Improving the Reliability of Sub‐Seasonal Forecasts of High and Low Flows by Using a Flow‐Dependent Nonparametric Model-Reference-Cited by-同舟云学术

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Improving the Reliability of Sub‐Seasonal Forecasts of High and Low Flows by Using a Flow‐Dependent Nonparametric Model

Published:2021-11 Issue:11 Volume:57 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resources Research

Author:

McInerney David¹^ORCID,Thyer Mark¹^ORCID,Kavetski Dmitri¹^ORCID,Laugesen Richard²^ORCID,Woldemeskel Fitsum³,Tuteja Narendra²,Kuczera George⁴

Affiliation:

1. School of Civil, Environmental and Mining Engineering University of Adelaide SA Australia

2. Bureau of Meteorology Canberra ACT Australia

3. Bureau of Meteorology Melbourne Victoria Australia

4. School of Engineering University of Newcastle Callaghan NSW Australia

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR029317

Reference37 articles.

1. A Markov Chain Monte Carlo Scheme for parameter estimation and inference in conceptual rainfall-runoff modeling

2. Constructing Confidence Sets Using Rank Statistics

3. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

4. A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context

5. An analysis of transformations;Box G. E. P.;Journal of the Royal Statistical Society, Series B,1964

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2. Enhancing probabilistic hydrological predictions with mixture density Networks: Accounting for heteroscedasticity and Non-Gaussianity;Journal of Hydrology;2024-09

3. A multi-model evaluation of probabilistic streamflow predictions via residual error modelling;Journal of Hydrology;2024-05

4. Neglecting hydrological errors can severely impact predictions of water resource system performance;Journal of Hydrology;2024-05

5. Improving probabilistic streamflow predictions through a nonparametric residual error model;Environmental Modelling & Software;2024-04

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