Hydrological risk specialist

Flood Hazard and Risk Analysis

Gabriele Coccia is a hydrological risk specialist and software developer at RED since 2015. From 2013 to 2014 he was a postdoctoral research associate at the Civil and Environmental Engineering Department of Princeton University (NJ, USA), working on analysing and modelling global hydro-meteorological datasets using remote sensed and reanalysis products, with a specific focus on combining data from different sources and assessing their uncertainty. From 2011 to 2012 he worked as hydrological modeler and software developer for Idrologia & Ambiente s.r.l (Italy), developing an improved version of the TOPKAPI hydrological model and applying it to several catchments in Europe. He holds a PhD degree in Physical Modeling for the Protection of the Environment from the University of Bologna (Italy), obtained in 2011, with a dissertation on assessing predictive uncertainty in real time flood forecasting. During his PhD he spent 7 months at the Polytechnic University of Valencia (Spain). He obtained his BSc (2004) and MSc (2007) in Environmental Engineering at the University of Bologna (Italy).

Gabriele is a hydrologist with a great passion for coding. His current work is mainly focused on developing hydrological, flood, excess rain and tropical cyclone risk models as well as producing user-friendly graphical interfaces to make models more suitable for non-technical users. Thanks to the collaboration with RED, Gabriele improved his knowledge about the analysis of flood impact and the parametric risk modelling for the rapid assessment of the impact of catastrophic events. In his work, he employs several programming languages, including Python, R, Java, C++, Fortran as well as web developing languages, such as Django, PHP and JavaScript.

Selected publications:

  • Barbetta, S., Coccia, G., Moramarco, T. and E. Todini: Case Study: A Real-Time Flood Forecasting System with Predictive Uncertainty Estimation for the Godavari River, India. Water 2016, 8, 463.
  • Reggiani, P., Coccia, G. and B. Mukhopadhyay: Predictive Uncertainty Estimation on a Precipitation and Temperature Reanalysis Ensemble for Shigar Basin, Central Karakoram. Water 2016, 8, 263.
  • Siemann, L. A., Coccia, G., Pan, M. and E. F. Wood: Development and Analysis of a Long-Term, Global, Terrestrial Land Surface Temperature Dataset Based on HIRS Satellite Retrievals. J Climate 29(10):3589-3606, 2016.
  • Coccia, G., Siemann, L. A. and E. F. Wood: Creating consistent datasets combining remotely-sensed data and Land Surface Model estimates through Bayesian uncertainty post-processing: The case of Land Surface Temperature from HIRS. Remote Sens. Environ. 170: 290-305, 2015.
  • Smith, M., and Coauthors: The distributed model intercomparison project, Phase 2: Experiment design and summary results of the western basin experiments. Journal of Hydrology, 507, 300-329, 2013.
  • Coccia, G. and E. Todini: Recent developments in predictive uncertainty assessment based on the model conditional processor approach, Hydrol. Earth Syst. Sci., 15(10), 3253-3274, 2011.
  • Curriculum vitae:
  • Phone: (+39) 0382-302945
  • Email: gabriele (dot) coccia (at) redpavia (dot) com