Preprints
https://doi.org/10.5194/hess-2021-179
https://doi.org/10.5194/hess-2021-179
28 May 2021
 | 28 May 2021
Status: a revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

Incorporating experimentally derived streamflow contributions into model parameterization to improve discharge prediction

Andreas Hartmann, Jean-Lionel Payeur-Poirier, and Luisa Hopp

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.

Andreas Hartmann et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-179', Christian Birkel, 16 Jun 2021
    • AC1: 'Reply on RC1', Andreas Hartmann, 11 Nov 2021
  • CC1: 'Comment on hess-2021-179', John Ding, 16 Jun 2021
    • AC3: 'Reply on CC1', Andreas Hartmann, 11 Nov 2021
  • RC2: 'Comment on hess-2021-179', Anonymous Referee #2, 21 Jul 2021
    • AC2: 'Reply on RC2', Andreas Hartmann, 11 Nov 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-179', Christian Birkel, 16 Jun 2021
    • AC1: 'Reply on RC1', Andreas Hartmann, 11 Nov 2021
  • CC1: 'Comment on hess-2021-179', John Ding, 16 Jun 2021
    • AC3: 'Reply on CC1', Andreas Hartmann, 11 Nov 2021
  • RC2: 'Comment on hess-2021-179', Anonymous Referee #2, 21 Jul 2021
    • AC2: 'Reply on RC2', Andreas Hartmann, 11 Nov 2021

Andreas Hartmann et al.

Andreas Hartmann et al.

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Short summary
We advance our understanding of including information derived from environmental tracers into hydrological modeling. We present a simple approach that integrates streamflow observations and tracer-derived stream flow contributions for model parameter estimation. We consider multiple observed streamflow components and their variation over time to quantify the impact of their inclusion for streamflow prediction at the catchment scale.