EGU2020-3205, updated on 12 Jun 2020
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

On the Robustness of Conceptual Rainfall-Runoff Models to Calibration and Evaluation Dataset Splits Selection: A Large Sample Investigation

Feifei Zheng1, Danlu Guo1,2, Hoshin Gupta3, and Holger Maier1,4
Feifei Zheng et al.
  • 1College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, Zhejiang, China (
  • 2Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC Australia (
  • 3Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, AZ, USA (
  • 4School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA Australia (

Conceptual Rainfall-Runoff (CRR) models are widely used for runoff simulation, and for prediction under a changing climate. The models are often calibrated with only a portion of all available data at a location, and then evaluated independently with another part of the data for reliability assessment. Previous studies report a persistent decrease in CRR model performance when applying the calibrated model to the evaluation data. However, there remains a lack of comprehensive understanding about the nature of this ‘low transferability’ problem and why it occurs. In this study we employ a large sample approach to investigate the robustness of CRR models across calibration/validation data splits. Specially, we investigate: 1) how robust is CRR model performance across calibration/evaluation data splits, at catchments with a wide range of hydro-climatic conditions; and 2) is the robustness of model performance somehow related to the hydro-geo-climatic characteristics of a catchment? We apply three widely used CRR models, GR4J, AWBM and IHACRE_CMD, to 163 Australian catchments having long-term historical data. Each model was calibrated and evaluated at each catchment, using a large number of data splits, resulting in a total of 929,160 calibrated models. Results show that: 1) model performance generally exhibits poor robustness across calibration/evaluation data splits; 2) lower model robustness is correlated with specific catchment characteristics, such as a higher runoff skewness, lower aridity and runoff coefficient. These results provide a valuable benchmark for future model robustness assessments, and useful guidance for model calibration and evaluation.

How to cite: Zheng, F., Guo, D., Gupta, H., and Maier, H.: On the Robustness of Conceptual Rainfall-Runoff Models to Calibration and Evaluation Dataset Splits Selection: A Large Sample Investigation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3205,, 2020


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