Krzysztof Lamorski

A TDR-Based Soil Moisture Monitoring System with Simultaneous Measurement of Soil Temperature and Electrical Conductivity

Elements of design and a field application of a TDR-based soil moisture and electrical conductivity monitoring system are described with detailed presentation of the time delay units with a resolution of 10 ps. Other issues discussed include the temperature correction of the applied time delay units, battery supply characteristics and the measurement results from one of the installed ground measurement stations in the Polesie National Park in Poland.

An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method

PUBLICATIONS Water Resources Research RESEARCH ARTICLE 10.1002/2016WR019533 Key Points: The main wetting branch of the soil water retention curve is estimated based on its main drying branch The machine learning method is used for analysis Results are compared with classical methods of estimating the main wetting branch Supporting Information: Supporting Information S1 Data Set S1 Correspondence to: K. Lamorski, k.lamorski@ipan.lublin.pl Citation: Lamorski, K., J. r Sim˚unek, C. Slawi nski, and J. Lamorska (2017), An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method, Water Resour. Res., 53, doi:10.1002/2016WR019533. Received 19 JUL 2016 Accepted 28 JAN 2017 Accepted article online 3 FEB 2017 An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method unek 2 , Cezary Slawi n ski 1 , and Joanna Lamorska 3 Krzysztof Lamorski 1 , Ji r i Sim˚ Institute of Agrophysics, Polish Academy of Sciences, Lublin, Poland, 2 Department of Environmental Sciences, University of California Riverside, Riverside, California, USA, 3 Institute of Agricultural Sciences, State School of Higher Education in Chelm, Chelm, Poland Abstract In this paper, we estimated using the machine learning methodology the main wetting branch of the soil water retention curve based on the knowledge of the main drying branch and other, optional, basic soil characteristics (particle size distribution, bulk density, organic matter content, or soil specific surface). The support vector machine algorithm was used for the models’ development. The data needed by this algorithm for model training and validation consisted of 104 different undisturbed soil core samples collected from the topsoil layer (A horizon) of different soil profiles in Poland. The main wetting and drying branches of SWRC, as well as other basic soil physical characteristics, were determined for all soil samples. Models relying on different sets of input parameters were developed and validated. The analysis showed that taking into account other input parameters (i.e., particle size distribution, bulk density, organic matter content, or soil specific surface) than information about the drying branch of the SWRC has essentially no impact on the models’ estimations. Developed models are validated and compared with well-known models that can be used for the same purpose, such as the Mualem (1977) (M77) and Kool and Parker (1987) (KP87) models. The developed models estimate the main wetting SWRC branch with estimation errors (RMSE 5 0.018 m 3 /m 3 ) that are significantly lower than those for the M77 (RMSE 5 0.025 m 3 /m 3 ) or KP87 (RMSE 5 0. 047 m 3 /m 3 ) models. 1. Introduction Soil water retention curves (SWRCs) are one of the most important soil hydrological characteristics required for both agricultural and environmental research related to the vadose zone. SWRCs link the soil water con- tent with the soil water potential and represent indispensable information for the modeling of soil water flow processes. Although laboratory measurements are the ultimate source of information about retention curves, for many reasons SWRCs are commonly estimated using various statistical models, such as the so- called pedotransfer functions (PTF) [e.g., Vereecken et al., 1989, 2016; Schaap et al., 2001]. The main reason for using PTF estimations of SWRCs instead of direct measurements is their long duration and high cost. Typical steady state equilibrium measurements of the retention curve for a full range of soil water potentials can last several months. PTFs estimate SWRCs based on various physical and chemical soil characteristics. For example, particle size dis- tribution and dry bulk density are commonly used predictor variables in PTFs. Additional soil variables such as organic carbon content, soil-specific surface area, and/or cation exchange capacity can also be used. There were numerous PTF models developed utilizing different statistical and/or soft computing methods for SWRC estimation. Early PTFs were often developed using statistical regression [Rawls et al., 1982; Vereecken et al., € sten et al., 1999; Walczak et al., 2006], leading to some still often used models. Various soft computing 1989; W o methods of statistical inference such as artificial neural networks (ANN) [Schaap et al., 2001; Jana et al., 2008], th et al., the k-nearest neighbors algorithm (k-NN) [Nemes et al., 2006; Botula et al., 2013], regression trees [T o 2012, 2015], or support vector machines (SVM) [Lamorski et al., 2008] were used later on. C 2017. American Geophysical Union. V All Rights Reserved. LAMORSKI ET AL. Soil water retention curves of many soil materials exhibit hysteretic behavior, which means that the depen- dence between the soil water potential (h) and the soil water content (h) is not unique and depends on the ESTIMATION OF WETTING FROM DRYING BRANCH OF RC

A System for Recording the Dynamics of the Water Drop’s Impact on a Surface

The splash phenomenon is the first stage of water erosion on the soil. It occurs when the water drops of the rain strike the surface. The impact of the water drop is fast. But if one has a fast enough measuring system, it can be considered as a temporally extended phenomenon. The aim of this paper is to describe a system for the measurement of the dynamic of the changing forces when a water drop interacts with the surface during impact. The constructive assumptions and validation of the measurement system, which has been based on piezoelectric sensors, are also presented. The system allows one to measure the changes of the force with the frequency of 150 kHz. The maximum force is 200 N, which is quite sufficient for all sizes of water drops which occur in nature and their maximum speeds. The results of test measurements, that is, the changes of the force obtained from water drops falling on the sensors, are also shown.

Dynamics of gas cell coalescence during baking expansion of leavened dough

The investigation of the dynamics of gas cell coalescence, i.e. a phenomenon that deteriorates the homogeneity of the cellular structure of bread crumb, was carried out performing simultaneously measurements of the dough volume, pressure, and viscosity. It was demonstrated that, during the baking expansion of chemically leavened wheat flour dough, the maximum growth rate of the gas cell radius determined from the ratio of pressure exerted by the expanded dough to its viscosity was on average four-fold lower than that calculated from volume changes in the gas phase of the dough. Such a high discrepancy was interpreted as a result of the course of coalescence, and a formula for determination of its rate was developed. The coalescence rate in the initial baking expansion phase had negative values, indicating nucleation of newly formed gas cells, which increased the number of gas cells even by 8%. In the next baking expansion phase, the coalescence rate started to exhibit positive values, reflecting dominance of the coalescence phenomenon over nucleation. The maximum coalescence rates indicate that, during the period of the most intensive dough expansion, the number of gas cells decreased by 2-3% within one second. At the end of the formation of bread crumb, the number of the gas cells declined by 55-67% in comparison with the initial value. The correctness of the results was positively verified using X-ray micro-computed tomography. The developed method can be a useful tool for more profound exploration of the coalescence phenomenon at various stages of evolution of the cellular structure and its determinants, which may contribute to future development of more effective methods for improving the texture and sensory quality of bread crumb.