TY - JOUR
T1 - Real-world hydrologic assessment of a fully-distributed hydrological model in a parallel computing environment
AU - Vivoni, Enrique
AU - Mascaro, Giuseppe
AU - Mniszewski, Susan
AU - Fasel, Patricia
AU - Springer, Everett P.
AU - Ivanov, Valeriy Y.
AU - Bras, Rafael L.
N1 - Funding Information:
We acknowledge financial support from the NSF Science and Technology Center for Sustainability of semi-Arid Hydrology and Riparian Areas (SAHRA). The Los Alamos National Laboratory (LANL) Directed Research and Development and Computing Programs also supported this effort. We also thank the ASU Ira A. Fulton Schools of Engineering High Performance Computing Initiative for the use of Saguaro for the final production runs. The comments from several anonymous reviewers also helped improve the quality of the manuscript.
PY - 2011/10/28
Y1 - 2011/10/28
N2 - A major challenge in the use of fully-distributed hydrologic models has been the lack of computational capabilities for high-resolution, long-term simulations in large river basins. In this study, we present the parallel model implementation and real-world hydrologic assessment of the Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS). Our parallelization approach is based on the decomposition of a complex watershed using the channel network as a directed graph. The resulting sub-basin partitioning divides effort among processors and handles hydrologic exchanges across boundaries. Through numerical experiments in a set of nested basins, we quantify parallel performance relative to serial runs for a range of processors, simulation complexities and lengths, and sub-basin partitioning methods, while accounting for inter-run variability on a parallel computing system. In contrast to serial simulations, the parallel model speed-up depends on the variability of hydrologic processes. Load balancing significantly improves parallel speed-up with proportionally faster runs as simulation complexity (domain resolution and channel network extent) increases. The best strategy for large river basins is to combine a balanced partitioning with an extended channel network, with potential savings through a lower TIN resolution. Based on these advances, a wider range of applications for fully-distributed hydrologic models are now possible. This is illustrated through a set of ensemble forecasts that account for precipitation uncertainty derived from a statistical downscaling model.
AB - A major challenge in the use of fully-distributed hydrologic models has been the lack of computational capabilities for high-resolution, long-term simulations in large river basins. In this study, we present the parallel model implementation and real-world hydrologic assessment of the Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator (tRIBS). Our parallelization approach is based on the decomposition of a complex watershed using the channel network as a directed graph. The resulting sub-basin partitioning divides effort among processors and handles hydrologic exchanges across boundaries. Through numerical experiments in a set of nested basins, we quantify parallel performance relative to serial runs for a range of processors, simulation complexities and lengths, and sub-basin partitioning methods, while accounting for inter-run variability on a parallel computing system. In contrast to serial simulations, the parallel model speed-up depends on the variability of hydrologic processes. Load balancing significantly improves parallel speed-up with proportionally faster runs as simulation complexity (domain resolution and channel network extent) increases. The best strategy for large river basins is to combine a balanced partitioning with an extended channel network, with potential savings through a lower TIN resolution. Based on these advances, a wider range of applications for fully-distributed hydrologic models are now possible. This is illustrated through a set of ensemble forecasts that account for precipitation uncertainty derived from a statistical downscaling model.
KW - Ensemble forecasting
KW - Parallel computing
KW - Rainfall-runoff processes
KW - Sub-basin partitioning
KW - Watershed model
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U2 - 10.1016/j.jhydrol.2011.08.053
DO - 10.1016/j.jhydrol.2011.08.053
M3 - Article
AN - SCOPUS:80054082804
SN - 0022-1694
VL - 409
SP - 483
EP - 496
JO - Journal of Hydrology
JF - Journal of Hydrology
IS - 1-2
ER -