RESUMEN
Simulation models are often affected by uncertainties that impress the modeling results. One of the important types of uncertainties is associated with the model input data. The main objective of this study is to investigate the uncertainties of inputs of the Heat-Flux (HFLUX) model. To do so, the Shuffled Complex Evolution Metropolis Uncertainty Algorithm (SCEM-UA), a Monte Carlo Markov Chain (MCMC) based method, is employed for the first time to assess the uncertainties of model inputs in riverine water temperature simulations. The performance of the SCEM-UA algorithm is further evaluated. In the application, the histograms of the selected inputs of the HFLUX model including the stream width, stream depth, percentage of shade, and streamflow were created and their uncertainties were analyzed. Comparison of the observed data and the simulations demonstrated the capability of the SCEM-UA algorithm in the assessment of the uncertainties associated with the model input data (the maximum relative error was 15%).
RESUMEN
Water temperature is a key characteristic defining chemical, physical, and biologic conditions in riverine systems. Models of riverine water quality require many inputs, which are commonly beset by uncertainty. This study presents an uncertainty analysis of inputs to the stream-temperature simulation model HFLUX. This paper's assessment relies on a Markov chain Monte Carlo (MCMC) analysis with the DREAM algorithm, which has fast convergence rate and good accuracy. The inputs herein considered are the river width and depth, percent shade, view to sky, streamflow, and the minimum and maximum values of inputs required for uncertainty analysis. The results are presented as histograms for each input specifying the input's uncertainty. A comparison of the observational data with the DREAM algorithm estimates yielded a maximum error equal to 7.5%, which indicates excellent performance of the DREAM algorithm in ascertaining the effect of uncertainty in riverine water quality assessment.