Options
Simulating daily rainfall fields over large areas for collective risk estimation
Publikationstyp
Journal Article
Date Issued
2014-05-06
Sprache
English
Author(s)
Serinaldi, Francesco
Journal
Volume
512
Start Page
285
End Page
302
Citation
Journal of Hydrology 512: 285-302 (2014)
Publisher DOI
Scopus ID
Publisher
Elsevier
Large scale rainfall models are needed for collective risk estimation in flood insurance, infrastructure networks and water resource management applications. There is a lack of models which can provide simulations over large river basins (potentially multi-national) at appropriate spatial resolution (e.g., 5-25km) that preserve both the local properties of rainfall (i.e., marginal distributions and temporal correlation) and the spatial structure of the field (i.e., the spatial dependence structure). In this study we describe a methodology which merges meta-Gaussian random fields and generalized additive models to simulate realistic rainfall fields at daily time scale over large areas. Unlike other techniques previously proposed in the literature, the suggested approach does not split the rainfall occurrence and intensity processes and resorts to a unique discrete-continuous distribution to reproduce the local properties of rainfall. This choice allows the use of a unique meta-Gaussian spatio-temporal random field substrate that is devised to reproduce the spatial properties and the short term temporal characteristics of the observed precipitation. The model is calibrated and tested on a 25km gridded daily rainfall data set covering the 817000 <sup>km2</sup> of the Danube basin. Standard and ad hoc diagnostics highlight the overall good performance over the whole range of rainfall values at multiple scales of spatio-temporal aggregation with particular attention to extreme values. Moreover, the modular structure of the model allows for refinements, adaptation to different areas and the introduction of exogenous forcing variables, thus making it a valuable tool for classical hydrologic analyses as well as for new challenges of network and reinsurance risk assessment over extensive areas. © 2014.
Subjects
Gaussian random fields
Generalized additive models
Generalized linear models
Large scale risk assessment
Rainfall modeling
DDC Class
577: Ecology
551: Geology, Hydrology Meteorology