Articles | Volume 16, issue 9
Hydrol. Earth Syst. Sci., 16, 3419–3434, 2012
https://doi.org/10.5194/hess-16-3419-2012

Special issue: Latest advances and developments in data assimilation for...

Hydrol. Earth Syst. Sci., 16, 3419–3434, 2012
https://doi.org/10.5194/hess-16-3419-2012

Research article 24 Sep 2012

Research article | 24 Sep 2012

Generating spatial precipitation ensembles: impact of temporal correlation structure

O. Rakovec1, P. Hazenberg1,*, P. J. J. F. Torfs1, A. H. Weerts2, and R. Uijlenhoet1 O. Rakovec et al.
  • 1Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands
  • 2Deltares, P.O. Box 177, 2600 MH Delft, The Netherlands
  • *now at: Atmospheric Sciences Department, The University of Arizona, Tucson, AZ, USA

Abstract. Sound spatially distributed rainfall fields including a proper spatial and temporal error structure are of key interest for hydrologists to force hydrological models and to identify uncertainties in the simulated and forecasted catchment response. The current paper presents a temporally coherent error identification method based on time-dependent multivariate spatial conditional simulations, which are conditioned on preceding simulations. A sensitivity analysis and real-world experiment are carried out within the hilly region of the Belgian Ardennes. Precipitation fields are simulated for pixels of 10 km × 10 km resolution. Uncertainty analyses in the simulated fields focus on (1) the number of previous simulation hours on which the new simulation is conditioned, (2) the advection speed of the rainfall event, (3) the size of the catchment considered, and (4) the rain gauge density within the catchment. The results for a sensitivity analysis show for typical advection speeds >20 km h−1, no uncertainty is added in terms of across ensemble spread when conditioned on more than one or two previous hourly simulations. However, for the real-world experiment, additional uncertainty can still be added when conditioning on a larger number of previous simulations. This is because for actual precipitation fields, the dynamics exhibit a larger spatial and temporal variability. Moreover, by thinning the observation network with 50%, the added uncertainty increases only slightly and the cross-validation shows that the simulations at the unobserved locations are unbiased. Finally, the first-order autocorrelation coefficients show clear temporal coherence in the time series of the areal precipitation using the time-dependent multivariate conditional simulations, which was not the case using the time-independent univariate conditional simulations. The presented work can be easily implemented within a hydrological calibration and data assimilation framework and can be used as an improvement over currently used simplistic approaches to perturb the interpolated point or spatially distributed precipitation estimates.