Erosion is a global problem and you need information across the whole watershed, not just a little piece of it as you would find with a tile.
Doing tile-based operations in Wilbur won't be easy and will be a whole lot of work beyond just the basic Wilbur work. The "erosion" operations in Wilbur happen over the surface in memory and have no way to take into account any information that's not on that surface (e.g. neighboring tiles). Getting usable results with Wilbur requires that you constrain your problem to things that Wilbur can work with. That's probably going to mean that you need 20% or more overlap between neighboring tiles. You probably also want to start with a coarse map that covers your area of interest to get things connected up and then chop up that coarse map into smaller overlapping pieces, paying particular attention to areas with altitude changes (i.e. mountain ranges). Reassembling them would be a matter of fading across edges, ideally with a graphcut and Poisson blending technique (not present in Wilbur).
Always save your work in Wilbur as MDR files if you intend to process them further. The MDR format stores the full 32-bit floating-point internal representation that Wilbur uses in its calculations. Only when you get things to your desired endpoint should you export those files as things like 16-bit DEMs (PNG files work well) to allow blending in programs like Photoshop.
The tool that you really seem to want (which, to the best of my understanding, does not completely exist) is a sort of patch-based texture mapper. You start with your collection of DEMs as input patches. You pick a patch and start painting, rotating and scaling as needed. What the system stores is not the height values, but rather the patch ID and texture coordinates. As you zoom in and out, the system samples the original images to synthesize your heightfield on the fly. For a dedicated terrain system, it would extract information about river and ridge connectivity in the original patches and force these things to match in the synthesized heightfield (see Howard Zhou's article at
http://www.howardzzh.com/research/terrain/ ) for an example. At a high level, drawn river definitions and terrain patches would define the overall landscape, with finer details providing points of interest like major mountain peaks and passes. By defining things using input coordinates, the system can synthesize things at any desired level of detail with fidelity up to the detail of the input data and can apply various synthesis techniques to generate some amount of detail beyond that. The primary benefit of a system such as I've described is that it can store a fairly small representation of the world and generate a potentially enormous image piecemeal from that representation and library. For terrain, it can also be retargeted across libraries because the patch coordinates can be easily converted to latitude/longitude values that can be used to index a new library.