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Generalization of Runoff Risk Prediction at Field Scales to a Continental‐Scale Region Using Cluster Analysis and Hybrid Modeling

CITE Title : Generalization of Runoff Risk Prediction at Field Scales to a Continental‐Scale Region Using Cluster Analysis and Hybrid Modeling Personal Author(s) : Ford, Chanse M.;Hu, Yao;Ghosh, Chirantan;Fry, Lauren M.;Malakpour‐Estalaki, Siamak;Mason, Lacey;Fitzpatrick, Lindsay;Mazrooei, Amir;Goering, Dustin C.; Published Date : 2022 Source : Geophysical Research Letters, 49(17) DOI : https://doi.org/10.1029/2022gl100667 Journal Title : Geophysical Research Letters Ford, Chanse M. et al. (2022). Generalization of Runoff Risk Prediction at Field Scales to a Continental‐Scale Region Using Cluster Analysis and Hybrid Modeling. 49(17). https://doi.org/10.1029/2022gl100667 Ford, Chanse M. et al. "Generalization of Runoff Risk Prediction at Field Scales to a Continental‐Scale Region Using Cluster Analysis and Hybrid Modeling" 49, no. 17 (2022), https://doi.org/10.1029/2022gl100667 Ford, Chanse M. et al. "Generalization of Runoff Risk Prediction at Field Scales to a Continental‐Scale Region Using Cluster Analysis and Hybrid Modeling" vol. 49, no. 17, 2022, https://doi.org/10.1029/2022gl100667 Export RIS Citation Information.

:

Ford, Chanse M. ;

Hu, Yao

2022 | Geophysical Research Letters, 49(17)

:

As surface water resources in the U.S. continue to be pressured by excess nutrients carried by agricultural runoff, the need to assess runoff risk at ...
A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA

CITE Title : A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA Personal Author(s) : Hu, Yao;Ghosh, Chirantan;Malakpour-Estalaki, Siamak; Published Date : 2023 Source : Geoscientific Model Development, 16(7), 1925-1936 DOI : https://doi.org/10.5194/gmd-16-1925-2023 Journal Title : Geoscientific Model Development Hu, Yao et al. (2023). A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA. 16(7). https://doi.org/10.5194/gmd-16-1925-2023 Hu, Yao and Ghosh, Chirantan and Malakpour-Estalaki, Siamak "A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA" 16, no. 7 (2023), https://doi.org/10.5194/gmd-16-1925-2023 Hu, Yao et al. "A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA" vol. 16, no. 7, 2023, https://doi.org/10.5194/gmd-16-1925-2023 Export RIS Citation Information.

:

Hu, Yao

;

Ghosh, Chirantan

2023 | Geoscientific Model Development, 16(7), 1925-1936

:

Geoscientific models are simplified representations of complex earth and environmental systems (EESs). Compared with physics-based numerical models, d...