02b3e380-8e22-4fda-b960-414229f585bc SANBI The Metadata Manager +27 21 799 8738 Kirstenbosch Research Centre Cape Town Claremont 7735 South Africa bgishelp@sanbi.org.za 2025-08-25T15:49:55 SANS 1878 FGDC-STD-001-1998 Albers 2021 Downscaled Mean Annual Precipitation for South Africa, Lesotho and eSwatini 2021 Rain gauge dataset from 8 319 rainfall stations was used to interpolate a rainfall surface using Empirical Bayesian Kriging (EBK) Regression Prediction geostatistical analysis tool available in ArcGIS Pro 2.7. The rain gauge dataset was received courtesy of the University of KwaZulu-Natal (Lynch 2004) and the dataset was last updated in 2002. Some rainfall stations had data going back to 1871 till the year 2001. Only those rainfall stations that have been active for long enough to be representative of the long-term mean were included (Lynch & Dent 1990). Mean annual rainfall values were extracted for each station and used as the dependent variable in the EBK Regression Prediction tool. This tool was selected as it is a recent and robust geostatistical method which considers local variation in explanatory variables and is in essence a hybrid between regression and kriging approaches to interpolation. To apply EBK Regression Prediction to the interpolation of Mean Annual Precipitation, six explanatory variables were used to predict precipitation at local scales. The explanatory variables that were selected included standard geographic and topographic variables known to influence rainfall. These datasets were either derived from the Shuttle Radar Topography Mission (SRTM) 90 m Digital Elevation Model (DEM) or created with the same resolution of 90 m pixels. Continentality refers to the climatic phenomenon whereby locations further inland are isolated from the climatic influence of the oceans. With regard to rainfall, the general pattern is that interior areas are further from the moist oceanic source of atmospheric water and are therefore drier. Continentality was included as a simple distance from the coastline, measured in km (a). Latitude in this instance simply represents the distance south from the equator (b). Most generally, rainfall decreases with increasing latitude, and particularly in tropical regions, latitude can influence the duration and intensity of rainfall. Altitude is the height of a point relative to mean sea level (c). The general pattern is that rainfall increases with altitude, as atmospheric water precipitates due to the effects of altitude, increasing altitude decreases air temperature and air density. The downscaling of the South African rainfall surface was a requirement for the fine-scale delineation of the national Strategic Water Source Areas (SWSAs). Because of the shared water catchments that feed water supply systems in South Africa, this surface was generated across South Africa, Lesotho and eSwatini. To incorporate the local effects of topography on rainfall, the Topographic Position Index (TPI) was also included as an explanatory variable. The TPI compares the elevation of each cell in a DEM to the mean elevation of a defined neighbourhood surrounding that cell. By doing so it calculates the degree to which a location is higher or lower than its surrounds and provides a way to highlight mountain ridges and valley bottoms. Positive TPI values represent locations that are higher than the average of their surroundings (mountains), negative values represent locations that are lower than their surroundings (valleys), and values near zero are flat areas. The inclusion of TPI is based on the concept of orographic precipitation where moist air precipitates as it is cooled while rising over a mountain range. Topography theoretically has a strong effect on orographic rainfall, whereby moist air rising over the windward side of a mountain will precipitate rainfall and the drier air on the leeward side creates a rain shadow. As TPI is strongly scale-dependent, it was calculated from the 90 m SRTM DEM using both a 50 km (d) and 250 km search radius (e). By default, the EBK Regression Prediction tool will create random overlapping polygons that incorporate on average around 500 sampling points. Each of these subset polygons will have its own semivariogram to model precipitation. However, the default is a random dataset, and it is recommended to rather select more representative subset polygons that are based on hydrologically or ecologically meaningful units. We used various model performance metrics to determine that the South African Weather Service Rainfall Districts produced the best result. Model performance was assessed using two primary model fit statistics, the Root Mean Square Error (RMSE) and the Continuous Rank Probability Score (CRPS). Both of these statistics are crossvalidation procedures, through which a data point is removed, then the model run with the remaining data points, and then the predicted value for the excluded data point is compared to its actual value. This is done for all the measurements. In both of these statistics, a lower value indicates better model fit. The CRPS is the most important model fit statistic for this analysis, and it was ultimately used to select the best performing model. The RMSE is useful as it is measured in the same units as the response variable, in this case mm precipitation per annum. The EBK Regression Prediction in ArcGIS also provides a Standard Error surface, which shows where the predictions are less certain. The final model with the selected parameters provided an improvement in model fit in comparison to other interpolation options tested. For comparison, using Simple Kriging resulted in an RMSE of 78 mm. Applying Empirical Bayesian Kriging improved this to 74 mm and the final EBK Regression Prediction had an error of only 68 mm. The EBK Regression Prediction process also provides a Standard Error surface that shows the uncertainty related to the predicted values (side graphic). The downscaled rainfall surface was refined as part of the work of the Biodiversity and Land Use (BLU) project which was implemented by SANBI in partnership with a range of public and private organisations and supported by the United Nations Development Programme (UNDP The Downscaled Mean Annual Precipitation surface for South Africa, Lesotho and eSwatini is a refined spatial layer that was a necessary input for delineating SWSAs at a finer scale. This new interpolated and downscaled mean annual precipitation surface is an advance on previously available data and will also be useful in many other applications, such as species distribution models. Mrs Precious Nokuthula Mahlangu South African National Biodiversity Institute (SANBI) Principal Researcher + 27 (21) 888 2407 Stellenbosch Stellenbosch Western Cape South Africa no.mahlangu2@sanbi.org.za Dr Mervyn Lötter Mpumalanga Tourism and Parks Agency Control Scientist: Biodiversity Planningng Pty Ltd Principal Hydrogelogist +27(0) 82 043 8075 Cape Town Office, 15 Bournemouth Road Muizenberg 7950 South Africa mervyn.lotter@mtpa.co.za Mean Annual Precipitation, Strategic Water Source Areas, surface water South Africa 10000 environment 2021-01-01 2021-01-01 15.292968749999995 33.486328125 -35.17380831799959 -21.616579336740585 SANBI bgishelp@sanbi.org.za ZIP 1 http://hdl.handle.net/20.500.12143/8521