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What Role Does Hydrological Science Play in the Age of Machine Learning?

Water resources research, 2021-03, Vol.57 (3), p.n/a

2021

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Deep learning rainfall–runoff predictions of extreme events

Hydrology and earth system sciences, 2022-07, Vol.26 (13), p.3377-3392

2022

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Benchmarking and Process Diagnostics of Land Models

Journal of hydrometeorology, 2018-11, Vol.19 (11), p.1835-1852

2018

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Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning

Water resources research, 2019-12, Vol.55 (12), p.11344-11354

2019

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The Plumbing of Land Surface Models: Is Poor Performance a Result of Methodology or Data Quality?

Journal of hydrometeorology, 2016-06, Vol.17 (6), p.1705-1723

2016

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Post‐Processing the National Water Model with Long Short‐Term Memory Networks for Streamflow Predictions and Model Diagnostics

Journal of the American Water Resources Association, 2021-12, Vol.57 (6), p.885-905

2021

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The quantity and quality of information in hydrologic models

Water resources research, 2015-01, Vol.51 (1), p.524-538

2015

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Information Theory for Model Diagnostics: Structural Error is Indicated by Trade‐Off Between Functional and Predictive Performance

Water resources research, 2019-08, Vol.55 (8), p.6534-6554

2019

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A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling

Hydrology and earth system sciences, 2021-05, Vol.25 (5), p.2685-2703

2021

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Debates-the future of hydrological sciences: A (common) path forward? Using models and data to learn: A systems theoretic perspective on the future of hydrological science

Water resources research, 2014-06, Vol.50 (6), p.5351-5359

2014

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Does Information Theory Provide a New Paradigm for Earth Science? Hypothesis Testing

Water resources research, 2020-02, Vol.56 (2), p.n/a

2020

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On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process

Hydrological processes, 2023-03, Vol.37 (3), p.n/a

2023

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The Impact of Vertical Measurement Depth on the Information Content of Soil Moisture for Latent Heat Flux Estimation

Journal of hydrometeorology, 2016-09, Vol.17 (9), p.2419-2430

2016

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Benchmarking NLDAS-2 Soil Moisture and Evapotranspiration to Separate Uncertainty Contributions

Journal of hydrometeorology, 2016-03, Vol.17 (3), p.745-759

2016

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A philosophical basis for hydrological uncertainty

Hydrological sciences journal, 2016-07, Vol.61 (9), p.1666-1678

2016

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impact of vertical measurement depth on the information content of soil moisture times series data

Geophysical research letters, 2014-07, Vol.41 (14), p.4997-5004

2014

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Performance Metrics, Error Modeling, and Uncertainty Quantification

Monthly weather review, 2016-02, Vol.144 (2), p.607-613

2016

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Parameter Sensitivity of the Noah-MP Land Surface Model with Dynamic Vegetation

Journal of hydrometeorology, 2018-05, Vol.19 (5), p.815-830

2018

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Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks

Hydrology and earth system sciences, 2022-11, Vol.26 (21), p.5493-5513

2022

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Model representation of the coupling between evapotranspiration and soil water content at different depths

Hydrology and earth system sciences, 2020-02, Vol.24 (2), p.581-594

2020

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Ensembles vs. information theory: supporting science under uncertainty

Frontiers of earth science, 2018-12, Vol.12 (4), p.653-660

2018

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Information loss in approximately Bayesian estimation techniques: A comparison of generative and discriminative approaches to estimating agricultural productivity

Journal of hydrology (Amsterdam), 2013-12, Vol.507, p.163-173

2013

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approach to quantifying the efficiency of a Bayesian filter

Water resources research, 2013-04, Vol.49 (4), p.2164-2173

2013

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Nonparametric triple collocation

Water resources research, 2017-07, Vol.53 (7), p.5516-5530

2017

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Coupling diffusion and maximum entropy models to estimate thermal inertia

Remote sensing of environment, 2012-04, Vol.119, p.222-231

2012

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What Role Does Hydrological Science Play in the Age of Machine Learning?

Deep learning rainfall–runoff predictions of extreme events

Benchmarking and Process Diagnostics of Land Models

Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning

The Plumbing of Land Surface Models: Is Poor Performance a Result of Methodology or Data Quality?

Post‐Processing the National Water Model with Long Short‐Term Memory Networks for Streamflow Predictions and Model Diagnostics

The quantity and quality of information in hydrologic models

Information Theory for Model Diagnostics: Structural Error is Indicated by Trade‐Off Between Functional and Predictive Performance

A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling

Debates-the future of hydrological sciences: A (common) path forward? Using models and data to learn: A systems theoretic perspective on the future of hydrological science

Does Information Theory Provide a New Paradigm for Earth Science? Hypothesis Testing

On strictly enforced mass conservation constraints for modelling the Rainfall‐Runoff process

The Impact of Vertical Measurement Depth on the Information Content of Soil Moisture for Latent Heat Flux Estimation

Benchmarking NLDAS-2 Soil Moisture and Evapotranspiration to Separate Uncertainty Contributions

A philosophical basis for hydrological uncertainty

impact of vertical measurement depth on the information content of soil moisture times series data

Performance Metrics, Error Modeling, and Uncertainty Quantification

Parameter Sensitivity of the Noah-MP Land Surface Model with Dynamic Vegetation

Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks

Model representation of the coupling between evapotranspiration and soil water content at different depths

Ensembles vs. information theory: supporting science under uncertainty

Information loss in approximately Bayesian estimation techniques: A comparison of generative and discriminative approaches to estimating agricultural productivity

approach to quantifying the efficiency of a Bayesian filter

Nonparametric triple collocation

Coupling diffusion and maximum entropy models to estimate thermal inertia

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