Estimation of soil properties by coupled inversion of electrical resistance, temperature, and moisture content data

Abstract

Abstract. Studies indicate greenhouse gas emissions following permafrost thaw will amplify current rates of atmospheric warming, a process referred to as the permafrost carbon feedback (PCF). However, large uncertainties exist regarding the timing and magnitude of the PCF, in part due to uncertainties associated with subsurface permafrost parameterization and structure. Development of robust parameter estimation methods for permafrost-rich soils is becoming urgent under accelerated warming of the Arctic. Improved parameterization of the subsurface properties in land system models would lead to improved predictions and reduction of modeling uncertainty. In this work we set the groundwork for future parameter estimation (PE) studies by developing and evaluating a joint PE framework that estimates soil properties from time-series of soil temperature, moisture, and electrical resistance measurements. The framework utilizes the PEST (Model Independent Parameter Estimation and Uncertainty Analysis) toolbox and coupled hydro-thermal-geophysical modeling. We test the framework against synthetic data, providing a proof-of-concept for the approach. We use specified subsurface parameters and coupled models to setup a synthetic state, perturb the parameters, then verify that our PE framework is able to recover the parameters and synthetic state. To evaluate the accuracy and robustness of the approach we perform multiple tests for a perturbed set of initial starting parameter combinations. In addition, we evaluate the relative worth of including various types and amount of data needed to improve predictions. The results of the PE tests suggest that using data from multiple observational datasets improves the accuracy of the estimated parameters.