Observations on soil-atmosphere interactions after long-term monitoring at two sample sites subjected to shallow landslides

Soil-atmosphere interaction has implications in different scientific research contexts and is increasingly investigated through field measurements. This paper reports a detailed description of interaction between shallow soil and atmosphere at two test sites in Oltrepò Pavese area (Northern Italy). The two test sites are in the same climatic area but are characterised by different geological features. In fact, the first objective is to compare the behaviour of two different soils, namely a clayey-sandy silt (CL) and a silty clay (CH), under similar meteorological events. Soil-atmosphere interaction is studied on the basis of long-term (about 87 and 42 months for the two test sites, respectively) monitoring data of both volumetric water content and soil water potential, recorded at different depths along two vertical soil profiles in the first two metres from ground level. Field measurements, together with meteorological data such as precipitation and air temperature, allow for clear identification of the seasonal fluctuations of unsaturated soil hydraulic properties. To infer detailed information, the recorded data were processed and relationships between soil water potential and water content were investigated. Different time spans, from several months to a few days, even including single rainy events, are considered to show the hydraulic soil behaviour. The hysteretic cycles of water content with respect to soil water potential and non-equilibrium flow are highlighted. In particular, the measured soil water potential is in the range of 0–800 kPa and of 0–1500 kPa for the CL and CH soil, respectively. At both sites, the observed hysteretic cycles are more frequent in the hot season (summer) than in the cold season (winter) and tend to reduce with depth. The experimental results are compared with the soil water characteristic curves (SWCCs) to assess whether and to what extent the SWCCs are reliable in modelling the hydraulic behaviour of partially saturated soils, under atmospheric forcing, at least in the considered climatic contexts

A data-driven method for the temporal estimation of soil water potential and its application for shallow landslides prediction

Soil water potential is a key factor to study water dynamics in soil and for estimating the occurrence of natural hazards, as landslides. This parameter can be measured in field or estimated through physically-based models, limited by the availability of effective input soil properties and pre-liminary calibrations. Data-driven models, based on machine learning techniques, could overcome these gaps. The aim of this paper is then to develop an innovative machine learning methodology to assess soil water potential trends and to implement them in models to predict shallow landslides. Monitoring data since 2012 from test-sites slopes in Oltrepò Pavese (northern Italy) were used to build the models. Within the tested techniques, Random Forest models allowed an outstanding recon-struction of measured soil water potential temporal trends. Each model is sensitive to meteorological and hydrological characteristics according to soil depths and features. Reliability of the proposed models was confirmed by correct estimation of days when shallow landslides were triggered in the study areas in December 2020, after implementing the modeled trends on a slope stability model, and by the correct choice of physically-based rainfall thresholds. These results confirm the potential application of the developed methodology to estimate hydrological scenarios that could be used for decision-making purposes

Robust Statistical Processing of Long-Time Data Series to Estimate Soil Water Content

The research presented in this paper aims at providing a statistical model that is capable of estimating soil water content based on weather data. The model was tested using a long-time series of field experimental data from continuous monitoring at a test site in Oltrepò Pavese (northern Italy). An innovative statistical function was developed in order to predict the evolution of soil–water content from precipitation and air temperature. The data were analysed in a framework of robust statistics by using a combination of robust parametric and non-parametric models. Specifically, a statistical model, which includes the typical seasonal trend of field data, has been set up. The proposed model showed that relevant features present in the field of experimental data can be obtained and correctly described for predictive purposes

Site-specific to local-scale shallow landslides triggering zones assessment using TRIGRS

Rainfall-induced shallow landslides are common phenomena in many parts of the world, affecting cultivation and infrastructure and sometimes causing human losses. Assessing the triggering zones of shallow landslides is fundamental for land planning at different scales. This work defines a reliable methodology to extend a slope stability analysis from the site-specific to local scale by using a well-established physically based model (TRIGRS-unsaturated). The model is initially applied to a sample slope and then to the surrounding 13.4 km2 area in Oltrepò Pavese (northern Italy). To obtain more reliable input data for the model, long-term hydro-meteorological monitoring has been carried out at the sample slope, which has been assumed to be representative of the study area. Field measurements identified the triggering mechanism of shallow failures and were used to verify the reliability of the model to obtain pore water pressure trends consistent with those measured during the monitoring activity. In this way, more reliable trends have been modelled for past landslide events, such as the April 2009 event that was assumed as a benchmark. The assessment of shallow landslide triggering zones obtained using TRIGRS-unsaturated for the benchmark event appears good for both the monitored slope and the whole study area, with better results when a pedological instead of geological zoning is considered at the regional scale. The sensitivity analyses of the influence of the soil input data show that the mean values of the soil properties give the best results in terms of the ratio between the true positive and false positive rates. The scheme followed in this work allows us to obtain better results in the assessment of shallow landslide triggering areas in terms of the reduction in the overestimation of unstable zones with respect to other distributed models applied in the past

Stochastic particle packing with specified granulometry and porosity

This work presents a technique for particle size generation and placement in arbitrary closed domains. Its main application is the simulation of granular media described by disks. Particle size generation is based on the statistical analysis of granulometric curves which are used as empirical cumulative distribution functions to sample from mixtures of uniform distributions. The desired porosity is attained by selecting a certain number of particles, and their placement is performed by a stochastic point process. We present an application analyzing different types of sand and clay, where we model the grain size with the gamma, lognormal, Weibull and hyperbolic distributions. The parameters from the resulting best fit are used to generate samples from the theoretical distribution, which are used for filling a finite-size area with non-overlapping disks deployed by a Simple Sequential Inhibition stochastic point process. Such filled areas are relevant as plausible inputs for assessing Discrete Element Method and similar techniques

Chitosan in Plant Protection

Chitin and chitosan are naturally-occurring compounds that have potential in agriculture with regard to controlling plant diseases. These molecules were shown to display toxicity and inhibit fungal growth and development. They were reported to be active against viruses, bacteria and other pests. Fragments from chitin and chitosan are known to have eliciting activities leading to a variety of defense responses in host plants in response to microbial infections, including the accumulation of phytoalexins, pathogen-related (PR) proteins and proteinase inhibitors, lignin synthesis, and callose formation. Based on these and other proprieties that help strengthen host plant defenses, interest has been growing in using them in agricultural systems to reduce the negative impact of diseases on yield and quality of crops. This review recapitulates the properties and uses of chitin, chitosan, and their derivatives, and will focus on their applications and mechanisms of action during plant-pathogen interactions

Development and First Validation of a Disease Activity Score for Gout

Objective: To develop a new composite disease activity score for gout and provide its first validation. Methods: Disease activity has been defined as the ongoing presence of urate deposits that lead to acute arthritis and joint damage. Every measure for each Outcome Measures in Rheumatology core domain was considered. A 3-step approach (factor analysis, linear discriminant analysis, and linear regression) was applied to derive the Gout Activity Score (GAS). Decision to change treatment or 6-month flare count were used as the surrogate criteria of high disease activity. Baseline and 12-month followup data of 446 patients included in the Kick-Off of the Italian Network for Gout cohort were used. Construct- and criterion-related validity were tested. External validation on an independent sample is reported. Results: Factor analysis identified 5 factors: patient-reported outcomes, joint examination, flares, tophi, and serum uric acid (sUA). Discriminant function analysis resulted in a correct classification of 79%. Linear regression analysis identified a first candidate GAS including 12-month flare count, sUA, visual analog scale (VAS) of pain, VAS global activity assessment, swollen and tender joint counts, and a cumulative measure of tophi. Alternative scores were also developed. The developed GAS demonstrated a good correlation with functional disability (criterion validity) and discrimination between patient- and physician-reported measures of active disease (construct validity). The results were reproduced in the external sample. Conclusion: This study developed and validated a composite measure of disease activity in gout. Further testing is required to confirm its generalizability, responsiveness, and usefulness in assisting with clinical decisions

An Open Transmission Line Method for the Dielectric Investigation of Bound Soil Water

A dielectric measuring technique, denoted open transmission line (OTL) method, has been developed. The OTL method is suitable for performing accurate permittivity measurements as a function of the sample moisture content, by allowing a free exchange of water with the environment. The characteristics of the OTL cell are presented, together with an electromagnetic model and a measurement approach to obtain the dielectric properties of porous materials as a function of the water content. A sensitivity analysis is conducted, comparing the performances of the OTL technique to those of other well known methods. The measurement results on a few dielectric materials are presented and discussed

Measurement errors of water retention curve using pressure plates: consequences on parameterization

Pressure plates are very common experimental devices to measure the soil water retention curve. However, recent studies have demonstrated the lack of reliability of pressure plates when measuring the soil water retention curve in the dry range, due to low plate and soil conductance, lack of soil-plate contact and soil dispersion. In a recent investigation on a silt loam soil, water retention data were determined using pressure plates only and a combination of pressure plates and a dew point meter, showing errors in the measurement of the soil water retention curve at potentials less than 20 m-H2O. This error led to unreliable evaluation of soil hydraulic properties and their parameterization. We extended the investigation on the effects of water retention measurement error to eighteen soils having different textural properties, by comparing measurements of soil water retention curves obtained with a combination of Stackman\u2019s beds and pressure plates and soil water retention curves obtained with the chilledmirror dew point technique. The aim of this research was to investigate the differences between the soil water retention curves as function of different soil textural properties and their effect of soil hydraulic properties and water drainage. Comparison between retention curves and fitting van Genuchten parameters, showed an error in measurements made by the combination Stackman\u2019s beds and Richards\u2019 pressure plates, for potential values below 1 m-H2O to 57 m-H2O. By characterizing textural properties by using geometric mean diameters, a clear relationship between texture and errors in water retention was established. In particular coarser soil displayed errors at lower potential (in absolute value) with respect to finer textures. The occurrence of these errors in the water retention measurement performed with pressure plates showed that it is advisable to use a combination of methodologies to correctly measure an entire soil retention curve and current parameters database should be used with caution