Ecosystem modeling usually requires a variety of data to run a complete simulation. These data usually include both time-variant and time-invariant datasets. For example, we usually consider Digital Elevation Model (DEM) as time-invariant data because surface topography is relatively stable for a given period of time unless extreme events such as earthquake occur.
However, most other driving datasets are actually time-variant. For example, climate data (temperature, precipitation.etc.) are constantly changing at any given time.
To date, there is yet no accurate definition whether a data should be defined as time-variant or time-invariant. And we always have to make some assumptions to simplify our models.
There are several reasons behind this and how they may be improved in future study. First, using time-variant data requires more data. For example, for an ecosystem model at daily time step, daily climate data are also required. While it may be relatively easy to retrieve climate data from meteorology sites, it may be much difficult to retrieve other types of data. For example, soil data survey usually requires much effort to produce a reliable soil map. It is practically impossible to provide soil data at daily time step.
Second, using time-variant data implies more computational demand and storage quota. For large ecosystem models, data preparation and I/O are among the most challenging jobs for a successful simulation. I will use one of my current projects as an example. For a 40-year simulation, I use approximate 6 time-variant datasets include temperature, precipitation and vapor pressure. That is 403656=87600 files of matrix. Needless to say the additional data produced during the preparation process, that amount of data will place certain requirement on the computational power and storage.transfer capacity.
Third, using time-variant data also make the model much more complex and complicated. It is obvious the model itself will become much more complex since data I/O inside the model will increase significantly. In addition, the time-variant data usually imply that some of our components must be flexible enough to be compatible with the changes.For example, if the land cover is changed due to wild fire within one week, what happens to the soil properties during this period? Does the surface albedo change linearly or nonlinear?
To conclude, there remains many challenges in time-variant datasets related subjects. Some of them can be gradually addressed using advanced approaches. For example, high performance computer (HPC) are more and more used to address the computational demand and storage capacity. Better data structures or formats such as HDF/NetCDF are also widely used to improve the data I/O.
Further, the first step is still to define whether a data is time-variant or time-invariant. Here I list a brief table to illustrate how I define different data in one of my current projects. | Data | Time-variant/invariant | Note | |:—————————-:|:———————-:|:——————————————————:| | Temperature | Variant | Daily | | Precipitation | Variant | Daily | | Land use and land cover type | Variant | Annually, but when extreme event occurs, it can change | | Vegetation type | Variant | Annually, but when extreme event occurs, it can change | | Soil type | Invariant | Change only extreme event occurs |