Calibration of infiltration, roughness and longitudinal dispersivity coefficients in furrow fertigation using inverse modelling with a genetic algorithm

Sedaghatdoost, Amir; Ebrahimian, Hamed

doi:10.1016/j.biosystemseng.2015.05.011

Cited by 9 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NSE values for drainage discharge, groundwater table depths and drainage water salinity were 0.83, 0.95 and 0.89, respectively, which were rated as excellent according to Skaggs et al's (2012) recommendation. This was consistent with Ritter et al (2004) and Sedaghatdoost and Ebrahimian's (2015) findings who showed that the best parameter sets came from an objective function that used all measured data. Last but not the least, the results proved that although inverse modelling using all measured data give the best results, monitoring of drainage discharge in combination with groundwater table depth proved to be sufficient.…”

supporting

confidence: 90%

“…In the Shoaybiyeh study area, among all studied objective functions, OF4 and OF7, which are based on two and three different sets of observed data, respectively, derived optimum values of parameters that simulated all model outputs precisely. Šimůnek and van Genuchten (1996), Ritter et al (2004), Sedaghatdoost and Ebrahimian (2015) also proved that applying an objective function that consists of a combination of data sets determined the unknown parameters suitably. Table VIII shows that parameters obtained by OF4 and OF7 differed substantially in initial values.…”

Section: Resultsmentioning

confidence: 88%

“…Šimůnek and van Genuchten (), Ritter et al . (), Sedaghatdoost and Ebrahimian () also proved that applying an objective function that consists of a combination of data sets determined the unknown parameters suitably. Table shows that parameters obtained by OF4 and OF7 differed substantially in initial values.…”

Section: Resultsmentioning

confidence: 90%

“…This was consistent with Ritter et al . () and Sedaghatdoost and Ebrahimian's () findings who showed that the best parameter sets came from an objective function that used all measured data. Last but not the least, the results proved that although inverse modelling using all measured data give the best results, monitoring of drainage discharge in combination with groundwater table depth proved to be sufficient.…”

Section: Resultsmentioning

confidence: 98%

See 3 more Smart Citations

Estimating Soil Hydraulic and Solute Transport Parameters in Subsurface Drainage Systems Using an Inverse Modelling Approach

Sedaghatdoost

Ebrahimian

Liaghat

2017

Irrigation and Drainage

Self Cite

View full text Add to dashboard Cite

The aim of this study is to present an inverse modelling approach with a genetic algorithm for estimating soil hydraulic and solute transport parameters in two subsurface drainage systems in Iran. In this study, measured data were obtained from Amirkabir and Shoaybiyeh sugarcane fields equipped with subsurface drainage systems. The SWAP model was used for simulating the outputs of subsurface drainage systems. The accuracy of different objective functions which were based on the discrepancies between measured and simulated values of drainage discharge, groundwater depth and drainage water salinity was evaluated in the inverse modelling approach. Based on sensitivity analysis of the SWAP model in studied areas, n shape parameter, lateral hydraulic conductivity, depth to impermeable layer, saturated water content and α shape parameter were selected in the inverse modelling approach. By applying the objective function which is based on drainage discharge and salinity in the Amirkabir study area, the Nash–Sutcliffe Efficiency (NSE) values for predicting drainage discharge and salinity were 0.63 and 0.79, respectively. In the Shoaybiyeh study area, the objective function which includes drainage discharge, groundwater depth and drainage water salinity resulted in NSE values of 0.83, 0.95 and 0.89 for predicting drainage discharge, groundwater depth and drainage water salinity, respectively. Copyright © 2017 John Wiley & Sons, Ltd.

show abstract

supporting

confidence: 90%

Section: Resultsmentioning

confidence: 88%

Section: Resultsmentioning

confidence: 90%

Section: Resultsmentioning

confidence: 98%

See 2 more Smart Citations

Estimating Soil Hydraulic and Solute Transport Parameters in Subsurface Drainage Systems Using an Inverse Modelling Approach

Sedaghatdoost

Ebrahimian

Liaghat

2017

Irrigation and Drainage

Self Cite

View full text Add to dashboard Cite

show abstract

“…The surface flow process may be modelled by many mathematical formulations, such as the Saint-Venant's Equations, the kinematic wave approximation [5,7,9], the diffusive wave approximation, the zero inertia model [7,10] or the full equations [4,8]. The kinematic wave approximation for one dimensional flow can be written as:…”

Section: Introductionmentioning

confidence: 99%

An Improved Method to Estimate Soil Hydrodynamic and Hydraulic Roughness Parameters by Using Easily Measurable Data During Flood Irrigation Experiments and Inverse Modelling

Alosman

Ruy

Buis

et al. 2018

Water

View full text Add to dashboard Cite

Surface irrigation is known as a highly water-consuming system and needs to be optimized to save water. Models can be used for this purpose but require soil parameters at the field scale. This paper aims to estimate effective soil parameters by combining: (i) a surface flow-infiltration model, namely CALHY; (ii) an automatic fitting algorithm based on the SIMPLEX method; and (iii) easily accessible and measurable data, some of which had never been used in such a process, thus minimizing parameter estimation errors. The validation of the proposed approach was performed through three successive steps: (1) examine the physical meaning of the fitted parameters; (2) verify the accuracy of the proposed approach using data that had not been served in the fitting process; and (3) validate using data obtained from independent irrigation events. Three parameters were estimated with a low uncertainty: the saturated hydraulic conductivity Ks, the hydraulic roughness k, and the soil water depletion ∆θ. The estimation uncertainty of the soil surface depressional storage parameter H0 was of the same order of magnitude of its value. All experimental datasets were simulated very well. Performance criteria were similar during both the fitting and validation stages.

show abstract

Estimation of Manning roughness coefficient for vegetated furrows

2018

View full text Add to dashboard Cite

Calibration of infiltration, roughness and longitudinal dispersivity coefficients in furrow fertigation using inverse modelling with a genetic algorithm

Cited by 9 publications

References 38 publications

Estimating Soil Hydraulic and Solute Transport Parameters in Subsurface Drainage Systems Using an Inverse Modelling Approach

Estimating Soil Hydraulic and Solute Transport Parameters in Subsurface Drainage Systems Using an Inverse Modelling Approach

An Improved Method to Estimate Soil Hydrodynamic and Hydraulic Roughness Parameters by Using Easily Measurable Data During Flood Irrigation Experiments and Inverse Modelling

Estimation of Manning roughness coefficient for vegetated furrows

Contact Info

Product

Resources

About