Predicting continuous form of soil-water characteristics curve from limited particle size distribution data

Samaneh Amanabadi,Mehran Vazirinia,Mohammad Hassan Masihabadi,Mohammad Hossien Mohammadi

doi:10.4314/wsa.v44i3.10

Abstract

Detailed information derived from a soil moisture characteristics curve (SMC) helps in water flow and solute transport management. Hence, prediction of the SMC from soil particle size distribution (PSD), which is easy to measure, would be convenient. In this study, we combine an integrated robust PSD-based model and a Van Genuchten SMC model to predict a continuous form of SMC using sand, silt and clay percentages for 50 soils selected from the UNSODA database. We compare the performance of the proposed approach with some previous prediction models. The results indicated that the SMC can be predicted and modelled properly by using sand, silt, clay and bulk density data. The model’s bias was attributed to the high fine particle and organic carbon (OC) content. We concluded that independence of the proposed method from the database and any empirical coefficients make predictions more reliable and applicable for large-scale water and solute transport management.

Highlights

The soil’s unsaturated zone forms a pivotal part of the hydrological cycle as it connects surface water to groundwater through the porous medium of soil
One of the most important challenges in soil physics is to deal with the estimation of the hydraulic conductivity curve (HCC) and soil moisture characteristic curve (SMC) (Futter et al, 2007; Balland et al, 2008)
1 and 2 are based on the MV model. This model assumes that all soil particles are spherical and that soil −sotrrƟguaiic(ntmiumc)r)me2caattneroncolynitnenfltu,epnacretiscoleilsbuurlfkacdeeennseitryg.yT, hleenesffaencdtsfiolfmsoil water volume are not supported by this model (Mohammadi and Vanclooster, 2011; Mohammadi and Meskini-Vishkaee, RfeeorvOrraoSltTurhEoae(TtMcdeTodaBARmtRuEaRRaps)sp=ieadntpreMgercfMRooittRMnhaRhnMeMcReetMdhRaRmmi=.RRanRRfeesRfi:aanR−∑ iins=RgnuRn1RaR5rRRbfeM0fRsdMRsoiiRoaRlNuR−inNRltSsdSe,PPpUeii rrNerodSriOc(tMDedAAmREcRo)oRRdia=sentsudwrRmReeRcreReoaRRnnRRtRRfbeRf niR−atRsRRRRRffRRRR2MMawRn0ReSeV1Sdtt3rhM)ma.onTeodgthdh2eeeosrlc.deoaFffno2oSrbrpMeera, oCltplhva.sierdatumeiancpldollyeenrass-itopstirtrleeisbndrutiectppetrridoeednstoeibcnttythieoMednaeissn,tsheuFosmipdge.p1c1tii,aaoMnlnldysetfoohfrotdthhe1e

Summary

Introduction

The soil’s unsaturated zone forms a pivotal part of the hydrological cycle as it connects surface water to groundwater through the porous medium of soil. Statistical techniques (pedo-transfer functions) or neural network models determine the correlation of basic soil properties (for instance sand, silt and clay percentages and organic matter content) to SMC points or parameters (Dashtaki et al, 2010; Vereecken et al, 2010; Abbasi et al, 2011). Meskini-Vishaee et al (2014) showed that the scaling approach improves the SMC predictions by 30% for all selected soils in comparison to the MV model, the MM model and ROSETTA (Schaap et al, 2001) software.

Results

Conclusion