Impact of marble powder amendment on hydraulic properties of a sandy soil

Marble powder is one of carbonate rock amendments that is used to improve soil reaction. We hypothesized that the powdered marble addition can cause favorable changes in hydraulic properties of sandy soils. Six levels of marble powder addition to an aridisol soil (0%; M0; 5%; M5; 10%; M10; 15%; M15; 20%; M20 and 25%, M25; by bulk volume) were analyzed in triplicate. The saturated hydraulic conductivity and soil water retention curves were obtained. Pore space properties were investigated using soil water retention curves, mercury intrusion porosimetry and scanning electron microscopy. The saturated hydraulic conductivity significantly decreased (between 83 and 97% for M5 and M25 respectively) and parameters α and n of the van Genuchten model significantly decreased in marble-amended soils. Both field capacity and permanent wilting point increased with the addition of marble powder. Plant-available water, increased significantly until 10% of marble powder application; higher percentages of application did not provide additional significant changes in the plant-available water. Pore space distributions from soil water retention curves parameters showed an increase in the pore size range and a decrease in the average pore size; pore space distribution from the scanning electron microscopy also showed the presence of a new family of dominant pore sizes which was not detected by the soil water retention curves parameters approach. It was concluded that the addition of marble powder can improve the ability of soil to store water providing an advantage for irrigation water management in water scarce environments. Further research will have to address the impact of marble powder amendment under field semi-arid conditions.This work was partially funded by the project GRE17-12 from the University of Alicante (2018-2019)

MATLAB algorithm to implement soil water data assimilation with the Ensemble Kalman Filter using HYDRUS

Data assimilation is becoming a promising technique in hydrologic modelling to update not only model states but also to infer model parameters, specifically to infer soil hydraulic properties in Richard-equation-based soil water models. The Ensemble Kalman Filter method is one of the most widely employed method among the different data assimilation alternatives. In this study the complete Matlab© code used to study soil data assimilation efficiency under different soil and climatic conditions is shown. The code shows the method how data assimilation through EnKF was implemented. Richards equation was solved by the used of Hydrus-1D software which was run from Matlab. • MATLAB routines are released to be used/modified without restrictions for other researchers • Data assimilation Ensemble Kalman Filter method code. • Soil water Richard equation flow solved by Hydrus-1D.This study forms part of the CGL2013-48802-C3-3-R project financed by the Spanish Ministry of Science and Innovation, the FPDI-2013-16742 from the Spanish Ministry of Economics, and GRE15-19 financed by the University of Alicante. A post-doctoral research fellowship (CAS 15/00244) funded by the Spanish Ministry of Science and Innovation was awarded to J. Valdes-Abellan for this project

Multifractal analysis of discretized X-ray CT images for the characterization of soil macropore structures

A correct statistical model of soil pore structure can be critical for understanding flow and transport processes in soils, and creating synthetic soil pore spaces for hypothetical and model testing, and evaluating similarity of pore spaces of different soils. Advanced visualization techniques such as X-ray computed tomography (CT) offer new opportunities of exploring heterogeneity of soil properties at horizon or aggregate scales. Simple fractal models such as fractional Brownian motion that have been proposed to capture the complex behavior of soil spatial variation at field scale rarely simulate irregularity patterns displayed by spatial series of soil properties. The objective of this work was to use CT data to test the hypothesis that soil pore structure at the horizon scale may be represented by multifractal models. X-ray CT scans of twelve, water-saturated, 20-cm long soil columns with diameters of 7.5 cm were analyzed. A reconstruction algorithm was applied to convert the X-ray CT data into a stack of 1480 grayscale digital images with a voxel resolution of 110 microns and a cross-sectional size of 690 × 690 pixels. The images were binarized and the spatial series of the percentage of void space vs. depth was analyzed to evaluate the applicability of the multifractal model. The series of depth-dependent macroporosity values exhibited a well-defined multifractal structure that was revealed by singularity and Rényi spectra. The long-range dependencies in these series were parameterized by the Hurst exponent. Values of the Hurst exponent close to one were observed indicating the strong persistence in variations of porosity with depth. The multifractal modeling of soil macropore structure can be an efficient method for parameterizing and simulating the vertical spatial heterogeneity of soil pore space

Drone-based imaging to assess the microbial water quality in an irrigation pond: A pilot study

Microbial water quality datasets are essential in irrigated agricultural practices to detect and inform measures to prevent the contamination of produce. Escherichia coli (E. coli) concentrations are commonly used to evaluate microbial water quality. Remote sensing imagery has been successfully used to retrieve several water quality parameters that can be determinants of E. coli habitats in waterbodies. This pilot study was conducted to test the possibility of using imagery from a small unmanned aerial vehicle (sUAV or drone) to improve the estimation of microbial water quality in small irrigation ponds. In situ measurements of pH, turbidity, specific conductance, and concentrations of dissolved oxygen, chlorophyll-a, phycocyanin, and fluorescent dissolved organic matter were taken at depths of 0–15 cm in 23 locations across a pond in Central Maryland, USA. The pond surface was concurrently imaged using a drone with three modified GoPro cameras, and a multispectral MicaSense RedEdge camera with five spectral bands. The GoPro imagery was decomposed into red, blue, and green components. Mean digital numbers for 1-m radius areas in the images were combined with the water quality data to provide input for a regression tree-based analysis. The accuracy of the regression-tree data description with “only imagery” inputs was the same or better than that of trees constructed with “only water-quality parameters” as inputs. From multiple cross-validation runs with “only imagery” inputs for the regression trees, the average (±SD) determination coefficient and root-mean-squared error of the decimal logarithm of E. coli concentrations were 0.793 ± 0.035 and 0.131 ± 0.011, respectively. The results of this study demonstrate the opportunities for using sUAV imagery for obtaining a more accurate delineation of the spatial variation of E. coli concentrations in irrigation ponds

How Critical Is the Assimilation Frequency of Water Content Measurements for Obtaining Soil Hydraulic Parameters with Data Assimilation?

Data assimilation (DA) is a promising alternative to infer soil hydraulic parameters from soil water dynamics data. Frequency of measurements and updates are important controls of DA efficiency; however, no strict guidance exists on determining the optimal frequency. In this study, DA was performed with the ensemble Kalman filter (EnKF) with a state augmentation approach to update both model states and parameters. We analyzed updates every 1, 2, 3, 5, 7, 9, 11, and 14 d. Two soil types (sandy loam and loam) and four climates (hot semiarid [Bwh], cold semiarid [Bsk], humid continental [Dfa], and humid subtropical [Cfa]) were considered. Results demonstrate that DA with high update frequencies does not provide better results than results obtained when using low frequencies. For sandy loam soil, assimilation of data every seven or more days yields better results for whatever climate considered. For loam soil, the same is true after 9 mo of assimilation. The chosen performance metric may affect the results, but the general trend of better results with low assimilation frequencies does not change.This study forms part of the projects GRE15-19 and GRE17-12 financed by the University of Alicante. A post-doctoral research fellowship (CAS 15/00244) funded by the Spanish Ministry of Science and Innovation was awarded to J. Valdes-Abellan for this project

Accounting for the three-dimensional distribution of Escherichia coli concentrations in pond water in simulations of the microbial quality of water withdrawn for irrigation

Evaluating the microbial quality of irrigation water is essential for the prevention of foodborne illnesses. Generic Escherichia coli (E. coli) is used as an indicator organism to estimate the microbial quality of irrigation water. Monitoring E. coli concentrations in irrigation water sources is commonly performed using water samples taken from a single depth. Vertical gradients of E. coli concentrations are typically not measured or are ignored; however, E. coli concentrations in water bodies can be expected to have horizontal and vertical gradients. The objective of this work was to research 3D distributions of E. coli concentrations in an irrigation pond in Maryland and to estimate the dynamics of E. coli concentrations at the water intake during the irrigation event using hydrodynamic modeling in silico. The study pond is about 22 m wide and 200 m long, with an average depth of 1.5 m. Three transects sampled at 50-cm depth intervals, along with intensive nearshore sampling, were used to develop the initial concentration distribution for the application of the environmental fluid dynamic code (EFDC) model. An eight-hour irrigation event was simulated using on-site data on the wind speed and direction. Substantial vertical and horizontal variations in E. coli concentrations translated into temporally varying concentrations at the intake. Additional simulations showed that the E. coli concentrations at the intake reflect the 3D distribution of E. coli in the limited pond section close to the intake. The 3D sampling revealed E. coli concentration hot spots at different depths across the pond. Measured and simulated 3D E. coli concentrations provide improved insights into the expected microbial water quality of irrigation water compared with 1D or 2D representations of the spatial variability of the indicator concentration

Macroporosity of 2-D cross sections of soil columns via X-ray CT: multifractal statistics and long range correlations for assessing 3-D soil pore structure

Soil pore structure controls important physical and biological processes in the soil-plant-microbial systems where microbial population dynamics, nutrient cycling, diffusion, mass flow and nutrient uptake by roots take place across many orders of magnitude in length scale. Over the last decades, fractal geometry has been proposed to deal with soil pore complexity and fractal techniques have been applied. Simple fractal models such as fractional Brownian motions, that have been proposed to capture the complex behavior of soil spatial variation, often cannot simulate the irregularity patterns displayed by spatial records of soil properties. It has been reported that these spatial records exhibit a behavior close to the so-called multifractal structures. Advanced visualization techniques such as X-ray computed tomography (CT) are required to assess and characterize the multifractal behavior of soil pore space. The objective of this work was to develop the multifractal description of soil porosity values (2-D sectional porosities) as a function of depth with data from binarized 2-D images that were obtained from X-ray CT scans of 12 water-saturated 20 cm-long soil columns with diameters of 7.5 cm. A reconstruction algorithm was applied to convert the X-ray CT data into a stack of 1480 grayscale digital images with a voxel resolution of 110 microns and a cross-sectional size of 690x690 pixels. The series corresponding to the percentage of void space of the sectional binarized images were recorded. These series of depth-dependent macroporosity values exhibited a well defined multifractal structure that was represented by the singularity and the Rényi spectra. We also parameterized the memory, or long range dependencies, in these series using the Hurst exponent and the multifractal model. The distinct behavior of each porosity series may be associated with pore connectivity and furthermore, correlated with hydraulic soil properties. The obtained multifractal spectra were consistent with multinomial multifractal measures where larger concentrations were less diverse but more common than the smaller ones. Therefore, models to assess pore space connectivity should incorporate a multifractal random structure compatible with this multinomial structure and the long range dependences that displayed these porosity series. Parameterization of the memory in depth dependencies of 2-D porosity series yields a useful representation of complex 3-D macropore geometry and topology

Multifractal features of 3-D macropore structures of discretized X-ray CT of undisturbed soil columns

The objective of this work was to develop the multifractal description of soil porosity values (2-D sectional porosities) as a function of depth with data from binarized 2-D images that were obtained from X-ray CT scans of 12 water-saturated 20 cm-long soil columns with diameters of 7.5 cm. A reconstruction algorithm was applied to convert the X-ray CT data into a stack of 1480 grayscale digital images with a voxel resolution of 110 microns and a cross-sectional size of 690x690 pixels. The series corresponding to the percentage of void space of the sectional binarized images were recorded. These series of depth-dependent macroporosity values exhibited a well defined multifractal structure that was represented by the singularity and the Rényi spectra. We also parameterized the memory, or long range dependencies, in these series using the Hurst exponent and the multifractal model

Sedimentation of fractal size distribution particles

Desde hace varios años, el modelo de fragmentación fractal ha atraido la atención de los investigadores, como un camino lógico para describir e interpretar distribuciones de partículas observadas. El análisis textural de un suelo ha mostrado ser muy importante, pues se utiliza para diagnosticar y predecir el funcionamiento y uso del mismo. Los métodos más populares para determinar la textura han sido los de sedimentación en agua utilizando el hidrómetro o la pipeta. Ambos tienen como objetivo encontrar la fracción de masa de partículas que se encuentran en suspensión a tiempos prefijados y relacionarla con los diámetros de las mismas. En este trabajo se ha desarrollado una nueva función potencial que relaciona la fracción de masa en suspensión con el tiempo de sedimentación. Utilizando la misma se puede determinar la dimensión fractal de fragmentación de una distribución de partículas en sedimentación. La nueva ecuación ha sido chequeada con datos propios obtenidos por el hidrómetro de Bouyoucos y otros publicados en la literatura, obtenidos mediante la pipeta de Robinson. El acuerdo logrado entre la teoría y los datos experimentales, mediante la técnica de regresión no lineal, ha sido excelente. Los valores de la dimensión fractal de fragmentación resultaron entre 2,404 y 2,512, para muestras de La Plata, Argentina, y entre 2,434 y 2,819 para los suelos de California, USA. El coeficiente de determinación, R2, fue en todos los casos mayor que 0,9.Since several years the fractal fragmentation model has attracted the attention of researchers, as a logic way to describe and interprete observed particle size distributions. Textural analysis has shown to be very important because of its usefulness in the dignosis and inferences about soil functioning and use. Most popular methods of textural analysis employ sedimentation of particles in water using the hydrometer or the pipet. Both have the objective of determining the particle fraction remaining in suspension at predetermined time and to relate them with particle diameters. In the present work a new power law relationship between the mass fraction in suspension and the time was developed. Using this relationship it was possible to determine the fragmentation fractal dimension of a set of particles in sedimentation. The new equation has been checked with data obtained in this research by the Bouyoucos's hydrometer, and others published in the literature, using the Robinson's pipet method. The agreement between the model and the experimental data, using non linear regression, was excellent. Resulting fractal fragmentation dimensions ranged from 2.404 to 2.512, for samples from La Plata, Argentina, and between 2.434 and 2.819 for soils from California, USA. Determination coefficients, R2, were always higher than 0.9.Facultad de Ciencias Agrarias y Forestale