currently I am exploring the Urban Flood Risk Mitigation Model from inVEST as potential tool for my master thesis. I wondered if land cover and its effect on runoff is processed considering the actual spatial location of LC types? Imagine afforestation would take place in the upper catchment area rather than in the lower catchment area of a watershed, it would/will differently effect the resulting runoff process, routing and/or amounts!
As far as I saw by the output formats provided by the tool (i.e. maps), this spatial explicity may be the case!? And, the only way to test an alternative scenario/spatial explicit land cover change (grassland to forest) would be to manipulate the input data (e.g. land cover file in e.g. QGIS), right? Or would such changes in the input maps not matter at all because input data is reduce to statistical information (percentages) when running the model?
I would be very glad to get insights here as analysis of the location specific changes are most important to my topic!
Thanks for your time and looking forward to the answer(s),
The InVEST Urban Flood Risk Mitigation model is a spatial model in that you provide spatial inputs and it will produce spatial outputs, and this can be useful for viewing the spatial distribution of flood risk in an urban setting. That being said, the model does not consider hydrological connectivity between pixels. So to answer your specific questions:
From the InVEST User’s Guide, runoff reduction is calculated per pixel in this model and does not affect the runoff of neighboring pixels.
Yep, that’s true! But if you’re considering things like afforestation, then this model, which was designed for and intended to be used within an urban context, might not be right for you. Instead, you may want to take a look at the InVEST Seasonal Water Yield model, which does consider the flow path of water in addition to landcover, precipitation and soil type.
Yes, we call these different sets of inputs “scenarios”, in that they allow you to explore multiple possible futures. This is a very common thing to do in spatial analyses: modify the landcover raster to see what happens with a different landscape.
While you can aggregate outputs of the Urban Flood Risk Mitigation model to a catchment scale (where the specific location of a pixel would not matter), the spatial distribution of the outputs of this model can be a useful output … it all depends on what you need to use this information for. This model will output both: rasters that include the spatial distribution of per-pixel values and summary statistics per catchment. These model outputs are also described in the UFRM user’s guide chapter: https://storage.googleapis.com/releases.naturalcapitalproject.org/invest-userguide/latest/urban_flood_mitigation.html#interpreting-outputs
But again, with the restorations/interventions you’ve mentioned so far, I’d wonder if something like the Seasonal Water Yield model might be more suitable to your study area.
Thanks for your detailed answered, it really helped me a lot
I see, I somehow missed going through the other models offered by NatCap inVEST - the Water Yield models were not though obvious to me to consider taking a sight at their documentation while searching with terms like flood* or runoff^^
I think I will use the SWY
