Insights on a global Extreme Rainfall Detection System

The Extreme Rainfall Detection System (ERDS) is an early warning system (EWS) developed for the monitoring and forecasting of rainfall events on a global scale. Within ERDS the near real-time rainfall monitoring is performed using the Global Precipitation Measurement data, while rainfall forecasts are provided by the Global Forecast System model. Rainfall depths determined on the basis of these data are then compared with a set of rainfall thresholds to evaluate the presence of heavy rainfall events: in places where the rainfall depth is higher than a rainfall threshold, an alert of a severe rainfall event is issued. The information provided by ERDS is accessible through a WebGIS application (http://erds.ithacaweb.org) in the form of maps of rainfall depths and related alerts to provide immediate and intuitive information also for nonspecialized users. This chapter is intended to describe the input data and the extreme rainfall detection methodology currently implemented in ERDS. Furthermore, several case studies (2019 Queensland flood event, 2017 Atlantic hurricane season, and 2017 Eastern Pacific hurricane season) are included to highlight the strengths and weaknesses of this EWS based on global-scale rainfall datasets

Geographically-based approaches to the statistical analysis of rainfall extremes

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A local regression approach to analyze the orographic effect on the spatial variability of sub-daily rainfall annual maxima

In this work we investigate the spatial variability of sub-daily rainfall extremes over Italy considering the influence of local orographic effects. We consider the average annual maxima computed from the recently-released Improved Italian – Rainfall Extreme Dataset (I2-RED) in about 3800 time series with at least 10 years of data (1916–2020 period) and we analyze the orographic effects through a local regression approach which gathers stations in a grid cell-centered area of 1 km resolution. Several constraints are considered to tackle problems determined by the low data density of some areas and by the extrapolation at low/high elevations. Different criteria for selecting the local sample are examined. This work confirms with increased detail previous findings, such as a generally positive gradient of the 24 h average annual maxima and the evidence of negative gradients in large mountainous areas for the 1 h maxima. The use of a local regression approach allows to identify the areas showing the reverse orographic effect, providing material for future investigations on the physical explanation of this evidence. Moreover, the reconstructed maps will allow to apply more accurate approaches in works related to the spatial variability of other rainfall statistics, such as the quantiles required for hydrologic design

I2-RED: A Massive Update and Quality Control of the Italian Annual Extreme Rainfall Dataset

The collection and management of hydrological data in Italy has been dealt with at national level, initially, by the National Hydrological Service (SIMN), and at regional level in the last 40 years. This change has determined problems in the availability of complete and homogeneous data for the whole country. As of 2020, an updated and quality-controlled dataset of the historical annual maxima rainfall in Italy is still lacking. The Italian Rainfall Extreme Dataset (I-RED) has recently been created to allow studies to be performed with a homogeneous dataset at a national level. In this paper, the methodological approach adopted to build an improved and quality-controlled version of I-RED (in terms of both the rainfall depth values and the position of the rain gauges) is presented. The new database can be used as a more reliable research support for the frequency analysis of the rainfall extremes. This new I2-RED database contains rainfall annual maxima rainfall of 1, 3, 6, 12 and 24 h from 1916 until 2019, counts 5265 rain gauges and has been corroborated by a re-positioning and elevation-checking of 15% of the stations. A descriptive analysis of the maximum values of the stations, which provides an additional quality check and reveals different intriguing spatial features of Super-Extreme rainfall events, is also presented

Long-term spatial and temporal rainfall trends over Italy

In this work, we investigate the spatial and temporal trend of short-duration (1 to 24 h) annual maximum rainfall depths, derived from the Improved Italian—Rainfall Extreme Dataset (I2-RED). The investigation is conducted using time series of at least 30 years of data both at the national and regional level using the record-breaking analysis, the Mann-Kendall test, the Regional Kendall test and the Sen’s slope estimator. The results confirm that rainfall extremes of different durations are not increasing uniformly over Italy and that separate tendencies emerge in different sectors, even at close distances

Flood Detection and Monitoring with EO Data Tools and Systems

A timely identification and monitoring of flood events by means of Earth Observation (EO) data is, nowadays, increasingly feasible thanks to recent advances achieved in remote sensing and hydrological process simulations. Despite the notable progress in these fields, a considerable effort will still be required to reduce the intrinsic inaccuracies of these types of approaches. The coarse spatial and temporal resolution of satellite measurements (compared to the one that characterizes in-situ instruments), in fact, continues to require a local-scale validation. Taking into account pros and cons of the approaches based on remotely-sensed data, this chapter reviews some of the most relevant open-access techniques, products, and services that research and academic institutes are currently providing for the detection and the near real-time monitoring of extreme hydrometeorological events