Pedotransfer functions for soil hydraulic properties developed from a hilly watershed of Eastern India

Geoderma ◽  
2008 ◽  
Vol 146 (3-4) ◽  
pp. 439-448 ◽  
Author(s):  
Priyabrata Santra ◽  
Bhabani Sankar Das
Keyword(s):  
Hydraulic Properties ◽  
Pedotransfer Functions ◽  
Eastern India ◽  
Soil Hydraulic Properties ◽  
Soil Hydraulic ◽  
Hilly Watershed

scholarly journals Geophysical mapping of soil texture variability in the root zone to improve modelling of the water and nutrient flow.

2020 ◽  
Author(s):  
Kim Schwartz Madsen ◽  
Bo Vangsø Iversen ◽  
Christen Duus Børgesen
Keyword(s):  
Soil Texture ◽  
Hydraulic Properties ◽  
Root Zone ◽  
Clay Fraction ◽  
Clay Content ◽  
Auxiliary Variable ◽  
Pedotransfer Functions ◽  
Soil Hydraulic Properties ◽  
Sampling Density ◽  
Soil Hydraulic

<p>Modelling is often used to acquire information on water and nutrient fluxes within and out of the root zone. The models require detailed information on the spatial variability of soil hydraulic properties derived from soil texture and other soil characteristics using pedotransfer functions (PTFs). Soil texture can vary considerably within a field and is cumbersome and expensive to map in details using traditionally measurements in the laboratory. The electrical conductivity (EC) of the soil have shown to correlate with its textural composition.</p><p>This study investigates the ability of electromagnetic induction (EMI) methods to predict clay content in three soil layers of the root zone. As the clay fraction often is a main predictor in PTFs predicting soil hydraulic properties this parameter is of high interest. EMI and soil textural surveys on four Danish agricultural fields with varying textural composition were used. Sampling density varied between 0.5 and 38 points per hectare. The EMI data was gathered with a Dualem21 instrument with a sampling density 200-3000 points per hectare. The EC values were used together with the measured values of the clay content creating a statistical relationship between the two variables. Co-kriging of the clay content from the textural sampling points with the EC as auxiliary variable produces clay content maps of the fields. Unused (80%) texture points were used for validation. EMI-predicted clay content maps and clay content maps based on the survey were compared. The two sets of soil texture maps are used as predictors for PTF models to predict soil hydraulic properties as input in field-scale root zone modelling.</p><p>The comparisons between EC and clay content show some degree of correlation with an R<sup>2</sup> in the range of 0.55 to 0.80 for the four fields. The field with the highest average clay content showed the best relationship between the two parameters. Co-kriging with EC decreased mean error by 0.016 to 0.52 and RMSE by 0.04 to 1.80 between observed and predicted clay maps.</p>

scholarly journals Pedotransfer in soil physics: trends and outlook — A review —

2015 ◽  
Vol 64 (2) ◽  
pp. 339-360 ◽  
Author(s):  
Ya. Pachepsky ◽  
K. Rajkai ◽  
B. Tóth
Keyword(s):  
Hydraulic Properties ◽  
Overland Flow ◽  
Extreme Event ◽  
Temporal Stability ◽  
Model Performance ◽  
Pedotransfer Functions ◽  
Soil Physics ◽  
Labor Costs ◽  
Soil Hydraulic Properties ◽  
Soil Hydraulic

Parameters governing the retention and movement of water and chemicals in soils are notorious for the difficulties and high labor costs involved in measuring them. Often, there is a need to resort to estimating these parameters from other, more readily available data, using pedotransfer relationships. This work is a mini-review that focuses on trends in pedotransfer development across the World, and considers trends regarding data that are in demand, data we have, and methods to build pedotransfer relationships. Recent hot topics are addressed, including estimating the spatial variability of water contents and soil hydraulic properties, which is needed in sensitivity analysis, evaluation of the model performance, multimodel simulations, data assimilation from soil sensor networks and upscaling using Monte Carlo simulations. Ensembles of pedotransfer functions and temporal stability derived from “big data” as a source of soil parameter variability are also described. Estimating parameter correlation is advocated as the pathway to the improvement of synthetic datasets. Upscaling of pedotransfer relationships is demonstrated for saturated hydraulic conductivity. Pedotransfer at coarse scales requires a different type of input variables as compared with fine scales. Accuracy, reliability, and utility have to be estimated independently. Persistent knowledge gaps in pedotransfer development are outlined, which are related to regional soil degradation, seasonal changes in pedotransfer inputs and outputs, spatial correlations in soil hydraulic properties, and overland flow parameter estimation. Pedotransfer research is an integral part of addressing grand challenges of the twenty-first century, including carbon stock assessments and forecasts, climate change and related hydrological weather extreme event predictions, and deciphering and managing ecosystem services. Overall, pedotransfer functions currently serve as an essential instrument in the science-based toolbox for diagnostics, monitoring, predictions, and management of the changing Earth and soil as a life-supporting Earth system.

scholarly journals Mapping soil hydraulic properties using random-forest-based pedotransfer functions and geostatistics

2019 ◽  
Vol 23 (6) ◽  
pp. 2615-2635 ◽  
Author(s):  
Brigitta Szabó ◽  
Gábor Szatmári ◽  
Katalin Takács ◽  
Annamária Laborczi ◽  
András Makó ◽  
...  
Keyword(s):  
Random Forest ◽  
Hydraulic Properties ◽  
Soil Depth ◽  
Pedotransfer Functions ◽  
Soil Hydraulic Properties ◽  
Matric Potential ◽  
Indirect Methods ◽  
Saturated Water Content ◽  
Soil Hydraulic ◽  
Wilting Point

Abstract. Spatial 3-D information on soil hydraulic properties for areas larger than plot scale is usually derived using indirect methods such as pedotransfer functions (PTFs) due to the lack of measured information on them. PTFs describe the relationship between the desired soil hydraulic parameter and easily available soil properties based on a soil hydraulic reference dataset. Soil hydraulic properties of a catchment or region can be calculated by applying PTFs on available soil maps. Our aim was to analyse the performance of (i) indirect (using PTFs) and (ii) direct (geostatistical) mapping methods to derive 3-D soil hydraulic properties. The study was performed on the Balaton catchment area in Hungary, where density of measured soil hydraulic data fulfils the requirements of geostatistical methods. Maps of saturated water content (0 cm matric potential), field capacity (−330 cm matric potential) and wilting point (−15 000 cm matric potential) for 0–30, 30–60 and 60–90 cm soil depth were prepared. PTFs were derived using the random forest method on the whole Hungarian soil hydraulic dataset, which includes soil chemical, physical, taxonomical and hydraulic properties of some 12 000 samples complemented with information on topography, climate, parent material, vegetation and land use. As a direct and thus geostatistical method, random forest combined with kriging (RFK) was applied to 359 soil profiles located in the Balaton catchment area. There were no significant differences between the direct and indirect methods in six out of nine maps having root-mean-square-error values between 0.052 and 0.074 cm3 cm−3, which is in accordance with the internationally accepted performance of hydraulic PTFs. The PTF-based mapping method performed significantly better than the RFK for the saturated water content at 30–60 and 60–90 cm soil depth; in the case of wilting point the RFK outperformed the PTFs at 60–90 cm depth. Differences between the PTF-based and RFK mapped values are less than 0.025 cm3 cm−3 for 65 %–86  % of the catchment. In RFK, the uncertainty of input environmental covariate layers is less influential on the mapped values, which is preferable. In the PTF-based method the uncertainty of mapping soil hydraulic properties is less computationally intensive. Detailed comparisons of maps derived from the PTF-based method and the RFK are presented in this paper.

scholarly journals A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models

Agronomy ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 285 ◽  
Author(s):  
M. H. J. P. Gunarathna ◽  
Kazuhito Sakai ◽  
M. K. N. Kumari ◽  
Manjula Ranagalage
Keyword(s):  
Sri Lanka ◽  
Hydraulic Properties ◽  
Growth And Yield ◽  
Pedotransfer Functions ◽  
Test Model ◽  
Sri Lankan ◽  
Soil Hydraulic Properties ◽  
Crop Models ◽  
Soil Hydraulic ◽  
Good Agreement

As measurements are expensive and laborious, the estimation of soil hydraulic properties using pedotransfer functions (PTFs) has become popular worldwide. However, the estimation of soil hydraulic properties is not the final aim but an essential input value for other calculations and simulations, mostly in environmental and crop models. This modeling approach is a popular way to assess agricultural and environmental processes. However, it is rarely used in Sri Lanka because soil hydraulic data are rare. We evaluated the functionality of PTFs (developed to estimate field capacity (FC) and the permanent wilting point (PWP) of Sri Lankan soils) for process-based crop models. We used the Agricultural Production Systems sIMulator (APSIM) as the test model. Initially, we confirmed the importance of PWP (LL15) and FC (DUL) by assessing the sensitivity of the soil input parameters on the growth and yield of rice under rainfed conditions. We simulated the growth and yield of rice and the four selected outputs related to the APSIM soil module using the measured and estimated values of FC and PWP. These simulations were conducted for ten years in 16 locations of Sri Lanka, representing wet, intermediate, and dry zones. The simulated total aboveground dry matter and weight of the rough rice, using both input conditions (the measured and PTF-estimated soil hydraulic properties), showed good agreement, with no significant differences between each other. Outputs related to the soil module also showed good agreement, as no significant differences were found between the two input conditions (measured and PTF-estimated soil hydraulic properties). Although the DUL and LL15 are the most influential parameters for the selected outputs of APSIM–Oryza, the estimated FC and PWP values did not change the predictive ability of APSIM. In this way, the functionality of PTFs for APSIM crop modeling is confirmed.

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