Long-term rainfall regression surfaces for the Kruger National Park, South Africa: a spatio-temporal review of patterns from 1981 to 2015

As an important bottom-up driver of ecosystem processes, rainfall is intrinsically linked to the dynamics of vegetation and species distributions through its effects on soil moisture content and surface water availability. Rainfall effects are thus spatially and temporally specific to different environmental role-players. Knowledge of its spatio-temporal pattern is therefore essential to understanding natural ecosystem flux and potential climate change effects. Climate change poses a serious threat to protected areas in particular, as they are often isolated in fragmented landscapes and confined within hard park boundaries. In consequence, a species' natural movement response to resulting climate-induced niche shifts is often obstructed. Long-term, accurate and consistent climate monitoring data are therefore important resources for managers in large protected areas like the Kruger National Park (Kruger). In this article we model local rainfall measurements as a function of global rainfall surfaces, elevation and distance to the ocean using a generalized additive mixed effects model to produce fine-scale (1 km²) monthly rainfall surfaces from July 1981 to June 2015. Results show a clear seasonal cycle nested within an oscillating multi-decadal trend. Most noticeably, seasonality is shifting both temporally and spatially as rainfall moves outside of the typical dry/wet periods and areas. In addition, high-rainfall seasons are generally receiving more rainfall while low-rainfall seasons are receiving less. Northwestern regions of the park are experiencing more extreme annual rainfall differences, while far northern and southern regions show greater seasonality changes. The well-described north–south and east–west rainfall gradient is still visible but the spatial complexity of this pattern is more pronounced than expected. Taken together, we show that Kruger's spatio-temporal rainfall patterns are changing significantly in the short to medium term. The resulting raster data set is made freely available here to promote holistic ecosystem studies and support longer-term climate change research.

Modelled 1 km² resolution monthly rainfall grids, derived from in situ rainfall measurements, environmental covariates and the Climate Hazards Infrared Precipitation with Stations (CHIRPS) global climate surfaces (Funk et al., 2015), are provided here. Monthly rainfall follows a Southern Hemisphere rainfall year i.e July year i to June year i+1 from 1981 to 2015. Files are provided in two formats, namely:

.GRD (Grid)
rain_monthly_July1981toJune2015_utm.grd
rain_monthly_July1981toJune2015_utm.gri

.TIF (GeoTIFF)
rain_monthly_July1981toJune2015_utm.tif
rain_monthly_July1981toJune2015_utm.tif.aux.xml

Layer names are stored in a separate .CSV file (names_rain_monthly_July1981toJune2015_utm.csv) and a R script is provided to assist reading files into R (R Core Team 2023): readRainfall.R. The full aricle is open access and avialable here: MacFadyen et al 2018