http://hdl.handle.net/10174/37667 Mon, 06 Apr 2026 09:08:40 GMT 2026-04-06T09:08:40Z http://hdl.handle.net/10174/41699 Title: Coupled Fire-Atmosphere modelling: Some Findings and current challenges based on Portuguese case studies Authors: Salgado, Rui; Couto, Flavio Tiago; Campos, Cátia; Santos, Filippe L. M.; Baggio, Roberta; Filippi, Jean-Baptiste Abstract: High-resolution atmospheric models and their coupling with fire propagation models are powerful tools for better understanding the behaviour of rural fires and their effects on the atmosphere. Portugal is one of the European countries with most burned area and numerous ignitions. In 2017, Portugal was affected by several megafires with burned areas larger than 10 000 hectares, some of which led to the formation of convective clouds: pyro-cumulus (pyroCu) and pyro-cumulonimbus (pyroCb). These phenomena can significantly influence the evolution of fire fronts by altering surface winds and raising spread rates, creating extra difficulties for firefighting and increasing burned areas. These rural fires of 2017 were the starting point for studying the atmospheric environment that favours ignitions and fire spread and the effects of fires on the atmosphere, particularly the generation of pyroconvection. In this study, Pedrogão Grande (June 17) and the Quiaios (October 15) mega-fires are chosen as case studies for numerical simulations with the MesoNH atmospheric model coupled with the ForeFire fire propagation model. The simulations show the development of pyroCu and pyroCb clouds produced by intense convective updrafts due the heat fluxes generated generated during combustion. The simulations have improved our understanding of the evolution of the fire environment and the role played by downbursts originating from pyroCb clouds and provided insights about numerical modelling of pyroconvective clouds using Meso-NH/ForeFire simulations. Finally, we use the results obtained in this work to illustrate the current state-of-the art of coupled fire-atmosphere modelling, its limitations and challenges. Sun, 18 May 2025 23:00:00 GMT http://hdl.handle.net/10174/41699 2025-05-18T23:00:00Z http://hdl.handle.net/10174/41697 Title: Live fuel moisture content estimation using remote sensing and numerical modelling approach Authors: Santos, Filippe L. M.; Couto, Flavio Tiago; Monteiro, Maria José; Ribeiro, Nuno Almeida; Le Moigne, Patrick; Salgado, Rui Abstract: Climate change has led to an increase in wildfires, particularly on the Iberian Peninsula. In recent years, Portugal suffered several devastating wildfires, such as in 2003, 2005, and 2017. In 2024, the situation repeated itself, with several large-scale wildfires occurring in the Central region during September. Wildfires are directly related to the availability of combustible material, weather conditions, and ignition factors. Currently, land use and occupation management is a way to reduce wildfires. Identifying areas with higher fuel availability is essential for wildfire prevention in this context. This work aims to improve the live fuel moisture content (LFMC) representation across mainland Portugal, using remote sensing and numerical modelling through a machine learning approach. First, a product was developed to estimate LFMC for Portugal based on remote sensing, through satellite imagery, and machine learning (LFMC-RS). Next, numerical simulations were performed with the MESO-NH (research) and AROME (operational) models, non-hydrostatic mesoscale atmospheric models, producing forcing files to initialize the SURFEX surface model. All output variables from the SURFEX model were utilized as predictors in a machine learning classifier to estimate the LFMC (LFMC-SFX). These results are useful for understanding the FMC spatiotemporal variability in Portugal The research received financial support from the Foundation for Science and Technology, I.P. (FCT) through the PyroC.pt initiative (Ref. PCIF/MPG/0175/2019) along with a PhD Grant (2022.11960.BD). This work also was cofounded by the European Union through the European Regional Development Fund (FEDER) in the framework of the Interreg VI-A España-Portugal (POCTEP) 2021–2027, FIREPOCTEP+ (0139_FIREPOCTEP_MAS_6_E). Sun, 18 May 2025 23:00:00 GMT http://hdl.handle.net/10174/41697 2025-05-18T23:00:00Z http://hdl.handle.net/10174/39218 Title: Optimizing CO2 Injection Under Geological Uncertainties in CCUS: A Lusitanian Basin Case Study Authors: Khudhur, Karwan; Pereira, Pedro; Carneiro, Júlio; Goldman, Matthew; Blin, Gwendoline; Santos, Mário; Casacão, João Abstract: This study, conducted within the PilotSTRATEGY project, explores the optimization of CO₂ injection under geological uncertainties in the Q4-TV1 prospect of the offshore Lusitanian Basin, Portugal. A static model integrating seismic and well data was coupled with dynamic simulations in the Big Loop framework, combining Bayesian optimization with uncertainty analysis. Geological risks, including intersecting faults and legacy wells, were evaluated to ensure safe containment and reservoir integrity. The integrated workflow identified an optimal injection strategy that minimizes leakage risks while ensuring stable plume behavior over the long term. The results demonstrate the value of combining Bayesian optimization and dynamic modelling to design secure and efficient CCUS injection strategies in geologically complex settings. Sun, 15 Jun 2025 23:00:00 GMT http://hdl.handle.net/10174/39218 2025-06-15T23:00:00Z http://hdl.handle.net/10174/39127 Title: 3D static modelling with uncertainties of an offshore area in Portugal for CO2 storage pilot site development Authors: Pereira, Pedro; Khudhur, Karwan; Carneiro, Julio; Caeiro, Maria Helena; Santos, Mário; Lopes, Ana Mafalda; Revaux, Charles Abstract: This study presents the construction of a 3D static geological model with uncertainty assessment for the Q4-TV1 prospect in the offshore northern Lusitanian Basin, Portugal, as part of the PilotSTRATEGY project. Using seismic interpretation, well data, and petrophysical logs, lithofacies and reservoir properties were simulated with geostatistical algorithms in Aspen SKUA. The models reproduced the geological framework, including heterogeneities and stratigraphic trapping features, and identified several reservoir flow units with varying porosity and clay content. Uncertainty analyses of structural and petrophysical parameters provided probabilistic estimates of net-porous rock volumes and storage capacity. The resulting static models form the basis for dynamic simulations, supporting the evaluation of this site’s potential for safe and effective CO₂ storage. Sun, 15 Jun 2025 23:00:00 GMT http://hdl.handle.net/10174/39127 2025-06-15T23:00:00Z