Time domain reflectometry waveform analysis with second order bounded mean oscillation

Time domain reflectometry (TDR) is a well-established electromagnetic technique used to estimate in situ soil water content, and thermo-TDR is an extension of the traditional TDR, which can measure both soil water content and thermal properties. Tangent line methods and adaptive waveform interpretation with Gaussian filters (AWIGF) are used in traditional TDR waveform analysis. However, due to the short probe design of thermo-TDR, those methods may not perform well. In this thesis, we present an alternative method in TDR waveform analysis based on the second order bounded mean oscillation (BMO) operator. Numerical analysis and laboratory calibration tests show that the second order BMO can return reasonable results compared with tangent line methods and AWIGF. Second order BMO analysis also performs well for some challenging waveforms. The second order BMO method is not totally automatic due to the large variation of TDR waveforms, and manual adjustment of some parameters is necessary. A MATLAB program of the implementation of second order BMO method is developed, and instructions for users of the program is also provided. The results show that second order BMO is a valid alternative method for TDR waveform analysis

Numerical methods in soil hydrology: TDR waveform analysis and water vapor diode simulation

Soil water content impacts all soil physical, chemical and biological properties. Soil water movement in shallow soil layers has critical importance for plant water use, foundation stability, energy transfer and chemical diffusion. Numerical analysis is one way to study soil water. New numerical methods are presented in this thesis to determine soil water content from time domain reflectometry (TDR) measurements and simulate soil water accumulation in selected soil layers. TDR enables nondestructive and continuous soil water content measurements. Traditional TDR waveguides have relatively long probes (\u3e150 mm), but new TDR waveguides tend to use short probes (\u3c40 mm) to enable the measurements of water content near the soil surface. However, analyzing TDR waveforms obtained with short TDR probes can be challenging for traditional numerical analysis methods. A new numerical method is needed for analyzing the short-probe TDR waveforms. Coupled heat and water movement can be used to describe the liquid water and water vapor fluxes under combined soil matric potential gradients and thermal gradients. Water vapor flux is the dominant means of soil water movements in relatively dry soil layers. If the naturally occurring water vapor fluxes can be controlled, it is possible to impact the water content distribution in soil profiles. A water vapor diode (WVD), acting as a check valve, allows water vapor flux to occur only in one direction but heat flux occurs in both directions. By installing a subsurface WVD, it is possible to impose direction-controlled vapor fluxes, and WVDs can be used to accumulate or remove water in particular soil layers. However, necessary properties of the WVDs should be clearly defined, and the performance of the WVD should be investigated. Thus, the objectives of this thesis are to (1) develop a new tangent line/second order bounded mean oscillation (TL-BMO) model for analyzing short-probe TDR waveforms to determine the soil water content, and compare TL-BMO with tradition models, such as tangent line (TL) and adaptive waveform interpretation with Gaussian filter (AWIGF); (2) introduce the concept of a WVD and use numerical simulations to analyze the influence of WVDs on soil water redistribution. The TL-BMO is evaluated with TDR waveforms obtained by short-probe sensors in Nicollet, Ida and Hanlon soil samples for a range of water contents to test its accuracy and stability. The root mean squared error of the TDR estimated water content and the gravimetric water content is \u3c2%. In order to compare TL-BMO with the traditional models, waveforms obtained with long- and short-probe TDR sensors in CaCl2 solutions for a range of electrical conductivities are used. The results indicate that the TL-BMO model is consistent with the traditional TDR waveform models for some of the waveforms, but the TL-BMO performs better than the traditional models on some challenging waveforms. Thus, TL-BMO can effectively analyze the waveforms from both long- and short-probe TDR sensors. Inspired by the methods used with TL-BMO, the AWIGF model was also revised with a newly designed corner-preserving filter. The performance of the revised AWIGF model on short-probe TDR waveforms was similar to that of the TL-BMO model. One dimensional numerical simulations of soil water redistribution with WVDs are conducted to illustrate the concept and properties of WVDs. Four WVD configurations are discussed to control soil water redistribution. Simulation results indicate that WVDs can increase the local water contents by at least 0.1 m3 m-3 in a silt loam, but the effects of WVDs varied with deployment depth and separation distance between two adjacent WVDs. Two dimensional numerical simulations are performed to evaluate the effects of two possible designs of the WVDs, i.e., an egg-carton design and a Tyvek design. The soil water content can be altered by 0.02 m3 m-3 with the WVDs in the numerical examples, and the unsaturated subsurface drainage can be increased due to the soil water accumulation induced by the WVDs. In conclusion, the TL-BMO model can provide stable and accurate analysis of short-probe TDR waveforms, and the TL-BMO model is flexible enough to be used on for both long- and short-probe TDR sensors. The WVD can effectively manipulate soil water redistribution of soil profile water due to the naturally occurring thermal gradients. WVDs can be deployed to cause water accumulation in specific soil layers, and to assist in unsaturated subsurface drainage of soil profile water

Time Domain Reflectometry Waveform Analysis with Second-Order Bounded Mean Oscillation

Tangent-line methods and adaptive waveform interpretation with Gaussian filtering (AWIGF) have been proposed for determining reflection positions of time domain reflectometry (TDR) waveforms. However, the accuracy of those methods is limited for short-probe TDR sensors. Second-order bounded mean oscillation (BMO) may be an alternative method to determine reflection positions of short-probe TDR waveforms. For this study, an algorithm of second-order BMO is developed. Example waveforms are analyzed with tangent-line methods, AWIGF method, and second-order BMO to illustrate the difference among the three methods. For some waveforms, second-order BMO appears be able to give more plausible results. Automatic implementation was challenging for the second-order BMO. With second-order BMO, it is difficult to set a default threshold suitably for all TDR waveforms. Thus, manual adjustment may be required to select a suitable threshold for second-order BMO analysi

Incorporation of carbon dioxide production and transport module into a Soil-Plant-Atmosphere continuum model

Carbon dioxide release from agricultural soils is influenced by multiple factors, including soil (soil properties, soil-microbial respiration, water content, temperature, soil diffusivity), plant (carbon assimilation, rhizosphere respiration), atmosphere (climate, atmospheric carbon dioxide), etc. Accurate estimation of the carbon dioxide (CO2) fluxes in the soil and soil respiration (CO2 flux between soil and atmosphere) requires a process-based modeling approach that accounts for the influence of all these factors. In this study, a module for CO2 production via root and microbial respiration and diffusion-based carbon dioxide transport is developed and integrated with MAIZSIM (a process-based maize crop growth model that accounts for detailed soil and atmospheric processes) based on a modularized architecture. The developed model simulates root respiration based on root mass, root age, soil water content, and temperature. Microbial respiration is based on the soil microbial processes by accounting for the carbon dynamics in the litter, humus, and organic fertilizer pools as moderated by the soil water content, temperature, microbial synthesis, humification, and decomposition of the carbon pools. Case studies presented include scenarios with different soil, climate, and carbon pools that simulated the soil respiration with an average index of agreement of 0.73 and root mean squared error of 11.4 kg carbon ha-1 between the measured and simulated soil respiration. The modular architecture used in the model development facilitates easy integration with other existing crop models and future modifications

Improving the cotton simulation model, GOSSYM, for soil, photosynthesis, and transpiration processes

GOSSYM, a mechanistic, process-level cotton crop simulation model, has a two-dimensional (2D) gridded soil model called Rhizos that simulates the below-ground processes daily. Water movement is based on gradients of water content and not hydraulic heads. In GOSSYM, photosynthesis is calculated using a daily empirical light response function that requires calibration for response to elevated carbon dioxide ( CO2). This report discusses improvements made to the GOSSYM model for soil, photosynthesis, and transpiration processes. GOSSYM’s predictions of below-ground processes using Rhizos are improved by replacing it with 2DSOIL, a mechanistic 2D finite element soil process model. The photosynthesis and transpiration model in GOSSYM is replaced with a Farquhar biochemical model and Ball-Berry leaf energy balance model. The newly developed model (modified GOSSYM) is evaluated using field-scale and experimental data from SPAR (soil–plant–atmosphereresearch) chambers. Modified GOSSYM better predicted net photosynthesis (root mean square error (RMSE) 25.5 versus 45.2 g CO2 m−2 day−1; index of agreement (IA) 0.89 versus 0.76) and transpiration (RMSE 3.3 versus 13.7 L m−2 day−1; IA 0.92 versus 0.14) and improved the yield prediction by 6.0%. Modified GOSSYM improved the simulation of soil, photosynthesis, and transpiration processes, thereby improving the predictive ability of cotton crop growth and development

A Comparison of Second‐Order Derivative Based Models for Time Domain Reflectometry Waveform Analysis

Core Ideas Two methods, AWIGF and second‐order BMO, were evaluated on long‐ and short‐probe TDR waveforms. A corner‐preserving filter was designed to improve the performance of AWIGF. With the new filter, AWIGF performance was consistent with second‐order BMO analysis. Adaptive waveform interpretation with Gaussian filtering (AWIGF) and second‐order bounded mean oscillation Z2(u,t,r) are time domain reflectometry (TDR) analysis models. The AWIGF was originally designed for relatively long‐probe (\u3e150 mm) TDR waveforms, while Z2(u,t,r) was originally designed for relatively short‐probe (\u3c50 mm) TDR waveforms. The performances of AWIGF and Z2(u,t,r) on both long and short TDR probes have not been evaluated. The main objective of this study was to evaluate theoretically and experimentally the AWIGF and Z2(u,t,r) performances on long and short TDR probes. The evaluations are performed via mathematical analysis, and measurements of long probe and short probe waveforms in CaCl2 solutions with various electrical conductivities (EC), adding Gaussian noise, and testing the stability of Z2(u,t,r) and AWIGF. A corner‐preserving filter (CPF) is proposed to improve the stability of AWIGF on short‐probe TDR waveforms. The CPF preserves the second order derivatives of the waveforms, and emphasizes the reflection positions (t2) compared to the original Gaussian filter. Both theoretical and experimental tests illustrate the consistency of Z2(u,t,r) and AWIGF. The standard deviations of relative permittivity (εr) are \u3c5% for all noise levels. In conclusion, Z2(u,t,r) and AWIGF can provide stable analysis for long and short probe TDR waveforms. The AWIGF with CPF is capable of stably analyzing challenging short probe TDR waveforms. The original AWIGF exhibits the lowest standard deviation of εr at a given EC, whereas AWIGF with CPF filter exhibits the lowest bias of εr across solutions varying in EC

Modeling vapor transfer in soil water and heat simulations: A modularized, partially-coupled approach

Coupled water and heat transfer models are widely used to analyze soil water content and temperature dynamics, evaluate agricultural management systems, and support crop growth modelling. In relatively dry soils, vapor transfer, rather than liquid water flux, becomes the main pathway for water redistribution. However, in some modularized soil simulators, e.g., 2DSOIL (Timlin et al., 1996), vapor transfer is not included, which may induce errors in soil water and heat modelling. Directly embedding vapor transfer into existing water and heat transfer modules may violate the modularized architecture of those simulators. Therefore, the objectives of this study are to design a vapor transfer model, evaluate its performance, and implement it as a separate module in a coupled soil water and heat simulator, e.g., 2DSOIL. The efficacy of the vapor transfer model is evaluated by comparing the simulated soil water content and temperature before and after including the new vapor transfer model, and the soil water content and temperature simulated with the standard Philip and de Vries (1957) model. By implementing vapor transfer as a separate module in 2DSOIL, modifications to existing water and heat transfer modules can be minimized and the modularized model architecture can be maintained. Numerical examples of 2DSOIL with the new vapor transfer model are presented to illustrate the effects of vapor flux on soil water and temperature redistributions. In conclusion, the new vapor transfer model provides an effective and easy-to-use method to account for the effects of vapor transfer on coupled soil water and heat simulations

The Influence of Concrete Grinding Residue on Soil Physical Properties and Plant Growth

Diamond grinding is a concrete pavement maintenance technique, and concrete grinding residue (CGR) is the byproduct. Concrete grinding residue deposited along roadsides affects soil chemical properties, but impacts of CGR on soil physical properties and plant growth are rarely studied. In this study, a controlled field experiment was performed to determine the influence of CGR on selected soil physical properties (i.e., bulk density [ρb], saturated hydraulic conductivity [Ks], and water infiltrability [It]) and on plant biomass and plant coverage under four application rates (0, 2.24, 4.48, and 8.96 kg m−2). Field measurements were performed before the CGR applications, and 1, 7, and 12 mo after the CGR applications. No significant CGR effects on soil physical properties were detected. The ρb was relatively stable for all of the treatments, whereas some nonsignificant variations (e.g., 10–30% of mean Ks values and mean It values among four CGR rates) were found. Plant biomass with a CGR rate of 2.24 kg m−2 tended to be 10 to 40% larger than biomass in the control treatment, whereas plant biomass with a CGR rate of 8.96 kg m−2 tended to be ∼10% smaller than the control treatment. Concrete grinding residue had no significant effects on plant coverage, richness, Simpson’s diversity, and evenness. Thus, CGR applications up to 8.96 kg m−2 did not significantly affect soil physical properties and plant growth in this controlled field study. This study can serve as a reference for results obtained from roadsides in Minnesota and Iowa that receive CGR applications