Interpretation of SWY and SDR results

Hi

I am still learning InVEST hydrological models. I have a question pertaining the interpretation of the results of these 2 models. I was able to run the models successfully now I have the results. The first question I have:
Am I supposed to use the output rasters as they are to quantify the sediment export (sed_export.tif), soil loss (usle_.tif), avoided erosion raster for the SDR model, and the quick flow (QF_.tif), local recharge (L_.tif) and baseflow (B_.tif) or I still need to use the raster calculator to calculate based on the calculation in the User guide.

I have also noticed that my quick flow values are the same for both scenarios which I find strange. They are the same even after I calculated the quick flow using the monthly quick flow rasters.

Lastly to show the spatial distribution of these results, do I use the output raster as they are, as from my study I am getting results that I am struggling to show where the different parameters I am quantifying are highly concentrated.

Thanks,
Simphiwe

Hi @Simphiwe -

There is no requirement to do post-processing on the model results, but we often do. Which model results you use, and how you use them, will depend on the needs of your analysis. Sometimes it’s useful to use the output rasters as they are, for example when your audience wants to know places to protect because they are retaining sediment (avoided erosion and/or avoided export reuslts), or places to do better land management because they are producing a lot of sediment export.

Sometimes it’s useful to aggregate results, so you’re quantifying the total amount of sediment export reaching something like a water treatment plant. There are also a lot of other post-processing calculations that you can do, but they are not required by InVEST.

What changed between your scenarios? Are the quick flow results exactly the same, or are there differences in some places, or very small differences?

~ Stacie

Thank you so much @swolny for the reply. Land cover is changing between the 2 scenarios where 1 is dominated by forests and the other one by agricultural land.

Ok @Simphiwe, then assuming that the curve numbers are different between forests and agricultural land, I’d think you should see a difference in quickflow between those scenarios. If you want to package up all of your model inputs and send them to me, I can try it out. You can send a link to swolny at stanford.edu.

~ Stacie