Development of Intensity-Duration-Frequency curves at basin scale using the ERA5 reanalysis product

Mianabadi, Ameneh; Omidvar, Javad; Pourreza-Bilondi, Mohsen

doi:10.22077/jdcr.2025.8636.1098

Development of Intensity-Duration-Frequency curves at basin scale using the ERA5 reanalysis product

نوع مقاله : مقاله پژوهشی

نویسندگان

Ameneh Mianabadi ¹

Javad Omidvar ²

Mohsen Pourreza-Bilondi ³

¹ Department of Ecology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran.

² Department of Water Sciences and Engineering, Faculty of Agricultural, Ferdowsi University of Mashhad, Mashhad, Iran.

³ Department of Water Engineering, University of Birjand, Birjand, Iran.

https://doi.org/10.22077/jdcr.2025.8636.1098

چکیده

IDF (Intensity-Duration-Frequency) curves play a crucial role in hydrological modeling, infrastructure design, and flood risk management. Traditional methods, relying on ground-based observations, face challenges such as limited spatial coverage, short temporal records, and the stationary assumption, particularly under climate change. This study addresses these issues by utilizing ERA5 reanalysis data to develop basin-scale IDF curves for the Karkheh River Basin (KRB) in Iran. Annual Maximum Precipitation (AMP) series for 6-, 12-, 18-, and 24-hour durations were extracted from ERA5 data and corrected for bias using observations from seven synoptic stations. Bias correction significantly improved ERA5 estimates, particularly in high-altitude regions prone to systematic errors. An elevation-bias relationship was established to extend corrections basin-wide. The corrected AMP data were modeled with the Generalized Extreme Value (GEV) distribution under stationary and non-stationary conditions to construct spatially distributed IDF curves. Based on 82 grid points, these curves provide detailed rainfall intensity estimates, overcoming limitations of station-based methods. The findings underscore ERA5 data's potential, combined with bias correction, to enhance hydrological analyses in data-scarce regions by better capturing spatial variability and extreme precipitation. This work supports improved flood management and infrastructure planning. However, future research must address uncertainties in bias correction and parameter estimation while extending data records. High-resolution reanalysis datasets are pivotal for adapting to evolving climatic conditions, extreme weather, and prolonged droughts.

کلیدواژه‌ها

global gridded precipitation

climate change

Annual Maximum Precipitation

Bias correction

موضوعات

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