Assessment of input uncertainty by seasonally categorized latent variables using SWAT

Haw Yen, Yu Wen Su, June E. Wolfe, Shien Tsung Chen, Yu Chao Hsu, Wen Hsiao Tseng, Dawn M. Brady, Jaehak Jeong, Jeffrey G. Arnold

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Watershed processes have been explored with sophisticated simulation models for the past few decades. It has been stated that uncertainty attributed to alternative sources such as model parameters, forcing inputs, and measured data should be incorporated during the simulation process. Among varying uncertainty sources, input uncertainty attributed to precipitation data exhibits a dominant role, as it is the source driving most hydrologically-related processes. In previous studies, latent variables (normally distributed random noise) have been implemented to explicitly incorporate input uncertainty from precipitation data. However, it may not be appropriate to apply the same set of latent variables throughout temporal series without considering seasonal effects. In this study, seasonally categorized latent variables were defined to investigate potential effects on model predictions and associated predictive uncertainty. Results show that the incorporation of seasonal latent variables resulted in better statistical solutions (NSE, Nash-Sutcliffe Efficiency coefficient) for both calibration (0.58[streamflow]/0.73[sediment]/0.59[ammonia]) and validation (0.57[streamflow]/0.45[sediment]/0.53[ammonia]) periods. Alternative definitions of Dry/Wet seasonality (two definitions are defined in this study) also affected model predictions. In addition, it was determined that predictive uncertainty can be enhanced by incorporating more latent variables during model calibration. The implementations of proposed seasonal latent variables have further substantiated the importance of incorporating seasonal effects when conducting comparable approaches. Applications of latent variables on future work should evaluate potential effects on model predictions before performing associated scientific studies or relevant decision making processes.

Original languageEnglish
Pages (from-to)685-695
Number of pages11
JournalJournal of Hydrology
Volume531
DOIs
Publication statusPublished - 2015 Dec 1

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

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