How catchment ecosystems globally manage root water access under different (climate) conditions

The root zone storage capacity (S_r) is the maximum volume of water in the subsurface that can potentially be accessed by vegetation for transpiration. S_r is an essential characteristic of hydrological systems as it controls the partitioning of precipitation into evaporation and runoff. Understanding the influence of climatic and landscape characteristics on S_r is essential for predicting how different ecosystems will respond to disturbances such as human activities and climate change. While the magnitude of S_r on ecosystem scale is partly influenced by landscape characteristics such slopes, bedrock properties and soil characteristics, there is widespread consensus that it is primarily controlled by climate conditions (i.e., the temporal dynamics of water and energy availability) as vegetation optimizes its root system to sustain atmospheric water demand.

Several studies have identified the influence of various climatic variables on Sr, but for different regions conflicting influences of these variables on Sr appeared. So far, it remains unclear what aspects of the climate are most important controls on Sr on global scale. This research aims to bridge this gap by exploring how different climatic and landscape characteristics influence the magnitude of S_r globally. Based on discharge measurements in a large sample of catchments worldwide (~4000), we estimated the actual S_r using the memory method as in Van Oorschot et al. (2021, 2023). With a random forest model we were able to adequately predict S_r using various climatic and landscape characteristics. Analysis of the driving variables of the random forest model show that the precipitation inter-storm duration is the most dominant control on S_r, and positively influences S_r in all regions. On the other hand, the influence of mean precipitation on S_r is conflicting in different regions. We found that in water limited regions, increased mean precipitation leads to increased S_r, while in energy limited regions, increased mean precipitation leads to decreased in S_r. Furthermore, the developed model is used to extrapolate the catchment S_r estimates to a global gridded map of S_r ensuring coverage of data-scarce regions. This extrapolated map can be used for more adequate modelling of subsurface vegetation water availability in large scale hydrological and land surface models.

van Oorschot, F., van der Ent, R. J., Hrachowitz, M., and Alessandri, A.: Climate-controlled root zone parameters show potential to improve water flux simulations by land surface models, Earth Syst. Dynam., 12, 725–743, https://doi.org/10.5194/esd-12-725-2021, 2021.

van Oorschot, F., van der Ent, R. J., Alessandri, A., and Hrachowitz, M.: Influence of irrigation on root zone storage capacity estimation, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2622, 2023.