A new software for spatio-temporal analysis of gridded data sources

Several long-term gridded datasets have become available in the last decades on a global scale, at increasing spatial and temporal resolution, with low latency times. These datasetshave opened new opportunities to advance Earth Sciences modelling studies at different spatial and temporal scales, especially in poorly gauged areas. However, working with (hundreds of) thousands of raster time series (e.g., for (sub)daily precipitation), usually in different vectorial and raster formats, impose high computational challenges to efficiently analyse all the gridded datasets.

In this work we introduce a new R package for easy processing and analysis of raster time series, to bring the use of gridded data closer to the Earth Sciences community. This package expands the large number of spatial functions provided by the terra package by taking advantage of the time attribute of raster objects. A particular emphasis of the package is exploring and comparing gridded datasets of hydrological variables with different time frequencies (e.g., sub-hourly, hourly, daily, monthly, seasonal, annual).

General purpose functions include temporal subsetting, resampling, cropping, extracting time series for points or polygons, comparing two datasets using summary statistics, and exporting a raster time series as a collection of daily/monthly/annual files, each one of them with several layers of a higher temporal frequency. Also, temporal aggregation is possible from sub-hourly to hourly/daily/weekly/monthly/annual, among others. Most functions can take advantage of multi-core computers and network clusters, to reduce the computational burden.

To illustrate the use of this new package, we compared different state-of-the-art gridded precipitation products (CHIRPSv2, CMORPH v1.0, IMERGv06B, MSWXv1.0, ERA5, ERA5-Land, CR2METv2.5), all of them with different data formats and spatial resolutions, using continental Chile as a case study. However, based on the package's flexibility and ease of use, we hope the broader community of hydro-scientists and water-engineers will use it to visualise the spatio-temporal variation of key hydrological/environmental variables,to carry out time series analysis, to combinedifferent types of models and data sources, and to improve our integrated knowledge of the water cycle.