Shakya Matina, Hess Amanda, Wadzuk Bridget M, Traver Robert G
Department of Civil, Architectural and Environmental Engineering, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA.
Department of Civil and Environmental Engineering, Villanova University, 800 Lancaster Avenue, Villanova, PA 19085, USA.
Sensors (Basel). 2024 Dec 24;25(1):27. doi: 10.3390/s25010027.
The ability to track moisture content using soil moisture sensors in green stormwater infrastructure (GSI) systems allows us to understand the system's water management capacity and recovery. Soil moisture sensors have been used to quantify infiltration and evapotranspiration in GSI practices both preceding, during, and following storm events. Although useful, soil-specific calibration is often needed for soil moisture sensors, as small measurement variations can result in misinterpretation of the water budget and associated GSI performance. The purpose of this research is to quantify the uncertainties that cause discrepancies between default (factory general) sensor soil moisture measurements versus calibrated sensor soil moisture measurements within a subsurface layer of GSI systems. The study uses time domain reflectometry soil moisture sensors based on the ambient soil's dielectric properties under different soil setups in the laboratory and field. The default 'loam' calibration was compared to soil-specific (loamy sand) calibrations developed based on laboratory and GSI field data. The soil-specific calibration equations used a correlation between dielectric properties (real dielectric: , and apparent dielectric: ) and the volumetric water content from gravimetric samples. A paired -test was conducted to understand any statistical significance within the datasets. Between laboratory and field calibrations, it was found that field calibration was preferred, as there was less variation in the factory general soil moisture reading compared to gravimetric soil moisture tests. Real dielectric permittivity () and apparent permittivity () were explored as calibration options and were found to have very similar calibrations, with the largest differences at saturation. The produced a 6% difference while the calibration produced a 3% difference in soil moisture measurement at saturation. was chosen over as it provided an adequate representation of the soil and is more widely used in soil sensor technology. With the implemented field calibration, the average desaturation time of the GSI was faster by an hour, and the recovery time was quicker by a day. GSI recovery typically takes place within 1-4 days, such that an extension of a day in recovery could result in the conclusion that the system is underperforming, rather than it being the result of a limitation of the soil moisture sensors' default calibrations.
在绿色雨水基础设施(GSI)系统中,利用土壤湿度传感器跟踪水分含量的能力,使我们能够了解该系统的水管理能力和恢复情况。土壤湿度传感器已被用于量化暴雨事件之前、期间和之后GSI实践中的入渗和蒸散。尽管土壤湿度传感器很有用,但通常需要针对特定土壤进行校准,因为微小的测量变化可能导致对水平衡和相关GSI性能的误解。本研究的目的是量化导致GSI系统地下层中默认(工厂通用)传感器土壤湿度测量值与校准后传感器土壤湿度测量值之间存在差异的不确定性。该研究在实验室和现场的不同土壤设置下,使用基于周围土壤介电特性的时域反射仪土壤湿度传感器。将默认的“壤土”校准与基于实验室和GSI现场数据开发的特定土壤(壤质砂土)校准进行了比较。特定土壤校准方程使用了介电特性(实介电常数: ,和视介电常数: )与重量法样品的体积含水量之间的相关性。进行了配对 检验,以了解数据集中的任何统计显著性。在实验室校准和现场校准之间,发现现场校准更可取,因为与重量法土壤湿度测试相比,工厂通用土壤湿度读数的变化较小。研究探讨了实介电常数( )和视介电常数( )作为校准选项,发现它们的校准非常相似,在饱和度时差异最大。在饱和度时, 校准在土壤湿度测量中产生了6%的差异,而 校准产生了3%的差异。选择 而不是 ,是因为它能充分代表土壤,并且在土壤传感器技术中应用更广泛。通过实施现场校准,GSI的平均去饱和时间快了一小时,恢复时间快了一天。GSI的恢复通常在1-4天内发生,因此恢复时间延长一天可能会导致得出该系统性能不佳的结论,而不是因为土壤湿度传感器默认校准的局限性。