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A scalable distributed parallel simulation tool for the SWAT model
Environmental Modelling & Software  (IF5.288),  Pub Date : 2021-07-13, DOI: 10.1016/j.envsoft.2021.105133
Qiaoying Lin, Dejian Zhang

High-fidelity hydrological models are increasingly built and used to investigate the effects of management activities and climate change on water availability and quality for large areas with datasets of high spatial and temporal resolution. However, these advantages come at the price of greater computational demand and run time. This becomes challenging when modeling routines involve iterative model simulations. In this study, we proposed a generic scheme to reduce the Soil and Water Assessment Tool (SWAT) runtime by decomposing a watershed model into subbasin models and optimizing the subbasin model simulations based on a parallel approach. Based on this scheme, we implemented a generic tool named Spark-SWAT, which allows subbasin models to be simulated in parallel on a Spark computer cluster. We then evaluated Spark-SWAT with two sets of experiments to demonstrate the potential of Spark-SWAT to accelerate single and iterative model simulations. In each test set, Spark-SWAT was applied to simulate 12 synthetic hydrological models in parallel with different I/O (input/output) burdens and river network complexities in a Spark cluster with five virtual machines. The single model parallelization results showed that Spark-SWAT yielded a speedup value of 7.84 for the most complex model but was less effective with simple models. When applied to use cases with iterative model runs, Spark-SWAT yielded a speedup of 6.55–24.58 depending on the model complexity. These results indicate that the proposed scheme can effectively solve high-computational-demand problems of complex models. As a subbasin-level parallelization tool, Spark-SWAT can be very computationally frugal and useful in use cases in which the model input changes pertain to only a few subbasins because only the changed and downstream subbasins require new computations. Moreover, it is possible to apply this generic method to other subbasin-based hydrological models to alleviate I/O demands and optimize model computational performance.