Optimizing parameter learning and calibration in an integrated hydrological model: Impact of observation length and information-Reference-Cited by-同舟云学术

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Optimizing parameter learning and calibration in an integrated hydrological model: Impact of observation length and information

Published:2024-11 Issue: Volume:643 Page:131889
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Jiang Peishi^ORCID,Shuai Pin^ORCID,Sun Alexander Y.^ORCID,Chen Xingyuan^ORCID

Funder

US Department of Energy

Publisher

Elsevier BV

Reference45 articles.

1. Sensitivity and uncertainty analysis of the conceptual HBV rainfall–runoff model: Implications for parameter estimation;Abebe;J. Hydrol.,2010

2. Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models;Anctil;Environ. Model. Softw.,2004

3. Technical note: “Bit by bit”: a practical and general approach for evaluating model computational complexity vs. model performance;Azmi;Hydrol. Earth Syst. Sci.,2021

4. The HBV Model. Computer Models of Watershed Hydrology, VP Singh, Ed;Bergström,1995

5. Evaluation of distributed process-based hydrologic model performance using only a priori information to define model inputs;Bhanja;J. Hydrol.,2023

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