Modelling Hurricane Exposure and Wind Speed on a Mesoclimate Scale: A Case Study from Cusuco NP, Honduras

High energy weather events are often expected to play a substantial role in biotic community dynamics and large scale diversity patterns but their contribution is hard to prove. Currently, observations are limited to the documentation of accidental records after the passing of such events. A more comprehensive approach is synthesising weather events in a location over a long time period, ideally at a high spatial resolution and on a large geographic scale. We provide a detailed overview on how to generate hurricane exposure data at a meso-climate level for a specific region. As a case study we modelled landscape hurricane exposure in Cusuco National Park (CNP), Honduras with a resolution of 50 m×50 m patches. We calculated actual hurricane exposure vulnerability site scores (EVVS) through the combination of a wind pressure model, an exposure model that can incorporate simple wind dynamics within a 3-dimensional landscape and the integration of historical hurricanes data. The EVSS was calculated as a weighted function of sites exposure, hurricane frequency and maximum wind velocity. Eleven hurricanes were found to have affected CNP between 1995 and 2010. The highest EVSS's were predicted to be on South and South-East facing sites of the park. Ground validation demonstrated that the South-solution (i.e. the South wind inflow direction) explained most of the observed tree damage (90% of the observed tree damage in the field). Incorporating historical data to the model to calculate actual hurricane exposure values, instead of potential exposure values, increased the model fit by 50%

Landslides in steep-slope agricultural landscapes

Agricultural landscapes cover a significant part of the Earth. In floodplains, we can find large areas dedicated to intensive agriculture. However, also on hills and mountains, agricultural activity can be relevant from the socio-economic point of view. Nowadays, such areas are increasingly under threat because of global environmental changes. Widespread growing rainfall aggressiveness due to climate change, in addition to land abandonment, lack of structural maintenance, and in some cases unsuitable agronomic practices are exposing steep-slope agricultural landscapes to increased hazard of landslides. A suitable hazard assessment and zonation of these phenomena would help better management of such agricultural landscapes. The purpose of this article is to provide an overview of this relevant problem focusing on (i) the contribution of remote sensing technologies (e.g., LiDAR and UAV photogrammetry) in mapping the investigated processes, and (ii) discussing advances and limitations of susceptibility modelling