Design and development of a python-based interface for parallel calibration of SWAT+ model at large scale using dynamically dimensioned search algorithm

Abstract

Efficient calibration of hydrological models such as the Soil and Water Assessment Tool Plus (SWAT+) is crucial for accurate watershed simulations and informed water resource management. Traditional calibration strategies often struggle to handle the computational complexities inherent in exploring high-dimensional parameter spaces. This research introduces an approach that harnesses the power of parallel computing for SWAT + model calibration, employing the Dynamically Dimensioned Search (DDS) algorithm. Parallel computing was utilized to distribute computational tasks across multiple processors or computing nodes, significantly reducing calibration time while maintaining precision. By leveraging parallel computing resources, the DDS algorithm explored more extensive parameter spaces for SWAT + calibration in a reasonable timeframe. In a case study conducted in the Upper Mississippi River Basin (UMRB), the effectiveness of parallel computing-enabled DDS was demonstrated for calibrating the SWAT + model. The algorithm efficiently navigated complex parameter spaces, leading to enhanced model performance in simulating critical hydrological processes such as streamflow dynamics, sediment yield, and nutrient transport. Comparisons of simulated and observed data demonstrated satisfactory performance of the calibrated model. The calibration method offered substantial benefits for large-scale hydrologic modeling, enabling researchers and water resource managers to calibrate more complex or fine-grained watershed models more quickly. The integration of parallel computing with DDS in SWAT + calibration facilitated accelerated model calibration and scenario analysis capabilities, enabling more timely decision-making for sustainable water management projects.