Chaopeng Shen
Chaopeng Shen
Professor in Water Resources Engineering, Pennsylvania State University
Verified email at - Homepage
Cited by
Cited by
A transdisciplinary review of deep learning research and its relevance for water resources scientists
C Shen
Water Resources Research 54 (11), 8558-8593, 2018
An overview of current applications, challenges, and future trends in distributed process-based models in hydrology
S Fatichi, ER Vivoni, FL Ogden, VY Ivanov, B Mirus, D Gochis, ...
Journal of Hydrology 537, 45-60, 2016
Improving the representation of hydrologic processes in Earth System Models
MP Clark, Y Fan, DM Lawrence, JC Adam, D Bolster, DJ Gochis, ...
Water Resources Research 51 (8), 5929-5956, 2015
Hillslope hydrology in global change research and earth system modeling
Y Fan, M Clark, DM Lawrence, S Swenson, LE Band, SL Brantley, ...
Water Resources Research 55 (2), 1737-1772, 2019
An investigation of the effect of pore scale flow on average geochemical reaction rates using direct numerical simulation
S Molins, D Trebotich, CI Steefel, C Shen
Water Resources Research 48 (3), 2012
Surface‐subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks
RM Maxwell, M Putti, S Meyerhoff, JO Delfs, IM Ferguson, V Ivanov, J Kim, ...
Water resources research 50 (2), 1531-1549, 2014
Enhancing streamflow forecast and extracting insights using long‐short term memory networks with data integration at continental scales
D Feng, K Fang, C Shen
Water Resources Research 56 (9), e2019WR026793, 2020
Prolongation of SMAP to spatiotemporally seamless coverage of continental US using a deep learning neural network
K Fang, C Shen, D Kifer, X Yang
Geophysical Research Letters 44 (21), 11,030-11,039, 2017
HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
C Shen, E Laloy, A Elshorbagy, A Albert, J Bales, FJ Chang, S Ganguly, ...
Hydrology and Earth System Sciences 22 (11), 5639-5656, 2018
A process-based, distributed hydrologic model based on a large-scale method for surface–subsurface coupling
C Shen, MS Phanikumar
Advances in Water Resources 33 (12), 1524-1541, 2010
Pore-scale controls on calcite dissolution rates from flow-through laboratory and numerical experiments
S Molins, D Trebotich, L Yang, JB Ajo-Franklin, TJ Ligocki, C Shen, ...
Environmental science & technology 48 (13), 7453-7460, 2014
From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale?
W Zhi, D Feng, WP Tsai, G Sterle, A Harpold, C Shen, L Li
Environmental Science & Technology 55 (4), 2357-2368, 2021
From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
WP Tsai, D Feng, M Pan, H Beck, K Lawson, Y Yang, J Liu, C Shen
Nature communications 12 (1), 5988, 2021
Evaluating controls on coupled hydrologic and vegetation dynamics in a humid continental climate watershed using a subsurface‐land surface processes model
C Shen, J Niu, MS Phanikumar
Water Resources Research 49 (5), 2552-2572, 2013
The value of SMAP for long-term soil moisture estimation with the help of deep learning
K Fang, M Pan, C Shen
IEEE Transactions on Geoscience and Remote Sensing 57 (4), 2221-2233, 2018
Near-real-time forecast of satellite-based soil moisture using long short-term memory with an adaptive data integration kernel
K Fang, C Shen
Journal of Hydrometeorology 21 (3), 399-413, 2020
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
F Rahmani, K Lawson, W Ouyang, A Appling, S Oliver, C Shen
Environmental Research Letters 16 (2), 024025, 2021
Transferring hydrologic data across continents–leveraging data‐rich regions to improve hydrologic prediction in data‐sparse regions
K Ma, D Feng, K Lawson, WP Tsai, C Liang, X Huang, A Sharma, C Shen
Water Resources Research 57 (5), e2020WR028600, 2021
Evaluating the potential and challenges of an uncertainty quantification method for long short‐term memory models for soil moisture predictions
K Fang, D Kifer, K Lawson, C Shen
Water Resources Research 56 (12), e2020WR028095, 2020
Adaptive mesh refinement based on high order finite difference WENO scheme for multi-scale simulations
C Shen, JM Qiu, A Christlieb
Journal of Computational Physics 230 (10), 3780-3802, 2011
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