利用基林图中的中值点降低蒸散同位素组成的不确定性

Yusen Yuan, Lixin Wang, Zhongwang Wei, H. Ajami, Honglang Wang, Taisheng Du
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引用次数: 0

摘要

在基于同位素的蒸散(ET)分区和水分循环研究中,蒸散的同位素组成δET是一个关键参数。Keeling 图法是计算 δET 的最常用方法,但它包含了最小二乘回归的较大外推不确定性。传统的 Keeling 回归使用单个测量值的平均值。在这里,我们提出了一个修改过的基林图框架,使用单个测量值的中位数点。我们使用均值点 [σET(均值)] 和中值点 [σET(中值)] 测试了 δET 的不确定性。使用来自六个独立站点的多分辨率输入和输出数据来测试这两种方法的性能。当反蒸气浓度()的平均值大于反蒸气浓度[]的中值时,σET(平均值)将大于σET(中值)。当应用 r2 > 0.8 的滤波器时,约 70% 的 σET(平均值)大于 σET(中位数)。这种现象可能是由于蒸汽浓度 Cυ 的正态性造成了 1/Cυ 的不对称分布。当输入高分辨率测量值(如 1 Hz)和水蒸气浓度 Cυ 相对较低时,中值法的性能明显优于均值法。与均值法相比,采用中值法平均可减少 6.88% 的蒸散发分配不确定性,平均可减少 9.00% 的水分循环不确定性。这项研究为基林图方法提供了新的见解,并强调从多个角度而非仅从输入参数来处理模型输出的不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Median Point in Keeling Plot to Reduce the Uncertainty of the Isotopic Composition of Evapotranspiration
The isotopic composition of evapotranspiration δET is a crucial parameter in isotope-based evapotranspiration (ET) partitioning and moisture recycling studies. The Keeling plot method is the most prevalent method to calculate δET, though it contains large extrapolated uncertainties from the least squares regression. Traditional Keeling regression uses the mean point of individual measurements. Here, a modified Keeling plot framework was proposed using the median point of individual measurements. We tested the δET uncertainty using the mean point [σET (mean)] and median point [σET (median)]. Multiple resolutions of input and output data from six independent sites were used to test the performance of the two methods. The σET (mean) would be greater than σET (median) when the mean value of inverse vapor concentration () is greater than the median value of inverse vapor concentration []. When applying the filter of r2 > 0.8, around 70% of σET (mean) was greater than σET (median). This phenomenon might be due to the normality of the vapor concentration Cυ producing the asymmetric distribution of 1/Cυ. The median method could perform significantly better than the mean method when inputting high-resolution measurements (e.g., 1 Hz) and when the water vapor concentration Cυ is relatively low. Compared to the mean method, applying the median method could on average reduce 6.88% of ET partitioning uncertainties and could on average reduce 9.00% of moisture recycling uncertainties. This study provided a new insight of the Keeling plot method and emphasized handling model output uncertainty from multiple perspectives instead of only from input parameters.
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