Rainfall partitioning by trees is an important hydrological process in the contexts of water resource management and climate change. It becomes even more complex where vegetation is sparse and in vulnerable natural systems, such as the Caatinga domain. Rainfall interception modelling allows extrapolating experimental results both in time and space, helping to better understand this hydrological process and contributing as a prediction tool for forest managers. In this work, the Gash model was applied in two ways of parameterization. One was the parameterization on a daily basis and another on a seasonal basis. They were validated, improving the description of rainfall partitioning by tree species of Caatinga dry tropical forest already reported in the scientific literature and allowing a detailed evaluation of the influence of rainfall depth and event intensity on rainfall partitioning associated with these species. Very small (0.0–5.0 mm) and low-intensity (0–2.5 mm h⁻¹) events were significantly more frequent during the dry season. Both model approaches resulted in good predictions, with absence of constant and systematic errors during simulations. The sparse Gash model parametrized on a daily basis performed slightly better, reaching maximum cumulative mean error of 9.8%, while, for the seasonal parametrization, this value was 11.5%. Seasonal model predictions were also the most sensitive to canopy and climatic parameters.

中文翻译：

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使用基于每日和季节性参数化的 Gash 模型模拟 Caatinga 植被的降雨拦截

在水资源管理和气候变化的背景下，树木划分降雨是一个重要的水文过程。在植被稀疏和脆弱的自然系统（例如卡廷加域）中，情况变得更加复杂。降雨拦截建模允许在时间和空间上推断实验结果，有助于更好地了解这一水文过程，并作为森林管理者的预测工具做出贡献。在这项工作中，Gash 模型以两种参数化方式应用。一种是每日参数化，另一种是季节性参数化。他们经过验证，改进了科学文献中已经报道的卡廷加干燥热带森林树种降雨划分的描述，并允许详细评估降雨深度和事件强度对与这些物种相关的降雨划分的影响。非常小 (0.0–5.0 mm) 和低强度 (0–2.5 mm h^-1 ) 事件在旱季明显更频繁。两种模型方法都产生了良好的预测，在模拟过程中不存在恒定和系统误差。每日参数化的稀疏 Gash 模型表现稍好，达到 9.8% 的最大累积平均误差，而对于季节性参数化，该值为 11.5%。季节性模型预测也是对冠层和气候参数最敏感的。