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distribution of Escherichia coli concentrations in pond water in simulations of the microbial quality of water withdrawn for irrigation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

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

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

Development and analysis of the Soil Water Infiltration Global database

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements ( ∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76% of the experimental sites with agricultural land use as the dominant type ( ∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it

Multiscale entropy-based analyses of soil transect data

A deeper understanding of the spatial variability of soil properties and the relationships between them is needed to scale up measured soil properties and to model soil processes. The object of this study was to describe the spatial scaling properties of a set of soil physical properties measured on a common 1024-m transect across arable fields at Silsoe in Bedfordshire, east-central England. Properties studied were volumetric water content ({theta}), total porosity ({pi}), pH, and N2O flux. We applied entropy as a means of quantifying the scaling behavior of each transect. Finally, we examined the spatial intrascaling behavior of the correlations between {theta} and the other soil variables. Relative entropies and increments in relative entropy calculated for {theta}, {pi}, and pH showed maximum structure at the 128-m scale, while N2O flux presented a more complex scale dependency at large and small scales. The intrascale-dependent correlation between {theta} and {pi} was negative at small scales up to 8 m. The rest of the intrascale-dependent correlation functions between {theta} with N2O fluxes and pH were in agreement with previous studies. These techniques allow research on scale effects localized in scale and provide the information that is complementary to the information about scale dependencies found across a range of scale

Scaling in Soil and Other Complex Porous Media

Scaling is becoming an increasingly important topic in the earth and environmental sciences as researchers attempt to understand complex natural systems through the lens of an ever-increasing set of methods and scales. The guest editors introduce the papers in this issue’s special section and present an overview of some of the work being done. Scaling remains one of the most challenging topics in earth and environmental sciences, forming a basis for our understanding of process development across the multiple scales that make up the subsurface environment. Tremendous progress has been made in discovery, explanation, and applications of scaling. And yet much more needs to be done and is being done as part of the modern quest to quantify, analyze, and manage the complexity of natural systems. Understanding and succinct representation of scaling properties can unveil underlying relationships between system structure and response functions, improve parameterization of natural variability and heterogeneity, and help us address societal needs by effectively merging knowledge acquired at different scales

Predicting microbial water quality with models: Over-arching questions for managing risk in agricultural catchments

The application of models to predict concentrations of faecal indicator organisms (FIOs) in environmental systems plays an important role for guiding decision-making associated with the management of microbial water quality. In recent years there has been an increasing demand by policy-makers for models to help inform FIO dynamics in order to prioritise efforts for environmental and human-health protection. However, given the limited evidence-base on which FIO models are built relative to other agricultural pollutants (e.g. nutrients) it is imperative that the end-user expectations of FIO models are appropriately managed. In response, this commentary highlights four over-arching questions associated with: (i) model purpose; (ii) modelling approach; (iii) data availability; and (iv) model application, that must be considered as part of good practice prior to the deployment of any modelling approach to predict FIO behaviour in catchment systems. A series of short and longer-term research priorities are proposed in response to these questions in order to promote better model deployment in the field of catchment microbial dynamics

Development and analysis of the Soil Water Infiltration Global database

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements ( ∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76% of the experimental sites with agricultural land use as the dominant type ( ∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it