Calibration of the SWAT Hydrological Model with the Particle Swarm Optimization Technique

Hydrological models are important tools for management of water resources at the basin scale. However, the outputs of these models might come with significant inaccuracies, often due to uncertainties in model parameters. One of the biggest challenges in working with a hydrological model is that these models require rigorous calibration, validation, and uncertainty analysis. In recent years, there has been an increase in the use of heuristic optimization techniques in water resources research. These techniques can yield more accurate and more reliable modeling results by searching the global optimum of multiple model parameter sets.

This study describes the application of a heuristic optimization method, the Particle Swarm Optimization (PSO), on a hydrological model, the Soil and Water Assessment Tool (SWAT). The model is applied to the Fetrek Stream watershed in western Turkiye, which is under environmental stress due to excessive groundwater abstraction and pollution from numerous wastewater discharges. The model includes data related to 41 point sources, and two inflowing tributaries. The model is configured with 8 sub-basins, and 484 hydrologic response units. Hydrological fluxes are obtained for a 30-year simulation period. The sensitivities of the model parameters and uncertainties of model results are investigated. PSO is used to calibrate sensitive model parameters, followed by a comparison with the calibration outcome using the SUFI-2 (Sequential Uncertainty Fitting) algorithm, which is the usual choice in calibrating SWAT models. The performances of both optimization approaches are evaluated with the Kling-Gupta Efficiency (KGE), the regression coefficient (R²), and the bias percentage  (PBIAS) Model results are presented with their associated prediction uncertainties in the form of the so-called p-factor and r-factor statistics, which represent envelopes of good model solutions. The results show that the PSO approach can achieve satisfactory results on a monthly time-scale thereby offering an alternative calibration with less parameters and a wider interval of the 95% prediction uncertainty.

This study is supported by the PRIMA program under grant agreement No: 2024 Project TRUST (management of industrial Treated wastewater ReUse as mitigation measures to water Scarcity in climaTe change context in two Mediterranean regions). The PRIMA program is supported by the European Union.