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: Amin Eidipour, Mohammad Amin Maddah, Ali Mohammad Akhoond-Ali
Format: Article
Language:English
Published: University of Birjand 2024-09-01
Series:Water Harvesting Research
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Online Access:https://jwhr.birjand.ac.ir/article_3146_191a6e4884bf9141027fee2b647b6ec8.pdf
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author Amin Eidipour
Mohammad Amin Maddah
Ali Mohammad Akhoond-Ali
author_facet Amin Eidipour
Mohammad Amin Maddah
Ali Mohammad Akhoond-Ali
author_sort Amin Eidipour
collection DOAJ
description 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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spelling doaj-art-d85f3da8a48c4a3ebe9a755912ceb4ca2025-08-20T03:11:10ZengUniversity of BirjandWater Harvesting Research2476-69762476-76032024-09-017224625710.22077/jwhr.2024.7445.11573146Assessment of Re-Forecast Data in the Modeling of Extreme Rainfall-Runoff Events (Case Study: Floods in the Bakhtiari Basin, Iran, March-April 2019)Amin Eidipour0Mohammad Amin Maddah1Ali Mohammad Akhoond-Ali2Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.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.https://jwhr.birjand.ac.ir/article_3146_191a6e4884bf9141027fee2b647b6ec8.pdfensemble forecastingflood warninggefsv12hydrological modelreservoir operation
spellingShingle Amin Eidipour
Mohammad Amin Maddah
Ali Mohammad Akhoond-Ali
Assessment of Re-Forecast Data in the Modeling of Extreme Rainfall-Runoff Events (Case Study: Floods in the Bakhtiari Basin, Iran, March-April 2019)
Water Harvesting Research
ensemble forecasting
flood warning
gefsv12
hydrological model
reservoir operation
title Assessment of Re-Forecast Data in the Modeling of Extreme Rainfall-Runoff Events (Case Study: Floods in the Bakhtiari Basin, Iran, March-April 2019)
title_full Assessment of Re-Forecast Data in the Modeling of Extreme Rainfall-Runoff Events (Case Study: Floods in the Bakhtiari Basin, Iran, March-April 2019)
title_fullStr Assessment of Re-Forecast Data in the Modeling of Extreme Rainfall-Runoff Events (Case Study: Floods in the Bakhtiari Basin, Iran, March-April 2019)
title_full_unstemmed Assessment of Re-Forecast Data in the Modeling of Extreme Rainfall-Runoff Events (Case Study: Floods in the Bakhtiari Basin, Iran, March-April 2019)
title_short Assessment of Re-Forecast Data in the Modeling of Extreme Rainfall-Runoff Events (Case Study: Floods in the Bakhtiari Basin, Iran, March-April 2019)
title_sort assessment of re forecast data in the modeling of extreme rainfall runoff events case study floods in the bakhtiari basin iran march april 2019
topic ensemble forecasting
flood warning
gefsv12
hydrological model
reservoir operation
url https://jwhr.birjand.ac.ir/article_3146_191a6e4884bf9141027fee2b647b6ec8.pdf
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