State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China.
Sci Data. 2024 Jun 7;11(1):604. doi: 10.1038/s41597-024-03425-7.
Transpiration (T) is pivotal in the global water cycle, responding to soil moisture, atmospheric stress, climate changes, and human impacts. Therefore, establishing a reliable global transpiration dataset is essential. Collocation analysis methods have been proven effective for assessing the errors in these products, which can subsequently be used for multisource fusion. However, previous results did not consider error cross-correlation, rendering the results less reliable. In this study, we employ collocation analysis, taking error cross-correlation into account, to effectively analyze the errors in multiple transpiration products and merge them to obtain a more reliable dataset. The results demonstrate its superior reliability. The outcome is a long-term daily global transpiration dataset at 0.1°from 2000 to 2020. Using the transpiration after partitioning at FLUXNET sites as a reference, we compare the performance of the merged product with inputs. The merged dataset performs well across various vegetation types and is validated against in-situ observations. Incorporating non-zero ECC considerations represents a significant theoretical and proven enhancement over previous methodologies that neglected such conditions, highlighting its reliability in enhancing our understanding of transpiration dynamics in a changing world.
蒸腾作用（T）在全球水循环中起着关键作用，对土壤湿度、大气压力、气候变化和人类活动的响应。因此，建立一个可靠的全球蒸腾数据集是至关重要的。协同分析方法已被证明可有效评估这些产品的误差，随后可用于多源融合。然而，以前的结果没有考虑误差的交叉相关，使得结果不太可靠。在本研究中，我们采用协同分析，考虑误差的交叉相关，有效地分析多个蒸腾产品的误差，并对其进行融合，以获得更可靠的数据集。结果表明其具有较高的可靠性。最终得到了一个 2000 年至 2020 年 0.1°分辨率的长期全球逐日蒸腾数据集。利用通量网站点分区后的蒸腾量作为参考，将融合产品的性能与输入数据进行比较。融合数据集在各种植被类型中表现良好，并与现场观测结果进行了验证。考虑非零 ECC 的情况是对以前忽略这些条件的方法的一个重大理论和实践上的改进，突出了其在增强我们对变化世界中蒸腾动态的理解方面的可靠性。
