The Future Change in Evaporation Based on the CMIP6 Merged Data Generated by Deep-Learning Method in China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Region and Data
2.1.1. Evaporation Reanalysis Data
2.1.2. CMIP6 Evaporation Data
2.2. Methods
2.2.1. Deep-Learning Method
2.2.2. Evaluation Metric
2.2.3. Training Setting and Strategy
3. Results
3.1. Evaluation of Merged Data
3.2. Future Evaporation Change
4. Discussion
4.1. Data Merge and Downscaling of Climate Data
4.2. Future Spatial-Temporal Changes in LET
4.3. Limitation and Prospect
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Institution (Country) | Model Name | Resolution (Lon × Lat) | Used Member |
---|---|---|---|
CSIRO (Australia) | ACCESS-CM2 | 1.875° × 1.25° | r1i1p1f1 |
BCC (China) | BCC-CSM2-MR | 2.25° × 2.25° | r1i1p1f1 |
CAMS (China) | CAMS-CSM1-0 | 1.125° × 1.125° | r1i1p1f1 |
NCAR (USA) | CESM2-WACCM | 2.5° × 1.875° | r1i1p1f1 |
CESM2 | r1i1p1f1 | ||
CNRM-CERFACS (France) | CNRM-CM6-1-HR | 1.25° × 0.9375° | r1i1p1f2 |
CNRM-ESM2-1 | r1i1p1f2 | ||
CCCMA (Canada) | CanESM5 | 2.8125° × 2.8125° | r1i1p1f1 |
EC-Earth Consortium (EU) | EC-Earth3-Veg | 0.703125° × 0.703125° | r1i1p1f1 |
CAS (China) | FGOALS-f3-L | 2.5° × 2° | r1i1p1f1 |
NOAA-GFDL (USA) | GFDL-ESM4 | 2.5° × 2° | r1i1p1f1 |
INM (Russia) | INM-CM4-8 | 2° × 1.5° | r1i1p1f1 |
INM-CM5-0 | r1i1p1f1 | ||
IPSL (France) | IPSL-CM6A-LR | 2.5° × 1.259° | r1i1p1f1 |
MIROC (Japan) | MIROC-ES2L | 2.8125° × 2.8125° | r1i1p1f2 |
MIROC6 | 2.8125° × 0.703125° | r1i1p1f1 | |
MPI-M (Germany) | MPI-ESM1-2-HR | 0.9375° × 0.9375° | r1i1p1f1 |
MPI-ESM1-2-LR | 1.875° × 1.875° | r1i1p1f1 | |
MRI (Japan) | MRI-ESM2-0 | 1.125° × 1.125° | r1i1p1f1 |
NCC (Norway) | NorESM2-LM | 5° × 3.75° | r1i1p1f1 |
NorESM2-MM | 2.5° × 1.875° | r1i1p1f1 | |
MOHC (UK) | UKESM1-0-LL | 1.875° × 1.25° | r1i1p1f2 |
Methods | MAE | RMSE | ubRMSD | R |
---|---|---|---|---|
DL data | 5.91 | 5.86 | 5.88 | 0.95 |
MEM data | 14.63 | 21.16 | 19.01 | 0.85 |
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Niu, X.; Wei, X.; Tian, W.; Wang, G.; Zhu, W. The Future Change in Evaporation Based on the CMIP6 Merged Data Generated by Deep-Learning Method in China. Water 2022, 14, 2800. https://doi.org/10.3390/w14182800
Niu X, Wei X, Tian W, Wang G, Zhu W. The Future Change in Evaporation Based on the CMIP6 Merged Data Generated by Deep-Learning Method in China. Water. 2022; 14(18):2800. https://doi.org/10.3390/w14182800
Chicago/Turabian StyleNiu, Xianghua, Xikun Wei, Wei Tian, Guojie Wang, and Wenhui Zhu. 2022. "The Future Change in Evaporation Based on the CMIP6 Merged Data Generated by Deep-Learning Method in China" Water 14, no. 18: 2800. https://doi.org/10.3390/w14182800