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How do we efficiently generate high-resolution hydraulic models at large numbers of riverine reaches?
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CITE Title : How do we efficiently generate high-resolution hydraulic models at large numbers of riverine reaches? Personal Author(s) : Nahorniak, Matthew;Wheaton, Joe;Volk, Carol;Bailey, Phillip;Reimer, Matt;Wall, Eric;Whitehead, Kelly;Jordan, Chris; Published Date : 2018 Source : Computers & Geosciences, 119, 80-91 DOI : https://doi.org/10.1016/j.cageo.2018.07.001 Journal Title : Computers & Geosciences Nahorniak, Matthew et al. (2018). How do we efficiently generate high-resolution hydraulic models at large numbers of riverine reaches?. 119https://doi.org/10.1016/j.cageo.2018.07.001 Nahorniak, Matthew et al. "How do we efficiently generate high-resolution hydraulic models at large numbers of riverine reaches?" 119 (2018), https://doi.org/10.1016/j.cageo.2018.07.001 Nahorniak, Matthew et al. "How do we efficiently generate high-resolution hydraulic models at large numbers of riverine reaches?" vol. 119, 2018, https://doi.org/10.1016/j.cageo.2018.07.001 Export RIS Citation Information.
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Personal Author:
Nahorniak, Matthew ;
Wheaton, Joe

...
2018 | Computers & Geosciences, 119, 80-91
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In support of efforts to quantify relationships between juvenile salmonid habitat and population dynamics in the Pacific Northwest, over 2200 hydrauli...
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Adaptive conditional bias-penalized Kalman filter with minimization of degrees of freedom for noise for superior state estimation and prediction of extremes
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CITE Title : Adaptive conditional bias-penalized Kalman filter with minimization of degrees of freedom for noise for superior state estimation and prediction of extremes Personal Author(s) : Seo, Dong-Jun;Shen, Haojing;Lee, Haksu; Published Date : 2022 Source : Computers & Geosciences, 166, 105193 DOI : https://doi.org/10.1016/j.cageo.2022.105193 Journal Title : Computers & Geosciences Seo, Dong-Jun et al. (2022). Adaptive conditional bias-penalized Kalman filter with minimization of degrees of freedom for noise for superior state estimation and prediction of extremes. 166https://doi.org/10.1016/j.cageo.2022.105193 Seo, Dong-Jun and Shen, Haojing and Lee, Haksu "Adaptive conditional bias-penalized Kalman filter with minimization of degrees of freedom for noise for superior state estimation and prediction of extremes" 166 (2022), https://doi.org/10.1016/j.cageo.2022.105193 Seo, Dong-Jun et al. "Adaptive conditional bias-penalized Kalman filter with minimization of degrees of freedom for noise for superior state estimation and prediction of extremes" vol. 166, 2022, https://doi.org/10.1016/j.cageo.2022.105193 Export RIS Citation Information.
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Personal Author:
Seo, Dong-Jun

;
Shen, Haojing
...
2022 | Computers & Geosciences, 166, 105193
Description:
We describe adaptive conditional bias (CB)-penalized ensemble Kalman filter (AEnKF) to improve estimation and prediction of extremes. Environmental va...
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[PDF - 808.50 KB]
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[PDF - 808.50 KB]