A relook at NEH‐4 curve number data and antecedent moisture condition criteria

This paper investigates the variation of the popular curve number (CN) values given in the National Engineering Hand Book–Section 4 (NEH‐4) of the Soil Conservation Service (SCS) with antecedent moisture condition (AMC) and soil type. Using the volumetric concept, involving soil, water, and air, a significant condensation of the NEH‐4 tables is achieved. This leads to a procedure for determination of CN for gauged as well as ungauged watersheds. The rainfall‐runoff events derived from daily data of four Indian watersheds exhibited a power relation between the potential maximum retention or CN and the 5‐day antecedent rainfall amount. Including this power relation, the SCS‐CN method was modified. This modification also eliminates the problem of sudden jumps from one AMC level to the other. The runoff values predicted using the modified method and the existing method utilizing the NEH‐4 AMC criteria yielded similar results. Copyright © 2006 John Wiley & Sons, Ltd.     

Water balance modelling in lateritic terrain

An analytical approximate model for unsaturated flow in a spatially variable field, coupled with infiltration and evapotranspiration at the upper boundary and a fluctuating water-table at the lower boundary, has been developed. The unsaturated flow equations depend on parameterizations of θ(Φ) and K(Φ). They are based on the notion of a moving, discontinuous front. The field heterogeneity refers to saturated hydraulic conductivity only. Horizontal variability is considered, and the flow medium is approximated as a set of uncorrelated, vertically homogeneous columns. Expectations and variances obtained with this approach have been compared with observations of the field hydrological processes. Three important aspects of the hydrology in this lateritic terrain are rapid water-table response, Hortonian surface runoff generation and soil suction variability. The stochastic conceptualization used explains to a high degree these characteristics, although some limitations are demonstrated.     

Generating soil thickness maps by means of geomorphological-empirical approach and random forest algorithm in Wanzhou County,Three Gorges Reservoir

Soil thickness, intended as depth to bedrock, is a key input parameter for many environmental models. Nevertheless, it is often difficult to obtain a reliable spatially exhaustive soil thickness map in wide-area applications, and existing prediction models have been extensively applied only to test sites with shallow soil depths. This study addresses this limitation by showing the results of an application to a section of Wanzhou County (Three Gorges Reservoir Area, China), where soil thickness varies from 0 to ～40 m. Two different approaches were used to derive soil thickness maps: a modified version of the geomorphologically indexed soil thickness (GIST) model, purposely customized to better account for the peculiar setting of the test site, and a regression performed with a machine learning algorithm, i.e., the random forest, combined with the geomorphological parameters of GIST (GIST-RF). Additionally, the errors of the two models were quantified, and validation with geophysical data was carried out. The results showed that the GIST model could not fully contend with the high spatial variability of soil thickness in the study area: the mean absolute error was 10.68 m with the root-mean-square error (RMSE) of 12.61 m, and the frequency distribution residuals showed a tendency toward underestimation. In contrast, GIST-RF returned a better performance with the mean absolute error of 3.52 m and RMSE of 4.56 m. The derived soil thickness map could be considered a critical fundamental input parameter for further analyses.     

Analysing the relationship between drought and soil erosion using vegetation health index and RUSLE models in Godavari middle sub-basin,India

Drought is a natural phenomenon posing severe implications for soil, groundwater and agricultural yield. It has been recognized as one of the most pervasive global change drivers to affect the soil. Soil being a weakly renewable resource takes a long time to form, but it takes no time to degrade. However, the response of soil to drought conditions as soil loss is not manifested in the existing literature. Thus, this study makes a concerted effort to analyze the relationship between drought conditions and soil erosion in the middle sub-basin of the Godavari River in India. MODIS remote sensing data was utilized for driving drought indices during 2000–2019. Firstly, we constricted Temperature condition index (TCI) and Vegetation Condition Index (VCI) from Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) derived from MODIS data. TCI and VCI were then integrated to determine the Vegetation Health Index (VHI). Revised Universal Soil Loss Equation (RUSLE) was utilized for estimating soil loss. The relationship between drought condition and vegetation was ascertained using the Pearson correlation. Most of the northern and southern watersheds experienced severe drought condition in the sub-basin during 2000–2019. The mean frequency of the drought occurrence was 7.95 months. The average soil erosion in the sub-basin was estimated to be 9.88 t ha^?1 year^?1. A positive relationship was observed between drought indices and soil erosion values (r value being 0.35). However, wide variations were observed in the distribution of spatial correlation. Among various factors, the slope length and steepness were found to be the main drivers of soil erosion in the sub-basin. Thus, the study calls for policy measures to lessen the impact of drought and soil erosion.     

应力分散型预应力抗拔桩系列技术研究与应用

根据目前国内外预应力抗拔抗浮桩、部分粘结预应力抗拔抗浮桩的优缺点以及使用的局限性——长螺旋钻机对于桩径＞800 mm或桩长＞30 m的桩力不从心,研究了应力分散型预应力抗拔桩及钢筋钢绞线笼系列技术,用于一个直径1 m、桩长60 m的抗拔桩工程,采用旋挖成孔,可实现在工程桩上进行大吨位试桩,受力合理,节省资金和工期,实践证明该系列技术有很好的应用前景。     

The Energy Relationship between Ions and Soil Particles based on Wien Effect and Its Application in Soil Science

The interaction between ions and soil particles plays an important role in the mobilization and bioavailability of ions in soils, which is one of the main research areas of soil chemistry. The new method based on suspension Wien effect has been developed recently to determine the binding energy and adsorption energy between ions and soil particles. Compared with other methods on the basis of adsorption isotherm or ions activity, Wien effect method has more advantages including convenience and direct measurement. The term suspension Wien effect refers to the increase of electrical conductivity of suspension with increasing applied electrical field. In this review paper, we introduced the fundamentals of suspension Wien effect, the apparatus about the Wien effect measurement, and along with demonstrating their application to quantifying the particles-ions interactions for several systems of soils. Our studies indicated that divalent cations have larger binding energies and adsorption energies on soil particles than monovalent because of the electrostatic interaction. However, few studies about the interaction between Cr³⁺ and La³⁺ on soil particles showed that the binding energy and adsorption energy of trivalent cations are lower than those of divalent cations because of hydrolysis. Soil properties such as soil organic matter, soil pH, and iron oxides significantly affected the binding energy and adsorption energy of ions. Our results deepened our understanding about the non specific adsorption of ions in soil chemistry, and enlarged the research area of soil chemistry in fundamental and methodology.     

Prediction of soil properties using imaging spectroscopy: Considering fractional vegetation cover to improve accuracy

Spectroscopic techniques have become attractive to assess soil properties because they are fast, require little labor and may reduce the amount of laboratory waste produced when compared to conventional methods. Imaging spectroscopy (IS) can have further advantages compared to laboratory or field proximal spectroscopic approaches such as providing spatially continuous information with a high density. However, the accuracy of IS derived predictions decreases when the spectral mixture of soil with other targets occurs. This paper evaluates the use of spectral data obtained by an airborne hyperspectral sensor (ProSpecTIR-VS – Aisa dual sensor) for prediction of physical and chemical properties of Brazilian highly weathered soils (i.e., Oxisols). A methodology to assess the soil spectral mixture is adapted and a progressive spectral dataset selection procedure, based on bare soil fractional cover, is proposed and tested. Satisfactory performances are obtained specially for the quantification of clay, sand and CEC using airborne sensor data (R² of 0.77, 0.79 and 0.54; RPD of 2.14, 2.22 and 1.50, respectively), after spectral data selection is performed; although results obtained for laboratory data are more accurate (R² of 0.92, 0.85 and 0.75; RPD of 3.52, 2.62 and 2.04, for clay, sand and CEC, respectively). Most importantly, predictions based on airborne-derived spectra for which the bare soil fractional cover is not taken into account show considerable lower accuracy, for example for clay, sand and CEC (RPD of 1.52, 1.64 and 1.16, respectively). Therefore, hyperspectral remotely sensed data can be used to predict topsoil properties of highly weathered soils, although spectral mixture of bare soil with vegetation must be considered in order to achieve an improved prediction accuracy.     

Arsenic accumulation in the roots of Helianthus annuus and Zea mays by irrigation with arsenic-rich groundwater: Insights from synchrotron X-ray fluorescence imaging

The aim of this study was to investigate the accumulation of arsenic (As) in and on roots of Zea mays (maize) and Helianthus annuus (sunflower) by means of synchrotron-based micro-focused X-ray fluorescence imaging (μ-XRF). Plant and soil samples were collected from two field sites in the Hetao Plain (Inner Mongolia, China) which have been regularly irrigated with As-rich groundwater. Detailed μ-XRF element distribution maps were generated at the Fluo-beamline of the Anka synchrotron facility (Karlsruhe Institute of Technology) to assess the spatial distribution of As in thin sections of plant roots and soil particles. The results showed that average As concentrations in the roots (14.5–27.4 mg kg⁻¹) covered a similar range as in the surrounding soil, but local maximum root As concentrations reached up to 424 mg kg⁻¹ (H. annuus) and 1280 mg kg⁻¹ (Z. mays), respectively. Importantly, the results revealed that As had mainly accumulated at the outer rhizodermis along with iron (Fe). We therefore conclude that thin crusts of Fe-(hydr)oxides cover the roots and act as an effective barrier to As, similar to the formation of Fe plaque in rice roots. In contrast to permanently flooded rice paddy fields, regular flood irrigation results in variable redox conditions within the silty and loamy soils at our study site and fosters the formation of Fe-(hydr)oxide plaque on the root surfaces.     

Methodological comparison for quantitative analysis of fossil and recently derived carbon in mine soils with high content of aliphatic kerogen

In mine soil, quantification of soil organic carbon (OC) derived recently from biomass decomposition is complicated by the presence of fossil (geogenic) C derived from coal, oil shale, or similar material in the overburden. The only reliable method for such measurement is ¹⁴C analysis (i.e. radiocarbon dating) using instrumentation such as accelerator mass spectrometry, which is too expensive for routine laboratory analysis. We tested two previously used and two new methods for recent C quantification and compared them with ¹⁴C AMS radiocarbon dating as a reference using a set of soil samples (n = 14) from Sokolov, Czech Republic: (i) ¹³C isotope ratio composition, (ii) cross polarization magic angle spinning ¹³C nuclear magnetic resonance (CPMAS ¹³C NMR) spectroscopy, (iii) near infrared spectroscopy (NIRS) coupled with partial least squares regression and (iv) Rock–Eval pyrolysis. Conventional methods for OC determination (dry combustion, wet dichromate oxidation, loss-on-ignition) were also compared to quantify any bias connected with their use. All the methods provided acceptable recent carbon estimates in the presence of mostly aliphatic fossil C from kerogen. However, the most accurate predictions were obtained with two approaches using Rock–Eval pyrolysis parameters as predictors, namely (i) S₂ curve components and (ii) oxygen index (OI). The S₂ curve approach is based on the lower thermal stability of recent vs. fossil organic matter. The OI approach corresponded well with ¹³C NMR spectra, which showed that samples rich in recent C were richer in carboxyl C and O-alkyl C. These two methods showed the greatest potential as routine methods for recent C quantification.