Effect of Initial Soil Water Content on Output Parameters of Sirmod Software Under Types of Different Irrigation Management

Javadi, A.; Mostafazadeh‐Fard, B.; Shayannejad, Mohammad; Ebrahimian, Hamed

doi:10.1002/ird.2371

Cited by 9 publications

(4 citation statements)

References 33 publications

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“…Therefore, the measured ET 0 and its estimated values with the best models of each group (KP, FAO24‐Rad, and FAO24‐BC) were considered more sensitive to the above‐mentioned parameters of T , T x , T n , VPD, and R s , respectively. In the present study, sensitivity analysis was performed based on the method applied by Javadi et al (2019). The sensitivity analysis of the models was reasonably in correspondence with this result.…”

Section: Resultsmentioning

confidence: 99%

Comparative analysis of 15 major reference evapotranspiration models based on weighing lysimeter measurements for two different grass species grown in three soil textures

Ahmadi̇far

Delirhasannia

Sadraddini

2022

Irrigation and Drainage

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In this study, 15 models in the combination, radiation, and temperature‐based groups were evaluated for three consecutive years (2017–2019) with lysimetric measurements in a semi‐arid region of Iran. To investigate the effects of reference plant species and soil textures on the amounts of reference evapotranspiration (ET0), two grass species including Festuca arundianacea Schreb. and Lolium perenne in the three soil textures sandy loam, clay, and silt loam were planted in the weighing lysimeters. ET0 was affected by soil texture and grass species (p < .01). The highest amount of ET0 occurred in the clay soil and Festuca grass. Comparison of the models' results with Lolium grass lysimetric data showed that the minimum and maximum root mean square error (RMSE) values 1.27 and 3.5 mm d−1 were obtained for the Kimberly‐Penman‐1996 (KP) and DeBruin‐Keijman (DK) models, respectively. Regarding the Festuca grass lysimetric data, the minimum RMSE (1.36 mm d−1) was obtained for the KP and the maximum RMSE (3.6 mm d−1) for the DK model. Group comparison of models showed that the combination models performed better than the other groups' models. The estimated ET0 values by the models had a better match with the measured data of Lolium grass than those of the Festuca grass.

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Section: Resultsmentioning

confidence: 99%

Comparative analysis of 15 major reference evapotranspiration models based on weighing lysimeter measurements for two different grass species grown in three soil textures

Ahmadi̇far

Delirhasannia

Sadraddini

2022

Irrigation and Drainage

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“…The van Genuchten-Mualem (1980) model parameters, including saturated moisture (θ s [-]), saturated hydraulic conductivity (K s [cm min −1 ]), shape parameter (n [-]) and scaling parameter (α [cm −1 ]) were optimized. For optimizing these parameters, the optimized soil hydraulic parameters were considered according to Javadi et al (2019a) (Table 4) [26]. In the research by Javadi et al (2019a), four parameters (α, K s , n and θ s ) were considered as uncertain parameters and soil water content at field capacity, drain water and cumulative infiltration were taken into consideration as specified parameters.…”

Section: Simulation Of Infiltration Process and Sensitivity Analysismentioning

confidence: 99%

“…where m is the number of outputs; X i is the new output, as a result of varying the input from P i−1 to P i ; X i−1 is the prior output, without varying the input (P i−1 ); S r is the relative sensitivity indicator (dimensionless); and S a is the absolute sensitivity indicator (dimensionless) [26]. Each infiltration equation (or coefficient) indicator near to zero has less sensitivity.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Effect of Different Irrigation Managements on Infiltration Equations and Their Coefficients

Javadi,

Ostad-Ali-Askari

2023

CivilEng

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The main aim of this paper was to analyze the sensitivity of the five infiltration equations (Kostiakov, Kostiakov–Lewis, Philip, Horton and SCS) and their coefficients to various ponding depths and initial soil moisture under different irrigation managements. The treatments included three qualities of water (electrical conductivity = 6, 3 and 0.6 dS/m), two managements of irrigation (intermittent irrigation and daily irrigation) and three irrigation periods (100, 45 and 8 days). The HYDRUS-1D model was calibrated to simulate infiltration in various initial soil moistures and ponding depths. Evaluating the performance of infiltration equations showed that the Horton and Kostiakov–Lewis had better accuracy and Kostiakov and SCS had less accuracy than the other equations. The empirical coefficients of SCS and Kostiakov had the most and least sensitivities, respectively. Furthermore, Horton was the most sensitive equation, while SCS was the least sensitive one. The output parameters under daily management were the most sensitive to variations in infiltration coefficients, especially when the salinity and sodium contents of water and soil were higher. The results also showed that the effect of the initial soil moisture on the infiltration coefficient in high permeable soil (arising from daily management) was greater; but in low permeable soil (arising from intermittent management), the ponding depth was more effective. It is concluded that the infiltration equations (specifically the SCS equation) and their coefficients (specifically coefficient c) should be calibrated relative to the initial soil moisture, ponding depth, soil solution and water irrigation quality. Particularly in areas with high permeable soil (in the daily management), the calibration of the infiltration equation should be conducted with the initial soil moisture. In these areas, the irrigation period should be controlled. In areas with low permeable soil (in intermittent management), calibration should be carried out relative to the ponding depth. In these areas, the inflow rate should be controlled.

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“…It simultaneously produces the best results when studying actual problems such as farming irrigation, fertilization techniques and groundwater pollution (Akbariyeh et al, 2018;Gu & Riley, 2010;Phogat, Cox, & Šimu ˚nek, 2018;Ramos et al, 2012;Tafteh & Sepaskhah, 2012). Models always involve uncertainties, which usually come from measured data, model parameters and model structures, such as soil spatial variability, physical and chemical properties and the variability of boundary conditions, leading to a high degree of nonlinearity in soil moisture and solute movement parameters (Javadi et al, 2019). Since the soil shows some randomness during sedimentation, resulting in a large spatial variation in soil physical and chemical properties, the water and N transport mechanisms are more complex at the field scale (Suarez-Rey et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The parametric uncertainty estimation of water and nitrogen transport simulation in a paddy field experiment using HYDRUS‐1D

Bian

Nie

et al. 2023

Irrigation and Drainage

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The HYDRUS model is an efficient technical means to study the process of complex nitrogen (N) transport in farmland. Nonetheless, spatial variability in soil water and N parameters has been observed at the field scale, leading to uncertainties in the HYDRUS model. In this study, the HYDRUS‐1D model was corrected and verified by the measured data. Generalized likelihood uncertainty estimation (GLUE) was used to estimate the uncertainty and sensitivity of the model parameters. The differences in simulated and measured data were expressed in terms of root mean square error, normalized root mean square error and coefficient of determination, and the model calibration and verification indicated that the result was reliable. The soil saturated water content, dispersion and sorption coefficient were well identified, with values ranging from 0.42 to 0.45 cm3/cm3, 0.26 to 0.29 cm and 0.141 to 0.158 L/mg, respectively. The denitrification rate and saturated hydraulic conductivity greatly influenced the uncertainty in the model results. Considering the uncertainty of the parameters, the predicted leaching of N accumulation from 0 to 100 cm depth ranged from 9.5 to 12.7 kg/ha. The HYDRUS‐1D model can simulate water and N transport in paddy fields. The prediction interval can be narrowed by considering the uncertainty of the model parameters.

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Effect of Initial Soil Water Content on Output Parameters of Sirmod Software Under Types of Different Irrigation Management

Cited by 9 publications

References 33 publications

Comparative analysis of 15 major reference evapotranspiration models based on weighing lysimeter measurements for two different grass species grown in three soil textures

Comparative analysis of 15 major reference evapotranspiration models based on weighing lysimeter measurements for two different grass species grown in three soil textures

Effect of Different Irrigation Managements on Infiltration Equations and Their Coefficients

The parametric uncertainty estimation of water and nitrogen transport simulation in a paddy field experiment using HYDRUS‐1D

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