INVESTIGADORES
JOBBAGY GAMPEL Esteban Gabriel
artículos
Título:
Global rainfall partitioning by dryland vegetation: Developing general empirical models
Autor/es:
MAGLIANO, PATRICIO N.; WHITWORTH-HULSE, JUAN I.; CID, FABRICIO D.; LEPORATI, JORGE L.; VAN STAN, JOHN T.; JOBBÁGY, ESTEBAN G.
Revista:
JOURNAL OF HYDROLOGY
Editorial:
ELSEVIER SCIENCE BV
Referencias:
Año: 2022 vol. 607
ISSN:
0022-1694
Resumen:
Rainfall partitioning by plant canopies can play key roles in dryland ecohydrology by altering the amount, timing and patterns of water receipt to soils. Here, we synthesized interception, throughfall and stemflow observations from 2,297 rainfall events across 40 dryland sites, including 48 plant species. Then, we developed general empirical models able to predict the response of rainfall partitioning into interception, throughfall and stemflow as a function of rainfall event size considering different functional factors (life form, bark type). Twelve linear models explained significant variability across all synthesized observations. In all cases, interception, throughfall and stemflow linearly increased as a function of rainfall event size with slope ranges of 0.149–0.174 mm of interception per mm of rainfall, 0.776–0.829 mm of throughfall per mm of rainfall, and 0.026–0.063 mm of stemflow per mm of rainfall. On the one hand, shrubs and rough-barked species presented more interception than trees and smooth-barked species, respectively. On the other hand, shrubs and smooth-barked species presented less throughfall than trees and rough-barked species, respectively. Finally, shrubs and smooth-barked species presented more stemflow than trees and rough-barked species, respectively. Our models confirm the scientific evidence found so far by other global meta-analyses but also allow us to explore the partitioning of rainfall against different scenarios. Our findings can be useful to help to predict and manage ecohydrological change in water-limited ecosystems in the context of shifting vegetation cover and climate conditions.