TG18 – The merits of multi constraints data sets for model parameter estimation

TG Leader: Andreas Ibrom

Description: Dynamic ecosystem models are simplified representations of ecosystems. Models are being adjusted to a specific system and situation with model parameters, i.e. site specific constants that are part of the process representations. There are two principal types of model parameters, a priori parameters that are known or can be measured (pm) or parameters that can only be derived from any kind of model-data fusion (MDF) technique (pd), i.e. their values are being deduced from a priori knowledge and information that is only indirectly related to the parameters. Generally the empirical data on the processes in ecosystems is incomplete and very sparse compared to the theoretical amount and diversity for the complete representation of the systems complexity. The less data exist, both in quantitative but as well in qualitative sense, the larger is the uncertainty for pd.

This task group will explore the relationship between data quantity and quality (data ‘richness’) and the power with which model-data fusion techniques can constrain certain parameter values to their realistic values. We will use a small number of the most complete data sets that are available from forest ecosystem research with a small number of models with varying complexity to examine the above described relationship between the data richness and quality of the parameter estimation. We invite both data providers and modellers to join this task group and help designing a feasible and systematic method for this task, and characterise the value of data for understanding ecosystem functioning and dynamics.

 

Participation: If you are interested in participating in the task group, please contact the task group coordinator directly. If you are interested in the working group in general, please contact the working group leaders.