# Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Countries**} & \multicolumn{3}{c}{**Predicted MASLR (Aggregate) \&**} & \multirow{2}{*}{**No. of GCMs**} & \multirow{2}{*}{**With Predicted C**} & \multirow{2}{*}{**References**} \\  & & **(\% Change)** & & & & **Used** & \& P factors (LULC) \\ \cline{2-6}  & **2030s** & 2050s & & & & \\ \hline Greece & - & 3.42 (+0.6\%) & 3.7 (+9\%) & 1 & No & [51] \\ Ethiopia & 26.13 (+4.5\%) & 26.78 (+7.1\%) & 27.44 (+9.8\%) & 20 & No & [24] \\ Ethiopia & - & 78 (+23\%) & - & 1 & Yes & [10] \\ Nigeria & 1428.3 (+12.2\%) & 1517.9 (19.3\%) & 1534.8 (20.6\%) & 4 & No & [53] \\ Europe & - & 3.61 (+17.8\%) & - & 19 & Yes & [100] \\ Iran & 12.9 (+21.7\%) & - & - & 1 & Yes & [82] \\ China & - & 3.54 (+28.3\%) & - & 6 & Yes & [25] \\ Uzbekistan & 574.6 (+17.1\%) & 608.2 (+20.5\%) & 636.6 (+23.3\%) & 5 & No & [101] \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Predicted mean soil erosion rates (t ha\({}^{-1}\) yr\({}^{-1}\)) and percentage change from the respective baseline soil erosion rates for different countries around the world. climate change signals, e.g., MIROC-5 and CCCMA_CANESM2, were employed based on previous climatology research in Kenya and other similar tropical regions. Different studies have utilized multiple GCMs in soil erosion predictions in order to reduce the uncertainties inherent to climate models. In this study, we selected four models among the top eight models (out of 22 CMPI5 models), which [PERSON] et al. [61] demonstrated to well produce rainfall estimations over East Africa. The present research only focused on the impact of the climatic factor (\(K\)) and overlooked both anthropogenic (\(P\)) and vegetation factors (\(C\)), which have been shown to have great significance on soil erosion rates. Holding such pertinent factors at a constant certainly raises questions about the reliability of the future RUSLE predictions [74]. [PERSON] et al. [31] utilized LULC projections based on the contribution of various land uses to greenhouse gas emissions to study future soil erosion rates within the tropical Brazilian Pantanal region and revealed an increasing trend ranging between 40-100%. It is thus important to integrate projected vegetation cover coefficients (based on projections of LULC) in order to improve on the integrity of the results or simulations. The ESRI very recently released a high-resolution Global Land Cover map (10 m) using state-of-the-art artificial intelligence ([[https://livingatlas.arcgis.com/landcover/](https://livingatlas.arcgis.com/landcover/)]([https://livingatlas.arcgis.com/landcover/](https://livingatlas.arcgis.com/landcover/)) accessed on 3 August 2021), as part of its contribution to climate change action, and it plans to continue this initiative on a yearly basis. This database will be of great importance to environmental modeling studies, e.g., climate change and food security, that require large-scale vegetation cover information (especially for developing countries). [PERSON] et al. [80] noted that there is limited knowledge of EA's current support practices; thus \(P\) estimations are often neglected in large-scale research or set to a value of \"1\", which represents no soil conservation measures as being the worst case scenario. Moreover, [PERSON] et al. [102] reported that the impact of soil conservation measures rapidly decline over time, especially in EA's ASAL regions.