基于区域剖分算法的土壤碳通量空间采样策略

由于土壤碳通量在空间分布上具有很强的异质性,传统的采样方法难以对区域土壤碳通量进行精确估算,因此确定适当的采样策略对区域土壤碳通量的估算具有重要意义.本文提出一种逐点递增式采样的区域剖分部署策略(RDPG):设定初始采样点,使用改进的凸包插值算法构造Delaunay三角网,根据邻近已知采样点插值计算三角形各边垂直平分线的交点的离散度,选择离散度最大的点作为新增采样点.采用该方法对变异系数为0.42～0.59的仿真试验区域进行多次试验,结果表明:在相同试验条件下,RDPG布局策略能够获得比随机采样和均匀采样策略更高的区域土壤碳通量估算准确度.RDPG方法考虑了区域土壤碳通量的空间异质性,提高了区域土壤碳通量拟合精度.     

面向区域土壤碳通量估算的多向插值空间采样策略

区域土壤碳通量的准确测量对陆地生态系统碳循环过程分析具有十分重要的作用。由于土壤碳通量空间异质性强,采用随机抽样的方法无法对区域土壤碳通量进行准确估算,而大范围的多点采样则需要大量的人力及设备成本。基于一种自制的仪器,提出了一种递增式采样的多向插值采样策略(MDI Multiple Directional Interpolation):在设定初始采样点的基础上,通过对已有采样点的测量,通过径向插值的方法计算采样点连线交点,将不同径向计算值差异最大的点作为新增采样点,以此逐步增加。通过对20幅的50×50网格区域仿真,结果表明(1)MDI布局策略能够针对土壤碳通量的变化情况而反馈采样点的疏密。(2)误差分析得出采样点数量(n=10)较少,MDI布局策略对碳通量的估算误差比随机布局策略低,比平均布局策略稍高;随采样点增多,3种布局策略误差均降低;采样点数量n=40,MDI布局策略对碳通量的估算误差(0.028)比平均布局策略的误差(0.32)降低了12.5%,比随机布局策略的误差(0.04)降低了30.0%。MDI布局策略根据土壤碳通量的变化梯度合理分配采样点,降低区域土壤碳通量监测误差。     

天目山常绿落叶阔叶混交林土壤温室气体排放特点

采用静态箱-气象色谱法, 将试验样地按照自上而下分为A、B、C、D 四个梯度的采样点。研究了浙江天目山常绿落叶阔叶混交林2013 年3 月-11 月期间土壤温室气体排放的时空变化特点, 并分析了不同梯度的土壤温湿度与气体排放通量的相关性。结果表明: (1)天目山常绿落叶阔叶混交林土壤CO₂ 和CH₄ 两种温室气体排放/吸收季节变化特征较一致, 即夏季>春季>秋季; N₂O 排放通量季节变化表现为夏季>秋季>春季。其中, CO₂ 和N₂O 表现为土壤的排放源, CH₄ 为大气的吸收汇。(2)空间上, CO₂ 通量大小表现为: D 采样点> A 采样点> C 采样点 > B 采样点; 土壤对CH4吸收速率表现为A 采样点 > C 采样点 > B 采样点 > D 采样点; 土壤N₂O 通量大小依次为: A 采样点 > C 采样点 > B采样点 > D 采样点。(3)温度是影响天目山常绿落叶阔叶混交林土壤CO₂ 通量重要因子; CH₄ 的吸收通量随温度的升高和湿度的降低而增大; 在海拔较低的地区, 温度是N₂O 通量的重要影响因子, 海拔较高地区, 湿度是N₂O 通量的重要限制因子。     

基于数字土壤制图技术的土壤有机碳储量估算

精准的土壤属性空间分布信息有助于提升土壤有机碳储量估算的精度。本研究以河南省济源市南山林场为研究区,以地形因子为预测因子,利用模糊C均值(FCM)聚类方法对土壤有机碳含量、土壤容重、土壤厚度和土壤砾石含量进行数字土壤预测制图,基于数字制图结果实现土壤有机碳密度预测制图和土壤有机碳储量估算。结果表明: 基于数字土壤制图方法得到的研究区土壤有机碳密度平均值为4.24 kg·m^-2,其预测图的平均误差(ME)为0.08 kg·m^-2,平均绝对误差(MAE)为2.80 kg·m^-2,均方根误差(RMSE)为5.03 kg·m^-2,与传统类型方法相比,预测结果的精度和稳定性更高,具有较高的可信度,最终估算得到研究区土壤有机碳储量为3.08×10⁸ kg。基于数字土壤制图技术仅采用少量土壤样点即可实现较高精度的土壤有机碳密度制图和储量估算,且能表征土壤有机碳密度空间分布特征。本研究为土壤有机碳储量估算提供了新途径,有助于提升土壤有机碳储量估算的精度和效率。     

农田表层土壤镉的典型空间插值方法对比研究

空间插值对于土壤重金属空间分布和污染评价具有重要意义, 以广东省广州市某地区农田表层土壤重金属镉的调查监测结果为例, 选取具有代表性的反距离加权、径向基函数、普通克里金、简单克里金、泛克里金5 种空间插值方法, 进行空间插值及土壤等级划分, 比较不同插值方法结果精度, 分析不同插值方法结果差异。研究结果表明: 不同插值方法识别的土壤镉浓度空间分布特征和土壤等级划分存在差异, 主要体现在局部极值向外过渡区域存在较大不确定性, 其中径向基函数精度最高, 克里金插值法对数据存在“压缩”效应较强。因此, 在开展土壤重金属污染调查时应考虑土壤重金属样本数据特征和空间结构特征, 选择合适的插值模型, 并适当加大土壤重金属浓度过渡区域采样密度。     

基于InVEST模型的黑河流域生态系统服务空间格局分析

本文基于InVEST模型对黑河流域2011年产水量、土壤保持、水质净化、生物多样性维持、碳储存、食物供给6项服务进行估算,并利用空间统计方法计算各项生态系统服务的冷热点分布格局、空间分异特征、服务综合热点区域。结果表明:各服务类型在空间分布上呈现出差异化规律;上游是该流域主要的产水区域,其他生态系统服务主要集中在中游,下游产水供给、水质净化服务都远低于中游;在主要生态系统类型中,草地是承担生态系统供给服务的主要角色;热点分布大多呈现"南高北低,中上游高、下游低"的空间格局,各类生态系统服务空间分异以低-低类型和低-高类型为主。本文通过分析流域生态系统服务的空间分布格局,为确定流域生态保护与建设目标提供了基础,也为其他流域开展生态系统服务空间评估研究提供了重要参考。     

基于Ts\|EVI特征空间的土壤水分估算

温度-植被指数特征空间耦合了地表温度和植被信息,是当前实现土壤水分遥感估算和农业旱情监测的重要方法.采用EOS-MODIS地表温度Ts和增强型植被指数EVI数据,研究Ts-EVI三角形特征空间中干边、湿边方程参数的确定方法,分析比较了温度植被干旱指数TVDI对不同土壤深度水分状况的估算能力,为利用特征空间法实现土壤水分监测提供理论依据.研究表明:特征空间中干边和湿边的确定以最大拐点处为始点进行线性拟合的常规方法并不完善,根据像元的分布频率,以采用能同时保留最大量有效信息和较高拟合精度的端点逼近法获取参数的效果较好;基于Ts-EVI特征空间构建的TVDI可以较好地估算土壤表层10、20cm和50cm土壤深度处土壤水分状况,其相关性均通过了α=0.001水平的t检验,但TVDI对表层土壤(20cm和10cm)水分的估算精度相对较高.     

河南封丘县域农田土壤固碳速率空间变异特征及其影响因素

农田土壤固碳速率是评价土壤固碳效应和潜力的重要指标,精确估算区域农田土壤固碳速率对土壤地力及环境效应均具有重要意义.本研究选取黄淮海平原典型潮土区河南省封丘县为研究区域,按照土壤利用-土壤类型联合单元布点法,于2011年采集了70个耕层土样,测定了土壤有机碳含量、机械组成、容重、pH,并与全国第二次土壤普查(1981年)数据进行对比分析,结合地统计方法和GIS技术研究了该地区近30年农田土壤固碳速率的空间变异特征,利用显著性检验、回归分析、方差分析等方法定量分析了该区域农田土壤固碳速率的影响因素.结果表明: 近30年封丘县域土壤固碳速率平均值为0.33 t C·hm^-2·a^-1,变异系数为74%,属于中等变异性;土壤固碳速率的变化在东西方向上表现为西高东低、中部高南北低,呈片状分布,区域结构性因素是引起农田土壤固碳速率空间分布差异的主导因素,如土壤类型、机械组成、容重、pH,可解释空间变异的59.5%,其次是随机性因素,如秸秆还田量、施肥量,可解释空间变异的40.5%.     

关中平原田间土壤含水量的空间变异性

为了明确田块尺度土壤含水量的空间变异特征,制定合理准确的土壤采样方法,支持田间精准灌溉,分7个日期对陕西省杨凌区曹新庄试验区土壤样品进行采集,利用经典统计学和地统计学方法,分析了0～60 cm不同土层土壤含水量的空间变异特点。结果表明: 田块尺度土壤含水量空间分布呈弱变异或中等偏弱变异;土壤含水量在11.7%～20.1%时,其值越低,空间变异性越强。采样间距显著影响土壤含水量空间变异性的计算精度,采样间距设置为东西方向间距27 m和南北方向间距9 m时的土壤含水量变异系数比采样间距设置为东西方向间距9 m和南北方向间距18 m大3.3%。随着采样密度的增大,土壤含水量分布的等值线变化增大;表征田块尺度土壤含水量空间变异性最少的网格数量为21个点。采样间距为东西方向间距18 m和南北方向间距9 m时,田块尺度土壤含水量具有较高的空间相关性,田块中间位置的土壤含水量比四周高3%～5%。本研究可为关中平原田间测定土壤含水量确定合理的采样方法,并为实现农业精准灌溉提供参考。     

喀斯特地区土壤表层CO2释放通量的影响因素Ⅱ.机制

贵州喀斯特地区土壤表层CO2 释放通量最高和最低分别出现在夏季和冬季。影响土壤表层CO2 释放通量的最基本因素是温度和土壤湿度 ;湿度对土壤表层CO2 释放通量的影响在温度大于 2 0℃时特别显著。温度和湿度对土壤表层CO2 释放通量的影响主要是借助于冷热交替及干湿循环下土壤微生物生物量碳、可溶性有机碳向土壤表层CO2 释放通量转化来实现的。     

The sensitivity of soil respiration to soil temperature,moisture, and carbon supply at the global scale

Soil respiration (Rs) is a major pathway by which fixed carbon in the biosphere is returned to the atmosphere, yet there are limits to our ability to predict respiration rates using environmental drivers at the global scale. While temperature, moisture, carbon supply, and other site characteristics are known to regulate soil respiration rates at plot scales within certain biomes, quantitative frameworks for evaluating the relative importance of these factors across different biomes and at the global scale require tests of the relationships between field estimates and global climatic data. This study evaluates the factors driving Rs at the global scale by linking global datasets of soil moisture, soil temperature, primary productivity, and soil carbon estimates with observations of annual Rs from the Global Soil Respiration Database (SRDB). We find that calibrating models with parabolic soil moisture functions can improve predictive power over similar models with asymptotic functions of mean annual precipitation. Soil temperature is comparable with previously reported air temperature observations used in predicting Rs and is the dominant driver of Rs in global models; however, within certain biomes soil moisture and soil carbon emerge as dominant predictors of Rs. We identify regions where typical temperature‐driven responses are further mediated by soil moisture, precipitation, and carbon supply and regions in which environmental controls on high Rs values are difficult to ascertain due to limited field data. Because soil moisture integrates temperature and precipitation dynamics, it can more directly constrain the heterotrophic component of Rs, but global‐scale models tend to smooth its spatial heterogeneity by aggregating factors that increase moisture variability within and across biomes. We compare statistical and mechanistic models that provide independent estimates of global Rs ranging from 83 to 108 Pg yr^?1, but also highlight regions of uncertainty where more observations are required or environmental controls are hard to constrain.     

短期增温和增加降水对内蒙古荒漠草原土壤呼吸的影响

利用红外辐射增温装置模拟短期持续增温和降水增加交互作用对内蒙古荒漠草原土壤呼吸作用的影响, 结果表明: 土壤含水量对月土壤呼吸的影响显著大于土壤温度增加的影响, 生长旺季的月土壤呼吸显著大于生长末季; 土壤温度和水分增加都显著影响日土壤呼吸, 但二者的交互作用对土壤呼吸无显著影响。荒漠草原7‒8月平均土壤呼吸速率为1.35 μmol CO₂·m ^-2·s ^-1, 7月份为2.08 μmol CO₂·m ^-2·s ^-1, 8月份为0.63 μmol CO₂·m ^-2·s ^-1。土壤呼吸与地下各层根系生物量呈幂函数关系, 0‒10 cm土层的根系生物量对土壤呼吸的解释率(79.2%)明显高于10‒20 cm土层的解释率(31.6%)。0-10 cm土层的根系生物量是根系生物量的主体, 根系生物量对土壤呼吸的影响具有层次性。在未来全球变暖和降水格局变化的情景下, 荒漠草原土壤水分含量是影响生物量的主导环境因子, 而根系生物量的差异是造成土壤呼吸异质性的主要生物因素, 土壤含水量可通过影响根系生物量控制土壤呼吸的异质性。     

Low soil moisture suppresses the thermal compensatory response of microbial respiration

The thermal compensatory response of microbial respiration contributes to a decrease in warming-induced enhancement of soil respiration over time, which could weaken the positive feedback between the carbon cycle and climate warming. Climate warming is also predicted to cause a worldwide decrease in soil moisture, which has an effect on the microbial metabolism of soil carbon. However, whether and how changes in moisture affect the thermal compensatory response of microbial respiration are unexplored. Here, using soils from an 8-year warming experiment in an alpine grassland, we assayed the thermal response of microbial respiration rates at different soil moisture levels. The results showed that relatively low soil moisture suppressed the thermal compensatory response of microbial respiration, leading to an enhanced response to warming. A subsequent moisture incubation experiment involving off-plot soils also showed that the response of microbial respiration to 100 d warming shifted from a slight compensatory response to an enhanced response with decreasing incubation moisture. Further analysis revealed that such respiration regulation by moisture was associated with shifts in enzymatic activities and carbon use efficiency. Our findings suggest that future drought induced by climate warming might weaken the thermal compensatory capacity of microbial respiration, with important consequences for carbon–climate feedback.     

Winter soil respiration during soil-freezing process in a boreal forest in Northeast China

Aims Boreal forest is the largest and contains the most soil carbon among global terrestrial biomes. Soil respiration during the prolonged winter period may play an important role in the carbon cycles in boreal forests. This study aims to explore the characteristics of winter soil respiration in the boreal forest and to show how it is regulated by environmental factors, such as soil temperature, soil moisture and snowpack.Methods Soil respiration in an old-growth larch forest (Larix gmelinii Ruppr.) in Northeast China was intensively measured during the winter soil-freezing process in 2011 using an automated soil CO₂ flux system. The effects of soil temperature, soil moisture and thin snowpack on soil respiration and its temperature sensitivity were investigated.Important findings Total soil respiration and heterotrophic respiration both showed a declining trend during the observation period, and no significant difference was found between soil respiration and heterotrophic respiration until the snowpack exceeded 20cm. Soil respiration was exponentially correlated with soil temperature and its temperature sensitivity (Q 10 value) for the entire measurement duration was 10.5. Snow depth and soil moisture both showed positive effects on the temperature sensitivity of soil respiration. Based on the change in the Q 10 value, we proposed a 'freeze–thaw critical point' hypothesis, which states that the Q 10 value above freeze–thaw critical point is much higher than that below it (16.0 vs. 3.5), and this was probably regulated by the abrupt change in soil water availability during the soil-freezing process. Our findings suggest interactive effects of multiple environmental factors on winter soil respiration and recommend adopting the freeze–thaw critical point to model soil respiration in a changing winter climate.     

Seasonal and spatial variability of soil respiration in four Sitka spruce stands

We investigated the causes for the seasonal and spatial variation of soil respiration in a first rotation Sitka spruce chronosequence composed of four age classes (10, 15, 31, and 47 year old) in Central Ireland. The study aimed at identifying easily determinable environmental parameters that explained the variation in soil respiration rates. The variation in temperature and soil water content influenced the seasonal trend observed in the spatial variability of soil respiration. The highest coefficients of variation in soil respiration were observed during autumn drought, while lower coefficients were generally observed during periods with highest soil respiration rates. On average, the sampling strategy of 30 sampling points per stand was adequate to obtain an average rate of soil respiration within 20% of its actual value at the 95% confidence level. Significantly higher soil respiration rates were observed at locations with high accumulation of organic matter and in collars established in close vicinity to tree stems. The organic layer thickness was the only variable that yielded significant regressions for explaining spatial variation in soil respiration in all the stands. Correlation analyses between the studied variables and soil respiration suggested the relative importance of heterotrophic and autotrophic components differed in their annual contribution to total soil respiration at each forest stand. Multiple regression analyses were used to assess the relative importance of primary temporal and spatial controls over soil respiration. Soil temperature and organic layer thickness explained most of the variance of soil respiration for the different sampling periods, while soil water content had a weaker effect as well as a different influence on soil respiration depending on the time of the year. The strong linear correlation between forest floor carbon and soil carbon stock further confirmed organic layer thickness as an integrative factor encompassing the effect of soil carbon pools on soil respiration. Moreover, its inclusion in the multiple regression analyses overrode the influence of both distance and fine root biomass. Overall, a multiple linear regression model driven by easily determinable environmental variables such as soil temperature, organic thickness, soil water content, soil bulk density, and soil organic carbon concentration allowed us to explain 54% of total variance of soil respiration over the different stand ages for the entire year (P < 0.05). Our results show that the adoption of an adequate sampling strategy, and the determination of some key environmental variables may help to explain a large proportion of total variation of soil respiration over the entire rotation length of afforested ecosystems.     

模拟降雨对西双版纳热带次生林和橡胶林土壤呼吸的影响

降雨作为一个重要的环境因子,对土壤呼吸具有重要的影响。研究土壤呼吸与降雨的关系,对准确估算大气中的CO2含量具有重要意义。本研究通过人工模拟降雨事件,应用野外原位测定方法,测量了热带次生林和橡胶林土壤呼吸速率、地下5cm土壤温度和土壤含水量的变化,以探究热带两种主要植被类型的土壤呼吸、土壤温度、土壤含水量对旱季单次降雨事件的响应过程与规律。研究发现,在旱季连续一周没有降雨的情况下,人工模拟降雨事件使土壤呼吸在降雨后的2h内被迅速激发,次生林的土壤呼吸最大达到11.15 μmolCO2·m-2·s-1,是对照的近7倍;橡胶林的土壤呼吸最大达到了15.88 μmolCO2·m-2·s-1,是对照的近11倍。随后激发效应迅速降低,尤其是橡胶林,在人工模拟降雨6h后处理与对照间无显著差异。人工模拟降雨前两种林型的土壤含水量与对照相比均无显著性差异,人工模拟降雨后的2d内土壤含水量均显著高于对照;人工模拟降雨前后土壤温度与对照相比均无显著性差异。本研究结果支持了"Birch effect",2种主要热带林型在旱季时期,由于单次降雨事件激发而释放到大气中的CO2是降雨前的数倍。     

三江源区不同退化程度高寒草原土壤呼吸特征

为了研究高寒草原退化对土壤呼吸的影响, 对三江源区不同退化程度的高寒草原土壤呼吸进行了测定, 分析了土壤呼吸与生物量、土壤温度以及土壤湿度的相关性, 结果表明: 1)不同退化程度的高寒草原土壤呼吸均表现出一定的月动态, 这种月动态在不同退化程度间各有不同。2)高寒草原在退化演替序列上生长季平均土壤呼吸速率呈先增加后降低的变化趋势, 其中在中度退化程度下达到最高值((2.46 ± 0.27) μmol·m^-2·s^-1), 显著高于未退化((1.92 ± 0.11) μmol·m^-2·s^-1)和重度退化((1.30 ± 0.16) μmol·m^-2·s^-1)水平(p < 0.01), 与轻度退化((2.22 ± 0.19) μmol·m^-2·s^-1)无显著差异(p > 0.05), 重度退化程度下呼吸速率显著低于其他退化水平(p < 0.01)。3)地上生物量和土壤呼吸存在极显著线性正相关关系(p = 0.004), 而地下生物量与土壤呼吸的相关性不很显著(p = 0.056)。4)除重度退化外, 未退化、轻度退化和中度退化高寒草原土壤呼吸与土壤温度显著正相关; 土壤呼吸与土壤湿度的二项式拟合方程在轻度退化程度下达到显著水平(p < 0.05), 而在未退化、中度退化和重度退化程度下均达到极显著水平(p < 0.01)。     

喀斯特地区土壤表层CO2释放通量的影响因素Ⅰ:规律

测定了贵州喀斯特地区土壤表层CO2释放通量,同时还测定了土壤微生物生物量碳以及土壤可溶性有机质含量和土壤湿度。研究表明,贵州喀斯特地区全年土壤表层CO2释放通量与温度变化呈正相关关系,与土壤微生物生物量碳呈负相关关系;当温度＞20℃时,土壤表层CO2释放通量与土壤湿度呈正相关,与土壤可溶性有机碳含量呈负相关。