12 Feb 2021

12 Feb 2021

Review status: a revised version of this preprint is currently under review for the journal HESS.

Satellite rainfall products outperform ground observations for landslide forecasting in India

Maria Teresa Brunetti1, Massimo Melillo1, Stefano Luigi Gariano1, Luca Ciabatta1, Luca Brocca1, Giriraj Amarnath2, and Silvia Peruccacci1 Maria Teresa Brunetti et al.
  • 1IRPI, via Madonna Alta 126, 06128, Perugia, Italia
  • 2International Water Management Institute, Colombo, Sri Lanka

Abstract. Landslides are among the most dangerous natural hazards, particularly in developing countries where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important tool to forecast landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide forecasting, to reduce the impact of this hazard on the population.

We have performed a comparative study of ground- and satellite-based rainfall products for landslide forecasting in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides occurred throughout India in the 13-year period between April 2007 and October 2019 has been used. Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (10 km vs 25 km) and temporal (hourly vs daily) resolution. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g., overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.

Maria Teresa Brunetti et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-42', Anonymous Referee #1, 13 Feb 2021
    • AC1: 'Reply on RC1', Maria Teresa Brunetti, 18 Feb 2021
    • AC2: 'Reply on RC1', Maria Teresa Brunetti, 04 Mar 2021
  • RC2: 'Comment on hess-2021-42', Ben Mirus, 29 Mar 2021
    • AC3: 'Reply on RC2', Maria Teresa Brunetti, 07 Apr 2021

Maria Teresa Brunetti et al.

Data sets

SM2RAIN-ASCAT (2007-June 2020): global daily satellite rainfall from ASCAT soil moisture Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., and Wagner, W.

Model code and software

CTRL–T (Calculation of Thresholds for Rainfall-induced Landslides–Tool) Melillo, M., Brunetti, M. T., Peruccacci, S., Gariano, S. L., and Guzzetti, F.

Maria Teresa Brunetti et al.


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Short summary
Satellite and rain gauge data are tested to forecast landslides in India, where the annual toll of human lives and loss of property urgently demands the implementation of strategies to prevent geo-hydrological instability. For the purpose, we calculated empirical rainfall thresholds for landslide initiation. The validation of thresholds showed that satellite-based rainfall data perform better than ground-based data, and the best performance is obtained with an hourly temporal resolution.