T. Mayr

Pedotransfer functions to estimate soil water retention parameters for a modified Brooks-Corey type model

Abstract This paper presents pedotransfer functions to estimate soil water retention parameters of the Hutson–Cass modified form of the Brooks–Corey equation from soil texture, bulk density and organic carbon content. Multiple regression equations are developed from a subset (286 soil horizons) of the soil physical properties database of England and Wales. For this dependent dataset, the average root mean square error (RMSE) in soil water content was 0.043 m 3 m −3 , while ca. 68% of the entries had RMSE values less than 0.05 m 3 m −3 . The functions were then validated against the remaining 1678 soil horizons in the database. This independent test gave a mean RMSE nearly as small (0.048 m 3 m −3 ) as that obtained for the original dataset. This degree of accuracy is considered acceptable, given the simple form assumed for the water retention function. The functions were shown to be robust, except for organic horizons (organic carbon content >5%) and/or for soils of small bulk density ( −3 ).

MACRO—DB: a decision-support tool for assessing pesticide fate and mobility in soils

Abstract A decision-support tool (MACRO—DB) for predicting pesticide fate and mobility in soils is described. MACRO—DB consists of soil, pesticide, climate and crop databases linked to parameter estimation routines and a simulation model (MACRO). The system currently allows access to three soils databases: SEISMIC ( c. 400 UK benchmark soils), MARKDATA (26 Swedish soils), and a database that the user can develop independently. Automatic estimation procedures (pedo-transfer functions) translate the soil information into model parameter values. Two pesticide databases are available. The PETE database contains information for over 600 common compounds. The user can also develop a parallel database for new compounds. Sorption and degradation constants are calculated automatically, combining soil and compound properties. A weather database contains long-term daily meteorological data and a weather generator enables synthetic daily weather data to be derived from long-term average climatic data. A separate database contains information on typical planting and harvest dates, root depths, etc. for some common agricultural crops. To account for parameter uncertainty, the user can simulate ‘worst-case’ or ‘average-case’ soil scenarios, utilizing available information on the mean and typical ranges of soil organic carbon content and pH. The user can view and analyse simulation results with flexible, in-built, graphical procedures.

SWBCM: a soil water balance capacity model for environmental applications in the UK

Abstract The paper presents a daily-time step, multi-horizon capacity model of soil-water balance (SWBCM—Soil Water Balance Capacity Model) suitable for ecological and environmental applications investigating the spatial and temporal variability of soil water content determined by changes in soil hydraulic conductivity, soil water storage capacity and the pathways of water movement through the soil and across soil types. SWBCM simulates soil water content at horizon level and encompasses limits on the amount of drainage from one horizon to the next to allow the formation of temporary perched water tables, lateral drainage, matric potential and surface runoff. The model incorporates a dynamic sub-model of grass growth (SWARD—Dowle, K., Armstrong, A.C., 1990. A model for investment appraisal of grassland drainage schemes on farms in the UK. Agric. Water Manage. 18, 101–120; Armstrong, A.C., Castel, D.A., Tyson, K.C., 1995. SWARD: a model of grass growth and the economic utilisation of grassland. In: Pereira L.S., van den Broek B.J., Kabat P., Allen R.G. (Eds.), Crop-Water Simulation Models in Practice. Wageningen Press, Wageningen, The Netherlands, pp. 189–197). SWBCM’s predictive ability is tested across a range of soil types under permanent grass in the UK and outputs are compared with predictions made by MACRO (Jarvis, N.J., 1994. The MACRO Model Version 3.1. Technical Description and Sample Simulations. Swedish University of Agricultural Sciences, Department of Soil Sciences, Reports and Dissertations 19, Uppsala, Sweden, 51 pp.), a mechanistic solute transport model which incorporates a physically-based preferential flow model in which total soil porosity is divided into two flow domains (macro-pores and micro-pores), each characterised by a flow rate; soil water flow in the micro-pore domain is modelled using Richards’ equation. In the modelling experiment, SWBCM simulations have been shown to provide good approximations of point-scale experimental data under a range of soil, climate and drainage management conditions in the UK. SWBCM simulations are close to those developed by the mechanistic MACRO model, suggesting that the capacity model can be applied to describe the water balance of multi-horizon UK soil profiles. The modelling approach used is considered to be applicable to the wide range of soil lower boundary conditions, ranging from free-draining to impermeable, occurring in the field and to simulate transient perched water tables that commonly occur in most temperate, high latitude countries such as the UK.

Landscape scale estimation of soil carbon stock using 3D modelling

Soil C is the largest pool of carbon in the terrestrial biosphere, and yet the processes of C accumulation, transformation and loss are poorly accounted for. This, in part, is due to the fact that soil C is not uniformly distributed through the soil depth profile and most current landscape level predictions of C do not adequately account the vertical distribution of soil C. In this study, we apply a method based on simple soil specific depth functions to map the soil C stock in three-dimensions at landscape scale. We used soil C and bulk density data from the Soil Survey for England and Wales to map an area in the West Midlands region of approximately 13,948 km(2). We applied a method which describes the variation through the soil profile and interpolates this across the landscape using well established soil drivers such as relief, land cover and geology. The results indicate that this mapping method can effectively reproduce the observed variation in the soil profiles samples. The mapping results were validated using cross validation and an independent validation. The cross-validation resulted in an R(2) of 36% for soil C and 44% for BULKD. These results are generally in line with previous validated studies. In addition, an independent validation was undertaken, comparing the predictions against the National Soil Inventory (NSI) dataset. The majority of the residuals of this validation are between ± 5% of soil C. This indicates high level of accuracy in replicating topsoil values. In addition, the results were compared to a previous study estimating the carbon stock of the UK. We discuss the implications of our results within the context of soil C loss factors such as erosion and the impact on regional C process models.

Two Methods for Using Legacy Data in Digital Soil Mapping

Legacy data are useful sources of information on the spatial variation of soil properties. There are, however, problems using legacy data, and in this paper we explore some of these problems. A common issue is often the uneven sample distribution over geographical and predictor space and the problems this generates for the subsequent modelling efforts. Furthermore legacy soil data often has a mixture qualitative and quantitative data. The current need is for quantitative data, whereas the available datasets are often qualitative; e.g. auger bores. In this paper we compare two methods:(i) a Generalized Linear modelling (GZLM) approach which uses scarce,measured soil property data and (ii) Bayesian Belief networks (BBN) which uses extensive but generic values of the soil property, linked to soil classes. We used digital soil mapping covariates such as small scale soil maps, geology, digital terrain model, climate data and landscape position in order to predict continuous surfaces for sand, silt, clay, bulk density and organic carbon. The objective is to present a qualitative comparison between the two methods, as a direct comparison was not possible due to the number and distribution of the legacy data. We found that the GZLM approach was significantly impacted by an uneven sampling of the predictor space. This study suggests that a more generalist approach such as BBN is better in the absence of few hard data but in the presence of many soft data.