## 2 Materials and Methods
### SPI (Standardized Precipitation Index) and Rainfall  
SPI [29] is used to quantify the rainfall deficit or surplus at different time scales and was calculated on an annual scale (SPI-12) for the Brazilian Pantanal for the years 2001 to 2019 to identify drought events during the historical time series. The SPI calculation was performed using the total cumulative precipitation records over 30 years fitted to a probability distribution function, which is then transformed into a normal probability distribution function. From this, the mean SPI value for a given location and period is set to zero.  
To determine the SPI, we initially used the calculation of the gamma distribution, which was defined by the probability density function (PDF) given in Equation (1).  
\[f(x)=\frac{1}{\Gamma(a)\beta^{a}}x^{a-1}e^{-\frac{x}{\beta}} \tag{1}\]  
where \(\alpha>0\) (\(\alpha\)) is a shape parameter (dimensionless); \(\beta>0\) (\(\beta\)) is a scale parameter (mm); \(x>0\) (\(x\)) is total rainfall (mm); and \(\Gamma\) (\(\alpha\)) is a Gamma function \(=\Gamma(a)=\int_{0}^{\infty}x^{a-1}e^{-x}dx\).  
All parameters and the gamma PDF were fitted to the frequency distribution of cumulative precipitation using remote sensing data. The parameters \(\alpha\) and \(\beta\) of the gamma PDF estimated for each of them were calculated at the above scales. The parameters \(\alpha\) and \(\beta\) were estimated through maximum likelihood estimation (MLE), which is the most suitable method, according to [PERSON] [30]. To find the cumulative probability of an observed rainfall event for the adopted scale, calculations of the parameters \(\alpha\) and \(\beta\) were performed. The cumulative probability is given by Equation (2).  
\[F(x)=\int_{0}^{x}f(x)dx=\frac{1}{\Gamma(a)\beta^{a}}\int_{0}^{x}fx^{a-1}e^{- \frac{x}{\beta}}dx \tag{2}\]  
Figure 2: Supra-annual variation in precipitation and temperature across the Pantanal biome over the time series examined in this study. Blue bars and the black solid line indicate rainfall and temperature, respectively.  
From the annual SPI values for the Pantanal, wet and dry periods were classified as: extremely wet (\(\geq\)2.00), very wet (1.5 to 1.99), moderately wet (1.00 to 1.49), close to normal (0.99 to \(-\)0.99), moderately dry (\(-\)1.00 to \(-\)1.49), very dry (\(-\)1.50 to \(-\)1.99), and extremely dry (\(\leq\)2.00) [29]. We used the CHIRPS dataset (Climate Hazards Group InfraRed Precipitation with Station data) developed by the U.S. Geological Survey (USGS) and Climate Hazards Group of the University of California, Santa Barbara. This is a product combining pentadal rainfall climatology, near-global geostationary TIR satellite observations of the CPC and National Climatic Data Center (NCDC) [31], atmospheric model rainfall fields of the NOAA Climate Forecast System (CFSv2) [32], and in situ rainfall observations [33]. In the first stage of the study, these products were used given the lack of temporal data from in situ weather stations in the Pantanal and because they are available from 1981 to the present, with a 0.05\({}^{\circ}\) (\(\pm\)5.3 km) spatial resolution and at monthly, pentadal, and decadal temporal resolutions at a global scale [34].