Journal of Resources and Ecology ›› 2022, Vol. 13 ›› Issue (1): 129-141.DOI: 10.5814/j.issn.1674-764x.2022.01.015
• Industry Ecology and Regional Development • Previous Articles Next Articles
ZHANG Xianzhou1,2(
), LI Meng3, WU Jianshuang4, HE Yongtao1,2, NIU Ben1,*()
Received:2021-09-13
Accepted:2021-10-13
Online:2022-01-30
Published:2022-01-08
Contact:
NIU Ben
About author:ZHANG Xianzhou, E-mail: zhangxz@igsnrr.ac.cn
Supported by:ZHANG Xianzhou, LI Meng, WU Jianshuang, HE Yongtao, NIU Ben. Alpine Grassland Aboveground Biomass and Theoretical Livestock Carrying Capacity on the Tibetan Plateau[J]. Journal of Resources and Ecology, 2022, 13(1): 129-141.
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| Time period | Study area (104 km2) | Methods | Variables considered | AGB (g m-2) | References |
|---|---|---|---|---|---|
| 1960-2002 | 147.74 | Century | Climate and soil data | 70.00 | Zhang et al., |
| 2002-2004 | 139.00 | Orchidee | Climate, soil and LAI data | 119.78 | Tan et al., |
| 1980-1990 | 113.60 | Area-weighted average | - | 58.11 | Ni, |
| - | 101.10 | Area-weighted average | - | 61.15 | Luo et al., |
| 2001-2004 | - | Filed observations | - | 59.30 | Yang et al., |
| 2001-2004 | 112.80 | Linear regression | EVI | 68.80 | Yang et al., |
| 1982-2006 | 129.50 | Exponential regression | NDVI | 74.11 | Ma et al., |
| 2005 | 122.80 | Exponential regression | NDVI | 43.33 | Xu et al., |
| - | 124.00 | Exponential regression | NDVI | 78.02 | Piao et al., |
| 1982-2013 | 154.48 | Exponential regression | NDVI | 104.40 | Jiao et al., |
| 2000-2014 | 151.11 | Random forest | Climate, terrain and NDVI | 77.12 | Zeng et al., |
| - | 132.00 | Random forest | Climate and NDVI | 78.40 | Xia et al., |
| 2000-2017 | - | Random forest | Climate, terrain and NDVI | 59.63 | Gao et al., |
Table 1 Estimated mean aboveground biomass (AGB) of alpine grassland on the Tibetan Plateau in various published studies
| Time period | Study area (104 km2) | Methods | Variables considered | AGB (g m-2) | References |
|---|---|---|---|---|---|
| 1960-2002 | 147.74 | Century | Climate and soil data | 70.00 | Zhang et al., |
| 2002-2004 | 139.00 | Orchidee | Climate, soil and LAI data | 119.78 | Tan et al., |
| 1980-1990 | 113.60 | Area-weighted average | - | 58.11 | Ni, |
| - | 101.10 | Area-weighted average | - | 61.15 | Luo et al., |
| 2001-2004 | - | Filed observations | - | 59.30 | Yang et al., |
| 2001-2004 | 112.80 | Linear regression | EVI | 68.80 | Yang et al., |
| 1982-2006 | 129.50 | Exponential regression | NDVI | 74.11 | Ma et al., |
| 2005 | 122.80 | Exponential regression | NDVI | 43.33 | Xu et al., |
| - | 124.00 | Exponential regression | NDVI | 78.02 | Piao et al., |
| 1982-2013 | 154.48 | Exponential regression | NDVI | 104.40 | Jiao et al., |
| 2000-2014 | 151.11 | Random forest | Climate, terrain and NDVI | 77.12 | Zeng et al., |
| - | 132.00 | Random forest | Climate and NDVI | 78.40 | Xia et al., |
| 2000-2017 | - | Random forest | Climate, terrain and NDVI | 59.63 | Gao et al., |
Fig. 1 Spatial distribution of eco-geographical regions and the sample sites on the Tibetan Plateau Note: (a) The eco-geographical regions of TP (Zheng, 1996) are the same as those listed in Table 2. (b) Sample sites in this study are from field observations and a previous study (Fu et al., 2017), which basically included all alpine grassland ecosystems on the TP.
| Classification | Abbreviation | Meaning |
|---|---|---|
| Eco-geographical regions | TP | Tibetan Plateau |
| IB1 | Golog-Nagqu high-cold shrub-meadow zone | |
| IIAB1 | Western Sichuan-eastern Tibet montane coniferous forest zone | |
| IC1 | Southern Qinghai high-cold meadow steppe zone | |
| IC2 | Qiangtang high-cold steppe zone | |
| ID1 | Kunlun high-cold desert zone | |
| IIC1 | Southern Tibet montane shrub-steppe zone | |
| IIC2 | Eastern Qinghai-Qilian montane steppe zone | |
| IID1 | Nagri montane desert-steppe and desert zone | |
| IID2 | Qaidam montane desert zone | |
| IID3 | Northern slopes of Kunlun montane desert zone | |
| OA1 | Southern slopes of Himalaya montane evergreen broad-leaved forest zone | |
| Grass and livestock | AGBp | (Potential, only climate-derived) Aboveground biomass of the grassland |
| LCCT | Theoretical livestock carrying capacity | |
| SSU | The standardized sheep unit (daily feed of 1.33 kg hay in this study) | |
| Climate and soil | GSDR | Growing season (May to September in each year) diurnal temperature range |
| GSP | Growing season precipitation | |
| GST | Growing season temperature | |
| NGSDR | Non-growing season diurnal temperature range | |
| NGSP | Non-growing season precipitation | |
| NGST | Non-growing season temperature | |
| SOM | Soil organic matter |
Table 2 List of abbreviations used in this study
| Classification | Abbreviation | Meaning |
|---|---|---|
| Eco-geographical regions | TP | Tibetan Plateau |
| IB1 | Golog-Nagqu high-cold shrub-meadow zone | |
| IIAB1 | Western Sichuan-eastern Tibet montane coniferous forest zone | |
| IC1 | Southern Qinghai high-cold meadow steppe zone | |
| IC2 | Qiangtang high-cold steppe zone | |
| ID1 | Kunlun high-cold desert zone | |
| IIC1 | Southern Tibet montane shrub-steppe zone | |
| IIC2 | Eastern Qinghai-Qilian montane steppe zone | |
| IID1 | Nagri montane desert-steppe and desert zone | |
| IID2 | Qaidam montane desert zone | |
| IID3 | Northern slopes of Kunlun montane desert zone | |
| OA1 | Southern slopes of Himalaya montane evergreen broad-leaved forest zone | |
| Grass and livestock | AGBp | (Potential, only climate-derived) Aboveground biomass of the grassland |
| LCCT | Theoretical livestock carrying capacity | |
| SSU | The standardized sheep unit (daily feed of 1.33 kg hay in this study) | |
| Climate and soil | GSDR | Growing season (May to September in each year) diurnal temperature range |
| GSP | Growing season precipitation | |
| GST | Growing season temperature | |
| NGSDR | Non-growing season diurnal temperature range | |
| NGSP | Non-growing season precipitation | |
| NGST | Non-growing season temperature | |
| SOM | Soil organic matter |
Fig. 2 Correlation coefficients of grassland AGBp with 14 environmental factors (a) and the relationships between observed grassland AGBp and estimated AGBp on the TP based on the RF model (b)
| Eco-geographical regions* | AGBp mean (g m-2) | AGBp trend (g m-2 yr-1) | ||
|---|---|---|---|---|
| Mean | Standard deviation (SD) | Mean | Standard deviation (SD) | |
| IB1 | 181.64 | 52.87 | 0.47 | 0.80 |
| ICI | 93.68 | 32.03 | 0.31 | 0.60 |
| IC2 | 53.81 | 24.90 | -0.11 | 0.30 |
| ID1 | 55.90 | 7.70 | 0.02 | 0.08 |
| IIAB1 | 196.47 | 46.29 | -0.23 | 0.69 |
| IIC2 | 167.11 | 44.29 | 1.04 | 0.59 |
| IIC1 | 80.47 | 25.17 | -0.16 | 0.29 |
| IID2 | 109.63 | 24.70 | 0.09 | 0.22 |
| OA1 | 228.15 | 35.00 | -0.53 | 0.48 |
| IID1 | 56.42 | 24.77 | 0.36 | 0.60 |
| IID3 | 92.39 | 23.35 | -0.02 | 0.11 |
| TP | 102.40 | 63.47 | 0.14 | 0.61 |
Table 3 The mean values and trends of grassland AGBp for each eco-region on the TP
| Eco-geographical regions* | AGBp mean (g m-2) | AGBp trend (g m-2 yr-1) | ||
|---|---|---|---|---|
| Mean | Standard deviation (SD) | Mean | Standard deviation (SD) | |
| IB1 | 181.64 | 52.87 | 0.47 | 0.80 |
| ICI | 93.68 | 32.03 | 0.31 | 0.60 |
| IC2 | 53.81 | 24.90 | -0.11 | 0.30 |
| ID1 | 55.90 | 7.70 | 0.02 | 0.08 |
| IIAB1 | 196.47 | 46.29 | -0.23 | 0.69 |
| IIC2 | 167.11 | 44.29 | 1.04 | 0.59 |
| IIC1 | 80.47 | 25.17 | -0.16 | 0.29 |
| IID2 | 109.63 | 24.70 | 0.09 | 0.22 |
| OA1 | 228.15 | 35.00 | -0.53 | 0.48 |
| IID1 | 56.42 | 24.77 | 0.36 | 0.60 |
| IID3 | 92.39 | 23.35 | -0.02 | 0.11 |
| TP | 102.40 | 63.47 | 0.14 | 0.61 |
Fig. 3 The spatial and temporal patterns of AGBp on the TP from 2000 to 2018 Note: The eco-geographical regions of the TP are the same as indicated in Table 2.
Fig. 4 The dynamics of growing season and non-growing season diurnal temperature, temperature, and precipitation on the Tibetan Plateau from 2000 to 2018. Note: The six climate variables from (a) to (f) are the same as indicated in Table 2.
Fig. 5 Correlations between AGBp and climatic variables from 2000 to 2018 on the TP Note: The six climate variables are the same as indicated in Table 2.
Fig. 6 The temporal-spatial patterns of the theoretical livestock carrying capacity from 2000 to 2018 on the TP Note: The eco-geographical regions of the TP are the same as indicated in Table 2.
Fig. 7 The grassland potential aboveground biomass (AGBp) changes in different future climate change scenarios for each eco-region on the TP compared to the past two decades Note: The eco-geographical regions of the TP are the same as indicated in Table 2. The different climate change scenarios of SSP1-2.6 SSP2-4.5, SSP3-7.0, and SSP5-8.5 (a-d) are the scenarios of low radiative forcing (2.6 W m-2), moderate radiative forcing (4.5 W m-2), medium to high radiative forcing (7.0 W m-2), and high radiative forcing (8.5 W m-2) in 2100 under the different social development conditions.
| Eco-geographical regions* | SSP1-2.6 | SSP2-4.5 | SSP3-7.0 | SSP5-8.5 |
|---|---|---|---|---|
| IB1 | -2.70±9.54 | -2.24±9.31 | -2.16±9.60 | -2.40±10.10 |
| IC1 | -16.33±10.27 | -14.05±10.32 | -14.37±10.69 | -16.29±11.05 |
| IC2 | -0.67±18.05 | 0.81±18.09 | 0.63±18.76 | -11.51±17.43 |
| ID1 | -7.79±8.29 | -7.74±8.37 | -8.59±8.73 | -17.47±7.44 |
| IIAB1 | 2.75±5.73 | 2.48±5.64 | 2.84±6.07 | 2.28±6.07 |
| IIC2 | -7.11±8.8 | -8.14±8.54 | -8.43±8.28 | -6.01±9.10 |
| IIC1 | -0.98±9.29 | -0.72±9.31 | -0.22±9.45 | -1.24±8.96 |
| IID2 | -4.66±7.11 | -4.71±6.85 | -4.76±6.81 | -4.33±7.11 |
| OA1 | 1.94±2.69 | 1.30±1.93 | 1.61±2.28 | 0.93±2.01 |
| IID1 | 4.73±17.9 | 4.21±18.01 | 1.39±17.27 | -1.07±21.35 |
| IID3 | -5.03±8.21 | -4.18±7.24 | -4.11±7.46 | -5.27±8.00 |
| Tibetan Plateau | -3.75±13.82 | -3.05±13.7 | -3.25±14.05 | -7.96±14.14 |
Table 4 Grassland potential aboveground biomass (AGBp) changes in different future climate change scenarios for each eco-region on the TP compared to the past two decades (Unit: g m-2)
| Eco-geographical regions* | SSP1-2.6 | SSP2-4.5 | SSP3-7.0 | SSP5-8.5 |
|---|---|---|---|---|
| IB1 | -2.70±9.54 | -2.24±9.31 | -2.16±9.60 | -2.40±10.10 |
| IC1 | -16.33±10.27 | -14.05±10.32 | -14.37±10.69 | -16.29±11.05 |
| IC2 | -0.67±18.05 | 0.81±18.09 | 0.63±18.76 | -11.51±17.43 |
| ID1 | -7.79±8.29 | -7.74±8.37 | -8.59±8.73 | -17.47±7.44 |
| IIAB1 | 2.75±5.73 | 2.48±5.64 | 2.84±6.07 | 2.28±6.07 |
| IIC2 | -7.11±8.8 | -8.14±8.54 | -8.43±8.28 | -6.01±9.10 |
| IIC1 | -0.98±9.29 | -0.72±9.31 | -0.22±9.45 | -1.24±8.96 |
| IID2 | -4.66±7.11 | -4.71±6.85 | -4.76±6.81 | -4.33±7.11 |
| OA1 | 1.94±2.69 | 1.30±1.93 | 1.61±2.28 | 0.93±2.01 |
| IID1 | 4.73±17.9 | 4.21±18.01 | 1.39±17.27 | -1.07±21.35 |
| IID3 | -5.03±8.21 | -4.18±7.24 | -4.11±7.46 | -5.27±8.00 |
| Tibetan Plateau | -3.75±13.82 | -3.05±13.7 | -3.25±14.05 | -7.96±14.14 |
Fig. 8 The theoretical livestock carrying capacity of alpine grasslands on the TP in the past two decades (2000-2018) and in the future two decades (2021-2040) under four climate change scenarios Note: The different climate change scenarios are the same as indicated in Fig. 7.
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