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首页> 外文期刊>Geoderma: An International Journal of Soil Science >Field use and calibration of a TDR-based probe for monitoring water content in a high-clay landslide soil in Austria
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Field use and calibration of a TDR-based probe for monitoring water content in a high-clay landslide soil in Austria

机译:基于TDR的探头的现场使用和校准,用于监测奥地利高粘土滑坡土壤中的水分

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摘要

Dielectric-based sensors are widely used for field monitoring of soil volumetric water content (theta(v)), including in situ applications in ecological monitoring programs. However, sensor response depends strongly on the location-specific soil properties, which in turn affects measurement accuracy and data processing. Published general or manufacturers' calibrations often misrepresent the theta(v)-sensor output relationship, requiring soil-specific calibration. We report on use of the CS615 Water Content Reflectometer (WCR) to monitor the soil water dynamics in a creeping flow at a landslide site (Bad Goisern, Austria), and on the soil-specific adjustment of measurement errors. Extraordinary soil conditions (high clay and water contents) caused anomalous overestimation of theta(v) via the manufacturer's standard calibration. Further, a laboratory calibration had to be aborted due to the intractable soil material. However an in situ field calibration and an ex situ field-soil calibration successfully provided relations between theta(v) and the probe output (multivibrator period, T). The calibration was performed as a two-stage procedure according to the inverse regression method. Linear (LR) and multiple (MR) regression models and polynomial (P2, P3) relations were generated via regression analyses. Bias, mean squared error (MSE) and mean deviation (MD) were used to evaluate the quality of theta(v) estimation using the inverse prediction function. LR and MR models provided better data adjustment than polynomial functions. Best results were derived from MR models including as additional variables temperature (T) and porosity (P), and subset-specific (S) to sensor position in the field (model MR TP S). Measurement error was reduced from 0.068 +/- 0.122 m(3) m(-3) (MSE +/- sigma for the standard calibration) to 0.001 +/- 0.002 m(3) m(-3) (MSE +/- sigma for the MR TP S model). Restricted sample size and moisture range impaired the statistical analyses of both field soil calibrations. Deviations of sensor response specific to soil layer and sensor position were observed and statistically confirmed. However, reasonable location-specific calibration functions were obtained for both the entire water content range and the site-specific high moisture range. Our results indicate an especially anomalous. soil pH-dependent response of the WCR (which operates in the lower frequency range 15 to 45 MHz) in a smectite-dominated soil, partly consistent with the findings of Ishida and Makino (1999) for the dielectric behaviour of montmorillonite suspensions. Unfavourable soil conditions, especially high moisture levels combined with high clay contents, demonstrated the limits of WCR-application. Our findings strongly support media-specific sensor calibration over general calibrations, especially for soils with extraordinary and challenging properties.
机译:基于介电的传感器广泛用于土壤体积水含量(theta(v))的现场监测,包括在生态监测程序中的原位应用。但是,传感器的响应在很大程度上取决于特定位置的土壤属性,这反过来又会影响测量精度和数据处理。已发布的一般或制造商的校准经常会误解theta(v)-传感器输出关系,因此需要特定于土壤的校准。我们报告了使用CS615水分反射仪(WCR)来监测滑坡站点(奥地利Bad Goisern)的蠕变流中的土壤水分动态,以及针对土壤的测量误差调整。异常的土壤条件(高粘土含量和水含量)导致通过制造商的标准校准对theta(v)的高估。此外,由于难处理的土壤材料,必须中止实验室校准。但是,原位场校准和异位场土壤校准成功地提供了theta(v)和探头输出(多谐振荡器周期T)之间的关系。根据逆回归方法,校准过程分为两步。通过回归分析生成了线性(LR)和多元(MR)回归模型以及多项式(P2,P3)关系。偏差,均方误差(MSE)和均方差(MD)用于使用逆预测函数评估theta(v)估计的质量。 LR和MR模型比多项式函数提供了更好的数据调整。最好的结果来自MR模型,包括温度(T)和孔隙率(P),以及传感器在现场的位置的子集特定(S)(模型MR TP S)。测量误差从0.068 +/- 0.122 m(3)m(-3)(标准校准为MSE +/- sigma)降至0.001 +/- 0.002 m(3)m(-3)(MSE +/- MR TP S模型的sigma)。样品大小和湿度范围受限会影响两种田间土壤标定的统计分析。观察到并具体确认了传感器对土壤层和传感器位置的响应偏差。但是,对于整个含水量范围和特定于地点的高湿度范围,都获得了合理的特定于位置的校准功能。我们的结果表明异常特别严重。在蒙脱石为主的土壤中,WCR(在15至45 MHz的较低频率范围内运行)的土壤pH依赖性响应,部分与Ishida和Makino(1999)关于蒙脱石悬浮液介电行为的发现一致。不利的土壤条件,特别是高水分含量和高粘土含量,证明了WCR应用的局限性。我们的发现有力地支持了特定介质传感器的校准,而不是一般的校准,尤其是对于具有非凡且具有挑战性的土壤。

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