Abstract
Uncertainties in hydrologic model outputs can arise for many reasons such as structural, parametric and input uncertainty. Identification of the sources of uncertainties and the quantification of their impacts on model results are important to appropriately reproduce hydrodynamic processes in karst aquifers and to support decision-making. The present study investigates the time-dependent relevance of model input uncertainties, defined as the conceptual uncertainties affecting the representation and parameterization of processes relevant for groundwater recharge, i.e. interception, evapotranspiration and snow dynamic, on the lumped karst model LuKARS. A total of nine different models are applied, three to compute interception (DVWK, Gash and Liu), three to compute evapotranspiration (Thornthwaite, Hamon and Oudin) and three to compute snow processes (Martinec, Girons Lopez and Magnusson). All the input model combinations are tested for the case study of the Kerschbaum spring in Austria. The model parameters are kept constant for all combinations. While parametric uncertainties computed for the same model in previous studies do not show pronounced temporal variations, the results of the present work show that input uncertainties are seasonally varying. Moreover, the input uncertainties of evapotranspiration and snowmelt are higher than the interception uncertainties. The results show that the importance of a specific process for groundwater recharge can be estimated from the respective input uncertainties. These findings have practical implications as they can guide researchers to obtain relevant field data to improve the representation of different processes in lumped parameter models and to support model calibration.
Highlights
Hydrologic models serve as important tools for the assessment of dominant hydrodynamic processes in karst systems (Hartmann et al 2013; Sivelle et al 2019)
The analyses focus on the comparison of the interquartile range of the LuKARS model outputs as an indicator for model uncertainty
As recent studies highlighted the specific role of snowmelt for groundwater recharge in alpine and pre-alpine catchments, this study further investigates if this specific importance is reflected in increased uncertainties in modeled spring discharge when snowmelt is the results of all model combinations highlight that considering more processes to be uncertain does not necessarily lead to an increase of the normalized interquartile range of modeled spring discharge
Summary
Hydrologic models serve as important tools for the assessment of dominant hydrodynamic processes in karst systems (Hartmann et al 2013; Sivelle et al 2019). The assessment of the reliability of a model output is an important step towards an improved description of the karst system (Hartmann et al 2014a) This assessment is usually done by uncertainty quantification techniques, which investigate the likelihood of a model outcome while considering the unknowns in a hydrologic model (Sarrazin et al 2018; Teixeira Parente et al 2019). Structural uncertainties evolve from the simplifications required while creating a conceptual model of a real-world system (Gupta and Govindaraju 2019; Rojas et al 2008) This conceptualization often neglects certain parts of the natural system due to a lack of knowledge, which can lead to an underrepresentation of important hydrodynamic processes (Butts et al 2004; Lee et al 2011). Parametric uncertainties arise from the fact that the exact values of model parameters, such as discharge
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