Toward Probabilistic Prediction of Flash Flood Human Impacts.

This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance-specific human losses during a flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle-related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time- and space-dependent human vulnerability and risk assessment for short-fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact-based predictive models for flash flood casualties using machine-learning approaches in the future.

Simulating the impact of varying vegetation on West African monsoon surface fluxes using a regional convection-permitting model.

This study assessed the sensitivity of the West African climate to varying vegetation fractions. The assessment of a such relationship is critical in understanding the interactions between land surface and atmosphere. Two sets of convection-permitting simulations from the UK Met Office Unified Model at 12 km horizontal resolution covering the monsoon period May-September (MJJAS) were used, one with fixed vegetation fraction (MF-V) and the other with time-varying vegetation fraction (MV-V). Vegetation fractions are based on MODIS retrievals between May and September. We focused on three climatic zones over West Africa: Guinea Coast, Sudanian Sahel, and the Sahel while investigating heat fluxes, temperature, and evapotranspiration. Results reveal that latent heat fluxes are the most strongly affected by vegetation fraction over the Sahelian and Sudanian regions while sensible heat fluxes are more impacted over the Guinea Coast and Sudanian Sahel. Also, in MV-V simulation there is an increase in evapotranspiration mainly over the Sahel and some specific areas in Guinea Coast from June to September. Moreover, it is noticed that high near-surface temperature is associated with a weak vegetation fraction, especially during May and June. Finally, varying vegetation seems to improve the simulation of surface energy fluxes and in turn impact on climate parameters. This suggests that climate modelers should prioritize the use of varying vegetation options to improve the representation of the West African climate system.