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Hydrology
Volume 9
Issue 1
10.3390/hydrology9010004
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Editorial

Editorial for Special Issue: “Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions”

by
Huidae Cho
1,* and
Lorena Liuzzo
2,*
1
Institute for Environmental and Spatial Analysis, University of North Georgia, Oakwood, GA 30566, USA
2
Facoltà di Ingegneria ed Architettura, Università degli Studi di Enna Kore, 94100 Enna, Italy
*
Authors to whom correspondence should be addressed.
Hydrology 2022, 9(1), 4; https://doi.org/10.3390/hydrology9010004
Submission received: 30 November 2021 / Accepted: 22 December 2021 / Published: 24 December 2021
(This article belongs to the Special Issue Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions)
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Physically-based or process-based hydrologic models play a critical role in hydrologic forecasting [...]

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MDPI and ACS Style

Cho, H.; Liuzzo, L. Editorial for Special Issue: “Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions”. Hydrology 2022, 9, 4. https://doi.org/10.3390/hydrology9010004

AMA Style

Cho H, Liuzzo L. Editorial for Special Issue: “Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions”. Hydrology. 2022; 9(1):4. https://doi.org/10.3390/hydrology9010004

Chicago/Turabian Style

Cho, Huidae, and Lorena Liuzzo. 2022. "Editorial for Special Issue: “Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions”" Hydrology 9, no. 1: 4. https://doi.org/10.3390/hydrology9010004

APA Style

Cho, H., & Liuzzo, L. (2022). Editorial for Special Issue: “Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions”. Hydrology, 9(1), 4. https://doi.org/10.3390/hydrology9010004

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