Geppert, M., Hartmann, K., Kirchner, I., Pfahl, S., Struck, U. & Riedel, F. (2022). Precipitation over southern Africa: Moisture sources and isotopic composition. Journal of Geophysical Research: Atmospheres, 127, e2022JD037005. doi: 10.1029/2022JD037005

Geppert et al. (2022) applied end-member modeling analysis (EMMA) to identify different groups of precipitation distributions in southern Africa. By inferring according rainfall zones the basis for further analysis regarding stable water isotope composition and moisture transport pathways was created. The results may aid interpretation of paleo-records and linkage to observational precipitation data. This in turn has the potential to enhance climate models and scenario calculation for precipitation changes and weather extremes, which are of particular relevance in the arid and semiarid regions of southern Africa vulnerable to anthropogenic climate change.

For a detailed description and example of the EMMA workflow in R please look into the previous section of this chapter. Here, only a brief summary of the analysis is presented.

Method

Average monthly precipitation data (in mm) for the period 1970-2000 and a spatial resolution of 10 min (∼340 km²) were downloaded from WorldClim 2.1.

Next, the end-member modeling algorithm implemented in the R package EMMAgeo was used to identify the main precipitation distributions in the data set.

Weighing the accuracy (high coefficient of determination) and simplicity (low number of end-members) of the model, a model with three robust end-members (EM) was selected. The final model explains 64 % of the variance of the data.

Results

The spatial distribution of the three identified end-members for precipitation distribution in southern Africa is represented in these maps:

Spatial distribution of the 3 end-members (Figure S1, Supplementary material for Geppert et al. (2022))

The spatial clustering of the precipitation distribution EM allowed the identification of five distinct precipitation regimes classified as rainfall zones (RZ). Based on the EM scores (relative amount of each end-member) each raster point of the study area was assigned to one of these five rainfall zones:

Spatial distribution of the five identified rainfall zones based on the 3 end-member model (Figure 5, Geppert et al. (2022). Used under a Creative Commons Attribution 4.0 International License).

Finally, the precipitation distribution groups identified through EMMA were analyzed with respect to their stable water isotope composition, moisture transport pathways, and sources. By linking these findings to typical circulation patterns the evaluation of isotope-enabled regional climate models and the interpretation of stable water isotope composition in paleo-records can be substantially improved.
Within the machine learning section of SOGA-R you will find a presentation of the algorithm for identification of co-variates determining stable isotope composition of precipitation using random forests!

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Citation

The E-Learning project SOGA-R was developed at the Department of Earth Sciences by Kai Hartmann, Joachim Krois and Annette Rudolph. You can reach us via mail by soga[at]zedat.fu-berlin.de.

You may use this project freely under the Creative Commons Attribution-ShareAlike 4.0 International License.

Please cite as follow: Hartmann, K., Krois, J., Rudolph, A. (2023): Statistics and Geodata Analysis using R (SOGA-R). Department of Earth Sciences, Freie Universitaet Berlin.