آمار

تعداد نشریات21
تعداد شماره‌ها346
تعداد مقالات3,642
تعداد مشاهده مقاله4,717,560
تعداد دریافت فایل اصل مقاله3,152,079
Mianabadi, Ameneh, Omidvar, Javad, Pourreza-Bilondi, Mohsen. (1403). Development of Intensity-Duration-Frequency curves at basin scale using the ERA5 reanalysis product. , 2(4), 121-140. doi: 10.22077/jdcr.2025.8636.1098
Ameneh Mianabadi; Javad Omidvar; Mohsen Pourreza-Bilondi. "Development of Intensity-Duration-Frequency curves at basin scale using the ERA5 reanalysis product". , 2, 4, 1403, 121-140. doi: 10.22077/jdcr.2025.8636.1098
Mianabadi, Ameneh, Omidvar, Javad, Pourreza-Bilondi, Mohsen. (1403). 'Development of Intensity-Duration-Frequency curves at basin scale using the ERA5 reanalysis product', , 2(4), pp. 121-140. doi: 10.22077/jdcr.2025.8636.1098
Mianabadi, Ameneh, Omidvar, Javad, Pourreza-Bilondi, Mohsen. Development of Intensity-Duration-Frequency curves at basin scale using the ERA5 reanalysis product. , 1403; 2(4): 121-140. doi: 10.22077/jdcr.2025.8636.1098

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

مجله پژوهش های خشکسالی و تغییراقلیم
دوره 2، شماره 4 - شماره پیاپی 8، اسفند 1403، صفحه 121-140    اصل مقاله (1.72 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22077/jdcr.2025.8636.1098
نویسندگان
Ameneh Mianabadi1؛ Javad Omidvar2؛ Mohsen Pourreza-Bilondi* 3
1Department of Ecology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran.
2Department of Water Sciences and Engineering, Faculty of Agricultural, Ferdowsi University of Mashhad, Mashhad, Iran.
3Department of Water Engineering, University of Birjand, Birjand, Iran.
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
مراجع

Ashraf Vaghefi, S., Mousavi, S. J., Abbaspour, K. C., Srinivasan, R., & Yang, H. (2014). Analyses of the impact of climate change on water resources components, drought and wheat yield in semiarid regions: Karkheh River Basin in Iran. Hydrological Processes, 28(4), 2018–2032. https://doi.org/10.1002/hyp.9747
Azadi, F., Sadough, S. H., Ghahroudi, M., & Shahabi, H. (2020). Zoning of flood risk in kashkan river basin using two models WOE and EBF. Journal of Geography and Environmental Hazards, 9(1), 45–60. [In Persian]. https://doi. org/ 10.22067/GEO.V9I1.83090.

Barbero, R., Fowler, H. J., Lenderink, G., & Blenkinsop, S. (2017). Is the intensification of precipitation extremes with global warming better detected at hourly than daily resolutions? Geophysical Research Letters, 44(2), 974–983. https://doi.org/10.1002/2016GL071917
Cheng, L., & AghaKouchak, A. (2015). Nonstationary precipitation intensity-durationfrequency curves for infrastructure design in a changing climate. Scientific Reports, 4(1), 7093. https://doi.org/10.1038/srep07093
Coles, S., Bawa, J., Trenner, L., & Dorazio, P. (2001). An introduction to statistical modeling of extreme values. Springer London. https:// doi.org/10.1007/978-1-4471-3675-0

Courty, L. G., Wilby, R. L., Hillier, J. K., & Slater, L. J. (2019). Intensity-durationfrequency curves at the global scale. Environmental Research Letters, 14(8), 084045. https://doi.org/10.1088/1748-9326/ab370a
Crévolin, V., Hassanzadeh, E., & BourdeauGoulet, S.-C. (2023). Updating the intensityduration-frequency curves in major Canadian cities under changing climate using CMIP5 and CMIP6 model projections. Sustainable Cities and Society, 92(September 2022), 104473. https://doi.org/10.1016/j.scs.2023.104473

Davtalab, R., Mirchi, A., Khatami, S., Gyawali, R., Massah, A., Farajzadeh, M., & Madani, K. (2017). Improving continuous hydrologic modeling of data-poor river basins using hydrologic engineering center’s hydrologic modeling system: case study of karkheh river Basin. Journal of Hydrologic Engineering, 22(8). https://doi.org/10.1061/(ASCE) HE.1943-5584.0001525
De Leo, F., Besio, G., Briganti, R., & Vanem, E. (2021). Non-stationary extreme value analysis of sea states based on linear trends. Analysis of annual maxima series of significant wave height and peak period in the Mediterranean Sea. Coastal Engineering, 167, 103896. https:// doi.org/10.1016/j.coastaleng.2021.103896

Frank, C. W., Wahl, S., Keller, J. D., Pospichal, B., Hense, A., & Crewell, S. (2018). Bias correction of a novel European reanalysis data set for solar energy applications. Solar Energy, 164, 12–24. https://doi.org/10.1016/j. solener.2018.02.012
Garibay, V. M., Gitau, M. W., Kiggundu, N., Moriasi, D., & Mishili, F. (2021). Evaluation of reanalysis precipitation data and potential bias correction methods for use in data-scarce areas. Water Resources Management, 35(5), 1587–1602. https://doi.org/10.1007/s11269-021-02804-8

Gilleland, E., & Katz, R. W. (2016). ExtRemes 2.0: an extreme value analysis package in R. Journal of Statistical Software, 72(8), 1–39. https://doi.org/10.18637/jss.v072.i08
Guhathakurta, P., Sreejith, O. P., & Menon, P. A. (2011). Impact of climate change on extreme rainfall events and flood risk in India. Journal of Earth System Science, 120(3), 359–373.

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., & Thépaut, J.- N. (2023). ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.adbb2d47
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., … Thépaut, J. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803

Hilbe, J. M., & Robinson, A. P. (2013). Methods of statistical model estimation. CRC Press. IPCC. (2021). Technical summary. Contribution of working group I to the sixth assessment report of the Intergovernmental panel on climate change. In climate change 2021: The Physical Science Basis.
Jalbert, J., Genest, C., & Perreault, L. (2022). Interpolation of precipitation extremes on a large domain toward IDF curve construction at unmonitored locations. Journal of Agricultural, Biological and Environmental Statistics, 27(3), 461–486. https://doi.org/10.1007/s13253-022-00491-5

Jiao, D., Xu, N., Yang, F., & Xu, K. (2021). Evaluation of spatial-temporal variation performance of ERA5 precipitation data in China. Scientific Reports, 11(1), 17956. https:// doi.org/10.1038/s41598-021-97432-y
Kavyani Malayeri, A., Saghafian, B., & Raziei, T. (2021). Performance evaluation of ERA5 precipitation estimates across Iran. Arabian Journal of Geosciences, 14(23), 2676. https:// doi.org/10.1007/s12517-021-09079-8
Kendall, M. G. (1975). Rank Correlation Methods. Charles Griffin.

Koutsoyiannis, D., Kozonis, D., & Manetas, A. (1998). A mathematical framework for studying rainfall intensity-duration-frequency relationships. Journal of Hydrology, 206(1–2), 118–135. https://doi.org/10.1016/S0022-1694(98)00097-3
Lavers, D. A., Simmons, A., Vamborg, F., & Rodwell, M. J. (2022). An evaluation of ERA5 precipitation for climate monitoring. Quarterly Journal of the Royal Meteorological Society, 148(748), 3152–3165. https://doi.org/10.1002/qj.4351

Luke, A., Vrugt, J. A., AghaKouchak, A., Matthew, R., & Sanders, B. F. (2017). Predicting nonstationary flood frequencies: Evidence supports an updated stationarity thesis in the <scp>U</scp> nited <scp>S</scp> tates. Water Resources Research, 53(7), 5469–5494. https://doi.org/10.1002/2016WR019676
Mann, H. B. (1945). Nonparametric Tests Against Trend. Econometrica, 13(3), 245–259.

Marra, F., Morin, E., Peleg, N., Mei, Y., & Anagnostou, E. N. (2017). Intensity–duration–frequency curves from remote sensing rainfall estimates: comparing satellite and weather radar over the eastern Mediterranean. Hydrology and Earth System Sciences, 21(5), 2389–2404. https://doi.org/10.5194/hess-21-2389-2017

Marra, F., Nikolopoulos, E. I., Anagnostou, E. N., Bárdossy, A., & Morin, E. (2019). Precipitation frequency analysis from remotely sensed datasets: A focused review. Journal of Hydrology, 574, 699–705. https://doi.org/10.1016/j.jhydrol.2019.04.081
Mianabadi, A. (2023). Evaluation of longterm satellite-based precipitation products for developing intensity-frequency (IF) curves of daily precipitation. Atmospheric Research, 286(February), 106667. https://doi.org/10.1016/j.atmosres.2023.106667
Noor, M., Ismail, T., Shahid, S., Asaduzzaman, M., & Dewan, A. (2021). Evaluating intensity-duration-frequency (IDF) curves of satellite-based precipitation datasets in Peninsular Malaysia. Atmospheric Research, 248, 105203. https://doi.org/10.1016/j. atmosres.2020.105203

Ombadi, M., Nguyen, P., Sorooshian, S., & Hsu, K. (2018). Developing intensity‐duration‐frequency (IDF) curves from satellite‐based precipitation: Methodology and Evaluation. Water Resources Research, 54(10), 7752–7766. https://doi.org/10.1029/2018WR022929
Parker, W. S. (2016). Reanalyses and Observations: What’s the difference? Bulletin of the American Meteorological Society, 97(9), 1565–1572. https://doi.org/10.1175/BAMS-D-14-00226.1
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., van Lipzig, N. P. M., & Leung, R. (2015). A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challenges. Reviews of Geophysics, 53(2), 323–361. https://doi. org/10.1002/2014RG000475

Probst, E., & Mauser, W. (2022). Evaluation of ERA5 and WFDE5 forcing data for hydrological modelling and the impact of bias correction with regional climatologies: A case study in the Danube River Basin. Journal of Hydrology: Regional Studies, 40, 101023. https://doi.org/10.1016/j.ejrh.2022.101023
Ragno, E., AghaKouchak, A., Love, C. A., Cheng, L., Vahedifard, F., & Lima, C. H. R. (2018). Quantifying changes in future intensity‐duration‐frequency curves using multimodel ensemble simulations. Water Resources Research, 54(3), 1751–1764. https:// doi.org/10.1002/2017WR021975

Shrestha, A., Babel, M., Weesakul, S., & Vojinovic, Z. (2017). Developing intensity–duration–frequency (IDF) curves under climate change uncertainty: The Case of Bangkok, Thailand. Water, 9(2), 1–22. https:// doi.org/10.3390/w9020145
Srivastava, A. K., Grotjahn, R., Ullrich, P. A., & Sadegh, M. (2021). Pooling data improves multimodel IDF estimates over median-based IDF estimates: Analysis over the Susquehanna and Florida. Journal of Hydrometeorology, 22(4), 971–995. https://doi.org/10.1175/JHM-D-20-0180.1

Tarek, M., Brissette, F. P., & Arsenault, R. (2020). Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America. Hydrology and Earth System Sciences, 24(5), 2527–2544. https://doi.org/10.5194/hess-24-2527-2020
Venkatesh, K., Maheswaran, R., & Devacharan, J. (2022). Framework for developing IDF curves using satellite precipitation: a case study using GPM-IMERG V6 data. Earth Science Informatics, 15(1), 671–687. https:// doi.org/10.1007/s12145-021-00708-0

Wambura, F. J. (2024). Using reanalysis precipitation data for developing intensityduration-frequency curves in a poorly gauged city. Journal of Hydrology: Regional Studies, 56, 102005. https://doi.org/10.1016/j. ejrh.2024.102005
Zambrano-Bigiarini, M., Soto, C., & Tolorza, V. (2024). Spatially-distributed intensityduration-frequency (IDF) curves for chile using sub-daily gridded datasets. EGU general assembly 2024, Vienna, Austria, 14–19 Apr 2024. https://doi.org/10.5194/egusphereegu24-21043

آمار
تعداد مشاهده مقاله: 353
تعداد دریافت فایل اصل مقاله: 277


نماد الکترونیک 

 

سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب