A review of parallel computing applications in calibrating watershed hydrologic models. (May 2022) - Asgari, Marjan; Yang, Wanhong; Lindsay, John; Tolson, Bryan; Dehnavi, Maryam Mehri
Abstract: In recent decades, parallel computing has been increasingly applied to address the computational challenges of calibrating watershed hydrologic models. The purpose of this paper is to review these parallelization studies to summarize their contributions, identify knowledge gaps, and propose future research directions. These studies parallelized models based on either random-sampling-based algorithms or optimization algorithms and demonstrated considerable parallel speedup gain and parallel efficiency. However, the speedup gain/efficiency decreases as the number of parallel processing units increases, particularly after a threshold. In future, various combinations of hydrologic models, optimization algorithms, parallelization strategies, parallelization architectures, and communication modes need to be implemented to systematically evaluate a suite of parallelization scenarios for improving speedup gain, efficiency, and solution quality. A standardized suite of performance evaluation metrics needs to be developed to evaluate these parallelization approaches. Interactive multi-objective optimization algorithms and/or integrated sensitivity analysis and calibration algorithms are potential future research fields, as well. Highlights: We developed a parallelization framework for calibrating watershed hydrologic models. We applied the framework to evaluate parallel model calibration applications. We discussed advancements and gaps of parallel model calibration applications. We advocated standardized reporting of parallelization framework and implementation. We promoted improving solution quality in addition to parallel speedup and efficiency.