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.
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.
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!
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.
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.