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Projected Changes in Precipitation Based on the CMIP6 Optimal Multi-Model Ensemble in the Pearl River Basin, China
- Source :
- Remote Sensing, Vol 15, Iss 18, p 4608 (2023)
- Publication Year :
- 2023
- Publisher :
- MDPI AG, 2023.
-
Abstract
- Precipitation fluctuations in the Pearl River Basin (PRB) have a significant impact on river runoff, causing huge economic losses and casualties. However, future precipitation variations in the PRB remain unclear. Therefore, we explored the projected changes in precipitation in the PRB based on the coupled model intercomparison project phase 6 (CMIP6) model via three shared socio-economic pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). In our study, the optimal ensemble of global climate models in the PRB was identified using the comprehensive rating index (CRI), which is based on climatology, spatial variation, and interannual variability, and it was used to analyze potential precipitation changes in the basin in the period 2025–2100. The results showed that the CMIP6 models underestimated precipitation in the PRB; the consistency between the observations and the multi-model ensemble mean of the four best models was higher than those of any other ensembles, and the CRI value was highest (0.92). The annual precipitation in the PRB shows a significant increasing trend under three scenarios from 2025 to 2100 (p < 0.01), with the highest rate of precipitation increase being seen under the high-emission scenario. By the end of the 21st century, the regional mean precipitation in the PRB will increase by 13%, 9.4%, and 20.1% under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. Spatially, the entire basin is projected to become wetter, except for a slight decrease of less than 6% in the central part of the basin and the Pearl River Delta in the near term in the 21st century, and the highest increases are projected to occur in the Xijiang River basin.
- Subjects :
- Pearl River Basin
precipitation
CMIP6 models
SSP-RCPs
Science
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 15
- Issue :
- 18
- Database :
- Directory of Open Access Journals
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.7e57521cc301434690bfccb33b03c9e7
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/rs15184608