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JungJin Kim
Texas A&M AgriLife Research (Texas A&M University System), P.O. Box 1658, Vernon, TX 76384, USA

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Journal article
Published: 18 May 2019 in Water
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We conducted a study on climate-driven flash flood risk in the Boise River Watershed using flood frequency analysis and climate-driven hydrological simulations over the next few decades. Three different distribution families, including the Gumbel Extreme Value Type I (GEV), the 3-parameter log-normal (LN3) and log-Pearson type III (LP3) are used to explore the likelihood of potential flash flood based on the 3-day running total streamflow sequences (3D flows). Climate-driven ensemble streamflows are also generated to evaluate how future climate variability affects local hydrology associated with potential flash flood risks. The result indicates that future climate change and variability may contribute to potential flash floods in the study area, but incorporating embedded-uncertainties inherited from climate models into water resource planning would be still challenging because grand investments are necessary to mitigate such risks within institutional and community consensus. Nonetheless, this study will provide useful insights for water managers to plan out sustainable water resources management under an uncertain and changing climate.

ACS Style

Jae Hyeon Ryu; JungJin Kim. A Study on Climate-Driven Flash Flood Risks in the Boise River Watershed, Idaho. Water 2019, 11, 1039 .

AMA Style

Jae Hyeon Ryu, JungJin Kim. A Study on Climate-Driven Flash Flood Risks in the Boise River Watershed, Idaho. Water. 2019; 11 (5):1039.

Chicago/Turabian Style

Jae Hyeon Ryu; JungJin Kim. 2019. "A Study on Climate-Driven Flash Flood Risks in the Boise River Watershed, Idaho." Water 11, no. 5: 1039.

Journal article
Published: 19 April 2019 in Water
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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.

ACS Style

JungJin Kim; Jae Hyeon Ryu. Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study. Water 2019, 11, 823 .

AMA Style

JungJin Kim, Jae Hyeon Ryu. Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study. Water. 2019; 11 (4):823.

Chicago/Turabian Style

JungJin Kim; Jae Hyeon Ryu. 2019. "Quantifying the Performances of the Semi-Distributed Hydrologic Model in Parallel Computing—A Case Study." Water 11, no. 4: 823.