Abstract
Summary 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., 5–25 km) 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 25 km gridded daily rainfall data set covering the 817 000 km 2 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.
Highlights
Dealing with large geographic areas, the modeling of the spatiotemporal evolution of rainfall is a challenging task that must be tackled to provide realistic scenarios to be used as an essential input of water resource or flood risk assessment analyses
The modeling and simulation approach can be summarized as follows: 1. A VGLM/VGAM/GAMLSS zero-adjusted distribution Fðrðs; tÞ; hðs;tÞÞ is fitted to every time series at each location to model the rainfall marginal distributions accounting for seasonality and covariate effects
This study presents a viable approach to simulate daily rainfall fields over large areas
Summary
Dealing with large geographic areas, the modeling of the spatiotemporal evolution of rainfall is a challenging task that must be tackled to provide realistic scenarios to be used as an essential input of water resource or flood risk assessment analyses. The method is suitable for fine scale temporal resolution by introducing ‘‘internal’’ rainfall patterns within each storm event, whereas the spatial domain might depend on the computational time required by the optimization/resampling procedures (see e.g., Haberlandt et al, 2008, 2011, and references therein) Time series models such as Markov chain models, generalized linear models (GLM; McCullagh and Nelder, 1989), generalized additive models (GAM; Hastie and Tibshirani, 1990), linear parametric models (ARMA and their extensions; Hipel and McLeod, 1994) are widely used to simulate hydrological variables. Models based on point processes have a well defined mathematical framework; their extension and incorporation of exogenous variables is not always easy; even in this case, good quality data at multiple time scales required for the model calibration are rarely available for large areas.
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