Calibration, measurement, and characterization of soil moisture dynamics in a central Amazonian tropical forest
Robinson Negrón‐Juárez, Sávio José Filgueiras Ferreira, Marcelo Crestani Mota, Boris Faybishenko, Maria Terezinha Ferreira Monteiro, Luiz Antônio Cândido, Rúbia Pereira Ribeiro, R. C. Oliveira, Alessandro Araùjo, J. M. Warren, Brent D. Newman, Bruno Gimenez, Charuleka Varadharajan, D. Agarwal, Laura S. Borma, Javier Tomasella, Níro Higuchi, Jeffrey Q. Chambers
Abstract
Abstract Soil moisture plays a key role in hydrological, biogeochemical, and energy budgets of terrestrial ecosystems. Accurate soil moisture measurements in remote ecosystems such as the Amazon are difficult and limited because of logistical constraints. Time domain reflectometry (TDR) sensors are widely used to monitor soil moisture and require calibration to convert the TDR's dielectric permittivity measurement ( K a ) to volumetric water content (θ v ). In this study, our objectives were to develop a field‐based calibration of TDR sensors in an old‐growth upland forest in the central Amazon, to evaluate the performance of the calibration, and then to apply the calibration to determine the dynamics of soil moisture content within a 14.2‐m‐deep vertical soil profile. Depth‐specific TDR calibration using local soils in a controlled laboratory setting yielded a novel K a –θ v third‐degree polynomial calibration. The sensors were later installed to their specific calibration depth in a 14.2‐m pit. The widely used K a –θ v relationship (Topp model) underestimated the site‐specific θ v by 22–42%, indicating significant error in the model when applied to these well‐structured, clay‐rich tropical forest soils. The calibrated wet‐ and dry‐season θ v data showed a variety of depth and temporal variations highlighting the importance of soil textural differentiation, root uptake depths, as well as event to seasonal precipitation effects. Data such as these are greatly needed for improving our understanding of ecohydrological processes within tropical forests and for improving models of these systems in the face of changing environmental conditions.