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Article

The Future Change in Evaporation Based on the CMIP6 Merged Data Generated by Deep-Learning Method in China

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Beijing Institute of Meteorological Applications, Beijing 100029, China
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National Key Laboratory of Geographic Information Engineering, Xi’an 710054, China
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School of Geographical Science, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
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School of Computer Science, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Academic Editor: Aizhong Ye
Water 2022, 14(18), 2800; https://doi.org/10.3390/w14182800
Received: 25 June 2022 / Revised: 28 August 2022 / Accepted: 5 September 2022 / Published: 8 September 2022
Land evaporation (LET) is an important variable in climate change, water cycle and water resources management. Mastering the projected changes in LET is significant for crop water requirements and the energy cycle. The global climate model (GCM) is a vital tool for future climate change research. However, the GCMs have low spatial resolution and relatively high errors. We use a deep learning (DL)-based model to deal with this problem. The DL approach can downscale the model data and merge simultaneously. We applied the DL approach to a suit of models from the Coupled Model Intercomparison Project 6th edition (CMIP6) LET data. From the result of all the evaluation metrics, the DL merged data greatly improved in both spatial and time dimensions. The mean RMSE is 5.85 mm and the correlation is 0.95 between the DL merged data and reference data (historical reliable evaporation data). The future LET evidently increases in four scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), and the upward intensity rises from the low to high emission scenarios. The highest increasing regions are in the Tibet Plateau and the south of China and the trend is larger than 10 mm/decade in the high scenarios. From the seasonal point of view, the increasing trend in spring and summer is far larger than for autumn and winter. The Tibet Plateau and the northeast of China have the largest upward trend in the spring of SSP5–8.5, higher than 1.6 mm/decade. View Full-Text
Keywords: downscaling; evaporation; deep-learning; climate change downscaling; evaporation; deep-learning; climate change
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MDPI and ACS Style

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

AMA Style

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 Style

Niu, 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

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