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AGU 2017 Poster Presentation by Ming Li and Presented by Tingyu Hou: Correlative assessment of two predictive soil hydrology models with measured surface soil geochemistry

Center for the Environment
December 14, 2017
8:00 AM - 12:20 PM
New Orleans Ernest N. Morial Convention Center - Poster Hall D-F


Spatial variability of surface soil organic matter on the hill slope scale is strongly influenced by topographic variation, especially in sloping terrains, where the coupled effects of soil moisture and texture are principle drivers for stabilization and decomposition. Topographic wetness index (TWI) calculations have shown reasonable correlations with soil organic carbon (SOC) content at broad spatial scales. However, due to inherent limitations of the “depression filling” approach, traditional TWI methods are generally ineffectual at capturing how small-scale micro-topographic (~1m2) variation controls water dynamics and, subsequently, poorly correlate to surface soil biogeochmical measures. For TWI models to capture biogeochmical controls at the scales made possible by LiDAR data they need to incoportate the dynamic connection between soil moisture, local climate, edaphic properties, and micro-topographic variability. We present the results of a study correlating surface soil geochemical data across field sites in the Upper Sangamon River Basin (USRB) in Central Illinois, USA with a range of land use types to SAGA TWI and a newly developed Dynamic Topographic Wetness Index (DTWI). The DTWI for all field sites were obtained from the probability distribution of long-term stochastically modeled soil moisture in between wilting point (WP) and field capacity (FC) using Dhara modeling framework. Whereas the SAGA TWI showed no correlation with soil geochemistry measures across the site-specific data, the DTWI, within a site, was strongly, positively correlated with soil nitrogen, organic carbon, and δ15N at three of the six sites and revealed controls potentially related to connectivity to local drainage paths. Overall, this study indicates that soil moisture derived by DTWI may offer a significant improvement in generating estimates in long-term soil moisture, and subsequently, soil biogeochemistry dynamics at a crucial landscape scale

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