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物探与化探, 2025, 49(1): 239-247 doi: 10.11720/wtyht.2025.3603

生态地质调查

黄土高原地区土壤有机碳和无机碳储量及含量特征

段星星,1,2,3, 刘小龙2,3, 韩宝华1, 阿地来·赛提尼亚孜2,3, 金梦婷2,3, 刘彤1

1.石河子大学 水利建筑工程学院,新疆 石河子 832003

2.中国地质调查局 乌鲁木齐自然资源综合调查中心,新疆 乌鲁木齐 830000

3.自然资源部 塔里木河下游水资源与生态野外科学观测研究站,新疆 乌鲁木齐 830000

Stocks and content of organic and inorganic carbon in soil of the Loess Plateau region

DUAN Xing-Xing,1,2,3, LIU Xiao-Long2,3, HAN Bao-Hua1, Adilai·Saitiniyazi 2,3, JIN Meng-Ting2,3, LIU Tong1

1. School of Hydraulic Engineering, Shihezi University, Shihezi 832003, China

2. Urumqi Comprehensive Survey Center on Natural Resources, China Geological Survey, Urumqi 830000, China

3. Field Observation and Research Station of Water Resources and Ecological Effect in Low Reaches of Tarim River Basin,Urumqi 830000, China

第一作者: 段星星(1983-),男,2006年毕业于中南大学,主要从事地球化学调查研究工作。Email:dxingxing@mail.cgs.gov.cn

责任编辑: 蒋实

收稿日期: 2022-12-3   修回日期: 2024-02-26  

基金资助: 新疆维吾尔自治区自然科学基金面上项目(2022D01A149)
中国地质调查局自然资源综合调查指挥中心科创基金项目(KC20230013)
中国地质调查局地质调查项目(DD20220887)

Received: 2022-12-3   Revised: 2024-02-26  

摘要

土壤碳库在调节全球碳平衡和减缓温室气体方面有着重要的地位,估算土壤碳储量对评价陆地生态系统碳循环具有重要意义。利用研究区土地质量地球化学调查获取的土壤碳数据,采用“单位土壤碳量”方法,估算了西北地区各层土壤全碳、有机碳和无机碳储量,分析了不同土壤、土地利用和地形地貌类型下的土壤有机碳和无机碳中碳含量特征。结果表明,研究区上下全层(0~2.0 m)土壤中累计求得总碳10 099.4 Mt,表层(0~0.2 m)总碳1 224.8 Mt,上层(0~1.0 m)5 345.9 Mt,下层(1.0~2.0 m)4 753.5 Mt,各层中总碳含量以无机碳为主,占比自上而下逐渐增大,有机碳主要集中在表层。无机碳含量高值区主要分布在青海湟水谷地、甘肃陇中、陕西北部和宁夏南部黄土高原等地区,有机碳高值区主要分布在祁连山一带。风沙土具有最低的表、深层有机碳、无机碳和总碳含量;黑垆土和黄绵土具有最高的表层无机碳含量;黑毡土具有最高的表层有机碳含量;黑垆土具有最高的深层有机碳含量,且具有最高的表层和深层土壤总碳含量。森林具有最高的表层和深层有机碳及表层总碳含量;草原具有最高的表层无机碳和总碳含量;耕地具有最高的深层无机碳含量;裸地具有最低的无机碳、有机碳和总碳含量。山地具有最高的表层和深层土壤有机碳和总碳含量;黄土具有最高的表层和深层无机碳含量;平原总体介于黄土和山地之间。高海拔地区具有极高的有机碳含量。

关键词： 黄土高原; 有机碳; 无机碳; 碳储量; 土地质量地球化学

Abstract

Soil carbon pools play a significant role in regulating global carbon balance and mitigating greenhouse gases. Hence, estimating soil carbon stocks is critical for assessing the carbon cycle in terrestrial ecosystems. Based on the soil carbon data obtained from the land quality geochemical survey in the study area, this study estimated the stocks of total, organic, and inorganic carbon of various soil layers in Northwest China using the unit soil carbon amount (USCA) method. It analyzed the content characteristics of organic and inorganic carbon in soil under different soil, land use, and topographic types. The results of this study are as follows: (1) All the soil layers at depths ranging from 0 to 2 m in the study area exhibited total carbon of 10 099.4 Mt, including 1 224.8 Mt in the topsoil layer (0~0.2 m), 5 345.9 Mt in the upper soil layer (0~1.0 m), and 4 753.5 Mt in the lower soil layer (1.0~2.0 m). Inorganic carbon predominated in all the soil layers, with its proportion gradually increasing from top to bottom, whereas organic carbon was principally concentrated in the topsoil layer; (2) The high-value areas of inorganic carbon content were primarily distributed in the Huangshui Valley of Qinghai Province, and the Loess Plateau region covering the Longzhong area of Gansu Province, northern Shaanxi Province, and southern Ningxia Province. In contrast, the high-value areas of organic carbon content were chiefly distributed in the Qilian Mountains; (3) The aeolian sandy soil exhibited the lowest organic, inorganic, and total carbon contents in the topsoil and deep soil layers. The dark loessial soil and the loessal soil showed the highest inorganic carbon content in the topsoil layer. The dark felty soil and the dark loessial soil displayed the highest organic carbon contents in the topsoil and deep soil layers, respectively. Additionally, the dark felty soil had the highest total carbon content in the topsoil and deep soil layers; (4) Forests exhibited the highest organic carbon content in the topsoil and deep soil layers, and the highest total carbon content in the topsoil layer. Grasslands showed the highest inorganic and total carbon contents in the topsoil layer. Cultivated land had the highest inorganic carbon content in the deep soil layer. Bare land manifested the lowest inorganic, organic, and total carbon contents; (5) Mountains displayed the highest organic and total carbon contents in the topsoil and deep soil layers. Loess had the highest inorganic carbon content in the topsoil and deep soil layers. Plains showed intermediate carbon contents generally between those of loess and mountains. Besides, high-altitude areas manifested extremely high organic carbon content.

Keywords： Loess Plateau; organic carbon; inorganic carbon; carbon stock; land quality geochemistry

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本文引用格式

段星星, 刘小龙, 韩宝华, 阿地来·赛提尼亚孜, 金梦婷, 刘彤. 黄土高原地区土壤有机碳和无机碳储量及含量特征[J]. 物探与化探, 2025, 49(1): 239-247 doi:10.11720/wtyht.2025.3603

DUAN Xing-Xing, LIU Xiao-Long, HAN Bao-Hua, Adilai·Saitiniyazi , JIN Meng-Ting, LIU Tong. Stocks and content of organic and inorganic carbon in soil of the Loess Plateau region[J]. Geophysical and Geochemical Exploration, 2025, 49(1): 239-247 doi:10.11720/wtyht.2025.3603

0 引言

工业革命以来,由于化石燃料的大量使用和土地利用方式的变化,大气中的CO2浓度快速升高[1],以2×10-6/年的速度持续增加,从工业化前的280×10-6上升至2005年的379×10-6,按此速度增长,到21世纪末有可能达到700×10-6[2],由此造成的全球气候变暖,将严重影响生态环境和人类社会经济的可持续发展[3-4]。因此,切实减少温室气体的排放,有效增汇、固碳成为缓解气候变暖的首要任务[5]。土壤作为陆地生态系统中重要的碳库之一,其碳储量大约是大气碳库的2倍,植被碳库的2~3倍[6-7],是陆地生态系统碳循环过程中的重要源—汇介质,其微小变化都会显著影响碳汇和碳源的大小和分布[8-9]。土壤碳储量的变化及其分布与土地利用活动密切相关,长期非持续性土地利用方式的改变对陆地生态系统碳循环有着极其重要的影响,准确评估目前土地利用方式下土壤碳动态平衡水平,可为指定响应的CO2减排措施提供依据[10-11]。

在全球气候变化背景下,国内有不少学者在国家或区域尺度上依据全国土地质量区域地球化学调查资料数据对土壤碳储量及其分布[12]、固碳潜力[13]、固碳调控以及土地利用变化与土壤有机碳关系[14]等方面进行了研究。Li等[15]在2007年对中国土壤有机碳和无机碳储量进行估计,预测储量分别为83.8 Pg和77.9 Pg。赵宁等[16]研究表明,近50年间中国陆地生态系统碳汇整体呈上升趋势,年平均为0.213±0.030 Pg。Wu等[17]通过研究人类活动对我国北方碳库的影响,发现人类活动造成土壤无机碳库1.6 Pg的损失。西北黄土高原表层土壤有机碳储量为78.56 Mt, 其储量呈上升趋势[18]。高原冻土区土壤有机碳储量约16.5 Pg, 土壤无机碳储量约14 Pg[19]。尽管已有大量土壤碳储量的研究,但数据资料精度及估算方法的不同,学者们所得研究结果也相差较大。加之自然因素和人类活动的影响,土地利用方式的复杂多样,土壤碳储量在水平空间上表现出的空间变异性较为复杂,以致难以准确获得土壤碳信息,使得同一地区的研究结果存在较大差异。同时,由于深层土壤取样工作较为困难,特别是在中、大尺度的研究中,有关土壤有机碳和无机碳空间变异的研究大都集中于表层30 cm以浅土壤中[20],忽略了对深层土壤有机碳和无机碳的垂直分布特征的研究,对于深层土壤碳库估算还存在很多不确定性。土壤碳储量在空间上的分布直接反映了其土壤肥力的分布特征,是植物健康生长的重要保障[21]。因此,准确评估土壤碳储量是评价区域土壤碳循环对全球气候变化反馈效应的基础。

本文选择黄土高原地区为研究区,利用土地质量地球化学调查获取的土壤碳数据,分析了不同土壤类型、土地利用方式及地形地貌中表层、上层和下层土壤碳含量分布特征,估算了表层、上层和下层土壤全碳、有机碳和无机碳储量,为研究区土壤碳库的评价、区域尺度土壤碳库和碳平衡研究提供科学参考和基础数据。

1 材料与方法

1.1 研究区概况

研究区位于黄土高原部分地区,范围西起青海省祁连山,东至太行山,南至秦岭,北抵黄河,涵盖了青海东部、甘肃中东部、陕西中部地区以及宁夏平原,总面积约67.8万km2(图1)。研究区位于中纬度的东部季风区,西风带南部,其中西部和北部属于暖温带—中温带半干旱区,降水量为150~250 mm;东部和南部属暖温带半湿润区,年降水量为600~750 mm,形成夏季、秋季多雨,冬季、春季干旱少雨,平均温度3.6~14.3 ℃,冬季严寒,夏季暖热的气候特点。区内地势西高东低,自西向东波状下降,总体地形地貌景观格局以山地、丘陵、高原、盆地、台地等相间分布。土壤类型主要有黄绵土、灰钙土、褐土、栗钙土、黑垆土、黑毡土、新积土、风沙土、黑钙土、灌淤土、灰褐土、潮土、盐土等;主要土地利用类型为耕地、草地、森林、裸地等。黄土高原是世界上被侵蚀最严重的地区之一[22],黄土沉积区表面已经变得支离破碎[23]。

图1

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图1   研究区采样点位分布

Fig.1   Distribution of sampling points in the study area


1.2 数据来源

本文数据来源于西北地区1∶25万土地质量地球化学调查,采用双层网格化方法进行点位布点和采样,表层土壤每1 km2采集1个样,深度0~0.2 m;深层土壤每4 km2采集1个样点,深度1.0~2.0 m,累计采集表层土壤样品43 298件和深层土壤样品10 974件(见图1)。土壤样品干燥后过20目筛,表层土壤以每4 km2分析1个组合样,深层土壤以每16 km2分析1个组合样。根据《多目标区域地球化学调查规范(1∶250 000)》要求,土壤样品中的有机碳采用重铬酸钾容量法分析,全碳采用X射线荧光光谱法分析。按照分析方法准确度和精密度要求,采取严格的质量检查措施。为了有效控制系统分析误差,对分析质量采用全国质量监控制度控制。土地利用类型数据源自https://doi.org/10.5281/zenodo.4417810[24],土壤类型源自 http://www.geodata.cn,土壤容重数据源自 https://www.fao.org/,底图高程数据源自 www.gscloud.cn。

1.3 数据处理

通过对一定区域内单位土壤碳量进行求和,计算得到土壤碳储量,单位土壤碳含量采用网格化计算单元,按照奚小环等[25]提出的可更加逼近真实含量的指数法模型法求取土壤碳储量。

1)表层和深层土壤平均碳含量(S)计算公式为:

①表层土壤(0~0.2 m)平均碳含量:

S0~0.2=TC表 ;

②深层土壤(1.5~2.0 m)平均碳含量:

S1.5~2.0=TC深 。

式中:TC表、TC深代表表层、深层土壤对应的土壤总碳、无机碳或有机碳含量,%。

2)单位碳含量(USCA)和碳密度(SCD)计算公式为:

①表层土壤(0~0.2 m)单位碳含量:

USCA0~0.2=16×SCD0~0.2=16×104×ρ×TC表×0.2;

②上层土壤(0~1.0 m)单位碳含量:

USCA0~1.0=16×SCD0~1.0=16×104×ρ×TC表-TC深×d1-d3+d3×lnd3-lnd2d3lnd1-lnd2+TC深×1.0;

③下层土壤(1.0~2.0 m)单位碳含量:

USCA1.0~2.0=USCA0~2.0-USCA0~1.0 ;

④上下全层土壤(0~2.0 m)单位碳含量:

USCA0~2.0=16×SCD0~2.0=16×104×ρ×　(TC表-TC深)×(d1-d2)d2lnd1-lnd2+TC深×2.0 。

式中:104为换算系数;ρ为土壤容重,t/m3;TC为土壤总碳、无机碳或有机碳含量,%;d为土壤深度,其中d1取表层土壤中心深度0.1 m,d2取深层样采样深度2.0 m,d3取1.0 m;USCA单位为t。按照深层土壤样品16 km2内组合为1件样品分析,表层样品通过前期数据处理后,与深层样品数据形成一一对应关系,因此以16 km2为最小统计单元后,即可求得研究区各层土壤有机碳总储量。

3)一定区域内土壤碳储量(SCR)计算公式为:

SCR = ∑i=1nUSCAi,

式中:USCAi为第i个统计单位土壤碳含量,t;n为土壤碳储量统计范围内单位土壤碳量加和个数。

2 结果与分析

2.1 土壤碳储量特征

据统计,研究区上下全层(0~2.0 m)土壤中累计求得总碳10 099.4 Mt,其中有机碳储量3 125.7 Mt,无机碳储量6 973.7 Mt,以无机碳为主,占69.1%;表层土壤(0~0.2 m)中累计求得总碳1 224.8 Mt,其中有机碳储量519.0 Mt,无机碳705.7 Mt,以无机碳为主,占57.6%;上层土壤(0~1.0 m)中累计求得总碳5 345.9 Mt,其中有机碳储量1 887.4 Mt,无机碳储量3 458.5 Mt,以无机碳为主,占64.7%;下层土壤(1.0~2.0 m)中累计求得总碳4 753.5 Mt,其中有机碳储量1 238.3 Mt,无机碳储量3 515.25 Mt,以无机碳为主,占74.0%。如图2所示,碳储量中无机碳大于有机碳,自上而下有机碳占比下降,无机碳占比上升。

图2

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图2   研究区土壤各层中有机碳和无机碳储量占比

Fig.2   The percentage of organic carbon and inorganic carbon reserves in different soil layers of the study area


2.2 土壤碳含量分布特征

图3和图4显示,表层和深层土壤的无机碳、有机碳之间分别具有基本类似的分布规律。表层土壤无机碳含量高值区主要分布在青海湟水谷地、甘肃陇中、陕西北部和宁夏南部黄土高原等地区,低值区主要分布在祁连山、六盘山、毛乌素沙地、银川盆地及渭河以南的关中盆地。表层有机碳高值区主要分布在祁连山一带,低值区主要分布在陕北黄土高原地区和宁夏中部干旱带,包括毛乌素沙地。

图3

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图3   研究区表层(a)和深层(b)土壤无机碳含量分布

Fig.3   Distribution of inorganic carbon content in surface (a) and deep (b) soil layers of the study area


图4

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图4   研究区表层(a)和深层(b)土壤有机碳含量分布

Fig.4   Distribution of organic carbon content in surface (a) and deep (b) soil layers of the study area


2.3 不同土壤类型碳含量特征

研究区土壤类型占比见图5,其中以黄绵土为主,占比32.6%。统计不同土壤类型中有机碳、无机碳和总碳含量(图6),发现有机碳含量在表、深层土壤中均呈现较大幅度变化,表层土壤有机碳含量依次为:黑毡土>黑钙土>灰褐土>栗钙土>褐土>黑垆土>新积土>黄绵土>潮土>灌淤土>盐土>灰钙土>风沙土;深层土壤有机碳含量依次为:黑毡土>黑钙土>灰褐土>栗钙土>黑垆土>褐土>黄绵土>灌淤土>新积土>灰钙土>盐土>潮土>风沙土。无机碳在同类型土壤间,表、深层中含量变化幅度不大,但在不同类型土壤间存在较明显的变化,表层为:黑垆土>黄绵土>灌淤土>栗钙土>新积土>盐土>灰钙土>灰褐土>黑毡土>黑钙土>潮土>褐土>风沙土;深层为:黑垆土>黄绵土>栗钙土>灌淤土>新积土>灰钙土>褐土>盐土>灰褐土>黑钙土>潮土>黑毡土>风沙土。总碳受有机碳和无机碳含量变化的影响,表层为:黑毡土>黑钙土>灰褐土>栗钙土>黑垆土>黄绵土>灌淤土>新积土>褐土>盐土>灰钙土>潮土>风沙土;深层为:毡土>灰褐土>黑钙土>黑垆土>栗钙土>黄绵土>灌淤土>新积土>褐土>灰钙土>盐土>潮土>风沙土。

图5

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图5   研究区土壤类型占比

Fig.5   The percentage of soil types


图6

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图6   研究区不同土壤类型碳含量(单位:%)

Fig.6   Carbon content of different soil types in the study area (unit:%)


如图6所示,研究区内风沙土具有最低的表、深层有机碳、无机碳和总碳含量,其中表层有机碳、无机碳和全碳含量分别是全区有机碳、无机碳、全碳含量的21.2%、40.9%、32.4%,深层有机碳、无机碳和全碳含量分别是全区对应含量的28.0%、53.5%、47.5%。黑垆土和黄绵土具有最高的表层无机碳含量,分别达1.63%和1.60%;黑毡土具有最高的表层有机碳含量,达到3.96%,是全区表层有机碳含量平均值的4倍;黑垆土具有最高的深层有机碳含量;黑毡土同时具有最高的表层和深层土壤总碳含量。

2.4 不同土地利用类型碳含量特征

表1显示,研究区以草地和耕地为主,表层土壤无机碳含量依次为草地>耕地>森林>裸地,有机碳含量为森林>草地>耕地>裸地,总碳含量为森林>草地>耕地>裸地;深层土壤无机碳含量为耕地>草地>森林>裸地,有机碳含量为森林>草地>耕地>裸地,总碳含量为草地>耕地>森林>裸地。森林具有最高的表层和深层有机碳及表层总碳含量;草原具有最高的表层无机碳和总碳含量;耕地具有最高的深层无机碳含量;裸地具有最低的无机碳、有机碳和总碳含量。

表1   研究区不同土地利用类型有机碳、无机碳和总碳含量

Table 1  Organic carbon and inorganic carbon content in different land use types in the study area

土地利
用类型		样品数表层土壤含量/%深层土壤含量/%
无机碳有机碳总碳无机碳有机碳总碳
草地59041.4141.1162.5301.4320.4751.907
耕地36621.3790.8252.2051.5010.3511.852
森林7921.1631.5022.6651.1030.5641.667
裸地1750.9320.6481.5800.9680.3141.282

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2.5 不同地形地貌碳含量特征

表2显示,研究区地形地貌中以黄土地貌为主,黄土、山地和平原具有不同的碳含量,山地具有最高的表层和深层土壤有机碳和总碳含量;黄土具有最高的表层和深层无机碳含量;平原总体介于黄土和山地之间。研究区以中海拔地区为主,高海拔地区具有极高的有机碳含量,表层达到4.208%,深层1.604%。

表2   研究区不同地形地貌有机碳、无机碳和总碳含量

Table 2  Organic and inorganic carbon content in different terrain and land forms of the study area

地形
地貌		样品数表层土壤含量/%深层土壤含量/%
无机碳有机碳总碳无机碳有机碳总碳
黄土46281.5510.7452.2961.6370.3421.979
山地25341.2981.8843.1821.2310.7822.013
平原28591.2620.9592.2221.3480.3521.700
高海拔4051.1594.2085.3670.9421.6042.546
中海拔47031.2990.6341.9321.3380.2661.604
低海拔14201.2110.8482.0591.5150.2691.784

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3 讨论

3.1 无机碳含量影响因素

研究表明,土壤无机碳随水分的垂直迁移是土壤无机碳分布格局的一个重要成因[26-27],无机碳含量随土层深度增加而增加,这可能是因为表层土壤中的碳酸盐通过雨水淋溶,导致钙质层由表层土壤向下层土壤转移,进而造成下层土壤无机碳含量高[28-29]。但由于研究区降雨量总体偏低,耕地、草地、裸地深层无机碳含量比表层无机碳略高,平均值分别增加0.122%、0.018%、0.036%,耕地因受长期的农业活动灌溉影响,变化最明显。森林则反之,平均值降低0.06%,这可能与表层无机碳较难发生垂直或水平方向的流失有关。

黄土高原是东亚季风的产物,由冬季风搬运上风向西北干旱区的粉尘物质堆积形成[30],同时受夏季风挟带的降雨改造[31];黄土高原土壤类型以黄绵土为主,初始粉尘中含有大量被风力搬运而来的碳酸盐矿物,土壤中无机碳含量主要受原始粉尘中碳酸盐输入通量和后期成壤改造作用影响。青海湟水谷地、甘肃陇中、陕西北部和宁夏南部黄土高原等地区降雨量低,植被稀疏,风化作用较弱,无机碳保存较好,因此表、深层无机碳含量均比较高。毛乌素沙地是我国面积最大的沙地[32],以固定、半固定沙丘为主,物质主要来自近源鄂尔多斯高原基岩风化产生的碎屑物质,和黄河上游物质不存在明显的物源联系[33],以中沙和细沙为主,累计占比介于81.0%~95.2%,基本不含黏土[34],因此区内具有极低的无机碳含量。学者认为流经青藏高原东北缘的黄河将大量碎屑物质搬运到银川盆地[33],因此盆地内沿黄河两岸具有比较高的表层无机碳含量。关中盆地受长期的农业活动灌溉影响,良好的土壤水分条件和密集的植物根系有利于碳酸氢盐的形成,从而加速了碳酸盐的溶解,特别是在雨季,表层无机碳受到较强的风化淋滤作用,使其遭受强烈的淋溶而离开表层土壤, 其中一部分在下伏黄土中淀积下来,使得深层无机碳含量高,尤其是渭河上游和秦岭山前地区水分条件更好,无机碳更容易通过地表和地下径流迁移,最终汇入黄河[35]。

3.2 有机碳含量影响因素

有机碳受到一系列自然因素和人为因素影响[36]。地形和土地利用方式是影响土壤有机碳库变化的重要因素[36⇓-38]。土壤有机碳与土壤质量和植物生产力有着内在的联系[39]。不同地形地貌中的山地和不同土地利用类型中森林分别具有最高的表层、深层土壤有机碳含量,表层有机碳高值区主要分布在祁连山一带山地区,表层和深层土壤有机碳含量分别达到1.884%和0.782%,是黄土和平原地区的2倍左右,该区海拔相对高,气温相对低,控制着植物残留物的碳输入和输出之间的平衡[40],抑制了有机碳的分解[41],土地利用类型多为天然森林和草地,具有高覆盖度和丰富根系,为土壤提供了大量的腐殖质和分泌物,且人为扰动小,使得有机碳累积。黄土地貌中表层和深层有机碳含量仅为0.745%和0.342%,黄土高原土壤侵蚀严重,有利于有机碳的去除,尤其是斜穿黄土高原中部的年降雨量400~500 mm的地区[42]。毛乌素沙地以风沙土为主,缺少可以为有机质提供物理保护的粉砂和黏土[43-44],有利于土壤向大气碳流失[36],风沙土具有所有土壤类型中最低的表、深层有机碳含量,植被以稀疏的荒漠草原为主,生物量低,有机碳输入量低,同时也因风蚀作用,有机碳以凋落物和细颗粒形式被去除[45]。银川盆地和关中盆地耕地区内,耕作过程会加速有机碳的分解和侵蚀,使土壤失去的有机碳进入大气中,从而增加大气二氧化碳[46]。耕地表层和深层有机碳含量分别仅为森林的54.9%和62.2%,与相关研究显示土地利用变化会导致30%~45%的损失基本一致[47-48],与不合理的耕作,破坏土壤大团聚体,降低土壤有机质稳定性,以及随后增加土壤温度和通气量,加速有机质的矿化[49]有关,也与耕地在收获过程中去除植物残留可以显著减少有机碳的输入有关[45]。

有机碳含量的剖面分布情况取决于生物量的形成、分解以及根系的剖面分布。研究区总有机碳储量中40%以上的有机碳分布在表层土壤中,主要是因为植物根系分布影响了土壤剖面中碳的垂直分布[35],同时以森林为例,其中的土壤大团聚体的数量会随着森林年龄的增长而增加,从而为有机碳提供更好的保护[22,50]。深层土壤中的有机碳一方面来自上层的浸出和土壤生物从浅层到深层的有机碳垂直混合;另一方面可能由于有机碳在更深的底土环境中较为稳定,不利于分解[51]。

4 结论

1)研究区表层和深层碳含量具有基本类似的分布规律,无机碳含量高值区主要分布在青海湟水谷地、甘肃陇中、陕西北部和宁夏南部黄土高原等地区,有机碳高值区主要分布在祁连山一带。上下全层(0~2.0 m)土壤中累计求得总碳10 099.4 Mt,表层(0~0.2 m)1 224.8 Mt,上层(0~1.0 m)5 345.9 Mt,下层(1.0~2.0 m)4 753.5 Mt。

2)区内以黄绵土为主,风沙土具有最低的表、深层有机碳、无机碳和总碳含量。黑垆土和黄绵土具有最高的表层无机碳含量;黑毡土具有最高的表层有机碳含量;黑垆土具有最高的深层有机碳含量。黑毡土同时具有最高的表层和深层土壤总碳含量。

3)区内土地利用类型以草地和耕地为主,森林具有最高的表层和深层有机碳及表层总碳含量;草原具有最高的表层无机碳和总碳含量;耕地具有最高的深层无机碳含量;裸地具有最低的无机碳、有机碳和总碳含量。

4)区内黄土、山地和平原具有不同的碳含量,山地具有最高的表层和深层土壤有机碳和总碳含量;黄土具有最高的表层和深层无机碳含量;平原总体介于黄土和山地之间。高海拔地区具有极高的有机碳含量。

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青藏高原是全球变化的敏感区，也是泛第三极地区气候变化的启动区。青藏高原土壤碳作为生态系统碳库的重要组成部分，对生态系统碳循环过程具有非常重要的作用。目前，对青藏高原土壤碳储量的估算仍存在很大的不确定性。为此，本文综述了近30年来关于青藏高原土壤碳储量研究，比较不同研究的土壤碳储量估算结果，以固有因子和变化因子两类影响因素作为切入点，分析了土壤碳储量时空分异规律。从估算模型和方法看，CENTURY和TEM模型综合考虑了影响土壤碳储量的多种机理过程，结果可信度高于EVI、NDVI模型以及插值估算法。青藏高原草地土壤表层（0～20 cm）有机碳储量约10 Pg C（1 Pg=10<sup>15</sup> g）。高原冻土区土壤有机碳储量（0～200 cm）约16.5 Pg C，土壤无机碳储量（0～100 cm）约14 Pg C。青藏高原土壤碳储量沿东南向西北方向逐渐降低，而关于变化因子对青藏高原土壤碳储量的作用规律还没有一致的认识。此外，采样点选择、数据源选择、估算深度以及估算方法等影响了青藏高原土壤碳储量估算结果的精确性。未来青藏高原土壤碳储量研究应建立土壤碳储量估算标准来提高结果的可比性；同时增大采样区、采样量以及采样深度并保障采样周期的时间连贯性等，有效减少土壤碳储量估算不确定性。以期更好地理解和预测未来青藏高原生态系统对气候变化的响应。

Wang L, Zeng H, Zhang Y J, et al.

A review of research on soil carbon storage and its influencing factors in the Tibetan Plateau

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[本文引用: 1]

The Tibetan Plateau is highly sensitive to global climate change, and is the controller for regional climate in the Pan-Third Pole region. On the Tibetan Plateau, soil carbon accounts for a high proportion of the ecosystem carbon and is extremely important for ecosystem carbon cycling. However, there are still plenty of uncertainties for current soil carbon storage estimation on the Tibetan Plateau, with different estimation methods also having great discrepancies. Here, we reviewed research progress on soil carbon storage on the Tibetan Plateau during the past 30 years, and compared the results of different studies. We also analyzed the spatial and temporal variation of soil carbon storage based on two kinds of influencing factors (inherent, such as geographical factor, soil property, vegetation type; and variable, such as climate change, human activities). In terms of estimation models and methods, the process models such as CENTURY and TEM, which consider multiple processes affecting soil carbon storage, had higher accuracy compared with the EVI and NDVI models, and interpolation estimation. Averaged across different studies, soil organic carbon storage in the top 20 cm of the alpine grasslands is about 10 Pg C (1 Pg=10<sup>15</sup> g), and that in the top 200 cm of the alpine permafrost is approximately 16.5 Pg C. Soil inorganic carbon storage in the top 100 cm of the alpine grassland is about 14 Pg C. The soil carbon storage on the Tibetan Plateau decreases gradually from southeast to northwest. The effects of variable factors on soil carbon storage varied greatly. The estimation accuracy of soil carbon storage is affected by sampling location, data source type, estimation method, and soil depth. Future studies of soil carbon storage on the Tibetan Plateau should pay attention to establishing a common standard for soil carbon storage estimation. Under the common standard, the comparability among different studies is boosted. Meanwhile, expanding sampling area and sample size, increasing sampling depth and maintaining the temporal coherence among each sampling period can efficiently abate uncertainty in soil carbon storage estimation on the Tibetan Plateau. With these improvements, our understanding on Tibetan Plateau ecosystem responses to climate change would be advanced and our prediction on its future status be more accurate.

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Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered dramatically with the economic development in the past few decades, sequential and fine-scale LC monitoring is in urgent need. However, currently, fine-resolution annual LC dataset produced by the observational images is generally unavailable for China due to the lack of sufficient training samples and computational capabilities. To deal with this issue, we produced the first Landsat-derived annual China land cover dataset (CLCD) on the Google Earth Engine (GEE) platform, which contains 30m annual LC and its dynamics in China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China's land-use/cover datasets (CLUDs) and visually interpreted samples from satellite time-series data, Google Earth and Google Maps. Using 335 709 Landsat images on the GEE, several temporal metrics were constructed and fed to the random forest classifier to obtain classification results. We then proposed a post-processing method incorporating spatial-temporal filtering and logical reasoning to further improve the spatial-temporal consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31% based on 5463 visually interpreted samples. A further assessment based on 5131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC and GlobeLand30. Besides, we intercompared the CLCD with several Landsat-derived thematic products, which exhibited good consistencies with the Global Forest Change, the Global Surface Water, and three impervious surface products. Based on the CLCD, the trends and patterns of China's LC changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease in cropland (-4.85 %) and grassland (-3.29 %), and increase in forest (+4.34 %). In general, CLCD reflected the rapid urbanization and a series of ecological projects (e.g. Gain for Green) in China and revealed the anthropogenic implications on LC under the condition of climate change, signifying its potential application in the global change research.

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[本文引用: 1]

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