Abstract. High-fidelity and large-scale hydrological models are
increasingly used to investigate the impacts of human activities and climate
change on water availability and quality. However, the detailed
representations of real-world systems and processes contained in these
models inevitably lead to prohibitively high execution times, ranging from
minutes to days. Such models become computationally prohibitive or even
infeasible when large iterative model simulations are involved. In this
study, we propose a generic two-level (i.e., watershed- and subbasin-level)
model parallelization schema to reduce the run time of computationally
expensive model applications through a combination of model spatial
decomposition and the graph-parallel Pregel algorithm. Taking the Soil and
Water Assessment Tool (SWAT) as an example, we implemented a generic tool
named GP-SWAT, enabling watershed-level and subbasin-level model
parallelization on a Spark computer cluster. We then evaluated GP-SWAT in
two sets of experiments to demonstrate the ability of GP-SWAT to accelerate
single and iterative model simulations and to run in different environments.
In each test set, GP-SWAT was applied for the parallel simulation of four
synthetic hydrological models with different input/output (I/O) burdens. The
single-model parallelization results showed that GP-SWAT can obtain a
2.3–5.8-times speedup. For multiple simulations with subbasin-level
parallelization, GP-SWAT yielded a remarkable speedup of 8.34–27.03 times.
In both cases, the speedup ratios increased with an increasing computation
burden. The experimental results indicate that GP-SWAT can effectively solve
the high-computational-demand problems of the SWAT model. In addition, as a
scalable and flexible tool, it can be run in diverse environments, from a
commodity computer running the Microsoft Windows operating system to a Spark
cluster consisting of a large number of computational nodes. Moreover, it is
possible to apply this generic tool to other subbasin-based hydrological
models or even acyclic models in other domains to alleviate I/O demands and
to optimize model computational performance.
THE ITERATIVE MODEL OF ETHICAL ANALYSIS FOR LARGE-SCALE IMPLEMENTATION OF ICT SOLUTIONS
