Information Gain in Rainfall-Runoff Modeling (Tunisia)

  • Aymen Ben JaafarEmail author
  • Zoubeida Bargaoui
Conference paper
Part of the Advances in Science, Technology & Innovation book series (ASTI)


Rainfall runoff conceptual models are used mainly to help make decisions in surface water resources and water quality management. The model calibration is an important step in selecting suitable sets of parameters. The model validation helps to demonstrate the model performance outside the calibration period, in extrapolation. Often, the calibration/validation of the model are carried out using the Generalized Split Sample Test (GSST) method. It is proposed to depict the validation analysis in view of the information gain between observed rainfall (mm day−1) and runoff (mm day−1). For illustration, we adopt the bucket bottom hole (BBH) model with a daily step. The model is lumped. The calibration is achieved by accepting the sets of parameters (solutions) that guarantee a relative error <20%. In addition, a Nash-Sutcliffe coefficient (NSH) condition is established with NSH >0.75. The evaluation of model performance in validation is based on NSH criterion at the monthly and decadal scales. The case study is the basin of the Sejnane River (376 km2) now controlled by a dam in northern Tunisia. It is found that the period with the highest information gain between rainfall and runoff observations constitutes a good “receiver” which means a good ability to reproduce runoff data regardless of the calibration period.


Rainfall Runoff NSH Information gain GSST 


  1. 1.
    Bargaoui, Z., Houcine, A.: Sensitivity to calibration data of simulated soil moisture related drought indices. Revue Sécheresse. 21(4), 1–7 (2010)Google Scholar
  2. 2.
    Beven, K.J.: A manifesto for the equifinality thesis. J. Hydrol. 320, 18–36 (2006)CrossRefGoogle Scholar
  3. 3.
    Coron, L., Andréassian, V., Perrin, C., Lerat, J., Vaze, J., Bourqui, M., Hendrickx, F.: Crash testing hydrological models in contrasted climate conditions: an experiment on 216 Australian basins. Water Resour. Res. 48(5), 17p (2012)CrossRefGoogle Scholar
  4. 4.
    Cosby, B.J., Hornberger, G.M., Clapp, R.B., Ginn, T.R.: A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. Water Resour. Res. 20(6), 682–690 (1984)CrossRefGoogle Scholar
  5. 5.
    Dakhlaoui, H., Ruelland, D., Tramblay, Y., Bargaoui, Z.: Evaluating the robustness of conceptual rainfall-runoff model under climate variability in northern Tunisia. J. Hydrol. 550, 201–217 (2017)CrossRefGoogle Scholar
  6. 6.
    Gray, R.M.: Entropy and Information Theory First Edition, Corrected. Springer, New York (2013).
  7. 7.
    Kobayashi, T., Matsuda, S., Nagai, H., Tesima, J.: A bucket with a bottom hole (BBH) model of soil hydrology. 5p. IAHS Publ. No. 270 (2001)Google Scholar
  8. 8.
    Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., Veith, T.L.: Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. Am. Soc. Agric. Biol. Eng. 50(3), 885–900 (2007)Google Scholar
  9. 9.
    Saxton, K.E., Rawls, W.J., Romberger, J.S., Papendick, R.I.: Estimating generalized soil-water characteristics from texture. Soil Soc. Am. J. 50, 1031–1036 (1986)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Laboratoire de Modélisation en Hydraulique et EnvironnementUniversité Tunis El Manar, ENITTunisTunisia

Personalised recommendations