Assessment of Re-Forecast Data in the Modeling of Extreme Rainfall-Runoff Events (Case Study: Floods in the Bakhtiari Basin, Iran, March-April 2019)
Predicting inflow into reservoirs is essential for their operation during floods, particularly in mountainous watersheds characterized by snow-rain regimes. The objective of this research is to evaluate the GEFSv12 re-forecast data as an input of the HEC-HMS model for forecasting floods due to the e...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Birjand
2024-09-01
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| Series: | Water Harvesting Research |
| Subjects: | |
| Online Access: | https://jwhr.birjand.ac.ir/article_3146_191a6e4884bf9141027fee2b647b6ec8.pdf |
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| Summary: | Predicting inflow into reservoirs is essential for their operation during floods, particularly in mountainous watersheds characterized by snow-rain regimes. The objective of this research is to evaluate the GEFSv12 re-forecast data as an input of the HEC-HMS model for forecasting floods due to the extreme precipitation in March/April 2019 in the reservoir of Bakhtiari dam in southwestern Iran. So, ensemble flood forecasting (control and ensemble members) was conducted using extracted precipitation and temperature data with the lead-time up to 10 days. A sequence of predictions for flood warnings was analyzed when 50% of the members exceeded the threshold inflows of 1000 and 1500 m³/s. The relative volume error values for the control member and the ensemble mean for five days ahead were -15% and -22%, respectively. While previous studies in catchments with snow-rain regimes anticipated challenges in flood forecasting at mid-lead times, this research demonstrated that the proposed probabilistic framework could effectively issue flood warnings for events with a lead time of five days. To address and predict flooding at the Bakhtiari Dam with a threshold of 1500 m³/s, flood warnings are issued with a lead time of 5 to 8 days. |
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| ISSN: | 2476-6976 2476-7603 |