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• W3014982014 abstract "Errors from hydrological simulations have substantial influence on hydrological applications. There are increasing interests to incorporate statistical models of the errors (error models) into hydrological applications to improve simulations. However, conventional error models are usually temporally unvaried and may be inadequate to handle model simulation errors when the underlying error statistics are strongly seasonal. To overcome this problem, the use of temporally varied error models in both prediction and forecasting applications are investigated. We analyze different levels of temporal granularity to identify the optimal temporally varied error model to best improve simulations and to quantify uncertainty. The Error Reduction and Representation In Stages (ERRIS) model is adapted in this study to improve streamflow simulations produced by the Variable Infiltration Capacity model for the Yarlung Tsangbo River basin. Well-marked seasonal and sub-seasonal variations in error statistics are found. Accordingly, three temporally varied ERRIS models are constructed at semi-annual (ERRIS-H), seasonal (ERRIS-S) and monthly (ERRIS-M) temporal granularity and compared with a benchmark model, the temporally unvaried model (ERRIS-A). Results show that the temporally varied ERRIS models are considerably more effective than the temporally unvaried one, with 34% reduction in continuous ranked probability score (CRPS) and 23% increase in Nash-Sutcliffe Efficiency (NSE) for prediction applications. With respect to forecasting applications, improvements of about 7% in CRPS are achieved by the temporally varied models. The performance of different temporally varied error models roughly follows the same order as the level of temporal granularity. Generally, ERRIS-S and ERRIS-M are similarly effective, with ERRIS-M providing additional improvement by more than 15% in CRPS for the spring season and by at least 30% for the autumn season. It is concluded that the consistency of temporal granularity between error models and variations of error statistics is the key to effective error reduction and uncertainty quantification. When complicated hydrological processes exist, hydrological model tends to produce seasonally and even sub-seasonally varied error statistics. Consequently, temporally finer error models are expected to lead to higher accuracy and reliability." @default.
• W3014982014 created "2020-04-10" @default.
• W3014982014 creator A5000108862 @default.
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• W3014982014 date "2020-07-01" @default.
• W3014982014 modified "2023-09-28" @default.
• W3014982014 title "Temporally varied error modelling for improving simulations and quantifying uncertainty" @default.
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• W3014982014 doi "https://doi.org/10.1016/j.jhydrol.2020.124914" @default.
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