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Dataset of "Water partitioning in a Neotropical Savanna forest (Cerrado s.s.): seasonal and non-seasonal responses at different time-scales using adapted versions of the Rutter and the Gash models"

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DataONE2022-04-15 更新2024-06-08 收录
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This is the dataset of Lívia Rosalem, Miriam Gerrits-Coenders, Jamil A. A. Anache, Sayed M. M. Sadeghi, and Edson Wendland to the paper \"Water partitioning in a Neotropical Savanna forest (Cerrado s.s.): seasonal and non-seasonal responses at different time-scales using adapted versions of the Rutter and the Gash models\", that it will be submitted to the Hydrological Earth System Sciences. Cerrado is the broadest Savvana ecosystem of South America and has an important role in our global climate. However, how rainfall finds its way through the vegetation layers in undisturbed Cerrado forests is largely unknown. Nonetheless, the interception of rainfall at both the canopy and forest floor can represent a significant amount of total rainfall. Additionally, as vegetation cover and weather changes over the seasons, it is important to consider seasonal differences. However, few studies evaluate how interception models perform at different time scales and study their seasonal response. Hence, this study aimed to evaluate the interception estimates at different time scales, and also the seasonal responses, by two commonly used interception models (Rutter and Gash) adapted to an undisturbed Cerrado s.s. forest including the forest floor interception. Our results show that the models are suitable to estimate throughfall and infiltration on a daily basis, but not the evaporative processes. Though, both models are able to reproduce the total interception well at the monthly scale (R² = 0.7–0.97, NSE = 0.63–0.85). The interception process in Cerrado s.s. forests has a seasonal trend that both models could represent. Nevertheless, the Rutter model seems to perform better when seasonal parameters are used than the Gash model, but both models are equally valuable to inter-annual analysis when non-seasonal parameters are used.
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2022-04-15
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