An improved calibration technique to address high dimensionality and non-linearity in integrated groundwater and surface water models
Article
Article Title | An improved calibration technique to address high dimensionality and non-linearity in integrated groundwater and surface water models |
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ERA Journal ID | 4673 |
Article Category | Article |
Authors | Rafiei, Vahid (Author), Nejadhashemi, A. Pouyan (Author), Mushtaq, Shahbaz (Author), Bailey, Ryan T. (Author) and An-Vo, Duc-Anh (Author) |
Journal Title | Environmental Modelling and Software |
Journal Citation | 149, pp. 1-15 |
Article Number | 105312 |
Number of Pages | 15 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 1364-8152 |
1873-6726 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.envsoft.2022.105312 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1364815222000184?via%3Dihub |
Abstract | The calibration of integrated groundwater-surface water models is often associated with high dimensionality and stagnation around local optimum solutions. Since these models are computationally demanding and also non-linear, finding their global optimum solution requires efficient optimization techniques. Here, we introduce the Multi-Memory Particle Swarm Optimization (MMPSO) algorithm. The swarm cognitive capacity is enhanced to minimize the number of local optimums and calibrate the model based on sub-objective functions. We used the MMPSO to simultaneously calibrate groundwater head, streamflow, baseflow, and nitrate loads in the SWAT-MODFLOW-RT3D model with 78 sensitive parameters. The results demonstrate that enhancing the cognitive capacity led to a marked improvement in discovering the global optimum solution. Furthermore, we evaluated the calibrated model's performance to quantify groundwater nitrate loads to streams and characterize the shallow surficial aquifer under intensive fertilizer land use. The results show the effectiveness of the MMPSO algorithm for calibrating complex hydrogeochemical models for large-scale applications. |
Keywords | groundwater; nitrate; SWAT-MODFLOW-RT3D; high dimensionality; particle swarm optimization; Great Barrier Reef |
ANZSRC Field of Research 2020 | 300201. Agricultural hydrology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Centre for Applied Climate Sciences |
Michigan State University, United States | |
Colorado State University, United States | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q70xx/an-improved-calibration-technique-to-address-high-dimensionality-and-non-linearity-in-integrated-groundwater-and-surface-water-models
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