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Effects of Landcover, Soil Property, and Temperature on Covariations of DOC and CDOM in Inland Waters
Author(s) -
Li Jiwei,
Yu Qian,
Tian Yong Q.,
Boutt David F.
Publication year - 2018
Publication title -
journal of geophysical research: biogeosciences
Language(s) - English
Resource type - Journals
eISSN - 2169-8961
pISSN - 2169-8953
DOI - 10.1002/2017jg004179
Subject(s) - colored dissolved organic matter , dissolved organic carbon , environmental science , evergreen , plant litter , deciduous , hydrology (agriculture) , ecosystem , ecology , chemistry , environmental chemistry , nutrient , geology , biology , geotechnical engineering , phytoplankton
Abstract Significant uncertainty exists in the estimation of dissolved organic carbon (DOC) concentration via remote sensing from colored dissolved organic matter (CDOM) absorption in inland waters pointing to a need for more process‐based understanding of the relationship between CDOM and DOC. In this study, we examine the factors affecting the covariations of DOC and CDOM using controlled experiments combined with field measurements at subbasin scale that have varying environmental and biological conditions. Our analysis reveals that the DOC:CDOM ratio is mainly related to landcover types. Higher DOC:CDOM linear regression slopes observed in evergreen leaf litter leachate suggest that CDOM comprises a smaller fraction of the DOC pool in evergreen sites in comparison to agricultural and deciduous leaf litter leachates. Given the same DOC concentrations, the range of CDOM levels from deciduous forest plant varied 3 times greater than that from other plant types. Results indicate that soil narrows the slope differences in the linear regressions of DOC from CDOM for all plant types (by 19% of evergreen, 18% of agriculture, and 77% of deciduous). Raising soil temperature by 5°C could double the range of DOC concentration and CDOM absorption for all scenarios. We present a mathematical model to estimate DOC concentration in freshwater environment via CDOM variations with reference to land cover and soil effects. The model was able to explain 95% of field measurements of multiple years in four subbasins. This improved understanding is critical for the remote sensing of DOC directly via observations of CDOM.