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Journal of Advances in Modeling Earth Systems Volume 11 ,Issue 4 ,2019-04-15
Explicit Representation of Grazing Activity in a Diagnostic Terrestrial Model: A Data‐Process Combined Scheme
Research Articles
Yizhao Chen 1 , 2 , 3 Weimin Ju 4 Shaojie Mu 5 Xinran Fei 6 Yuan Cheng 6 Pavel Propastin 7 , 8 Wei Zhou 9 Cuijuan Liao 2 Luxiao Chen 10 Rongjun Tang 10 Jiaguo Qi 11 Jianlong Li 2 Honghua Ruan 1
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DOI:10.1029/2018MS001352
Received 2017-09-14, accepted for publication 2019-03-11, Published 2019-03-11
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摘要

Abstract Grazing activity is a fundamental behavior in pasture ecosystems and, globally, is a major disturbance that leads to destruction of terrestrial biomass. However, its impact on ecosystem C sequestration at large scales is not well understood due to its obvious anthropogenic property. In this study, we proposed a Data‐Process combined Grazing Scheme (DPGS) to quantify the regional grazing impact on ecosystem C sequestration in the typical pasture ecosystem, Temperate Eurasian Steppe. First, a pixel‐based livestock distribution map was generated based on fine‐scale (province/prefecture) inventory data using a resource‐oriented livestock distribution approach. Then the C consumption due to grazing (Closs,graze) was simulated by combining a late version of a remote‐sensing‐based terrestrial model, the Boreal Ecosystem Productivity Simulator and the Shiyomi grazing model. The modeled regional livestock density was evaluated against the Gridded Livestock of the World data set. The DPGS was able to reproduce the spatial distribution of livestock. Because extralarge herbivores (camel and horse) were involved in the calculation, the DPGS predicts higher livestock density than the Gridded Livestock of the World data set over 70% of the region. The modeled Closs,graze and its seasonal variability were validated against multiple site‐based data sets. The results showed good agreements with the field observations of Closs,graze. With further tests and data incorporations, this scheme has the potential to produce high‐resolution data sets of livestock distribution and Closs,graze and become a useful diagnostic instrument for model evaluation, parameterization, and intercomparison.

关键词

livestock distribution;C sequestration;terrestrial carbon cycling;grazing C consumption;temperate Eurasian steppe;remote sensing model

授权许可

©2019. American Geophysical Union. All Rights Reserved.

图表

Map of the study region. Kazakh steppe and Mongol steppe are two subregions of the Temperate Eurasian Steppe. The green colored areas in the upper map represent the grassland areas. Some of the southern prefectures in Xinjiang were not included in this map because few grasslands exist in those areas.

The schematic diagram of the data‐process combined grazing scheme. NPP = Net Primary Productivity; DEM = digital elevation model.

The livestock distribution map of 2006 in Temperate Eurasian Steppe based on the administrative divisions. IM,CN = Inner Mongolia, China; KGZ = Kyrgyzstan; KAZ = Kazakhstan; TJK = Tajikistan; TKM = Turkmenistan; UZB = Uzbekistan; VO,RUS = Volgograd Oblast, Russia; XJ,CN = Xinjiang, China.

The outline of the model pool and flux scheme in the coupled model. BEPS = Boreal Ecosystem Productivity Simulator.

Comparison of the spatial distribution pattern of livestock. (a) The regional pattern of differences in livestock density between this study and the Gridded Livestock of the World data set (this study‐Gridded Livestock of the World data set). (b) The frequency distribution of livestock density differences. IM,CN = Inner Mongolia, China; KGZ = Kyrgyzstan; KAZ = Kazakhstan; TJK = Tajikistan; TKM = Turkmenistan; UZB = Uzbekistan; VO,RUS = Volgograd Oblast, Russia; XJ,CN = Xinjiang, China; MGL = Mongolia.

The linear correlation between the modeled annual livestock density in this study and in the GLW data set. The blue color represents the pixel density. The color becomes deeper with higher pixel density. The livestock density in sheep unit per square kilometer was log10 transformed. DPGS = data‐process combined grazing scheme; GLW = Gridded Livestock of the World.

Comparisons of the C consumption due to grazing from the data‐process combined grazing scheme against field control experiments under different grazing intensities; the values are averaged over 1990, 1993, and 1997. (a) Temporal trend and comparison; (b) linear correlation between measured and modeled C consumption.

Comparisons of the C consumption due to grazing from the data‐process combined grazing scheme against the observed results collected from recent publications. The classifications of grasslands were based on the recorded annual production: low productivity (0–100·gC·m−2·year−1, mainly desert grasslands), medium productivity (100–300·gC·m−2·year−1, mainly typical grasslands), and high productivity (>300·gC·m−2·year−1, meadow and alpine grasslands).

The spatial distributions of (a) potential NPP (i.e., the sum of NPP after grazing and Closs,graze), (b) C consumption due to grazing (Closs,graze), and (c) percentage of grazed C from potential NPP in Temperate Eurasian Steppe. NPP = Net Primary Productivity; IM,CN = Inner Mongolia, China; KGZ = Kyrgyzstan; KAZ = Kazakhstan; TJK = Tajikistan; TKM = Turkmenistan; UZB = Uzbekistan; VO,RUS = Volgograd Oblast, Russia; XJ,CN = Xinjiang, China; MGL = Mongolia.

The seasonal trends of grazing consumption in TES in 2006: conceptual projections and modeled results. The y axis represents the regional mean grazing C consumption accumulation. The red lines represent the modeling outputs of the regional accumulated grazing C consumptions with different parameterizations. Solid red line = standard parameterization; upper dashed red line = all sensitive parameter values increased by 10%; lower dashed red line = all sensitive parameter values decreased by 10%. The black lines represents the conditions with forage supply fully available throughout the year with (solid black line) and without (dashed black line) an upper limit of livestock weight. The blue line represents the C consumption due to grazing from pixels with the lowest forage supply from the pasture system (see a more detailed description in section 2.6). TES = Temperate Eurasian Steppe.

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通讯作者

1. Yizhao Chen.Joint Innovation Center for Modern Forestry Studies, College of Biology and the Environment, Nanjing Forestry University, Nanjing, Jiangsu, China;School of Life Science, Nanjing University, Nanjing, China;Ministry of Education Key Laboratory for Earth System Modeling, and Department for Earth System Science, Tsinghua University, Beijing, China.chenyzvest@gmail.com
2. Honghua Ruan.Joint Innovation Center for Modern Forestry Studies, College of Biology and the Environment, Nanjing Forestry University, Nanjing, Jiangsu, China.chenyzvest@gmail.com

推荐引用方式

Yizhao Chen,Weimin Ju,Shaojie Mu,Xinran Fei,Yuan Cheng,Pavel Propastin,Wei Zhou,Cuijuan Liao,Luxiao Chen,Rongjun Tang,Jiaguo Qi,Jianlong Li,Honghua Ruan. Explicit Representation of Grazing Activity in a Diagnostic Terrestrial Model: A Data‐Process Combined Scheme. Journal of Advances in Modeling Earth Systems ,Vol.11, Issue 4(2019)

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