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Data from Beyond MAP: A guide to dimensions of rainfall variability for tropical ecology

Citation

Schwartz, Naomi; Lintner, Benjamin R.; Feng, Xue; Powers, Jennifer S. (2020), Data from Beyond MAP: A guide to dimensions of rainfall variability for tropical ecology, Dryad, Dataset, https://doi.org/10.5061/dryad.f7m0cfxsq

Abstract

Tropical ecologists have long recognized rainfall as the key climate filter shaping tropical ecosystem structure and function across space and time. Still, tropical ecologists have historically had a limited toolkit for characterizing rainfall, largely relying on simple metrics like mean annual precipitation (MAP) and dry season length to characterize rainfall regimes that vary along many more dimensions. Here, we review methods for quantifying dimensions of rainfall variability on multiple time scales, with a focus on ecological applications of these methods. We also discuss key considerations for tropical ecologists looking to use rainfall metrics that better align with hypothesized biological or ecological mechanisms or that more effectively describe rainfall variability in the systems we study, and provide a toolkit (R scripts and gridded datasets) to do so. We argue that incorporating more sophisticated approaches to quantify rainfall variability into study design and statistical analyses will enhance our understanding of past, ongoing, and future changes in tropical ecosystems.  

Methods

Dataset contains four gridded datasets of dimensions of rainfall variation as described in Schwartz et al. All variables were derived from monthly rainfall time series, derived from TRMM data from 1997-2016 and are presented at 0.25 degree resolution. Each .tif file has two bands. Band 1 is the mean value for the DRV and band 2 is its standard deviation over the 20-year time period. 

Also included are R functions and example scripts for calculating DRVs. 

Usage Notes

Necessary information is in the readme files. 

Funding

National Science Foundation, Award: PRFB 1711366

U.S. Department of Energy, Award: DE-SC0014363