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A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Water resources research, 2018-11, Vol.54 (11), p.8558-8593

Shen, Chaopeng

2018

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Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

Geophysical research letters, 2017-11, Vol.44 (21), p.11,030-11,039

2017

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From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling

Nature communications, 2021-10, Vol.12 (1), p.5988-5988, Article 5988

2021

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An investigation of the effect of pore scale flow on average geochemical reaction rates using direct numerical simulation

Water resources research, 2012-03, Vol.48 (3), p.n/a

2012

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Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data

Environmental research letters, 2021-02, Vol.16 (2), p.024025

2021

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A process-based, distributed hydrologic model based on a large-scale method for surface–subsurface coupling

Advances in water resources, 2010-12, Vol.33 (12), p.1524-1541

2010

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An overview of current applications, challenges, and future trends in distributed process-based models in hydrology

Journal of hydrology (Amsterdam), 2016-06, Vol.537, p.45-60

2016

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Editorial: Broadening the Use of Machine Learning in Hydrology

Frontiers in water, 2021-05, Vol.3

2021

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Improving the representation of hydrologic processes in Earth System Models

Water resources research, 2015-08, Vol.51 (8), p.5929-5956

2015

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Pore-Scale Controls on Calcite Dissolution Rates from Flow-through Laboratory and Numerical Experiments

Environmental science & technology, 2014-07, Vol.48 (13), p.7453-7460

2014

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Surface-subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks

Water resources research, 2014-02, Vol.50 (2), p.1531-1549

2014

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Adaptive mesh refinement based on high order finite difference WENO scheme for multi-scale simulations

Journal of computational physics, 2011-05, Vol.230 (10), p.3780-3802

2011

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Quantifying the effects of data integration algorithms on the outcomes of a subsurface–land surface processes model

Environmental modelling & software : with environment data news, 2014-09, Vol.59, p.146-161

2014

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The fan of influence of streams and channel feedbacks to simulated land surface water and carbon dynamics

Water resources research, 2016-02, Vol.52 (2), p.880-902

2016

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Physics-Guided Long Short-Term Memory Network for Streamflow and Flood Simulations in the Lancang–Mekong River Basin

Water (Basel), 2022-05, Vol.14 (9), p.1429

2022

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MINErosion 3: Using measurements on a tilting flume-rainfall simulator facility to predict erosion rates from post-mining landscapes in Central Queensland, Australia

PloS one, 2018-03, Vol.13 (3), p.e0194230-e0194230

2018

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A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Water resources research, 2018-11, Vol.54 (11)

Shen, Chaopeng

2018

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Full‐flow‐regime storage‐streamflow correlation patterns provide insights into hydrologic functioning over the continental US

Water resources research, 2017-09, Vol.53 (9), p.8064-8083

2017

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Constructing a Large-Scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates

IEEE journal of selected topics in applied earth observations and remote sensing, 2022, Vol.15, p.4349-4370

2022

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Estimating longitudinal dispersion in rivers using Acoustic Doppler Current Profilers

Advances in water resources, 2010-06, Vol.33 (6), p.615-623

2010

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Enhancing Streamflow Forecast and Extracting Insights Using Long‐Short Term Memory Networks With Data Integration at Continental Scales

Water resources research, 2020-09, Vol.56 (9), p.n/a

2020

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Toward a Priori Evaluation of Relative Worth of Head and Conductivity Data as Functions of Data Densities in Inverse Groundwater Modeling

Water (Basel), 2019-06, Vol.11 (6), p.1202

2019

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Mitigating Prediction Error of Deep Learning Streamflow Models in Large Data‐Sparse Regions With Ensemble Modeling and Soft Data

Geophysical research letters, 2021-07, Vol.48 (14), p.n/a

2021

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Revealing Causal Controls of Storage-Streamflow Relationships With a Data-Centric Bayesian Framework Combining Machine Learning and Process-Based Modeling

Frontiers in water, 2020-11, Vol.2

2020

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Deep Learning: A Next-Generation Big-Data Approach for Hydrology

Eos (Washington, D.C.), 2018-04, Vol.99

Shen, Chaopeng

2018

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A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling

An investigation of the effect of pore scale flow on average geochemical reaction rates using direct numerical simulation

Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data

A process-based, distributed hydrologic model based on a large-scale method for surface–subsurface coupling

An overview of current applications, challenges, and future trends in distributed process-based models in hydrology

Editorial: Broadening the Use of Machine Learning in Hydrology

Improving the representation of hydrologic processes in Earth System Models

Pore-Scale Controls on Calcite Dissolution Rates from Flow-through Laboratory and Numerical Experiments

Surface-subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks

Adaptive mesh refinement based on high order finite difference WENO scheme for multi-scale simulations

Quantifying the effects of data integration algorithms on the outcomes of a subsurface–land surface processes model

The fan of influence of streams and channel feedbacks to simulated land surface water and carbon dynamics

Physics-Guided Long Short-Term Memory Network for Streamflow and Flood Simulations in the Lancang–Mekong River Basin

MINErosion 3: Using measurements on a tilting flume-rainfall simulator facility to predict erosion rates from post-mining landscapes in Central Queensland, Australia

A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

Full‐flow‐regime storage‐streamflow correlation patterns provide insights into hydrologic functioning over the continental US

Constructing a Large-Scale Landslide Database Across Heterogeneous Environments Using Task-Specific Model Updates

Estimating longitudinal dispersion in rivers using Acoustic Doppler Current Profilers

Enhancing Streamflow Forecast and Extracting Insights Using Long‐Short Term Memory Networks With Data Integration at Continental Scales

Toward a Priori Evaluation of Relative Worth of Head and Conductivity Data as Functions of Data Densities in Inverse Groundwater Modeling

Mitigating Prediction Error of Deep Learning Streamflow Models in Large Data‐Sparse Regions With Ensemble Modeling and Soft Data

Revealing Causal Controls of Storage-Streamflow Relationships With a Data-Centric Bayesian Framework Combining Machine Learning and Process-Based Modeling

Deep Learning: A Next-Generation Big-Data Approach for Hydrology

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