Partitioning evapotranspiration in a tallgrass prairie using micrometeorological and water use efficiency approaches under contrasting rainfall regimes

Abstract

Partitioning evapotranspiration (ET) into evaporation (E) and plant transpiration (T) is key to understanding ecosystem responses to rainfall variability resulting from climate change. The goal of this study was to quantify T and E using eddy covariance (EC) flux measurements in a tallgrass prairie in consecutive growing seasons with contrasting rainfall regimes. The field measurements were conducted at the National Ecological Observatory Network (NEON) KONZ site, in Kansas, U.S., during the growing seasons of 2017, 2018 and 2019. The ET partitioning was performed using an approach based on the concept of the underlying water use efficiency (uWUE). To evaluate the uWUE approach, we compared daily E estimates obtained from the uWUE with E observations provided by microlysimeters (ML). Green chromatic coordinate (GCC) was used to monitor the vegetation dynamics. In the 2017 growing season, the total rainfall was 23.1% below the site’s long-term average cumulative precipitation. On the other hand, in 2018 and 2019 the accumulated growing season precipitations were 7.2% and 40.2%, respectively, above the long-term precipitation average. The relationship between uWUE approach and ML E measurements showed a Pearson correlation coefficient (r) of 0.42 and a root mean square error (RMSE) of 0.58 mm d−1. The lowest T/ET average value (0.50) was observed in the 2017 growing season, while the largest T/ET average (0.65) was observed in 2018. The correlations between GCC and T/ET were reduced during the growing seasons that experienced drought periods. Air temperature was the main environmental driver of T/ET during the wet growing seasons (r = 0.49 and 0.72). The subsurface soil moisture (0.45 m) was the main environmental driver of T/ET during a dry growing season (r = 0.41). These results demonstrate that the precipitation variability not only has a direct impact on the ET components but also modulates the response of those components to other environmental drivers. Since ET partitioning studies at the ecosystem scale are still scarce, our results can improve T/ET and water use efficiency estimates in long-term modelling studies. This will help to better understand how ET in ecosystems will respond to global warming and increased CO2 concentration during wet and dry growing seasons.