Spatial and seasonal patterns of rainfall erosivity in the Lake Kivu region: Insights from a meteorological observatory network

Bagalwa Rukeza, Montfort [UCL] Chartin, Caroline [UCL] Baumgartner, Simon [UCL] Mercier, Sophie [UCL] Syauswa, M. [Observatoire Volcanologique de Goma, DR Congo] Samba, V.C. [Observatoire Volcanologique de Goma, DR Congo] Zabona, M.T. [Observatoire Volcanologique de Goma, DR Congo] Karume, K. [Observatoire Volcanologique de Goma, DR Congo] Cizungu, N.L. [Université Catholique de Bukavu, DR Congo] Barthel, M. [Swiss Federal Institute of Technology, ETH Zürich, Switzerland] Doetterl, S. [Swiss Federal Institute of Technology, ETH Zürich, Switzerland] Six, J. [Swiss Federal Institute of Technology, ETH Zürich, Switzerland] Boeckx, P. [Ghent University, Belgium] Van Oost, Kristof [UCL] et al.[show all]

In the Lake Kivu region, water erosion is the main driver for soil degradation, but observational data to quantify the extent and to assess the spatial-temporal dynamics of the controlling factors are hardly available. In particular, high spatial and temporal resolution rainfall data are essential as precipitation is the driving force of soil erosion. In this study, we evaluated to what extent high temporal resolution data from the TAHMO network (with poor spatial and long-term coverage) can be combined with low temporal resolution data (with a high spatial density covering long periods of time) to improve rainfall erosivity assessments. To this end, 5 minute rainfall data from TAHMO stations in the Lake Kivu region, representing ca. 37 observation-years, were analyzed. The analysis of the TAHMO data showed that rainfall erosivity was mainly controlled by rainfall amount and elevation and that this relation was different for the dry and wet season. By combining high and low temporal resolution databases and a set of spatial covariates, an environmental regression approach (GAM) was used to assess the spatiotemporal patterns of rainfall erosivity for the whole region. A validation procedure showed relatively good predictions for most months (R2 between 0.50 and 0.80), while the model was less performant for the wettest (April) and two driest months (July and August) (R2 between 0.24 and 0.38). The predicted annual erosivity was highly variable with a range between 2000 and 9000 MJ mm ha−1 h−1 yr−1 and showed a pronounced east–west gradient which is strongly influenced by local topography. This study showed that the combination of high and low temporal resolution rainfall data and spatial prediction models can be used to improve the assessments of monthly and annual rainfall erosivity patterns that are grounded in locally calibrated and validated data.