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\begin{document}

\title{Filling the gap between GRACE-and GRACE-FO-derived terrestrial water
storage anomalies with Bayesian convolutional neural networks}



\author[1]{Shaoxing Mo}%
\author[2]{Yulong Zhong}%
\author[1]{Xiaoqing Shi}%
\author[3]{Wei Feng}%
\author[4]{Xin Yin}%
\author[1]{Jichun Wu}%
\affil[1]{Nanjing University}%
\affil[2]{China University of Geosciences Faculty of Information Engineering}%
\affil[3]{Chinese Academy of Sciences}%
\affil[4]{Nanjing Hydraulic Research Institute}%


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  \date{May 18, 2024}


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\begin{abstract}
There is an approximately one-year observation gap of terrestrial water
storage anomalies (TWSAs) between the Gravity Recovery and Climate
Experiment (GRACE) satellite and its successor GRACE Follow-On
(GRACE-FO). This poses a challenge for water resources management, as
discontinuity in the TWSA observations may introduce significant biases
and uncertainties in hydrological model predictions and consequently
mislead decision making. To tackle this challenge, a Bayesian
convolutional neural network (BCNN) is proposed in this study to bridge
this gap using climatic data as inputs. Enhanced by integrating recent
advances in deep learning, BCNN can efficiently extract important
features for TWSA predictions from multi-source input data. The
predicted TWSAs are compared to the hydrological model outputs and three
recent TWSA prediction products. Results suggest the superior
performance of BCNN in bridging the gap. The extreme dry and wet events
during the gap period are also successfully identified by BCNN.%
\end{abstract}%



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