Abstract
A model for generating daily spatial correlated rainfall fields suitable for evaluating the impacts of climate change on water resources is presented. The model, termed Stochastic Rainfall Generating Process, is designed to incorporate two major nonstationarities: changes in the frequencies of different precipitation generating mechanisms (frontal and convective), and spatial nonstationarities caused by interactions of mesoscale atmospheric patterns with topography (orographic effects). These nonstationarities are approximated as discrete sets of the time-stationary Stochastic Rainfall Generating Process, each of which represents the different spatial patterns of rainfall (including its variation with topography) associated with different atmospheric circulation patterns and times of the year (seasons). Each discrete Stochastic Rainfall Generating Process generates daily correlated rainfall fields as the product of two random fields. First, the amount of rainfall is generated by a transformed Gaussian process applying sequential Gaussian simulation. Second, the delimitation of rain and no-rain areas (intermittence process) is defined by a binary random function simulated by sequential indicator simulations. To explore its applicability, the model is tested in the Upper Guadiana Basin in Spain. The result suggests that the model provides accurate reproduction of the major spatiotemporal features of rainfall needed for hydrological modeling and water resource evaluations. The results were significantly improved by incorporating spatial drift related to orographic precipitation into the model.
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Acknowledgements
This research was undertaken as part of the European Union (FP6) funded Integrated Project WATCH through contract number 036946. The meteorological data were provided by the Spanish state meteorological agency (AEMET). The fourth author received partial support from the Spanish Ministry of Science and Innovation (MEC) and from the Australian Research Council through the Centre of Excellence for Climate System Science (grant number CE110001028). Comments by the editor and referees are gratefully acknowledged.
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Sapriza Azuri, G., Jódar, J., Carrera, J. et al. Stochastic Simulation of Nonstationary Rainfall Fields, Accounting for Seasonality and Atmospheric Circulation Pattern Evolution. Math Geosci 45, 621–645 (2013). https://doi.org/10.1007/s11004-013-9467-0
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DOI: https://doi.org/10.1007/s11004-013-9467-0