Sequential Classification System Research Articles

Quantitative analysis of the impact of climate variability and human activities on grassland productivity of the Qilian Mountain National Park, China.

To quantitatively analyze the impact of climate variability and human activities on grassland productivity of China's Qilian Mountain National Park, this study used Carnegic-Ames-Stanford Approach model (CASA) and Integrated Vegetation model improved by the Comprehensive and Sequential Classification System (CSCS) to assess the trends of grassland NPP from 2000 to 2015, the residual trend analysis method was used to quantify the impact of human activities and climate change on the grassland based on the NPP changes. The actual grassland NPP accumulation mainly occurred in June, July and August (autumn); the actual NPP showed a fluctuating upward trend with an average increase of 2.2 g C·m-2 a-1, while the potential NPP increase of 1.6 g C·m-2 a-1 and human-induced NPP decreased of 0.5 g C·m-2 a-1. The annual temperature showed a fluctuating upward trend with an average increase of 0.1°C 10a-1, but annual precipitation showed a fluctuating upward trend with an average annual increase of 1.3 mm a-1 from 2000 to 2015. The area and NPP of grassland degradation caused by climate variability was significantly greater than that caused by human activities and mainly distributed in the northwest and central regions, but area and NPP of grassland restored caused by human activities was significantly greater than that caused by climate variability and mainly distributed in the southeast regions. In conclusion, grassland in Qilian Mountain National Park showed a trend of degradation based on distribution area, but showed a trend of restoration based on actual NPP. Climate variability was the main cause of grassland degradation in the northwestern region of study area, and restoration of grassland in the eastern region was the result of the combined effects of human activities and climate variability. Under global climate change, the establishment of Qilian Mountain National Park was of great significance to the grassland's protection and the grasslands ecological restoration that have been affected by humans.

Modelling analysis embodies drastic transition among global potential natural vegetations in face of changing climate

Potential natural vegetation (PNV) is a valuable reference for ecosystem renovation and has garnered increasing attention worldwide. However, there is limited knowledge on the spatio-temporal distributions, transitional processes, and underlying mechanisms of global natural vegetation, particularly in the case of ongoing climate warming. In this study, we visualize the spatio-temporal pattern and inter-transition procedure of global PNV, analyse the shifting distances and directions of global PNV under the influence of climatic disturbance, and explore the mechanisms of global PNV in response to temperature and precipitation fluctuations. To achieve this, we utilize meteorological data, mainly temperature and precipitation, from six phases: the Last Inter-Glacial (LIG), the Last Glacial Maximum (LGM), the Mid Holocene (MH), the Present Day (PD), 2030 (2021–2040) and 2090 (2081–2100), and employ a widely-accepted comprehensive and sequential classification system (CSCS) for global PNV classification. We find that the spatial patterns of five PNV groups (forest, shrubland, savanna, grassland and tundra) generally align with their respective ecotopes, although their distributions have shifted due to fluctuating temperature and precipitation. Notably, we observe an unexpected transition between tundra and savanna despite their geographical distance. The shifts in distance and direction of five PNV groups are mainly driven by temperature and precipitation, although there is heterogeneity among these shifts for each group. Indeed, the heterogeneity observed among different global PNV groups suggests that they may possess varying capacities to adjust to and withstand the impacts of changing climate. The spatio-temporal distributions, mutual transitions and shift tendencies of global PNV and its underlying mechanism in face of changing climate, as revealed in this study, can significantly contribute to the development of strategies for mitigating warming and promoting re-vegetation in degraded regions worldwide.

Spatiotemporal Variations of Chinese Terrestrial Ecosystems in Response to Land Use and Future Climate Change

Terrestrial ecosystems in China are threatened by land use and future climate change. Understanding the effects of these changes on vegetation and the climate-vegetation interactions is critical for vegetation preservation and mitigation. However, land-use impacts on vegetation are neglected in terrestrial ecosystems exploration, and a deep understanding of land-use impacts on vegetation dynamics is lacking. Additionally, few studies have examined the contribution of vegetation succession to changes in vegetation dynamics. To fill the above gaps in the field, the spatiotemporal distribution of terrestrial ecosystems under the current land use and climate baseline (1970–2000) was examined in this study using the Comprehensive Sequential Classification System (CSCS) model. Moreover, the spatiotemporal variations of ecosystems and their succession under future climate scenarios (the 2030s–2080s) were quantitatively projected and compared. The results demonstrated that under the current situation, vegetation without human disturbance was mainly distributed in high elevation regions and less than 10% of the national area. For future vegetation dynamics, more than 58% of tundra and alpine steppe would shrink. Semidesert would respond to climate change with an expansion of 39.49 × 104 km2, including the succession of the steppe to semidesert. Although some advancement of the temperate forest at the expense of substantial dieback of tundra and alpine steppe is expected to occur, this century would witness a considerable shrinkage of them, especially in RCP8.5, at approximately 55.06 × 104 km2. Overall, a warmer and wetter climate would be conducive to the occurrence and development of the CSCS ecosystems. These results offer new insights on the potential ecosystem response to land use and climate change over the Chinese domain, and on creating targeted policies for effective adaptation to these changes and implementation of ecosystem protection measures.

Preface to the Special Issue of

Rangelands are some of the most widely distributed ecosystems on Earth, covering ~20% of the land surface area. As an important part of terrestrial ecosystems, accurate rangeland classification plays a crucial role in carbon cycling, biodiversity conservation and forage production. The Comprehensive Sequential Classification System of Rangeland (CSCSR), a well accepted rangeland classification system, has attracted global attention and has undergone further development. This paper reviews the origin and development of the CSCSR and its application in the fields of climate change, rangeland and livestock production, even rangeland ecosystem services in detail.

Response of grassland net primary productivity to climate change in China

To protect the grassland ecosystem’s security, and coordinate the utilisation of grassland resources, explicitly estimating the response of Net Primary Productivity (NPP) of 10 grassland groups to future climate change is necessary. Based on the Comprehensive and Sequential Classification System (CSCS) and modified Carnegie Ames Stanford Approach (CASA) model, in conjunction with four Representative Concentration Pathways (RCP 2.6, RCP 4.5, RCP 6.0, RCP 8.5) of the Intergovernmental Panel on Climate Change (IPCC), the response of China’s grassland NPP to climate change was simulated and estimated. Results showed that: (1) the simulation accuracy of modified CASA model (R2 = 0.65) is 34% higher than that of CASA model, indicating that the modified CASA was suitable for estimating grassland NPP in China; (2) annual mean NPP and total NPP (2001–18) were 138.4 g C m−2 year−1 and 495.7 T g C, respectively; compared with the period 2001–18, NPP during the 2050 under RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5 were predicted with increases of 34.8%, 35.9%, 34.9% and 35.8%, respectively; and (3) from the present-day to the 2050, the NPP of Warm desert exhibited the largest increase (73.2–76.3%), while that of Tundra and alpine grassland the smallest (1.3–1.6%). These differences in NPP increase likely lead to differences in grassland carrying capacity. To respond to the impact of future climate change on grassland, grassland classification management strategies according to different groups should be implemented as grassland NPP changes differently in different grassland groups.

Loss of grassland ecosystem service values based on potential vegetation in China

Grassland resources in China have diverse categories and span extensive areas. Existing studies have estimated the value of ecosystem services in China at different scales, but there are few reports on the potential loss of grassland ecosystem services in China. On the basis of the class-level compatibility of the two existing grassland classification systems, this study separately estimated the ecosystem service valves (ESVs) of Chinese potential grassland ecosystems (Comprehensive Sequential Classification System, CSCS) and existing grassland ecosystems (Vegetation Habitatology Classification System, VHCS). In addition, we also calculated the loss of ESVs between the two grassland classification systems and further analysed the reasons for the loss. The results are as follows: (1) the total amount of ESVs provided by the existing grassland in China was 64 929.87 × 108 Ren Min Bi (RMB) per year, in which lowland meadow and alpine meadow provide higher ESVs than the other 16 grassland classes; (2) the total ESV of Chinese potential grasslands was 83 632.88 × 108 RMB per year, of which IE29 (frigid temperate humid tundra, alpine meadow) and IF36 (frigid perhumid rain tundra, alpine meadow) contributed most to the total ESV (16.9%); (3) the ESVs of grassland in China decreased by 18 703.01 × 108 RMB per year compared with the potential value; the ESV loss mainly occurred in areas where the original grassland decreased and the secondary grassland increased; and (4) anthropogenic disturbances such as grazing, felling and indiscriminate cultivation and climate change are the main reasons for loss of ESV owing to grassland degradation.

Spatio-temporal distribution of sensitive regions of potential vegetation in China based on the Comprehensive Sequential Classification System (CSCS) and a climate-change model

The potential vegetation can reflect climatic influence on vegetation type change, and provide a scientific reference and guide for restoration and reconstruction of vegetation ecosystems. Meteorological observation data from 1961 to 2017 and predicative data under the Representative Concentration Pathway (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) during 2030s, 2050s and 2080s, in conjunction with comprehensive sequential classification system (CSCS) model and Geographic Information System (GIS) technology, were used to analyse the spatio-temporal distribution and variation of Sensitive Regions of Potential Vegetation (SRPV) in China. Results suggested that SRPV presented a pattern of total dispersion and partial agglomeration under all scenarios. The agglomerate regions spread from north-eastern China (e.g. Inner Mongolia Plateau, Greater Xing’an Mountains, and North-east Plain) to south-western China (e.g. Loess Plateau, Qinling–Huaihe belt, Nanling Mountains, Jiangnan hills, Qinghai–Tibet Plateau, and Tarim Basin). From the 2030s to the 2080s, SRPV exhibited characteristics of expansion and migration to the north under RCP scenarios. The distributed area of SRPV increased with increased radiation emission intensity. These results both further expand the research on potential vegetation using the CSCS, and also provide reference for governments to ensure vegetation ecological protection.

Impacts of Future Climate Changes on Spatio-Temporal Distribution of Terrestrial Ecosystems over China

Understanding the response of terrestrial ecosystems to future climate changes would substantially contribute to the scientific assessment of vegetation–climate interactions. Here, the spatiotemporal distribution and dynamics of vegetation in China were projected and compared based on comprehensive sequential classification system (CSCS) model under representative concentration pathway (RCP) RCP2.6, RCP4.5, and RCP8.5 scenarios, and five sensitivity levels were proposed. The results show that the CSCS model performs well in simulating vegetation distribution. The number of vegetation types would increase from 36 to 40. Frigid–perhumid rain tundra and alpine meadow are the most distributed vegetation types, with an area of more than 78.45 × 104 km2, whereas there are no climate conditions suitable for tropical–extra-arid tropical desert in China. Some plants would benefit from climate changes to a certain extent. Warm temperate–arid warm temperate zone semidesert would expand by more than 1.82% by the 2080s. A continuous expansion of more than 18.81 × 104 km2 and northward shift of more than 124.93 km in tropical forest would occur across all three scenarios. However, some ecosystems would experience inevitable changes. More than 1.33% of cool temperate–extra-arid temperate zone desert would continuously shrink. Five sensitivity levels present an interphase distribution. More extreme scenarios would result in wider ecosystem responses. The evolutionary trend from cold–arid vegetation to warm–wet vegetation is a prominent feature despite the variability in ecosystem responses to climate changes.

Response of potential grassland vegetation to historical and future climate change in Inner Mongolia

Assessing current and potential future grassland vegetation types and distribution has important practical implications in grassland management. In this study, historical and projected climate data from Inner Mongolia were used to classify grassland classes for two historical (1960–1986 and 1986–2011) and two future periods (2021–2024 and 2041–2060) using the Comprehensive and Sequential Classification System (CSCS). Changes of grassland classes in these time periods were investigated. The results indicated that (1) using the CSCS provided an efficient approach to investigate the impact of historical and future climate on grassland classes and their distribution in Inner Mongolia over time and space; (2) since 1986, the precipitation in Inner Mongolia has declined, decreasing the semi-humid zone area and expanding that of arid and semi-arid areas. The area of Tundra and alpine steppe, Temperate zonal humid grassland and Temperate zonal forest steppe decreased, and that of Frigid desert, Semi-desert, and Steppe group increased; and (3) under the projected increase in temperature and decrease in precipitation in large areas in Inner Mongolia by 2060, the area of arid grassland classes will likely expand (Frigid desert, Semi-desert, and Steppe), and the grassland classes of Tundra and alpine steppe may gradually disappear. Government investment in infrastructure and grassland management measures such as introducing drought tolerant grass species and improved precipitation utilisation through irrigation are needed to adapt to the changing climate. Inner Mongolia should take advantage of the benefits of the increase in temperature in the eastern semi-humid region that would permit reduced grazing pressure in the western arid region.

Shift of potential natural vegetation against global climate change under historical, current and future scenarios

Potential natural vegetation (PNV), the final successional stage of vegetation, plays a key role in ecological restoration, the design of nature reserves, and development of agriculture and livestock production. Meteorological data from historical and current periods including the last inter-glacial (LIG), last glacial maximum (LGM), mid Holocene (MH) periods and the present day (PD), plus derived data from 2050 and 2070, in conjunction with the Comprehensive and Sequential Classification System (CSCS) model, were used to classify global PNV. The 42 classes of global PNV were regrouped into 10 groups to facilitate analysis of spatial changes. Finally, spatio-temporal patterns and successional processes of global PNV as well as the response to climate changes were analysed. Our study made the following five conclusions. (1) Only one missing class (IA1 frigid-extrarid frigid desert, alpine desert) arose in periods of LIG, MH, 2050, and 2070 for global PNV. (2) The frigid-arid groups were mainly distributed in higher latitudes and elevations, but temperate-humid groups and tropical-perhumid groups occurred in middle and low latitudes, respectively. Temperate zonal forest steppe, warm desert, savanna and tropical zonal forest steppe increased, while six other groups decreased. (3) The conversion from temperate zonal forest steppe to tundra and alpine steppe from LIG to LGM occupied the largest area, indicating a drastic shift in climate and the associated response of terrestrial vegetation sensitive to climate change. (4) The CSCS could be used to simulate the long-term succession of global PNV. (5) As a consequence of global warming, forests shifted to the northern hemisphere and Tibet, areas with much higher latitude and elevation. The PNV groups with greater shift distance revealed the more serious effects of global climate change on vegetation.

A new net primary productivity model and new management strategy of grassland classification based on CSCS in China

The discovery of grassland field, form and biomass in China was central to the sustainable development of grassland. In this study, the realistic spatial distribution patterns of grasslands were clarified through the combination of the International Geosphere-Biosphere Program (IGBP) and the Comprehensive and Sequential Classification System (CSCS). An optimal net primary productivity (NPP) model suitable for Chinese grasslands was introduced by integrating the classification indices-based model (CIM) with the Normalised Difference Vegetation Index (NDVI), and comparing it with the standard classical model (Miami, Schuur, CIM, CASA model). Using the optimal model as the algorithm basis, the net primary production spatial pattern of grassland in China was determined. The results showed that: (1) the total area of grassland was ~374.3 × 104 km2 in 2018, mainly distributed in north-western China. Among the grassland super-class groups, Tundra and alpine steppe were largest, and Warm desert smallest; (2) the optimal modified CIM had the highest prediction efficiency, and the overall accuracy was higher than the standard classical model (Miami, Schuur, CIM, CASA model). It achieved the accurate calculation of grassland NPP in China; (3) different grassland super-class groups had different carbon fixation efficiency per unit area, resulting in huge differences in total NPP. Among the various grassland super-class groups, the temperate humid grassland, steppe, tundra and alpine steppe had high conversion efficiency per unit area of NPP, whereas that for warm desert and the savanna was low. The total NPP was 388.04 × 1012 g C/year in the study area in 2018. The results provide a basis for the rational arrangement of grassland ecological and productive functions, and are significant for developing a new strategy of grassland classification management in China.

Spatiotemporal distribution of grassland NPP in Gansu province, China from 1982 to 2011 and its impact factors.

The modified Carnegie–Ames–Stanford Approach (CASA) model based on the comprehensive and sequential classification system of grasslands (CSCS, a unique vegetation classification system) was used to determine grassland net primary production (NPP) in Gansu province from 1982 to 2011 and its spatio-temporal variability. The relationship between NPP and climate drivers was analyzed. The results showed that annual NPP of grasslands in Gansu province averaged 139.30 gC m-2 yr -1 during the study period. NPP decreased from southeast to northwest across the province. Grassland NPP showed an increasing trend during the period 1982–2011, and the increase rate over the whole period was 92.91%. The highest NPP appeared in summer with more precipitation and higher cumulative temperature conditions; while the lowest values existed in winter. The largest correlation coefficient was found between the average annual NPP and the average annual precipitation (r = 0.77), followed by annual NPP and solar radiation (r = 0.70) or NDVI (r = 0.69), Annual NPP had no significant correlation with annual cumulative temperature (>0°C) or moisture index (K-value). Thus, precipitation is the major controlling factor on the average annual NPP in Gansu grassland. Solar radiation and NDVI also have important effects on grassland NPP in Gansu. These results may provide basic information for sustainable development and utilization of grassland and for the improvement and protection of the ecological environment as well.

Uncertainties on the GIS based potential natural vegetation simulation using Comprehensive and Sequential Classification System

ABSTRACT The use of GIS-based ecological models is increasing and the accuracy of input datasets of these models is improving. Still, there is a significant gap in quantifying the uncertainty related to the input data and the accuracy of these models’outputs. This study quantified error in annual cumulative temperature derived from using daily mean temperature and monthly mean temperature, and the uncertainty and error propagated in the Comprehensive and Sequential Classification System (CSCS) model that predicts Potential Natural Vegetation (PNV) in China. The error in annual cumulative temperature derived from daily mean temperature and monthly mean temperature is particularly high in Northwest and Northern China. The deviations in annual cumulative temperature have different effects on each PNV including edge effects on the model’s predictability due to error propagation in the interpolation method and overlay analysis. Future research can focus on the assessment of model behavior with the uncertainty of data itself and different spatial analysis methods including the spatial resolution of datasets. There is a need to develop a unique algorithm that would enable a better assessment of attribute error and location error in the spatial modeling.

Sequential classification system for recognition of malaria infection using peripheral blood cell images

AimsMorphological recognition of red blood cells infected with malaria parasites is an important task in the laboratory practice. Nowadays, there is a lack of specific automated systems able to differentiate...

A Modification of CIM for Prediction of Net Primary Productivity of the Three-River Headwaters, China

The Three-River Headwaters (TRH) region is covered dominantly with alpine meadow, a large part of which is confronting severe degradation as a result of climate change and human-induced influences. The estimation of net primary productivity (NPP) is essential to provide support for scientific management of TRH grassland resources to prevent further degradation. The classification indices-based model (CIM) has been applied in the estimation of NPP and its response to global warming because of its simple structure and easily obtained indices. However, CIM is considered to estimate the potential NPP rather than the actual value. Thus, its application has been restricted. In this study, the normalized difference vegetation index (NDVI) was applied to modify the CIM. Then, CIM and modified CIM were compared with the other three models. The assessment of NPP estimates indicated that the modified CIM had a fair performance among the NPP models (R2 = 0.42, RMSE = 178.08). All the NPP estimation models revealed that NPP increased from the northwest to the southeast. According to the modified CIM, the mean NPP of TRH grassland was 135.44 gC·m−2·yr−1 and the total NPP was 3.22 × 1013 gC·yr−1. Among the classes of the grassland of TRH in the comprehensive and sequential classification system (CSCS), the frigid perhumid rain tundra and alpine meadow occupied most of the grassland NPP, which was 3.06 × 1013 gC·yr−1. With the help of the NDVI, the modified CIM performed better than the CIM; however, there is still much room for the improvement of CIM in future research.

Spatial and temporal variability in the net primary production of grassland in China and its relation to climate factors

As an important component of the terrestrial carbon (C) cycle, variability in net primary productivity (NPP) plays a crucial role in the C input and accumulation in grasslands system. In this study, the spatial and temporal variability of grassland NPP in China during 2001–2010 and its relation to climate factors were analyzed by using a modified model of Carnegie–Ames–Stanford Approach based on the Comprehensive and Sequential Classification System. The results show that monthly grassland NPP increases from January to July. While the seasonal variability of NPP indicates peak productivity in summer. Annual mean grassland NPP follows a significant increasing trend with fluctuation from 2001 to 2010. The spatial pattern of grassland NPP shows increasing gradients from the west to the east and from the north to the south of China. Annual NPP differs significantly among different grassland types, with the highest NPP in the grassland distributed in sub-tropical perhumid evergreen broad leaved forest and tropical-perhumid rain forest. Time-lag correlation analysis at the monthly scale shows that grassland NPP responded more rapidly to changes in temperature than to precipitation. Among the climate factors, grassland NPP shows the strongest correlation at 1-month lag with moisture index K. There is a significant positive correlation between seasonal NPP and K. The seasonal NPP is significantly correlated with >0 °C annual cumulative temperature. The highest and the lowest NPP sensitivity to precipitation, K, and temperature were observed in the grassland distributed in tropical forest and semi-desert. The results indicate a complex mechanism of climate factors that control grassland C sequestration in terrestrial ecosystems.

A GIS simulation of potential vegetation in China under different climate scenarios at the end of the 21st century

The study of potential vegetation can reveal the impact of climate on changes in vegetation patterns. It is the starting point for studying vegetation-environmental classification and relationships, and it is the key point for studying global change and terrestrial ecosystems. By using the Comprehensive Sequential Classification System (CSCS) and the meteorological data under the four climate change scenarios from the IPCC5 publication, the present paper carries out a GIS simulation study of the spatial distribution of potential vegetation in China at the end of the 21st century. The results indicate that under the four climate scenarios at the end of the 21st century: (1) The potential vegetation in China shows significant horizontal and vertical distribution, which corresponds well to those of natural topographic features. (2) There are 40 classes of potential vegetation in China. Tropical-extrarid tropical desert (VIIA), which has no corresponding condition of growth in China, is commonly lacking, and differences exist among the potential vegetation classes and among the ratios of the classes under different scenarios. (3) From the perspective of categories, temperate forest is the most widely distributed, and savanna is the least widely distributed. Together with the strengthening of the radiation intensity according to RCP2.6 → RCP4.5 → RCP6.0 → RCP8.5, the area covered by cold-dry potential vegetation decreases as the area covered by warm-humid potential vegetation increases. As a result, the areas of tundra and alpine steppe, frigid desert, steppe, and temperate humid grassland tend to decrease, and those of semi-desert, temperate forest, sub-tropical forest, tropical forest, warm desert, and savanna tend to increase. Moreover, the potential vegetation in China at the end of the 21st century would change at different levels and in different directions when compared with that at the end of the 20th century. (4) In the same period, potential vegetation in different regions shows differences in their sensitivity to climate change, and by the end of the 21st century, 30.73% of land in China would be classified as a sensitive region, which highly corresponds to the current ecologically vulnerable zone, and whose potential vegetation easily evolves along with changes of climate scenarios.

Assessing the impact of historical and future climate change on potential natural vegetation types and net primary productivity in Australian grazing lands

The study investigated the impact of historical and future climate changes on potential natural vegetation (PNV) types and net primary productivity (NPP) in Australia, using the Comprehensive and Sequential Classification System model and the Miami model coupled with climate of the 1931–70 and 1971–2010 periods and the projected climate in 2050. Twenty-eight vegetation classes were classified based on the key climate indicators with four of them being the major vegetation classes corresponding to Australian rangelands and accounting for 75% of total land area. There was a substantial shift in areas of vegetation classes from the 1931–70 period to the 1971–2010 period due to the increased rainfall over large areas across Australia. The modelling projected a range of changes in vegetation classes for 2050 depending on the climate-change scenario used. Many vegetation classes with more intense land use (e.g. steppe and forest) were projected to decrease in 2050, which may have significant impact on the grazing industry and biodiversity conservation. By 2050, NPP was projected to increase in central and northern Australia and to decrease in southern and eastern coastal areas and was projected to be higher on average than that of the 1931–70 period. The vegetation classes approximately corresponding to Australian rangelands mostly had increased NPP projections compared with the 1931–70 period. Although actual response will partially depend on human management activities, fire and extreme events, the projected increase in average NPP in 2050 indicates that Australian vegetation, particularly the rangeland vegetation, will likely be a net carbon sink rather than a carbon source by 2050, with the exception of a ‘warm-dry’ scenario.

Modeling the dynamics of distribution, extent, and NPP of global terrestrial ecosystems in response to future climate change

Understanding how terrestrial ecosystems would respond to future climate change can substantially contribute to scientific evaluation of the interactions between vegetation and climate. To reveal the future climate impacts might on the nature and magnitude of global vegetation, the spatiotemporal distribution and net primary productivity (NPP) of global terrestrial biomes and their dynamics in this century were quantitatively simulated and compared by using the improved Comprehensive and Sequential Classification System and the segmentation model. The 33 general circulation models under the four scenarios of Representative Concentration Pathways (RCPs) were utilized to simulate the future climate change. The multi-model ensemble results showed that at the global scale, the distribution of forests and deserts would expand by more than 2% and 4% over this century, respectively. By contrast, more than 11% of grassland regions would shrink. Despite the considerable differences in the simulated responses of the biomes, the poleward movement or expansion of temperate forest were prominent features across all the scenarios. Meanwhile, the terrestrial NPP was projected to increase by 7.44, 9.51, 9.46, and 12.02PgDW·a−1 in 2070s relative to 1970s in the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The largest NPP decrease would occur in tundra & alpine steppe. NPP in the Tropical Zone, the North Temperate Zone, and the North Frigid Zone was estimated to increase in this century, whereas NPP in the South Temperate Zone was projected to decrease slightly across all scenarios. Overall, ecosystems in the mid-/high latitudes would be more vulnerable to future climate change in terms of distribution ranges and primary productivity despite the existing uncertainties. Some vegetation would benefit from the warmer and wetter climate. However, most of these plants would suffer and experience irreversible changes, particularly in the northern hemisphere.

Estimating net primary production of natural grassland and its spatio-temporal distribution in China

The net primary production (NPP) of grassland largely determines terrestrial carbon (C) sinks, and thus plays an important role in the global C cycle. Comprehensive and sequential classification system of grasslands (CSCS) is a unique vegetation classification system (mainly for grassland) that is dependent on quantitative measurement indices [>0°C annual cumulative temperature (Σθ) and moisture index (K-value)]. Based on the relationship of the quantitative classification of CSCS and grassland NPP, a modified model of Carnegie–Ames–Stanford Approach (CASA) was used to predict the grassland NPP and its temporal and spatial distribution in China from 2004 to 2008. The scatter plot of the estimated NPP and the observed NPP showed that the estimated data can be accepted with correlation coefficient of 0.896 (P<0.05). The average annual NPP of grassland from 2004 to 2008 in China ranged from 443.23 to 554.40gCm−2yr.−1. The NPP also showed spatial–temporal variations. There existed an increasing trend of NPP from the northwest to southeast due to the zonal distribution of vegetation. From the trend of monthly variations, it can be drawn that the NPP accumulation primarily occurred between April and October. The average NPP over seven months from April to October was 482.19gCm−2, or about 88.78% of the annual total. The spatial–temporal trend suggests the importance of water and thermal regimes in determining the grassland NPP (i.e. water and thermal are key limited factors for the grassland production), which is also confirmed by a cluster analysis. The mean annual NPP and the total annual NPP differed significantly among grassland classes corresponding with different Σθ and K-value. The results demonstrate that the grassland NPP and the classes/super-classes in CSCS achieve the optimum coupling.