Evaluation of different procedures to interpolate particle-size distributions to achieve compatibility within soil databases

Many environmental and agricultural problems are not restricted to national boundaries and therefore require international cooperation if solutions are to be found. Often, these solutions require the ability to use soil data as input in simulation models, however, despite a number of recognised international standards, soil data are rarely compatible across national frontiers. This problem was encountered when creating the draulic operties of uropean oils (HYPRES) database. The data, which includes particle-size distributions, were collected from 20 institutions in 12 countries. Only a few of these institutions adhered strictly to a recognised international system. Therefore, interpolation of the cumulative particle-size distribution was required to achieve compatibility of particle-size distributions within the HYPRES database. In this study, four different interpolation procedures were evaluated. The accuracy of the different procedures was found to vary with size intervals between measured points of the particle-size distribution. The loglinear interpolation of the cumulative particle-size distribution has previously been used in various studies but was found to give the least accurate estimation of the four procedures. Fitting the Gompertz curve, which is a special asymmetric type of curve described by a closed-form equation, showed less sensitivity to size intervals between measured points. However, interpolation within some of the particle-size distributions was not sufficiently accurate and this procedure could not be applied to particle-size distributions where the number of measured size fractions was less than the number of model parameters. Fitting a nonparametric spline function to the particle-size distributions showed a considerable increase in accuracy of the interpolation with decreasing size intervals between measured points. As a novel approach, the similarity procedure was introduced which does not use any mathematical interpolation functions. It uses an external source of soil information from which soils are selected with particle-size distributions that match the distribution of the soil under investigation. This similarity procedure was capable of giving the most accurate interpolations. Once an extensive external reference data set with well-quantified particle-size distributions is available, the similarity procedure becomes a very powerful tool for interpolations. Based on the number and distribution of measured points on the particle-size distributions, a general rule was formulated to decide whether to fit a spline function or use the novel similarity procedure to estimate missing values. Results of this study were used to classify all soils in the HYPRES database into the same soil texture classes used in the 1:1.000.000 scale Soil Geographical Database of Europe.     

Use of class pedotransfer functions based on texture and bulk density of clods to generate water retention curves

Abstract. Water retention properties of 219 horizons were measured in Cambisols, Luvisols and Fluvisols, mainly from the Paris basin. We derived class pedotransfer functions (class PTFs) based on texture alone and in a second stage class PTFs based on classes combining texture and clod bulk density. The performance of these two types of PTFs were discussed at −330 and −15000 hPa water potential on an independent set of 221 horizons. Results showed that PTFs based on sets grouped by texture and clod bulk density provide estimates with an accuracy that is (i) greater than with class PTFs based on texture alone, and (ii) similar to the estimation accuracy recorded with continuous PTFs. As a consequence, the lack of interest in class PTFs should be reconsidered to bridge the gap between the available basic soil data and hydraulic properties which are generally missing, particularly when pertinent soil characteristics can be derived from the data available in soil databases. The two types of class PTFs providing gravimetric water contents at seven water potentials ranging from −10 to −15 000 hPa were converted to volumetric water content using the soil bulk density. Finally, the parameters of van Genuchten's water retention curve model were computed for every class PTF.     

Hydraulic variability in space and time in a dark red latosol of the tropics

Detailed information of the variability of soil properties and processes in space and time is presented for a dark red latosol (Alfisol) of the county of Piracicaba, S.P., Brazil. Data were collected on 25 plots along a 125 m transect during the years 1989–1991, and consisted of the soil water content θ in the 0–150 cm soil layer and the water pressure heads h at 135 and 165 cm depths. These raw data were used to characterize variabilities in space and time using classical statistics and in a second step to analyse the difficulties in calculating soil water storage, soil hydraulic conductivities, hydraulic gradients, soil water fluxes and water balances. In general, there was a great variability of hydraulic properties and processes, which is fairly constant in time in the case of basic data like soil water content and potential, but not in the case of calculated data like hydraulic conductivities and gradients, and soil water flux densities. A discussion is presented of the difficulties of using Darcy's equation to estimate soil water flux densities due to the exponential K(θ) and K(h) relationships of the hydraulic conductivity K, and of the influence of variability in space and time on the establishment of water balance components.     

Estimating the water retention curve from soil properties: comparison of linear, nonlinear and concomitant variable methods

The unsaturated soil hydraulic functions involving the soil–water retention curve (SWRC) and the hydraulic conductivity provide useful integrated indices of soil quality. Existing and newly devised methods were used to formulate pedotransfer functions (PTFs) that predict the SWRC from readily available soil data. The PTFs were calibrated using a large soils database from Hungary. The database contains measured soil–water retention data, the dry bulk density, sand, silt and clay percentages, and the organic matter content of 305 soil layers from some 80 soil profiles. A three-parameter van Genuchten type function was fitted to the measured retention data to obtain SWRC parameters for each soil sample in the database. Using a quasi-random procedure, the database was divided into “evaluation” (EVAL) and “test” (TEST) parts containing 225 and 80 soil samples, respectively. Linear PTFs for the SWRC parameters were calculated for the EVAL database. The PTFs used for this purpose particle-size percentages, dry bulk density, organic matter content, and the sand/silt ratio, as well as simple transforms (such as logarithms and products) of these independent variables. Of the various independent variables, the eight most significant were used to calculate the different PTFs. A nonlinear (NL) predictive method was obtained by substituting the linear PTFs directly into the SWRC equation, and subsequently adjusting the PTF parameters to all retention data of the EVAL database. The estimation error (SSQ) and efficiency (EE) were used to compare the effectiveness of the linear and nonlinearly adjusted PTFs. We found that EE of the EVAL and the TEST databases increased by 4 and 7%, respectively, using the second nonlinear optimization approach. To further increase EE, one measured retention data point was used as an additional (concomitant) variable in the PTFs. Using the 20 kPa water retention data point in the linear PTFs improved the EE by about 25% for the TEST data set. Nonlinear adjustment of the concomitant variable PTF using the 20 kPa retention data point as concomitant variable produced the best PTF. This PTF produced EE values of 93 and 88% for the EVAL and TEST soil data sets, respectively.     

Evaluation of pedotransfer functions predicting hydraulic properties of soils and deeper sediments

Eight pedotransfer functions (PTF) originally calibrated to soil data are used for evaluation of hydraulic properties of soils and deeper sediments. Only PTFs are considered which had shown good results in previous investigations. Two data sets were used for this purpose: a data set of measured pressure heads vs. water contents of 347 soil horizons (802 measured pairs) from Bavaria (Southern Germany) and a data set of 39 undisturbed samples of tertiary sediments from deeper ground (down to 100 m depth) in the molasse basin north of the Alps, containing 840 measured water contents vs. pressure head and unsaturated hydraulic conductivity. A statistical analysis of the PTFs shows that their performance is quite similar with respect to predicting soil water contents. Less satisfactory results were obtained when the PTFs were applied to prediction of water content of sediments from deeper ground. The predicted unsaturated hydraulic conductivities show about the same uncertainty as for soils in a previous study. Systematic deviations of predicted values indicate that an adaptation of the PTFs to the specific conditions of deeper ground should be possible in order to improve predictions.     

青海三江源地区土壤水分常数转换函数的建立与比较

利用土壤理化性质数据建立转换函数是间接获得土壤水力参数的重要手段之一。基于测定的土壤理化性质和土壤水分常数数据,本文采用回归分析、BP神经网络和基于BP神经网络的Rosetta模型3种方式分别建立了青海三江源地区土壤饱和含水量、毛管持水量和田间持水量的转换函数,并对其预测精度进行了比较。结果表明:(1)回归分析方法总体预测效果比较理想,特别是田间持水量的平均误差(ME)和均方根误差(RMSE)都在3.397%以下,决定系数(R2)高达0.868;(2)BP神经网络方法的预测效果非常理想,各土壤水分常数平均误差和均方根误差都在4.685%以下,并且决定系数均在0.857以上;(3)Rosetta模型的预测效果相对较差,特别是饱和含水量和毛管持水量,平均误差(ME)和均方根误差(RMSE)相对较大,决定系数(R2)相对较小。3种方式中,BP神经网络方法所建立的毛管持水量和饱和含水量转换函数均为最佳,回归方法所建立的田间持水量的转换函数要好于BP神经网络方法和Rosetta模型,Rosetta模型对土壤水分常数的预测效果不如其他两种方式。研究可为青海三江源地区土壤水力特性参数研究以及区域尺度上土壤水分估算提供科学依据。     

Soil structure and pedotransfer functions

Accurate estimates of soil hydraulic properties from other soil characteristics using pedotransfer functions (PTFs) are in demand in many applications, and soil structural characteristics are natural candidates for improving PTFs. Soil survey provides mostly categorical data about soil structure. Many available characteristics such as bulk density, aggregate distribution, and penetration resistance reflect not only structural but also other soil properties. Our objective here is to provoke a discussion of the value of structural information in modelling water transport in soils. Two case studies are presented. Data from the US National Pedon Characterization database are used to estimate soil water retention from categorical field‐determined structural and textural classes. Regression‐tree estimates have the same accuracy as those from textural class as determined in the laboratory. Grade of structure appears to be a strong predictor of water retention at ?33 kPa and ?1500 kPa. Data from the UNSODA database are used to compare field and laboratory soil water retention. The field‐measured retention is significantly less than that measured in the laboratory for soils with a sand content of less than 50%. This could be explained by Rieu and Sposito's theory of scaling in soil structure. Our results suggest a close relationship between structure observed at the soil horizon scale and structure at finer scales affecting water retention of soil clods. Finally we indicate research needs, including (i) quantitative characterization of the field soil structure, (ii) an across‐scale modelling of soil structure to use fine‐scale data for coarse‐scale PTFs, (iii) the need to understand the effects of soil structure on the performance of various methods available to measure soil hydraulic properties, and (iv) further studies of ways to use soil–landscape relationships to estimate variations of soil hydraulic properties across large areas of land.