# Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya
## 4 Discussion
There was an overall dramatic positive change in rainfall erosivity in the investigated area across all the models, except for model MIROC-5 under the RCP 2.6 scenario for the 2030s and MIROC-5 under RCP 8.5 for the 2080s. The projected future erosion rates were within the range of estimates given other works in EA [10; 24; 55], as with the continental predictions [1] (Table 19). For instance, references [25; 100; 101] observed increases of the mean soil loss rate ranging between +17.8%-28.3% in China, Europe and Uzbekistan (Table 19). Similar to observations made by [PERSON] et al. [100] across Europe, Kenya having a lower erosion rate than Ethiopia (16.9 t ha\({}^{-1}\) yr\({}^{-1}\)) [14] is expected to record larger increments due climate change. High amounts of rainfall can increase the erosive power of runoff and significantly magnify soil loss rates, especially in steep terrains. It is also possible that increases in precipitation can promote vegetation growth and, in turn, enhance plant canopy protection against soil erosion [99]. The highest change in rainfall erosivity was noted in the rift valley, Lake Victoria and central and eastern regions, which consequently resulted in high soil erosion rates. This may lead to other hazardous off-site effects, including sedimentation in water bodies, landslides and flooding in these regions. For most cases, the highest variations in soil loss occurred in the central highlands and western regions of Kenya, which represent areas of intensive agricultural production including the Kenya's grain basket region [43]. Agricultural output in these regions will most likely be negatively affected by climate variability, due to possible increases in soil erosion rates and soil degradation. It is important for the country to adapt to these expected changes and devise soil and water conservation policies that will enhance agricultural resilience (ensure a balance between food security and acceptable soil loss rates). Effective soil conservation methods, as eco-sustainable agricultural technologies, should be continuously implemented in such areas. On the other hand, public policies should encourage more agricultural expansions in regions projected to have reductions in average rainfall erosivity [41] (and consequent low soil loss rates), e.g., the North Eastern region, Coastal region and northern parts of the Rift Valley. The recently launched Galana Kalalu irrigation scheme within the NER and CR can be seen as a profitable shift from the country's overdependence on rain-fed agriculture aimed at improving food security [6; 8]. Nevertheless, caution should be taken when converting such natural landscapes into croplands via irrigation, as increased farming in arid lands with no accompanying conservation practices can greatly enhance soil loss rates. The inability to reflect major rainfall erosivity variability arising from extreme rainfall phenomena is a major setback of the RUSLE empirical method applied. The monthly precipitation data used in the study would not be sufficient to account for the size of raindrops and rainfall duration, which bear a direct correlation to surface erosion. In addition, uncertainties inherent to GCMs, including bias correction methods (Delta method), spatial resolution (resampling to 1 km resolution) and other model assumptions, also pose a major limitation to the present study [53]. Different researchers across the globe have applied multiple climate models with variable future scenarios with an aim to mitigate such uncertainties. Likewise, four GCMs that address differences or inconsistencies arising from