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Article

Parameter Optimization for Uncertainty Reduction and Simulation Improvement of Hydrological Modeling

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Department of Earth and Environmental Science, School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2
China Institute of Water Resources and Hydropower Research, Beijing 100038, China
3
National Institute of Water and Atmospheric Research, Christchurch 8144, New Zealand
4
Guangxi Key Laboratory of Beibu Gulf Marine Biodiversity Conservation, Beibu Gulf University, Qinzhou 535011, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(24), 4069; https://doi.org/10.3390/rs12244069
Received: 9 November 2020 / Revised: 4 December 2020 / Accepted: 6 December 2020 / Published: 11 December 2020
(This article belongs to the Special Issue Remote Sensing in Hydrology and Water Resources Management)
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 modeling uncertainty (reduction in the R-factor from 0.9 to 0.1) in terms of the convergence of parameter ranges and the aggregation of parameters, in addition to iterative optimization. Furthermore, the combined approaches ensured the rationality of the parameters’ physical meanings and reduced the complexity of the model calibration procedure. We also found the simulation accuracy of ET improved substantially after adding remotely sensed ET data. The parameter ranges and optimal parameter sets obtained by multi-objective calibration (using streamflow plus ET) were more reasonable and the Nash–Sutcliffe coefficient (NSE) improved more rapidly using multiple objectives, indicating a more efficient parameter optimization procedure. Overall, the selected combined approach with multiple objectives can help reduce modeling uncertainty and attain a reliable hydrological simulation. The presented procedure can be applied to any hydrological model. View Full-Text
Keywords: combined approach; multi-objective optimization; modeling uncertainty; model constraint; SWAT combined approach; multi-objective optimization; modeling uncertainty; model constraint; SWAT
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MDPI and ACS Style

Hui, J.; Wu, Y.; Zhao, F.; Lei, X.; Sun, P.; Singh, S.K.; Liao, W.; Qiu, L.; Li, J. Parameter Optimization for Uncertainty Reduction and Simulation Improvement of Hydrological Modeling. Remote Sens. 2020, 12, 4069. https://doi.org/10.3390/rs12244069

AMA Style

Hui J, Wu Y, Zhao F, Lei X, Sun P, Singh SK, Liao W, Qiu L, Li J. Parameter Optimization for Uncertainty Reduction and Simulation Improvement of Hydrological Modeling. Remote Sensing. 2020; 12(24):4069. https://doi.org/10.3390/rs12244069

Chicago/Turabian Style

Hui, Jinyu, Yiping Wu, Fubo Zhao, Xiaohui Lei, Pengcheng Sun, Shailesh K. Singh, Weihong Liao, Linjing Qiu, and Jiguang Li. 2020. "Parameter Optimization for Uncertainty Reduction and Simulation Improvement of Hydrological Modeling" Remote Sensing 12, no. 24: 4069. https://doi.org/10.3390/rs12244069

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