Investigation of Soil Moisture - Vegetation Interactions in Oklahoma

and-atmosphere interactions are an important component of climate, especially in semi-arid regions such as the Southern Great Plains. Interactions between soil moisture and vegetation modulate land-atmosphere coupling and thus represent a crucial, but not well understood climate factor. This study examines soil moisture-vegetation health interactions using both in situ observations and land surface model simulations. For the observational study, soil moisture is taken from 20 in situ Oklahoma Mesonet soil moisture observation sites, and vegetation health is represented by MODIS-derived normalized difference vegetation index (NDVI). For the modeling study, the variable infiltration capacity (VIC) hydrologic model is employed with two different vegetation parameterizations. The first is the model default vegetation parameter which is interannually-invariant leaf area index (LAI). This parameter is referred to as the control parameter. The second is MODIS-derived LAI, which captures interannual differences in vegetation health. Soil moisture simulations from both vegetation parameterizations are compared and the VIC-simulated soil moisture’s sensitivity to the vegetation parameters is also examined. Correlation results from the observation study suggest that soil moisture-vegetation interactions in Oklahoma are inconsistent, varying both in space and time. The modeling results show that using a vegetation parameterization that does not capture interannual vegetation health variability could potentially result in dry or wet biased soil moisture simulations

MEWS: Real-time Social Media Manipulation Detection and Analysis

This article presents a beta-version of MEWS (Misinformation Early Warning System). It describes the various aspects of the ingestion, manipulation detection, and graphing algorithms employed to determine--in near real-time--the relationships between social media images as they emerge and spread on social media platforms. By combining these various technologies into a single processing pipeline, MEWS can identify manipulated media items as they arise and identify when these particular items begin trending on individual social media platforms or even across multiple platforms. The emergence of a novel manipulation followed by rapid diffusion of the manipulated content suggests a disinformation campaign