Due to inadequate data support, existing algorithms used to estimate soil hydraulic conductivity, K, in (eco)hydrological models ignore the effects of key site factors such as land use and climate and underplay the significant effects of soil structure on water flow at and near saturation. These limitations may introduce serious bias and error into predictions of terrestrial water balances and soil moisture status, and thus plant growth and rates of biogeochemical processes. To resolve these issues, we collated a new global database of hydraulic conductivity measured by tension infiltrometer under field conditions. The results of our analyses on this data set contrast markedly with those of existing algorithms used to estimate K. For example, saturated hydraulic conductivity, K s , in the topsoil (< 0.3 m depth) was found to be only weakly related to texture. Instead, the data suggests that K s depends more strongly on bulk density, organic carbon content and land use. In this respect, organic carbon was negatively correlated with K s , presumably due to water repellency, while K s at arable sites was, on average, ca. 2-3 times smaller than under natural vegetation, forests and perennial agriculture. The data also clearly demonstrates that clay soils have smaller K in the soil matrix and thus a larger contribution of soil macropores to K at and near saturation.Published by Copernicus Publications on behalf of the European Geosciences Union.

Dual-permeability models can account for the strong influence of soil macropores on contaminant transport, but their predictive application is hampered by the difficulty in estimating a priori values for the rate coefficient controlling lateral mass exchange between the two flow domains. Our aim was to investigate the possibility of estimating the mass transfer coefficient in the dual-permeability model MACRO from fundamental soil properties. To this end, we calibrated MACRO against transient chloride leaching tests carried out on 33 undisturbed soil columns taken from the topsoils of three agricultural fields, characterized by a wide range of texture and organic matter content. The global search algorithm SUFI was used to derive optimum values of the mass transfer coefficient in each column. A Monte Carlo procedure was carried out on two columns to investigate the stability of the estimates in the presence of potential errors in fixed parameters. Despite such errors, c. 50% of the variation in mass transfer for this data set could be explained by two fundamental soil properties: the geometric mean particle size and the organic matter content. Soils of coarser texture and larger organic matter content were characterized by stronger lateral mass exchange and therefore weaker macropore flow. Harrowing and a 6-year grass ley also reduced the extent of non-equilibrium transport. Our results suggest that a robust functional description of the effects of soil structure on chemical transport is possible for predictive management applications of dual-permeability models across larger areas (i.e. mapping leaching risks at field, farm and catchment scales).

The extent to which a fast, nonequilibrium, and highly transient pore‐scale process such as macropore flow can be predicted is very often debated, although little research has been conducted to investigate this issue. The validity of approaches to “upscaling” transport predictions from pore through Darcy to landscape scales critically depends on the answer to this question. We developed a simple conceptual model of soil susceptibility to macropore flow, based on a synthesis of existing experimental information. The conceptual model takes the form of a decision tree, which classifies soil horizons into one of four susceptibility classes on the basis of easily available site and soil factors. The model was tested against an independent database of tracer breakthrough experiments on undisturbed soil columns collated from the literature (n = 52), using the pore volumes drained at peak solute concentration, tp, as a measure of the strength of macropore flow. Analysis of variance for tp as a function of susceptibility class showed that the overall model was significant. A significant proportion of the residual variation in tp could be attributed to variation in clay content within one of the susceptibility classes. Some important sources of experimental error were also identified that may account for much of the remaining unexplained variation. We concluded that macropore flow is predictable to a sufficient degree from easily available soil properties and site factors. The simple classification tree developed in this study could be used to support hydropedological approaches to quantifying the spatial distribution of contaminant leaching at the landscape scale by providing the basis for class pedotransfer functions to estimate model parameters related to macropore flow. Such an approach has been implemented in the European project FOOTPRINT.

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Due to inadequate data support, existing algorithms used to estimate soil hydraulic conductivity, K, in (eco)hydrological models ignore the effects of key site factors such as land use and climate and neglect the significant effects of soil structure on water flow at and near saturation. These limitations may introduce serious bias and error into predictions of terrestrial water balances and soil moisture status, and thus plant growth and rates of biogeochemical processes. To resolve these issues, we collated a new global database of hydraulic conductivity measured by tension infiltrometer under field conditions. The results of our analyses on this dataset contrast markedly with those of existing algorithms used to estimate K. We show that the saturated hydraulic conductivity, K_s, in topsoil (< 0.3 m depth) is only very weakly related to texture. Instead, K_s depends more strongly on bulk density, organic carbon content and land use and management factors. In this respect, the results show that arable sites have, on average, ca. 2 to 3 times smaller K_s values than natural vegetation, forests and perennial agriculture. The data also clearly demonstrates that clay soils have smaller K in the soil matrix and thus a larger contribution of soil macropores to K at and near saturation

The objective of this study was to identify the main sources of variation in pesticide losses at field and catchment scales using the dual permeability model MACRO. Stochastic simulations of the leaching of the herbicide MCPA (4-chloro-2-methylphenoxyacetic acid) were compared with seven years of measured concentrations in a stream draining a small agricultural catchment and one year of measured concentrations at the outlet of a field located within the catchment. MACRO was parameterized from measured probability distributions accounting for spatial variability of soil properties and local pedotransfer functions derived from information gathered in field- and catchment-scale soil surveys. At the field scale, a single deterministic simulation using the means of the input distributions was also performed. The deterministic run failed to reproduce the summer outflows when most leaching occurred, and greatly underestimated pesticide leaching. In contrast, the stochastic simulations successfully predicted the hydrologic response of the field and catchment and there was a good resemblance between the simulations and measured MCPA concentrations at the field outlet. At the catchment scale, the stochastic approach underestimated the concentrations of MCPA in the stream, probably mostly due to point sources, but perhaps also because the distributions used for the input variables did not accurately reflect conditions in the catchment. Sensitivity analyses showed that the most important factors affecting MACRO modeled diffuse MCPA losses from this catchment were soil properties controlling macropore flow, precipitation following application, and organic carbon content.

All rights reserved. No part of this periodical may be reproduced or transmi ed in any form or by any means, electronic or mechanical, including photocopying, recording, or any informa on storage and retrieval system, without permission in wri ng from the publisher. O R Channels made by deep-burrowing (anecic) earthworms are known to strongly aff ect soil water fl ow and increase the leaching risk of agricultural pollutants. A classifi ca on tree that predicts the abundance of the anecic earthworm Lumbricus terrestris L. from readily available survey informa on (land use, management prac ces, and soil texture) was derived from literature data (n = 86). The most important factors favoring L. terrestris were perennial land use, no-ll arable cropping, organic addi ons (i.e., manure), and medium-textured soil. The classifi ca on scheme correctly predicted earthworm abundance for 71% of the studies in the database. Among other poten al applica ons, the classifi ca on tree could be used to iden fy areas at risk from groundwater pollu on in agricultural landscapes and to support catchment-and regional-scale models of contaminant leaching in the vadose zone.

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