Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers

Abstract

Vegetation roots play an essential role in regulating the hydrological cycle by removing water from the subsurface and releasing it to the atmosphere. However, the present understanding of the drivers of ecosystem-scale root development and their spatial variability globally is limited. This study investigates the varying roles of climate, landscape, and vegetation on the magnitude of root zone storage capacity ($S_{\mathrm{r}}$) worldwide, which is defined as the maximum volume of subsurface moisture accessible to vegetation roots. To this aim, we quantified $S_{\mathrm{r}}$ and evaluated 21 possible climate, landscape, and vegetation controls for 3612 river catchments worldwide using a random forest machine learning model. Our findings reveal climate as primary, but spatially varying, driver of ecosystem scale $S_{\mathrm{r}}$ with landscape and vegetation characteristics playing a minor role. More specifically, we found the mean inter-storm duration as most dominant control of $S_{\mathrm{r}}$ globally, followed by mean temperature, mean precipitation, and mean topographic slope. While the inter-storm duration, temperature, and slope exhibit a consistent relation with $S_{\mathrm{r}}$ globally, the relation between precipitation and $S_{\mathrm{r}}$ varies spatially. Based on this spatial variability, we classified two different regimes: precipitation driven and energy limited. The precipitation-driven regime exhibits a positive relation between precipitation and $S_{\mathrm{r}}$ for precipitation of up to 3 $\mathrm{mm}{\mathrm{d}}^{-1}$, above which the relation flattens and eventually becomes negative. The energy-limited regime exhibits a strictly negative relation between precipitation and $S_{\mathrm{r}}$. Using the random forest model based on these three dominant climate variables and the landscape variable slope, we generated a global gridded dataset of $S_{\mathrm{r}}$, which closely resembles other global datasets of root characteristics. This suggests that our parsimonious approach based on four globally available variables to estimate $S_{\mathrm{r}}$ on a global scale has the potential to be readily and easily integrated into the parameterization of $S_{\mathrm{r}}$ in global hydrological and land surface models. This may enhance the accuracy of global predictions of land–atmosphere exchange fluxes and hydrological extremes by providing a robust representation of both spatial and temporal variability in vegetation root characteristics.