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© 2022. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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 RD3 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 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 in true alarms by 8 %.

Details

Title
Potential of satellite-derived hydro-meteorological information for landslide initiation thresholds in Rwanda
Author
Uwihirwe, Judith 1   VIAFID ORCID Logo  ; Riveros, Alessia 2 ; Wanjala, Hellen 3 ; Schellekens, Jaap 3 ; Weiland, Frederiek Sperna 4 ; Hrachowitz, Markus 5   VIAFID ORCID Logo  ; Bogaard, Thom A 5   VIAFID ORCID Logo 

 Section Water Resources, Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, the Netherlands; Department of Irrigation and Drainage, School of Agricultural Engineering, College of Agriculture Animal Sciences and Veterinary Medicine, University of Rwanda, P.O. Box 210, Musanze, Rwanda 
 Section Water Resources, Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, the Netherlands; Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands 
 Planet Labs, PBC, Wilhelminastraat 43A, 2011 VK Haarlem, the Netherlands 
 Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands 
 Section Water Resources, Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, the Netherlands 
Pages
3641-3661
Publication year
2022
Publication date
2022
Publisher
Copernicus GmbH
ISSN
15618633
e-ISSN
16849981
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2732499873
Copyright
© 2022. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.