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The study evaluates the rain-on-grid (RoG) hydraulic model's sensitivity to digital elevation model (DEM) resolution when simulating an extreme flood in Slovenia. The RoG model is validated against a high-resolution benchmark, showing strong agreement with a Kling-Gupta efficiency of 0.913 and Pearson correlation of 0.964 for a 1 m DEM. Differences are observed in peak shapes and concentration times, attributed to rainfall propagation in RoG grids. DEM resolution significantly impacts performance, with the largest decrease between resolutions of 1 and 5 m. Coarser DEMs yielded higher depths, indicating slope decreases and terrain smoothing. The study concludes that high-resolution DEMs (<1 m) are needed for adequate RoG performance, while commercially available coarser DEMs (30 m) degraded accuracy and should be avoided using this method. Differences from semi-empirical concentration time models are also discussed, and emphasis is placed also on the impacts on water velocity and numerical stability. [ABSTRACT FROM AUTHOR]

In this work, we formulate a regionalization framework for rainfall intensity–time scale–return period relationships which is applied over the Greek territory. The methodology for single-site estimation is based on a stochastic framework for multi-scale modelling of rainfall intensity which is outlined in the companion paper. Five parameters are first fitted independently for each site and the resulting parameter variability is assessed. Following a systematic investigation of uncertainty and variability patterns, two parameters, i.e. the tail-index and a time scale parameter, are identified as constant in space and estimated using data pooling techniques. The other three parameters are regionalized over Greece by means of spatial interpolation and smoothing techniques that are assessed through cross-validation in a multi-model framework. The regionalization scheme is implemented in a sequential order that allows exploiting rainfall information both from rainfall stations with sub-daily resolution and from the more reliable network of daily raingauges. [ABSTRACT FROM AUTHOR]

This work presents a stochastic framework for the construction of rainfall intensity–time scale–return period relationships, which was applied in the recent regionalization of design rainfall curves over the Greek territory, described in a companion paper. The methodology outlined herein builds upon a widely-used mathematical framework, which has been recently revisited and upgraded, and incorporates two different versions: (a) a theoretically consistent stochastic model applicable for rainfall intensity over any scale of interest; and (b) a simplified version valid over small scales, which makes parameter estimation easier. Special attention is given to the presentation of the simplified version, which suffices for most engineering tasks. Parameter estimation approaches are presented in detail, including the K-moments framework that allows for reliable high-order moment estimation and handling of bias due to spatiotemporal dependence. [ABSTRACT FROM AUTHOR]

Clustering of extremes is critical for hydrological design and risk management and challenges the popular assumption of independence of extremes. We investigate the links between clustering of extremes and long-term persistence, else Hurst-Kolmogorov (HK) dynamics, in the parent process exploring the possibility of inferring the latter from the former. We find that (a) identifiability of persistence from maxima depends foremost on the choice of the threshold for extremes, the skewness and kurtosis of the parent process, and less on sample size; and (b) existing indices for inferring dependence from series of extremes are biased downward when applied to non-Gaussian processes. We devise a probabilistic index based on the probability of occurrence of peak-over-threshold events across multiple scales, which can reveal clustering, linking it to the persistence of the parent process. Its application shows that rainfall extremes may exhibit noteworthy departures from independence and consistency with an HK model. [ABSTRACT FROM AUTHOR]