Merging and Downscaling Soil Moisture Data From CMIP6 Projections Using Deep Learning Method

Abstract

Soil moisture (SM) is an important variable in mediating the land-atmosphere interactions. Earth System Models (ESMs) are the key tools for predicting the response of SM to future climate change. Many ESMs provide outputs for SM; however, the estimated SM accuracy from different ESMs varies geographically as each ESM has its advantages and limitations. This study aimed to develop a merged SM product with improved accuracy and spatial resolution in China for 2015-2100 through data fusion of 25 ESMs with a deep-learning (DL) method. A DL model that can simultaneously perform data fusion and spatial downscaling was used to analyze SM’s future trend in China. Through the model, monthly SM data in four future scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) from 2015 to 2100, with a high resolution at 0.25°, was obtained. The evaluation metrics include mean absolute error (MAE), root mean square difference (RMSD), unbiased root mean square difference (ubRMSD), and coefficient of correlation (r). The evaluation results showed that our merged SM product is significantly better than each of the ESMs and the ensemble mean of all ESMs in terms of accuracy and spatial distribution. In the temporal dimension, the merged product is equivalent to the original data after deviation correction and equivalent to reconstructing the fluctuation of the whole series in a high error area. By further analyzing the spatiotemporal patterns of SM with the merged product in China, we found that northeast China will become wetter whereas South China will become drier. Northwest China and the Qinghai-Tibet Plateau would change from wetting to drying under a medium emission scenario. From the temporal scale of the results, the rate of SM variations is accelerated with time in the future under different scenarios. This study demonstrates the feasibility and effectiveness of the proposed procedure for simultaneous data fusion and spatial downscaling to generate improved SM data. The merged data have great practical and scientific implications.