Jason D. Fridley

Understanding the distribution of soil moisture is notoriously difficult in topographically complex regions that are subject to both large‐scale climate gradients and fine‐scale effects of terrain, vegetation, and soil structure. Remote sensing approaches capture large‐scale moisture patterns but are limited in spatial and temporal resolution, while commercial field sensors remain too expensive to deploy intensively over large spatial extents. Here, we demonstrate the use of low‐cost (<20 USD) custom sensors to create a large monitoring network of surficial (0–15 cm depth) volumetric soil moisture content (VMC) across Great Smoky Mountains National Park (GSMNP) (NC, TN, USA). In laboratory tests, temperature‐calibrated VMC values approached the accuracy of commercial probes. We deployed over 80 sensors across multiple watersheds, topographic positions, and a 1,800‐m elevation gradient, and created hierarchical models to understand associations of VMC with spatial (30‐m resolution) and temporal (daily) variables related to water supply and demand. Elevation had the strongest association with VMC, with a fivefold increase across the gradient reflecting 1.5‐fold changes in both (increased) precipitation and (decreased) evapotranspiration; slope angle was a strong mediating factor. Common proxies for moisture including topographic convergence index were not associated with VMC, likely due to limited contributions of surface drainage to local water balance. Our model predicted daily VMC of a set of validation sensors with a root mean square error of 4.8%, which may be improved by site‐specific field calibration. Our study indicates that spatially extensive, field‐based soil moisture networks are practical, accurate, and an important component of regional environmental monitoring.