1,721,083 research outputs found
Automated single ring infiltrometer with a low-cost microcontroller circuit
No full textA method to automate data collection with a compact infiltrometer under constant head conditions was developed. The infiltrometer consists of a containment ring with a small quasi-constant head of water (i.e., 2–3 mm) that is controlled by a Mariotte reservoir and a data acquisition system based on the open source microcontroller platform Arduino and a differential pressure transducer. The presented design can be easily reproduced and operated.
The infiltrometer was tested in a citrus orchard on a sandy loam soil. A simple methodology was applied for accurate data acquisition from the initial stage of the process and to minimize the disturbance of the soil surface. A new approach to process the data was proposed for determining an accurate cumulative infiltration curve from transducer output. The BEST algorithm by Lassabatère et al. (2006) was applied to determine the hydraulic properties of the soil. A comparison between the automated procedure and the original BEST procedure was made.
Automatic data collection increases measurement speed, permits measurement at shorter time intervals, improves measurement precision, and allows for more efficient data handling and analysis. The proposed electronic data acquisition system based on the open source Arduino board has proved to be accurate and reliable, constituting a very cost effective alternative to previous proposed equipment. The very limited cost could represent a step toward a cheaper and widespread application of accurate and automated infiltration rate measurement. This infiltrometer could be used for situations where a large number of readings need to be collected
Comparing alternative algorithms to analyze the beerkan infiltration experiment
No full textThe increasing interest in the Beerkan Estimation of Soil Transfer parameters (BEST) procedure of soil hydraulic characterization justifies an assessment of alternative methods to analyze infiltration data. The BEST-slope and BEST-intercept algorithms allow estimation of soil sorptivity, S, and saturated soil hydraulic conductivity, K-s, using the transient part of the experimental infiltration curve and the slope and the intercept, respectively, of the linear portion of this curve. With reference to 401 runs performed in Sicily (Italy) and Burundi, this investigation showed that these two algorithms differed by the number of successful runs (positive S and K-s values), with BEST-intercept yielding a higher success percentage (93%) than BEST-slope (66%) at the expense of a poorer performance in terms of data representation by the infiltration model. On average, the two algorithms yielded S values differing by 3.3% and K-s values differing by a factor of 3.1. High discrepancies between two alternative K-s estimates, that is, by even more than two orders of magnitude, were occasionally detected at individual sampling points. The BEST-steady algorithm developed in this investigation, using steady-state cumulative infiltration data, was closer to BEST-intercept (individual S and K-s values differing at the most by 17% and a factor of 1.5, respectively) than to BEST-slope (differences by 22% for S and a factor of 186 for K-s). Data should initially be analyzed with BEST-slope and an attempt to apply BEST-intercept should be made only if the former algorithm fails in giving physically plausible S and K-s values. BEST-steady is an alternative algorithm to be considered in practice for a variety of reasons, including a success percentage of 100%, a simplified calculation of S and K-s, and the possibility to adjust the run duration directly in the field
An assessment of the BEST procedure to estimate the soil water retention curve: A comparison with the evaporation method
No full textThe Beerkan Estimation of Soil Transfer parameters (BEST) procedure is an attractive, easy, robust, and inexpensive way for a complete soil hydraulic characterization but testing the ability of this procedure to estimate the water retention curve is necessary as relatively little information is available in the literature. In this investigation the soil water retention curve was predicted for four differently textured soils by applying three existing BEST algorithms (i.e., slope, intercept and steady) and the results compared with those measured by the standard Wind evaporation method. A sensitivity analysis of the infiltration constants, beta and gamma, was also carried out and their impact on the estimated retention curve scale parameter, h(g), was evaluated. BEST-slope underestimated the soil water retention for three of the four soils under consideration, providing relatively low root mean squared differences between estimated and measured data (0.0261 cm(3)cm(-3) <= RMSD <= 0.0483 cm(3)cm(-3)). For one site (PAL, sandy-loam soil), BEST-steady provided the lowest RMSD value (0.0893 cm(3)cm(-3)) among the considered algorithms, but the water retention was systematically overestimated as a consequence of a relatively higher difference between field and lab saturated soil water contents. A specific calibration performed for beta and gamma highlighted that: i) the water retention estimations by BEST-slope were more sensitive to beta than those obtained by BEST-intercept and BEST-steady; ii) with the exception of PAL soil, the lowest RMSD values were obtained with BEST-slope. Estimation of the soil water retention curve was not significantly worse when reference values of infiltration constants (beta = 0.6 and gamma = 0.75) were used as detected by negligible differences in RMSDs as compared to calibrated beta and gamma. Therefore, it was concluded that the BEST slope algorithm yielded predictions of water retention closer to the laboratory estimated ones than the alternative BEST algorithms (i.e. BEST-intercept and-steady). For these algorithms, the less accurate estimates of the water retention data were attributed to h(g) overestimations due to the independence of the retention curve scale parameter from gamma
Estimating saturated soil hydraulic conductivity by the near steady-state phase of a Beerkan infiltration test
No full textSingle-ring infiltration experiments carried out in the field, such as the Beerkan runs, allow easy and inexpensive characterization of soil hydraulic properties, and specifically saturated soil hydraulic conductivity, K-s,K- by maintaining the functional connection of the sampled soil volume with the surrounding soil. However, a single infiltration experiment is not enough to determine K-s. The simplest way to obtain the necessary additional data is based on the assessment of the soil texture and structure characteristics. In this investigation, a simplified method, named SSBI (Steady version of the Simplified method based on a Beerkan infiltration run), was developed to estimate K-s by only using the near steady-state phase of a Beerkan infiltration run and an estimate of the alpha* parameter. Testing the method against analytically generated infiltration data revealed low prediction errors of K-s (<= 4.1%) for a wide range of soils and initial soil water conditions. A test with an extensive set of field data showed that the developed method yielded means and medians of K-s that were similar (i.e., differing by no more than a factor of two) to those obtained with a more data demanding procedure. Similar coefficients of variation, i.e. 126-141% or 62-67%, depending on the sampled soil from regions of Sicily and Burundi, respectively, were also obtained. Another field comparison of the SSBI method with the more classical single ring pressure infiltrometer method yielded statistically equivalent K-s values (100-143 mm h(-1)) but significantly shorter equilibration times in the former case (16 min) than the latter one (31 min). The developed method appears attractive due to the simplicity of both the experiment and the data analysis procedure. In addition, it allows quicker runs and makes use of smaller water volumes as compared with other, more popular, infiltrometer methods based on constant-head single ring experiments. Therefore, additional testing of the developed method is advisable
Determining short-term changes in the hydraulic properties of a sandy-loam soil by a three-run infiltration experiment
Full text linkSoil structure-dependent parameters can vary rapidly as a consequence of perturbing events such as intense rainfall. Investigating their short-term changes is therefore essential to understand the general behaviour of a porous medium. The aim of this study is to gain insight into the effects of wetting, perturbation and recovery processes through different sequences of Beerkan infiltration experiments performed on a sandy-loam soil. Two different three-run infiltration experiments (LHL and LLL) were carried out by pouring water at low (L, non-perturbing) and high (H, perturbing) heights above the soil surface and at short time intervals (hours, days). The results demonstrate that the proposed method allows one to capture short-term variations in soil structure-dependent parameters. The developed methodology is expected to simplify the parameterization of hydrological models with temporally variable soil hydraulic properties
Simplified estimation of field saturated soil hydraulic conductivity from ponded infiltration data
No full textSimple and reasonably rapid experiments are desirable to conduct a spatially distributed determination of field-saturated soil hydraulic conductivity, Kfs, that is a highly variable soil property. Bagarello et al. (2012) recently developed a simplified approach to estimate Kfs that is based on a ponded field infiltration experiment. A cylinder is inserted to a short depth into the soil, so to produce a minimal disturbance of the porous medium, and the infiltration time of a few small volumes of water repeatedly applied at the surface of the confined soil is measured. Calculating Kfs needs to determine the slope of the linearized cumulative infiltration vs. time relationship, the ring radius and an estimation of the so-called α* parameter basically from a rough knowledge of the soil texture. Bagarello et al. (2012) also showed that a site-specific prediction of α* can be obtained from the slope of the linearized cumulative infiltration curve.
Validation of the simplified approach was conducted with a relatively large data set that included approximately 200 soil sampling points from Burundi and Sicily. The Kfs values obtained by the simplified approach were compared with the ones determined by the well established One-Ponding-Depth approach by Reynolds and Elrick (1990). A more general α* estimating relationship was also developed.
The estimates of Kfs obtained with the simplified and the OPD approaches were significantly correlated (coefficient of determination, R2 = 0.94, R > 0, P = 0.05) and they differed by not more than a factor of two in 98% of the cases. Moreover, the differences between the two datasets were not significant according to a two tailed paired t test (P = 0.05).
The new α* estimating relationship allowed to obtain Kfs values that differed from those estimated with the complete BEST procedure for soil hydraulic characterization (Lassabatère et al., 2006) by less than a factor of two in 97% of the cases. In addition, the two sets of Kfs data were significantly correlated (R2 = 0.76), the means did not differ significantly according to a two-tailed paired t test, and the linear regression line between the two estimates of Kfs did not differ significantly from the identity line according to the calculated 95% confidence intervals for the intercept and the slope. Therefore, this investigation confirmed that the measured infiltration curve contained the necessary information to estimate α*.
The developed method is cheap, rapid and parsimonious in terms of both the devices that have to be transported and the measurements that have to be carried out in the field. Therefore, it is a good candidate method for intensively sampling an area of interest with a practically sustainable experimental effort and, hence, it could allow improved interpretation and simulation of soil hydrological processes, such as runoff generation
Estimating field-saturated soil hydraulic conductivity by a simplified Beerkan infiltration experiment
No full textField-saturated soil hydraulic conductivity, K-fs, is highly variable. Therefore, interpreting and simulating hydrological processes, such as rainfall excess generation, need a large number of K-fs data even at the plot scale. Simple and reasonably rapid experiments should be carried out in the field. In this investigation, a simple infiltration experiment with a ring inserted shortly into the soil and the estimation of the so-called * parameter allowed to obtain an approximate measurement of K-fs. The theoretical approach was tested with reference to 149 sampling points established on Burundian soils. The estimated K-fs with the value of first approximation of * for most agricultural field soils (*=0.012mm(-1)) differed by a practically negligible maximum factor of two from the saturated conductivity obtained by the complete Beerkan Estimation of Soil Transfer parameters (BEST) procedure for soil hydraulic characterization. The measured infiltration curve contained the necessary information to obtain a site-specific prediction of *. The empirically derived * relationship gave similar results for K-fs (mean=0.085mms(-1); coefficient of variation (CV)=71%) to those obtained with BEST (mean=0.086mms(-1); CV=67%), and it was also successfully tested with reference to a few Sicilian sampling points, since it yielded a mean and a CV of K-fs (0.0094mms(-1) and 102%, respectively) close to the values obtained with BEST (mean=0.0092mms(-1); CV=113%). The developed method appears attractive due to the extreme simplicity of the experiment. Copyright (c) 2012 John Wiley & Sons, Ltd
Rapid and accurate measurement methods for determining soil hydraulic properties: A review
Full text linkThe determination of soil hydraulic properties is important in several environmental sciences but may be expensive and time consuming. Therefore, during the last decades, a great effort has been made in soil sciences to develop relatively easy, robust, and inexpensive methods for soil hydraulic characterization. In this manuscript, we reviewed and discussed different infiltrometer techniques in light of the available experimental applications. More specifically, we considered the simplified falling head (SFH) infiltrometer technique and the single-ring infiltration experiment of the Beerkan type. Concerning this latter method, we considered different algorithms for data analysis: Two simplified methods based on the analysis of transient (TSBI) and steady (SSBI) Beerkan infiltration data, and the Beerkan Estimation of Soil pedoTransfer parameters algorithm (BEST), that allows to estimate the soil characteristics curves, i.e., the soil water retention curve and hydraulic conductivity functions. For a given method, after dealing briefly theory and practice, available literature references were reported to account for specific applications in order to provide findings on method validation and application. With the aim to provide practical information on available tools for a simpler application of the reviewed methods, several video tutorials were reported to show i) how to conduct correctly field experiments and ii) how to calculate saturated hydraulic conductivity or soil hydraulic functions using user-friendly tools for data analysis. Finally, details on a new automated single-ring infiltrometer for Beerkan infiltration experiments (i.e., construction, assembly and field use) were presented
Soil hydrology for a sustainable land management: Theory and practice
No full textSoil hydrology determines the water-soil-plant interactions in the Earth's system, because porous medium acts as an interface within the atmosphere and lithosphere, regulates main processes such as runoff discharge, aquifer recharge, movement of water and solutes into the soil and, ultimately, the amount of water retained and available for plants growth. Soil hydrology can be strongly affected by land management. Therefore, investigations aimed at assessing the impact of land management changes on soil hydrology are necessary, especially with a view to optimize water resources. This Special Issue collects 12 original contributions addressing the state of the art of soil hydrology for sustainable land management. These contributions cover a wide range of topics including (i) effects of land-use change, (ii) water use efficiency, (iii) erosion risk, (iv) solute transport, and (v) new methods and devices for improved characterization of soil physical and hydraulic properties. They involve both field and laboratory experiments, as well as modelling studies. Also, different spatial scales, i.e., from the field-to regional-scales, as well as a wide range of geographic regions are also covered. The collection of these manuscripts presented in this Special Issue provides a relevant knowledge contribution for effective saving water resources and sustainable land management
A test of the Beerkan Estimation of Soil Transfer parameters (BEST) procedure
No full textThe Beerkan Estimation of Soil Transfer parameters (BEST) procedure is attractive for a simple soil hydraulic characterization but testing the ability of this procedure to estimate soil properties is necessary. The BEST predictions were compared with soil water retention and hydraulic conductivity data measured in the laboratory and the field, respectively, at ten Sicilian field sites. Provided that BEST yielded physically possible scale parameters of the soil characteristic curves in most of the four replicated infiltration runs at a site, the measured water retention was satisfactorily predicted (i.e., not statistically significant differences between measurements and predictions, significant correlation between the data, regression line not significantly different from the identity one) when i) the infiltration run was relatively short (11 applied volumes of water); ii) the n shape parameter of the water retention curve was estimated on the basis of the measured sand and clay content of the soil; and iii) the saturated soil water content, theta(s), was set equal to 93% of the porosity. Possible field saturated soil hydraulic conductivity values were also obtained, although some trace of soil disturbance by the infiltration run was detected. The predicted unsaturated soil hydraulic conductivity was higher than the measured one, probably because the unimodal hydraulic conductivity function used in BEST does not reproduce the changes in the pore system of a real soil in the pressure head range close to saturation. It was concluded that BEST is promising to simply yield a reasonably reliable soil hydraulic characterization. An improved description of the unsaturated hydraulic conductivity function is desirable. (C) 2014 Elsevier B.V. All rights reserved
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