Spatial variability of soil properties and soil erodibility in the Alqueva reservoir watershed

The aim of this work is to investigate how the spatial variability of soil properties and soil erodibility (K factor) were affected by the changes in land use allowed by irrigation with water from a reservoir in a semiarid area. To this end, three areas representative of different land uses (agroforestry grassland, lucerne crop and olive orchard) were studied within a 900 ha farm. The interrelationships between variables were analyzed by multivariate techniques and extrapolated using geostatistics. The results confirmed differences between land uses for all properties analyzed, which was explained mainly by the existence of diverse management practices (tillage, fertilization and irrigation), vegetation cover and local soil characteristics. Soil organic matter, clay and nitrogen content decreased significantly, while the K factor increased with intensive cultivation. The HJ-Biplot methodology was used to represent the variation of soil erodibility properties grouped in land uses. Native grassland was the least correlated with the other land uses. The K factor demonstrated high correlation mainly with very fine sand and silt. The maps produced with geostatistics were crucial to understand the current spatial variability in the Alqueva region. Facing the intensification of land-use conversion, a sustainable management is needed to introduce protective measures to control soil erosion

Spatial Random Field Models Inspired from Statistical Physics with Applications in the Geosciences

The spatial structure of fluctuations in spatially inhomogeneous processes can be modeled in terms of Gibbs random fields. A local low energy estimator (LLEE) is proposed for the interpolation (prediction) of such processes at points where observations are not available. The LLEE approximates the spatial dependence of the data and the unknown values at the estimation points by low-lying excitations of a suitable energy functional. It is shown that the LLEE is a linear, unbiased, non-exact estimator. In addition, an expression for the uncertainty (standard deviation) of the estimate is derived.Comment: 10 pages, to appear in Physica A v4: Some typos corrected and grammatical change

Landslide risk management through spatial analysis and stochastic prediction for territorial resilience evaluation

Natural materials, such as soils, are influenced by many factors acting during their formative and evolutionary process: atmospheric agents, erosion and transport phenomena, sedimentation conditions that give soil properties a non-reducible randomness by using sophisticated survey techniques and technologies. This character is reflected not only in spatial variability of properties which differs from point to point, but also in multivariate correlation as a function of reciprocal distance. Cognitive enrichment, offered by the response of soils associated with their intrinsic spatial variability, implies an increase in the evaluative capacity of the contributing causes and potential effects in failure phenomena. Stability analysis of natural slopes is well suited to stochastic treatment of uncertainty which characterized landslide risk. In particular, this study has been applied through a back- analysis procedure to a slope located in Southern Italy that was subject to repeated phenomena of hydrogeological instability (extended for several kilometres in recent years). The back-analysis has been carried out by applying spatial analysis to the controlling factors as well as quantifying the hydrogeological hazard through unbiased estimators. A natural phenomenon, defined as stochastic process characterized by mutually interacting spatial variables, has led to identify the most critical areas, giving reliability to the scenarios and improving the forecasting content. Moreover, the phenomenological characterization allows the optimization of the risk levels to the wide territory involved, supporting decision-making process for intervention priorities as well as the effective allocation of the available resources in social, environmental and economic contexts

Identification of high-permeability subsurface structures with multiple point geostatistics and normal score ensemble Kalman filter

Alluvial aquifers are often characterized by the presence of braided high-permeable paleo-riverbeds, which constitute an interconnected preferential flow network whose localization is of fundamental importance to predict flow and transport dynamics. Classic geostatistical approaches based on two-point correlation (i.e., the variogram) cannot describe such particular shapes. In contrast, multiple point geostatistics can describe almost any kind of shape using the empirical probability distribution derived from a training image. However, even with a correct training image the exact positions of the channels are uncertain. State information like groundwater levels can constrain the channel positions using inverse modeling or data assimilation, but the method should be able to handle non-Gaussianity of the parameter distribution. Here the normal score ensemble Kalman filter (NS-EnKF) was chosen as the inverse conditioning algorithm to tackle this issue. Multiple point geostatistics and NS-EnKF have already been tested in synthetic examples, but in this study they are used for the first time in a real-world casestudy. The test site is an alluvial unconfined aquifer in northeastern Italy with an extension of approximately 3 km2. A satellite training image showing the braid shapes of the nearby river and electrical resistivity tomography (ERT) images were used as conditioning data to provide information on channel shape, size, and position. Measured groundwater levels were assimilated with the NS-EnKF to update the spatially distributed groundwater parameters (hydraulic conductivity and storage coefficients). Results from the study show that the inversion based on multiple point geostatistics does not outperform the one with a multiGaussian model and that the information from the ERT images did not improve site characterization. These results were further evaluated with a synthetic study that mimics the experimental site. The synthetic results showed that only for a much larger number of conditioning piezometric heads, multiple point geostatistics and ERT could improve aquifer characterization. This shows that state of the art stochastic methods need to be supported by abundant and high-quality subsurface data

Estimating exploitable stock biomass for the Maine green sea urchin (Strongylocentrotus droebachiensis) fishery using a spatial statistics approach

The objective of this study was to investigate the spatial patterns in green sea urchin (Strongylocentrotus droebachiensis) density off the coast of Maine, using data from a fishery-independent survey program, to estimate the exploitable biomass of this species. The dependence of sea urchin variables on the environment, the lack of stationarity, and the presence of discontinuities in the study area made intrinsic geostatistics inappropriate for the study; therefore, we used triangulated irregular networks (TINs) to characterize the large-scale patterns in sea urchin density. The resulting density surfaces were modified to include only areas of the appropriate substrate type and depth zone, and were used to calculate total biomass. Exploitable biomass was estimated by using two different sea urchin density threshold values, which made different assumptions about the fishing industry. We observed considerable spatial variability on both small and large scales, including large-scale patterns in sea urchin density related to depth and fishing pressure. We conclude that the TIN method provides a reasonable spatial approach for generating biomass estimates for a fishery unsuited to geostatistics, but we suggest further studies into uncertainty estimation and the selection of threshold density values

Modelling of radionuclide migration through the geosphere with radial basis function method and geostatistics

The modelling of radionuclide transport through the geosphere is necessary in the safety assessment of repositories for radioactive waste. A number of key geosphere processes need to be considered when predicting the movement of radionuclides through the geosphere. The most important input data are obtained from field measurements, which are not available for all regions of interest. For example, the hydraulic conductivity, as input parameter, varies from place to place. In such cases geostatistical science offers a variety of spatial estimation procedures. To assess the a long term safety of a radioactive waste disposal system, mathematical models are used to describe the complicated groundwater flow, chemistry and potential radionuclide migration through geological formations. The numerical solution of partial differential equations (PDEs) has usually been obtained by finite difference methods (FDM), finite element methods (FEM), or finite volume methods (FVM). Kansa introduced the concept of solving PDEs using radial basis functions (RBFs) for hyperbolic, parabolic and elliptic PDEs. The aim of this study was to present a relatively new approach to the modelling of radionuclide migration through the geosphere using radial basis functions methods and to determine the average and sample variance of radionuclide concentration with regard to spatial variability of hydraulic conductivity modelled by a geostatistical approach. We will also explore residual errors and their influence on optimal shape parameters

Combining the radial basis function Eulerian and Lagrangian schemes with geostatistics for modeling of radionuclide migration through the geosphere

To assess the long-term safety of a radioactive waste disposal system, mathematical models are used to describe groundwater flow, chemistry, and potential radionuclide migration through geological formations. A number of processes need to be considered, when predicting the movement of radionuclides through the geosphere. The most important input data are obtained from field measurements, which are not available for all regions of interest. For example, the hydraulic conductivity as an input parameter varies from place to place. In such cases, geostatistical science offers a variety of spatial estimation procedures. Methods for solving the solute transport equation can also be classified as Eulerian, Lagrangian and mixed. The numerical solution of partial differential equations (PDE) has usually been obtained by finite-difference methods (FDM), finite-element methods (FEM), or finite-volume methods (FVM). Kansa introduced the concept of solving partial differential equations using radial basis functions (RBF) for hyperbolic, parabolic, and elliptic PDEs. The aim of this study was to present a relatively new approach to the modeling of radionuclide migration through the geosphere using radial basis function methods in Eulerian and Lagrangian coordinates. In this study, we determine the average and standard deviation of radionuclide concentration with regard to variable hydraulic conductivity, which was modelled by a geostatistical approach. Radionuclide concentrations will also be calculated in heterogeneous and partly heterogeneous 2D porous media. (C) 2004 Elsevier Ltd. All rights reserved

Organic residues - a resource for arable soils

An increased recirculation of urban organic residues to arable soils has several environmental benefits, but there is a need for reliable test systems to ensure that soil quality is maintained. In this thesis, soil microbial, chemical and physical properties were included in an integrated evaluation to reflect the positive and negative effects of amending arable soils with organic residues. Efficient statistical tools and methods to describe intrinsic spatial variation are important when evaluating soil data. A new method was developed, combining near infrared reflectance (NIR) spectroscopy with principal component analysis (PCA). The first principal component (PC1) of NIR data described spatial soil variation better than the conventional soil variables total carbon, clay content and pH. A long-term field trial was established in which the soil was amended annually with organic residues (compost, biogas residues, sewage sludge) and fertilizers (pig manure, cow manure and mineral fertilizer, NPS). Annual measurements of soil and crop quality as well as yield revealed that biogas residues performed best among the organic residues. It improved several important microbiological properties, such as substrate-induced respiration (SIR) and potential ammonium oxidation (PAO), and it compared well with mineral fertilizer in terms of grain quality and harvest yield. Altogether, the results from the field trial showed no negative effects from any of the organic residues. Short- and moderately long-term effects of wood ash and compost on potential denitrification activity (PDA) and PAO were evaluated in a laboratory incubation experiment. Wood ash application had a profound toxic effect on PDA both in the short- and long-term. This toxic effect was mitigated when compost was added to the soil

Modelling a two-dimensional spatial distribution of mycotoxin concentration in bulk commodities to design effective and efficient sample selection strategies

Mycotoxins in agricultural commodities are a hazard to human and animal health. Their heterogeneous spatial distribution in bulk storage or transport makes it particularly difficult to design effective and efficient sampling plans. There has been considerable emphasis on identifying the different sources of uncertainty associated with mycotoxin concentration estimations, but much less on identifying the effect of the spatial location of the sampling points. This study used a two-dimensional statistical modelling approach to produce detailed information on appropriate sampling strategies for surveillance of mycotoxins in raw food commodities. The emphasis was on deoxynivalenol (DON) and ochratoxin A (OTA) in large lots of grain in storage or bulk transport. The aim was to simulate a range of plausible distributions of mycotoxins in grain from a set of parameters characterising the distributions. For this purpose, a model was developed to generate data sets which were repeatedly sampled to investigate the effect that sampling strategy and the number of incremental samples has on determining the statistical properties of mycotoxin concentration. Results showed that, for most sample sizes, a regular grid proved to be more consistent and accurate in the estimation of the mean concentration of DON, which suggests that regular sampling strategies should be preferred to random sampling, where possible. For both strategies, the accuracy of the estimation of the mean concentration increased significantly up to sample sizes of 40-60 (depending on the simulation). The effect of sample size was small when it exceeded 60 points, which suggests that the maximum sample size required is of this order. Similar conclusions about the sample size apply to OTA, although the difference between regular and random sampling was small and probably negligible for most sample sizes