Parchami-Araghi, F., Sadeghi-Lari, A. (2021). Uncertainty Analysis of the SWAP Model in a Sugarcane Field with Subsurface Controlled Drainage. , 35(2), 151-167. doi: 10.22092/jwra.2021.353950.859
Farzin Parchami-Araghi; Adnan Sadeghi-Lari. "Uncertainty Analysis of the SWAP Model in a Sugarcane Field with Subsurface Controlled Drainage". , 35, 2, 2021, 151-167. doi: 10.22092/jwra.2021.353950.859
Parchami-Araghi, F., Sadeghi-Lari, A. (2021). 'Uncertainty Analysis of the SWAP Model in a Sugarcane Field with Subsurface Controlled Drainage', , 35(2), pp. 151-167. doi: 10.22092/jwra.2021.353950.859
Parchami-Araghi, F., Sadeghi-Lari, A. Uncertainty Analysis of the SWAP Model in a Sugarcane Field with Subsurface Controlled Drainage. , 2021; 35(2): 151-167. doi: 10.22092/jwra.2021.353950.859

Uncertainty Analysis of the SWAP Model in a Sugarcane Field with Subsurface Controlled Drainage

پژوهش آب در کشاورزی
Article 5, Volume 35, Issue 2, September 2021, Pages 151-167    PDF (1.05 M)
Document Type: Research Paper
DOI: 10.22092/jwra.2021.353950.859
Authors
Farzin Parchami-Araghi* 1; Adnan Sadeghi-Lari2
1Assistant Prof., Agricultural Engineering Research Institute, Ardabil Agricultural and Natural Resources Research and Education Center, AREEO, Ardabil, Iran
2Department of Agriculture, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran
Abstract
It is important to assess the uncertainties involved in agro-hydrologic simulations because they are subject to varying degrees of uncertainty. Uncertainty analysis of the agro-hydrological models can provide useful insights into the degree of confidence in the model results. In this study, uncertainty analysis of a distributed application of the SWAP model to a sugarcane field with subsurface controlled drainage was conducted using a hybrid uncertainty analysis scheme, combining Generalized Likelihood Uncertainty Estimation (GLUE) and Unified Particle Swarm Optimization (UPSO). The results revealed a high variability of the calibrated parameters and the necessity of an uncertainty assessment for the SWAP simulations. Strong parameter correlations highlighted the need for calibration of the model parameters against diverse calibration data in a simultaneous manner. The 95% prediction uncertainty bands obtained for the  hydrological (soil water content, water table level, sub-surface drainage outflow), solute transport (soil water solute concentration and sub-surface drainage outflow salinity), and biophysical (leaf area index, cane, and sucrose dry yield) simulations enveloped 73-80%, 45-58%, and 75-100% of the corresponding total observed data (including both calibration and validation datasets), respectively, with an r-factor (the ratio of the average thickness of the 95PPU band to the standard deviation of the corresponding measured variable) of 0.83-0.98, 1.43-1.96, and 0.75-1.11. The thickness of the derived 95PPU bands for the biophysical simulations showed an increasing trend over the simulation period.
Keywords
: Generalized Likelihood Uncertainty Estimation; Soil salinity; Soil hydraulic parameters; Unified Particle Swarm Optimization
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