Objective refinements to a diagnostic terrestrial biosphere model using satellite data: North America carbon and water cycle simulations

We established a framework for objective improvement of a diagnostic terrestrial biosphere model (Terrestrial Observation and Prediction System; TOPS) using satellite-derived products including snow cover, evapotranspiration (ET), and gross primary productivity (GPP). Based on the TOPS model structure, we established an objective improvement process by first optimizing snow submodel, then soil water submodel, and finally gross primary production submodel. We used MODIS snow cover products (MOD10A2) for snow submodel improvements, Support Vector Machine (SVM) based ET estimation for soil water submodel improvements, and SVM-based GPP estimation for GPP model improvements as satellite-derived products. Snow submodel refinement has shown an improvement on snow dynamics, streamflow, ET, and GPP over high latitude areas. Soil water cycle submodel refinement has shown an improvement on seasonal ET and GPP variations for seasonally-dry regions. GPP submodel refinement has shown an improvement on seasonal and annual GPP over the vegetated regions. Our analysis shows that the objective improvement of terrestrial ecosystem model is an effective way for improving model performance. Carbon and water cycle simulation over North America were greatly improved as a result of the model improvements.