Estimation of above- and below-ground ecosystem parameters for DVM-DOS-TEM v0.7.0 using MADS v1.7.3

Abstract

The permafrost region contains a significant portion of the world’s soil organic carbon, and its thawing, driven by accelerated Arctic warming, could lead to substantial release of greenhouse gases, potentially disrupting the global climate system. Accurate predictions of carbon cycling in permafrost ecosystems hinge on the robust calibration of model parameters. However, manually calibrating numerous parameters in complex process-based models is labor-intensive and is complicated further by equifinality – the presence of multiple parameter sets that can equally fit the observed data. Incorrect calibration can lead to unrealistic ecological predictions. In this study, we employed the Model Analysis and Decision Support (MADS) software package to automate and enhance the accuracy of parameter calibration for carbon dynamics within the coupled Dynamic Vegetation Model, Dynamic Organic Soil Model, and Terrestrial Ecosystem Model (DVM-DOS-TEM), a process-based ecosystem model designed for high-latitude regions. The calibration process involved adjusting rate-limiting parameters to accurately replicate observed carbon and nitrogen fluxes and stocks in both soil and vegetation. Gross primary productivity, net primary productivity, vegetation carbon, vegetation nitrogen, and soil carbon and nitrogen pools served as synthetic observations for a black spruce boreal forest ecosystem. To validate the efficiency of this new calibration method, we utilized model-generated synthetic and actual observations. When matching model outputs to observed data, we encountered difficulties in maintaining mineral soil carbon stocks. Additionally, due to strong interdependencies between parameters and target values, the model consistently overestimated carbon and nitrogen allocation to the stems of evergreen trees. This study demonstrates the calibration workflow, offers an in-depth analysis of the relationships between parameters and observations (synthetic and actual), and evaluates the accuracy of the calibrated parameter values.