Abstract Soil vegetation atmosphere transport (SVAT) models are important for the quantification of water fluxes, soil water availability, drought stress and their uncertainties under climate change. We present LWFBrook90R , an enhanced implementation of the well-established, process-based SVAT model LWF-Brook90 for the R environment for statistical computing. The package provides new functions and sub-models for model parameterization, and facilitates parallel computing, sensitivity analysis and inverse calibration of the model. A case study comprising i) basic forward water balance simulations for temperate grassland vegetation, deciduous and evergreen forest, ii) a parallelized sensitivity analysis, and iii) Bayesian calibrations based on soil water storage observed in a poplar (Populus nigra × P. maximowiczii) Short Rotation Forest (SRF) demonstrates the utility of the R package. The sensitivity analysis revealed parameters affecting plant-available soil water storage capacity and the vegetation's timing and level of water demand to be most important for the annual course of simulated soil water storage, with seasonal and interannual differences in parameter importance rankings. The subsequent calibration yielded a very high agreement between daily simulated and observed soil water storage (0-200 cm soil depth) for the calibration and validation datasets, with Nash-Sutcliffe efficiencies of 0.97 and 0.95, respectively. The final model predicted high though realistic rates of annual evapotranspiration (2011: 844 ± 3.8 mm y-1, 2012: 733 ± 4.5 mm y-1) for the poplar SRF, regularly exceeding grass reference evaporation (ET0) by 20-47% during the months of the growing season. However, basing calibrations solely on observed soil water storage probably resulted in biased partitioning of evapotranspiration towards interception losses. The integration of the LWF-Brook90 hydrological model into R with its wide variety of extensions was successfully tested and may provide efficient, reliable and reproducible water balance predictions by facilitating complex statistical analyses and large-scale applications of the model.

中文翻译：

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在 R 中使用 LWF-Brook90 水文模型推进水通量、土壤水分和干旱胁迫的模拟

摘要 土壤植被大气输运（SVAT）模型对于量化气候变化下的水通量、土壤水分可用性、干旱胁迫及其不确定性具有重要意义。我们提出了 LWFBrook90R，它是用于统计计算的 R 环境的完善的、基于过程的 SVAT 模型 LWF-Brook90 的增强实现。该包为模型参数化提供了新的函数和子模型，并促进了模型的并行计算、灵敏度分析和逆校准。案例研究包括 i) 温带草原植被、落叶林和常绿林的基本前向水平衡模拟，ii) 并行敏感性分析，以及 iii) 基于在杨树 (Populus nigra × P.) 中观察到的土壤储水量的贝叶斯校准。maximowiczii) 短循环森林 (SRF) 演示了 R 包的实用性。敏感性分析揭示了影响植物可用土壤蓄水能力和植被的时间和需水水平的参数对模拟土壤蓄水的年度过程最重要，参数重要性排名的季节和年际差异。随后的校准在校准和验证数据集的每日模拟和观察到的土壤储水量（0-200 厘米土壤深度）之间产生了非常高的一致性，纳什-萨特克利夫效率分别为 0.97 和 0.95。最终模型预测了杨树 SRF 的年蒸散率虽然很高但很现实（2011 年：844 ± 3.8 mm y-1，2012 年：733 ± 4.5 mm y-1），在生长季节的几个月里，经常超过草参考蒸发量 (ET0) 20-47%。然而，仅基于观察到的土壤储水量进行校准可能会导致蒸发蒸腾量偏向于截留损失。LWF-Brook90 水文模型与 R 及其各种扩展的集成已成功测试，并且可以通过促进模型的复杂统计分析和大规模应用来提供高效、可靠和可重复的水平衡预测。