Scaling analysis of soil water storage with missing measurements using the second‐generation continuous wavelet transform

Information on the spatial variability of soil water storage (SWS) at different scales is important for understanding various hydrological, ecological and biogeochemical processes in the landscape. However, various obstructions such as roads or water bodies may result in missing measurements and create an irregular spatial series. The wavelet transform can quantify spatial variability at different scales and locations but is restricted to regular measurements. The objective of this study was to analyse the spatial variability of SWS with missing measurements using the second‐generation continuous wavelet transform (SGCWT). Soil water content (converted to SWS by multiplying with depth) was measured with a neutron probe and time‐domain reflectrometry along a transect of 128 points. Because there were missing measurements, I used SGCWT to partition the total variation into different scales and locations. Whilst there were some small‐scale variations (< 20 m) along the transect, the medium scale variations (20–70 m with an average of about 30–45 m) were mainly concentrated within the depressions along the transect. The strongest variations were observed at around 90–110 m scale, representing the variations resulting from alternating knolls and depressions. Similar spatial patterns at different scales were observed during different seasons, indicating temporal stability in the spatial pattern of SWS. Among the controlling factors, the wavelet spectra of relative elevation (RE) and organic carbon (OC) were very similar to that of SWS. The wavelet covariance was also large between SWS and RE and OC at all seasons. As the OC reflects the long‐term history of water availability and might be controlled by topographic setting or elevation, it can be concluded that elevation is an important controlling factor of SWS irrespective of seasons in this type of landscape. The SGCWT provides a new way of analysing the spatial variability of regularly measured soil properties or those with missing measurements.     

Spatial scaling of soil salinity indices along a temporal coastal reclamation area transect in China using wavelet analysis

High spatial variability of soil salinity in coastal reclamation regions makes it difficult to obtain accurate scale-dependent information. The objectives of this study were to describe the spatial patterns of saline-sodic soil properties (using soil pH, electrical conductivity (EC_1:5) and sodium ion content (SIC) as indicators) and to gain knowledge of the scaling relationships between those variables. The soil pH, EC_1:5 and SIC data were measured at intervals of 285 m along a 13,965-m temporal transect in a coastal region of China. The spatial variability of soil pH was weak but it was strong for soil EC_1:5 and SIC at the measurement scale. There was a significant positive correlation between soil EC_1:5 and SIC, while correlations between soil pH and either EC_1:5 or SIC were weak and negative. For each saline-sodic soil parameter, the variability changed with the decomposition scales. The high-variance area at the larger scales (≥570 m) occupied less than 10% of the total area in the local wavelet spectrum, which meant that the spatial variations of the salinity indicators were insignificant at these scales. For local wavelet coherency, at a scale of 1500–2800 m and a sampling distance of 0–4500 m, the covariance was statistically significant between any two of the saline-sodic soil parameters.     

Variability in soil properties at different spatial scales (1 m-1 km) in a deciduous forest ecosystem

The purpose of this research was to test the hypothesis that variability in 11 soil properties, related to soil texture and soil C and N, would increase from small (1 m) to large (1 km) spatial scales in a temperate, mixed-hardwood forest ecosystem in east Tennessee, USA. The results were somewhat surprising and indicated that a fundamental assumption in geospatial analysis, namely that variability increases with increasing spatial scale, did not apply for at least five of the 11 soil properties measured over a 0.5-km² area. Composite mineral soil samples (15 cm deep) were collected at 1, 5, 10, 50, 250, and 500 m distances from a center point along transects in a north, south, east, and westerly direction. A null hypothesis of equal variance at different spatial scales was rejected (P?0.05) for mineral soil C concentration, silt content, and the C-to-N ratios in particulate organic matter (POM), mineral-associated organic matter (MOM), and whole surface soil. Results from different tests of spatial variation, based on coefficients of variation or a Mantel test, led to similar conclusions about measurement variability and geographic distance for eight of the 11 variables examined. Measurements of mineral soil C and N concentrations, C concentrations in MOM, extractable soil NH₄-N, and clay contents were just as variable at smaller scales (1-10 m) as they were at larger scales (50-500 m). On the other hand, measurement variation in mineral soil C-to-N ratios, MOM C-to-N ratios, and the fraction of soil C in POM clearly increased from smaller to larger spatial scales. With the exception of extractable soil NH₄-N, measured soil properties in the forest ecosystem could be estimated (with 95% confidence) to within 15% of their true mean with a relatively modest number of sampling points (n?25). For some variables, scaling up variation from smaller to larger spatial domains within the ecosystem could be relatively easy because small-scale variation may be indicative of variation at larger scales.     

How representatively can we sample soil mineral nitrogen?

Spatial distribution of soil mineral‐N content (N_min) is a scale‐variant process. Precision farming assumes knowledge about the spatial distribution of N_min. Moreover, sampling in management zones is based on the assumption of spatial dependence between sampling points. In the present study, variability structure of N_min and the sources of variability were investigated. Within an agricultural landscape, N_min was investigated across a field in a nested design over four consecutive years. Temporally unstable structure of individual nests require a sampling with several nests in the field. In the investigated field, 35%–49% of the total variability derived from small‐scale variability observed at spatial distances of <5 m and from sampling and analytical errors. Differences between 10 and 26 kg N ha^–1 for the soil depth increment 0–60 cm can be expected. Uncertainty due to analytical errors were in the order of 5–10 kg N ha^–1 for a 0–60 cm layer.     

Variation of soil respiration at three spatial scales: Components within measurements, intra-site variation and patterns on the landscape

Soil respiration is an important component of terrestrial carbon cycling and can be influenced by many factors that vary spatially. This research aims to determine the extent and causes of spatial variation of soil respiration, and to quantify the importance of scale on measuring and modeling soil respiration within and among common forests of Northern Wisconsin. The potential sources of variation were examined at three scales: [1] variation among the litter, root, and bulk soil respiration components within individual 0.1 m measurement collars, [2] variation between individual soil respiration measurements within a site (<1 m to 10 m), and [3] variation on the landscape caused by topographic influence (100 m to 1000 m). Soil respiration was measured over a two-year period at 12 plots that included four forest types. Root exclusion collars were installed at a subset of the sites, and periodic removal of the litter layer allowed litter and bulk soil contributions to be estimated by subtraction. Soil respiration was also measured at fixed locations in six northern hardwood sites and two aspen sites to examine the stability of variation between individual measurements. These study sites were added to an existing data set where soil respiration was measured in a random, rotating, systematic clustering which allowed the examination of spatial variability from scales of <1 m to 100+ m. The combined data set for this area was also used to examine the influence of topography on soil respiration at scales of over 1000 m by using a temperature and moisture driven soil respiration model and a 4 km² digital elevation model (DEM) to model soil moisture. Results indicate that, although variation of soil respiration and soil moisture is greatest at scales of 100 m or more, variation from locations 1 m or less can be large (standard deviation during summer period of 1.58 and 1.28 μmol CO₂ m⁻² s⁻¹, respectively). At the smallest of scales, the individual contributions of the bulk soil, the roots, and the litter mat changed greatly throughout the season and between forest types, although the data were highly variable within any given site. For scales of 1-10 m, variation between individual measurements could be explained by positive relationships between forest floor mass, root mass, carbon and nitrogen pools, or root nitrogen concentration. Lastly, topography strongly influenced soil moisture and soil properties, and created spatial patterns of soil respiration which changed greatly during a drought event. Integrating soil fluxes over a 4 km² region using an elevation dependent soil respiration model resulted in a drought induced reduction of peak summer flux rates by 37.5%, versus a 31.3% when only plot level data was used. The trends at these important scales may help explain some inter-annual and spatial variability of the net ecosystem exchange of carbon.     

温带沙漠土壤空间分布的尺度依赖性研究

Spatial variation is a ubiquitous feature of natural ecosystems, especially in arid regions, and is often present at various scales in these regions. To determine the scale dependence of the heterogeneity of soil chemical properties and the dominant scales （factors） for soil heterogeneity in arid regions, the spatial variability of soil resources was investigated in the Gurbantunggut Desert of Central Asia at the scales of 10-3, 10-2, 10-1, 100, 101, 102, 103 and 104 m （from individual plant to population or community to ecosystem）. Soil chemical properties including pH, electrical conductivity （EC）, organic carbon, total nitrogen, available nitrogen, total phosphorus, and available phosphorus were considered in the investigation. At a scale of 10-1 m, which represented the scale of individual plant, significant enrichment of soil resources occurred under shrub canopy and ＂fertile islands＂ formed in the desert ecosystem. Soil EC exhibited the largest heterogeneity at this scale, indicating that individual plants exerted a great influence on soil salinity/alkalinity. Soil nutrients exhibited the greatest heterogeneity at a scale of 102 m, which represented the scale of sand dune/interdune lowlands （between communities）. The main important factors contributing to soil spatial heterogeneity in the Gurbantunggut Desert were individual plants and different topographic characteristics, namely, the appearance of vegetation, especially shrubs or small trees, and existing sand dunes. Soil salinity/alkalinity and soil nutrient status behaved differently in spatial heterogeneity, with an inverse distribution between them at the individual scale.     

Scale‐ and location‐dependent correlation of nitrous oxide emissions with soil properties: an analysis using wavelets

This paper shows how the wavelet transform can be used to analyse the complex spatial covariation of the rate of nitrous oxide (N₂O) emissions from the soil with soil properties that are expected to control the evolution of N₂O. We use data on N₂O emission rates from soil cores collected at 4‐m intervals on a 1024‐m transect across arable land at Silsoe in England. Various soil properties, particularly those expected to influence N₂O production in the soil, were also determined on these cores. We used the adapted maximal overlap discrete wavelet transform (AMODWT) coefficients for the N₂O emissions and soil variables to compute their wavelet covariances and correlations. These showed that, over the transect as a whole, some soil properties were significantly correlated with N₂O emissions at fine spatial scales (soil carbon content), others at intermediate scales (soil water content) and others at coarse spatial scales (soil pH). Ammonium did not appear to be correlated with N₂O emissions at any scale, suggesting that nitrification was not a significant source of N₂O from these soils in the conditions that pertained at sampling. We used a procedure to detect changes in the wavelet correlations at several spatial scales. This showed that certain soil properties were correlated with N₂O emissions only under certain conditions of topography or parent material. This is not unexpected given that N₂O is generated by biological processes in the soil, so the rate of emission may be subject to one limiting factor in one environment and a different factor elsewhere. Such changes in the relationship between variables from one part of the landscape to another is not consistent with the geostatistical assumption that our data are realizations of coregionalized random variables.     

Spatial variability of water retention parameters and saturated hydraulic conductivity in a calcareous Inceptisols (Khuzestan province of Iran) under sugarcane cropping

The objective of this study was to quantify inherent spatial variability and spatial cross-correlation of the van Genuchten retention parameters and saturated hydraulic conductivity (K_s) of surface and subsurface layers in a calcareous Inceptisols (Khuzestan province, Iran) under sugarcane cropping. Measurements were performed on 100-cm³ undisturbed soil cores collected at 94 locations along a 30-m-long transect with horizontal sampling distance intervals of 0.3 and 1 m at soil depths of 0–40 and 40–80 cm, respectively. Spatial variability was investigated using conventional statistics and geostatistical techniques. Coefficient of variation (CV) varied from 8.2% (for shape parameter, n at 40–80 cm depth) to 256.7% (for K_s at 0–40 cm depth). The n parameter and saturated water content, θ_s, showed a small-scale spatial heterogeneity with a maximum CV of 11.3% for the first depth and 9.2% for the second depth. Most of the hydraulic parameters at both depths showed a spatial structure and convex experimental semivariograms with dominant spherical models with the influence range of 3.2–41 m. In most cases, the extent of spatial correlation scales of cross-semivariograms for pairs of cross-correlated hydraulic variables was found to be different with reference to those relating to the direct semivariograms of correlated variables.     

Identifying scale specific controls of soil water storage in a hummocky landscape using wavelet coherency

High spatio-temporal variability of soil water is contributed from different ecohydrological and soil processes operating in different intensities at different scales. Traditional Pearson correlation analysis only examines linear correlation at the measurement scale. In this study, the correlation between soil water storage and its controlling factors was examined at different scales and locations in a hummocky landscape using wavelet coherency. Time domain reflectometry and neutron probe were used to measure soil water storage up to 1.4 m depth along a transect of 576 m long established in a hummocky landscape at St. Denis National Wildlife Area, Saskatchewan, Canada. In spite of visual similarity of the spatial pattern of soil water storage and elevation, the value of Pearson correlation coefficient was very small. However, wavelet coherency identified strong scale- and location-specific correlations between soil water storage and elevation. The total area of significant correlations as calculated from the total number of significant coherencies at different scales and locations was higher between soil water storage and elevation than between soil water storage and any other factors, which indicated a dominant control from elevation on soil water storage in the hummocky landscape. The largest area of significant correlation was observed at large scales (> 70 m), which can be attributed to the alternating knolls and depressions. The relationship between soil water storage and elevation at different scales was persistent at different times of the year or at different seasons with a slight reduction in the magnitude of correlation. The persistent relationship indicated the dominant control from elevation with slight change in the degree of the control. The scale-location specific correlation provides a complete picture on the controls of soil water storage, which was not possible with traditional correlation analysis.     

Spatial covariation of Azotobacter abundance and soil properties: A case study using the wavelet transform

The soil microflora is very heterogeneous in its spatial distribution. The origins of this heterogeneity and its significance for soil function are not well understood. A problem for understanding spatial variation better is the assumption of statistical stationarity that is made in most of the statistical methods used to assess it. These assumptions are made explicit in geostatistical methods that have been increasingly used by soil biologists in recent years. Geostatistical methods are powerful, particularly for local prediction, but they require the assumption that the variability of a property of interest is spatially uniform, which is not always plausible given what is known about the complexity of the soil microflora and the soil environment. We have used the wavelet transform, a relatively new innovation in mathematical analysis, to investigate the spatial variation of abundance of Azotobacter in the soil of a typical agricultural landscape. The wavelet transform entails no assumptions of stationarity and is well suited to the analysis of variables that show intermittent or transient features at different spatial scales.In this study, we computed cross-variograms of Azotobacter abundance with the pH, water content and loss on ignition of the soil. These revealed scale-dependent covariation in all cases. The wavelet transform also showed that the correlation of Azotobacter abundance with all three soil properties depended on spatial scale, the correlation generally increased with spatial scale and was only significantly different from zero at some scales. However, the wavelet analysis also allowed us to show how the correlation changed across the landscape. For example, at one scale Azotobacter abundance was strongly correlated with pH in part of the transect, and not with soil water content, but this was reversed elsewhere on the transect.The results show how scale-dependent variation of potentially limiting environmental factors can induce a complex spatial pattern of abundance in a soil organism. The geostatistical methods that we used here make assumptions that are not consistent with the spatial changes in the covariation of these properties that our wavelet analysis has shown. This suggests that the wavelet transform is a powerful tool for future investigation of the spatial structure and function of soil biota.     

Scale- and location-dependent correlations of soil strength and the yield of wheat

The relationship between soil strength and crop yield may be summarized by a linear correlation coefficient (usually negative). It is likely, however, that this over-simplifies a complex situation in which the relationship between these variables depends on spatial scale and location. We used the wavelet transform to assess this scale- and location-dependence. We established a transect on an arable field in Eastern England, and studied the correlations of soil strength (top- and subsoil) with crop yield. The transect comprised 267 contiguous 0.72 m × 0.72 m plots. Measurements were taken during two consecutive growing seasons of winter wheat (harvest dates of August 2004 and 2005). Soil strength was measured with a penetrometer in the spring of each growing season. As expected, the overall correlation of soil strength with yield was negative but weak. Wavelet analysis revealed that, at fine spatial scales, topsoil and subsoil strength were correlated more or less equally with yield; however, at coarse spatial scales, topsoil strength had a stronger correlation with yield than did subsoil strength. The correlation of topsoil strength with yield at fine spatial scales (corresponding to about 1 m on the ground) was negative. A likely source of this fine-scale variation was the soil compaction associated with tractor wheelings. The correlation of topsoil strength with yield at the coarsest spatial scale (corresponding to about 50 m on the ground) was positive. This correlation was temporally stable, and might have reflected how soil strength can act as a proxy for other soil attributes. In the 2005 growing season, we found evidence that, at intermediate spatial scales, the correlation of soil strength with yield changed depending on the position on the transect. This was probably due to an interaction between the compaction associated with tractor wheelings and the local soil conditions. There was no evidence of such location-dependence in the correlation of soil strength with yield in the 2004 growing season. In summary, the effect of soil strength on crop yield was not expressed in a constant negative correlation across all spatial scales and locations: the negative correlation occurred mainly at fine spatial scales, and the correlation changed according to the position in the landscape and the prevailing local soil conditions.     

A multiscale study of silty soil structure

Dependency of soil properties on scale is a crucial issue in soil physics. In this paper, fractal approaches are used in two case studies in France and Australia, respectively, to study how measured physical soil properties change with the sample spacing and the scale of observation. At a scale of 10–1000 m (10⁴ to 10⁶ mm), fractals were applied to sample data from a linear transect, while at the 10^?6 to 10² mm scale, fractals were applied in two dimensions to analyse both soil micro‐ and macrostructure, based on thin section samples. Porosity was characterized by short‐range spatial variations using sample spacings of 0.5 and 5 m (from the transect data), and a sample spacing of 1 cm (from the thin section analysis). The size of the representative elementary volume (REV) or representative elementary area (REA), required to represent statistically the elementary soil structure, was identified in three ways: (i) by the correlation length of a representative interconnected pore network, (ii) by the upper limit of the non‐linear increase with observation scale of mean porosity (upper limit of the solid mass fractal domain), and (iii) by the non‐linear decrease with observation scale of the coefficient of variation, CV, of mean porosity. Two embedded REAs were identified: the first (0.1–0.4 mm) related to the soil microstructure whereas a second (11–44 mm) related to the soil macrostructure. The solid mass fractal dimensions of the two embedded structural domains showed that hierarchical heterogeneity of soil structure was more pronounced for microstructures than for macrostructures. The mean area ratio of microstructural matrix/total surface and the CV of mean microporosity both scale similarly at observation scales smaller than the REA size. Their scaling exponents were both related to the fractal dimension of microstructural matrix. This preliminary study shows that the theory of fractals applied to soil structures at a specific scale range cannot be directly applied to predict soil physical properties at another scale range. This is because there are different interdependent structuring processes operating at different scales resulting in fractal dimensions being consistent only over particular domain limits.     

毛乌素沙区地下水对植被盖度空间格局影响分析

通过在榆溪河流域沿地下水埋深增大方向设置植被调查样带,基于植被盖度、地下水埋深及土壤含水量等数据,利用多元经验模态分解(MEMD)获取植被盖度及其影响因子所表征的空间尺度,结构方程解析植被盖度空间分布的驱动因素,并结合聚类分析划分了植被群落自然恢复演替空间格局状态变化特征。结果表明：(1)MEMD将空间多元数据分解为3个本征模态函数,经希尔伯特转换得到各模态函数相应的空间尺度分别为14,27,38 km;(2)结构方程模型和MEMD分解后的最大表征尺度相关分析表明,地下水埋深与植被盖度在整个样带尺度上呈显著负相关(R²=-0.95,p<0.001);土壤含水量与植被盖度以地下水埋深5 m空间尺度为分界点,<5 m的区域呈显著正相关(路径系数为0.68,p<0.001),>5 m的区域呈显著负相关(路径系数为-0.43,P<0.01);(3)在此基础上,结合系统聚类分类结果将植被盖度划分为核心区(地下水埋深0～3 m)、过渡区(地下水埋深3～4 m)、稳定区(地下水埋深4～5 m)、外围区(地下水埋深5～10 m)及边缘区(地下水埋深1...     

Spatial Variability of Soil Organic C,Inorganic N and Extractable P in a Mediterranean Grazed Area

In this paper, we studied the spatial variability of soil organic C (SOC), inorganic N (SIN) and extractable P (P_extr) in a grazed Mediterranean‐type vegetation formation. Sampling was conducted from a gently sloping area in northern Greece.. The grazing pressure was evenly distributed over the experimental area with the exception of an overgrazed passage zone 200–300 m from steeper foothills. Soil samples, from the upper 10 cm, were collected every 10 m along four replicate lines (400 m length with a distance of 10 m between lines). Sampling took place twice (October and February). Data were analysed by geostatistical tools, and spherical models were significantly fitted to the semivariograms. SOC in both samplings and SIN in the first one displayed moderate spatial dependence which indicates the non‐random distribution of their concentration. On the contrary, P_extr and SIN in winter exhibited weak spatial dependence, whereas P_extr in autumn showed spatial independence. For the parameters exhibiting spatial pattern, two scales of dependence were revealed: a fine scale within distances shorter than 10 m and a coarse scale varying between 80 and 130 m. The coarse distribution of SOC, SIN and P_extr invoked interplay among more predictable (composition of vegetation) and unpredictable (leaching, runoff) extrinsic factors, occurring at the landscape level. Specifically, SOC as a storage agent exhibited uniform spatial pattern in both samplings. By contrast, SIN by being susceptible to leaching exhibited time‐specific dependence, whereas P_extr which was affected by surface runoff displayed limited or even spatial independence. Copyright © 2012 John Wiley & Sons, Ltd.     

Small-Scale Spatial Variability in Winter Wheat Production

Spatial variability is well documented in agricultural crops. Research has shown that average differences in grain yield for neighboring corn (Zea mays L.) plants can vary by as much as 4211 kg ha^?1; however, little work has been done in winter wheat (Triticum aestivum L.) to determine the amount and scale of spatial variability that exists in grain yields. This study used 22-m?×?0.9-m transects, partitioned in 0.9-m?×?0.9-m subplots, to document the spatial variability that occurred in winter wheat yields. Average yields of each transect ranged from 1023 to 3807 kg ha^?1. Within transects, there was a 1.7- to 2.3-fold difference between the highest and lowest yielding units. This study documented large levels of variability over distances of <1 m. Agronomists working toward precisely managing crop inputs for their most efficient use should account for spatial variability, as significant differences in winter wheat grain yield were found in adjacent 1-m?×?1-m plots.     

Cotton lint yield variability in a heterogeneous soil at a landscape scale

Landscape variability associated with topographic features affects the spatial pattern of soil water and N redistribution, and thus N uptake and crop yield. A landscape-scale study was conducted in a center pivot irrigated field on the southern High Plains of Texas in 1999 to assess soil water, soil NO₃-N, cotton (Gossypium hirsutum L.) lint yield, and N uptake variability in the landscape, and to determine the spatial correlation between these landscape variables using a state-space approach. The treatments were irrigation at 50 and 75% cotton potential evapotranspiration (ET). Neutron access tubes were placed at a 15-m interval along a 710 m (50% ET) and 820 m (75% ET) transect across the field. Soil NO₃-N in early spring was autocorrelated at a distance varying between 60 and 80 m. Measured soil volumetric water content (WC), total N uptake, and lint yield were generally higher on lower landscape positions. Cotton lint yield was significantly correlated to soil WC (r=0.76), soil NO₃-N (r=0.35), and site elevation (r=−0.54). Differences of site elevation between local neighboring points explained the soil water, NO₃-N and lint yield variability at the micro-scale level in the landscape. Soil WC, cotton lint yield, N uptake, and clay content were crosscorrelated with site elevation across a lag distance of ±30–40 m. The state-space analysis showed that cotton lint yield was positively weighted on soil WC availability and negatively weighted on site elevation. Cotton lint yield state-space models give insights on the association of soil physical and chemical properties, lint yield, and landscape processes, and have the potential to improve water and N management at the landscape-scale.     

A spatial approach to soil‐ecological experimentation at landscape scale

The upscaling of soil‐ecological processes to larger landscape units represents a special challenge to soil ecology. Results from micro‐ or mesocosms cannot easily be transferred to other scales because effects are often scale‐dependent. In this context, field experiments which take into account the heterogeneity of the landscape may be promising. Therefore, we carried out an experiment based on a transect study in the agrolandscape of NE Germany on heterogeneous sandy soil in which the feeding activity of the soil‐organism community was assessed by means of the bait‐lamina test at each of the 101 transect locations. At every 4th position, prior to the measurement the soil biota were stimulated by a treatment consisting of adding easily available C and water to the soil. Our aim was to test whether this kind of spatial approach enables to separate effects induced by treatments from landscape effects. The results showed a highly variable feeding activity along the transect after 4 weeks. Despite this variability, a basic trend could be identified which was related to a landscape factor, i.e., the relief. On upper‐slope positions, the feeding activity tended to be less in comparison to positions down‐slope. At every 4th position of the transect, the stimulating effect of the substrate and water addition could be clearly detected and quantified with spectral and cross‐spectral analysis. It is concluded that effects of treatments in heterogeneous landscapes may be distinguished from site effects when the signal‐to‐noise ratio is high and soil and treatment effects on the variable of interest are sufficiently different from one another. In a heterogeneous landscape with gradients of site properties, a treatment based on the frequency domain and applied in regular intervals can be distinguished with spectral analysis techniques.     

Seasonal changes in multi-scale spatial structure of soil pH and related parameters along a tropical dry evergreen forest slope

Seasonal changes in multi-scale spatial variation in soil chemical properties, which may be controlled simultaneously by biotic and abiotic factors, have not been studied in tropical dry forests. We evaluated the spatial variation of physico-chemical soil properties, plant litter and terrain attributes at multiple scales in a tropical dry evergreen forest using multivariate geostatistics. Soil samples were collected at different depths using nested interval sampling during 1- and 10-m intervals in both the wet and dry seasons. We measured pH, exchangeable cations (Ex-K⁺ and Ex-Ca²⁺), acidity (Ex-H⁺ and Ex-Al³⁺), particle size (clay and sand contents), and forest floor mass (O_i and O_a). Pronounced spatial variation in pH was observed in surface soil (0-5 cm) but not in deeper soil (5-55 cm). Multi-scale spatial structures with short (20 m) and long (86 m) ranges were observed in the auto- and cross-variograms of soil, litter and slope gradient. Pronounced multi-scale structures were observed simultaneously in pH and Ex-Ca²⁺ both in the wet and dry seasons. Only a short-range structure was observed in Ex-K⁺ and O_a, whereas a long-range structure was pronounced in sand contents and slope gradients. Although the variograms had similar shapes between wet and dry seasons for almost all variables, the short-range structure of the cross-variogram between O_a with pH and base cations was more pronouncedly developed in the wet season than in the dry season. Scale-dependent correlation coefficients suggest that a small-scale spatial variation in pH was connected to heterogeneous litter accumulation via base-cation input, whereas long-range spatial variation was simultaneously linked to particle size and slope gradient. This multivariate geostatistical approach applied within a stand detected biotic and abiotic factors controlling spatial variation in soil properties at both short and long distances.     

Prediction of soil depth using environmental variables in an anthropogenic landscape, a case study in the Western Ghats of Kerala, India

Soil (regolith) depth is a crucial input for modeling earth surface phenomena. However, most studies ignore its spatial variability. Techniques that map the spatial variability of soil depth are of three types: (1) physically-based; (2) empirico-statistical from environmental correlates; and (3) interpolation from point observations. In an anthropogenic landscape, soil depth does not depend primarily on natural processes, making it difficult to apply a physically-based approach. The present study compares empirico-statistical methods with geostatistical methods for predicting soil depth in such a landscape: Aruvikkal catchment (9.5 km²) in the Western Ghats of Kerala, India. Regression kriging applied on blocks of 20 m by 20 m using the environmental covariates elevation, slope, aspect, curvature, wetness index, land use and distance from streams, proved to be the best predictor of soil depth. This model explains 52% of the variability of soil depth in the catchment; with a prediction variance of 0.05 to 0.19. A Gaussian simulation was attempted for a more realistic visualization of the depth, as opposed to the smooth kriging prediction. The most important explanatory variable of soil depth in this landscape is land use, as expected from the strong human intervention.     

Regional and local scale variations in soil organic carbon stocks in West Greenland

The soil organic carbon (SOC) pool of the Northern Hemisphere contains about half of the global SOC stored in soils. As the Arctic is exceptionally sensitive to global warming, temperature rise and prolonged summer lead to deeper thawing of permafrost‐affected soils and might contribute to increasing greenhouse gas emissions progressively. To assess the overall feedback of soil organic carbon stocks (SOCS) to global warming in permafrost‐affected regions the spatial variation in SOCS at different environmental scales is of great interest. However, sparse and unequally distributed soil data sets at various scales in such regions result in highly uncertain estimations of SOCS of the Northern Hemisphere and here particularly in Greenland. The objectives of this study are to compare and evaluate three controlling factors for SOCS distribution (vegetation, landscape, aspect) at two different scales (local, regional). The regional scale reflects the different environmental conditions between the two study areas at the coast and the ice margin. On the local scale, characteristics of each controlling factor in form of defined units (vegetation units, landscape units, aspect units) are used to describe the variation in the SOCS over short distances within each study area, where the variation in SOCS is high. On a regional scale, we investigate the variation in SOCS by comparing the same units between the study areas. The results show for both study areas that SOCS are with 8 kg m^?2 in the uppermost 25 cm and 16 kg m^?2 in the first 100 cm of the soil, i.e., 3 to 6 kg m^?2 (37.5%) higher than existing large scale estimations of SOCS in West Greenland. Our approach allows to rank the scale‐dependent importance of the controlling factors within and between the study areas. However, vegetation and aspect better explain variations in SOCS than landscape units. Therefore, we recommend vegetation and aspect for determining the variation in SOCS in West Greenland on both scales.