Accelerating the Lagrangian Particle Tracking in Hydrologic Modeling to Continental-Scale

Chen Yang, Carl Ponder, Bei Wang, Hoang Tran, Jun Zhang, Jackson Swilley, Laura Condon, Reed Maxwell

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Unprecedented climate change and anthropogenic activities have induced increasing ecohydrological problems, motivating the development of large-scale hydrologic modeling for solutions. Water age/quality is as important as water quantity for understanding the terrestrial water cycle. However, scientific progress in tracking water parcels at large-scale with high spatiotemporal resolutions is far behind that in simulating water balance/quantity owing to the lack of powerful modeling tools. EcoSLIM is a particle tracking model working with ParFlow-CLM that couples integrated surface-subsurface hydrology with land surface processes. Here, we demonstrate a parallel framework on distributed, multi-Graphics Processing Unit platforms with Compute Unified Device Architecture-Aware Message Passing Interface for accelerating EcoSLIM to continental-scale. In tests from catchment-, to regional-, and then to continental-scale using 25-million to 1.6-billion particles, EcoSLIM shows significant speedup and excellent parallel performance. The parallel framework is portable to atmospheric and oceanic particle tracking models, where the parallelization is inadequate, and a standard parallel framework is also absent. The parallelized EcoSLIM is a promising tool to accelerate our understanding of the terrestrial water cycle and the upscaling of subsurface hydrology to Earth System Models.

Original languageEnglish (US)
Article numbere2022MS003507
JournalJournal of Advances in Modeling Earth Systems
Volume15
Issue number5
DOIs
StatePublished - May 2023

All Science Journal Classification (ASJC) codes

  • Global and Planetary Change
  • Environmental Chemistry
  • General Earth and Planetary Sciences

Keywords

  • CUDA-Aware MPI
  • EcoSLIM
  • continental scale
  • multi-GPU
  • particle tracking

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