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Evaluation of Bias Correction Methods for CMIP6 Precipitation Projection: A Case Study of Torqabeh City, Iran | ||
| Water Harvesting Research | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 02 مرداد 1405 | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22077/jwhr.2026.11491.1206 | ||
| نویسندگان | ||
| Abolfazl akbarpour* 1؛ bahareh sadat Hamraz2؛ Mohammad Hosein Najafimood3 | ||
| 1Department of Civil Engineering, University of Birjand,Iran | ||
| 2Department of Water Engineering, University of Birjand, Birjand, iran | ||
| 3Department of Water Engineering, University of birjand, Birjand, Iran | ||
| چکیده | ||
| The present study aimed to investigate the effect of seven bias correction methods and a weighted ensemble of the five best-corrected models on the accuracy of 14 CMIP6 models in predicting precipitation. Bias correction was performed for the baseline period 1990–2014, and the corrected outputs were validated against observational data from nine meteorological stations using RMSE, PBIAS, KGE, and NSE metrics. Based on the results, the ensemble method was selected as the superior approach at all stations, improving KGE from 0.54 to 0.56 (3.7%), improving NSE from 0.19 to 0.42 (121%), and reducing RMSE from 23.48 to 19.79 (15.7%). The ensemble reduced PBIAS at six stations (by ~77% on average(. A one-tailed Wilcoxon signed-rank test confirmed that the superiority of the ensemble method is statistically significant (W = 45, n = 9, p = 0.0039). Among the individual methods, the Bernoulli gamma method, which is based on the separation of dry and wet days and the use of the gamma distribution, had the best performance. In the ensemble method, CESM2, CNRM-CM6-1, and MRI-ESM2-0 were consistently among the top five models at all stations. Precipitation projection using the optimal bias correction method (ensemble) and the best-performing CMIP6 model for the 2030–2055 period indicates that precipitation will decrease by an average of 10.46% under SSP1-2.6 (ranging from -6.1% to –13.7% across stations) and by an average of 14.6% under SSP5-8.5 (ranging from -9.2% to –18.7%). | ||
| کلیدواژهها | ||
| Bernoulli-Gamma؛ ensemble؛ KGE؛ SSP scenarios؛ Wilcoxon | ||
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