Development of a knowledge-sharing parallel computing approach for calibrating distributed watershed hydrologic models.

A research gap in calibrating distributed watershed hydrologic models lies in the development of calibration frameworks adaptable to increasing complexity of hydrologic models. Parallel computing is a promising approach to address this gap. However, parallel calibration approaches should be fault-tolerant, portable, and easy to implement with minimum communication overhead for fast knowledge sharing between parallel nodes. Accordingly, we developed a knowledge-sharing parallel calibration approach using Chapel programming language, with which we implemented the Parallel Dynamically Dimensioned Search (DDS) algorithm by adopting multiple perturbation factors and parallel dynamic searching strategies to keep a balance between exploration and exploitation of the search space. Our results showed that this approach achieved super-linear speedup and parallel efficiency above 75%. In addition, our approach has a low communication overhead, along with the positive impact of knowledge-sharing in the convergence behavior of the parallel DDS algorithm.