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\"Here is the definitive guide to unsaturated soil by the world's expert in the area of unsaturated soil mechanics. This volume features the latest information and replaces the leading text in the field, also written by this author team. The text offers state-of-the-art information to deal with the practical engineering problems resulting from unsaturated soil. Greater emphasis has been placed on the using the soil-water characteristic curve in solving practical engineering problems, as well as the quantification of thermal and moisture boundary conditions based on weather data\"--

In road safety applications involving W-beam guardrails embedded in the subgrade, soil plays a crucial role in the overall performance and crashworthiness of the barriers. Previous studies have often relied on simplistic (elastic-perfectly plastic) soil models, such as Mohr–Coulomb, in numerical simulations, failing to capture the intricate nonlinear soil characteristics. A primary challenge in incorporating advanced soil models is capturing the key factors influencing the soil–pile interaction during vehicle impacts, such as strain softening and strain-rate effect. This research aims to provide a better understanding of input parameters and implementation of the Federal Highway Administration (FHWA) soil model, as one of the commonly used advanced constitutive models for crash simulations. For this purpose, we conduct a single pile under pendulum impact and full-scale guardrail (supported by group piles) crash simulations. The W-beam guardrail simulations are validated by real crash test data from the Midwest Roadside Safety Facility. The parametric analyses investigate the effects of soil parameters related to strain softening and strain-rate effect on the overall crashworthiness of the barrier system, an area that has not been previously explored. Among these parameters, the residual friction angle and void formation energy are found to be the most critical parameters, showing a direct influence on vehicle redirection and crashworthiness outcome of the barrier systems.

Vegetation acclimation to changing climate, in particular elevated atmospheric concentrations of carbon dioxide (CO2), has been observed to include modifications to the biochemical and ecophysiological functioning of leaves and the structural components of the canopy. These responses have the potential to significantly modify plant carbon uptake and surface energy partitioning, and have been attributed with large-scale changes in surface hydrology over recent decades. While the aggregated effects of vegetation acclimation can be pronounced, they often result from subtle changes in canopy properties that require the resolution of physical, biochemical and ecophysiological processes through the canopy for accurate estimation. In this paper, the first of two, a multilayer canopy-soil-root system model developed to capture the emergent vegetation responses to environmental change is presented. The model incorporates both C3 and C4 photosynthetic pathways, and resolves the vertical radiation, thermal, and environmental regimes within the canopy. The tight coupling between leaf ecophysiological functioning and energy balance determines vegetation responses to climate states and perturbations, which are modulated by soil moisture states through the depth of the root system. The model is validated for three growing seasons each for soybean (C3) and maize (C4) using eddy-covariance fluxes of CO2, latent, and sensible heat collected at the Bondville (Illinois) Ameriflux tower site. The data set provides an opportunity to examine the role of important environmental drivers and model skill in capturing variability in canopy-atmosphere exchange. Vertical variation in radiative states and scalar fluxes over a mean diurnal cycle are examined to understand the role of canopy structure on the patterns of absorbed radiation and scalar flux magnitudes and the consequent differences in sunlit and shaded source/sink locations through the canopies. An analysis is made of the impact of soil moisture stress on carbon uptake and energy flux partitioning at the canopy-scale and resolved through the canopy, providing insight into the roles of canopy structure and metabolic pathway on the response of each crop to moisture deficits. Model calculations indicate increases in water use efficiency (WUE) with increasing moisture stress, with average maize WUE increases of 45% at the highest levels of plant stress examined here, relative to 20% increases for soybean.

Field observations and experimental data of effects of nitrogen (N) deposition on plant species diversity have been used to derive empirical critical N loads for various ecosystems. The great advantage of such an approach is the inclusion of field evidence, but there are also restrictions, such as the absence of explicit criteria regarding significant effects on the vegetation, and the impossibility to predict future impacts when N deposition changes. Model approaches can account for this. In this paper, we review the possibilities of static and dynamic multispecies models in combination with dynamic soil–vegetation models to (1) predict plant species composition as a function of atmospheric N deposition and (2) calculate critical N loads in relation to a prescribed protection level of the species composition. The similarities between the models are presented, but also several important differences, including the use of different indicators for N and acidity and the prediction of individual plant species vs. plant communities. A summary of the strengths and weaknesses of the various models, including their validation status, is given. Furthermore, examples are given of critical load calculations with the model chains and their comparison with empirical critical N loads. We show that linked biogeochemistry–biodiversity models for N have potential for applications to support European policy to reduce N input, but the definition of damage thresholds for terrestrial biodiversity represents a major challenge. There is also a clear need for further testing and validation of the models against long-term monitoring or long-term experimental data sets and against large-scale survey data. This requires a focused data collection in Europe, combing vegetation descriptions with variables affecting the species diversity, such as soil acidity, nutrient status and water availability. Finally, there is a need for adaptation and upscaling of the models beyond the regions for which dose–response relationships have been parameterized, to make them generally applicable.

Soil pollution is a worldwide problem caused by both natural and anthropogenic activities. This has resulted in health and physiological problems to both plants and animals. This study investigated heavy metals in soils within the immediate vicinity. Soils from Seven APIIC zones in Visakhapatnam were collected and analysed for physicochemical characteristics and heavy metals. The data obtained were subjected to the pollution index model and multivariate statistical analysis. The data obtained showed that the soils are rich in zinc, and heavy metals are above trace level with a minor positively skewed distribution. The analysis of pollution index, geoaccumulation index and ecological risk factors in soils in all the locations showed that they are mainly contaminated and polluted by Cd followed by Zn. The mean heavy metal concentrations around APIIC can be arranged in increasing order as Cr < Co < Pb < Cu < Cd < Zn. Element pairs such as Zn-Pb, Zn-Cu, Zn-Cd, Pb-Cu, Pb-Cd, Cu-Cr, Cd-Co and Cr-Co showed strong positive correlation coefficient \"r\" indicating their association in the study area. The observed concentrations of heavy metals revealed that soil contamination has been increasing and measures must be taken to ensure the adoption of more environment-friendly practices.

The primary objective of modern crop management is to enhance fruit sweetness and size while maximizing water and fertilizer efficiency. This requires optimizing irrigation to regulate soil‐plant‐atmosphere interactions and water fluxes. While dry soil conditions can reduce fruit yield, controlled root water stress can improve fruit carbohydrate and nutrient concentration. Understanding the relationship between soil water status and fruit quality necessitates models that predict water and carbohydrate distribution within the soil‐fruit system. Existing models, such as the SUGAR model, describe the biochemical processes influencing fruit composition, but simplify soil‐plant interactions. To enhance mechanistic linkages between soil processes and fruit development, this study integrates, for the first time, the HYDRUS hydrological model with the SUGAR model. By incorporating a root water uptake approach based on root hydraulic architecture, the model calculates stem water potential from soil hydraulic state and atmospheric conditions, providing a more comprehensive depiction of soil‐plant interactions. The model is calibrated and validated using experimental data on tomato crops under varied irrigation conditions. Results show that model predictions closely matched observations, with relative errors generally low across scenarios: 5%–6% for fruit water mass, 2%–18% for dry mass, 10%–11% for soluble sugars, and 7%–8% for starch. Finally, a breakdown of simulated fluxes indicates that phloem flux is the main driver of fruit growth and that the active uptake of carbohydrates is a key mechanism for sugar accumulation in fruits. The results offer insights into water and carbohydrate allocation under different watering regimes, advancing predictive capabilities for sustainable crop management strategies.

We propose here a general mathematical framework to represent soil organic matter dynamics. This framework is expressed in the language of dynamical systems and generalizes previous modeling approaches. It is based on a set of six basic principles about the decomposition of soil organic matter: (1) mass balance, (2) substrate dependence of decomposition, (3) heterogeneity of the speed of decay, (4) internal transformations of organic matter, (5) environmental variability effects, and (6) substrate interactions. We show how the majority of models previously proposed are special cases of this general model. This approach provides tools to classify models according to the main principles or concepts they include. It also helps to identify a priori the general behavior of different models or groups of models. Another important characteristic of the proposed mathematical representation is the possibility to develop particular models at any level of detail. This characteristic is described as a modeling hierarchy, in which a general model of a high degree of abstraction can accommodate specific realizations of model structure for specific modeling objectives. This framework also allows us to study general properties of groups of models such as their qualitative behavior, timescale of application, and their dynamic stability. For instance, we find conditions under which models are asymptotically stable, i.e., converge to a stable steady state in the long term, but may approach this state with or without oscillations. We also expand the concept of dynamic stability for models that include time dependencies and do not converge to a fixed steady state, but rather to a region of stability in the state-space. As an example of the application of the concept of dynamic stability, we show how this framework helps to explain the acclimation of soil respiration fluxes in soil-warming experiments.

Increased availability of soil phosphorus (P) has recently been recognised as an underlying driving factor for the positive relationship between plant diversity and ecosystem function. The effects of plant diversity on the bioavailable forms of P involved in biologically mediated rhizospheric processes and how the link between plant and soil microbial diversity facilitates soil P bioavailability, however, remain poorly understood. This study quantified four forms of bioavailable P (CaCl2‐P, citric‐P, enzyme‐P and HCl‐P) in mature subtropical forests using a novel biologically based approach, which emulates how rhizospheric processes influence the release and supply of available P. Soil microbial diversity was measured by Illumina high‐throughput sequencing. Our results suggest that tree species richness significantly affects soil microbial diversity (p < 0.05), increases litter decomposition, fine‐root biomass and length and soil organic carbon and thus increases the four forms of bioavailable P. A structural equation model that links plants, soil microbes and P forms indicated that soil bacterial and fungal diversity play dominant roles in mediating the effects of tree species richness on soil P bioavailability. An increase in the biodiversity of plants, soil bacteria and fungi could maintain soil P bioavailability and alleviate soil P limitations. Our results imply that biodiversity strengthens plant and soil feedback and increases P recycling. A plain language summary is available for this article. Plain Language Summary

Aims The interaction between plants and soil is an important internal driver of ecosystem evolution. Many studies have reported the unidirectional effects of soil nutrients on plant diversity and species turnover. However, there are still many gaps in our knowledge about how plant diversity and species turnover feedback to soil nutrients. Methods In the present study, three forest plots with different species composition and diversity were created through artificial disturbance in the same stand origin forest, and their long-term dynamics were observed. We identified underlying mechanisms of how plant diversity (Shannon-Wiener index) and species turnover (Bray-Curtis dissimilarity) affect soil total nitrogen (TN), total phosphorus (TP), available nitrogen (AN), and available phosphorus (AP). Results Plant diversity was associated with soil TN, TP, AN, and AP concentrations ( P  < 0.01). Species turnover was negatively correlated with the log-response ratio of TP ( LRR TP) ( P  < 0.001), but not correlated with LRR AP. Species turnover had significant positive correlations with LRR TN and LRR AN ( P  < 0.001). The structural equation model supports hypotheses that plant diversity and species turnover influenced soil N and P availability by affecting forest community growth (total tree basal area, TBA), litter quantity and quality, and soil physical and chemical properties (soil organic carbon, SOC; soil exchangeable base cations). Conclusions Collectively, our results highlighted the co-regulation of plant diversity and species turnover on soil N and P availability by “complementary” and “mass” effects during the long-term dynamics of forest ecosystems.

This work aims to compare the performance of new methods to estimate the Soil Moisture Content (SMC) of bare soils from their spectral signatures in the reflective domain (0.4-2.5 mu m) in comparison with widely used spectral indices like Normalized Soil Moisture Index (NSMI) and Water Index SOIL (WISOIL). Indeed, these reference spectral indices use wavelengths located in the water vapour absorption bands and their performance are thus very sensitive to the quality of the atmospheric compensation. To reduce these limitations, two new spectral indices are proposed which wavelengths are defined using the determination matrix tool by taking into account the atmospheric transmission: Normalized Index of Nswir domain for Smc estimatiOn from Linear correlation (NINSOL) and Normalized Index of Nswir domain for Smc estimatiOn from Non linear correlation (NINSON). These spectral indices are completed by two new methods based on the global shape of the soil spectral signatures. These methods are the Inverse Soil semi-Empirical Reflectance model (ISER), using the inversion of an existing empirical soil model simulating the soil spectral reflectance according to soil moisture content for a given soil class, and the convex envelope model, linking the area between the envelope and the spectral signature to the SMC. All these methods are compared using a reference database built with 32 soil samples and composed of 190 spectral signatures with five or six soil moisture contents. Half of the database is used for the calibration stage and the remaining to evaluate the performance of the SMC estimation methods. The results show that the four new methods lead to similar or better performance than the one obtained by the reference indices. The RMSE is ranging from 3.8% to 6.2% and the coefficient of determination R2 varies between 0.74 and 0.91 with the best performance obtained with the ISER model. In a second step, simulated spectral radiances at the sensor level are used to analyse the sensitivity of these methods to the sensor spectral resolution and the water vapour content knowledge. The spectral signatures of the database are then used to simulate the signal at the top of atmosphere with a radiative transfer model and to compute the integrated incident signal representing the spectral radiance measurements of the HYMAP airborne hyperspectral instrument. The sensor radiances are then corrected from the atmosphere by an atmospheric compensation tool to retrieve the surface reflectances. The SMC estimation methods are then applied on the retrieve spectral reflectances. The adaptation of the spectral index wavelengths to the HyMap sensor spectral bands and the application of the convex envelope and ISER models to boarder spectral bands lead to an error on the SMC estimation. The best performance is then obtained with the ISER model (RMSE of 2.9% and R2 of 0.96) while the four other methods lead to quite similar RMSE (from 6.4% to 7.8%) and R super(2) (between 0.79 and 0.83) values. In the atmosphere compensation processing, an error on the water vapour content is introduced. The most robust methods to water vapour content variations are WISOIL, NINSON, NINSOL and ISER model. The convex envelope model and NSMI index require an accurate estimation of the water vapour content in the atmosphere.