Abstract
Sensitivity analysis is a fundamental approach to identify the most significant and sensitive parameters, helping us to understand complex hydrological models, particularly for time-consuming distributed flood forecasting models based on complicated theory with numerous parameters. Based on Sobol’ method, this study compared the sensitivity and interactions of distributed flood forecasting model parameters with and without accounting for correlation. Four objective functions: (1) Nash–Sutcliffe efficiency (ENS); (2) water balance coefficient (WB); (3) peak discharge efficiency (EP); and (4) time to peak efficiency (ETP) were implemented to the Liuxihe model with hourly rainfall-runoff data collected in the Nanhua Creek catchment, Pearl River, China. Contrastive results for the sensitivity and interaction analysis were also illustrated among small, medium, and large flood magnitudes. Results demonstrated that the choice of objective functions had no effect on the sensitivity classification, while it had great influence on the sensitivity ranking for both uncorrelated and correlated cases. The Liuxihe model behaved and responded uniquely to various flood conditions. The results also indicated that the pairwise parameters interactions revealed a non-ignorable contribution to the model output variance. Parameters with high first or total order sensitivity indices presented a corresponding high second order sensitivity indices and correlation coefficients with other parameters. Without considering parameter correlations, the variance contributions of highly sensitive parameters might be underestimated and those of normally sensitive parameters might be overestimated. This research laid a basic foundation to improve the understanding of complex model behavior.
Highlights
A distributed hydrological model is an essential tool and has been widely used in hydrology and water resources management due to its good ability in simulating temporal and spatial variation of hydrological variables [1,2]
Even though the performance of validation was slightly worse than the calibration period, the former still met the second grade of the norm, which strengthened the confidence of the Liuxihe model and calibrated parameters
The extended and generalized Sobol’ methods were implemented to compare the sensitivity indices of the Liuxihe model parameters in the case of uncorrelated and correlated parameters based on four objective functions: (1) Nash–Sutcliffe efficiency (ENS); (2) water balance coefficient (WB); (3) peak discharge efficiency (EP); and (4) time to peak efficiency (ETP)
Summary
A distributed hydrological model is an essential tool and has been widely used in hydrology and water resources management due to its good ability in simulating temporal and spatial variation of hydrological variables [1,2]. Because of the existing phenomenon called “equifinality” [3,4], i.e., different parameter sets resulting in the same model simulations, there is large uncertainty in the application of hydrological models. Various selection of parameters will induce a large variety of simulation results; on the other hand, considering that the distributed hydrological models always contain complex structures with large number of parameters, the optimization choice of parameters is considered to be a difficult and time-consuming task. Some studies have stated that SA can improve parameter calibration efficiency, avoid over-parameterization [7], reduce model uncertainty [8,9,10], aid in understanding model structure [11,12], and generate more accurate model outputs [6,13]
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