  In this paper, we introduce two new model-based versions of the widely-used
standardized precipitation index (SPI) for detecting and quantifying the
magnitude of extreme hydro-climatic events. Our analytical approach is based on
generalized additive models for location, scale and shape (GAMLSS), which helps
as to overcome some limitations of the SPI. We compare our model-based
standardised indices (MBSIs) with the SPI using precipitation data collected
between January 2004 - December 2013 (522 weeks) in Caapiranga, a road-less
municipality of Amazonas State. As a result, it is shown that the MBSI-1 is an
index with similar properties to the SPI, but with improved methodology. In
comparison to the SPI, our MBSI-1 index allows for the use of different
zero-augmented distributions, it works with more flexible time-scales, can be
applied to shorter records of data and also takes into account temporal
dependencies in known seasonal behaviours. Our approach is implemented in an R
package, mbsi, available from Github.
