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

Abstract

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.

Global rainfall erosivity database (GloREDa) and monthly R-factor data at 1 km spatial resolution

“…In this call for data, contributors from Poland, Slovenia, Uruguay and the Democratic Republic of Congo (DRC) responded with data from 314 new stations. The contribution from DRC is very important, as the R-factor data are very limited for Africa [9] . Finally, the updated GloREDa 1.2 includes 3939 stations ( Fig.…”

Fluvial sediment export from pristine forested headwater catchments in the Congo Basin

“…Tropical ecosystems are especially affected by erosion because of high precipitation rates and intensities (Bagalwa et al, 2021;Labrière et al, 2015). The Congo Basin is home to some of the most pristine tropical and subtropical forests, covering distinct forest types.…”

“…The Congo Basin is home to some of the most pristine tropical and subtropical forests, covering distinct forest types. While the erosion potential is high in montane forests in the eastern parts of the Congo basin because of the combination of high rainfall erosivity and steep topography (Bagalwa et al, 2021), physical erosion is reported to be much lower in the dense lowland forests in the central part of the basin (Borrelli et al, 2017). In contrast to both the montane and lowland forests, the subtropical Miombo woodlands in the south of the Congo Basin have a sparser vegetation cover (especially during the dry season, where the understory vegetation is lost to seasonal fires) compared to other tropical forests, leaving their surface layers less protected against heavy rainfall.…”