Modelling soil moisture in a high‐latitude landscape using LiDAR and soil data

Soil moisture has a pronounced effect on earth surface processes. Global soil moisture is strongly driven by climate, whereas at finer scales, the role of non‐climatic drivers becomes more important. We provide insights into the significance of soil and land surface properties in landscape‐scale soil moisture variation by utilizing high‐resolution light detection and ranging (LiDAR) data and extensive field investigations. The data consist of 1200 study plots located in a high‐latitude landscape of mountain tundra in north‐western Finland. We measured the plots three times during growing season 2016 with a hand‐held time‐domain reflectometry sensor. To model soil moisture and its temporal variation, we used four statistical modelling methods: generalized linear models, generalized additive models, boosted regression trees, and random forests. The model fit of the soil moisture models were R² = 0.60 and root mean square error (RMSE) 8.04 VWC% on average, while the temporal variation models showed a lower fit of R² = 0.25 and RMSE 13.11 CV%. The predictive performances for the former were R² = 0.47 and RMSE 9.34 VWC%, and for the latter R² = 0.01 and RMSE 15.29 CV%. Results were similar across the modelling methods, demonstrating a consistent pattern. Soil moisture and its temporal variation showed strong heterogeneity over short distances; therefore, soil moisture modelling benefits from high‐resolution predictors, such as LiDAR based variables. In the soil moisture models, the strongest predictor was SAGA (System for Automated Geoscientific Analyses) wetness index (SWI), based on a 1 m² digital terrain model derived from LiDAR data, which outperformed soil predictors. Thus, our study supports the use of LiDAR based SWI in explaining fine‐scale soil moisture variation. In the temporal variation models, the strongest predictor was the field‐quantified organic layer depth variable. Our results show that spatial soil moisture predictions can be based on soil and land surface properties, yet the temporal models require further investigation. Copyright © 2017 John Wiley & Sons, Ltd.     

Electromagnetic induction prediction of soil salinity and groundwater properties in a Tunisian Saharan oasis

Abstract

Electromagnetic induction measurements (EM) were taken in a saline gypsiferous soil of the Saharan-climate Fatnassa oasis (Tunisia) to predict the electrical conductivity of saturated soil extract (ECe) and shallow groundwater properties (depth, Dgw, and electrical conductivity, ECgw) using various models. The soil profile was sampled at 0.2 m depth intervals to 1.2 m for physical and chemical analysis. The best input to predict the log-transformed soil salinity (lnECe) in surface (0–0.2 m) soil was the EMh/EMv ratio. For the 0–0.6 m soil depth interval, the performance of multiple linear regression (MLR) models to predict lnECe was weaker using data collected over various seasons and years (R _a ² = 0.66 and MSE = 0.083 dS m^-1) as compared to those collected during the same period (R _a ² = 0.97, MSE = 0.007 dS m^-1). For similar seasonal conditions, for the Dgw–EMv relationship, R ² was 0.88 and the MSE was 0.02 m for Dgw prediction. For a validation subset, the R ² was 0.85 and the MSE was 0.03 m. Soil salinity was predicted more accurately when groundwater properties were used instead of soil moisture with EM variables as input in the MLR.

Editor D. Koutsoyiannis; Associate editor K. Heal

Citation Bouksila, F., Persson, M., Bahri, A., and Berndtsson, R., 2012. Electromagnetic induction predictions of soil salinity and groundwater properties in a Tunisian Saharan oasis. Hydrological Sciences Journal, 57 (7), 1473–1486.     

Spatial distribution and content of soil organic matter in an agricultural field in eastern Canada,as estimated from geostatistical tools

Soil erosion induces soil redistribution within the landscape and thus contributes to the spatial variability of soil quality. This study complements a previous experimentation initiated by the authors focusing on soil redistribution as a result of soil erosion, as indicated by caesium‐137 (¹³⁷Cs) measurements, in a small agricultural field in Canada. The spatial variability of soil organic matter (SOM) was characterized using geostatistics, which consider the randomized and structured nature of spatial variables and the spatial distribution of the samples. The spatial correlation of SOM (in percentages) patterns in the topsoil was established taking into account the spatial structure present in the data. A significant autocorrelation and reliable variograms were found with a R² ≥ 0·9, thus demonstrating a strong spatial dependence. Ordinary Kriging (OK) interpolation provided the best cross validation (r² = 0·35). OK and inverse distance weighting power two (IDW2) interpolation approaches produced similar estimates of the total SOM content of the topsoil (0–20 cm) of the experimental field, i.e. 211 and 213 tonnes, respectively. However, the two approaches produced differences in the spatial distribution patterns and the relative magnitude of some SOM content classes. The spatialization of SOM and soil redistribution variability – as evidenced by ¹³⁷Cs measurements – is a first step towards the assessment of the impact of soil erosion on SOM losses to recommend conservation measures. Copyright © 2009 John Wiley & Sons, Ltd.     

Modelling the process of soil detachment by rill flow on steep loessial hillslopes

Soil detachment by rill flow is a key process of rill erosion, modelling this process can help in understanding rill erosion mechanisms. However, many soil detachment models are established on conceptual assumptions rather than experimental data. The objectives of this study were to establish a model of soil detachment by rill flow based on flume experimental data and to quantitatively verify the model. We simulated the process of soil detachment by rill flow in flume experiments with a soil-feeding hopper using loessial soil on steep slopes. Seven flow discharges, six slopes and five sediment loads were combined. Soil detachment capacity, sediment transport capacity, and soil detachment rate by rill flow under different sediment loads were measured. The process of soil detachment by rill flow can be modelled by a dual power function based on soil detachment capacity and transport capacity deficit as variables. The established model exhibited high credibility (NSE=0.97; R²=0.97). The contributions of soil detachment capacity and transport capacity deficit to soil detachment rate by rill flow reached 60% and 36%, respectively. Soil detachment capacity exerted more influence on soil detachment rate than did transport capacity deficit. The performance of the WEPP rill erosion equation is also favourable (NSE=0.95; R²=0.97). The two power exponents in the model we established strengthen the role of soil detachment capacity in soil detachment rate and weaken that for transport capacity deficit. Soil detachment capacity and transport capacity deficit played important roles in the determination of soil detachment rate by rill flow. The results can be applied to implement the numerical modeling and prediction of rill erosion processes on steep loessial hillslopes. © 2019 John Wiley & Sons, Ltd.     

Investigation of soil water hydrological process in the permafrost active layer using stable isotopes

Here, we studied the isotope characteristics and source contributions of soil water in the permafrost active layer by collecting soil samples in July 2018 in Yangtze River basin. Soil moisture and temperature showed decreasing trends from 0–80 cm, and an increasing trend from 80–100 cm. The value of δ¹⁸O and δD first increased and then decreased in the soil profile of 0–100 cm; however, d-excess increased from 0–100 cm. δ¹⁸O values became gradually positive from the southwest to northeast of the study area, while d-excess gradually increased from southeast to northwest. The evaporation water line (EL) was δD = 7.56 δ¹⁸O + 1.50 (R² = 0.90, p < 0.01, n = 96). Due to intense solar radiation and evaporation on the Tibetan Plateau, the elevation did not impact the surface soil. The altitude effect of the soil depths of 0–20 cm was not obvious, but the other soil layers had a significant altitude effect. Soil moisture and temperature were closely related to the stable isotopic composition of soil water. The contribution of precipitation to soil water on the sunny slope was 86%, while the contribution of the shady slope was 84%. However, the contribution of ground ice to soil water on sunny slope was 14% and the shady slope was 16%. The contribution of ground ice to soil water increased with increasing altitude on the sunny slope, but the contribution of ground ice to soil water had no obvious trend on the shady slope.     

Model calibration using hydropedological insights to improve the simulation of internal hydrological processes using SWAT+

Soils affect the distribution of hydrological processes by partitioning precipitation into different components of the water balance. Therefore, understanding soil-water dynamics at a catchment scale remains imperative to future water resource management. In this study, the value of hydropedological insights was examined to calibrate a processes-based model. Soil morphology was used as soft data to assist in the calibration of the Soil Water Assessment Tool (SWAT+) model at five different catchment scales (48, 56, 174, 674, and 2421 km²) in the Sabie River catchment, South Africa. The aim of this study was to calibrate the SWAT+ model to accurately simulate long-term monthly streamflow predictions as well as to reflect internal soil hydrological processes using a procedure focusing on hydropedology as a calibration tool in a multigauge system. Results indicated that calibration improved streamflow predictions where R² improved by 2%–8%. Nash-Sutcliffe Efficiency (NSE) improved from negative correlations to values exceeding 0.5 at four of the five catchment scales compared to the uncalibrated model. Results confirm that soil mapping units can be calibrated individually within SWAT+ to improve the representation of hydrological processes. Particularly, the spatial linkage between hydropedology and hydrological processes, which is captured within the soil map of the catchment, can be adequately reflected within the model simulations after calibration. This research will lead to an improved understanding of hydropedology as soft data to improve hydrological modelling accuracy.     

Development of pedotransfer functions for soil hydraulic properties in the critical zone on the Loess Plateau,China

Soil hydraulic properties (SHPs) including the soil water retention curve and saturated soil hydraulic conductivity (Ks) are crucial input data for simulations of soil water and solute transport in the Earth's critical zone. However, obtaining direct measurements of SHPs at a wide range of scales is time consuming and expensive. Pedotransfer functions (PTFs) are employed as an alternative method for indirectly estimating these parameters based on readily measured soil properties. However, PTFs for SHPs for the deep soil layer in the Earth's critical zone are lacking. In this study, we developed new PTFs in the deep soil profile for Ks and soil water retention curve on the Loess Plateau, China, which were fitted with the van Genuchten equation. In total, 206 data sets comprising the hydraulic and basic soil properties were obtained from three typical sites. Samples were collected from the top of the soil profile to the bedrock by soil core drilling. PTFs were developed between the SHPs and basic soil properties using stepwise multiple linear regression. The PTFs obtained the best predictions for Ks (R_adj² = 0.561) and the worst for van Genuchten α (R_adj² = 0.474). The bulk density and sand content were important input variables for predicting Ks, α, and θs, and bulk density, clay content, and soil organic carbon were important for n. The PTFs developed in this study performed better than existing PTFs. This study contains the first set of PTFs of SHPs to be developed for the deep profile on the Loess Plateau, and they may be applicable to other regions.     

Predicting physical equations of soil detachment by simulated concentrated flow in Ultisols (subtropical China)

Soil detachment in concentrated flow is due to the dislodging of soil particles from the soil matrix by surface runoff. Both aggregate stability and shear strength of the topsoil reflect the erosion resistance of soil to concentrated runoff, and are important input parameters in predicting soil detachment models. This study was conducted to develop a formula to predict soil detachment rate in concentrated flow by using the aggregate stability index (As), root density (R_d) and saturated soil strength (σ_s) in the subtropical Ultisols region of China. The detachment rates of undisturbed topsoil samples collected from eight cultivated soil plots were measured in a 3.8 m long, 0.2 m wide hydraulic flume under five different flow shear stresses (τ = 4.54, 9.38, 15.01, 17.49 and 22.54 Pa). The results indicated that the stability index (As) was well related with soil detachment rate, particularly for results obtained with high flow shear stress (22.54 Pa), and the stability index (As) has a good linear relationship with concentrated flow erodibility factors (K_c). There was a positive linear relationship between saturated soil strength (σ_s) and critical flow shear stress (τ_c) for different soils. A significant negative exponential relationship between erodibility factors (K_c) and root density (R_d) was detected. This study yielded two prediction equations that allowed comparison of their efficiency in assessing soil detachment rate in concentrated flow. The equation including the root density (R_d) may have a better correlation coefficient (R² = 0.95). It was concluded that the formula based on the stability index (As), saturated soil strength (σ_s) and root density (R_d) has the potential to improve methodology for assessing soil detachment rate in concentrated flow for the subtropical Chinese Ultisols. Copyright © 2012 John Wiley & Sons, Ltd.     

A comprehensive analysis of Universal Soil Loss Equation-based models at the Sparacia experimental area

Improving Universal Soil Loss Equation (USLE)-based models has large interest because simple and reliable analytical tools are necessary in the perspective of a sustainable land management. At first, in this paper, a general definition of the event rainfall- runoff erosivity factor for the USLE-based models, REF_e = (Q_R)^b1(EI₃₀)^b2, in which Q_R is the event runoff coefficient, EI₃₀ is the single-storm erosion index, and b₁ and b₂ are coefficients, was introduced. The rainfall-runoff erosivity factors of the USLE (b₁ = 0 and b₂ = 1), USLE-M (b₁ = b₂ = 1), USLE-MB (b₁ ≠ 1 and b₂ = 1), USLE-MR (b₁ = 1 and b₂ ≠ 1), USLE-MM (b₁ = b₂ ≠ 1), and USLE-M2 (b₁ ≠ b₂ ≠ 1) can be defined using REF_e. Then the different expressions of REF_e were simultaneously tested against a data set of normalized bare plot soil losses, A_eN, collected at the Sparacia (south Italy) site. As expected, the poorest A_eN predictions were obtained with the USLE. The observed tendency of this model to overestimate small A_eN values and underestimate high A_eN values was reduced by introducing in the soil loss prediction model both Q_R and an exponent for the erosivity term. The fitting to the data was poor with the USLE-MR as compared with the USLE-MB and the USLE-MM. Estimating two distinct exponents (USLE-M2) instead of a single exponent (USLE-MB, USLE-MR, and USLE-MM) did not appreciably improve soil loss prediction. The USLE-MB and the USLE-MM were recognized to be the best performing models among the possible alternatives, and they performed similarly with reference to both the complete data set and different sub-data sets, only including small, intermediate, and severe erosion events. In conclusion, including the runoff coefficient in the soil loss prediction model is important to improve the quality of the predictions, but a great importance has to be paid to the mathematical structure of the model.     

SfM-MVS Photogrammetry for Splash Erosion Monitoring under Natural Rainfall

An understanding of splash erosion is the basis to describe the impact of rain characteristics on soil disturbance. In typical splash cup experiments, splashed soil is collected, filtered, and weighed. As a way to collect additional data, our experiments have been supplemented by a photogrammetric approach. A total of three soils were tested across three sites, one in the Czech Republic and two in Austria, all equipped with rain gauges and disdrometers to measure rainfall parameters. The structure from motion multiview stereo (SfM-MVS) photogrammetric method was used to measure the raindrops impact on the soil surface. The images were processed using Agisoft PhotoScan, resulting in orthophotos and digital elevation models (DEMs) with a resolution of 0.1 mm/pix. The surface statistics included the mean surface height (whose standard deviation was used as a measure of surface roughness), slope, and other parameters. These parameters were evaluated depending on soil texture and rainfall parameters. The results show a linear correlation between consolidation and splash erosion with a coefficient of determination (R²) of approximately 0.65 for all three soils. When comparing the change in soil volume with rainfall parameters, the best correlation was found with the maximum 30-minintensity (I₃₀), resulting in R² values of 0.48 (soil A, silt loam, 26% clay), 0.59 (soil B, silt loam, 18% clay), and 0.68 (soil C, loamy sand, 12% clay). The initial increase in the sample volume for the lowest splashed mass corresponds with the increase in the clay content of each of the soils. Soil A swells the most. Soil B swells less. Soil C does not swell at all and consolidates the most. We derived the relationship between the photogrammetrically measured change in surface height and the splash erosion (measured by weight) by accounting for the effect of the clay content.     

Characterization of field scale soil variability using remotely and proximally sensed data and response surface method

Soil salinization of the reclaimed tidelands is problematic. Therefore, there is a need to characterize the spatial variability of soil salinity associated with soil moisture and other soil properties across the reclaimed tidelands. One approach is the use of easily-acquired ancillary data as surrogates for the arduous conventional soil sampling. In a reclaimed coastal tideland in the south of Hangzhou Gulf, backscattering coefficient (σ⁰) from remotely sensed ALOS/PALSAR radar imagery (HH polarization mode) and apparent soil electrical conductivity (ECa) from a proximally sensed EM38 were used to indicate the spatial distribution of soil moisture and salinity, respectively. After that, response surface methodology (RSM) was employed to determine an optimal set of 12 soil samples using spatially referenced σ⁰ and ECa data. Spatial distributions of three soil chemical properties [i.e. soil organic matter (SOM), available nitrogen (AN), and available potassium (AK)] were predicted using inverse distance weighted method based on the 12 samples and were then compared with the predictions generated using 42 samples obtained from a conventional grid sampling scheme. It was concluded that combination of radar imagery and EM induction data can delineate the spatial variability of two key soil properties (i.e. moisture and salinity) across the study area. Besides, RSM-based sampling using radar imagery and EM induction data was highly effective in characterizing the spatial variability of SOM, AN and AK, compared with the conventional grid sampling. This new approach may be used to assist site specific management in precision agriculture.     

Modeling rock permeability from NMR relaxation data by PLS regression

This study explores the application of the partial least squares regression (PLSR) technique to rock permeability prediction from nuclear magnetic resonance (NMR) relaxation data. A total of 68 Brazilian sandstone cores selected from reservoirs and outcrop analogs were fully saturated and analyzed by NMR. The permeability of the cores ranged from 0.007 to 9,800 mD. From their ¹H transverse relaxation times (T₂) measured at 2 MHz, two PLSR models were developed for the relaxation spectra and the raw relaxation curves. Both models led to more uniform and accurate predictions (RMSE = 0.47 and 0.50 log mD, respectively) compared with the classical Kenyon model (RMSE = 0.78 log mD).     

Characterization of Soil Particle Size Distribution with a Fractal Model in the Desertified Regions of Northern China

We constructed an aeolian soil database across arid, semi-arid, and dry sub-humid regions, China. Soil particle size distribution was measured with a laser diffraction technique, and fractal dimensions were calculated. The results showed that: (i) the predominant soil particle size distributed in fine and medium sand classifications, and fractal dimensions covered a wide range from 2.0810 to 2.6351; (ii) through logarithmic transformations, fractal dimensions were significantly positive correlated with clay and silt contents (R² = 0.81 and 0.59, P < 0.01), and significantly negative correlated with sand content (R² = 0.50, P < 0.01); (3) hierarchical cluster analysis divided the plots into three types which were similar to sand dune types indicating desertification degree. In a large spatial scale, fractal dimensions are still sensitive to wind-induced desertification. Therefore, we highly recommend that fractal dimension be used as a reliable and quantitative parameter to monitor soil environment changes in desertified regions. This improved information provides a firm basis for better understanding of desertification processes.     

A GIS‐based variable source area hydrology model

Effective control of nonpoint source pollution from contaminants transported by runoff requires information about the source areas of surface runoff. Variable source hydrology is widely recognized by hydrologists, yet few methods exist for identifying the saturated areas that generate most runoff in humid regions. The Soil Moisture Routing model is a daily water balance model that simulates the hydrology for watersheds with shallow sloping soils. The model combines elevation, soil, and land use data within the geographic information system GRASS, and predicts the spatial distribution of soil moisture, evapotranspiration, saturation‐excess overland flow (i.e., surface runoff), and interflow throughout a watershed. The model was applied to a 170 hectare watershed in the Catskills region of New York State and observed stream flow hydrographs and soil moisture measurements were compared to model predictions. Stream flow prediction during non‐winter periods generally agreed with measured flow resulting in an average r² of 0·73, a standard error of 0·01 m³/s, and an average Nash‐Sutcliffe efficiency R² of 0·62. Soil moisture predictions showed trends similar to observations with errors on the order of the standard error of measurements. The model results were most accurate for non‐winter conditions. The model is currently used for making management decisions for reducing non‐point source pollution from manure spread fields in the Catskill watersheds which supply New York City's drinking water. Copyright © 1999 John Wiley & Sons, Ltd.     

Rainfall‐induced consolidation and sealing effects on soil erodibility during concentrated runoff for loess‐derived topsoils

Flume experiments simulating concentrated runoff were carried out on remolded silt loam soil samples (0·36 × 0·09 × 0·09 m³) to measure the effect of rainfall‐induced soil consolidation and soil surface sealing on soil erosion by concentrated flow for loess‐derived soils and to establish a relationship between soil erodibility and soil bulk density. Soil consolidation and sealing were simulated by successive simulated rainfall events (0–600 mm of cumulative rainfall) alternated by periods of drying. Soil detachment measurements were repeated for four different soil moisture contents (0·04, 0·14, 0·20 and 0·31 g g^?1). Whereas no effect of soil consolidation and sealing is observed for critical flow shear stress (τ_cr), soil erodibility (Kc) decreases exponentially with increasing cumulative rainfall depth. The erosion‐reducing effect of soil consolidation and sealing decreases with a decreasing soil moisture content prior to erosion due to slaking effects occurring during rapid wetting of the dry topsoil. After about 100 mm of rainfall, Kc attains its minimum value for all moisture conditions, corresponding to a reduction of about 70% compared with the initial Kc value for the moist soil samples and only a 10% reduction for the driest soil samples. The relationship estimating relative Kc values from soil moisture content and cumulative rainfall depth predicts Kc values measured on a gradually consolidating cropland field in the Belgian Loess Belt reasonably well (MEF = 0·54). Kc is also shown to decrease linearly with increasing soil bulk density for all moisture treatments, suggesting that the compaction of thalwegs where concentrated flow erosion often occurs might be an alternative soil erosion control measure in addition to grassed waterways and double drilling. Copyright © 2007 John Wiley & Sons, Ltd.     

Identifying effects of local and nonlocal factors of soil water storage using cyclical correlation analysis

There are various factors governing the spatial and temporal variability of soil water storage including soil properties, topography and vegetation. Some factors act locally, whereas others act nonlocally, which means that a factor measured at one location has effect on soil water storage at another location. The objective of this study was to examine the effects of local and nonlocal controls of soil water storage in a hummocky landscape using cyclical correlation analysis. Soil water storage, soil properties and terrain indices were measured along a 128‐point transect of 576 m long from the semiarid, hummocky, prairie pothole region of North America. There are large coefficients of determination (r²) between soil water storage and sand content (r² = 0.32–0.53), organic carbon content (r² = 0.22–0.56), depth to carbonate layer (r² = 0.13–0.63), wetness index (r² = 0.25–0.45) and other variables at the measurement scale at different times, indicating strong local effects from these variables. The correlation coefficients were also calculated by physically shifting the spatial series of soil water storage with respect to that of controlling factors. The shifting improves the correlation between the spatial series, and the length of shifting indicated the difference in the response of soil water to its controlling factors. For example, the value of r² increased more than eightfold (r² = 0.47–0.64) after shifting the spatial series of soil water storage by 54 m, almost equal to the average length of existing slope, compared with the very weak correlation (r² = 0.02–0.08) at the measurement scale. This indicated the nonlocal effect from the relative elevation. The identification of nonlocal effects from factors improves the prediction of soil water storage. Copyright © 2011 John Wiley & Sons, Ltd.     

Evaluation of seasonal patterns of hydraulic redistribution in a humid subtropical area,East China

Antecedent soil moisture or soil moisture status has a great impact on hydrological processes. Hydraulic redistribution (HR), a widely observed phenomenon, is defined as water distributed (typically at night) from moist soil to drier soil via plant roots, which plays an important role in soil moisture replenishment. Knowledge on seasonal patterns of HR and on the relationship between HR and soil water use is not fully understood. We investigated temporal variations in HR and total daily water use (Δθ) at stands of camphor and peach by monitoring soil moisture content in a humid region in eastern China. HR at the three locations reached its maximum values in summer (0.68 mm d⁻¹ to 1.15 mm d⁻¹) at depths of 15 cm and 35 cm. Redistributed water replenished 41% of water depleted in the soil at a 5–45-cm depth. Interestingly, normalized HR (i.e., HR/Δθ) showed a constant pattern during the growing season implying it is independent of seasonal climate alterations. This also indicated that HR had a stable effect on the replenishment of daily water use. Positive linear relationships between HR and Δθ were found at three measuring locations (camphor: R² = .35, p < .01; peach1: R² = .57, p < .01; peach2: R² = .63, p < .01), suggesting a relatively stable inherent linkage between HR and Δθ. This study suggested that hydrological processes involving soil moisture status or antecedent soil moisture, needs to take the HR effect into account across timescales from intraday to seasonal.     

Impact of wildfires on surface water repellency in soils of northwest Spain

Soil water repellency (hydrophobicity) is a naturally occurring phenomenon that can be intensified by soil heating during fires. Fire‐induced water repellency, together with the loss of plant cover, is reportedly the principal source of increased surface runoff and accelerated erosion in burned soils. In this study, the surface water repellency of several soils affected by summer forest fires in northwest Spain was studied and compared with that of adjacent unburned soils. Soil water repellency was determined using the ethanol percentage test (MED). Most of the unburned soil samples exhibited water repellency that ranged from strong to very strong; only four of the unburned soil samples were non‐repellent. Water repellency in the unburned soils was significantly correlated with the organic carbon content (r = 0·64, p < 0·05). Overall, fires increased the surface water repellency in soils with previously low degrees of water repellency and caused little change in that of originally strongly hydrophobic soils. In order to examine in detail the changes in water repellency with temperature, three unburned soil samples were subjected to a controlled heating program. Water repellency increased between 25 and 220 °C, water repellency peaked between 220 and 240 °C and disappeared above 260–280 °C. Extrapolation of the results of the heating tests to field conditions suggested that the intensity of fire (temperature and time of residence) reached by most soils during fires is not too high. Based on the results, the determination of water repellency could be used as a simple test for the indirect estimation of the intensity levels reached on the soil surface during a fire. Copyright © 2005 John Wiley & Sons, Ltd.     

Modeling aggregate size distribution of eroded sediment resulting from rain-splash and raindrop impacted flow processes

Soil susceptibility to detachment and transport sub-processes of erosion is generally controled by the aggregate breakdown mechanism. Measuring particle size and aggregation to the estimate erodibility potential of soils is important under erosive rainfall conditions. The Aggregate Size Distribution (ASD) is one of the most important determinants of soil structure along with soil organic matter content for describing the efficiency of applied, sustainable management strategies. This study aimed to compare the performances of three different aggregate size distribution models to predict the characteristic aggregate size parameter (median diameter, D50) for eroded sediment from interrill erosion processes of Rain- Splash Transport (RST) and Raindrop Impacted Flow Transport (RIFT). The ASDs of 1143 collected sediment samples from the RST and RIFT processes were measured and modeled by the Log-normal, Fractal, and Weibull approaches. The D50 value, as a characteristic parameter for aggregate size distributions, derived from the cumulative ASD curve was compared for soils from different land use types and different slope and rainfall intensity conditions. The performance of each model was evaluated using the Mean Square Error (MSE) and Coefficient of Determination (R^2). The Weibull approach was the most accurate model showing the best fit with the lowest MSE values (0.0002 ≤MSE≤ 0.0048) and having the greatest R2 values (0.936≤ R^2≤ 0.998) when compared with the Log-normal and Fractal models. Herewith, for semi-arid land use and soil, specific shape and scale parameters for the Weibull distribution, the respective ASDs were successfully re-generated for modeling the eroded sediment of the simulated RST and RIFT interill processes.     

Temporal variation in properties of an uncropped,ploughed Miamian soil in relation to seasonal erodibility

Knowledge of seasonal variation in soil structural and related properties is important for the determination of critical periods during which soil is susceptible to accelerated erosion and other degradative processes. The purpose of this research was to evaluate the magnitude of seasonal variations in selected soil and deposited sediment properties in relation to soil erodibility for a Miamian silt-loam soil (Typic Hapludalf) in central Ohio. Erosion plots (USLE-type) were established on a 4·5% slope and maintained under bare, ploughed conditions from 1988 to 1991. Particle size distribution, bulk density(ρ_b), percentage water stable aggregates (WSA), soil organic carbon (SOC), and total soil nitrogen (TSN) of both soil and sediment samples were monitored between Autumn 1989 and Spring 1991. The soil and sediment particle size distributions followed no clear seasonal trends. Soil ρ_b increased following tillage (1·20 Mg m⁻³) and was highest (1·45 Mg m⁻³) during the autumn owing to soil slumping and consolidation upon drying. Low winter and spring values of ρ_b and %WSA (20–50% lower than in autumn) were attributed to excessive wetness and freeze–thaw effects. Both SOC and soil TSN contents progressively declined (from 2·18 to 1·79% and 1·97 to 1·75 g kg⁻¹, respectively) after ploughing owing to maintenance of plots under bare, fallow conditions. Spring highs and autumn lows of sediment SOC (3·12 vs. 2·44%) and TSN (2·70 vs. 1·96 g kg⁻¹) contents were a result of the combined effects of soil microbial activity and rainfall erosivity. Soil properties such as bulk density, SOC and WSA, which vary seasonally, can potentially serve as predictors of seasonal soil erodibility, which, in turn, could improve the predictive capacity of soil erosion prediction models. © 1998 John Wiley & Sons, Ltd.