Uncertainty Analysis of HYDRUS-1D Model to Simulate Soil Salinity Dynamics under Saline Irrigation Water Conditions Using Markov Chain Monte Carlo Algorithm
Utilizing degraded quality waters such as saline water as irrigation water with proper management methods such as leaching application is a potential answer to water scarcity in agricultural systems. Leaching application requires understanding the relationship between the amount of irrigation water...
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MDPI AG
2022-11-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/11/2793 |
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| author | Farzam Moghbel Abolfazl Mosaedi Jonathan Aguilar Bijan Ghahraman Hossein Ansari Maria C. Gonçalves |
| author_facet | Farzam Moghbel Abolfazl Mosaedi Jonathan Aguilar Bijan Ghahraman Hossein Ansari Maria C. Gonçalves |
| author_sort | Farzam Moghbel |
| collection | DOAJ |
| description | Utilizing degraded quality waters such as saline water as irrigation water with proper management methods such as leaching application is a potential answer to water scarcity in agricultural systems. Leaching application requires understanding the relationship between the amount of irrigation water and its quality with the dynamic of salts in the soil. The HYDRUS-1D model can simulate the dynamic of soil salinity under saline water irrigation conditions. However, these simulations are subject to uncertainty. A study was conducted to assess the uncertainty of the HYDRUS-1D model parameters and outputs to simulate the dynamic of salts under saline water irrigation conditions using the Markov Chain Monte Carlo (MCMC) based Metropolis-Hastings algorithm in the R-Studio environment. Results indicated a low level of uncertainty in parameters related to the advection term (water movement simulation) and water stress reduction function for root water uptake in the solute transport process. However, a higher level of uncertainty was detected for dispersivity and diffusivity parameters, possibly because of the study’s scale or some error in initial or boundary conditions. The model output (predictive) uncertainty showed a high uncertainty in dry periods compared to wet periods (under irrigation or rainfall). The uncertainty in model parameters was the primary source of total uncertainty in model predictions. The implementation of the Metropolis-Hastings algorithm for the HYDRUS-1D was able to conveniently estimate the residual water content (θ<sub>r</sub>) value for the water simulation processes. The model’s performance in simulating soil water content and soil water electrical conductivity (ECsw) was good when tested with the 50% quantile of the posterior distribution of the parameters. Uncertainty assessment in this study revealed the effectiveness of the Metropolis-Hastings algorithm in exploring uncertainty aspects of the HYDRUS-1D model for reproducing soil salinity dynamics under saline water irrigation at a field scale. |
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| publishDate | 2022-11-01 |
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| spelling | doaj.art-a4b5bcd439ca456690e5d2b1e3f9ca0a2023-11-24T03:22:24ZengMDPI AGAgronomy2073-43952022-11-011211279310.3390/agronomy12112793Uncertainty Analysis of HYDRUS-1D Model to Simulate Soil Salinity Dynamics under Saline Irrigation Water Conditions Using Markov Chain Monte Carlo AlgorithmFarzam Moghbel0Abolfazl Mosaedi1Jonathan Aguilar2Bijan Ghahraman3Hossein Ansari4Maria C. Gonçalves5Department of Water Science and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 9177948978, IranDepartment of Water Science and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 9177948978, IranSouthwest Research-Extension Center, Kansas State University, 4500 E. Mary St., Garden City, KS 67846, USADepartment of Water Science and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 9177948978, IranDepartment of Water Science and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 9177948978, IranInstituto Nacional de Investigação Agrária e Veterinária (INIAV), Av. República, 2780-157 Oeiras, PortugalUtilizing degraded quality waters such as saline water as irrigation water with proper management methods such as leaching application is a potential answer to water scarcity in agricultural systems. Leaching application requires understanding the relationship between the amount of irrigation water and its quality with the dynamic of salts in the soil. The HYDRUS-1D model can simulate the dynamic of soil salinity under saline water irrigation conditions. However, these simulations are subject to uncertainty. A study was conducted to assess the uncertainty of the HYDRUS-1D model parameters and outputs to simulate the dynamic of salts under saline water irrigation conditions using the Markov Chain Monte Carlo (MCMC) based Metropolis-Hastings algorithm in the R-Studio environment. Results indicated a low level of uncertainty in parameters related to the advection term (water movement simulation) and water stress reduction function for root water uptake in the solute transport process. However, a higher level of uncertainty was detected for dispersivity and diffusivity parameters, possibly because of the study’s scale or some error in initial or boundary conditions. The model output (predictive) uncertainty showed a high uncertainty in dry periods compared to wet periods (under irrigation or rainfall). The uncertainty in model parameters was the primary source of total uncertainty in model predictions. The implementation of the Metropolis-Hastings algorithm for the HYDRUS-1D was able to conveniently estimate the residual water content (θ<sub>r</sub>) value for the water simulation processes. The model’s performance in simulating soil water content and soil water electrical conductivity (ECsw) was good when tested with the 50% quantile of the posterior distribution of the parameters. Uncertainty assessment in this study revealed the effectiveness of the Metropolis-Hastings algorithm in exploring uncertainty aspects of the HYDRUS-1D model for reproducing soil salinity dynamics under saline water irrigation at a field scale.https://www.mdpi.com/2073-4395/12/11/2793BayesianHYDRUS-1DirrigationleachingMCMCMetropolis-Hastings |
| spellingShingle | Farzam Moghbel Abolfazl Mosaedi Jonathan Aguilar Bijan Ghahraman Hossein Ansari Maria C. Gonçalves Uncertainty Analysis of HYDRUS-1D Model to Simulate Soil Salinity Dynamics under Saline Irrigation Water Conditions Using Markov Chain Monte Carlo Algorithm Agronomy Bayesian HYDRUS-1D irrigation leaching MCMC Metropolis-Hastings |
| title | Uncertainty Analysis of HYDRUS-1D Model to Simulate Soil Salinity Dynamics under Saline Irrigation Water Conditions Using Markov Chain Monte Carlo Algorithm |
| title_full | Uncertainty Analysis of HYDRUS-1D Model to Simulate Soil Salinity Dynamics under Saline Irrigation Water Conditions Using Markov Chain Monte Carlo Algorithm |
| title_fullStr | Uncertainty Analysis of HYDRUS-1D Model to Simulate Soil Salinity Dynamics under Saline Irrigation Water Conditions Using Markov Chain Monte Carlo Algorithm |
| title_full_unstemmed | Uncertainty Analysis of HYDRUS-1D Model to Simulate Soil Salinity Dynamics under Saline Irrigation Water Conditions Using Markov Chain Monte Carlo Algorithm |
| title_short | Uncertainty Analysis of HYDRUS-1D Model to Simulate Soil Salinity Dynamics under Saline Irrigation Water Conditions Using Markov Chain Monte Carlo Algorithm |
| title_sort | uncertainty analysis of hydrus 1d model to simulate soil salinity dynamics under saline irrigation water conditions using markov chain monte carlo algorithm |
| topic | Bayesian HYDRUS-1D irrigation leaching MCMC Metropolis-Hastings |
| url | https://www.mdpi.com/2073-4395/12/11/2793 |
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