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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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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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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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A Deep Learning Data Fusion Model Using Sentinel-1/2, SoilGrids, SMAP, and GLDAS for Soil Moisture Retrieval

Journal of hydrometeorology, 2023-10, Vol.24 (10), p.1789-1823

2023

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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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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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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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Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM)

Österreichische Wasser- und Abfallwirtschaft, 2021, Vol.73 (7-8), p.270-280

2021

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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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Benchmarking and Process Diagnostics of Land Models

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

2018

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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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Estimating information entropy for hydrological data: One-dimensional case

Water resources research, 2014-06, Vol.50 (6), p.5003-5018

2014

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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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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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Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden

Österreichische Wasser- und Abfallwirtschaft, 2021, Vol.73 (7-8), p.295-307

2021

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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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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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Caravan - A global community dataset for large-sample hydrology

Scientific data, 2023-01, Vol.10 (1), p.61-11, Article 61

2023

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

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

Deep learning rainfall–runoff predictions of extreme events

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

A Deep Learning Data Fusion Model Using Sentinel-1/2, SoilGrids, SMAP, and GLDAS for Soil Moisture Retrieval

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

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

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

Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM)

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

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

Benchmarking and Process Diagnostics of Land Models

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

Estimating information entropy for hydrological data: One-dimensional case

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

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

Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden

Performance Metrics, Error Modeling, and Uncertainty Quantification

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

Caravan - A global community dataset for large-sample hydrology

Ensembles vs. information theory: supporting science under uncertainty

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