Spatial and seasonal patterns of rainfall erosivity in the Lake Kivu region: Insights from a meteorological observatory network
Autor: | Simon Baumgartner, R.M. Bagalwa, Caroline Chartin, N.L. Cizungu, Matti Barthel, Johan Six, M.T. Zabona, K. Karume, V.C. Samba, K. Van Oost, S. Mercier, Pascal Boeckx, Sebastian Doetterl, M. Syauswa |
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Přispěvatelé: | UCL - SST/ELI/ELIC - Earth & Climate |
Rok vydání: | 2021 |
Předmět: |
Planning and Development
Water erosion Geography 010504 meteorology & atmospheric sciences Geography Planning and Development 0207 environmental engineering 02 engineering and technology Atmospheric sciences 01 natural sciences Soil retrogression and degradation Meteorological observatory Earth and Planetary Sciences (miscellaneous) General Earth and Planetary Sciences Environmental science 020701 environmental engineering 0105 earth and related environmental sciences |
Zdroj: | Progress in Physical Geography: Earth and Environment, p. 030913332110017 (2021) |
ISSN: | 1477-0296 0309-1333 |
Popis: | 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. |
Databáze: | OpenAIRE |
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