测定灌水基质水分释放曲线的滤纸法

本文介绍一种快速测定灌水基质水分释放曲线的简单方法，水分饱和基质在筛子上干燥到不同含水量后，放入铺有华特曼４２号滤纸的密封分层玻璃容器中，利用已知的持水力关系式由滤纸的水分含量计算平衡后的水势。平衡所需的时间不超过２４小时，五种不同基质材料包括玻璃纤维垫和聚酯毛毡都已建立了具有高度再现性的水分释放曲线，本文对该方法的优点和变异来源也进行了讨论。     

轻质土壤水分特征曲线估计的简便方法

以黄淮海平原封丘地区的潮土和风沙土为研究对象,根据大量的土壤基本物理性质和土壤持水数据,利用多元逐步回归分析方法,建立了轻质土壤在不同基质势下土壤含水量(θ)的传递函数模型,并进行了模型验证。结果表明,利用轻质土壤的基本物理性质估计其水分特征曲线是一种简便可行的方法,并且,在回归方程中,增加-30kPa含水量项可提高-30kPa以上土壤含水量的估计精度;增加-1500kPa含水量项可以明显提高-100kPa至-1500kPa间土壤含水量的估计精度。     

温度对砂型土壤和石英砂水分特征曲线的影响

为了研究温度变化对土壤和石英砂持水特性差异的影响机理,在离心机试验的基础上结合理论分析,对不同温度下土壤和石英砂水分特征曲线实测数据、低吸力段比水容及Van Genuchten经验模型的拟合参数进行了研究分析。结果表明:温度的变化对土壤水分特征曲线的位置影响显著,对曲线的走势无太大影响;而对于石英砂样本受温度变化的影响并不明显;温度对土壤持水能力的影响很大部分是由土壤中水分的性质和土壤自身性质共同决定的;同时,结合比水容的概念和水分特征曲线的VG模型参数较准确地评价温度变化下两种供试样品的持水性能和水分的有效程度。     

科尔沁沙地土壤水分特征曲线传递函数的构建与评估

为了快速准确的获取某一区域的水力性质,该文以科尔沁沙地典型沙丘-草甸相间地区为研究区,在对该区49个不同地貌类型采样点土壤水分特征曲线与土壤基本物理化学特性参数测试分析的基础上,采用函数参数非线性规划法构建了土壤干容重、粒径分布、有机质、pH值、电导率值等基本参数与水分特征曲线之间的传递函数,并进行了精度评估与分析。结果表明:1)研究区土壤水分特征曲线的坡度陡峭,不同地貌类型与土地利用方式下,土壤水分特征曲线有较大差异,相同负压下,土壤持水量按照流动沙丘-半固定沙丘-固定沙丘-农田-草甸的顺序递增;土壤的供水能力按照流动沙丘-半固定沙丘-固定沙丘-草甸-农田的顺序递减;2)利用土壤基本物化特性参数通过函数参数非线性规划法建立了研究区土壤水分特征曲线的传递函数,干容重和砂粒含量是预测土壤水分特征曲线模型参数的主要变量,增加土壤的理化指标可以提高预测精度,然而有机质含量、pH值、电导率值对本区土壤水分常数的影响并不大,对水分特征曲线模型3个参数的影响略微增加;3)通过对传递函数的检验与精度评估分析,各参数的平均误差均在0附近;饱和含水率、残余含水率的均方根误差分别为0.017、0.023;土壤水分常数的相关系数在0.95附近,饱和含水率、残余含水率的误差比的几何标准偏分别为1.04、1.27。表明所建土壤水分特征曲线传递函数的精度较高,可用于该区土壤水分特性研究。该研究可为该区水分、溶质运移、水-热-盐耦合运移模拟提供技术支持和理论保证。     

根据土壤粒径分形估计紫色土水分特征曲线

选择不同质地紫色土,分别根据土壤颗粒的数量和重量分布计算土壤粒径分形维数,并与Tyler和Wheatcraft土壤水分特征曲线模型的拟合分形维数进行比较分析。结果表明,三种途径获得的分形维数与土壤质地密切相关,均表现出土壤黏粒含量越高,质地越细,分形维数越高。土壤粒径数量分布分形维数(2.98～3.26)和土壤粒径重量分布分形维数(2.73～2.81)存在较大的差异,但二者间却存在显著的线性关系。土壤粒径分形维数与土壤水分特征曲线模型拟合分形维数(2.72～2.84)均存在显著的线性关系,尤其是土壤粒径重量分布分形维数与土壤水分特征曲线模型拟合分形维数数值十分接近。通过建立的土壤水分特征曲线模型分形维数与土壤粒径分布分形维数关系式,结合Tyler和Wheatcraft模型进行土壤水分特征曲线预测,预测值与实测值具有良好的一致性,因而根据土壤粒径分形预测紫色土水分特征曲线是可行的。     

不同侵蚀强度黑土的土壤水分特征曲线模拟

为研究土壤侵蚀对土壤水分特征曲线的影响,选择轻、中、重3种侵蚀强度黑土的15个样点,通过测定8个土壤水吸力对应的土壤水分,估算了不同侵蚀强度土壤水分特征曲线Brooks-Corey(BC)模型和Van Genuchten(VG)模型参数,并分析其差异性,比较2个模型模拟不同侵蚀强度黑土土壤水分特征曲线的精度。结果表明:轻、中度侵蚀黑土的田间持水量和凋萎湿度无显著差异,重度侵蚀则与其差异显著,随侵蚀加剧,黑土的持水能力明显降低。BC和VG模型共同的参数饱和含水量和滞留含水量参数值相同,且饱和含水量轻、中度侵蚀差异不显著,重度侵蚀与其差异显著。滞留含水量、形状参数λ和n分别接近于0cm~3/cm~3,0.10和1.10。2个模型反映土壤通气性指标的进气吸力hb和尺度参数α表现出一致性,即中度侵蚀黑土通气性最好。修正了VG模型形状参数m和n之间的关系,与原关系式相比中度侵蚀黑土m降低13%,轻度和重度侵蚀黑土分别降低30%和46%,修正后的VG模型更适合遭受不同强度侵蚀黑土的土壤水分特征曲线模拟。     

温度对南方红壤和水稻土水分特征曲线影响差异性分析

为了研究温度变化对土壤水分特征曲线的影响,采用高速离心机法测定南方红壤和水稻土在4,12,20,28,36℃温度条件下土体脱湿过程的水分特征曲线,并运用VG模型进行参数提取,探讨了温度对土壤当量孔径分布、非饱和导水率及水分扩散度等参数的影响。结果表明:相同水势条件下红壤和水稻土的含水率随温度增加而显著减少(P0.05),表现出4℃12℃20℃28℃36℃的关系,且同一温度下的红壤持水性能显著高于水稻土(P0.05);不同温度处理下红壤和水稻土的非饱和导水率以及水分扩散度均表现出20℃最高、36℃最小的显著差异性(P0.05),这表明土壤在20℃时的导水能力和入渗能力相对最佳,且同一温度下红壤的非饱和导水率和水分扩散度均低于水稻土;红壤和水稻土的饱和含水率θ_s、进气值相关参数α、形状系数n均随温度增加而缓慢减小,拟合参数随温度变化而差异显著(P0.05),这说明土壤水分特征曲线参数对温度有一定的敏感性。研究结果可为进一步探讨温度对南方土壤持水性能和水分运移机制研究提供合理的参考依据。     

台湾青枣盆栽基质的筛选

选用塘泥、菇泥、椰糠3种基质进行6种不同的配比作为台湾青枣的栽培基质,对台湾青枣进行盆栽研究,并以塘泥作为对照。结果表明:不同配方的栽培基质对台湾青枣的营养生长和生殖生长均有不同的影响,其中处理C(塘泥:菇泥=3:2)的新叶片数和花序数与对照A相比差异达到显著水平,开花数达到极显著水平,是台湾青枣盆栽栽培基质的理想配方。     

容重对铁尾矿水分运移特征的影响

[目的]探究紧实度对铁尾矿水分运移特性的影响,为铁矿区水土保持及植被恢复提供科学依据。[方法]采用室内一维土柱试验和张力计法,分别测定1.50,1.55,1.60,1.65,1.70g/cm~3这5个容重水平下的入渗过程及水分特征曲线。[结果](1)随着容重增加,铁尾矿入渗能力降低,容重与稳渗率、湿润峰距离、累积入渗量呈显著线性负相关关系(p0.05);(2)铁尾矿剖面水分含量随土层深度的增加而下降,1.50～1.60g/cm~3含水量在不同深度上分层明显,1.65和1.70g/cm~3尾矿砂剖面含水量分异性较小;(3)Green-Ampt模型拟合铁尾饱和导水率与实测值相关系数为0.886,计算精度较高,Kostiakov模型拟合不同容重铁尾矿入渗过程效果最佳(R~2=0.989);(4)铁尾矿在脱湿过程中,高容重下持水能力明显下降,1.60g/cm~3持水保水性最好,van Genuchten模型拟合水分特征曲线表明,饱和含水率(θ_s)、残余含水率(θ_r)、进气值(α)、形状系数(n)等参数随容重增大而减小。[结论]高度压实严重抑制铁尾矿水分入渗和持水能力,因此复垦修复中应以疏松尾矿基质改善结构特性为主。     

保水剂与基质水分对侧柏水分利用特征的影响

[目的]研究基质含水量和保水剂浓度对侧柏水势、水分利用效率和蒸腾速率的影响,为不同降雨地区铅锌尾矿库植被重建与生态恢复中保水剂的科学施用提供理论依据和技术支撑。[方法]以一年生侧柏为研究对象,采用二次通用旋转组合设计,测定侧柏水势、净光合速率、蒸腾速率,计算水分利用效率,建立侧柏水势(ψ)、水分利用效率(WUE)、蒸腾速率(T_r)与保水剂浓度和基质含水量之间的回归模型,分析基质含水量和保水剂浓度对侧柏水势、水分利用效率、蒸腾速率的影响。[结果]①水势和蒸腾速率的回归模型主效应顺序为:基质含水量保水剂浓度;水分利用效率的回归模型中保水剂浓度基质含水量;②水势最高时保水剂浓度为0.1%,基质含水量为19.73%;水分利用效率最大时保水剂浓度为0.1%,基质含水量为7.89%,蒸腾速率最小时保水剂浓度为1%,基质含水量为7.89%。[结论]使用0.1%～0.23%低保水剂浓度,有利于侧柏从改良铅锌尾矿基质中获取水分,起到促进叶片二氧化碳固定,提高水分利用效率的作用。     

Pedotransfer functions for estimating soil water retention curve of Sicilian soils

Pedotransfer functions (PTFs) make use of routinely surveyed soil data to estimate soil properties but their application to soils different from those used for their development can yield inaccurate estimates. This investigation aimed at evaluating the water retention prediction accuracy of eight existing PTFs using a database of 217 Sicilian soils exploring 11 USDA textural classes. PTFs performance was assessed by root mean square differences (RMSD) and average differences (AD) between estimated and measured data. Extended Nonlinear Regression (ENR) technique was adopted to recalibrate or develop four new PTFs and Wind’s evaporation method was applied to validate the effectiveness of the relationships proposed. PTFs evaluation resulted in RMSD and AD values in the range 0.0630–0.0972 and 0.0021–0.0618 cm³ cm^–3, respectively. Best and worst performances were obtained respectively by PTF-MI and PTF-ZW. ENR allowed to recalibrate PTF-MI and PTF-ZW with improvements of RMSD (0.0594 and 0.0508 cm³ cm^–3) and to develop two relationships that improved RMSD by 75–78% as compared to PTF-MI. The results confirmed the potential of ENR technique in calibrating existing PTFs or developing new ones. Validation conducted with an independent dataset suggested that recalibrated/developed PTFs represent a viable alternative for water retention estimation of Sicilian soils.     

土壤水分特征曲线的分形模拟

Many empirical models have been developed to describe the soil water retention curve （SWRC）. In this study, a fractal model for SWRC was derived with a specially constructed Menger sponge to describe the fractal scaling behavior of soil; relationships were established among the fractal dimension of SWRC, the fractal dimension of soil mass, and soil texture; and the model was used to estimate SWRC with the estimated results being compared to experimental data for verification. The derived fractal model was in a power-law form, similar to the Brooks-Corey and Campbell empirical functions. Experimental data of particle size distribution （PSD）, texture, and soil water retention for 10 soils collected at different places in China were used to estimate the fractal dimension of SWRC and the mass fractal dimension. The fractal dimension of SWRC and the mass fractal dimension were linearly related. Also, both of the fractal dimensions were dependent on soil texture, i.e., clay and sand contents. Expressions were proposed to quantify the relationships. Based on the relationships, four methods were used to determine the fractal dimension of SWRC and the model was applied to estimate soil water content at a wide range of tension values. The estimated results compared well with the measured data having relative errors less than 10% for over 60% of the measurements. Thus, this model, estimating the fractal dimension using soil textural data, offered an alternative for predicting SWRC.     

基于ANN的复垦土壤水分特征曲线的预测研究

为能较容易且更准确地获取复垦土壤水分特征曲线,将易测定的土壤特性如土壤质地、容重和饱和含水量作为输入变量,采用基于bagging算法的神经网络法建立了用于预测土壤水分特征曲线的土壤转换函数法(PTFs)模型,并对徐州矿区复垦土壤的水分特征曲线进行了预测,同时与普通BP算法预测精度进行了比较.研究结果表明所建立的PTFs参数模型具有较高的估计精度,bagging算法均方根预测误差比普通BP算法减少了7.5%～27.0%,说明该模型的建立与求解为复垦土壤水分特征曲线的预测研究提供了一条新途径.     

添加蚓粪和蛭石对沟塘底泥育苗基质培育番茄幼苗的影响

以沟塘底泥为主要原料,添加蚓粪和蛭石,复配成育苗基质,研究其对番茄幼苗生长的影响。结果表明,与市售基质相比,沟塘底泥复配的基质能够显著增加番茄幼苗的叶片数、叶绿素、根粗和茎叶鲜重,显著减小根长和根鲜重,而株高、茎粗、根表面积、根体积、根系活力、根干重、茎叶干重以及壮苗指数等无显著差异。沟塘底泥与蚓粪按8∶1(体积比)混合基质中番茄幼苗的茎粗、根粗、根鲜重、茎叶鲜重、根干重、茎叶干重以及壮苗指数显著大于4∶1体积比的基质,沟塘底泥与蛭石按4∶1(体积比)混合基质中番茄幼苗的株高、根长、根表面积、根鲜重、茎叶鲜重、根干重以及壮苗指数显著大于8∶1(体积比)的基质。沟塘底泥与蚓粪和蛭石按8∶1∶1(体积比)混合的基质与沟塘底泥分别与蚓粪以及蛭石按4∶1(体积比)混合的基质总体上对幼苗生长影响无显著差异。总之,沟塘底泥可以作为基质材料培育番茄幼苗,沟塘底泥中单独添加蚓粪(8∶1)比添加蛭石(4∶1)更有利于番茄幼苗的生长发育。     

砒砂岩风化物对土壤水分特征曲线及蒸发的影响

运用高速离心机法和室内模拟蒸发的方法,对比研究了红色(RS)、灰色(GS)和白色(WS)三种颜色砒砂岩风化物在不同添加比例条件下对沙黄土和风沙土水分特征曲线、供水能力和干旱过程中失水过程的影响。研究表明:砒砂岩风化物的添加对沙黄土和风沙土水分特征曲线有较明显的影响。砒砂岩风化物的持水能力均高于沙黄土和风沙土,砒砂岩风化物添加提高了风沙土和沙黄土的持水能力,降低了沙黄土和风沙土失水速率;较低比例的砒砂岩风化物提高了沙黄土和风沙土的比水容量,但对风沙土和沙黄土低吸力段供水能力的提高作用并不明显。整个蒸发期间,砒砂岩风化物降低了沙黄土的标准蒸发量,而对于风沙土,其作用是蒸发前期降低,中后期增加。红色、灰色、白色三种砒砂岩风化物分别使风沙土的蒸发失水比提高了11.59%、10.14%和0.01%,添加三种砒砂岩风化物后的处理的最终平均含水量分别是风沙土的4倍、2.33倍、1.33倍,使沙黄土的蒸发失水比降低了13.33%、13.33%、29.52%,最终含水量降低了45.83%、54.17%、66.67%。因此,从提高土壤持水能力方面考虑,砒砂岩可以用于当地矿区和排土场区土壤改良,以促进生态修复。     

Estimating the water retention curve from soil properties: comparison of linear, nonlinear and concomitant variable methods

The unsaturated soil hydraulic functions involving the soil–water retention curve (SWRC) and the hydraulic conductivity provide useful integrated indices of soil quality. Existing and newly devised methods were used to formulate pedotransfer functions (PTFs) that predict the SWRC from readily available soil data. The PTFs were calibrated using a large soils database from Hungary. The database contains measured soil–water retention data, the dry bulk density, sand, silt and clay percentages, and the organic matter content of 305 soil layers from some 80 soil profiles. A three-parameter van Genuchten type function was fitted to the measured retention data to obtain SWRC parameters for each soil sample in the database. Using a quasi-random procedure, the database was divided into “evaluation” (EVAL) and “test” (TEST) parts containing 225 and 80 soil samples, respectively. Linear PTFs for the SWRC parameters were calculated for the EVAL database. The PTFs used for this purpose particle-size percentages, dry bulk density, organic matter content, and the sand/silt ratio, as well as simple transforms (such as logarithms and products) of these independent variables. Of the various independent variables, the eight most significant were used to calculate the different PTFs. A nonlinear (NL) predictive method was obtained by substituting the linear PTFs directly into the SWRC equation, and subsequently adjusting the PTF parameters to all retention data of the EVAL database. The estimation error (SSQ) and efficiency (EE) were used to compare the effectiveness of the linear and nonlinearly adjusted PTFs. We found that EE of the EVAL and the TEST databases increased by 4 and 7%, respectively, using the second nonlinear optimization approach. To further increase EE, one measured retention data point was used as an additional (concomitant) variable in the PTFs. Using the 20 kPa water retention data point in the linear PTFs improved the EE by about 25% for the TEST data set. Nonlinear adjustment of the concomitant variable PTF using the 20 kPa retention data point as concomitant variable produced the best PTF. This PTF produced EE values of 93 and 88% for the EVAL and TEST soil data sets, respectively.