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Global estimation of effective plant rooting depth: Implications for hydrological modeling
Author(s) -
Yang Yuting,
Donohue Randall J.,
McVicar Tim R.
Publication year - 2016
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2016wr019392
Subject(s) - environmental science , biogeochemical cycle , evapotranspiration , hydrological modelling , grid , biome , drainage basin , hydrology (agriculture) , satellite , atmospheric sciences , climatology , mathematics , geology , geography , cartography , ecosystem , geotechnical engineering , engineering , ecology , chemistry , geometry , aerospace engineering , environmental chemistry , biology
Plant rooting depth ( Z r ) is a key parameter in hydrological and biogeochemical models, yet the global spatial distribution of Z r is largely unknown due to the difficulties in its direct measurement. Additionally, Z r observations are usually only representative of a single plant or several plants, which can differ greatly from the effective Z r over a modeling unit (e.g., catchment or grid‐box). Here, we provide a global parameterization of an analytical Z r model that balances the marginal carbon cost and benefit of deeper roots, and produce a climatological (i.e., 1982–2010 average) global Z r map. To test the Z r estimates, we apply the estimated Z r in a highly transparent hydrological model (i.e., the Budyko‐Choudhury‐Porporato (BCP) model) to estimate mean annual actual evapotranspiration ( E ) across the globe. We then compare the estimated E with both water balance‐based E observations at 32 major catchments and satellite grid‐box retrievals across the globe. Our results show that the BCP model, when implemented with Z r estimated herein, optimally reproduced the spatial pattern of E at both scales (i.e., R 2 = 0.94, RMSD = 74 mm yr −1 for catchments, and R 2 = 0.90, RMSD = 125 mm yr −1 for grid‐boxes) and provides improved model outputs when compared to BCP model results from two already existing global Z r data sets. These results suggest that our Z r estimates can be effectively used in state‐of‐the‐art hydrological models, and potentially biogeochemical models, where the determination of Z r currently largely relies on biome type‐based look‐up tables.