Department of Civil, Environmental, and Architectural Engineering – University of Padova

Contact: marco.marani@unipd.it

Call for Application: https://www.dicea.unipd.it/sites/dicea.unipd.it/files/01_ICEA_Bando_Tipo…

To apply, you will need to first register in the Pica System: https://pica.cineca.it/

Application link: https://pica.cineca.it/unipd.  Search for “assegni-icea-23-2022-MARANI

The application may only be submitted by completing the online procedure available from November 14, 2022 al 3 p.m. to December 13, 2022 at 3 p.m. (CET).

Space-time downscaling of rainfall extremes

The RESILIENCE project (see Summary description below and http://resilience.stat.unipd.it/) brings together an interdisciplinary group of scientists, from hydrologists, to climate modelers, to statisticians, to forest science experts. The research group involves Marco Marani (lead at the Department of Civil, Environmental, and Architectural Engineering – University of Padova), Marco Borga and Carlo Gregoretti (Department of Department of Land, Environment, Agriculture and Forestry, UniPD), Antonio Canale (Department of Statistics, UniPD), Francesco Marra (National Research Council, Bologna), Giorgia Fosser (IUSS, Pavia). The group at the Department of Civil, Environmental, and Architectural Engineering (DICEA) will focus on novel statistical tools to infer local-scale (~point to 1 km scale) and short time scale (10 min- 1hr) extreme value statistics from Convection-Permitting and Regional Climate Model rainfall outputs (2.5-50 km in space and 1 day in time), as well as from remote sensing rainfall estimates. We envision using theoretical results based on general stochastic process properties (Marani, 2003; 2005) and applied successfully to the case of ordinary (i.e. non-extreme) temporal rainfall (Marani and Zanetti, 2007) and to ordinary and extreme space-time rainfall (Zorzetto and Marani, 2019).  Results will be tested against high-resolution rain-gauge observations and weather radar information. Other methods that will be explored include, but will not be limited to, machine learning algorithms (e.g. convolutional neural networks). The work will focus on the Italian Northeast region, for which ground and remote sensing rainfall estimates are available to the team, along with results from Convection Permitting Models and Regional Climate Models. Outcomes are expected to be used and useful for impact studies and engineering design, objectives that will be pursued through the collaborations within RESILIENCE.

Candidate profile. The successful candidate will be based at DICEA and will work within an interdisciplinary group, collaborating with statisticians and hydrologists at UniPD, climate modelers at IUSS Pavia, and atmospheric physicists at CNR Bologna. Specifically, candidates who completed PhD theses in Hydrology, Atmospheric Physics, Environmental Sciences, Statistics, or other related disciplines are sought. A strong background in stochastic modelling will be preferred. Other qualifications include significant programming skills and research abilities demonstrated by publications.

ExtREme Storms in the ItaLIan North-East: frequeNCy, impacts and projected changEs (RESILIENCE)

Global warming is leading to a significant increase of short and intense precipitation in the next future, with a specific impact on flash floods and associated hydro-geomorphic processes (such as shallow landslides and debris flows). As shown by the extreme Vaia storm occurred on 2018 in North-eastern Italy, the joint occurrence of intense precipitation and strong wind is particularly relevant for forested mountainous catchments, where extensive uprooting may strongly enhance the triggering of landslides and debris flows, and lead to the formation of large woody debris. RESILIENCE aims to develop an integrated methodology to assess the impact of climatic variations and changes on the intense precipitation and wind regimes, and on the ensuing triggering of flash floods, debris-flows and wind-related forest damages. To meet this main objective, RESILIENCE develops based on two main scientific advances. The first advance is the advent of Convection-Permitting Models, which substantially improves the representation of both precipitation and wind field at the sub-daily scales compared to the standard Regional Climate Models. However, due to the computational costs of these high-resolution simulations, outputs for only short (typically ten years) time slices are available. These time series are too short to provide reliable statistics of extremes if analyzed using the traditional extreme value theory. This limitation can now be overcome by exploiting a second recent advance in the field of extreme value theory, the Metastatistical Extreme Value Distribution. With RESILIENCE, the novel statistical method is further developed and exploited to quantify changes in the frequency of extreme impacts (flash flood peak/volume, debris-flow sediment volumes; forest damages) through the end of the current century focusing on the Veneto Region and on three key study areas where detailed process observations are available. RESILIENCE is based on the interaction with three key Project Stakeholders, and will communicate and disseminate the project results to a wide audience of residents in the Veneto region and beyond through collaborations with Museums, Academies and Local Authorities.