%0 Dataset
%T Daily Evapotranspiration Fusion Data Set in Qinghai Province Based on GCN_GRU Model (1990-2023)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/d624ed8f-6c77-4b4d-9eb5-37ffce8fcb6a
%W NCDC
%A quan chen
%A zhang xiao dan
%A liu chang
%A wang hui ping
%K evapotranspiration;GCN_GRU model
%X Evapotranspiration (ET), as a key link connecting the land and atmosphere water cycle, plays an important role in the global water cycle. This study evaluated the accuracy of two actual evapotranspiration data sets (GLEAM and ERA5_Land) by comparing in-situ observation data. In order to improve data accuracy, we introduced surface temperature and net radiation as covariates for data fusion, and proposed a multi-source ET fusion model based on deep learning. This model integrates corresponding data by mining spatio-temporal dependencies, and its core is graph convolution. Composed of neural network (GCN) and gated loop unit (GRU). Experimental results show that:(1) The GCN-GRU fusion model proposed in this study is significantly better than a single data source in terms of accuracy;(2) Model tests show that its root-mean-square error (RMSE) is less than 1.25 mm/day, the mean absolute error (MAE) is less than 1.1 mm/day, the relative deviation (RB) is less than 22%, and the correlation coefficient (CC) reaches 0.83;(3) The model also improves the ET spatial accuracy of raw GLEAM data and ERA5_Land reanalysis data in Qinghai Province, with the root-mean-square error reduced by 65% and 54% respectively, and the average absolute error reduced by 67% and 53% respectively;(4) Finally, the GCN-GRU model fusion was used to generate the daily ET data set from 2012 to 2016, which has higher spatial resolution (0.01°) and better data accuracy in the Qinghai Province region.