Improved data assimilation in regional frequency analysis of rainfall extremes across large and morphologically complex geographical areas

In locations where measured timeseries are not available or not sufficiently long, reliable predictions of the rainfall depth associated with a given duration and exceedance probability may be obtained through regional frequency analysis (RFA). The scientific literature reports on a large number of different approaches to RFA of rainfall extremes, each one characterized by specific advantages and disadvantages. One of the most common drawbacks is that regional models specifically refer to a single duration or a single exceedance probability. Second, several approaches require the definition of a homogeneous region where the model is trained; this leads to higher accuracy, but also the applicability of the model is limited to those locations that are hydrologically similar to the homogeneous group used in the training. Moreover, most models require filtering the available gauged stations based on the length of the measured timeseries to perform reliable frequency analysis. These aspects lead to discard a significant amount of data, which could turn out to be detrimental to the accuracy of the regional prediction in some cases.

We set up a few alternative models aiming to investigate and discuss a different and innovative approach for RFA of rainfall extremes. We want to address three main research questions: (1) Can a single model represent the frequency of extreme rainfall events over a large, climatically, and morphologically complex geographical area? (2) Can a single RFA model handle all sub-daily  durations (i.e., from 1 to 24h)? (3) Is it possible to exploit all available annual maximum series, regardless of their length (i.e., very short ones too)? We select a large study area that is located in north-central Italy. We make use of more than 2300 Annual Maximum Series of rainfall depth for different time-aggregation intervals between 1 and 24 hours, that have been collected between 1928 and 2011 in the Italian Rainfall Extreme Dataset (I2-RED). For each gauged station, several morpho-climatic descriptors are retrieved (e.g., minimum distance to the coast, elevation of orographic barriers, aspect, terrain slope, etc.) and used as covariates for the prediction models. Our models are based on ensembles of unsupervised artificial neural networks (ANNs) and are able to predict parameters of a Gumbel distribution for any location and any duration in the 1-24 hours range based on the morphoclimatic descriptors. Through the analysis of results over 100 gauged validation stations, a profitable discussion is enabled on the potential and drawbacks of ensembles of unsupervised ANNs for regional frequency analysis of sub-daily rainfall extremes.