Agreement, opposition, and dataset influence in global evapotranspiration trends

Abstract. Evapotranspiration (ET) is a key component of the terrestrial water and energy balance, and numerous global gridded ET products are routinely used to assess historical variability and trends. However, differences in forcing data, model structure and physics in these products complicate robust ET trend analyses. Here, we present a systematic intercomparison of 14 global terrestrial ET datasets for the period 2000–2019. We introduce a topology framework that categorizes ET datasets according to their trend signatures within multi-product ensembles, providing insight into the structural role of each dataset and revealing how certain products consistently amplify or oppose dominant trends, patterns that are not evident from standard ensemble statistics. We find that products which amplify negative trends consistently oppose the dominant ensemble trend direction, whereas products that amplify positive trends tend to produce statistically significant trends where most datasets indicate weak or non-significant change. We quantify the magnitude, direction, and statistical significance of ET trends across products and evaluate their spatial consistency. The analysis reveals substantial divergence among datasets. While many products indicate predominantly positive ET trends, agreement on the magnitude and direction of change is lacking across many regions. In many regions, trends differ by more than an order of magnitude, and the spatial patterns of significant trends are highly product-dependent. The resulting harmonized trend estimates and classification provide a reference resource for evaluating current and future ET products, assessing uncertainty in trend studies, and guiding the use and improvement of ET datasets. More broadly, the topology framework can be extended beyond ET to geoscientific data product ensembles in general, enabling fitness for purpose evaluation, uncertainty assessment, and more systematic intercomparison across datasets.