Infiltration-soil moisture redistribution under natural conditions: experimental evidence as a guideline for realizing simulation models

Abstract. The evolution in time, t, of the experimental soil moisture vertical profile under natural conditions is investigated in order to address the corresponding simulation modelling. The measurements were conducted in a plot with a bare silty loam soil. The soil water content, θ, was continuously monitored at different depths, z, using a Time Domain Reflectometry (TDR) system. Four buriable three-rod waveguides were inserted horizontally at different depths (5, 15, 25 and 35 cm). In addition, we used sensors of air temperature and relative humidity, wind speed, solar radiation, evaporation and rain as supports for the application of selected simulation models, as well as for the detection of elements leading to their improvement. The results indicate that, under natural conditions, very different trends of the θ(z, t) function can be observed in the given fine-textured soil, where the formation of a sealing layer over the parent soil requires an adjustment of the simulation modelling commonly used for hydrological applications. In particular, because of the considerable variations in the shape of the moisture content vertical profile as a function of time, a generalization of the existing models should incorporate a first approximation of the variability in time of the saturated hydraulic conductivity, K1s, of the uppermost soil. This conclusion is supported by the fact that the observed shape of θ(z, t) can be appropriately reproduced by adopting the proposed approach with K1s kept constant during each rainfall event but considered variable from event to event, however the observed rainfall rate and the occurrence of freeze-thaw cycles with high soil moisture contents have to be explicitly incorporated in a functional form for K1s(t)

Development and analysis of the Soil Water Infiltration Global database.

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements (~76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76% of the experimental sites with agricultural land use as the dominant type (~40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it

A statistical approach for the assessment of the saturated hydraulic conductivity applied to an Austrian region

Study region: This study refers to the Hydrological Open Air Laboratory (HOAL) watershed, located in South-West Austria. Study focus: The spatial variability of saturated hydraulic conductivity, Ks, is sometimes synthetized by geometric mean, K̃s, and coefficient of variation, while areal-average infiltration models rely upon the arithmetic mean, K¯sl, associated to log-transformed Ks, and relative coefficient of variation, CVal. Robust estimation of K¯sl and CVal, as well as of K̃s and associated coefficient of variation, CVg,would require a large number of Ks observations. The determination of the minimum number of Ks measurements, n*, for obtaining sufficiently accurate values of each aforementioned quantity over an area is an open issue addressed here. A statistical approach has been applied to Ks datasets on three grassy plots for an uncertainty analysis based on the non-parametric bootstrap method with replacement. The uncertainty of each quantity has been derived for different observation numbers and areas. New hydrological insights for the region: Considering different sub-regions in the largest plot the uncertainty is almost invariant with increasing the sub-region area beyond a threshold. Furthermore, for a given n*, the uncertainty of K¯sl and CVal is much smaller than that of K̃s and CVg. Our approach defines a methodology for determining over an area the n* associated to a fixed uncertainty level in the joint estimation of the selected quantities. Guidelines for investigations over different plots are also proposed

A plot-scale uncertainty analysis of saturated hydraulic conductivity of a clay soil

Simulating soil hydrological processes at the plot or field scale requires using spatially representative values of the saturated soil hydraulic conductivity, Ks. Sampling campaigns should yield a reliable mean of Ks with a sustainable workload since measuring Ks at many points is challenging. Uncertainty analysis can be used to determine the lowest number of measurements that yield a mean Ks value with a specified accuracy level. Potential and limitations of this analysis were tested in this investigation for different extents of the sampled area and sampling densities. A clay soil was sampled intensively on two plots (plot area = 44 m2), two dates and using both small (0.15 m in diameter) and large (0.30 m) rings. With the small rings, intensively sampling an appropriate portion of the total plot area should be enough to establish the number of measurements yielding a certain accuracy level for the entire plot since this level remained nearly constant when the same number of measurements was performed on larger areas. Moreover, for these areas, the spatial resolution of the measurements did not influence appreciably the width of the confidence interval of the mean Ks value. However, working with larger rings was recommended since, in this case, the sampled area did not affect at all normalized confidence levels that, in addition, varied only a little with the number of the considered measurements of Ks. In practice, characterizing the plots required about 20 and 10 measurements with the smaller and the larger rings, respectively. The uncertainty analysis appears promising to plan practically sustainable soil sampling campaigns