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I'm working on a script for calculating the Rainfall Anomaly Index1 (RAI) for an area using satellite imagery to estimate precipitation. The context I have seen the RAI used in before is for weather station estimates, which essentially corresponds to a single pixel in my imagery. What I would like to do is calculate droughts using the imagery, but the RAI formula changes depending on if the difference in rainfall in the given time period is negative (drought) or positive (flood).

So my scenario is that within the area I would like to calculate the RAI for I have some pixels that experience a drought, and some that technically experience flooding. My ideas to deal with this is to a) take an average for the entire area and based on that either use the positive or the negative formula or b) calculate the index for each pixel individually, then averaging the index at the end.

Any suggestions as to how I should approach this problem?

1The definition of RAI that I'm using is this from Space-Time Distribution of Rainfall Anomaly Index (RAI) for the Salgado Basin, Ceará State - Brazil , which in turn cites

  • ROOY MP. van. A Rainfall Anomaly Index Independent of Time and Space. Notos. 1965; 14, 43p.
  • FREITAS MAS. Um sistema de suporte à decisão para o monitoramento de secas meteorológicas em regiões semiáridas. Rev. Tecnol. 2005; (suppl 19): p. 84-95.

For the time being I've decided to use a fairly simple index that only considers precipitation as an input to get my code to work.

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    $\begingroup$ Can you add a link or cite a source to any definition of RAI? I found two here, but there may be better ones out there, perhaps one you are using yourself? agrimetsoft.com/faq/What%20is%20RAI(Rainfall%20Anomaly%20Index) and also droughtmanagement.info/rainfall-anomaly-index-rai $\endgroup$
    – uhoh
    Jul 18, 2021 at 3:41
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    $\begingroup$ The definition I'm using is this one: semanticscholar.org/paper/… , which in turn cites "ROOY MP. van. A Rainfall Anomaly Index Independent of Time and Space. Notos. 1965; 14, 43p." and "FREITAS MAS. Um sistema de suporte à decisão para o monitoramento de secas meteorológicas em regiões semiáridas. Rev. Tecnol. 2005; (suppl 19): p. 84-95." For the time being I've decided to use a fairly simple index that only considers precipitation as an input to get my code to work. $\endgroup$
    – Mach
    Jul 20, 2021 at 23:01
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    $\begingroup$ Great, thanks! I've added that information back into your question. Some people don't read the comments. $\endgroup$
    – uhoh
    Jul 21, 2021 at 0:35
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    $\begingroup$ Sweet, thanks for the help! Also I talked it over with a friend of mine, and after some back and forth I came to the conclusion that if the goal of the research question requires you to quantify a drought over an area, then it would make sense to take the average of that area. Given that the index is applied to data from a single weather station, I figured you could treat the precipitation from an entire area as that of a weather station. E.g. there was a drought in a certain region in 2018, so if I consider that entire region to be my rain gauge, it would make sense to take the average. $\endgroup$
    – Mach
    Jul 26, 2021 at 12:14

1 Answer 1

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I think Standardized Precipitation Index (SPI) is good fit with your objective. The SPI value is spanning from -3 to +3, positive SPI values indicate greater than median precipitation (Wet) and negative values indicate less than median precipitation (Dry).

  1. You can read the official guideline from WMO here: http://www.wamis.org/agm/pubs/SPI/WMO_1090_EN.pdf

  2. To calculate SPI using satellite precipitation estimates, you can follow a training from NASA: https://appliedsciences.nasa.gov/join-mission/training/english/arset-applications-gpm-imerg-reanalysis-assessing-extreme-dry-and-wet

  3. You can check this website https://livingatlas.arcgis.com/droughtaware/drought/index.html on how SPI used to monitor drought.

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  • $\begingroup$ That's fantastic, thanks for the help! Actually, I wanted to make an attempt at developing an app using the SPI as well, as it's much more useful than the RAI, but I figured I'd give the RAI a shot first as it's simpler. The resources you've linked are very helpful! $\endgroup$
    – Mach
    Oct 25, 2021 at 11:28

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