Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3267-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-3267-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Jun 2021
Research article |
| 16 Jun 2021
| 16 Jun 2021
Satellite rainfall products outperform ground observations for landslide prediction in India
Maria Teresa Brunetti
CORRESPONDING AUTHOR
CNR IRPI, Via Madonna Alta 126, 06128, Perugia, Italy
Massimo Melillo
CNR IRPI, Via Madonna Alta 126, 06128, Perugia, Italy
Stefano Luigi Gariano
CNR IRPI, Via Madonna Alta 126, 06128, Perugia, Italy
Luca Ciabatta
CNR IRPI, Via Madonna Alta 126, 06128, Perugia, Italy
Luca Brocca
CNR IRPI, Via Madonna Alta 126, 06128, Perugia, Italy
Giriraj Amarnath
International Water Management Institute, Colombo, Sri Lanka
Silvia Peruccacci
CNR IRPI, Via Madonna Alta 126, 06128, Perugia, Italy
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- Accounting for satellite rainfall uncertainty in rainfall-triggered landslide forecasting B. Zhao et al. 10.1016/j.geomorph.2021.108051
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- A comprehensive assessment of SM2RAIN-NWF using ASCAT and a combination of ASCAT and SMAP soil moisture products for rainfall estimation M. Saeedi et al. 10.1016/j.scitotenv.2022.156416
- An hourly shallow landslide warning model developed by combining automatic landslide spatial susceptibility and temporal rainfall threshold predictions Y. Cao et al. 10.1007/s11629-022-7370-1
- Satellite-based precipitation error propagation in the hydrological modeling chain across China J. Gou et al. 10.1016/j.jhydrol.2024.130906
- Geoinformatic Analysis of Rainfall-Triggered Landslides in Crete (Greece) Based on Spatial Detection and Hazard Mapping A. Argyriou et al. 10.3390/su14073956
- Big Data, Small Island: Earth Observations for Improving Flood and Landslide Risk Assessment in Jamaica C. Avalon-Cullen et al. 10.3390/geosciences13030064
- Challenges of modeling rainfall triggered landslides in a data-sparse region: A case study from the Western Ghats, India C. Vishnu et al. 10.1016/j.geogeo.2022.100060
- Assessment of Satellite Precipitation Data Sets for High Variability and Rapid Evolution of Typhoon Precipitation Events in the Philippines P. Aryastana et al. 10.1029/2022EA002382
- Which Rainfall Errors Can Hydrologic Models Handle? Implications for Using Satellite‐Derived Products in Sparsely Gauged Catchments C. Stephens et al. 10.1029/2020WR029331
- A Landslide Numerical Factor Derived from CHIRPS for Shallow Rainfall Triggered Landslides in Colombia C. Cullen et al. 10.3390/rs14092239
- DEWS: A QGIS tool pack for the automatic selection of reference rain gauges for landslide-triggering rainfall thresholds O. Al-Thuwaynee et al. 10.1016/j.envsoft.2023.105657
- Feasibility of satellite-based rainfall and soil moisture data in determining the triggering conditions of debris flow: The Jiangjia Gully (China) case study H. Yang et al. 10.1016/j.enggeo.2023.107041
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18 citations as recorded by crossref.
- Rainfall thresholds estimation for shallow landslides in Peru from gridded daily data C. Millán-Arancibia & W. Lavado-Casimiro 10.5194/nhess-23-1191-2023
- Evaluation of rainfall thresholds triggering debris flows in western China with gauged- and satellite-based precipitation measurement Z. Liu 10.1016/j.jhydrol.2023.129500
- Assessment of bottom-up satellite rainfall products on estimating river discharge and hydrologic signatures in Brazilian catchments A. Almagro et al. 10.1016/j.jhydrol.2021.126897
- Landslides forecasting using satellite rainfall estimations and machine learning in the Colombian Andean region D. Gómez et al. 10.1016/j.jsames.2023.104293
- Accounting for satellite rainfall uncertainty in rainfall-triggered landslide forecasting B. Zhao et al. 10.1016/j.geomorph.2021.108051
- Open gridded climate datasets can help investigating the relation between meteorological anomalies and geomorphic hazards in mountainous areas R. Paranunzio & F. Marra 10.1016/j.gloplacha.2023.104328
- Regional-scale evaluation of 14 satellite-based precipitation products in characterising extreme events and delineating rainfall thresholds for flood hazards G. Moura Ramos Filho et al. 10.1016/j.atmosres.2022.106259
- A comprehensive assessment of SM2RAIN-NWF using ASCAT and a combination of ASCAT and SMAP soil moisture products for rainfall estimation M. Saeedi et al. 10.1016/j.scitotenv.2022.156416
- An hourly shallow landslide warning model developed by combining automatic landslide spatial susceptibility and temporal rainfall threshold predictions Y. Cao et al. 10.1007/s11629-022-7370-1
- Satellite-based precipitation error propagation in the hydrological modeling chain across China J. Gou et al. 10.1016/j.jhydrol.2024.130906
- Geoinformatic Analysis of Rainfall-Triggered Landslides in Crete (Greece) Based on Spatial Detection and Hazard Mapping A. Argyriou et al. 10.3390/su14073956
- Big Data, Small Island: Earth Observations for Improving Flood and Landslide Risk Assessment in Jamaica C. Avalon-Cullen et al. 10.3390/geosciences13030064
- Challenges of modeling rainfall triggered landslides in a data-sparse region: A case study from the Western Ghats, India C. Vishnu et al. 10.1016/j.geogeo.2022.100060
- Assessment of Satellite Precipitation Data Sets for High Variability and Rapid Evolution of Typhoon Precipitation Events in the Philippines P. Aryastana et al. 10.1029/2022EA002382
- Which Rainfall Errors Can Hydrologic Models Handle? Implications for Using Satellite‐Derived Products in Sparsely Gauged Catchments C. Stephens et al. 10.1029/2020WR029331
- A Landslide Numerical Factor Derived from CHIRPS for Shallow Rainfall Triggered Landslides in Colombia C. Cullen et al. 10.3390/rs14092239
- DEWS: A QGIS tool pack for the automatic selection of reference rain gauges for landslide-triggering rainfall thresholds O. Al-Thuwaynee et al. 10.1016/j.envsoft.2023.105657
- Feasibility of satellite-based rainfall and soil moisture data in determining the triggering conditions of debris flow: The Jiangjia Gully (China) case study H. Yang et al. 10.1016/j.enggeo.2023.107041
1 citations as recorded by crossref.
Latest update: 24 Apr 2024
Short summary
Satellite and rain gauge data are tested to predict 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 this 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.
Satellite and rain gauge data are tested to predict landslides in India, where the annual toll...
