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首页> 外文期刊>Journal of hydrometeorology >Modeling Rainfall Interception Loss for an Epiphyte-Laden Quercus virginiana Forest Using Reformulated Static- and Variable-Storage Gash Analytical Models
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Modeling Rainfall Interception Loss for an Epiphyte-Laden Quercus virginiana Forest Using Reformulated Static- and Variable-Storage Gash Analytical Models

机译：使用重新构造的静态存储和可变存储Gash分析模型对附生植物栎森林的降雨截留损失进行建模

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Barrier island forests are sensitive to changing precipitation characteristics as they typically rely on a precipitation-fed freshwater lens. Understanding and predicting significant rainfall losses is, therefore, critical to the prediction and management of hydrometeorological processes in the barrier island forest ecosystem. This study measures and models one such loss, canopy rainfall interception, for a barrier island forest common across subtropical and tropical coastlines: epiphyte-laden Quercus virginiana on St. Catherine's Island (Georgia, United States). Reformulated Gash analytical models (RGAMs) relying on static-and variable-canopy-storage formulations were parameterized using common maximum water storage (minimum, mean, maximum, and laboratory submersion) and evaporation (Penman-Monteith, saturated rain-throughfall regression, and rain-interception regression) estimation methods. Cumulative interception loss was 37% of rainfall, and the epiphyte community contribution to interception loss was 11%. Variable-storage RGAMs using inferred evaporation and maximum water storage estimates performed best: mean absolute error of 1-2mm, normalized mean percent error of 15%-25%, and model efficiency of 0.88-0.97, resulting in a 2%-5% overestimate of cumulative interception. Static-and variable-storage RGAMs using physically derived evaporation (Penman-Monteith) underestimated observed interception loss (40%-60%), yet the error was significantly lowered for submersion estimates of maximum water storage. Greater apparent error when using Penman-Monteith rates may result from unknown drying times, evaporation sources, and/or in situ epiphyte storage dynamics. As such, it is suggested that future research apply existing technologies to quantify evaporative processes during rainfall (e.g., eddy covariance) and to develop new methods to directly monitor in situ epiphyte water storage.
机译：屏障岛森林对降水特征的变化非常敏感，因为它们通常依赖于以降水为食的淡水晶状体。因此，了解和预测巨大的降雨损失对于屏障岛森林生态系统中水文气象过程的预测和管理至关重要。这项研究针对亚热带和热带海岸线常见的障碍岛森林（在圣凯瑟琳岛（美国乔治亚州）上附生植物的栎属栎）测量并模拟了这种损失（冠层降雨截留）。使用常见的最大水存储量（最小，均值，最大和实验室浸没）和蒸发量（Penman-Monteith，饱和降雨通过回归以及），对依赖于静态和可变冠层存储公式的重新构造的Gash分析模型（RGAM）进行参数化降雨截断回归）估计方法。累积拦截损失为降雨量的37％，附生植物群落对拦截损失的贡献为11％。使用推断的蒸发量和最大储水量估算的可变存储RGAM表现最佳：平均绝对误差为1-2mm，归一化平均误差百分比为15％-25％，模型效率为0.88-0.97，得出2％-5％高估了累积拦截。静态和可变存储的RGAM使用物理衍生的蒸发技术（Penman-Monteith）低估了所观察到的拦截损失（40％-60％），但对于最大储水量的淹没估计，该误差显着降低。使用Penman-Monteith比率时，较大的表观误差可能是由于未知的干燥时间，蒸发源和/或原位附生植物的储存动力学所致。因此，建议未来的研究应用现有技术来量化降雨期间的蒸发过程（例如，涡度协方差）并开发新的方法来直接监测原位附生植物的水储量。

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