Evaluation of Water Retention Functions and Computer Program “Rosetta” in Predicting Soil Water Characteristics of Seasonally Impounded Shrink–Swell Soils

Patil, N. G.; Rajput, G. S.

doi:10.1061/(asce)ir.1943-4774.0000007

Cited by 23 publications

(11 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was demonstrated that the comparison of observed and estimated SWRC showed an increase in the regression c 2013 NSP Natural Sciences Publishing Cor. [27][28][29][30]. Therefore, the hierarchical level of item (5) of input data was used for this study.…”

Section: Parameters Determination Of Numeric Methodsmentioning

confidence: 99%

Estimating Parameters of Van Genuchten Model for Soil Water Retention Curve by Intelligent Algorithms

Yang¹,

You²

2013

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

An improved particle swarm optimization (IPSO) was proposed and the intelligent algorithms such as IPSO, genetic algorithm (GA), and simulated annealing algorithm (SA) were introduced to determine parameters of Van Genuchten (VG) model for soil water retention curve (SWRC) of four typical agricultural soil textures (clay, clay loam, silt loam and sand loam) in China. For comparison, the SWRC in term of VG model was also fitted by a computer program RETC and pedotransfer function Rosetta, respectively. For four soil textures, the value of determination coefficient (R 2 ) and root mean square error (RMSE) in the estimation of VG equation parameters by IPSO are the highest and lowest in the above three intelligent algorithms, respectively. The simulated values of water content by IPSO are much closed to the measured values (R 2 = 0.990). It was found that the Rosetta is unable to estimate the SWRC adequately and the highest RMSE value is up to 1.096E − 01cm 3 cm −3 . The predicted values of moisture content by Rosetta are far away from the measured values (R 2 = 0.585). The RETC provided good simulation results of water content (R 2 = 0.974). However, the soil residual water content (θ r ), of VG equation can not be obtained. It was concluded that the IPSO presented here is more reasonable and reliable to estimate the SWRC in term of VG model than the method of GA, SA, RETC and Rosetta.

show abstract

Section: Parameters Determination Of Numeric Methodsmentioning

confidence: 99%

Estimating Parameters of Van Genuchten Model for Soil Water Retention Curve by Intelligent Algorithms

Yang¹,

You²

2013

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Investigations reported here are limited to the clay horizons because no adequate data for other textures was available for developing PTFs. For more details the reader is referred to Patil and Rajput (2009). A statistical summary of the physical properties data is shown in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Estimation of bulk density of waterlogged soils from basic properties

Patil¹,

Chaturvedi²

2012

Archives of Agronomy and Soil Science

View full text Add to dashboard Cite

Pedotransfer functions (PTFs) to predict bulk density (BD) from basic soil data are presented. Available data pertaining to seasonally impounded shrink-swell soils of Jabalpur district in the Madhya Pradesh state of India were used for the study. The data included horizon-wise information of 41 soil profiles in the study area covering nearly 5 million ha. Six independent variables, namely textural data (sand, silt and clay), field capacity (FC), permanent wilting point (PWP) and organic carbon content (OC) were used as input in hierarchical steps to establish dependencies, with bulk density as the dependent variable, using statistical regression and artificial neural networks. The PTFs derived using neural networks [average root mean square error (RMSE) 0.05] were relatively better than statistical regression PTFs (average RMSE 4 0.1). The best-performing PTFs required input data on sand, silt content, FC and PWP, with lowest prediction errors (RMSE 0.01, maximum absolute error (MAE) 0.01) and highest values of index of agreement (d, 0.95) and R 2 (0.65). Use of measures of structure, as well as information on pore structure, was found to be essential to derive acceptable PTFs. Inclusion of OC as an input variable showed relatively better fitting to the training data set, implying an underlying relationship between OC and BD, but the neural networks could not mimic the relationship when tested against subset.

show abstract

“…For state variables many authors use mean (absolute) error (M(A)E), root mean 260 square error (RMSE), index of agreement (IoA;Willmott, 1982), and less often the Nash-Sutcliffe modelling 261 efficiency (NSE; Nash-Sutcliffe, 1970) (e.g., Donatelli et al, 2004;Gribb et al, 2009;Herbst et al, 2005; 262 Khodaverdiloo et al, 2011;Patil and Rajput, 2009;Ritter et al, 2003;Vereecken et al, 2010). For rate variables 263 authors generally use MAE, mean difference (MD), absolute maximum error (AME), RMSE, IoA, NSE, 264 coefficient of determination (R 2 ), percentage of error (PE), percentage of bias (P bias ) (e.g., Akkal-Corfini et al, 265 2010;Ale et al, 2012;Dawson et al, 2007Dawson et al, , 2010Jabro et al, 2012;Jachner et al, 2007; Kersebaum et al, 266 2007; Krause et al, 2005;Moriasi et al, 2007;Qi et al, 2012;Reusser et al, 2009;Stumpp et al, 2009; Van der 267 Laan et al, 2011;Wang et al, 2006;Willmott et al, 1985).…”

mentioning

confidence: 99%

Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data

Groenendijk

Heinen

Klammler

et al. 2014

Science of The Total Environment

View full text Add to dashboard Cite

Elsevier Groenendijk, P.; Heinen, M.; Klammler, G.;Kupfersberger, H.; Pisinaras, V.; Gemitzi, A.... (2014). Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data. Science of the Total Environment. 499: 463-480. doi:10.1016/j.scitotenv.2014.07.002 . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 2 The agricultural sector faces the challenge of ensuring food security without an excessive burden on the 24 environment. Simulation models provide excellent instruments for researchers to gain more insight into relevant 25 processes and best agricultural practices and provide tools for planners for decision making support. The extent 26 to which models are capable of reliable extrapolation and prediction is important for exploring new farming 27 systems or assessing the impacts of future land and climate changes. 28A performance assessment was conducted by testing six detailed state-of-the-art models for simulation of nitrate 29 leaching (ARMOSA, COUPMODEL, DAISY, EPIC, SIMWASER/STOTRASIM, SWAP/ANIMO) for 30 lysimeter data of the Wagna experimental field station in Eastern Austria, where the soil is highly vulnerable to 31 nitrate leaching. 32Three consecutive phases were distinguished to gain insight in the predictive power of the models: 1) a blind test 33 for 2005 -2008 in which only soil hydraulic characteristics, meteorological data and information about the 34 agricultural management were accessible; 2) a calibration for the same period in which essential information on 35 field observations was additionally available to the modellers; and 3) a validation for 2009 -2011 with the 36 corresponding type of data available as for the blind test. A set of statistical metrics (mean absolute error, root 37 mean squared error, index of agreement, model efficiency, root relative squared error, Pearson's linear 38 correlation coefficient) was applied for testing the results and comparing the models. 39None of the models performed good for all of the statistical metrics. Models designed for nitrate leaching in high 40 input farming systems had difficulties in accurate predicting leaching in low input farming systems that are 41 strongly influenced by the retention of nitrogen in catch crops and nitrogen fixation by legumes. An accurate 42 calibration does not guarantee a good predictive power of the model. Nevertheless all models were able to 43 identify years and crops with high and low leaching rates. 44 Keywords 45Lysimeter, model comparison, nitrate leaching, performance assessment, predictive power, simula...

show abstract

Evaluation of Water Retention Functions and Computer Program “Rosetta” in Predicting Soil Water Characteristics of Seasonally Impounded Shrink–Swell Soils

Cited by 23 publications

References 30 publications

Estimating Parameters of Van Genuchten Model for Soil Water Retention Curve by Intelligent Algorithms

Estimating Parameters of Van Genuchten Model for Soil Water Retention Curve by Intelligent Algorithms

Estimation of bulk density of waterlogged soils from basic properties

Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data

Contact Info

Product

Resources

About