Document Type : Research Paper
Authors
Department of Water Engineering and Management, Faculty of Agriculture, Tarbiat Modarres University, Tehran, Iran
Abstract
Keywords
Main Subjects
Comparison of CMIP6 Climate Models and Quantile Mapping Bias Correction Methods in the Simulation of Precipitation
EXTENDED ABSTRACT
Globally, rising temperature due to continuous greenhouse gas emissions (GHG) have caused climate change. Accordingly, many important sectors to human existence including water resources, agriculture, health, energy, and the environment have been affected. Due to the consequences of climate change, there have been numerous studies on climate change projections and impacts in which General Circulation Models (GCMs) have been popularly used to analyze the simulated historical and future changes in climatic variables. Due to inherent limitations of global climate models, their outputs are significantly biased in comparison to observed values which could provide unreliable climate projections. To fix different problems with GCM output, researchers use various methods to correct bias. This study evaluates the performance of 10 global climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) for simulating precipitation in the Rafsanjan study area over a historical reference period. Various quantile mapping-based bias correction methods and approaches are also used and evaluated to correct simulated precipitation by climate models.
Rafsanjan study area has hot summers and cold and dry winters due to its location on the edge of the desert. This area is located near the desert, so it does not get much rain and does not have a river all year round. It mostly depends on underground water for its water supply. The region's geographical conditions, along with the negative impacts of climate, can put water resources at risk in this area. Evaluating the performance of various climate models and quantile mapping-based bias correction methods and approaches are carried out through multiple statistical metrics including NSE, PBIAS, MAE, and KGE as well as Taylor's diagram in calibration (1986-2005) and validation (2006-2014) periods. The procedure used in this study was as follows:
Downloading simulated precipitation data for the Rafsanjan study area from the Copernicus Climate Change Service website
Comparing selected climate models, various quantile mapping-based bias correction methods and approaches for simulated precipitation values in calibration and validation periods
Evaluating the performance of each model and bias correction methods and approaches, using selected evaluation metrics.
Projecting future precipitation by selected model and bias correction method
To check how accurate climate models are, GCMs-simulated Precipitation data are calibrated against the corresponding observed data from 1986 to 2005 (calibration period) and then validated from 2006 to 2014 (validation period). According to the evaluation metrics used, MPI-ESM1-2-LR performed the best in estimating precipitation in the Rafsanjan study area. Unlike MPI-ESM1-2-LR, GFDL-ESM4 performs poorly compared to other models and is not recommended for simulating precipitation in the mentioned study area. Simply put, the bernlnorm method was found to be better at correcting simulated precipitation values compared to other methods when tested in both periods. On the other hand, the bernexp performs poorly compared to other methods in terms of correcting simulated values due to evaluating criteria. In general, with the exception of the bernexp method, all the other methods show acceptable abilities in correcting biased data according to the evaluation criteria. After evaluating each bias correction method separately, the results of comparing the NTP, PT, and DDT approaches indicate that NTP and PT demonstrate greater capability compared to the DDT approach. Specifically, the NTP approach demonstrated satisfactory performance compared to both PT and DDT approaches. Finally, MPI-ESM1-2-LR model and the bernlnorm bias correction method were used to estimate future precipitation changes (2024-2043) in the study area of Rafsanjan.
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