PASS4SWAT: Orchestration of containerized SWAT for facilitating computational reproducibility of model calibration and uncertainty analysis.

When performing modeling routines with iterative simulations, a research gap exists in developing the parallelizing frameworks that are adaptable to the increasing complexities of the hydrologic models. This study addresses this gap by proposing a parallel simulation stack for SWAT (PASS4SWAT) that leverages the power of cloud-native infrastructure. Cloud-native applications are designed to be scalable, agile, and resilient, making them ideal for computationally intensive tasks such as hydrologic modeling. To achieve this goal, PASS4SWAT was created by integrating a message broker, a container runtime and orchestration tool, and certain components from SWAT-CUP. We evaluated PASS4SWAT using the Jinjiang watershed model and a synthetic model with high input/output demands to demonstrate its effectiveness. The results show that PASS4SWAT can consistently replicate the results obtained with SWAT-CUP and significantly reduce the runtime, achieving 91.5% and 93.2% reductions in single-node and multinode Kubernetes clusters, respectively. Therefore, in this paper, we conclude that PASS4SWAT can effectively address the high computational demands of the SWAT model by scaling out parallel tasks on a cluster, and can adapt flexibly to diverse environments, including single-node clusters, multinode clusters, and potential cloud platforms. [Display omitted] • PASS4SWAT significantly reduces the runtime needed for calibration and uncertainty analysis. • PASS4SWAT can adapt to diverse environments, including single-node and multinode clusters. • PASS4SWAT can autonomously manage subtasks and obviate the need for explicit failover and load balancing. • PASS4SWAT has great potential for application in other models and can be linked with other calibration tools. [ABSTRACT FROM AUTHOR]