Hi! I’ve been reviewing the InVEST Sediment Retention model, particularly the Sediment Downslope Trapping component (which outputs the “Avoided Export” layer), and I’d like to try a similar type of analysis using a nutrient dataset generated with the NDR model. My goal is to identify where nutrients are actually being “trapped.” From what I understand, this would involve using the modified_load and NDR layers. I might be missing something here, but I’m not sure why this component wasn’t implemented in the Nutrient Retention model.
The main challenge I’m facing is running these analyses outside the InVEST platform, since I’d like to use intermediate outputs from the NDR model as inputs for the Sediment Downslope Trapping algorithm.
Would anyone happen to have advice on how I could approach this? Is anyone else working on something similar?
One approach we’ve used with the raster results is to subtract export from intermediate_outputs\modified_load , so:
N_retention = modified_load_n - n_total_export
P_retention = modified_load_p - p_surface_export
I’m using the terms “retention”, “trapping”, and “avoided export” interchangeably here. But be aware that this simplified approach is a bit of a workaround, because SDR includes a deposition/trapping component, which is what is retained from upslope pixels, whereas NDR does not. The nutrient load results do not consider upslope pixels, only the user-provided parameter values from the biophysical table input. Here is another way my colleague, @adrianvogl , explained it:
“there is no way to calculate nitrogen retention on a pixel, only that pixel’s reduced export as a function of downstream pixels. The equation that Jesse suggested (modified load - export) works at a watershed scale, but at a pixel scale it only tells you how much N is retained between that pixel and the stream, not how is much is retained on that pixel.”
Another option is to run multiple scenarios and then compare results, like creating an all bare soil scenario to get at the biophysical “retention supply” provided by the existing land cover. This would be similar to what the authors of this study did (“Including Additional Pollutants into an Integrated Assessment Model for Estimating Nonmarket Benefits from Water Quality”, Griffin, R. et al. 2020, Land Economics, https ://doi.org/10.3368/wple.96.4.457 ).
You’re not the only one trying to solve this. Perhaps others could provide better insight @elonsdorf , @lons0011 , or @swolny .