The depth-wise distribution of root water uptake is typically inferred through linear mixing models that utilize knowledge of stable water isotopes in soil and plants. However, these existing models often represent the water uptake profile in discrete segments, potentially introducing significant uncertainty and bias into results. In this study, we introduced a novel root water uptake mixing model that combines a Bayesian linear mixing framework with a continuous root water uptake pattern, named CrisPy. To evaluate the performance of CrisPy, we conducted virtual and field-based tests under several types of prior information. CrisPy showed accurate and robust reconstruction of the true root water uptake profile under various prior information settings in the virtual test. By contrast, the discrete mixing model, MixSIAR was greatly influenced by the prior information and deviated from the true profile. The root mean squared error of the uptake proportions from CrisPy ranged from 3.6% to 7.4%, while MixSIAR exhibited values of 6.3%–15.2%. Furthermore, posterior predictive checking indicated that CrisPy effectively reconstructed the mean and standard deviations of plant water isotopic compositions in both virtual and field-based tests. MixSIAR, however, underestimated the mean and overestimated the standard deviation of these compositions. These findings collectively support the enhanced accuracy, greater robustness, and reduced uncertainty of CrisPy in comparison to MixSIAR. Therefore, CrisPy provides a powerful tool for partitioning plant water sources.

中文翻译：

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连续根系吸水同位素混合模型


根部吸水的深度分布通常是通过线性混合模型推断的，该模型利用土壤和植物中稳定水同位素的知识。然而，这些现有模型通常代表离散部分的吸水曲线，可能会给结果带来显着的不确定性和偏差。在本研究中，我们引入了一种新颖的根系吸水混合模型，该模型将贝叶斯线性混合框架与连续根系吸水模式相结合，称为 CrisPy。为了评估 CrisPy 的性能，我们在几种类型的先验信息下进行了虚拟和现场测试。 CrisPy 在虚拟测试中的各种先验信息设置下显示了真实根部吸水曲线的准确且稳健的重建。相比之下，离散混合模型MixSIAR受先验信息的影响很大，偏离了真实的轮廓。 CrisPy 摄取比例的均方根误差范围为 3.6% 至 7.4%，而 MixSIAR 的值为 6.3%–15.2%。此外，事后预测检查表明，CrisPy 在虚拟和现场测试中有效地重建了植物水同位素组成的平均值和标准偏差。然而，MixSIAR 低估了这些成分的平均值并高估了标准差。这些发现共同支持 CrisPy 与 MixSIAR 相比具有更高的准确性、更强的鲁棒性和更低的不确定性。因此，CrisPy 提供了划分植物水源的强大工具。