Exposure - Consequences Plot in HRA

Hi Everyone

I have a question concerning exposure - consequences plot produced by Habitat Risk Assessment. My question is:

  1. Can exposure - consequence results determine whether one particular human activities would give high/moderate/low risk? If yes, then
  2. how to determine whether it is high/moderate/low risk? The scores that I used is ranged from 0,1,2 and 3.

For instance, lets have a look into Tourism in blue point. Please refer to the picture attached
the exposure value is 2.7. the consequence is 1.57. Then, does it categorized as high/moderate risk?

I am still confused to determine high/moderate/low risk from exposure and consequence plot.
Sumba Tengah

So guys, would you please to help me to solve this problem?

thank you #staysafe

1 Like

Hi @Aryolejandro,
The exposure and consequence scores are on the same scale as the criteria scores that you input in the criteria scores CSV. For example with the Tourism point, I would say that it is high exposure / moderate consequence.

The HRA model calculates risk directly from the exposure and consequence results. There are two options for risk calculation (see equations (3) and (4) in the user’s guide) Basically it’s either the length of the (exposure, consequence) vector (Euclidean method); or it is exposure * consequence (Multiplicative method).

See step 4 of this section of the user’s guide for explanation of how risk levels are classified for each pixel. You can apply the same method to classify the overall results in your exposure/consequence plot.

For instance, if you calculate risk with the Euclidean method, your maximum possible risk score is 2.87: √( (3 - 1)^2 + (3 - 1)^2) = 2.87. If you use the multiplicative method, your maximum possible risk score is 3*3 = 9.

A point is classified as high risk if its score is >66% of the maximum; moderate risk if between 33% - 66%; and low risk if 0% - 33%. For your Tourism point, the risk score by the Euclidean method is 1.79, and by the multiplicative method is 4.24.
1.79 / 2.87 = 62%
4.24 / 9 = 47%
So by either method, your Tourism point would be classified as moderate risk. I hope that helps!

3 Likes

Yes. Correct. I totally understand now. Thank you so much.

maybe just one correction is that the maximum possible risk score is 2.83 because √8 is 2.83
not 2.87.

Another 2 question are:

  1. In the excel produced by the model in output folder. There are (E_min, E_max, E_mean), (C_mean, C_min, C_max), (R_mean, R_min, R_max), as well as (R_%mean, R_%min, R_%max). How can i read this?

  2. From the picture below. “From all stressor” point (in color beige) is located in the very low exposure and low consequence. However, there two two beige points are in high exposure, which are settlement and agriculture. Does it mean that the exposure of settlement and agriculture are above the cumulative risk from all stressor towards mangrove ?

Thank you so much for helping me.

Hi @Aryolejandro,

You’re right, that should be 2.83 not 2.87. Thanks for catching that!

  1. E = exposure, C = consequence, and R = risk. These are summary statistics giving the minimum, mean, and maximum values. The R% columns show the percentage of the total area that is low, medium, or high risk.
  2. I believe that the ‘From All Stressors’ point is relatively low because it’s calculated over the entire subregional area. The user’s guide states, “At the subregional scale, score for spatial overlap (a default exposure criteria) is based on the fraction of habitat area in a subregion that overlaps with a human activity (see below for more detail). The subregional score for all other E and C criteria are the average E and C score across all grid cells in the subregion.” So the ‘From All Stressors’ point is the average total E and C scores, across all stressors, across all grid cells in the subregion. I think it does not equal the average of all stressor points because they are weighted by area.
1 Like

Thank you so much for the answers.

yes, it is true. all “From All Stressors” point are always relatively low from my results.

Another question is :

the result maps from " Total_Risk_habitat" is totally different with the result maps from “Reclass_Risk_Habitat”. Likewise, the result map from “Total_Risk_Ecosystem” is also totally different from “Reclass_Risk_Ecosystem”". Then, which one that I need to choose? I know that the website has explained the differences, however, i still dont understand how to put it into context.

After read a couple of HRA journals, all of the writers didnt explicitly explained which maps that they used in their journals between (Total_Risk_habitat or Reclass_Risk_Habitat). Likewise, which map they they choose between (Total_Risk_Ecosystem or Reclass_Risk_Ecosystem).

Moreover, most of the writers didnt put the whole results maps. They only put one for ecosystems and one for habitats. But they didnt inform which one that they choose for ecosytem (total risk ecosystem or reclass risk ecosystem) as well as they didnt inform which one that they choose for habitat (total risk habitat or reclass risk habitat).

I am so sorry if i ask many questions. Thank you so much. #staysafe

Hi @Aryolejandro,

I’m not sure which map the journal writers used, but I think it doesn’t really matter because the Total_risk_ecosystem and reclass_risk_ecosystem represent the same data. The Total_risk represents the average risk across all habitats in a grid cell. This is a continuous value between 0 and the maximum possible risk value (e.g. 2.83 for the Euclidean method). The Reclass_risk is simply the Total_risk but classified into the high/medium/low risk categories using the 66-100%, 33-66%, 0-33% categories like we talked about. So Reclass_risk is discrete data where every value will be 0, 1, 2, or 3.

I think which one you choose depends on the kind of analysis you want to do. If you are interested in the finer-grain information, or your analysis takes in continuous data, then use the Total_risk. If your analysis takes in categorical data, then use the Reclass_risk. I hope that helps!

2 Likes

Yes, it really help. Thank you so much for the discussion.

2 Likes