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Soil enthalpy(H) contains the combined effects of both soil moisture(w) and soil temperature(T) in the land surface hydrothermal process. In this study, the sensitivities of H to w and T are investigated using the multi-linear regression method.Results indicate that T generally makes positive contributions to H, while w exhibits different(positive or negative) impacts due to soil ice effects. For example, w negatively contributes to H if soil contains more ice; however, after soil ice melts,w exerts positive contributions. In particular, due to lower w interannual variabilities in the deep soil layer(i.e., the fifth layer), H is more sensitive to T than to w. Moreover, to compare the potential capabilities of H, w and T in precipitation(P) prediction, the Huanghe–Huaihe Basin(HHB) and Southeast China(SEC), with similar sensitivities of H to w and T,are selected. Analyses show that, despite similar spatial distributions of H–P and T –P correlation coefficients, the former values are always higher than the latter ones. Furthermore, H provides the most effective signals for P prediction over HHB and SEC, i.e., a significant leading correlation between May H and early summer(June) P. In summary, H, which integrates the effects of T and w as an independent variable, has greater capabilities in monitoring land surface heating and improving seasonal P prediction relative to individual land surface factors(e.g., T and w). 相似文献
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Soil carbon sequestration 总被引：1，自引：0，他引：1
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不同土壤类型的热通量变化特征 总被引：3，自引：0，他引：3
利用2004—2007年中国科学院中国生态系统研究网络(CERN)生态站实测土壤热通量、辐射等资料,分析了不同土壤类型表层热通量的日变化和季节变化,以及不同土壤类型的热通量与总辐射、净辐射的关系。结果表明,由于导热率越大,热量传输就越快;热容量越小,热量传输也越快,造成土壤热通量的日较差和年较差较大,所以黄绵土和紫色土的表层热通量日较差最大(220～280 W.m-2),高寒草甸土和水稻土最小(55W.m-2);季节变化中土壤表层热通量的年较差变化范围在12～28W.m-2之间,灰漠土最大,为28W.m-2,热通量年较差从大到小依次为灰漠土、黄绵土、盐碱潮土、红壤土、紫色土、沼泽土、水稻土和高寒潮土,高寒潮土最小,为12W.m-2。不同土壤类型的热通量与总辐射、净辐射呈正相关关系,但不同土壤类型的土壤热通量在12:00(地方时)所占净辐射的比例各不相同,高寒草甸土最小,约为8%;黄绵土最大,为38%,多数土壤的热通量占净辐射的比例在15%～20%之间,这充分表明不同土壤类型表层热通量的传输存在很大差异。 相似文献
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Sensitivity of evapotranspiration E and root zone soil moisture content θ to the parameterization of soil water retention Ψ(θ) and soil water conductivity K(Ψ), as well as to the definition of field capacity soil moisture content, is investigated by comparing Psi1-PMSURF and Theta-PMSURF models. The core of PMSURF (Penman–Monteith Surface Fluxes) consists of a 3-layer soil moisture prediction module based on Richard’s equation in combination with the Penman–Monteith concept for estimating turbulent heat fluxes. Psi1- PMSURF and Theta-PMSURF differ only in the parameterization of the moisture availability function Fma. In Psi1,Fma is parameterized by using Ψ(θ) and K(Ψ) hydrophysical functions; in Theta, Fma is parameterized by using hydrophysical parameters: the field capacity θf and wilting point θw soil moisture contents. Both Psi1 and Theta are based on using soil hydrophysical data, that is, there is no conceptual difference between them in the parameterization of E even if in Psi1Fma depends on 12 parameters, while in Theta only on two soil/vegetation parameters. Sensitivity tests are performed using the Cabauw dataset. Three soil datasets are used: the vG (van Genuchten), CH/vG (Clapp and Hornberger/van Genuchten) and CH/PILPS (Clapp and Hornberger/Project for Intercomparison of Land-surface Parameterization Schemes) datasets. The vG dataset is used in van Genuchten’s parameterization, while in Clapp and Hornberger’s the CH/vG and CH/PILPS datasets are used. It is found that the consistency of soil hydrophysical data in the simulation of transpiration is quite important. The annual sum of E obtained by Psi1EPsi1, differs from the annual sum of E obtained by Theta, ETheta, because of the inconsistency between the fitting parameters of Ψ(θ) and K(Ψ) and the θf, and not because of the differencies in the parameterization of Fma. Further, θf can be estimated not only on the basis of using soil hydrophysical functions (the θf so obtained is θSoilf) but also on the basis of analysing the transpiration process (the θfso obtained is θtrf). θtrf values estimated from the condition ETheta ≈ EPsi1 are in acceptable accordance with the θSoilf values proposed by Wösten and co-workers. The results are useful in optimizing the parameterization of transpiration in land-surface schemes. 相似文献
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文章基于呼和浩特市区76个观测点数据,采用文纳四极法,测量观测点的土壤电阻率。结果表明:呼和浩特地区的土壤电阻率分布规律呈明显的带状分布,在北部沿大青山一线土壤电阻率明显较高,由北向南成逐级递减趋势。 相似文献
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通过对察右中旗2011年人工观测与GStar-I C型自动土壤水分仪观测的数据进行对比,分析两种观测方法存在的差异,为自动土壤水分监测仪投入业务化运行提供一定的科学依据。 相似文献
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通用陆面模式对土壤质地和亮度的敏感性分析 总被引：6，自引：2，他引：4
使用前苏联Valdai 1966～1971年的气象观测资料,研究了通用陆面模式(Common Land Model,CoLM)模拟的水分循环和地表通量在12种土壤质地和8种土壤亮度条件下的差异。结果表明,在相同的气象条件下,模拟的热通量对土壤质地和亮度都比较敏感,而地表水文过程只对土壤质地敏感。土壤亮度相同时,相对砂性土壤,粘土含量高的土壤保水性强,土壤湿度、地表蒸发和径流量都比较大（月均最大差值：土壤湿度约为5 kg·m-2,地表蒸发和径流量约为年降水量的7%和1.2 %）,相应地在热通量分配上存在明显差异（月均最大差值为8 W·m-2）;土壤质地相同,亮度由亮变暗时,潜热通量变化很小,地表温度略有升高,而感热通量和净辐射增加显著（月均最大差值为7 W·m-2）。土壤质地和亮度对模拟的影响主要存在于降水少、植被覆盖度低的3～5月。 相似文献
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土壤温度和湿度对冬小麦田土壤空气 CO2浓度的影响 总被引：5，自引：2，他引：3
通过同步观测耕层土壤空气CO2浓度廓线、土壤温度和土壤含水量,主要研究和讨论了华东地区典型稻麦轮作农田旱地阶段的土壤空气CO2浓度的变化规律,及土壤温度和含水量对它的影响.结果表明:麦田土壤空气CO2浓度与植物生长密切相关.土壤空气CO2浓度受土壤温度的影响较为显著,且深层的相关性要明显大于浅层.观测阶段的麦田土壤含水量介于30%和44%之间,与土壤空气CO2浓度有较好的相关性(相关性R2=0.61,统计显著性p＜0.001).土壤空气CO2浓度与土壤含水量呈正相关性的原因可能是:高土壤含水量导致的低充气孔隙度降低了土壤空气CO2扩散速率,从而导致土壤空气CO2聚积,浓度升高.在0～30 cm土层中,上层土壤气体中的CO2向上垂直扩散要比下层土壤快.土壤温度对土壤空气CO2浓度的影响大于土壤含水量. 相似文献
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Soil Carbon Sequestration in India 总被引：4，自引：0，他引：4
R. Lal 《Climatic change》2004,65(3):277-296
With a large land area and diverse ecoregions, there is a considerable potential of terrestrial/soil carbon sequestration in India. Of the total land area of 329 million hectares (Mha), 297 Mha is the land area comprising 162 Mha of arable land, 69 Mha of forest and woodland, 11 Mha of permanent pasture, 8 Mha of permanent crops and 58 Mha is other land uses. Thesoil organic carbon (SOC) pool is estimated at 21 Pg (petagram = Pg = 1 ×1015 g= billion ton) to 30-cm depth and 63 Pg to 150-cm depth. The soil inorganic carbon (SIC) pool is estimated at 196 Pg to 1-m depth. The SOC concentration in most cultivated soils is less than 5 g/kg compared with 15 to 20 g/kg in uncultivated soils. Low SOC concentration is attributed to plowing, removal of crop residue and other biosolids, and mining of soil fertility. Accelerated soil erosion by water leads to emission of 6 Tg C/y. Important strategies of soil C sequestration include restoration of degraded soils, and adoption of recommended management practices (RMPs) of agricultural and forestry soils. Potential of soil C sequestration in India is estimated at 7 to 10 Tg C/y for restoration of degraded soils and ecosystems, 5 to 7 Tg C/y for erosion control, 6 to 7 Tg C/y for adoption of RMPs on agricultural soils, and 22 to 26 Tg C/y for secondary carbonates. Thus, total potential of soil C sequestration is 39 to 49 (44± 5) Tg C/y. 相似文献
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根据国内外大量文献资料的分析以及作者的科研成果和实际工作经验,探讨了土壤电学产生的背景,给出了土壤电学的定义、学科分支、研究领域、发展趋势,提出了土壤电学的分支学科--土壤电腐蚀学和土壤电导学,旨在为开展雷电流入地引起的土壤物理化学变化研究,从微观和宏观角度揭示雷电流入地后的散流机理、增强土壤导电性的机理、降低土壤电阻率的机理、地下设施雷电防护机理,完善雷电从大气中形成到土壤中消散的物理过程的科学研究体系,为开发接地工程技术、地下金属设施防腐工程技术提供理论基础. 相似文献
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为研究不同土壤类型、不同测定距离下人工及DZN2型自动站土壤湿度的差异,在河南省泛区、鹤壁、许昌三个试验点选择代表性较强的砂土、粘土和壤土三种土壤类型为研究对象,于2013年、2014年在冬小麦全生育期分别距离自动站探头140—150 cm、100—110 cm位置人工取土,进行土壤湿度差异分析。结果表明:在冬小麦生育期内,人工与自动站所测土壤湿度的差值粘土最小,壤土的次之,砂土的最大,粘土、壤土播种时差值最大,砂土中最大差值出现在抽穗前。差值随土壤深度的垂直变化,总体表现为砂土波动范围最大,壤土、粘土的相对较小。在10—30 mm降水后,三种土壤类型差值均有不同程度的增加,其中砂土中差值增加最大;降水量在30—50 mm后,粘土和壤土的差值较小,砂土的差值较大。三种土壤类型下,人工取土点距离自动站探头150 cm以内,总体差异不显著。 相似文献
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两种土壤温度算法的对比分析 总被引：8，自引：1，他引：7
为了定量理解黄土高原土壤的物理特性和过程, 为进一步提高陆面模式对该地区地表能量平衡模拟能力奠定基础, 本文利用2005年黄土高原陆面过程试验中7月22～26日期间裸土地表观测站土壤温度观测资料, 采用热传导（结合数学拟合法)、热传导－对流两种方法分别计算了该地区土壤热扩散率。本文还利用热传导－对流方法计算0.05～0.1 m浅薄土壤层的热扩散率垂直梯度与水通量密度之和, 其值介于0.80×10-6～2.43×10-6m/s之间。在此基础之上, 以0.05 m深度的土壤层为上边界, 分别利用上述两种方法模拟0.10 m深度的土壤层温度, 结果表明: 由于忽略土壤的垂直不均匀性和水分的垂直运动而只考虑热传导过程, 热传导方法不仅高估了土壤温度振幅, 而且高估了位相的延迟。而热传导－对流方法对温度振幅和位相的模拟值与实际观测值吻合较好, 白天 (北京时间08:00～20:00) 的温度模拟值相对测量值的平均误差、 标准差和归一化标准差分别为0.19 K、0.18 K和0.08%。 相似文献