Coupling the strengths of optimization and simulation for calibrating Poisson cluster models

P. Kossieris, A. Efstratiadis, and D. Koutsoyiannis, Coupling the strengths of optimization and simulation for calibrating Poisson cluster models, Facets of Uncertainty: 5th EGU Leonardo Conference – Hydrofractals 2013 – STAHY 2013, Kos Island, Greece, doi:10.13140/RG.2.2.15223.21929, European Geosciences Union, International Association of Hydrological Sciences, International Union of Geodesy and Geophysics, 2013.



Many hydrological applications require use of rainfall data across a wide range of time scales. To simulate rainfall at fine time scales, stochastic approaches are usually enrolled. A leading representative is the Bartlett-Lewis model, which belongs to the family of Poisson-cluster processes that represent rainfall events. The usual approach of model calibration comprises the incorporation of the theoretical model equations in an objective function and the optimization of that function. However, it is obvious that this procedure is limited to the case that analytical equations exist for the modelled stochastic properties of the process. Yet such analytical equations cannot be derived for key characteristics such as skewness and parameters determining the distribution of extreme values. Here we present an innovative approach that remedies those weaknesses through the combined use of simulation and optimization. During model calibration, the model statistics are derived by Monte Carlo simulation, instead of theoretical equations. Various calibration criteria as well as statistical parameters are introduced aiming at more faithful representation of the rainfall process at different time scales. The efficiency of the proposed method is demonstrated using a long data series from a rain gauge in Athens.

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Our works that reference this work:

1. P. Kossieris, C. Makropoulos, C. Onof, and D. Koutsoyiannis, A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures, Journal of Hydrology, 556, 980–992, doi:10.1016/j.jhydrol.2016.07.015, 2018.

Other works that reference this work (this list might be obsolete):

1. De Luca, D. L., and L. Galasso, Calibration of NSRP models from extreme value distributions, Hydrology, 6(4), 89, doi:10.3390/hydrology6040089, 2019.
2. Park, J., D. Cross, C. Onof, Y. Chen, and D. Kim, A simple scheme to adjust Poisson cluster rectangular pulse rainfall models for improved performance at sub-hourly timescales, Journal of Hydrology, 598, 126296, doi:10.1016/j.jhydrol.2021.126296, 2021.
3. De Luca, D. L., and A. Petroselli, STORAGE (STOchastic RAinfall GEnerator): A user-friendly software for generating long and high-resolution rainfall time series, Hydrology, 8(2), 76, doi:10.3390/hydrology8020076, 2021.

Tagged under: Rainfall models, Students' works