EGU2020-7147
https://doi.org/10.5194/egusphere-egu2020-7147
EGU General Assembly 2020
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

The additional value of using proxy data besides runoff for calibrating a conceptual hydrological model in a small agricultural catchment

Borbála Széles1, Juraj Parajka1, Patrick Hogan1, Rasmiaditya Silasari1, Lovrenc Pavlin1, Peter Strauss2, and Günter Blöschl1
Borbála Széles et al.
  • 1Vienna University of Technology, Institute of Hydraulic Engineering and Water Resources Management , Vienna, Austria (szeles@waterresources.at)
  • 2Federal Agency of Water Management, Institute for Land and Water Management Research, Petzenkirchen, Austria

The aim of this study was to explore the additional value of using proxy data besides runoff for calibrating a conceptual hydrological model. The study area was the Hydrological Open Air Laboratory (HOAL), a 66 ha large experimental catchment in Austria. A conceptual, HBV type, spatially lumped hydrological model was calibrated following two approaches. First, the model was calibrated in one step using only runoff data. Second, we proposed a step-by-step approach, where the modules of the model (snow, soil moisture and runoff generation) were calibrated using proxy data besides runoff, such as snow, actual evapotranspiration, soil moisture, overland flow and groundwater level. The two approaches were evaluated on annual, seasonal and daily time scales. Using the proposed step-by-step approach, the runoff volume errors in the calibration and validation periods were 0% and -1%, the monthly Pearson correlation coefficients were 0.92 and 0.82, and the daily logarithmic Nash Sutcliffe efficiencies were 0.59 and 0.18, respectively. The additional benefit of using proxy data besides runoff was the improved overall process consistency compared to the approach when only runoff was used for model calibration. Soil moisture and evapotranspiration observations had the largest influence on simulated runoff, while the calibration of the snow and runoff generation modules had a smaller influence.

How to cite: Széles, B., Parajka, J., Hogan, P., Silasari, R., Pavlin, L., Strauss, P., and Blöschl, G.: The additional value of using proxy data besides runoff for calibrating a conceptual hydrological model in a small agricultural catchment, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7147, https://doi.org/10.5194/egusphere-egu2020-7147, 2020.

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  • CC1: Comment on EGU2020-7147, Katya Dimitrova Petrova, 04 May 2020

    Hi Borbála, thanks for your interesting contribution. Is this part of ongoig work or published research? 

    Sorry, I could not understand from the poster: I am curious, which part of the HBV storage did you calibrate with your soil moisture data?

    In a humid mixed-agricultural landscape in Scotland, UK, we obtained similar results i.e. that including soil moisture estimates in the model calibration improved internal model structure. 

    Thank you!

    • AC1: Reply to CC1, Borbála Széles, 04 May 2020

      Dear Katya,

      Many thanks for the questions!

      This work is currently under review in Water Resources Research:

      Széles, B., Parajka, J., Hogan, P., Silasari, R., Pavlin, L., Strauss, P., & Blöschl, G. (2020). The added value of different data types for calibrating and testing a hydrologic model in a small catchment. Water Resources Research, Under Review.

      Using soil moisture (and/or actual evapotranspiration) measurements, we calibrated the soil moisture accounting module of the model (Step 2 in the Methodology on the poster). This is the root zone soil storage, i.e. the soil moisture of a top soil layer, which controls runoff generation and actual evapotranspiration. Considering that the model is lumped and we had point measurements for soil moisture, we standardized the soil moisture time series to make the simulated soil moisture comparable with the measurements.

      Many thanks for the information, I checked your paper in the Journal of Hydrology (2020), it is a very interesting study, and as I could see, you distinguished between near-surface soil storage and catchment-scale storage.

      Thanks again for the questions!

      • CC2: Reply to AC1, Katya Dimitrova Petrova, 05 May 2020

        Dear Borbala,

        Thanks for your reply. It is interesting (and encourading) indeed to see how slightly different ways to conceptualise the soil moisture storage still work well to improve model internal dynamics.

        Thanks for the reference as well, I will make sure to follow your publication.