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Creators/Authors contains: "Bras, Rafael L."

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  1. Abstract

    Hurricanes are expected to intensify throughout the 21st century, yet the impact of frequent major hurricanes on tropical ecosystems remains unknown. To investigate tropical forest damage and recovery under different hurricane regimes, we generate a suite of scenarios based on Coupled Model Intercomparison Project Phase 6 climate projections and increased hurricane recurrence and intensity for the Luquillo Experimental Forest, Puerto Rico. We then use the Ecosystem Demography model to predict changes in carbon stocks, forest structure and composition. Our results indicate that frequent hurricane disturbances in the future would decrease the overall aboveground biomass, decrease the dominance of late‐successional species, but increase the dominance of palm species. Warmer climates with increased CO2would have little effect on the functional‐type composition but increase the aboveground biomass. However, the predicted climate and CO2fertilization effects would not compensate for the biomass loss due to more frequent severe‐hurricane disturbances.

     
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  2. Abstract

    The potentially important influence of climate change on landscape evolution and on critical zone processes is not sufficiently understood. The relative contribution of hydro‐climatic factors on hillslope erosion rates may significantly vary with topography at the watershed scale. The objective of this study is to quantify the hydro‐geomorphic behavior of two contrasting landscapes in response to different climate change scenarios in the Luquillo Critical Zone Observatory, a site of particular geomorphological interest, in terms of hillslope erosion and rainfall‐triggered landslides. We investigate the extent to which hillslope erosion and landslide occurrence remain relatively invariant with future hydro‐climatic perturbations. The adjacent Mameyes and Icacos watersheds are studied, which are underlain by contrasting lithologies. A high resolution coupled hydro‐geomorphic model based on tRIBS (Triangulated Irregular Network‐based Real‐time Integrated Basin Simulator) is used. Observations of landslide activity and hillslope erosion are used to evaluate the model performance. The process‐based model quantifies feedbacks among different hydrologic processes, landslide occurrence, and topsoil erosion and deposition. Simulations suggest that the propensity for landslide occurrence in the Luquillo Mountains is controlled by tropical storms, subsurface water flow, and by non‐climatic factors, and is expected to remain significant through 2099. The Icacos watershed, which is underlain by quartz diorite, is dominated by relatively large landslides. The relative frequency of smaller landslides is higher at the Mameyes watershed, which is underlain by volcaniclastic rock. While projections of precipitation decrease at the study site may lead to moderate decline in hillslope erosion rates, the simulated erosional potential of the two diverse landscapes likely remains significant. © 2018 John Wiley & Sons, Ltd.

     
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  3. Abstract

    Flooding impacts are on the rise globally, and concentrated in urban areas. Currently, there are no operational systems to forecast flooding at spatial resolutions that can facilitate emergency preparedness and response actions mitigating flood impacts. We present a framework for real‐time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in probabilistic methods. The framework overcomes the prohibitive computational demands of high‐fidelity modeling in real‐time by using a probabilistic learning method relying on surrogate models that are trained prior to a flood event. This shifts the overwhelming burden of computation to the trivial problem of data storage, and enables forecasting of both flood hazard and its uncertainty at scales that are vital for time‐critical decision‐making before and during extreme events. The framework has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high‐fidelity computations in real‐time.

     
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