Preprints
https://doi.org/10.5194/egusphere-2022-596
https://doi.org/10.5194/egusphere-2022-596
 
11 Jul 2022
11 Jul 2022
Status: this preprint is open for discussion.

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

Judith Uwihirwe1,2, Alessia Riveros1,3, Hellen Wanjala4, Jaap Schellekens4, Frederiek Sperna Weiland3, Markus Hrachowitz1, and Thom A. Bogaard1 Judith Uwihirwe et al.
  • 1Section Water Resources, Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, PO Box 5048, 2600 GA, Delft, The Netherlands
  • 2Department of Irrigation and Drainage, School of Agricultural Engineering, College of Agriculture Animal Sciences and Veterinary Medicine, University of Rwanda, PO Box 210, Musanze, Rwanda
  • 3Deltares, PO Box 177, 2600 MH Delft
  • 4Planet Labs, PBC, Wilhelminastraat 43A, 2011 VK Haarlem, The Netherlands

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 system in data scarce regions. In this study, we used statistical metrics to compare gauge-based to 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 Rwanda weather station sites. Based on statistical indicators, the NASA GPM-based IMERG rainfall product showed the highest skill to reproduce the main spatiotemporal precipitation patterns at the studies 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 multiple day rainfall event. Second, the cumulative rainfall over the past few day(s). For each category, the antecedent soil moisture recorded at three levels of soil depth, top 5 cm by satellite-based technologies as well as top 50 cm and 2 m through 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 day rainfall RD3 as 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 TPR 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 of 84 %). The hydro-meteorological threshold models that incorporate the Seroot(t-3) and RD3 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 of true alarms by ~8 %.

Judith Uwihirwe et al.

Status: open (until 22 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Review to egushere-2022-596 by Uwihirwe et al.', Anonymous Referee #1, 10 Aug 2022 reply

Judith Uwihirwe et al.

Judith Uwihirwe et al.

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Short summary
This study compared gauge-based to satellite-based precipitation products. Similarly, satellite and hydrological model derived soil moisture was compared to in situ soil moisture and used in landslide hazard assessment and warning. The results reveal the cumulative 3 day rainfall from the NASA GPM as the most effective landslide trigger. The modelled antecedent soil moisture in the root zone was the most informative hydrological variable for landslide hazard assessment and warning in Rwanda.