Can I read Parameter Optimization for Uncertainty Reduction and Simulation Improvement of Hydrological Modeling on EtoBox?

Parameter Optimization for Uncertainty Reduction and Simulation Improvement of Hydrological Modeling by Hui, Jinyu; Wu, Yiping; Zhao, Fubo; Lei, Xiaohui; Sun, Pengcheng; Singh, Shailesh Kumar; Liao, Weihong; Qiu, Linjing; Li, Jiguang is a Environmental Science article available to read on EtoBox.

What is Parameter Optimization for Uncertainty Reduction and Simulation Improvement of Hydrological Modeling about?

Hydrological modeling has experienced rapid development and played a significant role in water resource management in recent decades. However, modeling uncertainties, which are propagated throughout model runs, may affect the credibility of simulation results and mislead management decisions. Therefore, analyzing and reducing uncertainty is of significant importance in providing greater confidence in hydrological simulations. To reduce and quantify parameter uncertainty, in this study, we attempted to introduce additional remotely sensed data (such as evapotranspiration (ET)) into a common parameter estimation procedure that uses observed streamflow only. We undertook a case study of an application of the Soil Water Assessment Tool in the Guijiang River Basin (GRB) in China. We also compared the effects of different combinations of parameter estimation algorithms (e.g., Sequential Uncertainty Fitting version 2, particle swarm optimization) on reduction in parameter uncertainty and improvement in modeling precision improvement. The results indicated that combining Sequential Uncertainty Fitting version 2 (SUFI-2) and particle swarm optimization (PSO) can substantially reduce the mod

Who reads Parameter Optimization for Uncertainty Reduction and Simulation Improvement of Hydrological Modeling?

Author

Hui, Jinyu; Wu, Yiping; Zhao, Fubo; Lei, Xiaohui; Sun, Pengcheng; Singh, Shailesh Kumar; Liao, Weihong; Qiu, Linjing; Li, Jiguang

Publisher

MDPI AG; Multidisciplinary Digital Publishing Institute (MDPI); Basel: MDPI, 2009- (ISSN 2072-4292)

Published

2020

Language

EN

Field

Environmental Science (Physical Sciences)