Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models-Reference-Cited by-同舟云学术

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Leveraging ensemble meteorological forcing data to improve parameter estimation of hydrologic models

Published:2021-11 Issue:11 Volume:35 Page:
ISSN:0885-6087
Container-title:Hydrological Processes
language:en
Short-container-title:Hydrological Processes

Author:

Liu Hongli¹²³^ORCID,Tolson Bryan A.¹,Newman Andrew J.²,Wood Andrew W.²

Affiliation:

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

2. Research Applications Laboratory National Center for Atmospheric Research Boulder Colorado USA

3. Now at Centre for Hydrology University of Saskatchewan Canmore Alberta Canada

Funder

National Science Foundation of Sri Lanka

Natural Sciences and Engineering Research Council of Canada

Publisher

Wiley

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.14410

Reference86 articles.

1. The CAMELS data set: catchment attributes and meteorology for large-sample studies

2. A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds

3. Comparison of Stochastic Optimization Algorithms in Hydrological Model Calibration

4. Is point uncertain rainfall likely to have a great impact on distributed complex hydrological modeling?

5. Robust estimation of hydrological model parameters

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1. BULL Database – Spanish Basin attributes for Unravelling Learning in Large-sample hydrology;Scientific Data;2024-07-06

2. Precipitation uncertainty estimation and rainfall-runoff model calibration using iterative ensemble smoothers;Advances in Water Resources;2024-04

3. Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian‐based integrated approach;Hydrological Processes;2024-01

4. The Impact of Meteorological Forcing Uncertainty on Hydrological Modeling: A Global Analysis of Cryosphere Basins;Water Resources Research;2023-06

5. Ensemble Dressing of Meteorological Fields: Using Spatial Regression to Estimate Uncertainty in Deterministic Gridded Meteorological Datasets;Journal of Hydrometeorology;2022-10

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