A Matlab function to calculate Palmer Drought Severity Index (PDSI) using monthly temperature and precipitation data. Can determine values for multiple sites simultaneously.
Matlab 2020b or higher is recommended, however this function should work on versions as low as 2016b.
To use, download this repository and add it to your MATLAB path.
Please see the demo for detailed instructions on using this function. Basic syntax is:
[X, Xm, Z, PE] = pdsi(T, P, years, lats, awcs, awcu, cafecYears, timeDim, progressbar)
---- Inputs -----
T: A numeric array holding monthly temperature data at various data sites. Data should begin in a January, and end in a December. Should be in units of Celsius.
P: Monthly precipitation data. Should correspond same data sites and time steps as the temperature data. Should be in units of mm/month.
years: A two element vector indicating the first and last year of the temperature data.
lats: The latitudes of the data sites in degrees. A numeric array. Must be the same size as the temperature data, except for the monthly time dimension, which should have a length of 1.
awcs: Available water capacity of the surface layer for each site (in mm). A numeric array the same size as lats. A common default value is 25.4 mm.
awcu: Available water capacity of the underlying layer for each site (in mm). A numeric array the same size as lats. A common default value is 127 mm.
cafecYears: A two element vector indicating the first and last years of the period to use for CAFEC normalizations.
timeDim: A positive integer indicating which dimension of the temperature and precipitation data is the monthly time step. If not provided, pdsi assumes time is along the first dimension.
progressbar: An optional input that can be used to display a progress bar for long computations. A scalar logical. Set it to true to display progress.
----- Outputs -----
X: Palmer Drought Severity Index at each site in each month.
Xm: Modified PDSI at each site in each month
Z: Z indices at each site in each month.
PE: Computed potential evapotranspiration at each site in each month (in mm).
It is usually fastest to run pdsi on all sites at once. However, the pdsi function scales poorly for very large datasets that require MATLAB to swap data to disk.
If you are experiencing slow run times, try running pdsi on chunks of data sites. Running on 1000 to 10000 sites at a time could be a good starting point.
This PDSI function is based on a script written by Dave Meko. It was updated by Jonathan King in 2020 to include the Southern Hemisphere, and to efficiently process multiple sites.