Print Email Facebook Twitter Potential of satellite-derived hydro-meteorological information for landslide initiation thresholds in Rwanda Title Potential of satellite-derived hydro-meteorological information for landslide initiation thresholds in Rwanda Author Uwihirwe, J. (TU Delft Water Resources; University of Rwanda) Riveros Pavez, A.D. (Deltares; Student TU Delft) Wanjala, Hellen (Planet Labs PBC) Schellekens, Jaap (Planet Labs PBC) Sperna Weiland, Frederiek (Deltares) Hrachowitz, M. (TU Delft Water Resources) Bogaard, T.A. (TU Delft Water Resources) Date 2022 Abstract 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 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 ∼ 0.72), but also the resulting true positive alarms (TPRs) of ∼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 ∼ 0.74 and TPR 84 %). The hydro-meteorological threshold models that incorporate the Seroot(t−3) and 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 ∼20 % at the expense of a minor reduction in true alarms by ∼8 %. To reference this document use: http://resolver.tudelft.nl/uuid:29e74874-7f72-4fbf-b0a9-9ed20d7f97b0 DOI https://doi.org/10.5194/nhess-22-3641-2022 ISSN 1561-8633 Source Natural Hazards and Earth System Sciences, 22 (11), 3641–3661 Part of collection Institutional Repository Document type journal article Rights © 2022 J. Uwihirwe, A.D. Riveros Pavez, Hellen Wanjala, Jaap Schellekens, Frederiek Sperna Weiland, M. Hrachowitz, T.A. Bogaard Files PDF nhess_22_3641_2022.pdf 6.46 MB Close viewer /islandora/object/uuid:29e74874-7f72-4fbf-b0a9-9ed20d7f97b0/datastream/OBJ/view