Comparison of methods to estimate soil water characteristics from soil texture, bulk density, and limited data

1992 ◽  
Vol 29 (6) ◽  
pp. 355
Keyword(s):  
Bulk Density ◽  
Soil Water ◽  
Soil Texture ◽  
Comparison Of Methods ◽  
Limited Data ◽  
Water Characteristics

scholarly journals Establishing relationship between measured and predicted soil water characteristics using SOILWAT model in three agro-ecological zones of Nigeria

2016 ◽  
Author(s):  
OrevaOghene Aliku ◽  
Suarau O. Oshunsanya
Keyword(s):  
Soil Water ◽  
Soil Texture ◽  
Irrigation Planning ◽  
Ecological Zones ◽  
Available Water ◽  
Water Characteristics ◽  
Permanent Wilting Point ◽  
Predicted Values ◽  
Wilting Point ◽  
Agro Ecological Zones

Abstract. Soil available water (SAW) affects soil nutrients availability and consequently affects crop performance. However, field determination of SAW for effective irrigated farming is laborious, time consuming and expensive. Therefore, experiments were initiated at three agro-ecological zones of Nigeria to compare the measured laboratory and predicted soil available water using SOILWAT model for sustainable irrigated farming. One hundred and eighty soil samples were collected from the three agro-ecological zones (Savannah, Derived savannah and rainforest) of Nigeria and analysed for physical and chemical properties. Soil texture and salinity were imputed into SOILWAT model (version 6.1.52) to predict soil physical properties for the three agro-ecological zones of Nigeria. Measured and predicted values of field capacity, permanent wilting point and soil available water were compared using T-test. Predicted soil textural classes by SOILWAT model were similar to the measured laboratory textural classes for savannah, derived savannah and rainforest zones. However, bulk density, maximum water holding capacity, permanent wilting point and soil available water were poorly predicted as significant (p < 0.05) differences existed between measured and predicted values. Therefore, SOILWAT model could be adopted for predicting soil texture for savannah, derived savannah and rainforest zones of Nigeria. However, the model needs to be upgraded in order to accurately predict soil water characteristics of the aforementioned locations for sustainable irrigation planning.

Global mapping of the soil water characteristics curve using machine-learning, a comprehensive dataset and spatial covariates

2021 ◽  
Author(s):  
Surya Gupta ◽  
Andreas Papritz ◽  
Peter Lehmann ◽  
Tom Hengl ◽  
Sara Bonetti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Content ◽  
Soil Water ◽  
Soil Texture ◽  
Land Surface ◽  
Arable Land ◽  
Pedotransfer Functions ◽  
Soil Water Retention ◽  
Environmental Covariates ◽  
Water Characteristics

&lt;p&gt;The representation of land surface processes in hydrologic and climatic models is critically dependent on the soil water characteristics curve (SWCC) that defines the hydrologic behavior of unsaturated soil. The SWCC depends not only on soil texture, but it is also shaped by biopores, soil structure, and clay type. To capture climate, vegetation and other soil formation processes on SWCC in spatial context, we predict how SWCC parameter values vary with local environmental covariates using a machine learning approach. The model was trained using (i) a novel and comprehensive compilation of global dataset of soil water retention measurements collected from the literature (approximately 13,000 pairs of water content and matric potential data) and (ii) global maps of environmental covariates and soil texture developed at 250 m resolution. Because in many cases only few measurements per sample are available to fit the SWCC, the estimated parameters are often highly uncertain and could yield unrealistic predictions of related physical quantities. To address these limitations, we added constraints to the values of residual and saturated water content based on clay content and mineralogy and ensured that the shape parameters related to air-entrance and pore size distribution honor other physical constraints, such as the characteristic length of evaporation and the ponding time. The resulting global maps of SWCC parameters are compared with predictions using pedotransfer functions (PTFs) based on soil information alone that were trained on data mainly collected for samples from arable land in temperate regions. We anticipate that our model including environmental covariates and geospatial data (covariate-based geotransfer functions CoGTFs) would enable us to provide more reliable predictions (compared to traditional PTFs) of SWCC that can be implemented in Earth system models.&lt;/p&gt;

Associations between soil texture, soil water characteristics and earthworm populations in grassland

2011 ◽  
Vol 61 (7) ◽  
pp. 583-592 ◽  
Author(s):  
Martin Holmstrup ◽  
Mathieu Lamandé ◽  
Søren B. Torp ◽  
Mogens H. Greve ◽  
Rodrigo Labouriau ◽  
...  
Keyword(s):  
Soil Water ◽  
Soil Texture ◽  
Water Characteristics ◽  
Earthworm Populations

scholarly journals Soil-water characteristics of weathered crust elution-deposited rare earth ores

2021 ◽  
Vol 31 (5) ◽  
pp. 1452-1464
Author(s):  
Zhong-qun GUO ◽  
Jian-rong ZHOU ◽  
Ke-fan ZHOU ◽  
Jie-fang JIN ◽  
Xiao-jun WANG ◽  
...  
Keyword(s):  
Rare Earth ◽  
Soil Water ◽  
Water Characteristics ◽  
Weathered Crust

Measurement of Soil Suction and Soil-Water Characteristics of Bentonite-Sand Mixtures

2010 ◽  
Author(s):  
Pan Hu ◽  
Qing Yang ◽  
Maotian Luan
Keyword(s):  
Soil Water ◽  
Vapor Phase ◽  
Unsaturated Soils ◽  
Characteristic Curve ◽  
Phase Method ◽  
Soil Suction ◽  
Compacted Bentonite ◽  
Water Characteristics ◽  
Wide Range ◽  
High Level

The soil-water characteristic curve (SWCC) is a widely used experimental means for assessing fundamental properties of unsaturated soils for a wide range of soil suction values. The study of SWCC is helpful because some properties of unsaturated soils can be predicted from it. Nowadays, much attention has been paid to the behaviours of highly compacted bentonite-sand mixtures used in engineering barriers for high level radioactive nuclear waste disposal. It is very important to study the various performances of bentonite-sand mixtures in order to insure the safety of high-level radioactive waste (HLW) repository. After an introduction to vapor phase method and osmotic technique, a laboratory study has been carried out on compacted bentonite-sand mixtures. The SWCC of bentonite-sand mixtures has been obtained and analyzed. The results show that the vapor phase method and osmotic technique is suitable to the unsaturated soils with high and low suction.

A generally applicable pedotransfer function that estimates field capacity and permanent wilting point from soil texture and bulk density

2008 ◽  
Vol 88 (5) ◽  
pp. 761-774 ◽  
Author(s):  
J. A. P. Pollacco
Keyword(s):  
Organic Matter ◽  
Soil Moisture ◽  
Bulk Density ◽  
Soil Texture ◽  
The United States ◽  
Pedotransfer Functions ◽  
Pedotransfer Function ◽  
Data Set ◽  
Wide Range ◽  
Fitting Parameters

Hydrological models require the determination of fitting parameters that are tedious and time consuming to acquire. A rapid alternative method of estimating the fitting parameters is to use pedotransfer functions. This paper proposes a reliable method to estimate soil moisture at -33 and -1500 kPa from soil texture and bulk density. This method reduces the saturated moisture content by multiplying it with two non-linear functions depending on sand and clay contents. The novel pedotransfer function has no restrictions on the range of the texture predictors and gives reasonable predictions for soils with bulk density that varies from 0.25 to 2.16 g cm-3. These pedotransfer functions require only five parameters for each pressure head. It is generally accepted that the introduction of organic matter as a predictor improves the outcomes; however it was found by using a porosity based pedotransfer model, using organic matter as a predictor only modestly improves the accuracy. The model was developed employing 18 559 samples from the IGBP-DIS soil data set for pedotransfer function development (Data and Information System of the International Geosphere Biosphere Programme) database that embodies all major soils across the United States of America. The function is reliable and performs well for a wide range of soils occurring in very dry to very wet climates. Climatical grouping of the IGBP-DIS soils was proposed (aquic, tropical, cryic, aridic), but the results show that only tropical soils require specific grouping. Among many other different non-climatical soil groups tested, only humic and vitric soils were found to require specific grouping. The reliability of the pedotransfer function was further demonstrated with an independent database from Northern Italy having heterogeneous soils, and was found to be comparable or better than the accuracy of other pedotransfer functions found in the literature. Key words: Pedotransfer functions, soil moisture, soil texture, bulk density, organic matter, grouping

Water retention in Australian soils. 2.* Prediction using particle size, bulk density, and other properties

Soil Research ◽  
1996 ◽  
Vol 34 (5) ◽  
pp. 679 ◽  
Author(s):  
Z Paydar ◽  
HP Cresswell
Keyword(s):  
Particle Size ◽  
Bulk Density ◽  
Soil Water ◽  
Multiple Regression ◽  
Empirical Relationship ◽  
Organic Matter Content ◽  
Matter Content ◽  
Point Method ◽  
Eastern Australia ◽  
The One

Different approaches were investigated for estimating the parameters in the Campbell soil water characteristic (SWC) equation from soil attributes such as particle size distribution (PSD), bulk density, and organic matter content. Predicted soil water characteristics were compared with measured values for soils of the wheatbelt of south-eastern Australia. A method of prediction is proposed incorporating an empirical relationship for estimating the slope of the SWC from the slope of the cumulative PSD. A power-law form is assumed for both the SWC and PSD functions. One measured SWC point is then used to locate and thus define the SWC curve. When SWC points predicted with this 'one-point' method were compared with measured values, the mean absolute value of the difference between each measured and predicted SWC point was 0.016 m3/m3 for the Geeves data and 0.027 m3/m3 for the Forrest data. Eight sets of predictive equations, previously developed using multiple regression analysis, were also evaluated. Whilst the equations predicted the slope of the SWC curves reasonably well, predictions of the air entry potential were poor. Although less accurate, the equations developed by multiple regression are less demanding in data requirement compared with alternative SWC prediction methods. The one-point method gave better predictions than the multiple regression approach but was less accurate than the 'two-point' method proposed in the first paper in this series. The one-point method should be considered where PSD data and 1 measured SWC point are available. In most other circumstances it will be more accurate and cost-effective to measure 2 SWC points to define the soil water characteristic function (the two-point method).* Part I, Aust. J. Soil Res., 1996, 34, 195–212.

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