Abstract
The successful application of hydrological models relies on careful calibration and uncertainty analysis. However, there are many different calibration/uncertainty analysis algorithms, and each could be run with different objective functions. In this paper, we highlight the fact that each combination of optimization algorithm-objective functions may lead to a different set of optimum parameters, while having the same performance; this makes the interpretation of dominant hydrological processes in a watershed highly uncertain. We used three different optimization algorithms (SUFI-2, GLUE, and PSO), and eight different objective functions (R2, bR2, NSE, MNS, RSR, SSQR, KGE, and PBIAS) in a SWAT model to calibrate the monthly discharges in two watersheds in Iran. The results show that all three algorithms, using the same objective function, produced acceptable calibration results; however, with significantly different parameter ranges. Similarly, an algorithm using different objective functions also produced acceptable calibration results, but with different parameter ranges. The different calibrated parameter ranges consequently resulted in significantly different water resource estimates. Hence, the parameters and the outputs that they produce in a calibrated model are “conditioned” on the choices of the optimization algorithm and objective function. This adds another level of non-negligible uncertainty to watershed models, calling for more attention and investigation in this area.
Highlights
Distributed hydrologic models are useful tools for the simulation of hydrologic processes, planning and management of water resources, investigation of water quality, and prediction of the impact of climate and landuse changes worldwide [1,2,3,4,5]
The current paper focuses on the Generalized Likelihood Uncertainty Estimation method (GLUE), SUFI-2, and PSO algorithms and the objective functions R2, bR2, Nash-Sutcliffe efficiency (NSE), Modified Nash-Sutcliffe efficiency (MNS), root mean square error (RSR), sum of squares (SSQR), Kling-Gupta efficiency (KGE), and Percent bias (PBIAS)
We investigated the sensitivity of parameters, model calibration performance, and water resource components to different objective functions (R2, bR2, NSE, MNS, RSR, SSQR, KGE, and PBIAS) and optimization algorithms (e.g., SUFI-2, GLUE, and PSO) using Soil and Water Assessment Tool (SWAT) in two watersheds
Summary
Distributed hydrologic models are useful tools for the simulation of hydrologic processes, planning and management of water resources, investigation of water quality, and prediction of the impact of climate and landuse changes worldwide [1,2,3,4,5]. Process-based distributed hydrologic models are generally characterized by a large number of parameters, which are often not measurable and must be calibrated. Calibration is performed by carefully selecting the values for model input parameters (within their respective uncertainty ranges) and by comparing model simulation (outputs) for a given set of assumed conditions with observed data for the same conditions [7]. Hydrological model predictions are affected by four sources of error, leading to uncertainties in the results of the model.
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