Conditional interval reduction method: A possible new direction for the optimization of process based models

Application of process-based models at different spatial scales requires their proper parameterization. This task is typically executed using trial-and-error parameter adjustment or a probabilistic method. Practical application the methods hampered by methodological complexity and lack interpretability. Here we present novel approach for parameterization that call as conditional interval refinement method (CIRM). The can be best described combination advantages expert-based adjustment. CIRM was demonstrated optimizing Biome-BGCMuSo biogeochemical model maize yield observations. proposed uses General Likelihood Uncertainty Estimation (GLUE) with additional expert knowledge, supplemented construction interpretation decision trees. It iterative, fully automatic successfully constrained intervals meanwhile our confidence on parameters increased. algorithm easily implemented other models.