Scale factor for rain gages

[MitchHeineman]

Users often create multiple design storm rainfall timeseries that are scaled from a base or unit hyetograph, e.g. a 10-y storm that is 1.5x a 2-y event, and a 25-y storm that is 2x the 2-y event. This can also be achieved through using [Adjustments], but a more intuitive solution would be to add a scale factor field to the [Raingages] section.

[dickinsonre]

Yes, please to Mitch's idea.

[dickinsonre]

Including a scale factor field in the [Raingages] section is a good idea because it streamlines and simplifies the common practice of creating multiple design storms based on a single baseline hyetograph. Here’s why:

1. Reduces Duplication of Data:
   Currently, to generate multiple design storms (e.g., 2-year, 10-year, 25-year events) from a single base hyetograph, users often have to create multiple separate rainfall time series files or adjust their input data repeatedly. A scale factor field would eliminate the need to keep multiple versions of essentially the same data, reducing file clutter and potential errors.

2. More Intuitive Workflow:
   The concept of scaling a single base event to create other return-period events is common practice among hydrologists. Having a dedicated scale factor in the [Raingages] section makes this process more transparent and straightforward. Instead of applying adjustments indirectly or re-entering data, users can intuitively enter a scale factor right where the rainfall data are defined.

3. Fewer Steps and Faster Setup:
   Setting scale factors within the raingage properties would be more direct than using the [Adjustments] block or multiple time series entries. This eliminates several steps in the model setup process, making it quicker and simpler to test scenarios or update design storms.

4. Easier Scenario Management:
   When performing a series of design storm simulations, it’s common to tweak storm intensities to see how the system responds. A scale factor field allows a user to rapidly switch between different return period events by just changing a single factor, rather than generating or importing new data sets or re-configuring adjustments.

5. Reduced Risk of Configuration Errors:
   Minimizing multiple data sources and scaling steps helps avoid confusion and the risk of making mistakes. A direct scale factor in the raingage definition keeps scaling logic in one place, clearly visible and easy to maintain.

In summary, adding a scale factor field to [Raingages] provides a more elegant, user-friendly, and error-resistant approach to generating multiple design storms from a single baseline time series. It aligns directly with how modelers commonly work, making their workflows more efficient and transparent.