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Showing 5 results for Water Retention
Effects of Flooding and Fire on Some of Soil Properties in Lakan Forest in Guilan Province
M. Norouzi, H. Ramezanpour,
Volume 16, Issue 61 (10-2012)
Abstract
Flooding and fire are important phevent which could impact the forests of north of Iran periodically. These phenomena could have undesirable effects on properties and quality of soil. This study was conducted in order to investigative the effects of flooding and fire on some soil properties in Lakan forest, Guilan province. Soil sampling was carried out on three replicates from three depths 0-3, 3-6 and 6-9 cm in flooding, burned and intact regions. Results of this study indicated that clay, silt, pH, electrical conductivity (EC), Na and K values (in all of depths), organic carbon (OC) and N values (in second and third depths) significantly increased and sand content (in all depths) significantly decreased in flooding soils in comparison with intact soils. In burned soils, pH values (in first and second depths), EC, K and P values (in first depth) significantly increased and clay, OC and N values (in first depth) significantly decreased in comparison with intact soils. Soil water retention capacity showed that the flooding and burned soils had maximum and minimum levels soil moisture that can be related to clay and OC changes. Results of WDPT test showed the water repellency in the first depth in burned soils. Generally, flooding and fire phenomena significantly affected physical and chemical properties.
Parametric Estimation of the Van Genuchten and Fredlund and Xing’s Moisture Retention Curve Models Using Soft Computing Techniques in Some Khozestan Soils
F. Moradi, B. Khalilimoghadam, S. Jafari, S. Ghorbani Dashtaki,
Volume 18, Issue 69 (12-2014)
Abstract

Soft computing techniques have been extensively studied and applied in the last three decades for scientific research and engineering computing. The purpose of this study was to investigate the abilities of multilayer perceptron neural network (MLP) and neuro-fuzzy (NF) techniques to estimate the soil-water retention curve (SWRC) from Khozestan sugarcane Agro-Industries data. Sensitivity analysis was used for determining the model inputs and appropriate data subset. Also, in this paper, the van Genuchten and Fredlund and xing models were used to predict SWRC. Measured soil variables included particle size distribution, organic matter, bulk density, calcium carbonate, sodium adsorption ratio, electrical conductivity, acidity, mean weight diameter, plastic and liquid limit, resistance of soil penetration, water saturation percentage and water content for matric potentials -33, -100, -500 and -1500 kPa. The results of this study in terms of various statistical indices indicated that both MLP and NF provide good predictions but the neural network provides better predictions than neuro-fuzzy model. For example, using MLP and NF models values of NMSE at prediction θs, θr, α, n and m in Fredlund and Xing equation corresponded to (0.059, 0.065), (0.154, 0.162), (0.109, 0.117), (0.129, 0.135) and (0.129, 0.145), respectively. Furthermore, α and n parameters at the first depth, and θr and α parameters at the second depth in Fredlund and Xing equation were estimated with higher accuracy compared with equivalent parameters in van Genuchten equation


The Effect of Ash and Coal Produced from Fire on Some of Soil Properties Especially Water Retention
Z. Sepehri, Z. Jafarian, A. Kavian, Gh. Heydari,
Volume 21, Issue 1 (6-2017)
Abstract
Ash and coal produced from fire influence the soil and few studies about these effects are available. For this purpose, this study was performed to investigate the effect of ash and coal on hydrological, physical and chemical properties of soil in Charat rangeland that has a history of fire. Systematic-random sampling was implemented in two plant types Astragalus gossypinus and Artemisia aucheri using 6 transects and 60 4m2 quadrates and plant and soil samples were obtained. Experimental treatments including control soil, composition soil and ash were prepared manually and also with artificial rain, composition soil and coal manually and also with artificial rain in the laboratory and characteristics of the texture, saturation moisture, pH, organic matter, field capacity, wilting point, available water and retention capacity were measured. ANOVA results for mean comparison soil, ash and coal properties showed that ash had more percent of silt and pH than soil and organic matter and lower bulk density than soil. In addition, multivariate analysis to show the effect of two types of treatments showed that manual composition of ash and coal with soil had increased field capacity and available water while composition of soil and coal with artificial rain had no significant effect on them. According to the results of this study, the presence of ash and coal resulted from burning vegetation caused great changes in soil properties, especially water retention.
 

The Influence of Using Soil Particles Percentage in Comparison with Geometric Characteristics of Soil Particles on the Accuracy of the Pedotransfer Equations and the Critical Point of SWRC
A. Javidi, A. Shabani, M. J. Amiri,
Volume 23, Issue 1 (6-2019)
Abstract
Soil water retention curve (SWRC) reflects different states of soil moisture and describes quantitative characteristics of the unsaturated parts of the soil. Direct measurement of SWRC is time-consuming, difficult and costly. Therefore, many indirect attempts have been made to estimate SWRC from other soil properties. Using pedotransfer functions is one of the indirect methods for estimating SWRC. The aim of this research was to assess the effect of using soil particles percentage in comparison with the geometric characteristics of soil particles on the accuracy of the pedotransfer equations of SWRC and the critical point of it. Accordingly, 54 soil samples of Isfahan province from seven texture classes were used. The most suitable functions for estimating SWRC, parameters of van Genuchten and Brooks-Corey equations, and the critical point of SWRC were selected based on statistical indices. The results indicated that the pedotransfer equations fitted the SWRC data well and the outputs from them were in a good agreement with the independent (validation) SWRC data. The results revealed that using soil particles percentage (sand and clay), bulk density and organic matter content in the point estimation of SWRC was better than applying geometric properties of the soil particle diameter. On the other hand, in the estimation of parametric and critical point of SWRC, using the geometric properties of soil particle diameters resulted in more satisfactory results, as compared with using the soil particles percentage. The NRMSE values indicated that the accuracy of the pedotransfer equations in the lower matric head was greater than that of the higher matric head.

Estimation of Soil Hydraulic Properties Curve through Beerkan Infiltration Experiment Algorithms
T. Ahmady, M. Delbari, P. Afrasiab,
Volume 23, Issue 2 (9-2019)
Abstract
Nowadays, the Beerkan computational algorithms (BESTslope and BESTsteday) are known as the suitable indirect methods for estimating soil saturated hydraulic conductivity (Ks) and sorptivity (S), as well as the scale parameter (hg) in van Genuchten soil water retention equation through the data collected in the Beerkan infiltration experiment and other required data. The purpose of this study was to compare these algorithms in estimating Ks and S, as well as the soil water content corresponding to the suctions of 33 kPa, 100 kPa, 200 kPa, 300 kPa and 1500 kPa. For this purpose, a total of 40 Beerkan infiltration experiments were carried out in Sistan dam research field. From all Beerkan experiments, 30 tests in loam and sandy loam textures having a relative error less than 5.5% (Er <5.5%) were selected for further analysis. The statistical criteria RMSE, ME and ωr2 were used to compare the measured and estimated water content values at each suction. The results showed that the BESTsteday algorithm, which had a more simple calculating process than the main algorithm (i.e. BESTslope), could provide the Ks and S values and the soil water content of the near field capacity with an acceptable accuracy. The model performance in estimating water content corresponding to the 1500 kpa suction head (i.e. θfc) was not acceptable for both algorithms. Moreover, the relative error of estimating soil water content (Er(h,θ)) was decreased gradually by an increase in clay %.

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