Articles | Volume 27, issue 10
https://doi.org/10.5194/hess-27-1987-2023
https://doi.org/10.5194/hess-27-1987-2023
Research article
 | 
25 May 2023
Research article |  | 25 May 2023

Why do our rainfall–runoff models keep underestimating the peak flows?

András Bárdossy and Faizan Anwar

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Cited articles

Ahmed, S. and De Marsily, G.: Comparison of geostatistical methods for estimating transmissivity using data on transmissivity and specific capacity, Water Resour. Res., 23, 1717–1737, https://doi.org/10.1029/WR023i009p01717, 1987. a
Balin, D., Lee, H., and Rode, M.: Is point uncertain rainfall likely to have a great impact on distributed complex hydrological modeling?, Water Resour. Res., 46, https://doi.org/10.1029/2009WR007848, 2010. a, b
Bárdossy, A. and Das, T.: Influence of rainfall observation network on model calibration and application, Hydrol. Earth Syst. Sci., 12, 77–89, https://doi.org/10.5194/hess-12-77-2008, 2008. a
Bárdossy, A., Stehlík, J., and Caspary, H.-J.: Automated objective classification of daily circulation patterns for precipitation and temperature downscaling based on optimized fuzzy rules, Clim. Res., 23, 11–22, https://doi.org/10.3354/cr023011, 2002. a
Bárdossy, A., Seidel, J., and El Hachem, A.: The use of personal weather station observations to improve precipitation estimation and interpolation, Hydrol. Earth Syst. Sci., 25, 583–601, https://doi.org/10.5194/hess-25-583-2021, 2021. a
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Short summary
This study demonstrates the fact that the large river flows forecasted by the models show an underestimation that is inversely related to the number of locations where precipitation is recorded, which is independent of the model. The higher the number of points where the amount of precipitation is recorded, the better the estimate of the river flows.