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Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study

1
Texas A&M AgriLife Research (Texas A&M University System), P.O.Box 1658, Vernon, TX 76384, USA
2
Department of Soil and Water Systems, University of Idaho, 322E. Front ST, Boise, ID 83702, USA
*
Author to whom correspondence should be addressed.
Water 2019, 11(4), 823; https://doi.org/10.3390/w11040823
Received: 21 March 2019 / Revised: 15 April 2019 / Accepted: 16 April 2019 / Published: 19 April 2019
(This article belongs to the Section Hydrology)
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Abstract

The research features how parallel computing can advance hydrological performances associated with different calibration schemes (SCOs). The result shows that parallel computing can save up to 90% execution time, while achieving 81% simulation improvement. Basic statistics, including (1) index of agreement (D), (2) coefficient of determination (R2), (3) root mean square error (RMSE), and (4) percentage of bias (PBIAS) are used to evaluate simulation performances after model calibration in computer parallelism. Once the best calibration scheme is selected, additional efforts are made to improve model performances at the selected calibration target points, while the Rescaled Adjusted Partial Sums (RAPS) is used to evaluate the trend in annual streamflow. The qualitative result of reducing execution time by 86% on average indicates that parallel computing is another avenue to advance hydrologic simulations in the urban-rural interface, such as the Boise River Watershed, Idaho. Therefore, this research will provide useful insights for hydrologists to design and set up their own hydrological modeling exercises using the cost-effective parallel computing described in this case study. View Full-Text
Keywords: hydrologic simulation; HSPF; BEOBEST; parallel computing; Boise River Watershed hydrologic simulation; HSPF; BEOBEST; parallel computing; Boise River Watershed
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Kim, J.; Ryu, J.H. Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study. Water 2019, 11, 823.

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