Modelling Hurricane Exposure and Wind Speed on a Mesoclimate Scale: A Case Study from Cusuco NP, Honduras
Modelling Hurricane Exposure and Wind Speed on a Mesoclimate Scale: A Case Study from Cusuco NP, Honduras
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在中气候尺度上模拟飓风暴露和风速：以洪都拉斯库苏科国家公园为例
作者：
S. Batke;M. Jocqué;D. Kelly
高能天气事件通常被认为在生物群落动态和大规模多样性模式中发挥重要作用，但它们的贡献难以证明。目前，观察仅限于这些事件过去后的偶然记录文件。一种更全面的方法是综合一个地点在很长一段时间内的天气事件，理想情况下是在高空间分辨率和大地理范围内。我们提供了一个详细的概述如何产生飓风暴露数据在一个特定地区的中气候水平。作为一个案例研究，我们模拟了洪都拉斯库苏科国家公园（CNP）的景观飓风暴露，分辨率为50 m×50 m。我们通过结合风压模型（一个可以在三维景观中包含简单风动力学的暴露模型）和历史飓风数据来计算实际的飓风暴露脆弱性站点得分（EVVS）。EVSS是作为站点暴露、飓风频率和最大风速的加权函数计算的。1995年至2010年间，共有11次飓风影响了CNP。EVSS指数最高的地点位于公园的南面及东南面。地面验证表明，南解（即南风入流方向）解释了观测到的大部分树木损害（占现场观测到的树木损害的90%）。将历史数据纳入模型以计算实际飓风暴露值，而不是潜在暴露值，将模型拟合提高了50%。
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%.