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The PowerSDI: an R-package for implementing and calculating the SPI and SPEI using data from the NASAPOWER project

ABSTRACT

The standardized precipitation (SPI) and standardized precipitation-evapotranspiration (SPEI) indices are important tools for monitoring drought events, but the low density of weather station networks limits their use in many regions. To address this issue, we developed the PowerSDI R-package, which calculates these two indices using gridded-data from the NASA-POWER project (NASA-SPI and NASA-SPEI). Different from other packages, the PowerSDI package has two modes: the scientific, and the operational. In the scientific mode, the users may assess the quality of the indices estimates through their agreement with a reference/observed series and through the evaluation of how well these estimates meet the conceptual assumptions required for calculating both SPI and SPEI. This evaluation is based on measure of accuracy (e.g., Willmott index of agreement), goodness-of-fit tests (e.g., Anderson-Darling), and normality tests (e.g., Shapiro-Wilk’s test), which are calculated by the ScientSDI.R, Reference.R, and Accuracy.R functions. In the operational mode, users can calculate both indices routinely using the OperatSDI.R function. The package also uses a quasi-weekly time scale, allowing for index calculations four times a month. The OperatSDI.R enables users to download NASA-POWER data for all available period or only for the quasi-week they intend to monitor (reducing the function’s running time). In short, the PowerSDI facilitates the routine use of these two widely used drought indices and, unlike others existing software, it provides a solid scientific basis for using NASA-POWER data in drought monitoring systems, which can help improve drought preparedness and response efforts worldwide. The package is freely available at two repositories: Github (https://github.com/gabrielblain/PowerSDI), and CRAN (https://CRAN.R-project.org/package=PowerSDI).

Key words
standardized drought indices; drought monitoring; R-software

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