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. Different global transpiration products exhibit significant differences, necessitating the evaluation of errors. 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 of this research is a long-term daily global transpiration dataset at 0.1° resolution from 2000 to 2020. Using the transpiration after partitioning at FLUXNET sites as a reference, we compare the performance of the merged product with input datasets. 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.

Pan evaporation decrease has been reported worldwide over them past decades. A recovery trend, even an increasing pan evaporation trend, has been recently found. Remarkably, most studies on Chinese pan evaporation change in China were based on simulations involving meteorological variables, including temperature, radiation (sunshine duration), wind speed and relative humidity, due to the pan evaporation observation inconsistency caused by the micropan (D20) replacement with large pans (E601) around 2002. In addition, it has been reported that a large-scale humidity sensor replacement across China has occurred since the 2000s, which can cause an underestimation of relative humidity and in turn leads to an inconsistency in simulated pan evaporation. Therefore, the recent pan evaporation trend independent of the observed relative humidity in China must be revisited. In this study, we complete the D20 pan evaporation from 1988 to 2017 according to E601 observations under the constant conversion coefficient assumption between the evaporation observations of these pans in the same month of every year at each station and conduct trend and attribution analysis through linear regression and PenPan-D20 model partial differential methods, respectively. A significant 2.68 mm/a/a upward pan evaporation trend (P<0.05) from 1988-2017 is revealed, primarily driven by the air temperature rise across China. Humidity sensor replacement causes an ~1.3% relative humidity underestimation, producing nonnegligible pan evaporation trend simulation errors.