Application of Parameter Screening to Derive Optimal Initial State Adjustments for Streamflow Forecasting-Reference-Cited by-同舟云学术

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Application of Parameter Screening to Derive Optimal Initial State Adjustments for Streamflow Forecasting

Published:2020-08-25 Issue:9 Volume:56 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Mai Juliane¹^ORCID,Arsenault Richard²^ORCID,Tolson Bryan A.¹^ORCID,Latraverse Marco³,Demeester Kenjy³

Affiliation:

1. Department of Civil and Environmental Engineering University of Waterloo Waterloo Ontario Canada

2. Département de génie de la construction École de Technologie Supérieure Montreal Quebec Canada

3. Aluminium division Rio Tinto Jonquière Quebec Canada

Funder

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR027960

Reference49 articles.

1. Comparison of Sequential and Variational Streamflow Assimilation Techniques for Short-Term Hydrological Forecasting

2. The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework

3. Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo

4. Multi-parametric variational data assimilation for hydrological forecasting

5. A review of operational methods of variational and ensemble‐variational data assimilation

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1. Performance evaluation of various hydrological models with respect to hydrological responses under climate change scenario: a review;Cogent Engineering;2024-06-03

2. Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia;Water;2024-05-17

3. Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment;Journal of Hydrology;2023-12

4. Evaluating the parameter sensitivity and impact of hydrologic modeling decisions on flood simulations;Advances in Water Resources;2023-11

5. Sensitivity analysis of the hyperparameters of an ensemble Kalman filter application on a semi-distributed hydrological model for streamflow forecasting;Journal of Hydrology;2023-11

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