Uncertainty analysis of hydrological modeling in a tropical area using different algorithms

• Published in: Frontiers of earth science Vol. 12; no. 4; pp. 661 - 671
• Main Authors: RAFIEI EMAM, Ammar, KAPPAS, Martin, FASSNACHT, Steven, Khanh LINH, Nguyen Hoang
• Format: Journal Article
• Language: English
• Published: Beijing Higher Education Press 01.12.2018; Springer Nature B.V
• Subjects: Algorithms; Analysis; Earth and Environmental Science; Earth Sciences; GLUE; Hydrologic analysis; Hydrologic models; Hydrology; issues and policy; Modelling; Parameter sensitivity; Parameter uncertainty; Parameters; ParaSol; Particle swarm optimization; Policies; PSO; Reliability analysis; Research Article; Sensitivity analysis; Soil; Soil water; SUFI2; SWAT-CUP; Tropical climate; Uncertainty; Uncertainty analysis; Vietnam; Watersheds; Vietnam; Vietnam; GLUE; SUFI2; ParaSol; PSO; SWAT-CUP
• ISSN: 2095-0195; 2095-0209
• DOI: 10.1007/s11707-018-0695-y

Hydrological modeling outputs are subject to uncertainty resulting from different sources of errors (e.g., error in input data, model structure, and model parameters), making quantification of uncertainty in hydrological modeling imperative and meant to improve reliability of modeling results. The uncertainty analysis must solve difficulties in calibration of hydrological models, which further increase in areas with data scarcity. The purpose of this study is to apply four uncertainty analysis algorithms to a semi-distributed hydrological model, quantifying different source of uncertainties (especially parameter uncertainty) and evaluate their performance. In this study, the Soil and Water Assessment Tools (SWAT) eco-hydrological model was implemented for the watershed in the center of Vietnam. The sensitivity of parameters was analyzed, and the model was calibrated. The uncertainty analysis for the hydrological model was conducted based on four algorithms: Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting (SUFI), Parameter Solution method (ParaSol) and Particle Swarm Optimization (PSO). The performance of the algorithms was compared using P-factor and R-factor, coefficient of determination ( R 2), the Nash Sutcliffe coefficient of efficiency (NSE) and Percent Bias (PBIAS). The results showed the high performance of SUFI and PSO with P-factor>0.83, R-factor<0.56 and R 2>0.91, NSE>0.89, and 0.18<PBIAS<0.32. Hence, we would suggest to use SUFI-2 initially to set the parameter ranges, and further use PSO for final analysis. Indeed, the uncertainty analysis must be accounted when the outcomes of the model use for policy or management decisions.