Assessment of multi-frequency electromagnetic induction for determining soil moisture patterns at the hillslope scale
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Pedotransfer function
Pedotransfer function
Soil texture
Macropore
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Pedotransfer function
Cambisol
Electrical Resistivity Tomography
Soil morphology
Cation-exchange capacity
Soil test
Digital Soil Mapping
Soil texture
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Abstract This study was conducted to evaluate the performance of pedotransfer functions (PTF S ) in estimating the saturated hydraulic conductivity in gypsum soil. The saturated hydraulic conductivity was measured for 26 unexcited samples using the permeability of the falling head, and then some chemical and physical properties of the study soil were measured. Rosetta and a number of models were used to predict the saturated hydraulic conductivity. Three statistical criteria, namely NSE, RMSE, and R 2 , were used to evaluate the performance of the models. The results showed that the second model of Rosetta, which depends on sand, silt, clay, and bulk density as input, is the best, as the values of NSE and RMSE were -13.28 and 0.31, respectively, while the other models, Puckett, Smettem, Bristow, and Saxton, were the best in predicting the saturated hydraulic conductivity of Among the models, the RMSE values were 0.52, 0.54, and 0.47, respectively, and the NSE values were 0.92, 0.91, and 0.93, respectively.
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Indirect Estimation of Near-saturated Hydraulic Conductivity From Readily Available Soil Information
Application of process-based water flow and solute transport models is often hampered by insufficient knowledge of soil hydraulic properties. This is certainly true for dual- or multi-porosity models that account for non-equilibrium flow of water in macropores, where the saturated ‘matrix’ hydraulic conductivity is a particularly critical parameter. Direct measurement is possible, but this is impractical for larger scale studies (i.e. catchment or regional), where estimation methods (pedotransfer functions) are usually required. This paper presents pedotransfer functions for hydraulic conductivity at a pressure head of � 10 cm, K10, based on measurements of near-saturated hydraulic conductivity made with tension infiltrometers in 70 soil horizons at 37 different sites in
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Macropore
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The experiment was conducted to predict saturated hydraulic conductivity at South Sumatera and Riau from soil particles distribution, porosity and horizon median depth. Saturated hydraulic conductivity is one of soil physics properties for predicting the water movement and dissolved in the water through the soil. Most of the saturated hydraulic conductivity which analysed on laboratory was impractical, and requires a lot of cost and time. Saturated hydraulic conductivity is related to clay content, porosity adn median depth horizon. In this study 78 set of soil samples were taken from South Sumatera and Riau from May 2012 until June 2013 using permeameter. The result showed approximately success in predicting saturated conductivity from clay content, porosity median depth soil horizon (R 2 = 0,675): Log Ks = -7,245 + 0,077 clay + 0,084 porosity - 0,011 median depth horizon. Where in this equation clay are fraction from particle size distribution, porosity are total porosity from soil structure and median depth are the middle depth of each soil horizon, and Log Ksat, saturated hydraulic conductivity expressed in cm/h. The result of regression analysis showed that clay content play more significant role with respect to Log Ks, saturated hydraulic conductivity and has been named as the effective parameter in Log Ksat calculation.
Pedotransfer function
Permeameter
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Indirect methods for prediction of soil hydraulic properties play an important role in understanding site‐specific unsaturated water flow and transport processes, usually via numerical simulation models. Specifically, pedotransfer functions (PTFs) to predict soil‐water retention have been widely developed. However, few datasets that include unsaturated hydraulic conductivity data are available for prediction purposes. Moreover, those available employ a variety of measurement techniques. We show that prediction of soil‐water retention and unsaturated hydraulic conductivity curves from basic soil properties can be improved if hydraulic data are determined by a single measurement method that is consistently applied to all soil samples. Here, we present a unique dataset that consists of 310 soil‐water retention and unsaturated hydraulic conductivity functions, all of which were obtained from the multistep outflow method. With this dataset, neural networks coupled with bootstrap aggregation were used to predict the soil‐water retention and hydraulic conductivity characteristics from basic soil properties, that is, sand, silt, and clay content, bulk density (ρ b ), saturated water content, and saturated hydraulic conductivity. The prediction errors of water content were about 3 to 4% by volume. Unsaturated hydraulic conductivity predictions improved significantly when a so‐called performance‐based algorithm was utilized to minimize residuals of soil hydraulic data rather than hydraulic parameters. The root mean squared of residuals for predicted values of water content and unsaturated hydraulic conductivity were reduced by about 50% when compared with predicted hydraulic functions using a published neural networks program Rosetta Results from a sensitivity analysis suggest that the hydraulic parameters are mostly sensitive to sand content and saturated water content.
Pedotransfer function
Water retention curve
Outflow
Silt
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Pedotransfer function
Permanent wilting point
Biometeorology
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Pedotransfer function
Sodium adsorption ratio
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Unsaturated hydraulic conductivity is important to many vadose zone processes. However, there is uncertainty regarding how laboratory‐ and field‐measured hydraulic property measurements can be combined with soil textural data to improve the description of the unsaturated hydraulic conductivity function at the field scale. In this investigation, we examine a scaled‐predictive method for defining the hydraulic conductivity function. This approach uses field‐measured data to adjust the hydraulic conductivity relationship developed using either laboratory measurements or soil textural data. In addition, both hydraulic property models were scaled using field‐measured data collected during a controlled infiltration experiment. Data from a second controlled infiltration experiment were used to evaluate the hydraulic property models based on accuracy of prediction of the arrival time of a wetting front and the water content distribution with time. The unadjusted laboratory‐derived parameters provide the best first approximation for predicting the wetting front arrival. Both hydraulic property models gave more accurate predictions of the wetting front arrival after adjustment with field‐measured data. Both scaled models predicted the water content distributions poorly. These predictions were improved, especially for the pedotransfer model, if the scaling was applied to only the lower portion of the soil profile.
Pedotransfer function
Infiltration (HVAC)
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Soil Retention Curve | Soil Hydraulic Conductivity | Arid and Semi-Arid Environment | Available Water | PTFs | منحنيات الشد الرطوبي | الموصلية الهيدروليكية | الأراضي الجافة وشبة الجافة |الماء المتوفر للنبات | معادلات التنبؤ | وادي الأردن
Pedotransfer function
Water retention curve
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