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Abstract
The complex terrain, seasonality, and cold region hydrology of the Nelson Churchill River Basin (NCRB) presents a formidable challenge for hydrological modeling, which complicates the calibration of model parameters. Seasonality leads to different hydrological processes dominating at different times of the year, which translates to time variant sensitivity in model parameters. In this study, Hydrological Predictions for the Environment model (HYPE) is set up in the NCRB to analyze the time variant sensitivity analysis (TVSA) of model parameters using a Global Sensitivity Analysis technique known as Variogram Analysis of Response Surfaces (VARS). TVSA can identify parameters that are highly influential in a short period but relatively uninfluential over the whole simulation period. TVSA is generally effective in identifying model’s sensitivity to event-based parameters related to cold region processes such as snowmelt and frozen soil. This can guide event-based calibration, useful for operational flood forecasting. In contrast to residual based metrics, flow signatures, specifically the slope of the mid-segment of the flow duration curve, allows VARS to detect the influential parameters throughout the timescale of analysis. The results are beneficial for the calibration process in complex and multi-dimensional models by targeting the informative parameters, which are associated with the cold region hydrological processes.
Highlights
Hydrological models are imprecise representations of real-world hydrological processes governed by mathematical equations and assumptions
We have applied three different Sensitivity analysis (SA) methods based on differing levels of temporal aggregation and different error metrics to analyze the sensitivity of the model parameters in Hydrological Predictions for the Environment model (HYPE) for cold region catchment (NCRB)
Monthly SA can reveal shorter-term, seasonal variations in the sensitivity of the model parameters associated with seasonally variant hydrological processes
Summary
Hydrological models are imprecise representations of real-world hydrological processes governed by mathematical equations and assumptions Such imperfect representation of real-world hydrological processes often leads to uncertainty in model outputs, which are attributed to different sources of error including measurement error, model structural error and the model parameter uncertainty [1,2]. Most of the parameters associated with the hydrological processes are unknown and often require calibration to match the model output with the measured dataset(s). Sensitivity analysis (SA) can assist the modeler in identifying the relative influence each model parameter has on the variation in model output. This information is typically used by the modeler
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