Potential of satellite-derived hydro-meteorological information for landslide initiation thresholds in Rwanda

Satellite and hydrological model-based technologies provide estimates of rainfall and soil moisture over larger spatial scales and now cover multiple decades, sufficient to explore their value for the development of landslide early warning systems in data-scarce regions. In this study, we used statistical metrics to compare gauge-based and satellite-based precipitation products and assess their performance in landslide hazard assessment and warning in Rwanda. Similarly, the value of high-resolution satellite and hydrological model-derived soil moisture was compared to in situ soil moisture observations at Rwandan weather station sites. Based on statistical indicators, rainfall data from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM_IMERG) showed the highest skill in reproducing the main spatiotemporal precipitation patterns at the study sites in Rwanda. Similarly, the satellite- and model-derived soil moisture time series broadly reproduce the most important trends of in situ soil moisture observations. We evaluated two categories of landslide meteorological triggering conditions from IMERG satellite precipitation: first, the maximum rainfall amount during a multi-day rainfall event, and second, the cumulative rainfall over the past few day(s). For each category, the antecedent soil moisture recorded at three levels of soil depth, the top 5 cm by satellite-based technologies as well as the top 50 cm and 2 m by modelling approaches, was included in the statistical models to assess its potential for landslide hazard assessment and warning capabilities. The results reveal the cumulative 3 d rainfall R-D3 to be the most effective predictor for landslide triggering. This was indicated not only by its highest discriminatory power to distinguish landslide from no-landslide conditions (AUC similar to 0.72), but also the resulting true positive alarms (TPRs) of similar to 80 %. The modelled antecedent soil moisture in the 50 cm root zone Seroot(t-3) was the most informative hydrological variable for landslide hazard assessment (AUC similar to 0.74 and TPR 84 %). The hydro-meteorological threshold models that incorporate the Seroot(t-3) and R-D3 following the cause-trigger concept in a bilinear framework reveal promising results with improved landslide warning capabilities in terms of reduced rate of false alarms by similar to 20 % at the expense of a minor reduction in true alarms by similar to 8 %.

Automated summary

Satellite and hydrological model-based technologies are valuable for developing landslide early warning systems in data-scarce regions. Statistical metrics were used to compare gauge-based and satellite-based precipitation products in Rwanda, showing high skill in reproducing precipitation patterns and soil moisture observations. Hydro-meteorological threshold models incorporating antecedent soil moisture and rainfall data demonstrate promising results in improving landslide warning capabilities.