Weighting in the cooling capacity index equation

I try to understand the weighting in the equation of CCi. It was cited in the website “The default weighting (0.6; 0.2; 0.2) is based on empirical data”. What do you mean empirical data here? can you explain more so I can use the data in my case study to adjust the weighting please?

Hi @tng44,

Thank you for writing in with your question.

InVEST’s Urban Cooling model’s default weight is 0.6 for the effect of shade, 0.2 for albedo, and 0.2 for evapotranspiration (ET). The empirical data mentioned in the User Guide refer to the findings described in “Zardo, L., D. Geneletti, M. Pérez-Soba, M. Van Eupen, Estimating the cooling capacity of green infrastructures to support urban planning, Ecosystem Services, Volume 26, Part A, 2017, Pages 225-235, ISSN 2212-0416, https://doi.org/10.1016/j.ecoser.2017.06.016.”

The authors state that, "Shading, evapotranspiration, and wind are the three ecosystem functions that determine the cooling capacity of green urban infrastructures… The relative contribution of shading and evapotranspiration to the overall cooling capacity is determined by the third component, i.e. the size of the [green space]… Evapotranspiration and shading jointly reduce the air temperature, but the impact of evapotranspiration becomes predominant as the area gets larger (Akbari et al., 1992). Unfortunately, only limited information is available in the literature on how to combine the contribution of evapotranspiration and shading, and the relationship between size and cooling capacity is non-linear (Chang et al., 2007). According to Chang et al. (2007), green areas larger than 2–3 hectares are much cooler than their surroundings. Additionally, parks between 3 and 12 hectares are cooler than most surrounding measurements; whereas parks smaller than 2 hectares have a limited effect. Many studies identify the threshold between “small” parks and “large” parks around a value of 2 hectares… Akbari et al. (1992)… concluded that the cooling capacity depends mainly on ET for large areas, reaching a distance as far as five times the tree. [They] found that shading contributes up to 95% when directly under the canopy, but its contribution in the decrease of temperature and consequently of energy consumption for air conditioning for the 40% for large areas (larger than two hectares). Chang et al. (2007) found that size contributes to 60% of the cooling capacity, and directly affects the contribution of ET. In areas smaller than two hectares, empirical studies determined the contribution of shading to be around 80% of the total cooling capacity, with the remaining 20% determined by ET (Shashua-Bar and Hoffman, 2000).

These findings suggested to assess the overall cooling capacity of [urban green spaces] through a weighted summation of their ET and shading scores, using different weights according to size. Particularly, in areas smaller than two hectares ET was assigned a weight of 0.2 and shading of 0.8. In areas larger than two hectares the weights were changed to 0.6 and 0.4, for ET and shading respectively."

Does this excerpt answer your questions? Please also refer to the InVEST User Guide’s Urban Cooling chapter’s Appendix: Data sources and guidance for parameter selection.


1 Like

Hi JesseG,
Thanks for your reply.
I did go through sources. Which I don’t understand here is Zardo suggested " in areas smaller than two hectares ET was assigned a weight of 0.2 and shading of 0.8. In areas larger than two hectares the weights were changed to 0.6 and 0.4, for ET and shading respectively. ". Later, it is modified by UrbanInVEST by the weighting (0.6; 0.2; 0.2) and add albedo in the equation. I just thought simplily that the effect of shading in areas smaller than two hectares and greatedr than 2 hectares should be dealt in the equation from the start:
areas smaller than two hectares CCi=ET of 0.2 and shading of 0.8 and ?albedo
areas larger than two hectares CCi=ET of 0.6 and shading of 0.4 and ?albedo
Not after calculate CCi and then HMi as only shading is the one affects the CCi not the whole equation

Hi @tng44,

I apologize if I do not fully understand your question.

To calculate HMi, the model amends CCi to account for the fact that large green spaces have a disproportionately large cooling effect on surrounding areas.

HMi is either:

  • equal to CCi if the pixel (i) is unaffected by a large green space OR
  • set to a distance-weighted average of CCi and a large green space if one is close enough to have an impact.

Zardo et. al. (https://doi.org/10.1016/j.ecoser.2017.06.016) did not consider the effect of albedo in their study. They did find that in areas >2 hectares (ha), ET had a greater effect relative to shading and albedo than it did in areas <2 ha. InVEST’s equations are intended to differentiate CC from HM. Both are affected by all three factors. Perhaps @chris could provide more insight as to the model’s construction.

If you have data or insights from your city of interest, you are welcome to use them to aid you in manually adjusting CC index weights, but we caution against changing them dramatically from the defaults without intimate knowledge of your particular city. My understanding is that this model has been tested more extensively in temperate climates and its parameters may need to be tweaked for use in tropical cities.

I hope this information is helpful to you.


Hi Jesse,
As your said "this model has been tested more extensively in temperate climates ", do you have any published paper to cite this? My case study is New Zealand so it is temperate climate but I want to support Why i select this model by providing evidence that the weighting has been tested in many case studies in temperate climates as you said.
And you said "Zardo et. al. (https://doi.org/10.1016/j.ecoser.2017.06.016) did not consider the effect of albedo in their study. They did find that in areas >2 hectares (ha), ET had a greater effect relative to shading and albedo than it did in areas <2 ha. " but Zardo only consider ET and shading.
I found the way to calculate HM is complicated to me so I just use CCi equation.

Hi @tng44,

Ah, sorry for the contradictory sentences. I should have said, “Zardo et al. (2017) found that in areas >2 hectares (ha), ET had a greater effect relative to shading than it did in areas <2 ha.

Here is a recent publication that used InVEST’s Urban Cooling model in the Upper Midwest of the United States:

Lonsdorf, E.V., Nootenboom, C., Janke, B., and Horgan, B.P., Assessing urban ecosystem services provided by green infrastructure: Golf courses in the Minneapolis-St. Paul metro area, Landscape and Urban Planning, Volume 208, 2021, 104022, ISSN 0169-2046, https://doi.org/10.1016/j.landurbplan.2020.104022.


Hi Jesse,
The paper you shared doesn’t include the information I want: weighting. Sorry for asking but if you can share any document which natural capital project team worked on how to decide the weighting I would be really appreciated.


Hi @tng44,

This paper:

Phelan, P. E., Kaloush, K., Miner, M., Golden, J., Phelan, B., Iii, H. S., & Taylor, R. A. (2015). Urban Heat Island : Mechanisms , Implications , and Possible Remedies. Annual Review of Environment and Resources, 285-309. https://doi.org/10.1146/annurev-environ-102014-021155

is cited by the model developers for its guidance in weighting the effects of albedo, ET, and shading.

Kunapo, J., Fletcher, T. D., Ladson, A. R., Cunningham, L., & Burns, M. J. (2018). A spatially explicit framework for climate adaptation. Urban Water Journal, 15(2), 159-166. https://doi.org/10.1080/1573062X.2018.1424216

may also be helpful to you.

I am seeker deeper insights from @chris and we’ll let you know if he has any more resources to share.


Hi @tng44

Thanks for your question (and thanks Jesse for your prompt and well-researched answers!). I can give some insight into the weights question, although the literature on this particular section of the model is somewhat sparse.

The weights are based on the papers Jesse referenced, and I typically use the defaults for any initial study I undertake as they best reflect the literature (i.e. Zardo et al. 2017 and Phelan et al. 2015). There have been few publications using the model as it is relatively new, but one such study relied on the initial weights to great success (https://doi.org/10.1016/j.scs.2020.102459).

I am currently working on a paper that optimizes the model to fit empirical data on urban heat islands in two cities. We are using an approach pioneered by Marti Bosch (see GitHub - martibosch/invest-ucm-calibration: Automated calibration of the InVEST urban cooling model with simulated annealing) that calibrates these weights to better fit the regional heatwave. The results are still preliminary so I cannot share the weights we are using, but we are hoping to submit in the next few months so with any luck there will be a citation soon. If you have access to local heatwave data you can use the code in the repository linked above to calibrate your model, as an empirical way of deriving more accurate weights.

I hope these resources help you improve your work! There is no one ‘correct’ combination of weights–it is a simplification of a much more complex climatological process. But the defaults should at least capture the current literature consensus on the relative importance of shade, albedo, and evapotranspiration, at least until more literature using the model is published.



Hi Chris,
Thanks for giving more explanations.
Please share with us the document when it is published.