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Yumeng Tao, Xiaogang Gao, Alexander Ihler, Soroosh Sorooshian, and Kuolin Hsu

extracting useful information in the satellite imageries and linking it to precipitation estimates ( Nasrollahi et al. 2013 ; Sorooshian et al. 2011 ). In recent years, deep learning techniques, also known as deep neural networks, have been developed and widely applied in the machine learning and computer vision areas ( Bengio 2009 ; Hinton et al. 2006 ; Vincent et al. 2010 ). Tao et al. (2016a , b) applied the methods to satellite-based precipitation estimation and demonstrated their effectiveness

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Kuai Fang and Chaopeng Shen

function can be regarded as piecewise linear, to be estimated locally at each pixel. However, it is not clear if such a linear model could fully describe the process of soil moisture change, and, especially, provide strong performance for forecasts with a few days of latency. Deep learning (DL) is well known for its ability to learn nonlinear mapping relationships and model dynamical systems ( Shen 2018 ; LeCun et al. 2015 ; Schmidhuber 2015 ). In our previous work ( Fang et al. 2017 ), we employed

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Xiaodong Chen, L. Ruby Leung, Yang Gao, and Ying Liu

: NeuralHydrology – Interpreting LSTMs in hydrology. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning , W. Samek et al., Eds., Springer, 347–362 . 10.1007/978-3-030-28954-6_19 Li , D. , M. L. Wrzesien , M. Durand , J. Adam , and D. P. Lettenmaier , 2017 : How much runoff originates as snow in the western United States, and how will that change in the future? Geophys. Res. Lett. , 44 , 6163 – 6172 , . 10.1002/2017GL073551 Li , Z

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Yumeng Tao, Kuolin Hsu, Alexander Ihler, Xiaogang Gao, and Soroosh Sorooshian

related to rainfall. In recent years, deep learning algorithms, also known as deep neural networks (DNNs), have been widely applied in many fields, including signal and image processing, computer vision, and language, in part for their ability to perform complex feature extraction ( Bengio 2009 ; Hinton et al. 2006 ; LeCun et al. 2015 ). According to many recent studies ( Glorot et al. 2011 ; Hinton et al. 2006 ; Lu et al. 2013 ; Tao et al. 2016a , 2018 ; Vincent et al. 2008 ; Yang et al. 2017

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Yumeng Tao, Xiaogang Gao, Kuolin Hsu, Soroosh Sorooshian, and Alexander Ihler

key to making the best use of these datasets is promoting advanced methods that assist in the extraction of valuable information from the raw data ( Nasrollahi et al. 2013 ; Sorooshian et al. 2011 ). In recent years, multiple novel techniques for deep learning have been developed in the scientific discipline of machine learning, which is a breakthrough for dealing with large and complex datasets, especially for feature extraction from a large amount of image data ( Bengio 2009 ; Hinton et al

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Sungmin O, Emanuel Dutra, and Rene Orth

. , 52 , 8343 – 8373 , . 10.1002/2016WR018850 Budyko , M. , 1974 : Climate and Life . Academic Press , 507 pp. Chollet , F. , 2017 : Deep Learning with Python . 1st ed . Manning Publications Co. , 384 pp. Cornes , R. C. , G. van der Schrier , E. J. M. van den Besselaar , and P. D. Jones , 2018 : An ensemble version of the e-OBS temperature and precipitation data sets . J. Geophys. Res. Atmos. , 123 , 9391 – 9409 ,

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Zhongkun Hong, Zhongying Han, Xueying Li, Di Long, Guoqiang Tang, and Jianhua Wang

networks are used to improve the prediction of numerical models and provide more accurate precipitation estimates, which perform better than linear regression, nearest neighbor, random forest, or fully connected deep neural networks across the contiguous United States ( Pan et al. 2019 ). Random forest, one of the machine learning approaches, outperforms the bilinear interpolation in downscaling precipitation data and can reproduce reasonable spatiotemporal patterns of precipitation across North

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Mojtaba Sadeghi, Ata Akbari Asanjan, Mohammad Faridzad, Phu Nguyen, Kuolin Hsu, Soroosh Sorooshian, and Dan Braithwaite

related and co-related factors. Therefore, applying more advanced data-driven methodologies for automatically extracting features from the input data will enhance precipitation estimation accuracy. Recent advances in the field of machine learning (ML) offer exciting opportunities to expand our knowledge about the Earth system ( Lary et al. 2016 ). Among the different machine learning methods, the deep neural network (DNN) method is a fast-growing branch characterized by its flexibility and capacity to

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Liqing Peng, Zhongwang Wei, Zhenzhong Zeng, Peirong Lin, Eric F. Wood, and Justin Sheffield

-scale R sd directly from these records because the traditional interpolation techniques for upscaling point observations are not appropriate for the sparsely distributed radiation network over a large domain. Machine-learning is a powerful tool to draw information from both ground observations and satellite products of surface radiation ( Mellit et al. 2010 ; Wang et al. 2012 ; Yang et al. 2018 ; Wei et al. 2019 ). The model tree ensemble (MTE) technique ( Jung et al. 2010 ) is one of the machine-learning

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Anne Felsberg, Gabriëlle J. M. De Lannoy, Manuela Girotto, Jean Poesen, Rolf H. Reichle, and Thomas Stanley

-derived data products of soil moisture in deeper layers (at least 1 m) rather than satellite retrievals of surface (0–5 cm) soil moisture conditions, because the shear planes of both deep-seated and shallow landslides are typically much deeper than 5 cm. Estimates of the deeper soil moisture based on physically based land surface models were preferred ( Brocca et al. 2016 ; Kirschbaum et al. 2020 ; Thomas et al. 2019 ) and found to be superior compared to those of, for example, simple exponential filters

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