Gabriel Mantegna

I am a PhD student at Princeton University, where I research energy systems engineering and policy. My research is focused on developing methods for electric grid planning that will help to increase the resilience of the electric grid to climate change, and also better prepare us for deeply uncertain factors such as changes in demand, technological development, and cost. I employ methods from energy economics, systems engineering, risk analysis, and operations research.

I previously worked at Energy and Environmental Economics (E3), a consulting firm based in San Francisco, where I advised state agencies and electric utilities on their decarbonization plans. While at E3, I helped the California Public Utilities Commission integrate the air quality impacts of electric generation into their decision making process, and I advised the California Air Resources Board on their Climate Change Scoping Plan process. I was also a principal developer of the PATHWAYS and RESOLVE models for energy system planning.

Outside of work, I greatly enjoy planning piano, riding bikes, sailing, and photography. You can check out some of my photography using the link at the top of the page.

My email is gabe.mantegna@princeton.edu. Please feel free to reach out if you would like to collaborate!

latest posts

Jul 10, 2023	test post !

selected publications

2017

JHydrology
Simulating sub-daily Intensity-Frequency-Duration curves in Australia using a dynamical high-resolution regional climate model

Gabriel A. Mantegna, Christopher J. White, Tomas A. Remenyi, and 2 more authors

Journal of Hydrology, Nov 2017

Abs Bib HTML

Climate change has the potential to significantly alter the characteristics of high-intensity, short-duration rainfall events, potentially leading to more severe and more frequent flash floods. Research has shown that future changes to such events could far exceed expectations based on temperature scaling and basic physical principles alone, but that computationally expensive convection-permitting models are required to accurately simulate sub-daily extreme rainfall events. It is therefore crucial to be able to model future changes to sub-daily duration extreme rainfall events as cost effectively as possible, especially in Australia where such information is scarce. In this study, we seek to determine what the shortest duration of extreme rainfall is that can be simulated by a less computationally expensive convection-parametrizing Regional Climate Model (RCM). We examine the ability of the Conformal Cubic Atmospheric Model (CCAM), a ∼10km high-resolution convection-parametrizing RCM, to reproduce sub-daily Intensity-Frequency-Duration (IFD) curves corresponding to two long-term observational stations in the Australian island state of Tasmania, and examine the future model projections. We find that CCAM simulates observed extreme rainfall statistics well for 3-h durations and longer, challenging the current understanding that convection-permitting models are needed to accurately model sub-daily extreme rainfall events. Further, future projections from CCAM for the end of this Century show that extreme sub-daily rainfall intensities could increase by more than 15% per °C, far exceeding the 7% scaling estimate predicted by the Clausius-Clapeyron vapour pressure relationship and the 5% scaling estimate recommended by the Australian Rainfall and Runoff guide.
@article{mantegna_simulating_2017, title = {Simulating sub-daily {Intensity}-{Frequency}-{Duration} curves in {Australia} using a dynamical high-resolution regional climate model}, volume = {554}, copyright = {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND)}, issn = {0022-1694}, url = {https://www.sciencedirect.com/science/article/pii/S0022169417306297}, doi = {10.1016/j.jhydrol.2017.09.025}, language = {en}, urldate = {2023-07-10}, journal = {Journal of Hydrology}, author = {Mantegna, Gabriel A. and White, Christopher J. and Remenyi, Tomas A. and Corney, Stuart P. and Fox-Hughes, Paul}, month = nov, year = {2017}, keywords = {Depth-Duration-Frequency, Extremes, Flood, Intensity-Frequency-Duration, RCM, Sub-daily rainfall}, pages = {277--291}, }