| Title: | Parameter optimization for uncertainty reduction and simulation improvement of hydrological modeling |
| Authors: | Hui, J., Y. Wu, F. Zhao, X. Lei, P. Sun, S.K. Singh, W. Liao, L. Qiu and J. Li |
| Year: | 2020 |
| Journal: | Remote Sensing |
| Volume (Issue): | 12(24) |
| Pages: | |
| Article ID: | 4069 |
| DOI: | 10.3390/rs12244069 |
| URL (non-DOI journals): | |
| Model: | SWAT |
| Broad Application Category: | hydrologic only |
| Primary Application Category: | uncertainty analysis |
| Secondary Application Category: | evapotranspiration assessment |
| Watershed Description: | 19,288 km^2 Guijiang River, a tributary of the Xijiang River (and larger Pearl River system) located in Guangxi Province in southern China. |
| Calibration Summary: | |
| Validation Summary: | |
| General Comments: | |
| Abstract: | 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. |
| Language: | English |
| Keywords: | combined approach, multi-objective optimization, modeling uncertainty, model constraint, SWAT |