Global Datasets of Rooting Zone Depth Inferred from Inverse Methods

Abstract

Two inverse methods are applied to a land surface model to infer global patterns of the hydrologically active depth of the vegetation's rooting zone. The first method is based on the assumption that vegetation is optimally adapted to its environment, resulting in a maximization of net carbon uptake [net primary production (NPP)]. This method is implemented by adjusting the depth such that the simulated NPP of the model is at a maximum. The second method assumes that water availability directly affects the leaf area of the vegetation, and therefore the amount of absorbed photosynthetically active radiation (APAR). Rooting depth in the model is adjusted such that the mismatch between simulated and satellite-derived APAR is at a minimum. The inferred patterns of rooting zone depth from both methods correspond well and reproduce the broad patterns of rooting depth derived from observations. Comparison to rooting depth estimates from root biomass distributions point out that these may underestimate the hydrological significance of deep rooted vegetation in the Tropics with potential consequences for large-scale land surface and climate model simulations.