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Global Dynamics of Stored Precipitation Water in the Topsoil Layer From Satellite and Reanalysis Data
Author(s) -
Kim Hyunglok,
Lakshmi Venkat
Publication year - 2019
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 - 10.1029/2018wr023166
Subject(s) - topsoil , environmental science , precipitation , water content , surface runoff , soil water , vegetation (pathology) , arid , hydrology (agriculture) , atmospheric sciences , soil science , geology , meteorology , geography , ecology , medicine , paleontology , geotechnical engineering , pathology , biology
The amount of soil water in the topsoil layer (from 0 to 10 cm) has been regarded as a key factor in controlling land‐atmosphere interaction by determining the fraction of net radiation. In the present study, we investigate spatial trends of the stored precipitation fraction in the topsoil layer for varying vegetation and aridity indices by utilizing four satellites and two reanalysis data sets on a global scale. Using the Budyko framework, we relate climate regimes to the stored precipitation fraction on a global scale. A positive relation between the stored precipitation fraction with aridity index and a negative relation between the stored precipitation fraction and free parameter, vegetation optical depth, and isohydric slope are discovered. Even though the stored precipitation fraction values were calculated from different soil moisture and precipitation sources, they share an similar spatial trend: the drier and less vegetated the soil is, the more precipitation is retained in the top layer of the soil. Specifically, the topsoil retains 37% ± 11% of precipitated water three days after a rainfall event where the aridity index was greater than 5. Over wet and forest areas, due to large runoff fluxes and plants intercepting water before the precipitated water reached the ground, the topsoil retains 21% ± 2% of precipitated water three days after a rainfall event. Furthermore, by using the modeled data sets in the calculation of the stored precipitation fraction metric, we are able to conduct a sensitivity analysis of F P ( f ) metrics with respect to different sampling frequency values.