Soil Variability and Biogeochemical Fluxes: Toward a Better Understanding of Soil Processes at the Land Surface

Core Ideas Pattern recognition techniques can help explain biogeochemical flux variability. Dynamic factors and their impact on biogeochemical flux variability need better identification. Controls on biogeochemical fluxes are time and space scale dependent. Soil biogeochemical fluxes in the vadose zone are characterized by a large degree of variability in space and time. This fact leads to the need for the development and application of appropriate methodologies to better understand the high nonlinearity and complex feedback mechanisms responsible for such fluxes. In this sense, there still exists a lack of knowledge in topics such as the scale dependence of the spatial and temporal variability of the controls on soil moisture and biodegradation rates and the dynamic behavior of flow and transport model parameter, and its association with the presence of roots. Knowledge of the variability of biogeochemical fluxes is needed for assorted applications ranging from natural hazards and environmental pollution risk assessment to agricultural production and water resources management. The contributions to this special section epitomize the ongoing effort toward the characterization, quantification, modeling, and understanding of biogeochemical fluxes in the vadose zone at several spatial and temporal scales. The main progress has been the identification of different controls on soil moisture and biodegradation rates depending on the scale of the study as well as the important dependence of the spatial and temporal variability of biogeochemical fluxes on dynamic properties such as vegetation and weather variables

Pedotransfer in soil physics: trends and outlook — A review —

Parameters governing the retention and movement of water and chemicals in soils are notorious for the difficulties and high labor costs involved in measuring them. Often, there is a need to resort to estimating these parameters from other, more readily available data, using pedotransfer relationships. This work is a mini-review that focuses on trends in pedotransfer development across the World, and considers trends regarding data that are in demand, data we have, and methods to build pedotransfer relationships. Recent hot topics are addressed, including estimating the spatial variability of water contents and soil hydraulic properties, which is needed in sensitivity analysis, evaluation of the model performance, multimodel simulations, data assimilation from soil sensor networks and upscaling using Monte Carlo simulations. Ensembles of pedotransfer functions and temporal stability derived from “big data” as a source of soil parameter variability are also described. Estimating parameter correlation is advocated as the pathway to the improvement of synthetic datasets. Upscaling of pedotransfer relationships is demonstrated for saturated hydraulic conductivity. Pedotransfer at coarse scales requires a different type of input variables as compared with fine scales. Accuracy, reliability, and utility have to be estimated independently. Persistent knowledge gaps in pedotransfer development are outlined, which are related to regional soil degradation, seasonal changes in pedotransfer inputs and outputs, spatial correlations in soil hydraulic properties, and overland flow parameter estimation. Pedotransfer research is an integral part of addressing grand challenges of the twenty-first century, including carbon stock assessments and forecasts, climate change and related hydrological weather extreme event predictions, and deciphering and managing ecosystem services. Overall, pedotransfer functions currently serve as an essential instrument in the science-based toolbox for diagnostics, monitoring, predictions, and management of the changing Earth and soil as a life-supporting Earth system

Estimation of hydraulic conductivity and its uncertainty from grain-size data using GLUE and artificial neural networks

peer reviewedaudience: researcher, professionalVarious approaches exist to relate saturated hydraulic conductivity (Ks) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods, i.e.multiple linear regression and artificial neural networks, that use the entire grain-size distribution data as input for Ks prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling. Artificial neural networks (ANNs) are combined with a generalized likelihood uncertainty estimation (GLUE) approach to predict Ks from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from literature demonstrates the importance of site specific calibration. The dataset used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size -Ks pairs. Finally, an application with the optimized models is presented for a borehole lacking Ks data

A neighborhood statistics model for predicting stream pathogen indicator levels

Because elevated levels of water-borne Escherichia coli in streams are a leading cause of water quality impairments in the U.S., water-quality managers need tools for predicting aqueous E. coli levels. Presently, E. coli levels may be predicted using complex mechanistic models that have a high degree of unchecked uncertainty or simpler statistical models. To assess spatio-temporal patterns of instream E. coli levels, herein we measured E. coli, a pathogen indicator, at 16 sites (at four different times) within the Squaw Creek watershed, Iowa, and subsequently, the Markov Random Field model was exploited to develop a neighborhood statistics model for predicting instream E. coli levels. Two observed covariates, local water temperature (degrees Celsius) and mean cross-sectional depth (meters), were used as inputs to the model. Predictions of E. coli levels in the water column were compared with independent observational data collected from 16 in-stream locations. The results revealed that spatio-temporal averages of predicted and observed E. coli levels were extremely close. Approximately 66 % of individual predicted E. coli concentrations were within a factor of 2 of the observed values. In only one event, the difference between prediction and observation was beyond one order of magnitude. The mean of all predicted values at 16 locations was approximately 1 % higher than the mean of the observed values. The approach presented here will be useful while assessing instream contaminations such as pathogen/pathogen indicator levels at the watershed scale

QUANTITATIVE DESCRIPTION OF PLANT DENSITY EFFECTS ON BRANCHING AND LIGHT INTERCEPTION IN SOYBEAN

The objective of this study was to quantify the effects of plant population density (PPD) on branching, light interception, and vegetative stages of soybean (Glycine max L.). A field study was conducted in Maryland, USA on a Beltsville silt loam soil (Fine-loamy mixed mesic Typic Fragiudult). The planting dates were 20 July 1992 and 14 June 1993. There were 10 plant densities that varied from 10 to 59 plants m^<-2>. Significant differences in plant heights among the different plant population densities were found in 1993 but not in 1992. The vegetative stage progression rates and number of branches were significantly related to PPD in both years. The internodal lengths increased with increase in PPD. Fewer branches were produced in 1992 than in 1993 at the lower PPD\u27s and the number of branches were similar at the highest PPD\u27s. The low PPD plants in 1992 did not have time to grow large enough canopy to capture all available light. We fit a logistic equation to the change in branch number with time. The maximum number of branches per plant as a function of PPD was described by a gaussian type equation. The fitted parameters and equations described the addition of branches

Comparison of two techniques to develop pedotransfer functions for water retention

Two pedotransfer function (PTF) approaches can be used for obtaining the analytical expression of the whole retention curve: (i) soil basic data is used to estimate soil water retention at specific water potentials; and then an analytical expression of the retention curve is fitted to the estimated soil moisture values; and (ii) soil basic data is used for estimating the parameters of an analytical expression of water retention curves. The objective of this study was to compare the performance of both techniques using data representing the main Brazilian soils. First, we derived PTFs for the parameters of van Genuchten equation and for water contents at -6, -10, -33, -100, and -1500 kPa for the same development data set. Second, we compared the performance of both techniques for the same validation data set. The approach, based on the estimation of water contents at specific water potentials, provided better results: for the validation data set, this technique showed an average root mean squared error of 0.036 m 3 m -3 , compared with an averaged error of 0.098 m 3 m -3 of the technique based on the direct estimation of van Genuchten parameters. A possible explanation for this result might be related to the fact that soil moisture is controlled by different independent variables at different ranges of soil water potential, and those differences are not directly related to the van Genuchten parameters.Pages: 1085-109