. With E value of-113.16, the Thornthwaite Memorial Model was the least suitable. Overall, comparison of the predicted with the observed grassland NPP indicated that the CVRMSE value using the Classification Indices-based Model was less than 10%, which denotes that the prediction suitability of the Classification Indices-based Model is considered excellent, while the prediction of the Chikugo Model is considered good with CVRMSE=14.29%. As the CVRMSE value using the Synthetic Model is 28.19%, the prediction of the model is considered fair. Because of the CVRMSE values beyond 30%, the prediction using the other models is considered poor (Table 1).


**Table 1.** Comparison of the seven models in terms model performance values

#### *3.1.3. Discussion*

regression line of the Classification Indices-based Model is much closer to zero than that (17.13)

**Figure 2.** Predicted net primary productivity (NPP) versus Observed NPP from seven different models compared in this

study. Each data point represents average of five replicates. Arabic numeral denotes the study site No..

of the regression line of the Chikugo Model.

176 CO2 Sequestration and Valorization

Seven different methods were compared in terms of their performance in predicting grassland NPP. The relatively high model efficiency in predicting grassland NPP using the Classification Indices-based Model and Chikugo Model indicates that these models outperform the others. The comparison of models involves the input data requirements from each model into account, as these factors affect the value of model efficiency (E), if they are optimized to maximize E. Generally, models with a large number of input variables and parameters would result in higher values of E than models with a small number of input variables and parameters. When comparing the Chikugo Model with the Classifica‐ tion Indices-based Model, it is evident that the Chikugo Model is more complicated in structure and highly intensive in its data requirements. The Chikugo Model provides insight into the relative importance of individual environmental variables in the evapotranspira‐

tion process [7, 11, 42, 43]. The model is a mechanistic approach to estimate grassland NPP and always slightly overestimated the actual NPP. Furthermore, the Classification Indicesbased Model is based on the IOCSG and used the classification indices as independent variables. It not only takes into account dynamic grassland classes [17], but also simu‐ lates NPP of corresponding grassland classes [22, 24, 25, 27, 28]. Thus it can be very easy to implement and understand. These factors support the Classification Indices-based model as a more accurate prediction among grassland classes in this study. So the Classification Indices-based Model was found to be the best choice for the given grassland classes. The model is especially suitable for grassland NPP research and application in many developing/ undeveloped countries or regions that generally lack the detailed and complex observa‐ tion data required by other models (i.e., BIOME4).

The results presented in this study were not only specific to this region, but more importantly, were specific to the given grassland classes according to the IOCSG approach, which can be scaled up from plots to estimate landscape-scale effects. The ascending order of grassland NPP and its class may have significant implications for grassland succession in Chinese grassland ecosystems with predicted changes in spatio-temporal patterns of precipitation under the influence of global climate change. So there is a need to extrapolate in order to calculate the grassland's response to climate change (for instance changing temperature and rainfall). Continued climate change is expected to result in changes in temperature and precipitation, the same as the changes along the altitudinal gradient in this study area. The changing climate will in turn affect the growth of plants, and result in changes to grassland classes and subse‐ quently NPP. The combined affect of precipitation and temperature, that is, the moisture index (K), is to some extent most important to NPP spatial distribution. Thus, the Classification Indices-based Model has the potential to evaluate the possible effects of climate change on grassland classes and their NPP in the future, in order to improve the accuracy of NPP prediction and reduce the evaluation uncertainty of the possible effects of climate change in the future.

#### **3.2. Model validation and modelling the potential net primary productivity of grassland in China**

China is located in the southeast monsoon climate of the Eurasian continent, and covers a vast territory, with complicated terrain and distinctive distribution patterns of tempera‐ ture and precipitation with different climatic zones, ranging from cold temperate to tropical and from moist to extreme drought. In addition, the ascent of the Qinghai-Tibetan Plateau(the so-called third polar region of the world) leads to a special plateau climate and thus forms a special pattern of vegetation distribution, termed as 'plateau zonality of vegetation' to distinguish horizontal zonality and vertical zonality of vegetation. The vegetation distribution of China can be characterized by the latitudinal zonality, longitudi‐ nal zonality, vertical zonality, and plateau zonality [44]. Thus, China's climate is unique and forms a special pattern of vegetation distribution. Grasslands account for 41.7% of land area in China [17]. Traditionally, China's grasslands have been economically productive areas and have provided important natural resources for the nation, including meat, milk,

wool and animal hides [35]. Grassland NPP can be used as an indicator of the capacity of the grassland ecosystem to accumulate carbon and to support grazing animals. The ability to accurately estimate grassland NPP is critical to our understanding of grassland dynam‐ ics [45]. But evaluation of the precision of grassland NPP estimates in China is difficult. Since it is a developing country, some mechanistic models cannot be used because longterm data required for the parameters of these models are not available in China [16]. But the required data are available for NPP-climate models, and these models have been shown to yield 'reasonable estimates' of global patterns of productivity [4, 9, 14, 16]. A growing number of research efforts [18] are focused on developing NPP-climate relationships. The Miami Model [38], the Schuur Model [15] and the Classification Indices-based Model [22, 24, 25, 27, 28] are examples of such efforts. But a simultaneous comparison using a consistent classification system has not been attempted in China.

The purpose of this section is: 1) to compared NPP estimated using the Miami Model [38], Schuur Model [15], and the Classification Indices-based Model [22,24,25,27,28] with NPP derived from measurements at 3767 sites in China, to evaluate the applicability and reliability of the Classification Indices-based Model; and 2) to simulate the spatial distribution patterns and its NPP characteristics of China's potential grassland under current climate scenarios using the IOCSG approach and NPP-climate models.

#### *3.2.1. Data acquisition and methods*

### *3.2.1.1. Observed NPP database*

tion process [7, 11, 42, 43]. The model is a mechanistic approach to estimate grassland NPP and always slightly overestimated the actual NPP. Furthermore, the Classification Indicesbased Model is based on the IOCSG and used the classification indices as independent variables. It not only takes into account dynamic grassland classes [17], but also simu‐ lates NPP of corresponding grassland classes [22, 24, 25, 27, 28]. Thus it can be very easy to implement and understand. These factors support the Classification Indices-based model as a more accurate prediction among grassland classes in this study. So the Classification Indices-based Model was found to be the best choice for the given grassland classes. The model is especially suitable for grassland NPP research and application in many developing/ undeveloped countries or regions that generally lack the detailed and complex observa‐

The results presented in this study were not only specific to this region, but more importantly, were specific to the given grassland classes according to the IOCSG approach, which can be scaled up from plots to estimate landscape-scale effects. The ascending order of grassland NPP and its class may have significant implications for grassland succession in Chinese grassland ecosystems with predicted changes in spatio-temporal patterns of precipitation under the influence of global climate change. So there is a need to extrapolate in order to calculate the grassland's response to climate change (for instance changing temperature and rainfall). Continued climate change is expected to result in changes in temperature and precipitation, the same as the changes along the altitudinal gradient in this study area. The changing climate will in turn affect the growth of plants, and result in changes to grassland classes and subse‐ quently NPP. The combined affect of precipitation and temperature, that is, the moisture index (K), is to some extent most important to NPP spatial distribution. Thus, the Classification Indices-based Model has the potential to evaluate the possible effects of climate change on grassland classes and their NPP in the future, in order to improve the accuracy of NPP prediction and reduce the evaluation uncertainty of the possible effects of climate change in

**3.2. Model validation and modelling the potential net primary productivity of grassland in**

China is located in the southeast monsoon climate of the Eurasian continent, and covers a vast territory, with complicated terrain and distinctive distribution patterns of tempera‐ ture and precipitation with different climatic zones, ranging from cold temperate to tropical and from moist to extreme drought. In addition, the ascent of the Qinghai-Tibetan Plateau(the so-called third polar region of the world) leads to a special plateau climate and thus forms a special pattern of vegetation distribution, termed as 'plateau zonality of vegetation' to distinguish horizontal zonality and vertical zonality of vegetation. The vegetation distribution of China can be characterized by the latitudinal zonality, longitudi‐ nal zonality, vertical zonality, and plateau zonality [44]. Thus, China's climate is unique and forms a special pattern of vegetation distribution. Grasslands account for 41.7% of land area in China [17]. Traditionally, China's grasslands have been economically productive areas and have provided important natural resources for the nation, including meat, milk,

tion data required by other models (i.e., BIOME4).

178 CO2 Sequestration and Valorization

the future.

**China**

A large reference data set (n = 3767) of observations of grassland NPP with paired climatic variables was compiled for this study. This data set came from surveys conducted by the National Animal Husbandry and Veterinary Service of the Ministry of Agriculture of the People's Republic of China from 2004 to 2005. Total observed NPP dataset from the 3767 sites was the sum of above- and belowground NPP following the standard methods [3, 40] and it was based on the average of five duplicated plots at each sampling site. Locations of 3767 sampling sites were plotted using their associated geographic coordinates, as shown in Figure 3.

### *3.2.1.2. Climate data*

Meteorological data for annual rainfall and GDD at 2348 meteorological stations, including longitude, latitude and altitude data of each meteorological station, were obtained from a database of the China Meteorological Data Sharing Service System from 1961 to 1990 (down‐ loaded from the website: http://cdc.cma.gov.cn). The locations of the 2348 meteorological stations are shown in Figure 4. The Digital Elevation Model (DEM) was derived from the global 1km×1km DEM issued by the U.S. Geological Survey (USGS) (downloaded from the website: http://edcdaac.usgs.gov/gtopo30/ README. asp#h17). Regional data were derived from a database of 1:4 million Chinese regional data points, which was last updated in May 2004, and were used to confirm the Chinese boundary.

**Figure 3.** Locations of observation sites of grassland NPP in China.

#### *3.2.1.3. Estimating grassland NPP and its distribution patterns in China*

Spatialization of GDD and K value was created by introduction into the interpolation analysis of meteorological elements. The 1km×1km grid data of the moisture index averaged over years was obtained by the Kriging interpolation method (Figure 5). The inverse distance squared method (IDS) [46], modified by DEM in the smaller regions, was used to spatialize GDD data and to improve the interpolation precision of GDD in China [47]. By mosaicking the grid data of GDD on the actual ground of eight smaller areas and clipping the integrated interpolation area by the Chinese regionalism, we finally obtained the 1km×1km grid data of GDD (Figure 6). The 1km×1km grid data of the moisture index and GDD were used to predict the potential classes recognized by the IOCSG (Figure 7), and were then used to drive the Miami Model, Schuur Model, and the Classification Indices-based Model, respectively. To more explicitly reflect the spatial distribution patterns of potential biomes in China, and for ease of comparison and application, we merged the 42 classes of IOCSG to Grassland and Forest categories. The Grassland category consists of seven grassland super-class groups (biomes): Tundra and alpine steppe, Frigid desert, Semi-desert, Steppe, Temperate humid grassland, Warm desert and Savanna. The Forest category includes Temperate forest, Sub-tropical forest and Tropical forest (Table 2). The summed NPP has a unit of g C for pixels or Tg C (1 Tg = 1012g) for each grassland super-class group (biome) or Pg C (1 Pg = 1015g) for Grassland or Forest category in China.

The Classification Indices-Based Model for NPP According to the Integrated Orderly Classification System of... http://dx.doi.org/10.5772/57297 181

**Figure 4.** Spatial distribution of 2348 meteorological observation stations used for plotting spatial distribution of GDD and K value

*3.2.1.3. Estimating grassland NPP and its distribution patterns in China*

**Figure 3.** Locations of observation sites of grassland NPP in China.

180 CO2 Sequestration and Valorization

China.

Spatialization of GDD and K value was created by introduction into the interpolation analysis of meteorological elements. The 1km×1km grid data of the moisture index averaged over years was obtained by the Kriging interpolation method (Figure 5). The inverse distance squared method (IDS) [46], modified by DEM in the smaller regions, was used to spatialize GDD data and to improve the interpolation precision of GDD in China [47]. By mosaicking the grid data of GDD on the actual ground of eight smaller areas and clipping the integrated interpolation area by the Chinese regionalism, we finally obtained the 1km×1km grid data of GDD (Figure 6). The 1km×1km grid data of the moisture index and GDD were used to predict the potential classes recognized by the IOCSG (Figure 7), and were then used to drive the Miami Model, Schuur Model, and the Classification Indices-based Model, respectively. To more explicitly reflect the spatial distribution patterns of potential biomes in China, and for ease of comparison and application, we merged the 42 classes of IOCSG to Grassland and Forest categories. The Grassland category consists of seven grassland super-class groups (biomes): Tundra and alpine steppe, Frigid desert, Semi-desert, Steppe, Temperate humid grassland, Warm desert and Savanna. The Forest category includes Temperate forest, Sub-tropical forest and Tropical forest (Table 2). The summed NPP has a unit of g C for pixels or Tg C (1 Tg = 1012g) for each grassland super-class group (biome) or Pg C (1 Pg = 1015g) for Grassland or Forest category in

**Figure 5.** 1km x 1km grid map of China showing the moisture index (K value) obtained by Kriging interpolation. The legends show the zones of K value.

**Figure 6.** 1km x 1km grid map of China showing spatial distribution of GDD by DEM+ IDS interpolation method. DEM, the Digital Elevation Model; IDS, the inverse distance squared method. The legends show the zones of GDD.


a The class name refers to Figure7. Explanation

**Table 2.** The relationships between super-classes (biomes) and classes according to IOCSG approach

**Figure 6.** 1km x 1km grid map of China showing spatial distribution of GDD by DEM+ IDS interpolation method. DEM,

VD 26, VID 27, VIID 28, IIIE 31, IVE 32, VE 33, VIE 34, VIIE 35, IIF

37, IIIF 38, IVF 39,V F40, VIF 41, VIIF 42

the Digital Elevation Model; IDS, the inverse distance squared method. The legends show the zones of GDD.

**Name of super-class group (biome) Corresponding class codea**

Semi-desert II B 9, III B 10, IV B 11, V B 12 Steppe 16IIC,17IIIC,18IV,19VC,25IVD

Savanna VIB 13, VIIB 14, VIC 20, VIIC 21

**Table 2.** The relationships between super-classes (biomes) and classes according to IOCSG approach

Frigid desert II A 2, III A 3, IV A 4

Temperate humid grassland IID 23, IIID 24, IIE30 Warm desert VA 5, VIA 6, VIIA 7

Forest, including Temperate forest, Sub-tropical forest

The class name refers to Figure7. Explanation

and Tropical forest

182 CO2 Sequestration and Valorization

a

Tundra and alpine steppe I A 1,I B 8, I C 15,I D 22, I E 29, I F 36

**Explanation:** IA 1 Frigid-extrarid frigid desert, alpine desert; IIA 2 Cold temperate-extrarid montane desert; IIIA 3 Cool temperate-extrarid temperate zonal desert; IVA 4 Warm temperate-extrarid warm temperate zonal desert; VA 5 Warm-extrarid subtropical desert; VIA 6 Subtropical-extrarid subtropical desert; VIIA 7 Tropical-extrarid tropical des‐ ert; IB 8 Frigid-arid frigid zonal semidesert,alpine semidesert; IIB 9 Cold temperate-arid montane semidesert; IIIB 10 Cool temperate-arid temperate zonal semidesert; IVB 11 Warm temperate-arid warm temperate zonal semidesert; VB 12 Warm-arid warm subtropical semidesert; VIB 13 Subtropical arid subtropical desert brush; VIIB 14 Tropical arid tropical desert brush; IC 15 Frigid-semiarid dry tundra,alpine steppe; IIC 16 Cold temperate-semiarid montane steppe; IIIC 17 Cool temperate-semiarid temperate typical steppe; IVC 18 Warm temperate-semiarid warm temperate typical steppe; VC 19 Warm-semiarid subtropical grasses-fruticous steppe; VIC 20 Subtropical-semiarid subtropical brush steppe; VIIC 21 Tropical-semiarid savanna; ID 22 Frigid-subhumid moist tundra,alpine meadow steppe; IID 23 Cold temperate subhumid montane meadow steppe; IIID 24 Cool temperate-subhumid meadow steppe; IVD 25 Warm temperate-subhumid forest steppe; VD 26 Warm-subhumid deciduous broad leaved forest; VID 27 Subtropical-sub‐ humid sclerophyllous forest; VIID 28 Tropical-subhumid tropical xerophytic forest; IE 29 Frigid-humid tundra, alpine meadow; IIE 30 Cold temperate-humid montane meadow; IIIE 31 Cool temperate-humid forest steppe, deciduous broad leaved forest; IVE 32 Warm temperate-humid deciduous broad leaved forest; VE 33 Warm-humid evergreendeciduous broad leaved forest; VIE 34 Subtropical-humid evergreen broad leaved forest; VIIE 35 Tropical-humid sea‐ sonal rain forest; IF 36 Frigid perhumid rain tundra,alpine meadow; IIF 37 Cold temperate perhumid taiga forest; IIIF 38 Cool temperate perhumid mixed coniferous broad leaved forest; IVF 39 Warm temperate perhumid deciduous broad leaved forest; VF 40 Warm-perhumid deciduous-evergreen broad leaved forest; VIF 41 Sub-tropical perhumid evergreen broad leaved forest; VIIF 42 Tropical-perhumid rain forest. The legend above also applies to Table 2)

**Figure 7.** Map of China showing the spatial distribution of potential class recognized by the IOCSG.

#### *3.2.2. Results*

#### *3.2.2.1. Comparison between NPP observations and predictions*

Figure 8. plots predicted grassland NPP versus observed NPP values. The results indicate that the Schuur Model underestimated grassland NPP. The Miami Model overestimated the large observations, while the Classification Indices-based Model overestimated as many data points as it underestimated. Considering the slopes and intercepts of the regression of predicted NPP by the Miami Model and Classification Indices-based Model versus observed NPP, the regression of the Miami Model has a slope similar to that of the Classification Indices-based Model (0.79 and 0.72, respectively), but the intercept of the regression line of the Classification Indices-based Model (64.19) is much closer to zero than is that of the Miami Model (113.40). The intercept differences suggest that the Miami Model overestimated the high values of observed NPP, whereas the Classification Indices-based Model slightly underestimated the high values.

**Figure 8.** Performance of the Miami, Schuur and Classification Indices-based Models expressed as scatter-plots of pre‐ dicted versus observed grassland NPP in China.

Comparison of the predicted with the observed grassland NPP indicated that the CVRMSE value using the Classification Indices-based Model was <10%, demonstrating that the predic‐ tion of the Classification Indices-based Model is considered excellent, while the output of the Miami Model is considered good, with CVRMSE 13.0%. As the CVRMSE value using the Schuur Model is 23.9%, its predictive ability is considered fair. The results of linear regression showed that the Classification Indices-based Model explained, on average, 65.3% (R2 ) of the variation in observed NPP, while the Miami Model explained 63.0% (R2 ) of the variation. With the highest E value, the Classification Indices-based Model ranked first for predicting NPP, followed by the Miami and Schuur Models, respectively, indicating that the Classification Indices-based Model can estimate grassland NPP more precisely (Table 3).

#### *3.2.2.2. Distribution patterns of grassland NPP in China*

Almost all classes (42 classes) occur in China, except for tropical-extra-arid tropical desert (VIIA7) under current climate conditions (Figure 7). The spatial distribution of grassland


*3.2.2. Results*

184 CO2 Sequestration and Valorization

high values.

dicted versus observed grassland NPP in China.

*3.2.2.1. Comparison between NPP observations and predictions*

Figure 8. plots predicted grassland NPP versus observed NPP values. The results indicate that the Schuur Model underestimated grassland NPP. The Miami Model overestimated the large observations, while the Classification Indices-based Model overestimated as many data points as it underestimated. Considering the slopes and intercepts of the regression of predicted NPP by the Miami Model and Classification Indices-based Model versus observed NPP, the regression of the Miami Model has a slope similar to that of the Classification Indices-based Model (0.79 and 0.72, respectively), but the intercept of the regression line of the Classification Indices-based Model (64.19) is much closer to zero than is that of the Miami Model (113.40). The intercept differences suggest that the Miami Model overestimated the high values of observed NPP, whereas the Classification Indices-based Model slightly underestimated the

**Figure 8.** Performance of the Miami, Schuur and Classification Indices-based Models expressed as scatter-plots of pre‐

Comparison of the predicted with the observed grassland NPP indicated that the CVRMSE value using the Classification Indices-based Model was <10%, demonstrating that the predic‐ tion of the Classification Indices-based Model is considered excellent, while the output of the Miami Model is considered good, with CVRMSE 13.0%. As the CVRMSE value using the Schuur Model is 23.9%, its predictive ability is considered fair. The results of linear regression showed that the Classification Indices-based Model explained, on average, 65.3% (R2

the highest E value, the Classification Indices-based Model ranked first for predicting NPP, followed by the Miami and Schuur Models, respectively, indicating that the Classification

Almost all classes (42 classes) occur in China, except for tropical-extra-arid tropical desert (VIIA7) under current climate conditions (Figure 7). The spatial distribution of grassland

variation in observed NPP, while the Miami Model explained 63.0% (R2

*3.2.2.2. Distribution patterns of grassland NPP in China*

Indices-based Model can estimate grassland NPP more precisely (Table 3).

) of the

) of the variation. With

**Table 3.** The mean bias error (MBE), coefficient of variation of the root mean square error (CVRMSE), coefficients of determination (R2), and model efficiency (E) for the comparison of the predicted with the observed grassland NPP in China.

biomes (Table 2) under current conditions is shown in Figure 9a. The Grassland category was predicted to be distributed primarily in the arid and semi-arid regions of Northern China, the Qinghai-Tibetan Plateau, and scattered throughout warm-temperate and tropical regions (Figure 9a). From north to south, grassland biomes were predicted to be mainly distributed as follows: Tundra and alpine steppe (accounting for 20.42% of Chinese land area); Cold desert (13.1%); Semi-desert (8.1%); Steppe (8.0%); Temperate humid grassland (6.0%); Warm desert (0.1%) and Savanna (0.1%) (Figure 9a and Table 4). These percentages correspond to the actual grassland distribution.

Compared with the Classification Indices-based Model and Miami Model, the Schuur Model estimated substantially lower TNPP, especially in Tundra and alpine steppe, Cold desert, Semi-desert, Steppe, Temperate humid grassland and Savanna areas (Table 4), but the simulated spatial pattern for grassland NPP is very similar (Figure 9b, c, d). Comparing the NPP maps (Figure 9b, c, d) with the data in Table 4 shows highest TNPP of grassland biomes in Tundra and alpine steppe, followed by Steppe and Temperate humid grassland, respec‐ tively. The Tundra and alpine steppe, which covers the largest area, was predicted to be mainly distributed in the Tibet Autonomous Region (94.4 million hectares), Qinghai Province (49.1 million hectares) and Xinjiang Uygur Autonomous Region (32.1 million hectares). Although Tundra and alpine steppe has lower NPP values than Temperate humid grassland on a unit area basis, the TNPP for Tundra and alpine steppe, estimated by the Classification Indicesbased Model, was predicted to be nearly twice as large as that of Temperate humid grassland because Tundra and alpine steppe was predicted to cover a greater area (Table 4). The total area of Steppe and Temperate humid grassland was predicted to be 130.75 million hectares, accounting for 25.0% of the total grassland area, with nearly half of distributed on the Inner Mongolian Plateau. Savanna and Warm desert super-classes were predicted to cover less than 1.3 million hectares. Savanna was predicted to be located in southern China, particularly in Hainan Province, and Warm desert was predicted to be located in the Tarim Basin in Xinjiang Uygur Autonomous Region and the Qaidam Basin in Qinghai Province (Figure 9a). Warm desert and Savanna were predicted to have the lowest TNPP, and Semi-desert and Cold desert to have moderate TNPP values (Figure 9 and Table 4). The Semi-desert was predicted to be mainly distributed in Xinjiang Uygur Autonomous Region (33.8 million hectares) and Inner Mongolia Autonomous Region (26.4 million hectares). The Cold desert was predicted to be mainly distributed in Xinjiang Uygur Autonomous Region (84.3 million hectares), the western region of Inner Mongolia Autonomous Region (18.7million hectares) and Gansu Province (11.9 million hectares).

**Figure 9.** Spatial distribution of (a) potential grassland biomes (super-class group) and (b, c, d) geographical distribu‐ tion of grassland NPP evaluated by the Miami Model, Schuur Model and Classification Indices-based Model at current climatic condition, respectively.


**Table 4.** Total NPP (TNPP) of major China's terrestrial biomes estimated by the Miami Model, Schuur Model and Classification Indices-based Model under current climatic condition.

In conclusion, under current climate conditions, the main parts of China's grassland are predicted to be the Tundra and alpine steppe and Steppe, and they account for 50.88% China's total grassland and 63.77-63.90% China's grassland TNPP (Table 4).

#### *3.2.3. Discussion*

**Figure 9.** Spatial distribution of (a) potential grassland biomes (super-class group) and (b, c, d) geographical distribu‐ tion of grassland NPP evaluated by the Miami Model, Schuur Model and Classification Indices-based Model at current

climatic condition, respectively.

186 CO2 Sequestration and Valorization

#### *3.2.3.1. Model-data comparison*

The performance of the different models can be compared on the basis of their error frequency distributions. Figure 10 shows the distribution of errors for the three models. Even though the pattern is to some extent controlled by the heterogeneous distribution of the validation points, the direct influence of the different models on the error structure remains evident. In the Miami Model, the error distribution (Figure 10a) shows a unimodal trend with a peak centered on a negative value (average local error of about -48 g C m-2 per year) and an error range from -419 to 492 g C m-2 per year. The largest underestimated values occur with the Schuur model, although a few overestimated predictions are also present in the same areas. In the histogram of error frequency (Figure 10b), a large single maximum is clearly centered on values of approximately 132 g C m-2 per year, indicating that grassland NPP is underestimated almost everywhere within the test area (Figure 8). The fact that the Schuur Model produced a model efficiency value of 0.58 is an indication that any predictions arising from this model must be treated cautiously. The Classification Indices-based and Miami models have more consistent behavior throughout the validation points, and Figure 10c indicates that the results of the Classification Indices-based Model are in good agreement with observed data (mean error 23 g C m-2 per year with a standard deviation of 105.6 g C m-2 per year) and the mean absolute error is lower than that obtained from the Miami Model.

**Figure 10.** Frequency distribution of residuals for (a) the Miami, (b) the Schuur, and (c) the Classification Indices-based Models. Residuals are expressed as the difference between observed and predicted NPP.

The Classification Indices-based Model performs slightly better than the Miami Model, with a narrower error range and an error histogram that approximates a bell-shaped distribu‐ tion (Figure 10). In general, the distribution of residuals is homogeneous, although a few larger errors occur in the Classification Indices-based model, which can compromise its reliability. Comparing the Miami Model with the Classification Indices-based Model, the Miami Model determines NPP for a particular location by comparing the minimum value of either temperature or precipitation functions. Hence, it only considers a single factor, and there are no interactions between the two variables. In contrast, the Classification Indices-based Model considers three meteorological measurements and their interactions in its formulation. The reduction in errors produced by the Classification Indices-based Model should lead to significant improvements in applications as a newer NPP-climate relation‐ ship model (Figure 10).

#### *3.2.3.2. Grassland carbon budgets*

The Classification Indices-based Model has two advantages: 1) it needs few input parameters; and 2) remote sensing data can be incorporated easily. Consequently, the Classification Indices-based Model is effective and practical for large-area application. But the simulated NPP by the Classification Indices-based Model was ideally potential value and land-use practices were not taken into account. The potential grassland, as a final state of succession which achieves the balance with its habitat, is the most stable and mature climax grassland class of the habitat without human interference, and is the trend of the regional grassland development. The study of potential grassland can substantially reveal the impact of climate on the changes of grassland patterns. It is the starting point of the vegetation-environmental classification and relationship study, as well as the key point of the global change and grassland ecosystem study [27].

The Classification Indices-based Model estimate of China's TNPP at 2.95 Pg C (Table 4) is close to the estimates of 2.65 Pg C and 2.24 Pg C by the light-use efficiency model [48] and the BEPS (Boreal Ecosystem Productivity Simulator) model [49] under current climate conditions, respectively. The Classification Indices-based Model outputs suggest that potential TNPP of grassland in China is 0.72 Pg C under current climate conditions (Table 4). China's grassland TNPP was estimated by Piao et al. [50] as 1.04 Pg C from 1980 to 1988, while Ni [51], using the carbon density method, estimated the TNPP of China's grasslands as 3.06 Pg C between 1949 and1978 and also between 1979 and 1990. This wide range in the reported potential TNPP could be due to their use of different estimation methods, inconsistent classification systems, and/or different time periods with changing grassland areas.

#### *3.2.3.3. Human Appropriation of Net Primary Production (HANPP)*

efficiency value of 0.58 is an indication that any predictions arising from this model must be treated cautiously. The Classification Indices-based and Miami models have more consistent behavior throughout the validation points, and Figure 10c indicates that the results of the Classification Indices-based Model are in good agreement with observed data (mean error 23 g C m-2 per year with a standard deviation of 105.6 g C m-2 per year) and the mean absolute

**Figure 10.** Frequency distribution of residuals for (a) the Miami, (b) the Schuur, and (c) the Classification Indices-based

The Classification Indices-based Model performs slightly better than the Miami Model, with a narrower error range and an error histogram that approximates a bell-shaped distribu‐ tion (Figure 10). In general, the distribution of residuals is homogeneous, although a few larger errors occur in the Classification Indices-based model, which can compromise its reliability. Comparing the Miami Model with the Classification Indices-based Model, the Miami Model determines NPP for a particular location by comparing the minimum value of either temperature or precipitation functions. Hence, it only considers a single factor, and there are no interactions between the two variables. In contrast, the Classification Indices-based Model considers three meteorological measurements and their interactions in its formulation. The reduction in errors produced by the Classification Indices-based Model should lead to significant improvements in applications as a newer NPP-climate relation‐

The Classification Indices-based Model has two advantages: 1) it needs few input parameters; and 2) remote sensing data can be incorporated easily. Consequently, the Classification Indices-based Model is effective and practical for large-area application. But the simulated NPP by the Classification Indices-based Model was ideally potential value and land-use practices were not taken into account. The potential grassland, as a final state of succession which achieves the balance with its habitat, is the most stable and mature climax grassland class of the habitat without human interference, and is the trend of the regional grassland development. The study of potential grassland can substantially reveal the impact of climate on the changes of grassland patterns. It is the starting point of the vegetation-environmental

Models. Residuals are expressed as the difference between observed and predicted NPP.

ship model (Figure 10).

188 CO2 Sequestration and Valorization

*3.2.3.2. Grassland carbon budgets*

error is lower than that obtained from the Miami Model.

One of the central issues of sustainable development is the question of how to meet the globally growing demand for biomass in an ecologically sound way. Land use activities, primarily for agricultural expansion and economic growth, have transformed one-third to one-half of our planet's land surface from forest or grassland clearance, agricultural practices and urban expansion, which make profound impacts on ecosystem service, food production and the environment [52]. China, occupying only 7% of the world's total arable land, supports nearly 21% of the world's total human population. Agriculture is the major sector of growth in the Chinese economy and with 80% of Chinese living in villages, a large proportion of these people are still dependent upon agriculture. However, with rapid industrialization, urbanization is accelerating and leading to changes in grassland and forest land uses. With increasing scientific and political interest in regional aspects of the global carbon cycle, there is a strong impetus to better understand the carbon balance of China. This is not only because China is the largest emitter of fossil-fuel CO2 into the atmosphere, but also because it has experienced regionally distinct land-use histories and climate trends, which together control the carbon budget of its ecosystems [53, 54]. Changes in terrestrial ecosystems through human land use and manage‐ ment are also important in land degradation and carbon balance studies. Environmental degradation is a major concern for China and research aimed at understanding the linkages between land use changes, socioeconomics and the biophysical variables governing environ‐ mental degradation is needed. Despite the vast amount of research conducted independently on land use changes and social indicators in China, an integrated assessment combining them to study the socioeconomic metabolic flows and their impact on land resources on a large spatial scale is absent. In this context, the concept of HANPP (human appropriation of net primary production) is an important tool for assessing the impact of anthropogenic forces through land use changes on biomass.

HANPP is an indicator of the extent to which ecosystem processes are altered by human activities. HANPP equates to the difference between the NPP of the potential natural vegeta‐ tion and the amount of NPP remaining in ecosystems. The notion of HANPP represents the food supply for humans, providing the free energy for humans to sustain their metabolic activities and allowing them to perform physical work. For example, agriculture and forestry harness biomass energy for socio-economic purposes and thereby reduce the amount of NPP remaining in ecological food chains [52, 55-58]. HANPP alters the composition of the atmos‐ phere, levels of biodiversity, energy flows within food webs and the provision of important ecosystem services [59-61]. This removal of carbon naturally comes at a cost in that increased intensities of human appropriation should directly result in less vegetative growth compared to its natural state, if all other factors are held constant, such as nutrient availability. In this study, we present a comprehensive assessment of Chinese HANPP based on vegetation modelling using our model, agricultural and forestry statistics, and geographical information systems data on land use, land cover, and soil degradation that localizes human impact on ecosystems. We find, overall, that the HANPP values suggest that more than 43.2% of the available NPP has already been modified and exploited in China. This value is comparatively higher than the range of global estimates of 20% to 40% for HANPP [62, 63]. Furthermore, compared with HANPP estimates cited for Austria [57, 59], where HANPP declined from 1980 to 1995, HANPP has increased in China. Increases in HANPP may lead to carbon fluxes from biota to the atmosphere, they may contribute to biodiversity loss, and they may result in diminished resilience of ecosystems [62]. As Chinese human population grows and with mounting political interest in increasing GDP, there is likely to be an increased requirement for more agricultural areas and further increases in HANPP over the present value. These analyses suggest policy options for slowing future growth of HANPP. It also offers a means to aid people in using the aforementioned carbon sinks to fulfill China's commitment of reducing greenhouse gases.

#### **3.3. Model-data comparison and modelling the potential net primary productivity of global grassland**

Grassland biomes account for 25% of the world's land area and have global significance for climate-carbon feedback [1], containing approximately 30% of global soil carbon stocks [64]. Ranging from the savannas of Africa to the North American prairies and the converted grasslands of Latin America and Southeast Asia, grassland ecosystems support the majority of the world's livestock and large mammals [65], besides playing a very important role in regulating the global carbon cycle and providing meat and milk for human beings.

As one of the most widespread ecosystem types, natural grassland plays a significant but poorly recognized role in the global carbon cycle [1, 2]. In order to effectively manage grassland ecosystems and maintain their sustainability, large-scale analysis and modelling of grassland NPP are needed to develop a better grasp of the spatial distribution of grasslands and their productivity [3, 4, 13, 20, 66]. Continuous monitoring of global grassland productivity has never been possible because of technological limitations. So it is necessary to use computer models, calibrated with existing data, to study the spatial and temporal variations of grassland NPP [3]. Recently, climate-vegetation models have been drawing much attention and have been widely applied internationally [4, 9, 18], having been shown to yield 'reasonable esti‐ mates' of global patterns of productivity [4, 9, 14, 16]. The Miami Model [38], Schuur Model [15] and Classification Indices-based Model [22, 24, 25, 27, 28] are examples of these climatevegetation models. But model inter-comparison under a consistent classification system simultaneously has not been attempted. In particular, the quantitative and spatial descriptions of global grassland classes and their NPP values have not been reported.

The purpose of this section is: 1) to compare NPP estimated from the Miami Model [38], Schuur Model [15], and the Classification Indices-based Model [22,24,25,27,28] with NPP derived from measurements at 37 sites around the world, to evaluate the applicability and reliability of the Classification Indices-based Model; and 2) to simulate the spatial distribution patterns and associated NPP characteristics of global potential grassland under recent past climate scenario using the IOCSG approach and NPP-climate models.

## *3.3.1. Data acquisition and methods*

### *3.3.1.1. Observed NPP database*

activities and allowing them to perform physical work. For example, agriculture and forestry harness biomass energy for socio-economic purposes and thereby reduce the amount of NPP remaining in ecological food chains [52, 55-58]. HANPP alters the composition of the atmos‐ phere, levels of biodiversity, energy flows within food webs and the provision of important ecosystem services [59-61]. This removal of carbon naturally comes at a cost in that increased intensities of human appropriation should directly result in less vegetative growth compared to its natural state, if all other factors are held constant, such as nutrient availability. In this study, we present a comprehensive assessment of Chinese HANPP based on vegetation modelling using our model, agricultural and forestry statistics, and geographical information systems data on land use, land cover, and soil degradation that localizes human impact on ecosystems. We find, overall, that the HANPP values suggest that more than 43.2% of the available NPP has already been modified and exploited in China. This value is comparatively higher than the range of global estimates of 20% to 40% for HANPP [62, 63]. Furthermore, compared with HANPP estimates cited for Austria [57, 59], where HANPP declined from 1980 to 1995, HANPP has increased in China. Increases in HANPP may lead to carbon fluxes from biota to the atmosphere, they may contribute to biodiversity loss, and they may result in diminished resilience of ecosystems [62]. As Chinese human population grows and with mounting political interest in increasing GDP, there is likely to be an increased requirement for more agricultural areas and further increases in HANPP over the present value. These analyses suggest policy options for slowing future growth of HANPP. It also offers a means to aid people in using the aforementioned carbon sinks to fulfill China's commitment of

**3.3. Model-data comparison and modelling the potential net primary productivity of global**

Grassland biomes account for 25% of the world's land area and have global significance for climate-carbon feedback [1], containing approximately 30% of global soil carbon stocks [64]. Ranging from the savannas of Africa to the North American prairies and the converted grasslands of Latin America and Southeast Asia, grassland ecosystems support the majority of the world's livestock and large mammals [65], besides playing a very important role in

As one of the most widespread ecosystem types, natural grassland plays a significant but poorly recognized role in the global carbon cycle [1, 2]. In order to effectively manage grassland ecosystems and maintain their sustainability, large-scale analysis and modelling of grassland NPP are needed to develop a better grasp of the spatial distribution of grasslands and their productivity [3, 4, 13, 20, 66]. Continuous monitoring of global grassland productivity has never been possible because of technological limitations. So it is necessary to use computer models, calibrated with existing data, to study the spatial and temporal variations of grassland NPP [3]. Recently, climate-vegetation models have been drawing much attention and have been widely applied internationally [4, 9, 18], having been shown to yield 'reasonable esti‐ mates' of global patterns of productivity [4, 9, 14, 16]. The Miami Model [38], Schuur Model [15] and Classification Indices-based Model [22, 24, 25, 27, 28] are examples of these climate-

regulating the global carbon cycle and providing meat and milk for human beings.

reducing greenhouse gases.

190 CO2 Sequestration and Valorization

**grassland**

A reference data set (n = 113) of grassland NPP field observations with paired climatic variables was compiled for this study. Total observed NPP data were gathered from the Oak Ridge National Laboratories (ORNL) Net Primary Production database (http://www-eos‐ dis.ornl.gov/NPP/npp\_home. html), and were the sum of aboveground and belowground NPP following standard methods [9]. The grasslands including the Tundra and alpine steppe, Cold desert, Semi-desert, Steppe, Temperate humid grassland, Warm desert and Savanna, or other intensively managed sites were omitted from this study. Locations of sampling sites were plotted using their associated geographic coordinates, as shown in Figure 11.

**Figure 11.** Spatial distribution of grassland NPP observations collected from the ORNL DAAC NPP database (http:// www-eosdis.ornl.gov/NPP/npp\_home.html).

#### *3.3.1.2. Monthly precipitation and mean temperature grid data*

The monthly precipitation and mean temperature grid data, with spatial resolution of 30 arc seconds (i.e., about 1 km), for global land areas excluding Antarctica over 50 years from 1950 to 2000, were generated using the software package ANUSPLIN Version 4.3 (Australian National University, Canberra, ACT, Australia) [67,68]. We chose this data set because the method that created it has been used in other global studies [69, 70], and it has performed well in comparison with the other multiple interpolation techniques [68, 71].

#### *3.3.1.3. IOCSG approach and NPP-climate model operation*

A common approach to extrapolating grassland NPP to the biosphere is to use vegetation maps together with models of plant productivity at a biome level. The IOCSG potential biome maps and its NPP were created and processed by the ArcGIS software (Esri Inc., Redlands, CA, USA) under current climate (1950-2000). In the IOCSG biome (super-class group) classification map, lake water, permanent snow and ice were excluded by using the Moderate Resolution Imaging Spectroradiometer-International Geosphere Biosphere Program (MODIS-IGBP) land-cover classification dataset in 2001, found at http://wist.echo.nasa.gov. The MODIS-IGBP land-cover classification dataset was used to calculate the area of potential grassland biome. The simulated NPP was an ideal potential value, and land-use practices were not taken into account. The total NPP has a unit of Tg C (1 Tg = 1012g) for biome or Pg C (1 Pg = 1015g) for world.

#### *3.3.2. Results*

#### *3.3.2.1. Comparison between NPP observations and predictions*

Figure 12 plots predicted grassland NPP versus observed NPP values. Data results indicate that the Schuur Model over-predicted grassland NPP. The Miami Model over-predicted the large observations while the Classification Indices-based Model achieved a better balance between over-predicted and under-predicted data points. A 1:1 line fits almost throughout the entire cloud of data points of the Classification Indices-based Model. Considering the slopes and intercepts for the regression of predicted NPP by the three models versus observed NPP, the regression of the Miami Model has a slope (0.77) similar to that (0.70) of the Classification Indices-based Model, but the intercept of the regression line (151.41) of the Classification Indices-based Model is much closer to zero than the regression line (232.52) of the Miami Model. The intercept differences suggested that the Miami Model overestimated the high values of observed NPP, whereas the Classification Indices-based Model slightly underesti‐ mated the high values.

Comparison of the predicted with the observed grassland NPP indicated that the CVRMSE value using the Classification Indices-based Model was less than 30%, demonstrating that the prediction suitability of the Classification Indices-based Model is considered fair, while the prediction suitability of the Miami model is considered poor, with the CVRMSE=34.12%. As the CVRMSE value using the Schuur Model is 42.13%, its predictive ability is considered the poorest. The results of linear regression showed that the Classification Indices-based Model explained, on average, a variation of 65.86% (R2 ) in observed NPP, while the Miami Model showed the explanations of 59.46% (R2 ). With the highest E value, the Classification Indicesbased Model ranked first for predicting NPP, indicating that the model can estimate the grassland NPP more precisely (Table 5).

The Classification Indices-Based Model for NPP According to the Integrated Orderly Classification System of... http://dx.doi.org/10.5772/57297 193

National University, Canberra, ACT, Australia) [67,68]. We chose this data set because the method that created it has been used in other global studies [69, 70], and it has performed well

A common approach to extrapolating grassland NPP to the biosphere is to use vegetation maps together with models of plant productivity at a biome level. The IOCSG potential biome maps and its NPP were created and processed by the ArcGIS software (Esri Inc., Redlands, CA, USA) under current climate (1950-2000). In the IOCSG biome (super-class group) classification map, lake water, permanent snow and ice were excluded by using the Moderate Resolution Imaging Spectroradiometer-International Geosphere Biosphere Program (MODIS-IGBP) land-cover classification dataset in 2001, found at http://wist.echo.nasa.gov. The MODIS-IGBP land-cover classification dataset was used to calculate the area of potential grassland biome. The simulated NPP was an ideal potential value, and land-use practices were not taken into account. The total

Figure 12 plots predicted grassland NPP versus observed NPP values. Data results indicate that the Schuur Model over-predicted grassland NPP. The Miami Model over-predicted the large observations while the Classification Indices-based Model achieved a better balance between over-predicted and under-predicted data points. A 1:1 line fits almost throughout the entire cloud of data points of the Classification Indices-based Model. Considering the slopes and intercepts for the regression of predicted NPP by the three models versus observed NPP, the regression of the Miami Model has a slope (0.77) similar to that (0.70) of the Classification Indices-based Model, but the intercept of the regression line (151.41) of the Classification Indices-based Model is much closer to zero than the regression line (232.52) of the Miami Model. The intercept differences suggested that the Miami Model overestimated the high values of observed NPP, whereas the Classification Indices-based Model slightly underesti‐

Comparison of the predicted with the observed grassland NPP indicated that the CVRMSE value using the Classification Indices-based Model was less than 30%, demonstrating that the prediction suitability of the Classification Indices-based Model is considered fair, while the prediction suitability of the Miami model is considered poor, with the CVRMSE=34.12%. As the CVRMSE value using the Schuur Model is 42.13%, its predictive ability is considered the poorest. The results of linear regression showed that the Classification Indices-based Model

based Model ranked first for predicting NPP, indicating that the model can estimate the

) in observed NPP, while the Miami Model

). With the highest E value, the Classification Indices-

in comparison with the other multiple interpolation techniques [68, 71].

NPP has a unit of Tg C (1 Tg = 1012g) for biome or Pg C (1 Pg = 1015g) for world.

*3.3.1.3. IOCSG approach and NPP-climate model operation*

*3.3.2.1. Comparison between NPP observations and predictions*

explained, on average, a variation of 65.86% (R2

showed the explanations of 59.46% (R2

grassland NPP more precisely (Table 5).

*3.3.2. Results*

192 CO2 Sequestration and Valorization

mated the high values.

**Figure 12.** Performance of the Miami, Schuur and Classification Indices-based Models expressed as scatter-plots of predicted versus observed grassland NPP in World.


**Table 5.** The MBE, CVRMSE, coefficients of determination (*R*2), and model efficiency (E) for the comparison of the predicted with the observed grassland NPP in World.

#### *3.3.2.2. Spatial distribution patterns and characters of grassland biomes over 1950-2000*

The spatial distribution of grassland biomes during the period of 1950-2000 is shown in Figure 13a. From Figure 13a, the potential grassland is characterized by a significant distribution pattern in latitudinal and altitudinal directions. From the Equator to the North Pole, grassland biomes were mainly distributed in sequence as follows: (1) Savanna as the largest terrestrial biome, mainly distributed in the east and middle of Africa, Central America and Oceania; (2) Warm desert, mainly distributed in North Africa, North America and the largest part in Asia; (3) Semi-desert and Cold desert, and mixed with Steppe in central Eurasia Continent and southwest of North America, as well as Tundra and alpine steppe in the Tibetan Plateau; (4) Temperate humid grassland, distributed over the entire Eurasian continent, and extending from the east of North America to the west coast with the Pacific Ocean; and (5) Tundra and alpine steppe, mainly distributed in the north of North America, Greenland and the most northerly part of Eurasia. In the Southern Hemisphere, from the Equator to the southernmost edge of Oceania, there are two major zones, including (1) dominated by Savanna, and mixed with a little bit of Steppe, and Warm desert, mainly distributed in the south and middle of Africa, the north and middle of South America, and the south of Southeast Asia and the north of Oceania; (2) dominated by Semi-desert, mainly distributed in southernmost South America, Africa and Oceania. Probably affected by interactions among topography, climate and vegetation, there are more grassland biomes distributed in the east of South American (Figure 13a). Results of statistical analysis indicated that Grassland and Forest covers 55.76% and 44.24% of global land area, respectively (Table 6). Grassland biomes were mainly distributed as follows: Savanna (accounting for 17.19% of global land area); Warm desert (16.29%); Tundra and alpine steppe (6.32%); Semi-desert (6.03%); Steppe (4.83%); Temperate humid grassland (3.86%) and Cold desert (1.25%) (Table 6). These percentages correspond to the actual grass‐ land distribution.


**Table 6.** Total NPP (TNPP) of major global terrestrial biomes estimated by Miami, Schuur and Classification Indicesbased Models in recent past (1950- 2000)

Comparisons with the Classification Indices-based Model and Miami Model, Schuur Model estimated substantially higher TNPP, especially for Temperate humid grassland, Steppe and Warm desert (Table 6), but the simulated spatial pattern for global NPP is very similar (Figure 13bcd). The NPP increases from the South and North poles toward the Equator, corresponding to changes in precipitation and temperature. Comparing the NPP maps (Figure 13bcd) with the Table 6 shows highest TNPP of grassland in Savanna, followed by Tundra and alpine grassland and Steppe, respectively. Although Warm desert grassland covers the second greatest area among grassland super-classes, TNPP for Warm desert grassland is still lower due to lower NPP.

In conclusion, under recent past climate conditions, the main parts of global grassland are the Savanna and Tundra and alpine grassland, which together account for 42.15% of total global grassland area and 63.76-68.91% of global grassland TNPP (Table 6).

The Classification Indices-Based Model for NPP According to the Integrated Orderly Classification System of... http://dx.doi.org/10.5772/57297 195

13a). Results of statistical analysis indicated that Grassland and Forest covers 55.76% and 44.24% of global land area, respectively (Table 6). Grassland biomes were mainly distributed as follows: Savanna (accounting for 17.19% of global land area); Warm desert (16.29%); Tundra and alpine steppe (6.32%); Semi-desert (6.03%); Steppe (4.83%); Temperate humid grassland (3.86%) and Cold desert (1.25%) (Table 6). These percentages correspond to the actual grass‐

**Miami Model Schuur Model**

505.43 1049.52 7515.71 841.33

5795.39 38904.59 40147.86 39128.38

Tundra and alpine steppe 827.96 1884.82 2269.96 2492.34 Frigid desert 163.57 120.65 23.87 92.18 Semi-desert 789.81 1576.73 1177.32 1365.04 Steppe 633.15 1706.27 4705.38 1371.51

Warm desert 2134.27 1516.60 3138.55 1111.00 Savanna 2251.42 8616.21 2536.42 8103.96

**Table 6.** Total NPP (TNPP) of major global terrestrial biomes estimated by Miami, Schuur and Classification Indices-

Comparisons with the Classification Indices-based Model and Miami Model, Schuur Model estimated substantially higher TNPP, especially for Temperate humid grassland, Steppe and Warm desert (Table 6), but the simulated spatial pattern for global NPP is very similar (Figure 13bcd). The NPP increases from the South and North poles toward the Equator, corresponding to changes in precipitation and temperature. Comparing the NPP maps (Figure 13bcd) with the Table 6 shows highest TNPP of grassland in Savanna, followed by Tundra and alpine grassland and Steppe, respectively. Although Warm desert grassland covers the second greatest area among grassland super-classes, TNPP for Warm desert grassland is still lower

In conclusion, under recent past climate conditions, the main parts of global grassland are the Savanna and Tundra and alpine grassland, which together account for 42.15% of total global

grassland area and 63.76-68.91% of global grassland TNPP (Table 6).

**TNPP(Tg C)**

**Classification Indices-based Model**

land distribution.

194 CO2 Sequestration and Valorization

**(super-class group)**

Temperate humid grassland

Forest, including Temperate forest, Subtropical forest and Tropical forest

due to lower NPP.

based Models in recent past (1950- 2000)

**Area (million hectares)**

**Biome**

*Note*: (a) Potential biome (super-class group) classification map; (b, c, d) global pattern of grassland NPP evaluated by the Miami Model, Schuur Model and Classification Indices-based Model.

**Figure 13.** Global spatial pattern of the potential biome and the grassland NPP for recent past (1950-2000)

#### *3.3.3. Discussion*

#### *3.3.3.1. Model-data comparison*

GDD, MAP and the moisture index (K value) are used as criteria to determine the class in the IOCSG. The Classification Indices-based Model connected with the IOCSG by using the classification indices as independent variables and considers three meteorological measure‐ ments and their interactions in its formulation. The use of these variables allows the Classifi‐ cation Indices-based Model to more accurately predict changes of NPP among grassland classes or super-classes [17, 22, 24, 25, 27, 28]. The model's relative simplicity and ability to make reasonable estimates of the patterns of NPP in global grasslands is attractive. Despite being a newer NPP-climate relationship model, the Classification Indices-based Model provides more accurate conclusions and may also reduce the uncertainty of climate change impacts on grasslands on a global scale. Furthermore, it has the potential to rapidly advance grassland NPP research and application in developing/undeveloped regions or countries that generally lack the detailed and complex data required by other models (i.e., BIOME4).

#### *3.3.3.2. Grassland carbon budgets*

The Classification Indices-based Model estimates global TNPP at 54.51 Pg C compared to 55.38 Pg C and 61.52 Pg C using the Miami model and the Schuur Model under recent past climate conditions (Table 6). The Classification Indices-based Model estimate is closer to the estimates of 46 Pg C, 48 Pg C and 55 Pg C by the National Center for Ecological Analysis and Synthesis (NCEAS) model [4], the Carnegie-Ames-Stanford approach (CASA) Biosphere model [72], and the MODIS NPP [73], respectively. This wide range in the reported potential global TNPP could be due to their use of different estimation methods, inconsistent classification systems, and/or different time periods [74]. The potential carbon accumulated in plants was calculated based on simulated NPP. This calculation does not account for the potential impacts from human disturbances, but assumes that the full potential NPP is reached in the grassland. The Classification Indices-based Model estimates global grassland TNPP at 15.38 Pg C compared to 16.47 Pg C and 21.37 Pg C using the Miami model and the Schuur Model under recent past climate conditions (Table 6). Our global-scale NPP estimates can be used to improve under‐ standing of environmental controls on global-scale NPP, help understand how NPP might have been altered by land use [75] and assess the human appropriation of net primary productivity.

#### **4. Grassland in response to climate change**

#### **4.1. Introduction**

Global climate change will seriously affect terrestrial ecosystems. In global climate change research, modelling is a key method for the study of climate-vegetation, which is an important component in the area of global change research. The current period of climate *3.3.3. Discussion*

196 CO2 Sequestration and Valorization

*3.3.3.1. Model-data comparison*

*3.3.3.2. Grassland carbon budgets*

productivity.

**4.1. Introduction**

**4. Grassland in response to climate change**

GDD, MAP and the moisture index (K value) are used as criteria to determine the class in the IOCSG. The Classification Indices-based Model connected with the IOCSG by using the classification indices as independent variables and considers three meteorological measure‐ ments and their interactions in its formulation. The use of these variables allows the Classifi‐ cation Indices-based Model to more accurately predict changes of NPP among grassland classes or super-classes [17, 22, 24, 25, 27, 28]. The model's relative simplicity and ability to make reasonable estimates of the patterns of NPP in global grasslands is attractive. Despite being a newer NPP-climate relationship model, the Classification Indices-based Model provides more accurate conclusions and may also reduce the uncertainty of climate change impacts on grasslands on a global scale. Furthermore, it has the potential to rapidly advance grassland NPP research and application in developing/undeveloped regions or countries that generally lack the detailed and complex data required by other models (i.e., BIOME4).

The Classification Indices-based Model estimates global TNPP at 54.51 Pg C compared to 55.38 Pg C and 61.52 Pg C using the Miami model and the Schuur Model under recent past climate conditions (Table 6). The Classification Indices-based Model estimate is closer to the estimates of 46 Pg C, 48 Pg C and 55 Pg C by the National Center for Ecological Analysis and Synthesis (NCEAS) model [4], the Carnegie-Ames-Stanford approach (CASA) Biosphere model [72], and the MODIS NPP [73], respectively. This wide range in the reported potential global TNPP could be due to their use of different estimation methods, inconsistent classification systems, and/or different time periods [74]. The potential carbon accumulated in plants was calculated based on simulated NPP. This calculation does not account for the potential impacts from human disturbances, but assumes that the full potential NPP is reached in the grassland. The Classification Indices-based Model estimates global grassland TNPP at 15.38 Pg C compared to 16.47 Pg C and 21.37 Pg C using the Miami model and the Schuur Model under recent past climate conditions (Table 6). Our global-scale NPP estimates can be used to improve under‐ standing of environmental controls on global-scale NPP, help understand how NPP might have been altered by land use [75] and assess the human appropriation of net primary

Global climate change will seriously affect terrestrial ecosystems. In global climate change research, modelling is a key method for the study of climate-vegetation, which is an important component in the area of global change research. The current period of climate change and high CO2 concentrations in recent years has resulted in warm summers and longer growing seasons, to a change in vegetation patterns can be expected. This change would cause changes in vegetation classes and their NPP. For example, the dominance of some cold-season C3 grasses would decrease, while that of warm season C4 grasses could be expected to increase. Global warming also causes changes in precipitation patterns and the total amount of precipitation. The far-reaching consequences of climatic phenomena such as La Niña and El Niño receive widespread attention and appear to be occurring more frequently in recent decades. Zhang et al. [26] recently showed that global warm‐ ing is already affecting the world's rainfall patterns, bringing more precipitation to northern Europe, Canada and northern Russia, but less to sub-Saharan Africa, southern India and Southeast Asia. They explained that human activity is causing alterations to the water cycle, moving more water vapor away from the warmest parts of the planet and pushing it toward the poles. As a result, wet areas are becoming wetter and dry areas are becom‐ ing drier. Thus, there has been an increase in the demand by the broader scientific community and policymakers for better projections of regional impacts of future green‐ house-gas-induced climatic change. Global environmental change, signified by 'global warming' and its possible effects on ecosystems have been drawing increasing attention from the scientists and governments of every country and from all circles of the world [9, 13, 76]. Study of responses of terrestrial NPP to climate changes will help us understand the feedback between climate systems and terrestrial ecosystems and the mechanisms of increased NPP in the northern, middle and high latitudes [77]. NPP is not only used for assessing the carbon balance on regional and global scales, it also plays an important role in demonstrating compliance with the Kyoto Protocol on greenhouse gas reduction [11]. So, accurate estimates of NPP are very important not only for scientifically guiding ecosystem management but also for the study of global climate change [53]. Therefore, study of NPP and its response to global change is one of the key focuses for the global scientific community [9, 78].

Climate change has been identified as having far-reaching implications for the world's grasslands [16, 79-83]. Therefore, understanding the sensitivity of grassland to climate change and the effect of these changes on grassland ecosystems is a key issue in global carbon cycling. NPP in grassland is a key variable in our understanding of carbon exchange between the biosphere and atmosphere, both currently and under climate change condi‐ tions [3, 4, 13, 84]. The mechanisms and modelling of responses in grassland NPP to global change will enhance understanding of how grassland ecosystems will respond to global change and the projection of grassland NPP to global change. This will enable the develop‐ ment of countermeasures to maximize the grassland NPP under conditions of global change, especially to provide sustainable development of pasturing in arid and semiarid areas, by using these theoretical and technological methods. The IPCC report [85] states that climate change will present challenges for future grassland use by livestock and other grazers, and for the resulting public policy designed to manage grasslands. Given their vast area and diversity of classes, China's grasslands play an important role in both regional and global carbon cycling [33]. In order to effectively manage grassland ecosystems and maintain their sustainability, a deeper understanding of how these ecosystems will respond to growing pressures is needed. Large-scale analysis and modelling is needed to develop a better grasp of the spatial distribution of grasslands, their productivity, and potential variations in response to climate changes [3, 4, 13, 20, 23, 66].

Climate change will have profound effects on the class level, which in turn will influence vegetation types and their NPP. Clearly, this requires improved projections of regional climate and understanding of how vegetation types will respond to climate change. Climate is a major driver of variation in NPP, but a clear understanding of the impact of climate change on NPP is lacking [13]. Not only are grassland researchers concerned with the performance of NPP simulation models under contemporary climate, but also they want to know the behavior of these models under extrapolated future environmental condi‐ tions [3, 4, 13]. A growing number of research efforts have demonstrated the importance of climate-vegetation interaction in understanding climate sensitivity and climate change [18]. The study of climate-vegetation interaction is the basis for research on terrestrial ecosystems' responses to global change and mainly comprises two important compo‐ nents: climate vegetation classification and NPP of natural vegetation. From the view of generic relationships among all the vegetation types, the IOCSG can be used to predict climate-linked spatial or temporal succession from an original class to a new class as these climatic factors change. Hence, there is potential to develop future scenarios based on possible changes in vegetation type and its NPP in response to climate change in coming years. The IOCSG is a suitable tool to incorporate the effects of both climate change and vegetation management measures on plant growth activity into NPP estimation at the same resolution as satellite data. The Classification Indices-based Model connects with the IOCSG by using the classification indices as independent variables. The use of these variables allows the Classification Indices-based Model to project changes of NPP among grassland classes or super-classes more accurately [17, 22, 24, 25, 27, 28]. The section on Model Validation and Model Inter-comparison has shown that the Classification Indices-based Model is suitable to predict grassland NPP at regional, Chinese or global scale. In addition, the Classification Indices-based Model presented the closest values to the observed in situ meteorological variables. Furthermore, its relative simplicity and ability to generate reasonable patterns of NPP is attractive. Also, GDD and the moisture index (K value) are used as proxies to represent the water and thermal properties of the growing season, and are effective drivers for modelling NPP. The Classification Indices-based Model not only takes into account dynamical classes [17], it also simulates NPP of corresponding classes. As a new NPP-climate relationship model, the Classification Indices-based Model has the potential to evaluate the possible effects of climate change by improving the accuracy of the NPP prediction and reducing the evaluation uncertainty of the possible effects of climate change [22, 24, 25, 27, 28].

In order to effectively model climate change impacts on global grassland distributions and associated NPP, it is important to understand climate dynamics in the recent past (1950-2000) as well as climatic projections for the future (2001-2050). Hence, in this section we: (1) simulate the spatial distribution patterns and associated NPP characteristics of the world's and China's potential grassland under a future climate scenario using the IOCSG approach and the Classification Indices-based Model; and (2) estimate future trends in response to climate change in the first half of the 21st century by comparing the varia‐ tion in distribution of NPP with the potential total NPP (TNPP) of grassland between the recent past and the projected future climate scenario. Such findings should improve knowledge about changes in global grassland primary productivity under global warming.

#### **4.2. Data acquisition and methods of analysis**

#### *4.2.1. Climate data*

pressures is needed. Large-scale analysis and modelling is needed to develop a better grasp of the spatial distribution of grasslands, their productivity, and potential variations in

Climate change will have profound effects on the class level, which in turn will influence vegetation types and their NPP. Clearly, this requires improved projections of regional climate and understanding of how vegetation types will respond to climate change. Climate is a major driver of variation in NPP, but a clear understanding of the impact of climate change on NPP is lacking [13]. Not only are grassland researchers concerned with the performance of NPP simulation models under contemporary climate, but also they want to know the behavior of these models under extrapolated future environmental condi‐ tions [3, 4, 13]. A growing number of research efforts have demonstrated the importance of climate-vegetation interaction in understanding climate sensitivity and climate change [18]. The study of climate-vegetation interaction is the basis for research on terrestrial ecosystems' responses to global change and mainly comprises two important compo‐ nents: climate vegetation classification and NPP of natural vegetation. From the view of generic relationships among all the vegetation types, the IOCSG can be used to predict climate-linked spatial or temporal succession from an original class to a new class as these climatic factors change. Hence, there is potential to develop future scenarios based on possible changes in vegetation type and its NPP in response to climate change in coming years. The IOCSG is a suitable tool to incorporate the effects of both climate change and vegetation management measures on plant growth activity into NPP estimation at the same resolution as satellite data. The Classification Indices-based Model connects with the IOCSG by using the classification indices as independent variables. The use of these variables allows the Classification Indices-based Model to project changes of NPP among grassland classes or super-classes more accurately [17, 22, 24, 25, 27, 28]. The section on Model Validation and Model Inter-comparison has shown that the Classification Indices-based Model is suitable to predict grassland NPP at regional, Chinese or global scale. In addition, the Classification Indices-based Model presented the closest values to the observed in situ meteorological variables. Furthermore, its relative simplicity and ability to generate reasonable patterns of NPP is attractive. Also, GDD and the moisture index (K value) are used as proxies to represent the water and thermal properties of the growing season, and are effective drivers for modelling NPP. The Classification Indices-based Model not only takes into account dynamical classes [17], it also simulates NPP of corresponding classes. As a new NPP-climate relationship model, the Classification Indices-based Model has the potential to evaluate the possible effects of climate change by improving the accuracy of the NPP prediction and reducing the evaluation uncertainty of the possible effects of climate

In order to effectively model climate change impacts on global grassland distributions and associated NPP, it is important to understand climate dynamics in the recent past (1950-2000) as well as climatic projections for the future (2001-2050). Hence, in this section we: (1) simulate the spatial distribution patterns and associated NPP characteristics of the world's and China's potential grassland under a future climate scenario using the IOCSG

response to climate changes [3, 4, 13, 20, 23, 66].

198 CO2 Sequestration and Valorization

change [22, 24, 25, 27, 28].

Two different global climatic datasets were used in this study. The first was the monthly precipitation and mean temperature grid datasets over 50 years from 1950-2000, descri‐ bed at section of 3.3.1.2. The second dataset was the global monthly precipitation and mean temperature prediction data set (also with 30 arc-second resolution) excluding Antarctica from 2001 to 2050 under the A2a scenario (see below), which was simulated by Austral‐ ia's Commonwealth Scientific and Industrial Research Organisation (CSIRO) and is available through the website http://www.worldclim.org /futdown.htm [86]. The A2a scenario takes into consideration the following land-use changes: (1) high rate of population growth; (2) slow technological change; and (3) increased energy use, and describes a highly heteroge‐ neous future world in light of regionally-oriented economies. The gridded baseline climate (recent past) and gridded future (2000-2050) A2a climate scenarios were used to estimate changes in mean annual temperature (MAT), growing degree-days (GDD) and mean annual precipitation (MAP).

#### *4.2.2. IOCSG approach and NPP simulation*

The IOCSG potential super-class group (biome) maps and its NPP were created and processed by the ArcGIS software (ESRI Inc., Redlands, CA, USA) under the baseline climate (1950-2000) and future climate A2a scenario (2000-2050) on average level. In the IOCSG biome (super-class group) classification map, lake water, permanent snow and ice were excluded by using the Moderate Resolution Imaging Spectroradiometer-International Geosphere Biosphere Pro‐ gram (MODIS-IGBP) land-cover classification dataset in year 2001, found at http://earthda‐ ta.nasa.gov. The MODIS-IGBP land-cover classification dataset was used to calculate the area of potential grassland biome. The simulated NPP by the Classification Indices-based Model was an ideal potential value and land-use practices were not taken into account. The recent past map was chosen as a baseline and was used for comparison with projected future climate to estimate trends in changes of class or grassland biome distribution and grassland NPP distribution in response to climate change. Both the IOCSG and the Classification Indicesbased Model were applied to analyze the changes in grassland biomes and to measure the change in TNPP of grassland biomes from the recent past (1950-2000) to the future A2a scenario (2001-2050). The total NPP has a unit of Tg C (1 Tg = 1012g) for biome or Pg C (1 Pg = 1015g) for China or the world.

#### **4.3. Modelling global-scale potential grassland changes in spatio-temporal patterns to global climate change**

#### *4.3.1. Characteristics of potential grassland over 2001-2050 under A2a scenario*

In the A2a scenario, the Savanna, Warm desert, Semi-desert, Tundra and alpine steppe, Steppe, Temperate humid grassland and Cold desert occupy 17.68%, 16.06%, 5.97%, 5.91%, 4.83%, 4.30% and 1.24% of global land area, respectively (Table 7). Table 7 shows the highest TNPP of grassland biomes in the Savanna, followed by Steppe and Semi-desert.

In conclusion, the main grassland biomes found in the world were the Savanna, Steppe and Semi-desert, occupying 50.86% of total global grassland areas and 78.06% of global grassland TNPP under future A2a scenario (Table 7).


**Table 7.** TNPP of major global terrestrial biomes estimated by Classification Indices-based Model in future (2001-2050) A2a scenario

#### *4.3.2. Numerical simulations of potential grassland dynamics*

Both the IOCSG approach and the Classification Indices-based Model were applied to analyze the succession of classes and super-classes and to measure the change of super-class TNPP from recent past (1950-2000) to future A2a scenario (2001-2050).

#### *4.3.2.1. Change of class and super-class area*

The class called Frigid-extrarid frigid desert, alpine desert (IA1), mainly distributed in America and Asia, continuously decreased from recent past to A2a scenario and the rate of decrease per decade was 4.72%. If decreasing rates persist, Frigid-extrarid frigid desert, alpine desert (IA1) could potentially disappear by the second half of this century, while the class called Cold temperate subhumid montane meadow steppe (II D23), mainly distributed in Asia and America, continuously increased from recent past to A2a scenario, by 18.40%.

Climate change also influences the spatial distribution of super-classes. The distribution area for various super-classes in these two time periods shows differing dynamic characteristics from recent past (1950-2000) to future A2a scenario (2001-2050) (Tables 6 and 7). The area of Tundra and alpine steppe will decrease by 53.26 million hectares compared with its size during 1950-2000. Likewise, Warm desert will decrease by 29.97 million hectares. However, trends show that Savanna and Temperate humid grassland will strongly increase, to nearly 123.16 million hectares. Overall, the Forest category will decrease by 29.29 million hectares (Tables 6 and 7).

### *4.3.2.2. Shift trend of super-classes*

**4.3. Modelling global-scale potential grassland changes in spatio-temporal patterns to**

In the A2a scenario, the Savanna, Warm desert, Semi-desert, Tundra and alpine steppe, Steppe, Temperate humid grassland and Cold desert occupy 17.68%, 16.06%, 5.97%, 5.91%, 4.83%, 4.30% and 1.24% of global land area, respectively (Table 7). Table 7 shows the highest TNPP

In conclusion, the main grassland biomes found in the world were the Savanna, Steppe and Semi-desert, occupying 50.86% of total global grassland areas and 78.06% of global grassland

**Area**

**(million hectares) total net primary productivity (TNPP)(Tg C)**

5766.10 40389.90

*4.3.1. Characteristics of potential grassland over 2001-2050 under A2a scenario*

of grassland biomes in the Savanna, followed by Steppe and Semi-desert.

Tundra and alpine steppe 774.70 1313.62 Cold desert 162.80 84.76 Semi-desert 782.41 1440.56 Steppe 633.15 1854.16 Temperate humid grassland 563.89 1157.55 Warm desert 2104.30 1235.21 Savanna 2316.12 10197.64

**Table 7.** TNPP of major global terrestrial biomes estimated by Classification Indices-based Model in future

Both the IOCSG approach and the Classification Indices-based Model were applied to analyze the succession of classes and super-classes and to measure the change of super-class TNPP

The class called Frigid-extrarid frigid desert, alpine desert (IA1), mainly distributed in America and Asia, continuously decreased from recent past to A2a scenario and the rate of decrease per decade was 4.72%. If decreasing rates persist, Frigid-extrarid frigid desert, alpine desert (IA1) could potentially disappear by the second half of this century, while the class called Cold temperate subhumid montane meadow steppe (II D23), mainly distributed in Asia and

America, continuously increased from recent past to A2a scenario, by 18.40%.

*4.3.2. Numerical simulations of potential grassland dynamics*

*4.3.2.1. Change of class and super-class area*

from recent past (1950-2000) to future A2a scenario (2001-2050).

**global climate change**

200 CO2 Sequestration and Valorization

**Biome (super-class group)**

Forest, including Temperate forest, Subtropical forest and Tropical forest

(2001-2050) A2a scenario

TNPP under future A2a scenario (Table 7).

The A2a scenario predicts changes in global temperature and rainfall patterns, making wet areas wetter, and dry areas drier (Figure 14), resulting in class conversions from an original class to a new class due to changes in climate conditions. For example, the Frigid perhumid rain tundra, alpine meadow (IF36) class will change to Cold temperate-humid montane meadow (IIE30) with climate warming, or Cold temperate perhumid taiga forest (IIF37) with climate drying. The conversion of classes from recent past to future A2a scenario will result in changes in area and TNPP distribution patterns among biomes (Table 8 and Figure 15a). For example, in the super-class of Tundra and alpine steppe, an area of 656.09 million hectares will be converted to Forest (Legend 18 in Figure 15a and Table 8) in this long-run sequence, which will mainly take place in Asia (accounting for 58.96%) and America (32.21%), while another 182.92 million hectares will be converted to Temperate humid grassland (Legend 15 in Figure 15a and Table 8), a change that will mainly take place in Asia (65.90%). An area of 590.06 million hectares of Forest under recent past climate conditions will be converted into the Temperate humid grassland under A2a scenario (Legend 85 in Figure 15a and Table 8), mainly taking place in Asia (51.14%), the Americas (26.22%) and Europe (22.58%). An area of 389.29 million hectares of Forest will be converted into Savanna (Legend 87 in Figure 15a and Table 8), mainly taking place in the Americas (49.62%) and Africa (35.52%). An area of 253.28 million hectares of Temperate humid grassland under recent past climate conditions will be converted into Steppe under the A2a scenario (Legend 54 in Figure 15a and Table 8), mainly taking place in Asia (48.76%), the Americas (28.89%) and Europe (22.22%). An area of 243.37 million hectares of Steppe will be converted into Semi-desert (Legend 43 in Figure 15a and Table 8), mainly taking place in Asia (49.72%) (Table 8).

An area of 856.54 million hectares of Grassland category will be converted to Forest category and an area of 1089.77 million hectares of Forest category will be converted into Grassland category (Figure 15b. and Table 8). There is a clear increasing trend in the Grassland category. As a whole, areas of Grassland category will increase by 31.76 million hectares, while areas of Forest category will decrease by 29.29 million hectares. The relative area of Grassland category will increase by 0.43% over this period (1950-2050), whereas the relative area of Forest category will decrease by 0.51% over the same period (Tables 6 and 7).

**Figure 14.** Maps of anomalies in future A2a climatic scenario (relative to the average for 1950-2000) for (a) mean an‐ nual temperature (MAT), (b) growing degree-days (GDD) and (c) mean annual precipitation (MAP).

The Classification Indices-Based Model for NPP According to the Integrated Orderly Classification System of... http://dx.doi.org/10.5772/57297 


*Note*: the two digits in the code represents the biome conversion, where the first number depicts the biome under recent past climatic conditions and the second number is under A2a scenario; numbers 1-8 represent Tundra and al‐ pine steppe, cold desert, Semi-desert, Steppe, Temperate humid grassland, Warm desert, Savanna, and Forest, respec‐ tively. For example, Code 18 means Tundra and alpine Steppe under recent past climatic conditions would convert into forest in A2a scenario. The number in table is the area of conversion in unit of million hectares.

**Table 8.** The global biomes conversion matrix from recent past (1950-2000) to future (2001-2050) A2a scenario

**Figure 14.** Maps of anomalies in future A2a climatic scenario (relative to the average for 1950-2000) for (a) mean an‐

nual temperature (MAT), (b) growing degree-days (GDD) and (c) mean annual precipitation (MAP).

CO2 Sequestration and Valorization

*Note*: (a) biomes shift. In the legend, the two digits represent the biomes conversion, where the first number depicts the biome under recent past climatic conditions and the second number is under A2a scenario. The numbers 1-8 rep‐ resent Tundra and alpine steppe, Cold desert, Semi-desert, Steppe, Temperate humid grassland, Warm desert, Savan‐ na, and Forest, respectively. For example, Legend 54 means Temperate humid grassland under the recent past climatic conditions would convert into Steppe under the A2a scenario. (b) Grassland (including Tundra and alpine steppe, Cold desert, Semi-desert, Steppe, Temperate humid grassland, Warm desert, and Savanna) and Forest (including Temper‐ ate forest, Sub-tropical forest, and Tropical forest) categories change: Grassland–forest means Grassland category would be converted into the Forest category, Forest-grassland means the Forest category would be converted into the Grassland category; the others are no change.

**Figure 15.** Spatial distribution dynamics of global potential biomes from recent past to future A2a scenario.

## *4.3.2.3. Change of TNPP*

*Note*: (a) biomes shift. In the legend, the two digits represent the biomes conversion, where the first number depicts the biome under recent past climatic conditions and the second number is under A2a scenario. The numbers 1-8 rep‐ resent Tundra and alpine steppe, Cold desert, Semi-desert, Steppe, Temperate humid grassland, Warm desert, Savan‐ na, and Forest, respectively. For example, Legend 54 means Temperate humid grassland under the recent past climatic conditions would convert into Steppe under the A2a scenario. (b) Grassland (including Tundra and alpine steppe, Cold desert, Semi-desert, Steppe, Temperate humid grassland, Warm desert, and Savanna) and Forest (including Temper‐ ate forest, Sub-tropical forest, and Tropical forest) categories change: Grassland–forest means Grassland category would be converted into the Forest category, Forest-grassland means the Forest category would be converted into the

**Figure 15.** Spatial distribution dynamics of global potential biomes from recent past to future A2a scenario.

Grassland category; the others are no change.

204 CO2 Sequestration and Valorization

The Classification Indices-based Model shows NPP either neutral or increasing for most global grid cells during the periods from recent past to A2a scenario (Figure 16).

*Note*: The legends show the differentiated NPP and negative values indicate C loss for NPP.

**Figure 16.** Trends in global NPP anomalies from 1950 to 2050 computed by the Classification Indices-based Model.

At the class level, the largest increases are in Tropical-humid seasonal rain forest (VIIE 35), Tropical arid tropical desert brush (VIIB14), Tropical-subhumid tropical xerophytic forest (VIID28) and Tropical-semiarid savanna (VIIC 21), as a result of increased MAT and MAP in Africa and the Americas, while TNPP of Frigid perhumid rain tundra, alpine meadow (IF 36), Cold temperate perhumid taiga forest (IIF 37), Sub-tropical perhumid evergreen broad leaved forest (VIF 41), Warm-perhumid deciduous-evergreen broad leaved forest (VF 40), Warm temperate perhumid deciduous broad leaved forest (IVF 39), and Subtropicalsubhumid sclerophyllous forest (VID27) decrease sharply. The TNPP of Subtropicalsemiarid subtropical brush steppe (VIC20) in Africa shows a marked decrease due to decreased precipitation (Table 9).

At the super-class level, the TNPP of Savanna shows the largest increase, followed by Steppe, Temperate humid grassland, Warm desert and Semi-desert. These increases in TNPP are 2093.68 Tg C, 482.65 Tg C, 316.22 Tg C, 124.21 Tg C and 75.52 Tg C, respectively. The TNPP of Tundra and alpine steppe shows the largest decrease. The simulated TNPP of Tundra and alpine steppe will decrease by 1178.72 Tg C compared with its size during 1950-2000. The decrease of TNPP for Cold desert will be 7.42 Tg C (Table 9).

At the category level, the TNPP of Grassland category will increase 12.40% to 1906.14 Tg C over this period (1950-2050) (Tables 6, 7 and 9).



*Note*: The class name refers to Figure 7 Explanation.Unit of TNPP is TgC.

**Table 9.** Comparison of TNPP of biomes and classes estimated by the Classification Indices-based Model under the baseline climate (recent past (1950-2000)) and those under future (2000-2050) climatic scenario (A2a)

#### *4.3.3. Discussion*

Biome

(super-class group)

Tundra and alpine steppe

Frigid desert

Semi-desert

Steppe

Temperate humid grassland

Warm desert

Savanna

Forest, including temperate forest, Subtropical forest and tropical forest

**Class-ID**

206 CO2 Sequestration and Valorization

**Current**

**A2a**

**Current**

**A2a**

**Current**

**Global Asia Europe Africa America Oceania**

**Current**

**A2a**

**Current**

**A2a**

**Current**

**A2a**

**A2a**

IA1 0.02 0.96 0.01 0.23 - 0.07 - 0.05 0.01 0.46 - 0.15 IB8 5.47 6.82 4.35 6.38 - - - - 1.12 0.44 - - IC15 4.77 5.58 4.04 4.46 - - - - 0.74 1.13 - - ID22 7.10 12.38 6.15 5.95 - - - - 0.95 6.44 - - IE29 38.79 70.51 27.49 34.69 - - - - 11.30 35.83 - - IF36 2436.19 1217.36 1249.53 433.86 215.92 71.70 0.12 0.04 967.04 710.92 3.58 0.84

IIA2 2.51 1.77 1.81 1.06 - - - - 0.70 0.71 - - IIIA3 23.32 15.78 21.16 12.20 - - - - 2.16 3.58 - - IVA4 66.35 67.21 60.71 59.96 - - 0.54 0.01 5.10 7.24 - -

IIB9 26.13 21.89 19.38 18.90 - - - - 6.75 3.00 - - IIIB10 357.26 276.52 263.64 214.83 1.75 3.90 - - 91.88 57.78 - - IVB11 470.08 762.99 218.25 400.46 56.49 134.40 35.45 3.53 158.16 224.59 1.73 - VB12 511.56 379.16 88.41 104.32 37.32 56.06 98.73 41.67 161.61 151.16 125.50 25.94

IIC16 27.93 26.26 22.02 21.05 - - - - 5.91 5.20 - - IIIC17 363.16 312.94 203.41 194.63 33.32 9.36 0.07 - 126.35 108.94 0.01 - IVC18 308.63 611.41 85.69 162.77 111.39 284.52 17.09 5.38 84.42 156.78 10.04 1.97 VC19 271.69 235.16 16.99 60.69 62.59 65.19 52.30 27.77 103.17 53.83 36.64 27.69 IVD25 400.10 668.39 96.81 105.43 178.71 423.78 23.22 6.62 75.78 119.67 25.59 12.90

IID23 53.99 101.44 43.96 60.01 - - - - 10.03 41.44 - - IIID24 460.59 573.19 193.04 342.42 90.80 64.68 0.67 0.02 175.48 166.03 0.60 0.05 IIE30 326.75 482.92 218.82 338.35 0.25 - - - 107.68 144.57 - -

VA5 56.55 52.91 42.65 44.25 - - 7.83 1.50 6.07 7.17 - - VIA6 351.22 265.31 97.70 109.45 0.25 0.22 96.29 56.62 24.16 31.20 132.82 67.82 VIIA7 703.24 916.99 212.04 248.00 - 0.03 336.03 420.98 9.29 32.51 145.87 215.47

VIB13 1367.24 1337.50 77.23 140.47 19.07 106.51 566.23 342.92 288.82 408.11 415.89 339.49 VIIB14 3357.22 4848.10 487.68 655.00 - 1.38 1685.12 2316.33 476.02 780.40 708.40 1094.98 VIC20 714.88 573.17 16.79 75.25 18.77 74.33 372.22 167.42 215.48 204.94 91.63 51.23 VIIC21 2664.61 3438.87 533.02 629.19 - 0.11 1355.63 1592.66 581.57 973.80 194.40 243.12

VD26 275.02 252.60 65.16 46.27 30.99 70.36 52.70 20.71 99.26 78.71 26.91 36.56 VID27 768.75 569.52 32.99 136.59 7.98 23.34 438.75 157.32 260.74 229.46 28.30 22.82 VID28 2991.91 4141.40 668.94 674.43 - - 1187.64 1629.08 973.51 1643.08 161.82 194.82 IIIE31 846.52 1313.47 232.00 549.65 433.70 411.61 2.20 0.63 175.74 350.61 2.88 0.96 IVE32 628.69 860.05 125.12 116.91 257.76 383.06 27.15 8.52 160.99 319.82 57.68 31.74 VE33 475.38 488.95 211.14 79.74 19.27 63.57 45.45 20.42 176.58 293.46 22.94 31.75 VIE34 1876.90 1763.09 279.98 419.33 2.34 7.47 785.68 310.50 780.53 993.43 28.38 32.35 VIIE35 6155.32 7697.00 912.79 1054.92 - - 2556.00 3506.24 2617.13 3036.96 69.40 98.88 IIF37 3558.44 2714.89 1447.30 1357.99 658.84 320.88 1.54 0.27 1424.26 1022.19 26.50 13.56 IIIF38 2575.79 2967.37 466.08 798.74 1064.25 868.80 9.44 4.09 946.25 1233.80 89.76 61.93

#### *4.3.3.1. Biome conversions*

The changing climate will affect the growth of plants, alter the species composition of plant communities and ultimately change community structure [87]. Under these conditions, the classes will shift [3,5] and result in biome conversions [3,5]. Our analysis indicated that Steppe will experience the largest variation in biome conversions. The result is the same as the conclusion of Liu et al. [88], who confirmed that under climate warming, the temper‐ ate steppe in the arid and semiarid regions of northern China will act as a net C source. The Savanna and Temperate humid grassland will have the highest percentage of growth. The Tundra and alpine steppe biome will have the greatest change in total area, followed by Forest (Tables 6, 7 and 9).

#### *4.3.3.2. Trends in grassland NPP in response to climate change*

The Classification Indices-based Model estimates global potential grassland TNPP at 16.47 Pg C and global TNPP at 57.67 Pg C under future climatic A2a scenario (Table 7). Climate change is expected to affect the TNPP of grassland. Understanding climate and NPP feedback cycles in grasslands requires knowledge of the changes in spatial patterns on the NPP of grasslands. The dynamic change characteristics of potential biomes (Table 8 and Figure 15) reflect further evidence of increasing global warming since the early 1980s (Intergovernmental Panel on Climate Change, 2007). The Classification Indices-based Model estimated an increase of 1.09 Pg C in TNPP for global potential grassland from recent past to future A2a scenario (Tables 6, 7 and 9). It will bring an increase of 3.91 Pg in annual sequestration of CO2 and 2.88 Pg of annual contribution to the atmospheric O2 cycle respectively (1.63 g of CO2 is absorbed and 1.2 g of O2 is released to generate 1 g of DM according to photosynthesis stoichiometry; [89]), which will impose a new issue for future grassland research to support sustainable develop‐ ment and to provide relevant knowledge to meet the challenge of climate change [90].

#### **4.4. Spatio-temporal dynamics on the distribution, extent and NPP of potential grassland in response to climate changes in China**

#### *4.4.1. Characters of potential grassland over 2001-2050 under A2a scenario*

In the A2a scenario, the Tundra and alpine steppe, Cold desert, Semi-desert, Steppe, Temperate humid grassland, Warm desert, and Savanna were projected to occupy 8.9%, 8.8%, 11.1%, 12.0%, 5.9%, 6.2%, and 1.8% of land area of China, respectively (Table 10). Under scenario A2a, the Classification Indices-based Model projected the highest TNPP to be in the Forest category. Table 10 shows that the highest TNPP values of grassland biomes were projected to be in the Steppe, followed by Semi-desert, Tundra and alpine steppe, and Temperate humid grassland, respectively. In conclusion, it was projected that the two main grassland biomes found in China will be in the Steppe and Semi-desert, and they will occupy 42.2% of China's total grassland areas and 58.3% of China's Grassland TNPP in the future climatic A2a scenario (Table 10).

#### *4.4.2. Comparing the area and TNPP of China's major terrestrial biomes of 1950-2000 with those of 2001-2050 under the A2a scenario*

Climate change was projected to influence the spatial distribution of grassland biomes, with a combined distribution ranging from the northeastern plain adjacent to Mongolia to the southern Tibetan Plateau, with warm desert showing the greatest discrepancies. The distri‐ bution of the area for various grassland biomes in these two time periods was projected to change considerably from the recent past (1950-2000) (Table 4) to the future (2001-2050) in the A2a scenario (Table 10). The area of Tundra and alpine steppe was projected to decrease by 107.9 m ha compared with its size during 1950-2000. The TNPP of Tundra and alpine steppe was projected to change from 250.7 to114.0 Tg C. This is a huge reduction of 54.5% due to global climate change. Likewise, the areas of the Cold desert and Temperate humid grassland were projected to decrease by 40.9 and 0.5 m ha, respectively, while the TNPPs of the Cold desert and Temperate humid grassland were projected to decrease by 1.4 Tg C and 14.1 Tg C, respectively. The Warm desert and Savanna super-class groups were projected to increase the most. The area of Warm desert, Steppe, Semi-desert, and Savanna were projected to increase considerably, by nearly 138.2 m ha (Tables 4 and 10).

#### *4.4.3. Numerical simulations on potential grassland dynamics*

#### *4.4.3.1. Changes in class*

The changing climate is likely to affect the growth of plants, alter the species composition of plant communities, and ultimately change plant community structure [87]. It is anticipated that the classes will shift [3, 5] and will result in biome conversions. Under the A2a scenario, a new class (sub-tropical-extra-arid sub-tropical desert, VIA6) was projected to appear in conjunction with climate warming and drying, and this would be distributed in the Xinjiang Uygur Autonomous Region. The class called Frigid per-humid rain tundra, alpine meadow (IF 36), mainly distributed in the Tibetan Plateau, is projected to decrease continuously under the A2a scenario, with a rate of decrease per decade of 6.8%. If such rates persist, Frigid per-


**Table 10.** TNPP of major China's terrestrial biomes estimated by the Classification Indices-based Model from 2001 to 2050 under A2a scenario

humid rain tundra, alpine meadow (IF 36) could potentially disappear by the end of the century.

#### *4.4.3.2. Trends in super-classes*

**4.4. Spatio-temporal dynamics on the distribution, extent and NPP of potential grassland**

In the A2a scenario, the Tundra and alpine steppe, Cold desert, Semi-desert, Steppe, Temperate humid grassland, Warm desert, and Savanna were projected to occupy 8.9%, 8.8%, 11.1%, 12.0%, 5.9%, 6.2%, and 1.8% of land area of China, respectively (Table 10). Under scenario A2a, the Classification Indices-based Model projected the highest TNPP to be in the Forest category. Table 10 shows that the highest TNPP values of grassland biomes were projected to be in the Steppe, followed by Semi-desert, Tundra and alpine steppe, and Temperate humid grassland, respectively. In conclusion, it was projected that the two main grassland biomes found in China will be in the Steppe and Semi-desert, and they will occupy 42.2% of China's total grassland areas and 58.3% of China's Grassland TNPP in the future climatic A2a scenario (Table 10).

*4.4.2. Comparing the area and TNPP of China's major terrestrial biomes of 1950-2000 with those of*

Climate change was projected to influence the spatial distribution of grassland biomes, with a combined distribution ranging from the northeastern plain adjacent to Mongolia to the southern Tibetan Plateau, with warm desert showing the greatest discrepancies. The distri‐ bution of the area for various grassland biomes in these two time periods was projected to change considerably from the recent past (1950-2000) (Table 4) to the future (2001-2050) in the A2a scenario (Table 10). The area of Tundra and alpine steppe was projected to decrease by 107.9 m ha compared with its size during 1950-2000. The TNPP of Tundra and alpine steppe was projected to change from 250.7 to114.0 Tg C. This is a huge reduction of 54.5% due to global climate change. Likewise, the areas of the Cold desert and Temperate humid grassland were projected to decrease by 40.9 and 0.5 m ha, respectively, while the TNPPs of the Cold desert and Temperate humid grassland were projected to decrease by 1.4 Tg C and 14.1 Tg C, respectively. The Warm desert and Savanna super-class groups were projected to increase the most. The area of Warm desert, Steppe, Semi-desert, and Savanna were projected to increase

The changing climate is likely to affect the growth of plants, alter the species composition of plant communities, and ultimately change plant community structure [87]. It is anticipated that the classes will shift [3, 5] and will result in biome conversions. Under the A2a scenario, a new class (sub-tropical-extra-arid sub-tropical desert, VIA6) was projected to appear in conjunction with climate warming and drying, and this would be distributed in the Xinjiang Uygur Autonomous Region. The class called Frigid per-humid rain tundra, alpine meadow (IF 36), mainly distributed in the Tibetan Plateau, is projected to decrease continuously under the A2a scenario, with a rate of decrease per decade of 6.8%. If such rates persist, Frigid per-

*4.4.1. Characters of potential grassland over 2001-2050 under A2a scenario*

**in response to climate changes in China**

208 CO2 Sequestration and Valorization

*2001-2050 under the A2a scenario*

*4.4.3.1. Changes in class*

considerably, by nearly 138.2 m ha (Tables 4 and 10).

*4.4.3. Numerical simulations on potential grassland dynamics*

The A2a scenario projects changes in China's temperature and rainfall patterns, making wet areas wetter and dry areas drier (Figure 17), resulting in class succession from an original class to a new class if one just considers changes in climate conditions. The model outputs suggest that the conversion of classes from the recent past to the A2a scenario would result in changes in distribution patterns of the area and TNPP among biomes. Table 11 showed the projected results of the changes in areas of biome conversions between the recent past and the future climatic A2a scenario. For example, in this long time sequence, an area of 58.0 million hectares of Tundra and alpine steppe would be converted to Forest (Legend 18 in Figure 18a and Table 11), which would take place mainly in the Tibet Autonomous Region (accounting for 40.1% of the change) and Qinghai Province (35.9% of change), while another 28.9 million hectares would change to Temperate humid grassland (Legend 15 in Figure 18a and Table 11), and this change would mainly take place in the Tibet Autonomous Region (78.1%). Likewise, an area of 56.9 million hectares of Cold desert under recent climate conditions would be converted into Warm desert under the A2a scenario (Legend 26 in Figure 18a and Table 11), mainly taking place in the Xinjiang Uygur Autonomous Region (99.1%). An area of 43.7 million hectares of Temperate humid grassland would be converted into Steppe (Legend 54 in Figure 18a and Table 11), mainly taking place in the Inner Mongolia Autonomous Region (42.5%). An area of 36.3 million hectares of Forest under recent climate conditions would be converted into Temperate humid grassland under the A2a scenario (Legend 85 in Figure 18a and Table 11), mainly taking place in both Heilongjiang Province (38.9%) and Inner Mongolia Autonomous Region (29.3%), respectively. There would be an area of 28.5 million hectares of Steppe changing to Semi-desert (Legend 43 in Figure 18a and Table 11), mainly taking place at the Inner Mongolia Autonomous Region (44.8%).

**Figure 17.** Maps of changes under the future climatic A2a scenario (1950-2000) relative to the average for 1950-2000 for (a) mean annual temperature (MAT) and (b) mean annual precipitation (MAP) in China.

The Classification Indices-Based Model for NPP According to the Integrated Orderly Classification System of... http://dx.doi.org/10.5772/57297 211

(Legend 43 in Figure 18a and Table 11), mainly taking place at the Inner Mongolia Autonomous

**Figure 17.** Maps of changes under the future climatic A2a scenario (1950-2000) relative to the average for 1950-2000

for (a) mean annual temperature (MAT) and (b) mean annual precipitation (MAP) in China.

Region (44.8%).

210 CO2 Sequestration and Valorization

**Figure 18.** Spatial distribution dynamics of potential biomes in China from the recent past to the future (2001-2050) in the A2a scenario.


*Note*: The symbols are defined in the same way as in Table 8.

**Table 11.** Projected changes in area(million hectares) of biome conversions from 1950 to 2050

An area of 81.5 million hectares of the Grassland category would be converted to the Forest category and 67.2 million hectares of the Forest category would be converted into the Grassland category (Figure 18b and Tables 4, 10, 11). There is a clear trend for a decrease in the area of the Grassland category by 11.1 million hectares, whereas areas of the Forest category would increase by 11.3 million hectares (Tables 4, 10 and 11). The relative area of the Forest category would increase by 2.7% over this period (1950-2050), whereas the relative area of the Grassland category would decrease by 2.1% over the same period.

#### *4.4.3.3. Change in TNPP*

**Code China**

212 CO2 Sequestration and Valorization

**Inner Mongolia Autonomous Region**

*Note*: The symbols are defined in the same way as in Table 8.

**Table 11.** Projected changes in area(million hectares) of biome conversions from 1950 to 2050

An area of 81.5 million hectares of the Grassland category would be converted to the Forest category and 67.2 million hectares of the Forest category would be converted into the Grassland

**Tibet Autonomous Region**

**Gansu Province**

 83.54 - 39.62 1.29 24.41 18.76 - 2.60 - 0.91 0.01 0.03 1.65 - 12.95 - 5.66 0.37 1.49 5.44 - 8.46 - 5.70 0.32 0.81 1.62 - 28.90 - 22.56 0.70 2.31 3.33 - 58.02 0.01 23.26 1.67 20.82 2.22 - 66.61 18.72 - 11.40 8.41 28.09 - 56.94 - - 0.49 - 56.45 - 13.48 3.52 0.30 2.37 0.71 6.57 - 62.17 22.95 0.16 4.47 3.98 26.51 - 0.81 - - - - 0.81 - 0.06 - - 0.01 - - - 28.53 12.77 0.07 1.73 0.44 3.35 - 18.81 8.33 0.01 0.38 0.48 0.58 0.12 4.64 - - 0.08 - - - 0.76 - 0.03 0.03 - 0.71 - 43.72 18.49 0.55 3.06 1.07 3.34 3.45 33.00 12.15 3.86 1.04 2.16 1.59 6.76 23.36 0.20 0.72 2.66 0.02 0.07 8.77 0.80 - - - - 0.80 - 1.25 - - - - - - 0.09 - - - - - - 18.26 - 0.03 1.52 - - - 36.31 10.62 2.95 1.36 2.08 1.32 14.10 12.64 - - 0.13 - - - 329.27 6.93 14.33 5.50 2.52 0.46 12.02

**Qinghai Province** **Xinjiang Uygur Autonomous Region**

**Heilongjiang Province**

> The Classification Indices-based Model shows NPP either neutral or increasing for most Chinese grid cells during the periods from the recent past through the A2a scenario (Figure 19). At the class level, the largest increases would be in Subtropical-humid evergreen broadleaved forest (VIE 34), Tropical-per-humid rain forest (VIIF 42), Subtropical per-humid evergreen broad-leaved forest (VIF 41), Subtropical-subhumid sclerophyllous forest (VID 27), and Tropical-humid seasonal rain forest (VIIE 35), as a result of increased MAT and MAP in southern China. The TNPP of Warm temperate-semi-arid warm temperate typical steppe (IVC, 18) in the north-east of the Inner Mongolia Plateau and the Plain of Northern China would increase by 71.69 Tg C year-1 because of increased precipitation in these regions. Warmer temperatures would be primarily responsible for increases in NPP in the Tibet Autonomous Region, i.e., the TNPP of Warm temperate-arid warm temperate zonal semi-desert (IVB 11), Cold temperate per-humid taiga forest (IIF 37), and Cold temperate-humid montane meadow (IIE 30) would increase, whereas the TNPP of Frigid per-humid rain tundra, alpine meadow (IF 36) would decrease sharply. The TNPP of Cool temperate-arid temperate zonal semi-desert (IIIB 10) in both the middle of the Xinjiang Uygur Autonomous Region and the northern area of the Inner Mongolia Autonomous Region would show a marked decrease due to decreased precipitation (Table 12).



*Note*: The class name refers to Fig.7. Explanation

**Table 12.** Comparison of the total net primary productivity (1011 g C) of biomes and classes of 1950-2000(recent past) with those of 2000-2050 under A2a scenario

The Classification Indices-Based Model for NPP According to the Integrated Orderly Classification System of... http://dx.doi.org/10.5772/57297 215

**Figure 19.** Trend of Changes in net primary productivity (NPP) from the recent past to the future (2001-2050) in the A2a scenario. The changes show the differentiated NPP, and negative values indicate C loss for NPP.

At the grassland biome level, the TNPP of Steppe would show the largest increase, followed by Semi-desert, Savanna and Warm desert. These increases in TNPP are 107.3, 69.9, 68.8 and 11.0 Tg C, respectively. The TNPP of Tundra and alpine steppe would show the largest decrease, followed by Temperate humid grassland and Cold desert. The simulated TNPP of Tundra and alpine steppe would decrease by 136.7 Tg C compared with its size during 1950-2000. The decrease of TNPP for Temperate humid grassland and Cold desert would be 14.1 Tg C and 1.4 Tg C, respectively (Tables 4, 10 and 12).

At the category level, the TNPP of Grassland category would increase by 14.5% to 104.7 Tg C over this period (1950-2050), whereas the TNPP of Forest category would increase by 10.2% to 228.0 Tg C (Tables 4, 10 and 12).

#### *4.4.4. Discussion*

**Biome (super-class group)**

Steppe

Temperate humid grassland

Warm desert

Savanna

Forest, including Temperate forest, Subtropical forest and Tropical forest

with those of 2000-2050 under A2a scenario

**Class-ID**

214 CO2 Sequestration and Valorization

**China**

**A2a**

**Recent past**

**Inner Mongolia Autonomous Region**

**A2a**

**Recent past**

**Tibet Autonomous Region**

IVB11 188.23 793.74 38.62 318.08 - - 8.51 71.95 - 0.30 92.40 276.57 VB12 2.14 267.67 - - - - 0.17 0.08 - - - 0.71

IIC16 40.90 110.53 3.26 - 0.68 66.13 3.37 4.21 7.00 11.72 26.59 28.47 IIIC17 597.23 483.52 414.72 342.19 - 8.80 35.78 17.42 5.90 22.27 31.95 51.72 IVC18 519.03 1235.92 2.49 279.73 0.31 4.67 4.37 107.37 - 6.05 - 10.31 VC19 117.75 509.91 - - - 0.62 1.56 19.49 - - - - IVD25 841.62 849.66 - 6.07 0.72 16.09 61.37 119.16 - 0.58 - 2.12

IID23 62.18 187.51 14.37 0.05 4.53 138.15 4.05 4.80 9.43 18.23 29.68 26.27 IIID24 976.28 594.58 397.16 263.99 9.38 59.84 63.01 25.79 10.86 31.26 15.83 30.75 IIE30 208.57 323.66 75.29 0.17 55.82 233.95 10.61 8.16 24.67 43.97 40.26 37.40

VA5 0.39 109.54 - - - - - 0.28 - - 0.39 109.26 VIA6 - 0.74 - - - - - 0.74

VIB13 1.47 117.36 - - - - - 1.32 - - - - VIIB14 3.85 7.68 - - - - - - - - - - VIC20 31.08 568.06 - - - - 0.02 8.37 - - - - VIIC21 30.73 61.80 - - - - - - - - - -

VD26 583.92 393.29 - - - 0.35 2.50 20.88 - - - - VID27 219.17 1299.22 - - - - - 1.80 - - - - VIID28 90.26 189.93 - - - - - - - - - - IIIE31 1247.72 1240.25 209.53 322.60 24.87 62.07 104.06 36.77 11.71 36.23 6.52 23.37 IVE32 1118.61 925.41 - - 1.64 54.73 48.60 47.17 - - - - VE33 2050.76 755.60 - - - 4.75 - 0.69 - - - - VIE34 1911.89 3859.07 - - - 0.20 - - - - - - VIIE35 622.82 1623.86 - - - - - - - - - - IIF37 1502.54 1879.78 388.80 81.96 229.04 611.51 96.39 75.86 95.61 546.35 33.71 49.97 IIIF38 1827.77 1456.20 34.63 105.08 96.09 177.73 56.28 70.14 0.53 31.00 0.18 8.03 IVF39 1904.99 1027.94 - - 119.58 71.14 0.82 2.83 - - - - VF40 3402.34 1221.15 - - 99.21 75.22 - - - - - - VIF41 5166.77 6509.44 - - 229.74 231.03 - - - - - - VIIF42 628.69 2177.01 - - 54.25 203.87 - - - - - -

*Note*: The class name refers to Fig.7. Explanation

**Table 12.** Comparison of the total net primary productivity (1011 g C) of biomes and classes of 1950-2000(recent past)

**A2a**

**Recent past**

**Gansu Province**

**A2a**

**Recent past**

**Qinghai Province**

**A2a**

**Recent past**

**Xinjiang Uygur Autonomous Region**

**A2a**

**Recent past**

#### *4.4.4.1. Grassland carbon budgets*

The Classification Indices-based Model projects China's TNPP at 3.28 Pg C (Table 10) under the future climatic A2a scenario and is close to the projections of 3.33 and 3.34 Pg C by the Vegetation Interaction Model (AVIM) [91] and the CEVSA (Carbon Exchange between Vegetation, Soil, and Atmosphere) model [92], both derived by using long-term annual average data (1980-2005). The results could potentially favor China's position in supporting its effort to reduce global warming gas as outlined in the Kyoto Protocol. The model outputs suggest that the potential TNPP of grasslands in China is 0.83 Pg C in the A2a scenario (Table 10).

Climate change is expected to affect the TNPP of grassland. Understanding climate and NPP feedback cycles in grasslands requires knowledge of the changes in spatial patterns on the NPP of grasslands. The dynamic change characteristics of potential biomes (Table 12 and Figure 18) highlights the risk that China's grassland faces with a climate that is 'already hot, dry and variable' [85] under global warming. The Classification Indices-based Model estimated an increase of 104.71Tg C in TNPP for potential grasslands from recent past to the future A2a scenario. The increase in TNPP for grasslands at northern latitudes was mainly driven by increased temperature and, in lower latitudes, by changes in precipitation (Figure 17 and Figure 19). The increase in TNPP for grasslands will bring an increase of 375.5 Tg of annual sequestration of CO2 and 276.4 Tg of annual contribution to the atmospheric O2 cycle respec‐ tively (1.63g of CO2 are absorbed and 1.2 g of O2 are released to generate 1g of dry matter according to photosynthesis stoichiometry) [89].

#### *4.4.4.2. 'Safe' livestock carrying capacity*

The goal of sustainable livestock husbandry is to balance the consumption of forage by livestock with the annual production of the grassland, so that future grassland production is not diminished. The concept of a 'safe' livestock carrying capacity was developed to estimate the capacity of the grassland resource to sustainably carry livestock (and other herbivores) in the long term (>30 years) [83]. Predictions of NPP give a good estimate of the annual production of the grasslands, and therefore NPP can be used to balance the food demands of different kinds of livestock, allowing a calculation of the maximum carrying capacity of livestock that can be supported on the grassland. This maximum carrying capacity of the grassland should be used to develop appropriate policies of grazing control to preserve sustainable production on the grassland [16]. The implication of climate change projections for 'safe' livestock carrying capacity remains an open question. Global warming causes changes in temperature and precipitation patterns, resulting in fluctuations in the NPP of grasslands. Several researchers have predicted that increases in temperature will cause a shift in the ratio of C4 to C3 grasses, with C4 increasing [17, 93]. This may decrease carrying capacity, since C4 grasses are generally considered to be of lower nutritive value to grazing animals than C3 species [94]. Moreover, increasing temperature and declining precipitation are also considered to decrease dietary crude protein, protein availability, and digestible organic matter [93]. In situations where climates become warmer and drier, grazing livestock are likely to encounter a protein-limited diet. The future declines in forage quality would also produce greater methane production from ruminant livestock [93]. Thus, it is important to assess climate change projections in terms of their implications for future 'safe' livestock carrying capacity and the risk of resource degradation [83]. However, the questions of what the 'safe' livestock carrying capacity is and of how best to integrate a wide range of factors, such as grassland classes, climatic variability and animal nutrition, are unresolved. So further research and development is needed to identify the regional trends for the 'safe' livestock carrying capacity to maintain sustainable resource condition and reduce the risk of resource degradation. This important task remains a challenge for all grassland scientists and practitioners.

## **Acknowledgements**

Climate change is expected to affect the TNPP of grassland. Understanding climate and NPP feedback cycles in grasslands requires knowledge of the changes in spatial patterns on the NPP of grasslands. The dynamic change characteristics of potential biomes (Table 12 and Figure 18) highlights the risk that China's grassland faces with a climate that is 'already hot, dry and variable' [85] under global warming. The Classification Indices-based Model estimated an increase of 104.71Tg C in TNPP for potential grasslands from recent past to the future A2a scenario. The increase in TNPP for grasslands at northern latitudes was mainly driven by increased temperature and, in lower latitudes, by changes in precipitation (Figure 17 and Figure 19). The increase in TNPP for grasslands will bring an increase of 375.5 Tg of annual sequestration of CO2 and 276.4 Tg of annual contribution to the atmospheric O2 cycle respec‐ tively (1.63g of CO2 are absorbed and 1.2 g of O2 are released to generate 1g of dry matter

The goal of sustainable livestock husbandry is to balance the consumption of forage by livestock with the annual production of the grassland, so that future grassland production is not diminished. The concept of a 'safe' livestock carrying capacity was developed to estimate the capacity of the grassland resource to sustainably carry livestock (and other herbivores) in the long term (>30 years) [83]. Predictions of NPP give a good estimate of the annual production of the grasslands, and therefore NPP can be used to balance the food demands of different kinds of livestock, allowing a calculation of the maximum carrying capacity of livestock that can be supported on the grassland. This maximum carrying capacity of the grassland should be used to develop appropriate policies of grazing control to preserve sustainable production on the grassland [16]. The implication of climate change projections for 'safe' livestock carrying capacity remains an open question. Global warming causes changes in temperature and precipitation patterns, resulting in fluctuations in the NPP of grasslands. Several researchers have predicted that increases in temperature will cause a shift in the ratio of C4 to C3 grasses, with C4 increasing [17, 93]. This may decrease carrying capacity, since C4 grasses are generally considered to be of lower nutritive value to grazing animals than C3 species [94]. Moreover, increasing temperature and declining precipitation are also considered to decrease dietary crude protein, protein availability, and digestible organic matter [93]. In situations where climates become warmer and drier, grazing livestock are likely to encounter a protein-limited diet. The future declines in forage quality would also produce greater methane production from ruminant livestock [93]. Thus, it is important to assess climate change projections in terms of their implications for future 'safe' livestock carrying capacity and the risk of resource degradation [83]. However, the questions of what the 'safe' livestock carrying capacity is and of how best to integrate a wide range of factors, such as grassland classes, climatic variability and animal nutrition, are unresolved. So further research and development is needed to identify the regional trends for the 'safe' livestock carrying capacity to maintain sustainable resource condition and reduce the risk of resource degradation. This important task remains

according to photosynthesis stoichiometry) [89].

a challenge for all grassland scientists and practitioners.

*4.4.4.2. 'Safe' livestock carrying capacity*

216 CO2 Sequestration and Valorization

The research was funded by the National Natural Science Foundation of China (No. 31172250) and the project of the Humanities and Social Sciences Planning Fund of the Chinese Ministry of Education (No. 10YJAZH047). This chapter is dedicated to Wanquan Lin, the father of Huilong Lin, who left this earth on July 3, 2013. He was a source of constant encouragement, wise counsel, and a remarkable example of hard work, integrity and a loving father.

## **Author details**

Huilong Lin\*

Address all correspondence to: linhuilong@lzu.edu.cn

State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou City, China

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**Provisional chapter**

## **Carbon Sequestration in Central European Forest Ecosystems Carbon Sequestration in Central European Forest Ecosystems**

Robert Jandl and Andreas Schindlbacher Additional information is available at the end of the chapter

Robert Jandl, Andreas Schindlbacher

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/57264

## **1. Introduction**

10.5772/57264

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with English abstract).

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The forests of Central Europe play an important role in the retention of atmospheric carbon dioxide. Carbon sequestration is rarely the main objective of forest management and is often an underappreciated by-effect of practical forest management, whereas timber production and protection against natural hazards in mountain regions are the main forest functions. Climate change is expected to alter practical forest management profoundly, by causing changes in productivity in two directions: some regions will be increasingly affected by prolonged drought periods and the growth rate of forests is expected to decline; in mountain regions the growth rate of forests will often increase because climate change will manifest itself by the prolongation of the growing season. Many forests will be affected by an increasing pressure from pests and pathogens, both due to the expansion of the natural habitat, and due to the inadvertent introduction of pathogens in an increasingly globalized market. In the long run climate change will have implications for the tree species distribution. The presently successful Norway spruce will be found in a smaller area because parts of the present habitat will not fulfill the site requirements of this tree species in the future. Such tendencies have far-reaching consequences for the timber industry, so concepts for adaptive forest management need to be formulated. Besides increasing efforts for monitoring forest conditions, development of the opportunities offered by the genetic variability of tree species is an example of pro-active planning. In order to base recommendations for adaptive forest management on a sound basis, climate manipulation experiments are conducted. A network of such experiments enables researchers to make educated guesses on the expected climate change impact on forests. Nevertheless, due to manifold uncertainties with respect to the extent of climate change, its regional realization, the future expectations of society towards forest ecosystems and other imponderabilities, the predictions remain uncertain.

the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2014 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

©2012 Jandl and Schindlbacher, licensee InTech. This is an open access chapter distributed under the terms of

**Figure 1.** Forest cover of Europe with share of forest in the total land area in Central Europe (inset). European forest cover map provided by the European Forest Institute [2–4].

## **2. Spatial extent of forests in Central Europe with emphasis on mountain forests**

Europe has a total forest area of more than 1 billion (109) hectares and has a an average forest cover of ≈ 45% (Figure 1). Forests are unevenly distributed. Densely forested areas are in the Nordic countries and in high elevation ecosystems. Central Europe, here comprising Austria, Czech Republic, Germany, Hungary, Italy, Poland, Slovakia, Slovenia and Ukraine, has a total forest area of more than 52 million (106) ha with an average forest cover of 35%. This figure is close to the global average of a forest cover of 31% [1]. The regional distribution of forests is determined by the human population density, the land management options, and by physio-geographic constraints. In low-elevation regions agriculture and settlements are often dominant land covers, whereas in mountainous areas the share of forests increases. A part of the land at high elevation is unproductive because it is situated above the timberline (Figure 2). The economic relevance of agriculture is often inversely reflected in the forest cover. Currently, abandonment of agricultural land allows the expansion of forests, both by encroachment of marginal crop- and grassland, and by concerted afforestation programs.

2 Carbon Sequestration

**Figure 1.** Forest cover of Europe with share of forest in the total land area in Central Europe (inset). European forest cover

**2. Spatial extent of forests in Central Europe with emphasis on mountain**

Europe has a total forest area of more than 1 billion (109) hectares and has a an average forest cover of ≈ 45% (Figure 1). Forests are unevenly distributed. Densely forested areas are in the Nordic countries and in high elevation ecosystems. Central Europe, here comprising Austria, Czech Republic, Germany, Hungary, Italy, Poland, Slovakia, Slovenia and Ukraine, has a total forest area of more than 52 million (106) ha with an average forest cover of 35%. This figure is close to the global average of a forest cover of 31% [1]. The regional distribution of forests is determined by the human population density, the land management options, and by physio-geographic constraints. In low-elevation regions agriculture and settlements are often dominant land covers, whereas in mountainous areas the share of forests increases. A part of the land at high elevation is unproductive because it is situated above the timberline (Figure 2). The economic relevance of agriculture is often inversely reflected in the forest cover. Currently, abandonment of agricultural land allows the expansion of forests, both by encroachment of marginal crop- and grassland, and by concerted afforestation programs.

map provided by the European Forest Institute [2–4].

**forests**

**Figure 2.** Typical elevational distribution of forests in a mountainous region with productive forests at low elevation and an increasing share of protection forests at high elevation; Data from Austrian National Forest Inventory [5].

The forest cover on managed land increases with altitude and its carbon pools in the aboveground and belowground biomass and in the soil are highly relevant for climate change policies. Although the rate of carbon sequestration in slowly growing forests is low, the maintenance of the carbon pool by minimizing forest disturbances is important. Mountain forests are often well adapted to harsh climate conditions and are, despite natural disturbances, stable elements of mountain ranges. However, when the reporting modalities for national greenhouse gas budgets are confined to actively managed ecosystems, as under the Kyoto Reporting Rules, the role of many mountain forests is not fully recognized because they are not managed [6, 7].

Natural forest dynamics are driven particularly by climate factors. Under increasingly favorable climate conditions, the major constraints to forest growth at high altitudes, such as short growing seasons and low temperatures, are being alleviated and the timberline is moving upwards. Superimposed on this tendency are changes in land use practices. In Central Europe the active management of mountain pastures is declining for economic reasons, and forests are expanding into these areas [8, 9]. The main economic reason is that high-altitude cattle grazing is no longer competitive in an increasingly globalized market for agricultural produce; the political reasons are strong public preferences towards ecosystem conservation in mountain ranges at the cost of practical forestry.

Climate change has positive outcomes for some tree species but may lead to reduction of habitat size or even extinction of other trees [10, 11]. Such changes have far-reaching consequences such as the alteration of the provision of drinking water from forested mountain watersheds, alteration of the protective function of for forests against natural hazards, and the biological and structural diversity of landscape. The impact of these changes is felt in timber production, but also by the local community that is making a living in mountain areas. While rising temperatures benefit the growth of trees, they also facilitate the more rapid growth and expansion of populations of pests and disease-causing organisms [12, 13]. This pressure is further exacerbated by the rapid distribution of such organisms through global market streams. Together, these global change processes increase the pressure on mountain forests. In regions of extensive forestry, outbreaks of pests and diseases have caused major damage to mountain forests: for instance, millions of hectares of lodgepole pine (*Pinus contorta*) in the Canadian Rocky Mountains have been lost to mountain pine beetles (*Dendroctonus ponderosae*), releasing considerable quantities of carbon and putting forest-dependent communities at risk. An often observed phenomenon is bark beetle infestation in the wake of storm damages that has been observed in Central and Northern Europe [14–16]. In regions of intensive forestry, monitoring of pest and pathogen densities is becoming ever more essential and widespread, and silvicultural costs will increase due to the need for international and national alerting and monitoring programs to combat insect and disease outbreaks at an early stage.

In regions where precipitation is increasing, weather conditions will contribute to reduced fire frequency but, at the same time, increased tree cover promotes fire. Regions that are becoming drier and often warmer are likely to have a higher risk of wildfires [17]. Both of these trends require better understanding of the processes involved, and more effective monitoring and rapid response in the case of emerging problems. Nevertheless, many fires are started intentionally or accidentally by people, so that climate change is only one of many factors to be considered. While changes in average temperature and precipitation lead to certain negative impacts, changes likely to result from increased numbers of extreme events are at least as critical. Examples include windstorms and heavy rain and snowfall. Such events will endanger the stability of mountain forests and hence the provision of their ecosystem services.

The role of mountain forests in the mitigation of climate change is rather passive. Their loss would release large amounts of CO2 into the atmosphere. Their protection, conservation and management are therefore important, so some governments are promoting the establishment of forests in mountain areas as part of their climate change policies or to benefit from carbon credits, sometimes through the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD). Active mitigation - the absorption of CO2 in biomass and soils - is slow because the growth of trees and chemical processes at high altitudes is slow. A second field of mitigation is the substitution of fossil fuels with renewable forms of energy. In this context, mountain forests play a modest role since trees generally grow slowly. Nevertheless, some species, which are not of great value for timber, can be regarded as reliable sources of biofuel - as can parts of large trees, such as smaller branches, that are not valuable as timber. Given growing energy demands, all efforts to increase the efficient use of energy are important, so that key needs are to ensure that fuelwood is used more efficiently and that, wherever possible, other renewable energy sources are used.

Adaptation to climate change may be reactive or planned. Reactive adaptation includes actions such as changes in harvesting levels and schedules, the removal of downed trees after storms and the use of the resulting wood, and the development of socioeconomic support programs for communities experiencing negative impacts. Planned adaptation implies the redefinition of forestry goals and practices, taking into consideration the risks and uncertainties associated with climate change [18]. Management plans will have to include uncertainty and the increased probability of extreme events, and be regularly updated. Farmers, communities and forestry companies and agencies alike need to identify plant species or provenances which will grow well and ensure stable and productive forests in the climates of the later decades of the 21*st* century, contribute to local livelihoods and, in particular, ensure forest cover in areas with an increased risk of erosion, landslides or other natural hazards. For communities, adaptation may include better governance of forest resources, capacity building to monitor and cope with extreme events, increased use of agroforestry practices, and diversification of income sources. At national and global levels, key actions may include the development and implementation of monitoring and reporting systems and tools for vulnerability assessments and adaptation planning, as well as increasing the flexibility of organizational cultures, structures, and forest-related policies before crises arise [19].

## **3. The carbon pools in forests**

The United Nations Framework Convention on Climate Change calls for its Parties to develop, periodically update and report national inventories of anthropogenic emissions by sources and removals by sinks of all greenhouse gases not controlled by the Montreal Protocol, using comparable methodologies. Estimating and reporting anthropogenic emissions and removals to the international community are important to develop policies to control the emissions and removals of greenhouse gases (GHG), which are believed to be the principle causes of the recent anthropogenic climate change. Each country needs to submit annual reports on emissions and removals of GHGs for all relevant sectors of the economy. The estimation and reporting must be complete, accurate, transparent, consistent over time and comparable between countries [7, 20]. For forest ecosystems, five compartments are reported:


4 Carbon Sequestration

and disease outbreaks at an early stage.

ecosystem services.

energy sources are used.

is felt in timber production, but also by the local community that is making a living in mountain areas. While rising temperatures benefit the growth of trees, they also facilitate the more rapid growth and expansion of populations of pests and disease-causing organisms [12, 13]. This pressure is further exacerbated by the rapid distribution of such organisms through global market streams. Together, these global change processes increase the pressure on mountain forests. In regions of extensive forestry, outbreaks of pests and diseases have caused major damage to mountain forests: for instance, millions of hectares of lodgepole pine (*Pinus contorta*) in the Canadian Rocky Mountains have been lost to mountain pine beetles (*Dendroctonus ponderosae*), releasing considerable quantities of carbon and putting forest-dependent communities at risk. An often observed phenomenon is bark beetle infestation in the wake of storm damages that has been observed in Central and Northern Europe [14–16]. In regions of intensive forestry, monitoring of pest and pathogen densities is becoming ever more essential and widespread, and silvicultural costs will increase due to the need for international and national alerting and monitoring programs to combat insect

In regions where precipitation is increasing, weather conditions will contribute to reduced fire frequency but, at the same time, increased tree cover promotes fire. Regions that are becoming drier and often warmer are likely to have a higher risk of wildfires [17]. Both of these trends require better understanding of the processes involved, and more effective monitoring and rapid response in the case of emerging problems. Nevertheless, many fires are started intentionally or accidentally by people, so that climate change is only one of many factors to be considered. While changes in average temperature and precipitation lead to certain negative impacts, changes likely to result from increased numbers of extreme events are at least as critical. Examples include windstorms and heavy rain and snowfall. Such events will endanger the stability of mountain forests and hence the provision of their

The role of mountain forests in the mitigation of climate change is rather passive. Their loss would release large amounts of CO2 into the atmosphere. Their protection, conservation and management are therefore important, so some governments are promoting the establishment of forests in mountain areas as part of their climate change policies or to benefit from carbon credits, sometimes through the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD). Active mitigation - the absorption of CO2 in biomass and soils - is slow because the growth of trees and chemical processes at high altitudes is slow. A second field of mitigation is the substitution of fossil fuels with renewable forms of energy. In this context, mountain forests play a modest role since trees generally grow slowly. Nevertheless, some species, which are not of great value for timber, can be regarded as reliable sources of biofuel - as can parts of large trees, such as smaller branches, that are not valuable as timber. Given growing energy demands, all efforts to increase the efficient use of energy are important, so that key needs are to ensure that fuelwood is used more efficiently and that, wherever possible, other renewable

Adaptation to climate change may be reactive or planned. Reactive adaptation includes actions such as changes in harvesting levels and schedules, the removal of downed trees after storms and the use of the resulting wood, and the development of socioeconomic support programs for communities experiencing negative impacts. Planned adaptation


Among the principles of estimating changes in the carbon pool size is that the demonstration should be "scientifically sound". However, the accounting rules are not explicit about the strategy of dealing with uncertainty. A common approach is the calculation of a "central estimate" (mean value) and the uncertainty around it (standard deviation). Another approach is to assess the margins of the uncertainty range [21].

The reporting is done differently in individual countries. The Good Practice Guidance identifies key categories that have a significant influence on a country's greenhouse gas inventory. When land use (agriculture, forestry, other land use 'AFOLU') is a key category countries use methods of higher complexity and with higher data demands (tier levels). Tier 1, the simplest version, applies to countries in which forests and the biomass carbon pool) is not a key category and where no country-specific activity data are available. Tier 2 applies where forests and biomass carbon form a key category and where country-specific estimates of activity data (*e.g.* forest inventories) are available. Tier 3 applies where the "forests remaining forests" and biomass carbon form a key category. It requires detailed national forest inventory data supplemented by dynamic models or allometric equations calibrated to national circumstances that allow for direct calculation of biomass increment [7, 22].

The most reliable data are available for the aboveground biomass due to the highly developed forest inventory systems in Europe [23]. The rotation period of managed forests is variable between ecosystem types, yet well recorded. Therefore, the estimates of the longevity of carbon residing in the aboveground biomass are well supported by data.

The information on the belowground biomass of forest ecosystems is much scarcer for several reasons. The root biomass is economically irrelevant and the required sampling effort is high. There is substantial uncertainty about the representative rooting depth of trees and the arbitrarily chosen reporting depth of 30 to 50 cm may greatly underestimate total belowground biomass. The longevity of the root biomass varies in a wide range. Fine roots turn over quickly, whereas the decomposition of root stumps requires many decades and is affected by numerous site factors [24–26]. Often the belowground biomass is estimated by means of generic equations [27]. Despite the challenges in quantifying the standing stock of roots and their turnover rate, it is of paramount interest to improve this understanding. Roots are important sources of soil carbon. It has even been argued that the intricate network of roots and fungi is the most important source of slowly turning over soil carbon in boreal forests. Hence their role for long-term soil carbon sequestration is large [28].

Dead organic matter, *i.e.*, the residues of fallen logs and decomposing branches and twigs, is also difficult to quantify. The amount of coarse woody debris left on-site after harvesting operations depends on the local demand for biomass on the industrial market, and on the pursued forest management concept.

Litter layer and soil organic matter are difficult to assess because their carbon pool sizes are spatially heterogeneous. There is no unambiguous evidence for either positive or negative impacts of climate change on soil carbon stocks. There are suggestions for enhancing soil C stocks at higher atmospheric CO2 concentration and reducing soil C stocks when temperatures are rising. The factors of climate change affect soil C, with the effect on soils of CO2 being indirect through photosynthesis and the effects of weather factors being both direct and indirect. Climate change affects soil carbon pools by affecting each of the processes in the C-cycle: photosynthetic C-assimilation, litter fall, decomposition, surface erosion, and hydrological transport. Due to the relatively large gross exchange of CO2 between atmosphere and soils and the significant stocks of carbon in soils, relatively small changes in these large but opposing fluxes of CO2 may have significant impact on climate and soil quality. Therefore, managing these fluxes through proper soil management can help mitigate climate change considerably [29–31].

## **4. Role of forestry sector in national greenhouse gas budgets**

Greenhouse gases comprise several gases but not all of them are equally relevant in forest ecosystems. The dominant gas with the highest relevance for European forest ecosystems

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is CO2, whereas nitrogen oxides (*e.g.*, N2O) and methane play a smaller role. In Central Europe, forests are mostly sinks of carbon dioxide.1 The reasons are the increase in the forest area at the expense of agricultural land and the discrepancy between high growth rates and low harvesting rates in forests. Land-use change is an ongoing process and there have been successful policy incentives for a more efficient use of forests. Forest products are carrying several elements of a knowledge-based bioeconomy of the European Union. Fibers from woody plants are relevant as raw material for industrial processes and the use of wood biomass as a source of energy is being promoted in order to reduce the dependency of the continent from fossil fuels. However, the entire sector plays a rather marginal role in greenhouse gas emissions. Less than 5% of the total greenhouse gas emissions are compensated by the sector 'land use, land-use change, forestry' (Figure 3). This figure is in reasonable agreement with the estimate of 7 to 12% of CO2 absorption in terrestrial ecosystems based on modeling results [32]. Nevertheless, the graph shows that forestry is the only sector that is a reliable sink of greenhouse gases. A second observation is that the emissions of greenhouse gases are declining because of the economic crisis in 2007. On a global scale no decline in greenhouse gas emissions was observed. On the contrary, due to the rapid development in previously less developed regions in Asia the emissions remained at high levels and the rate of annual emissions is even accelerating [33]. The top four emitters in 2011 covered 62% of the global emissions: China (28%), United States (16%), EU27 (11%), and India (7%). Among them, emissions have strongly increased in 2010/11 in China and India, and slightly decreased in the United States and Europe [34, 35].

6 Carbon Sequestration

[7, 22].

pool) is not a key category and where no country-specific activity data are available. Tier 2 applies where forests and biomass carbon form a key category and where country-specific estimates of activity data (*e.g.* forest inventories) are available. Tier 3 applies where the "forests remaining forests" and biomass carbon form a key category. It requires detailed national forest inventory data supplemented by dynamic models or allometric equations calibrated to national circumstances that allow for direct calculation of biomass increment

The most reliable data are available for the aboveground biomass due to the highly developed forest inventory systems in Europe [23]. The rotation period of managed forests is variable between ecosystem types, yet well recorded. Therefore, the estimates of the longevity of

The information on the belowground biomass of forest ecosystems is much scarcer for several reasons. The root biomass is economically irrelevant and the required sampling effort is high. There is substantial uncertainty about the representative rooting depth of trees and the arbitrarily chosen reporting depth of 30 to 50 cm may greatly underestimate total belowground biomass. The longevity of the root biomass varies in a wide range. Fine roots turn over quickly, whereas the decomposition of root stumps requires many decades and is affected by numerous site factors [24–26]. Often the belowground biomass is estimated by means of generic equations [27]. Despite the challenges in quantifying the standing stock of roots and their turnover rate, it is of paramount interest to improve this understanding. Roots are important sources of soil carbon. It has even been argued that the intricate network of roots and fungi is the most important source of slowly turning over soil carbon in boreal

Dead organic matter, *i.e.*, the residues of fallen logs and decomposing branches and twigs, is also difficult to quantify. The amount of coarse woody debris left on-site after harvesting operations depends on the local demand for biomass on the industrial market, and on the

Litter layer and soil organic matter are difficult to assess because their carbon pool sizes are spatially heterogeneous. There is no unambiguous evidence for either positive or negative impacts of climate change on soil carbon stocks. There are suggestions for enhancing soil C stocks at higher atmospheric CO2 concentration and reducing soil C stocks when temperatures are rising. The factors of climate change affect soil C, with the effect on soils of CO2 being indirect through photosynthesis and the effects of weather factors being both direct and indirect. Climate change affects soil carbon pools by affecting each of the processes in the C-cycle: photosynthetic C-assimilation, litter fall, decomposition, surface erosion, and hydrological transport. Due to the relatively large gross exchange of CO2 between atmosphere and soils and the significant stocks of carbon in soils, relatively small changes in these large but opposing fluxes of CO2 may have significant impact on climate and soil quality. Therefore, managing these fluxes through proper soil management can help mitigate

Greenhouse gases comprise several gases but not all of them are equally relevant in forest ecosystems. The dominant gas with the highest relevance for European forest ecosystems

carbon residing in the aboveground biomass are well supported by data.

forests. Hence their role for long-term soil carbon sequestration is large [28].

**4. Role of forestry sector in national greenhouse gas budgets**

pursued forest management concept.

climate change considerably [29–31].

Only recently was the carbon-dioxide sink strength of forests on a global level evaluated as strong and persistent, although the absolute size of the sink and the major contributing regions remained unclear [36, 37]. However, climate change includes elements that in the long run are prone to lead to carbon losses from terrestrial ecosystems [38]. However, currently European forests are a sink of CO2. For the 25 member states of the European Union (EU-25) and the timespan 1990 to 2005, the long-term carbon forest sink was estimated to be 75±20 gC/m2/year, with approximately a third of the carbon going into the forest soil and two thirds ending up in unharvested tree biomass [39]. For European forests there are already some indications that the sink strength of forests will not continue at the present rate. The rate of stem growth is decreasing and thus the sink is declining after decades of increase. In addition, the increasing demand for agricultural land and for the establishment of settlements and infrastructures leads to deforestation. The subsequent vegetation cover after forest clearance has a lower carbon density. And finally, an increasing rate of ecosystem disturbances due to extreme climate events and due to biotic hazards exerted by pests and pathogens are leading to the loss of forest stands [40].

The narrative for the greenhouse-gas sink in the forestry sector is given by the example of the Austrian National Inventory Report of the year 2012 [42].

In 2010 Article 3.3 activities, *i.e.* afforestation, reforestation and deforestation activities, were a net sink in Austria: Net CO2 removals amounted to 1259 Gg CO2. Removals from afforestation/reforestation amounted to 2621 Gg CO2. About 2/3 of these gains were caused by the C stock increases in soil and litter, 1/3 was due to

<sup>1</sup> National Inventory Reports: http://unfccc.int/national\_reports/annex\_i\_ghg\_inventories/national\_inventories\_ submissions/items/7383.php; accessed Aug 18, 2013

**Figure 3.** Overview of EU-27 GHG emissions in the main source and sink categories 1990 to 2011 in CO2- equivalents (Tg); [41]

biomass growth in the afforestation/reforestation areas. Approximately 37% of these removals occurred in afforestation/reforestation areas from grassland, 18% in afforestation/reforestation areas from 'other land', 17% from settlement, 16% from cropland and 12% from wetlands. In the same year, emissions from deforestation amounted to 1362 Gg CO2. A bit more than 2/3 were due to C stock losses in litter and soil, and 1/3 due to biomass losses in the deforestation areas. Approximately 30% of these losses occurred in deforestation areas to grassland, 28% to settlement, 25% to other land and the rest in deforestation areas to cropland and wetlands.

The net carbon stock changes of forests vary considerably between single years. The annual growth rate and the annual harvest rate can differ significantly year by year due to variations in weather conditions, the market situation for timber, and the intensity of ecosystem disturbances. In the example of Austria, forests have been a strong and reliable CO2 sink for many years. The reason is that harvesting rates were far below the annual increment. A successful political campaign for the utilization of timber resources and high timber prices gave sufficient incentives for increased harvesting rates. The positive effect on the timber market had the immediate by-effect that the carbon sink strength of forests was reduced. In addition, a series of storm damages of forests unintentionally led to high harvest and salvaging rates. For a single year these disturbances even caused forests to be a source of CO2 rather than a sink. Figure 4 gives a clear indication that the main inter-annual volatility of the GHG-budget in forests is determined by forests remaining forests. Land-use change, *e.g.*, the conversion of cropland to forest and grassland, to forest plays a marginal role.

### **5. Effect of land use change on carbon sequestration**

The establishment of the agricultural societies starting 60 000 years ago has shaped the European landscape. Forests were cleared to enable the establishment of cropland and

8 Carbon Sequestration

**Figure 3.** Overview of EU-27 GHG emissions in the main source and sink categories 1990 to 2011 in CO2- equivalents (Tg); [41] biomass growth in the afforestation/reforestation areas. Approximately 37% of these removals occurred in afforestation/reforestation areas from grassland, 18% in afforestation/reforestation areas from 'other land', 17% from settlement, 16% from cropland and 12% from wetlands. In the same year, emissions from deforestation amounted to 1362 Gg CO2. A bit more than 2/3 were due to C stock losses in litter and soil, and 1/3 due to biomass losses in the deforestation areas. Approximately 30% of these losses occurred in deforestation areas to grassland, 28% to settlement, 25% to

The net carbon stock changes of forests vary considerably between single years. The annual growth rate and the annual harvest rate can differ significantly year by year due to variations in weather conditions, the market situation for timber, and the intensity of ecosystem disturbances. In the example of Austria, forests have been a strong and reliable CO2 sink for many years. The reason is that harvesting rates were far below the annual increment. A successful political campaign for the utilization of timber resources and high timber prices gave sufficient incentives for increased harvesting rates. The positive effect on the timber market had the immediate by-effect that the carbon sink strength of forests was reduced. In addition, a series of storm damages of forests unintentionally led to high harvest and salvaging rates. For a single year these disturbances even caused forests to be a source of CO2 rather than a sink. Figure 4 gives a clear indication that the main inter-annual volatility of the GHG-budget in forests is determined by forests remaining forests. Land-use change, *e.g.*, the conversion of cropland to forest and grassland, to forest plays a marginal role.

The establishment of the agricultural societies starting 60 000 years ago has shaped the European landscape. Forests were cleared to enable the establishment of cropland and

other land and the rest in deforestation areas to cropland and wetlands.

**5. Effect of land use change on carbon sequestration**

**Figure 4.** Removals and emissions of carbon dioxide in the forests of Austria according to the IPCC category 'Forest remaining forest', and forest land conversions from 1990 to 2010 (GT) CO<sup>2</sup> equivalents).

pastures. Forests were used both for the wood and non-wood products [43]. Extensive European deforestation started around 1000 BC [44].

Mountain regions play a peculiar role. Their settlements are exposed to natural hazards such as flooding and avalanches. Forests can mitigate these dangers because they affect the hydrology and the ability to stabilize a snow cover. A prerequisite for the protective function is the sustainable maintenance of an optimal stand structure [45, 46]. With awareness of the primary relevance of permanent forest cover in alpine regions, forest management schemes were greatly restricted. Already in the 16*th* century the exploitation of mountain forests in the Alps was constrained by Forest Acts in order to avoid natural disasters and the rapid degradation of mountain regions. However, mountain regions were often poverty traps because the low productivity of the land impaired economic development and the politcal representation was low. In politically labile times such as in the aftermath of the Napoleonic Wars the legal status of the forests in parts of the Alps was unclear. Quickly, overexploitation of the forests took place as a manifestation of the "tragedy of the commons" [47]. Often farmers were in economically desperate situations and means of increasing the productivity of agricultural lands such as mineral fertilizers were not yet available. The remedy was the deforestation of land at high altitudes to provide pasture land for an increasing number of livestock. The dire consequences followed within a short period of time. As early as 1852 a stringent Forest Act ("Reichsforstgesetz") was implemented and administrative and technical bodies for torrent control were established. Incontestable proof of the relevance of a dense and sustainable forest cover was given by the classic 'Sperbelgraben/Rappengraben' hydrological experiment in Switzerland [30, 48]. Even the establishment of academic forestry curricula in Central Europe was a direct consequence of a series of natural disasters.

**Figure 5.** The poor condition of mountain forests led to numerous natural disasters. Mourning the death after avalanches in Villgraten, Eastern Tyrol in 1931 (left), catastrophic flooding in the city of Sterzing, Southern Tyrol (center), and soil erosion in Eastern Tyrol (right); [50].

In recent history, the forest area in mountain regions was reduced several times. The Oetztal in the western Austrian province of Tyrol is such an example. Between 1774 and 1880 the forest area was reduced by more than 50%. The political unrest during the Napoleonic Wars led to little control of land use. The precarious income situation for agriculture in the valleys was based on potato, beets, and cabbage as main crops. The land was used at its maximum carrying capacity [49]. The extension of agricultural land in high altitudes offered one of the few options to increase the income in agriculture. The immediate consequence of this overuse of mountain regions and the removal of mountain forests was an increase in the frequency and severity of natural disasters 5. It was even pondered whether permanent settlements in some parts of the Alps should be given up and whether the population should be relocated to safer ground [45].

Today, the reverse process is underway. Mountain areas in the Alps are prospering economically as tourism is growing as a pillar of the economy. They are widely recognized as water towers with an immense impact on the lowlands. In addition, a part of the increase in the Central European forest area is due to the expansion of mountain forests. In particular at high elevation were mountain pastures are abandoned, forests are encroaching on the now unmanaged land [5]. The process has several implications. One is a loss in biodiversity because the forested landscape is becoming more uniform in comparison to the variety of pasture lands with a wide range of management intensities. However, from the perspective of climate mitigation the tendency towards more forests is beneficial because the carbon density in forests is higher than in other types of terrestrial ecosystems [36, 51–53]. It has been concluded that the effect of climate change has an impact on the soil carbon pool size in the longer term. However, the climate signal is much weaker than the effect of land use change and land management [31].

## **6. Expected climate change impact on tree species and pests and pathogens**

There is agreement that in many regions of Europe, and even globally, climatic change will manifest itself in terms of little change in the precipitation regime, but in higher temperatures and longer growing seasons. Precise regionally valid climate predictions are still elusive and the uncertainty of the estimates is expected to remain large [54, 55]. Even apparently simple phenomena such as the length and frequency of drought periods are difficult to quantify and widely different statements on the expected temporal trend of drought periods are

10.5772/57264

presented [56, 57]. However, there is clear evidence that unprecedented rates of tree mortality are happening. Particularly in already dry areas, it is questionable whether forests can re-establish themselves or whether they are regionally going to be replaced by shrublands. Nevertheless, almost all forest types are expected to be affected by drought [17, 58]. The effects of that for carbon sequestration are dramatic. Currently, forests in Europe are a potent sink for carbon dioxide in the atmosphere. However, under increasing stress, the sink strength will be difficult to maintain [39, 40]. Outside of Europe there is already widespread evidence of drought events with massive deleterious effects on the carbon storage in forests [59].

10 Carbon Sequestration

Eastern Tyrol (right); [50].

to safer ground [45].

change and land management [31].

**pathogens**

**Figure 5.** The poor condition of mountain forests led to numerous natural disasters. Mourning the death after avalanches in Villgraten, Eastern Tyrol in 1931 (left), catastrophic flooding in the city of Sterzing, Southern Tyrol (center), and soil erosion in

In recent history, the forest area in mountain regions was reduced several times. The Oetztal in the western Austrian province of Tyrol is such an example. Between 1774 and 1880 the forest area was reduced by more than 50%. The political unrest during the Napoleonic Wars led to little control of land use. The precarious income situation for agriculture in the valleys was based on potato, beets, and cabbage as main crops. The land was used at its maximum carrying capacity [49]. The extension of agricultural land in high altitudes offered one of the few options to increase the income in agriculture. The immediate consequence of this overuse of mountain regions and the removal of mountain forests was an increase in the frequency and severity of natural disasters 5. It was even pondered whether permanent settlements in some parts of the Alps should be given up and whether the population should be relocated

Today, the reverse process is underway. Mountain areas in the Alps are prospering economically as tourism is growing as a pillar of the economy. They are widely recognized as water towers with an immense impact on the lowlands. In addition, a part of the increase in the Central European forest area is due to the expansion of mountain forests. In particular at high elevation were mountain pastures are abandoned, forests are encroaching on the now unmanaged land [5]. The process has several implications. One is a loss in biodiversity because the forested landscape is becoming more uniform in comparison to the variety of pasture lands with a wide range of management intensities. However, from the perspective of climate mitigation the tendency towards more forests is beneficial because the carbon density in forests is higher than in other types of terrestrial ecosystems [36, 51–53]. It has been concluded that the effect of climate change has an impact on the soil carbon pool size in the longer term. However, the climate signal is much weaker than the effect of land use

**6. Expected climate change impact on tree species and pests and**

There is agreement that in many regions of Europe, and even globally, climatic change will manifest itself in terms of little change in the precipitation regime, but in higher temperatures and longer growing seasons. Precise regionally valid climate predictions are still elusive and the uncertainty of the estimates is expected to remain large [54, 55]. Even apparently simple phenomena such as the length and frequency of drought periods are difficult to quantify and widely different statements on the expected temporal trend of drought periods are Forest fires are recognized as a substantial future risk for Central European forests. Due to active fire suppression, the incidents of forest fires are still rare and are often caused by human negligence rather than being triggered by climatic phenomena. However, this danger has to been kept on the agenda, particularly because the lengthening of the growing seasons will also extend the fire season. Wildfires not only diminish the tree biomass, they also have long-term adverse effects on the soil carbon pool [60, 61].

Despite the widespread recognition of drought as a threat to forests, currently the greatest damages in Central European forests are caused by storms and bark beetle attacks. There is some evidence that the frequency and severity of storms is increasing [62]. However, the observed signal needs to be interpreted with high caution. The scientific evidence whether or not the strength of storms is increasing is still insufficiently corroborated, although indications are recorded [63, 64]. Furthermore, the predictability of storms in global climate models is confined to advection events whereas locally severe events can be caused by convective processes that are not fully covered in regionalized climate models. A consequence of devastative storm damages is the attack of the broken stems by bark beetles. Due to constraints in the available work force and the difficult accessibility of storm-damaged sites, dead wood can be left on site for long periods. Especially in the first weeks after the disaster the still moist wood presents an ideal breeding ground for *Ips typographus* and other bark beetles. Without effective and often expensive counter-measures, the bark beetle effect can spread from the damaged sites (Figure 6). Foresters in Central Europe often have the chance to salvage damaged forest stands, thereby containing the risk of large-scale damages. In parts of the world where forests are unmanaged or only extensively managed bark beetle infestations can reach dramatic sizes, as shown from the example of forests in Western Canada [15].

An emerging threat is the occurrence of introduced pests and pathogens. This phenomenon is not new. Elm disease, the spread of potato weevils (*Cylas puncticollis*), and the vine fretter (*Phylloxera vastatrix*) are iconic examples. Globalization aggravates the challenge. In globally transported woody packaging material and in transported organic material such as soils, plants and fruits that are vectors of diseases are inadvertently carried to new ecosystems. A recent example of the introduction of new pests is the infestation of trees in northern Austria with the Asian longhorned beetle (*Anoplophora glabripennis*) and the longhorned citrus beetle (*Anoplophora chinensis*) [65, 66]. These species are native to Asia and have been introduced into Europe mainly with packaging wood material and ornamental plants from China or other Asian countries. Under suitable site conditions these endemic species can be persistent and even invade new territories. Favored by climate change the new pests can often use the new habitat as a starting point for a widerrs a poleward shift in the distribution of pests and pathogens by almost 3 km/year has been observed [12].

**Figure 6.** The bark beetle, *Ips typographus*, is a common pest in secondary spruce forests (left). The infestation extends from single trees to the adjacent previously healthy trees (center), and can ultimately kill the entire forest stand within a few months (right); pictures©: Christian Tomiczek.

From a human perspective, disturbances of forests mostly have a negative connotation. Besides the economic loss, carbon dioxide is also lost from the ecosystem. The slogan 'slow-in/fast-out' has been coined for the quick release of carbon dioxide due to a storm event after decades of carbon accrual in the biomass [67]. However, disturbances are also acknowledged to be a part of the natural ecosystem dynamics [68–71]. Many trees in harsh environments can only germinate and develop because uprooted trees provide site conditions that are far better than in the immediate vicinity of a fallen stem. Moreover, the recent increase in damages in Central Europe can be explained by a combination of climatic factors and forest management strategies. Forests have become more vulnerable: (i) ecologically less stable tree species have been planted for economic reasons, and (ii) the low harvesting rates have led to denser and higher forest stands that are less able to withstand storms [72].

The tree species in Central Europe are currently distributed as shown in Figure 7. The distribution only partly reflects the potential natural vegetation. The proportion of Norway spruce (*Picea abies* Karst.) has been greatly increased by forest managers because of the high productivity of spruce forests. The instability of secondary spruce forests has been known for more than 100 years. Nevertheless, the economic opportunities of a spruce-based timber industry in the Alps have made the higher production risk acceptable [73].

Predictions on the effect of climate change on the tree species composition are highly uncertain because a complex interaction of species-specific parameters and competitive processes within forest stands needs to be considered. In the past the potential natural tree species distribution has served as a useful reference for a target composition of adapted forests for a given set of site parameters [74]. Under the influence of climate change this reference is losing relevance as an indication of the 'non-analogues future'. The present potential natural vegetation is of little relevance when climate change affects the thermal regime and when nitrogen deposition leads to fundamentally altered soil fertility conditions, thereby creating site conditions that have not been encountered before. Niche models are a useful and transparent tool for creating 'species-distribution maps' [75–77]. These maps show a potential distribution and as such an end-member of a long process of a fundamental change in the vegetation. Niche models yield no information on the temporal course of species migration and do not include the adaptive capacity of species. In addition, they are based on information on the currently realized niche of tree species. The fundamental niche may or may not be substantially larger. Hence, their main purpose is constraining the many

12 Carbon Sequestration

(right); pictures©: Christian Tomiczek.

**Figure 6.** The bark beetle, *Ips typographus*, is a common pest in secondary spruce forests (left). The infestation extends from single trees to the adjacent previously healthy trees (center), and can ultimately kill the entire forest stand within a few months

From a human perspective, disturbances of forests mostly have a negative connotation. Besides the economic loss, carbon dioxide is also lost from the ecosystem. The slogan 'slow-in/fast-out' has been coined for the quick release of carbon dioxide due to a storm event after decades of carbon accrual in the biomass [67]. However, disturbances are also acknowledged to be a part of the natural ecosystem dynamics [68–71]. Many trees in harsh environments can only germinate and develop because uprooted trees provide site conditions that are far better than in the immediate vicinity of a fallen stem. Moreover, the recent increase in damages in Central Europe can be explained by a combination of climatic factors and forest management strategies. Forests have become more vulnerable: (i) ecologically less stable tree species have been planted for economic reasons, and (ii) the low harvesting rates have led to denser and higher forest stands that are less able to withstand storms [72].

The tree species in Central Europe are currently distributed as shown in Figure 7. The distribution only partly reflects the potential natural vegetation. The proportion of Norway spruce (*Picea abies* Karst.) has been greatly increased by forest managers because of the high productivity of spruce forests. The instability of secondary spruce forests has been known for more than 100 years. Nevertheless, the economic opportunities of a spruce-based timber

Predictions on the effect of climate change on the tree species composition are highly uncertain because a complex interaction of species-specific parameters and competitive processes within forest stands needs to be considered. In the past the potential natural tree species distribution has served as a useful reference for a target composition of adapted forests for a given set of site parameters [74]. Under the influence of climate change this reference is losing relevance as an indication of the 'non-analogues future'. The present potential natural vegetation is of little relevance when climate change affects the thermal regime and when nitrogen deposition leads to fundamentally altered soil fertility conditions, thereby creating site conditions that have not been encountered before. Niche models are a useful and transparent tool for creating 'species-distribution maps' [75–77]. These maps show a potential distribution and as such an end-member of a long process of a fundamental change in the vegetation. Niche models yield no information on the temporal course of species migration and do not include the adaptive capacity of species. In addition, they are based on information on the currently realized niche of tree species. The fundamental niche may or may not be substantially larger. Hence, their main purpose is constraining the many

industry in the Alps have made the higher production risk acceptable [73].

**Figure 7.** Tree species in Central Europe based on compiled data from National Forest Inventories. Green shades represent coniferous forests, while reddish shades are deciduous forests; [79].

future possible tree species composition to a transparent model realization. The challenge for practical foresters is the evaluation of adaptive management no-regret strategies in order to foster a development where forests can fulfill the expected ecosystem services [78].

A comprehensive niche-modeling exercise led to a prediction of the future tree species distribution for Europe [10]. It is predicted that under now realistic climate change scenarios the share of Norway spruce is going to decline. Spruce will still be the dominant tree in the Alps, but the spruce stands at low elevation are not expected to cope with future climate conditions (Figure 8). Beech (*Fagus sylvatica* L.) is expected to benefit in the next decades. However, in the long run its habitat is expected to shrink. The tree species that are expected to expand as a consequence of climate change are pines (*Pinus sp.*) and oaks (*Quercus sp.*). Such a development is highly undesirable for the forestry industry because the most productive tree species will be less abundant whereas tree species with low growth rates or less economic value are on the rise. The benefit of niche models is that practical forestry is already supplied with this information now. Independent scrutiny of the modeling results is given by observations in the southern part of Switzerland, where the proposed species shift according to Figure 8 has been documented in the field [80]. There is still time to develop strategies to deal with climate change effects. Among the emerging fields is the efficient use of the genetic variability of tree species. This strategy is supported by the inherent genetic heterogeneity of many tree species. In particular trees that have a history of migration into different types of ecosystems after the last glaciation have potentially formed several phenotypes that can be efficiently used in forest management [81–83].

The forced migration of a tree species due to climate change is already an extreme manifestation of the change in site conditions. For a substantial amount of time it is likely that trees will adjust their growth rate to the prevailing climate conditions. In Figure 9 the change in productivity is given for different time intervals with the main confounding factor being the pressure from bark beetles (Figure 6). The analysis is based on data from

**Figure 8.** Potential trend of the share of tree species in Europe under the influence of climate change; [10].

**Figure 9.** Altered productivity of Austrian forests due to climate change effects, differentiated by provinces.

an earlier experiment [84]. Obviously, climate change does not affect the regions equally. In mountain areas such as Tyrol, Vorarlberg, and Salzburg, the productivity is even going to increase because the main effect of climate change is extension of the growing season. At lower elevations, droughts are going to limit tree growth and the pressure from bark beetles is expected to increase. Therefore, the geographic heterogeneity of landscapes will lead to widely different climate change effects on forest ecosystems.

10.5772/57264

**Figure 10.** The effect of changes in temperature distribution on extremes. Different changes in temperature distributions between present and future climate and their effects on extreme values of the distributions: (a) effects of a simple shift of the entire distribution toward a warmer climate; (b) effects of an increase in temperature variability with no shift in the mean; (c) effects of an altered shape of the distribution, in this example a change in asymmetry toward the hotter part of the distribution ; [87].

## **7. Climate manipulation experiments**

14 Carbon Sequestration

**Figure 8.** Potential trend of the share of tree species in Europe under the influence of climate change; [10].

**Figure 9.** Altered productivity of Austrian forests due to climate change effects, differentiated by provinces.

widely different climate change effects on forest ecosystems.

an earlier experiment [84]. Obviously, climate change does not affect the regions equally. In mountain areas such as Tyrol, Vorarlberg, and Salzburg, the productivity is even going to increase because the main effect of climate change is extension of the growing season. At lower elevations, droughts are going to limit tree growth and the pressure from bark beetles is expected to increase. Therefore, the geographic heterogeneity of landscapes will lead to Climate extremes, such as severe drought, heat waves and periods of heavy rainfall, will affect tree growth greatly. Global climate change is expected to increase both the frequency and intensity of climate extremes and there is an urgent need to understand their ecological consequences, in particular with respect to carbon sequestration [85, 86]. The Special Report on Extreme Events by the IPCC distinguishes three forms of the change [87] (Figure 10).

Field experiments are essential for the assessment of ecosystem responses to changing conditions, especially when the complexity of the ecosystems does not allow the deduction of results based on existing knowledge. A challenge in climate change manipulation

**Figure 11.** Elements of climate manipulation experiments. Roofs under the tree canopy enable the creation of artificial drought conditions (left), automatic chambers allows the continuous measurement of greenhouse-gas emissions from soils (center), stem diameter growth is a common response variable for the quantification of climate change effects (right).

experiments in forests is their size and longevity. For the assessment of soil processes under conceivable warmer conditions, soil warming experiments have been set up in different forest ecosystems [88–91] (Figure 11).

Another avenue of climate change experiments is the exposure of trees to elevated concentrations of CO2. Several Free-Air-Carbon dioxide-Enrichment (FACE) experiments have been conducted with young and fast-growing trees [92, 93]. Maximum stimulation of tree growth occurred at concentrations up to 560 ppm unless other limiting factors such as water shortage and insufficient nutrient supply set in. For the long-term C sequestration potential of forest ecosystems the FACE experiments are not entirely relevant because the response to elevated CO2 is probably transient [94].

A challenge is setting up experiments with a sufficient duration. Funding schemes of scientific experiments often favor short-duration experiments whereas long-term experiments in ecology are often burdened with insufficient funding [30, 95]. Among the climate manipulation experiments the investigation of droughts has been quite successful and is widely applied. One reason is that the installation of roofs covering experimental plots is rather simple (Figure 11). The technique is already long known and has been used in acid rain research since the 1980s [96].

Much more difficult is the assessment of other extreme events in climate manipulation experiments. Early and late frost events are difficult to create under field conditions. A partial remedy is the formation of scientific networks such as Climmani (http.//www.climmani. org) that bring together key researchers within climate change experiments to build coherent interdisciplinary databases. Such climate manipulation experiments can be combined with long-term ecological research installations [97].

## **Acknowledgements**

This chapter is an outcome of several projects, such as the MANFRED Project ("Management strategies to adapt Alpine Space forests to climate change risks projects" (Interreg Alpine Space Programme), Ecosoc Cuvée, sponsored by the Austrian Academy of Sciences, and COST 639.

## **Author details**

16 Carbon Sequestration

ecosystems [88–91] (Figure 11).

response to elevated CO2 is probably transient [94].

in acid rain research since the 1980s [96].

long-term ecological research installations [97].

**Acknowledgements**

COST 639.

**Figure 11.** Elements of climate manipulation experiments. Roofs under the tree canopy enable the creation of artificial drought conditions (left), automatic chambers allows the continuous measurement of greenhouse-gas emissions from soils (center), stem

experiments in forests is their size and longevity. For the assessment of soil processes under conceivable warmer conditions, soil warming experiments have been set up in different forest

Another avenue of climate change experiments is the exposure of trees to elevated concentrations of CO2. Several Free-Air-Carbon dioxide-Enrichment (FACE) experiments have been conducted with young and fast-growing trees [92, 93]. Maximum stimulation of tree growth occurred at concentrations up to 560 ppm unless other limiting factors such as water shortage and insufficient nutrient supply set in. For the long-term C sequestration potential of forest ecosystems the FACE experiments are not entirely relevant because the

A challenge is setting up experiments with a sufficient duration. Funding schemes of scientific experiments often favor short-duration experiments whereas long-term experiments in ecology are often burdened with insufficient funding [30, 95]. Among the climate manipulation experiments the investigation of droughts has been quite successful and is widely applied. One reason is that the installation of roofs covering experimental plots is rather simple (Figure 11). The technique is already long known and has been used

Much more difficult is the assessment of other extreme events in climate manipulation experiments. Early and late frost events are difficult to create under field conditions. A partial remedy is the formation of scientific networks such as Climmani (http.//www.climmani. org) that bring together key researchers within climate change experiments to build coherent interdisciplinary databases. Such climate manipulation experiments can be combined with

This chapter is an outcome of several projects, such as the MANFRED Project ("Management strategies to adapt Alpine Space forests to climate change risks projects" (Interreg Alpine Space Programme), Ecosoc Cuvée, sponsored by the Austrian Academy of Sciences, and

diameter growth is a common response variable for the quantification of climate change effects (right).

Robert Jandl, Andreas Schindlbacher

Forest Research Center (BFW), Vienna, Austria

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