Abstract
Environmental tracers have been used to separate streamflow components for many years. They allow to quantify the contribution of water originating from different sources such as direct runoff from precipitation, subsurface stormflow or groundwater to total streamflow at variable flow conditions. Although previous studies have explored the value of incorporating experimentally derived fractions of event and pre-event water into hydrological models, a thorough analysis of the value of incorporating hydrograph separation derived information on multiple streamflow components at varying flow conditions into model parameter estimation has not yet been performed. This study explores the value of such information to achieve more realistic simulations of catchment discharge. We use a modified version of the process-oriented HBV model that simulates catchment discharge through the interplay of hillslope, riparian zone discharge and groundwater discharge at a small forested catchment which is located in the mountainous north of South Korea subject to a monsoon season between June and August. Applying a Monte Carlo based parameter estimation scheme and the Kling Gupta efficiency (KGE) to compare discharge observations and simulations across two seasons (2013 & 2014), we show that the model is able to provide accurate simulations of catchment discharge (KGE ≥ 0.8) but fails to provide robust predictions and realistic estimates of the contribution of the different streamflow components. Using a simple framework to incorporate experimental information on the contributions of hillslope, riparian zone and groundwater to total discharge during four sub-periods, we show that the precision of simulated streamflow components can be increased while remaining with accurate discharge simulations. We further show that the additional information increases the identifiability of all model parameters and results in more robust predictions. Our study shows how tracer derived information on streamflow contributions can be used to improve the simulation and predictions of streamflow at the catchment scale without adding additional complexity to the model. The complementary use of temporally resolved observations of streamflow components and modelling provides a promising direction to improve discharge prediction by representing model internal dynamics more realistically.
Highlights
At many catchments, in temperate regions, subsurface stormflow (SSF) is an important event-scale mechanism of streamflow generation (Bachmair and Weiler, 2011; Barthold and Woods, 2015; Blume et al, 2016; Chifflard et al, 2019)
Applying a Monte Carlo based parameter estimation scheme and the Kling Gupta efficiency (KGE) to compare discharge observations and simulations across two seasons (2013 & 2014), we show that the model is able to provide accurate 20 simulations of catchment discharge (KGE ≥ 0.8) but fails to provide robust predictions and realistic estimates of the contribution of the different streamflow components
Discarding all parameter sets that deviate more than 20% from the observed hillslope contributions, results in 555 15 remaining parameter sets and in 29 parameter sets when the groundwater contributions are added
Summary
In temperate regions, subsurface stormflow (SSF) is an important event-scale mechanism of streamflow generation (Bachmair and Weiler, 2011; Barthold and Woods, 2015; Blume et al, 2016; Chifflard et al, 2019). Other than early approaches that 15 split streamflow into event and pre-event water (Kendall et al, 2001; Sklash et al, 1979), these approaches rely on the assumption that streamflow is a mixture of distinct water sources within the catchment This hydrograph separation technique and more advanced multivariate statistical tools for comprehensive data sets, such as the End Member Mixing Analysis employing a principal component analysis, have extensively been used in streamflow generation studies (Brown et al, 1999; Burns et al, 2001; Christophersen and Hooper, 1992; Inamdar et al, 2013). The initiation, pathways, residence times, 20 quantity, or spatial origin of SSF in various landscapes are still poorly understood Due to this lack of a general understanding of the occurrence of and controls on SSF, only few modelling studies focus on the realistic simulation of SSF (Appels et al, 2015; Chifflard et al, 2019; Hopp and McDonnell, 2009)
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