CSDMS 2.0: Moving Forward
Building a Network for Sediment Experimentalists and Modelers
Abstract
In the modeler community, hindcasting (a way to test models based on knowledge of past events) is required for all computer models before providing reliable results to users. CSDMS 2.0 “Moving forward” has proposed to incorporate benchmarking data into its modeling framework. Data collection in natural systems has been significantly advanced, but is still behind the resolution in time and space and includes natural variability beyond our understanding, which makes thorough testing of computer models difficult.
In the experimentalist community, research in Earth-surface processes and subsurface stratal development is in a data-rich era with rapid expansion of high-resolution, digitally based data sets that were not available even a few years ago. Millions of dollars has been spent to build and renovate flume laboratories. Advanced technologies and methodologies in experiment allow more number of sophisticated experiments in large scales at fine details. Joint effort between modelers and experimentalists is a natural step toward a great synergy between both communities.
Time for a coherent effort for building a strong global research network for these two communities is now. First, the both communities should initiate an effort to figure out a best practice, metadata for standardized data collection. Sediment experimentalists are an example community in the “long tail”, meaning that their data are often collected in one-of-a-kind experimental set-ups and isolated from other experiments. Second, there should be a centralized knowledge base (web-based repository for data and technology) easily accessible to modelers and experimentalists. Experimentalists also have a lot of “dark data,” data that are difficult or impossible to access through the Internet. This effort will result in tremendous opportunities for productive collaborations.
The new experimentalist and modeler network will be able to achieve the CSDMS current goal by providing high quality benchmark datasets that are well documented and easily accessible.
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