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Diagnosis of the hydrology of a small Arctic permafrost catchment using HBV conceptual rainfall-runoff model
Diagnosis of the hydrology of a small Arctic permafrost catchment using HBV conceptual rainfall-runoff model
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Diagnosis of the hydrology of a small Arctic permafrost catchment using HBV conceptual rainfall-runoff model
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Diagnosis of the hydrology of a small Arctic permafrost catchment using HBV conceptual rainfall-runoff model
Diagnosis of the hydrology of a small Arctic permafrost catchment using HBV conceptual rainfall-runoff model

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Diagnosis of the hydrology of a small Arctic permafrost catchment using HBV conceptual rainfall-runoff model
Diagnosis of the hydrology of a small Arctic permafrost catchment using HBV conceptual rainfall-runoff model
Journal Article

Diagnosis of the hydrology of a small Arctic permafrost catchment using HBV conceptual rainfall-runoff model

2019
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Overview
Changes in active layer thickness (ALT) over Arctic and permafrost regions have an important impact on rainfall-runoff transformation. General warming is observed across Svalbard Archipelago and corresponds to increases in ground temperatures. Permafrost thaw and changes in ALT due to climate warming alter how water is routed and stored in catchments, and thus impact both surface and subsurface processes. The overall aim of the present study is to examine the relationships between temporal changes of active layer depth and hydrological model parameters, together with variation in the catchment response. The analysis was carried out for the small unglaciated catchment Fuglebekken, located in the vicinity of the Polish Polar Station Hornsund on Spitsbergen. For hydrological modelling, the conceptual rainfall-runoff HBV (Hydrologiska Byråns Vattenbalansavdelning) model was used. The model was calibrated and validated on runoff within subperiods. A moving window approach (3 weeks long) was applied to derive temporal variation of parameters. Model calibration, together with an estimation of parametric uncertainty, was carried out using the Shuffled Complex Evolution Metropolis algorithm. This allowed the dependence of HBV model parameters on ALT to be analysed. Also, we tested the influence of model simplification, correction of precipitation, and initial conditions on the modelling results.