The potential of Satellite and model derived precipitation and soil moisture for estimation of landslide hazard thresholds in Rwanda

<p>A combination of extreme environmental conditions such as high soil moisture content and heavy or prolonged precipitation contribute to landslide initiation in mountainous areas worldwide. On-site soil moisture monitoring equipment and rain gauge have been widely used to record these variables despite the sparse spatial coverage. Satellite‐based technologies provide estimates of rainfall and soil moisture over large spatial areas sufficient to be explored for landslide hazard assessment in data scarce regions. In this study, we used statistical metrics to compare the gauge based to the satellite precipitation products: TRMM42, CHIRPS, PERSIANN-CDR, GLDAS-2.1, CFSV2, GPM-IMERG, and ERA-5 and assess their performance. Similarly, high resolution satellite and hydrological model derived soil moisture was compared to the automated soil moisture observations at Rwanda weather station sites to assess the usefulness in empirical landslide hazard assessment thresholds in Rwanda. Based on statistical indicators, the NASA GPM based IMERG showed the highest skill to reproduce the main spatiotemporal precipitation patterns. Similarly, the satellite and hydrological model derived soil moisture broadly reproduce the in situ measured soil moisture. The landslide explanatory variables from IMERG satellite precipitation; event rainfall volume E and Duration D in bilinear thresholds framework reveal promising results with improved landslide prediction capabilities in terms of true positive alarms ~80-90% and low rate of false alarms ~14-16%. However, the incorporation of satellite and model derived antecedent soil moisture to the empirical landslide hydro-meteorological thresholds showed no significant improvement. This may be attributed to the probable long and no constant timescale of the defined landslide triggering events that could be shortened to further improve the landslide prediction and support the early warning system development in Rwanda.</p>