Articles | Volume 23, issue 6
https://doi.org/10.5194/hess-23-2647-2019
https://doi.org/10.5194/hess-23-2647-2019
Research article
 | 
19 Jun 2019
Research article |  | 19 Jun 2019

Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data

Yingchun Huang, András Bárdossy, and Ke Zhang

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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
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Bárdossy, A. and Pegram, G.: Combination of radar and daily precipitation data to estimate meaningful sub-daily point precipitation extremes, J. Hydrol., 544, 397–406, https://doi.org/10.1016/j.jhydrol.2016.11.039, 2016a. a
Bárdossy, A. and Pegram, G.: Space-time conditional disaggregation of precipitation at high resolution via simulation, Water Resour. Res., 52, 920-937, https://doi.org/10.1002/2015wr018037, 2016b. a, b, c
Bárdossy, A. and Singh, S. K.: Robust estimation of hydrological model parameters., Hydrol. Earth Syst. Sci., 12, 1273–1283, https://doi.org/10.5194/hess-12-1273-2008, 2008. a
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Short summary
This study investigates whether higher temporal and spatial resolution of rainfall can lead to improved model performance. Four rainfall datasets were used to drive lumped and distributed HBV models to simulate daily discharges. Results show that a higher temporal resolution of rainfall improves the model performance if the station density is high. A combination of observed high temporal resolution observations with disaggregated daily rainfall leads to further improvement of the tested models.