Distribution Of Soil Thickness Research Articles

Evaluation of key parameters in rainfall-induced shallow landslide analysis: Are Di Village Case

Are Di Village experienced a devastating landslide in September 2020. This research utilized the transient rainfall infiltration and a grid-based regional slope stability model (TRIGRS) to assess the causes of slope instability. Input parameters such as soil properties, rainfall data, slope geometry, and soil thickness were integrated into the analysis. The models were validated using landslide scars from the event. The analysis results indicate that soil thickness significantly influences landslide susceptibility. In this study, the empirical equation utilized to estimate soil thickness considered the influence of topography on the distribution of soil thickness. The results calculated from TRIGRS showed that the model with a maximum soil thickness input of 3.5 m produced the most reliable landslide susceptibility map with an %LSclass of 40% and %LRclass of 65%.

Climate and land use changes explain variation in the A horizon and soil thickness in the United States

Understanding spatio-temporal changes in soil thickness and their natural and anthropogenic driving factors are essential for earth system modeling and natural resource conservation. It remains challenging to accurately quantify the spatial pattern of soil thickness, and there is no assessment of its temporal changes at the national scale across eco-climatic zones. Here we compiled a long-term (1950–2018), large-scale (conterminous United States of America) topsoil (A horizon, n = 37,712) and solum (22,409) thickness data to quantify their spatial and temporal variations using generalized additive models and selected chronosequences in land resource regions. Climate was found associated with the spatial distribution of soil thickness, and land use and erosion associated with its temporal variation. The A horizon and solum thickness displayed strong longitudinal patterns, correlated with soil moisture and temperature, respectively. Temporal changes in the thickness varied across land resource regions, affected by topography, land use, and erosion. Severe A horizon loss primarily occurred in Mollisols of the Central Great Plains, Alfisols on steep slopes, and soils under cropping. These findings enhanced our fundamental understanding of soil formation and biogeochemical cycles during the Anthropocene across scales and identified regions for conservation practices to reduce further topsoil loss.

Exploring extreme rainfall-triggered landslides using 3D unsaturated flow, antecedent moisture and spatially distributed soil depth

The susceptibility to shallow landslides is affected by several factors. Rainfall intensity, soil thickness distribution, and antecedent moisture conditions are components that influence the spatio-temporal prediction of landslides at the watershed scale. Yet, the combined impact of these factors on the susceptibility to landslides has been overlooked. The purpose of this paper is to investigate landslides triggered by extreme precipitation in the Tijuca Massif, Rio de Janeiro, southeastern Brazil. Our approach couples a 3D variably saturated flow solver with the infinite slope stability method to calculate the statistical distributions of the safety factor and the pore pressure at the soil–bedrock interface. Numerical simulations were performed for 6 scenarios considering 1-year spin-up time for soil moisture and different spatially distributed soil depths. The results show that during extreme precipitation events, soil depth and initial moisture conditions may not have much influence on the safety factor, as the intensity and duration of rainfall will be the triggering agents. Comparison of simulated unstable zones and field data from mapped landslide scars further supported this conclusion. We conclude that simple soil depth models are feasible options for regional studies of landslide susceptibility. Our findings are relevant to understanding shallow landslides induced by extreme rainfall in Rio de Janeiro and other regions with similar geological and climate settings.

Overview and large-scale representative estimate of radon-222 flux data in China

• Surface radon-222 flux (RF) is poorly known at large spatial scales worldwide. • We collected 2622 RF data using 69 publications from databases available in China. • Substratum lithology dominantly controls RF mean value at large spatial scales. • RF is affected by soil thickness, on average reduced for larger thickness. • Representative RF for China after soil thickness correction is 20±6 mBq m −2 s −1 . The inert radioactive gas radon-222, ubiquitous in the environment, is the first cause of lung cancer in non-smokers and a powerful asset to trace geological fluids and constrain atmospheric transport models. In all of these applications, radon flux (RF) from the ground needs to be estimated. However, obtaining a large-scale representative estimate of RF in continental land is a challenging task and a starting basis is to focus on large countries in the world. Here, we collected a total of 2622 direct RF measurements carried out in continental China from 1988 to 2021 using 69 publications in a 494-document pool. Over the whole dataset, the RF results were classified into geographical provinces, time periods and substratum types. Mean RF values yield 180±32, 36.9±4.8, 31.1±1.0, 66.1±1.6 and 1510±100 mBq m −2 s −1 in metamorphic and igneous ( n =71), sedimentary ( n =24), Quaternary ( n =585), faulted areas ( n =832) and uranium-related sites ( n =898), respectively. Considering geology only, mean RF for China (66±11 mBq m −2 s −1 ) is larger than most large-scale estimates worldwide. To account for the distribution of soil thickness, RF values were extrapolated to representative soil thickness values per lithology using a scaling law inferred from natural sites. The inferred corrected mean RF for China yields 20.2±6.1 mBq m −2 s −1 , corresponding to a total radon-222 emission of (191±43) × 10 9 Bq s −1 , and appears compatible with available large-scale estimates in Europe or at global scale. Potential sources of systematic bias and variability, such as vegetation cover, meteorological and seasonal effects, remain. The contribution of urban areas, estimated for the first time, was found significant. Our study suggests future research directions to better constrain the RF source to the atmosphere in China, and, more generally, to extract robust global-scale representative RF values from direct RF measurements.

PERBANDINGAN METODE MULTIPLE LINEAR REGRESSION (MLR) DAN REGRESSION KRIGING (RK) DALAM PEMETAAN KETEBALAN TANAH DIGITAL

Soil thickness has a significant influence on many of earth surface processes, and it can be mapped using various methods. Digital soil mapping can be used to estimate the spatial distribution of soil thickness and can estimate the uncertainty of the soil prediction map. Digital soil mapping using regression methods such as Multiple Linear Regression (MLR) and Regression Krigging (RK) was used to estimate soil thickness of the slope of Bener Reservoir. Bener Dam is a national strategic project which is built for irrigation to improve farming quantity. The aim of this research was to determine the spatial variation of the soil thickness at the slope of Bener Reservoir. The accuracy of MLR and RK were compared to select the best soil thickness prediction. There were 212 and 53 soil thickness samples from fieldwork which were used for data training and testing, respectively. There were 5 environmental variables such as elevation, distance from river, slope, plan curvature, and topographic wetness index. R programming language with gstat, krige, and stats Packages was employed for MLR and RK prediction. The result showed that KR is more accurate than MLR.

Development and Evaluation of Relative Relief Based Soil Thickness Model: A Comparative Study in Hilly Terrain, South Korea

Soil thickness is a major parameter to better understand slope stability, surface erosion, groundwater storage, and vegetation growth. In this study, the main focus is the development and application of a relative relief (RR)-based spatial soil thickness model. Intensive field works were also carried out to gather ground-truthing soil thickness data using traditional drilling and excavation methods. The spatial distribution of soil thickness obtained from the RR model was validated with the results of the field measurements, and compared with the predictions derived from S and multiple linear regression (MLR) models, which are already known in the literature. In this study, we tested how raster resolutions (5, 10, 20, 30, 50, 60 and 90 m) influence the spatial prediction of soil thickness. Based on the comparison between the predicted soil thickness and the measured soil thickness, a map of 10 m resolution contributed reasonable delineation of soil thickness over the study area. A comparison of the predicted results was performed using the agreement coefficient (AC) which showed that the RR model has a better predictive ability (AC = 0.970) than the S (AC = 0.945) and MLR (AC = 0.710) soil thickness models. The results indicate that an adjustment to the soil thickness and spatial resolution can significantly improve the modelling efficiency.

Incorporating the Effects of Complex Soil Layering and Thickness Local Variability into Distributed Landslide Susceptibility Assessments

Incorporating the influence of soil layering and local variability into the parameterizations of physics-based numerical models for distributed landslide susceptibility assessments remains a challenge. Typical applications employ substantial simplifications including homogeneous soil units and soil-hydraulic properties assigned based only on average textural classifications; the potential impact of these assumptions is usually disregarded. We present a multi-scale approach for parameterizing the distributed Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model that accounts for site-specific spatial variations in both soil thickness and complex layering properties by defining homogeneous soil properties that vary spatially for each model grid cell. These effective properties allow TRIGRS to accurately simulate the timing and distribution of slope failures without any modification of the model structure. We implemented this approach for the carbonate ridge of Sarno Mountains (southern Italy) whose slopes are mantled by complex layered soils of pyroclastic origin. The urbanized foot slopes enveloping these mountains are among the most landslide-prone areas of Italy and have been subjected to repeated occurrences of damaging and deadly rainfall-induced flow-type shallow landslides. At this scope, a primary local-scale application of TRIGRS was calibrated on physics-based rainfall thresholds, previously determined by a coupled VS2D (version 1.3) hydrological modeling and slope stability analysis. Subsequently, by taking into account the spatial distribution of soil thickness and vertical heterogeneity of soil hydrological and mechanical properties, a distributed assessment of landslide hazard was carried out by means of TRIGRS. The combination of these approaches led to the spatial assessment of landslide hazard under different hypothetical rainfall intensities and antecedent hydrological conditions. This approach to parameterizing TRIGRS can be adapted to other spatially variable soil layering and thickness to improve hazard assessments.

Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods

Spatially continuous soil thickness data at large scales are usually not readily available and are often difficult and expensive to acquire. Various machine learning algorithms have become very popular in digital soil mapping to predict and map the spatial distribution of soil properties. Identifying the controlling environmental variables of soil thickness and selecting suitable machine learning algorithms are vitally important in modeling. In this study, 11 quantitative and four qualitative environmental variables were selected to explore the main variables that affect soil thickness. Four commonly used machine learning algorithms (multiple linear regression (MLR), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGBoost) were evaluated as individual models to separately predict and obtain a soil thickness distribution map in Henan Province, China. In addition, the two stacking ensemble models using least absolute shrinkage and selection operator (LASSO) and generalized boosted regression model (GBM) were tested and applied to build the most reliable and accurate estimation model. The results showed that variable selection was a very important part of soil thickness modeling. Topographic wetness index (TWI), slope, elevation, land use and enhanced vegetation index (EVI) were the most influential environmental variables in soil thickness modeling. Comparative results showed that the XGBoost model outperformed the MLR, RF and SVR models. Importantly, the two stacking models achieved higher performance than the single model, especially when using GBM. In terms of accuracy, the proposed stacking method explained 64.0% of the variation for soil thickness. The results of our study provide useful alternative approaches for mapping soil thickness, with potential for use with other soil properties.

Prediction of soil thicknesses in a headwater hillslope with constrained sampling data

The spatial distribution of soil thickness plays a critical role in upland hydrological and biogeochemical processes. Process-based models are thought to have great potentials for predicting soil thickness. However, applications of process-based approaches have been greatly limited by parameter estimation, which typically requires specialized laboratories. In this study, on the basis of field works, an approach for parameter estimation under steady-state assumption is developed using linear and nonlinear soil transport models. Under each model, parameter, namely the ratios between the maximum soil production rate and the diffusion coefficient can be determined. Moreover, the optimal simulation time is derived by a power function of the diffusion coefficient derived under the linear model. The method is then used in a 0.31 ha headwater hillslope in the Hemuqiao catchment, China. According to the suggested method, parameters are determined using soil thickness data collected at 45 different locations on the hillslope, and then soil thickness is predicted. We find the distribution of predicted soil thickness and the root mean square error between the measured and predicted soil thickness for all the soil pits are all the same with the estimated ratios in both models. Furthermore, the derived power function between the optimal simulation time and the diffusion coefficient is verified and the optimal simulation time is determined. Comparisons of simulations and field observations indicate that both models can achieve comparable results, although the procedure developed using the linear model is more suitable for the study area. Furthermore, the linear model is more efficient in parameter estimation, and thus is simpler to employ in practical applications. With our framework, field studies are constrained either on ridges or sideslopes, thus reducing the aimless hunting for sampling sites. This framework provides a useful approach for applying process-based geomorphic models in real catchments.

Landslide hazard mapping using limit equilibrium method with GIS application of roadway traversing mountain slopes: The case of Kitaotao Bukidnon, Philippines

The national highway section of the Municipality of Kitaotao along the Davao City route plays a vital role in the transport of goods and services in the region. However, this national highway is prone to rainfall-induced landslides. Records show that almost every year this highway is being disrupted by landslide and road clearing operations would take several hours or even up to several days. This study aimed to improve the present landslide hazard map using Limit Equilibrium Method (LEM) and GIS techniques in order to present the spatial distribution of Factor of Safety (FS) in the area. A LEM based software (SLOPE /W) was utilized in the present study and Bishop Simplified slope stability model was adopted in calculating the FS. Geotechnical properties like cohesion, friction angle and unit weight were used in the analyses. The soil thickness data gathered were correlated to the slope angle in order to determine the spatial distribution of soil thickness in the area. The LHZM revealed that areas with low to very low stability are extensively found in the southern part and some areas in the northern portion. The accuracy of the model was validated through actual field observations and also by overlying the landslide inventory over the LHZM. It was found that 77.80 % of inventoried landslides are in low to very low stability zones. The present study adapts a deterministic approach using LEM of slope stability analysis and clusters the hazard zonation in terms of the spatial variation of FS in every 20 m x 20 m slope area employing GIS techniques; hence, this provides better insight on the landslide susceptibility throughout the study area and can be used directly for engineering design purposes.

A simple geomorphic‐based analytical model for predicting the spatial distribution of soil thickness in headwater hillslopes and catchments

Soil thickness acts as an important control for headwater hydrologic processes. Yet, its spatial distribution remains one of the least understood in catchment hydrology. Analytic methods are desirable to provide a simple way for predicting soil thickness distribution over a hillslope or a catchment. In this paper, a simple geomorphic‐based analytical model is derived from the dynamic equations of soil thickness evolution in areas with no tectonic uplift or lowering since the recent geological past. The model employs terrain attributes (slope gradient, curvature, and upstream contributing area) as inputs on grid‐based DEMs for predicting soil thickness evolution over time. The analytic model is validated first on nine abstract hillslopes through comparing 10 kyr simulation results between our proposed model and the numerical solution. The model is then applied to predict soil thickness evolution over 13 kyr in the 7.9 ha Shale Hills catchment (one of the Critical Zone Observatories in the U.S. located in central Pennsylvania). Field observed and model predicted values of soil thickness are in good agreement (with a root mean squared error of 0.39 m, R2 = 0.74, and absolute errors <0.10 m in 70% of 106 sample points). Moreover, our model verifies that terrain shape and position are the first‐order control on soil thickness evolution in the headwater catchment. Therefore, the derived geomorphic‐based analytical model can be helpful in understanding soil thickness change over geological time and is useful as a simple tool for deriving spatially distributed soil thickness needed for hydrologic modeling.

Predicting the thickness and aeolian fraction of soils in upland watersheds of the Mojave Desert

Soil thickness and soil texture, and their spatial variability at hillslope scales, are important inputs for hydrologic models. Recent studies have demonstrated that mechanistic models can accurately predict the spatial distribution of soil thickness in areas where soil thickness varies gradually along hillslope profiles. To date, however, these models have not been tested in arid regions, where soils are often thin and highly variable at small scales (i.e., 1–10m) and where the influence of aeolian accretion on soil thickness and soil texture may be significant. In addition, most of the data available for the fraction of the soil derived from aeolian deposition (which can serve as a proxy for soil texture) originate from soils developed on stable depositional landforms; data for aeolian fractions from upland soils are scarce. In this study, we developed and tested a numerical model for the prediction of thickness and aeolian fraction of soils in arid upland areas. The model assumes a long-term balance between the rates of soil production from bedrock, aeolian accretion, and soil erosion. Rates of soil production are quantified in the model using the exponential and humped soil production functions, and soil erosion rates are quantified using the nonlinear depth- and slope-dependent transport functions, assuming that only the upper soil horizons (A+B) are capable of transport. The rate of aeolian deposition is assumed to be spatially uniform. We calibrated the three free parameters of the model at a site in the Mojave Desert with a granite lithology using a combination of 1) field-measured data for soil thickness and 2) estimation of the aeolian fraction in the soil using laboratory analysis of immobile element concentrations. We validated the model results in three nearby sites using a combination of measured soil thickness in soil pits and the presence/absence of soil, and laboratory analysis of the aeolian fraction of soil. The study area is characterized by generally thin soils (<1m) and extreme variability in soil thickness at small scales. The model does a relatively poor job of predicting the soil thickness on a point-by-point basis due to the extreme small-scale variability, but the model does a much better job at predicting the average soil thickness at the scale of tens of meters and greater. The model predicts soil thickness with a root-mean-square error of 15–32cm in regions characterized by poorly- to moderately-developed calcic soils, but fails to accurately predict soil thickness at a site characterized by well-developed soils (carbonate stages II–IV) and low-relief topography. The model predicts the aeolian fraction and content in soil with root-mean-square errors of 10–13% and 8cm, respectively. The prediction of aeolian content captures the overall pattern of increasing trend in aeolian content towards the lower portions of slopes. Our results show that the model can simultaneously predict the spatial variation of thickness and aeolian fraction/content of soils even in arid, dust-rich environments where soils are thin and spatial variability is extreme.

Comparison of Soil Thickness in a Zero-Order Basin in the Oregon Coast Range Using a Soil Probe and Electrical Resistivity Tomography

AbstractAccurate estimation of the soil thickness distribution in steepland drainage basins is essential for understanding ecosystem and subsurface response to infiltration. One important aspect of this characterization is assessing the heavy and antecedent rainfall conditions that lead to shallow landsliding. In this paper, we investigate the direct current (DC) resistivity method as a tool for quickly estimating soil thickness over a steep (33–40°) zero-order basin in the Oregon Coast Range, a landslide prone region. Point measurements throughout the basin showed bedrock depths between 0.55 and 3.2 m. Resistivity of soil and bedrock samples collected from the site was measured for degrees of saturation between 40 and 92%. Resistivity of the soil was typically higher than that of the bedrock for degrees of saturation lower than 70%. Results from the laboratory measurements and point-depth measurements were used in a numerical model to evaluate the resistivity contrast at the soil-bedrock interface. A dec...

Combined geophysical methods for detecting soil thickness distribution on a weathered granitic hillslope

The usefulness of electrical resistivity imaging (ERI) as a highly accurate method for determining the soil thickness distribution on hillslopes was validated by combining intensive measurements using invasive methods, i.e., cone penetration testing and boreholes, with ERI in three granitic watersheds. Areas of high electrical resistivity (ρ) contrast reflecting soil–bedrock interfaces were found in all three study watersheds. However, ρ values of soil and weathered granite just below the soil mantle varied over a relatively wide range at each site, as well as considerably from site to site. The patterns of low–high contrast in ρ profiles, reflecting the soil–bedrock interface, also differed from site to site despite similarly dry conditions. Differences in the water retention characteristics of soil and weathered granitic bedrock, as found by a previous study of bedrock hydrological properties, may have been a major factor in the observed subsurface ρ variations. The ERI method, with electrode spacing of 0.5 to 2.0m, was successful in determining soil thickness distributions ranging from about 0.5 to 3m depth based on its ability to detect high contrast in ρ in the subsurface zone. Closer electrode spacings are expected to more sensitively reveal the distribution of ground material properties and thus more accurately replicate the soil–bedrock interface. ERI failed to clearly identify the soil–bedrock interface at some points along our measurement lines because of local intermediate materials with different properties such as unconsolidated soil and clayey intermediation just below the soil–bedrock interface. Two types of seismic survey (SS) techniques were also used, combining seismic refraction (SR) and the surface wave method (SWM) with the ERI method in a granitic watershed to compare ERI with other geophysical methods. The profile of S-wave velocity (Vs) by SWM also reasonably duplicated the soil–bedrock interface; the Vs profile showed larger variation in lateral direction and corresponded to the soil thickness distribution better than the P-wave velocity (Vp) profile by SR. The combined use of ERI and SWM may be more effective in detecting the soil–bedrock interface because each method compensates for the deficiencies of the other method.

Influences of spatial distribution of soil thickness on shallow landslide prediction

Shallow landslides usually occur during hevy rainfall and result in casualties and property losses. Thus, the possible locations where landslides are likely to occur must be identified in advance in order to avoid or reduce the harm they cause. When performing a slope-instability analysis, soil thickness is an important factor; however, soil thickness information from landslide-prone areas is rarely obtained. The objective of this study is to realize the influences of spatial distribution of soil thickness on shallow landslide prediction. Three different spatial soil-thickness distributions were applied to perform a slope-instability analysis, and uniform-distributed soil thicknesses from 0.4 m to 2.0 m were also applied for comparison. Geomorphologic information and hydrological records from a landslide-prone area in southern Taiwan were collected. Results show that the spatial distribution of soil thickness related to wetness index provides a reasonable estimation in order to avoid an over-prediction for landslide-prone areas or an under-prediction for stable areas. The analytical procedure used in this study is a simple way for assessing hillslope instability for shallow landslide prediction.

Prediction of landslide occurrence based on slope-instability analysis and hydrological model simulation

Hillslope failure usually occurs as soil resistance deteriorates in the presence of the acting stress developed by a rising groundwater level during rainstorms. The present study adopted a slope-instability analysis and a hydrological model for landslide prediction during heavy rainstorms. Variation of the groundwater table on hillslope was simulated by using the hydrological model and then the temporal groundwater level at each grid was substituted into the slope-instability analysis to determine the instability of the grids in watersheds for prediction of massive landslides. Hydrological records from two landslide-prone areas in northern Taiwan were collected. Digital elevation model was adopted to obtain the geomorphologic factors required for the slope-instability analysis and the hydrological model. The spatial distribution of soil thickness required for performing the infinite slope model was estimated by using a wetness index. Results showed that the temporal variation of the percentage of unstable grids in the study watersheds basically followed the variation of rainfall hyetographs. The percentage of the unstable grids reached a maximum value when the centroid of the hyetograph passed. A comparison between the landslide records and the model analytical results revealed that a massive landslide might occur if more than 50% of the grids in the subwatershed were classified as unstable in the study areas. The predicted time and location of landslide occurrence were consistent with those obtained from field investigations. It is therefore considered promising to apply the developed analytical method for landslide warning to alleviate the loss of lives and property.

Distribution and characteristics of soil thickness and effects upon water storage in forested areas of Cambodia

AbstractSoil layers play an important role in hydrological processes, particularly in forested basins. For example, thick soil layers can store rainwater temporarily and drain it gradually. In the Mekong River Basin, especially in Cambodia, evergreen and deciduous forests are widely distributed. However, we have no data on soil conditions such as thickness and hardness that affect water storage capacity in forested areas. To clarify the water storage role of forest soils in the lower reaches of the Mekong River Basin, we investigated the distribution of soil thickness using a handy dynamic cone penetrometer in three Cambodian provinces: Kampong Thom, Kratie, and Mondul Kiri. Soil thickness was greater than 8 m in a deciduous forest in Kampong Thom. In some evergreen forests, apparent soil thickness varied from the rainy to dry seasons because of specific grain size distributions; for example, the soils were rock hard in the dry season and soft in the rainy season. In Kratie, deciduous forests prevailed and soil thickness was shallow at less than 3 m. In Mondul Kiri, forests were mainly formed on a basaltic plateau, and soil thickness was greater than 7 m. Soil thickness and hardness differed among the soil types in these three provinces, as did water storage capacity. The maximum water storage capacity was 1350 mm in an evergreen forest distributed on Acrisols in Kampong Thom. In contrast, the minimum water storage capacity was 225 mm in a deciduous forest distributed on Leptosols in Mondul Kiri. We constructed a soil thickness map for the study area in Kampong Thom that was mainly covered with evergreen forest; the distribution of soil thickness was related to elevation, with soil thickness increasing 0·13 m per 1 m increase in elevation, except in a deciduous mixed swamp forest area. Copyright © 2008 John Wiley &amp; Sons, Ltd.

Spatial distribution of soil structure in a suburban forest catchment and its effect on spatio-temporal soil moisture and runoff fluctuations

Intensive hydrological observations and model calculations reveal that heterogeneous distributions of soil thickness and soil physical characteristics greatly influence long-term spatio-temporal fluctuations of soil moisture and runoff in a suburban forest, which has rich (thickly wooded) forest but undeveloped forest soil. This paper presents a simple, physical-contour-based model that can describe topography and spatially heterogeneous distributions, and uses the model to clarify how spatial heterogeneous variability influences long-term rainfall runoff processes. The 1.5-ha study catchment features deciduous-evergreen secondary forest and is located near an urban area about 30 km southeast of Nagoya City, in central Japan. This model can simulate saturated/unsaturated throughflow, excess flow, and soil moisture, and has not been calibrated. No input parameters have been fitted and all input parameters are based on published and measured data. Rainfall runoff processes are affected by structural distributions of colluvial/residual soil and spatial heterogeneous distributions of soil thickness. The heterogeneous soil thickness distribution contributes, in particular, to headwater conservation, which moderates storm flow and inhibits drought water flow. The magnitude of headwater conservation and the alignment of individual hillslope elements help to control runoff. The mechanism is attributable to the greater degree of headwater conservation in a suburban forest compared with a homogeneous natural forest. The model also clarifies and predicts water circulation on a larger scale than has been possible to date.