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.
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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Jacopo Dari, Paolo Filippucci, and Luca Brocca
Hydrol. Earth Syst. Sci., 28, 2651–2659, https://doi.org/10.5194/hess-28-2651-2024, https://doi.org/10.5194/hess-28-2651-2024, 2024
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Søren Julsgaard Kragh, Jacopo Dari, Sara Modanesi, Christian Massari, Luca Brocca, Rasmus Fensholt, Simon Stisen, and Julian Koch
Hydrol. Earth Syst. Sci., 28, 441–457, https://doi.org/10.5194/hess-28-441-2024, https://doi.org/10.5194/hess-28-441-2024, 2024
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This study provides a comparison of methodologies to quantify irrigation to enhance regional irrigation estimates. To evaluate the methodologies, we compared various approaches to quantify irrigation using soil moisture, evapotranspiration, or both within a novel baseline framework, together with irrigation estimates from other studies. We show that the synergy from using two equally important components in a joint approach within a baseline framework yields better irrigation estimates.
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Stefan Steger, Mateo Moreno, Alice Crespi, Peter James Zellner, Stefano Luigi Gariano, Maria Teresa Brunetti, Massimo Melillo, Silvia Peruccacci, Francesco Marra, Robin Kohrs, Jason Goetz, Volkmar Mair, and Massimiliano Pittore
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Jacopo Dari, Luca Brocca, Sara Modanesi, Christian Massari, Angelica Tarpanelli, Silvia Barbetta, Raphael Quast, Mariette Vreugdenhil, Vahid Freeman, Anaïs Barella-Ortiz, Pere Quintana-Seguí, David Bretreger, and Espen Volden
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Hydrol. Earth Syst. Sci., 26, 4685–4706, https://doi.org/10.5194/hess-26-4685-2022, https://doi.org/10.5194/hess-26-4685-2022, 2022
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This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
Lorenzo Alfieri, Francesco Avanzi, Fabio Delogu, Simone Gabellani, Giulia Bruno, Lorenzo Campo, Andrea Libertino, Christian Massari, Angelica Tarpanelli, Dominik Rains, Diego G. Miralles, Raphael Quast, Mariette Vreugdenhil, Huan Wu, and Luca Brocca
Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, https://doi.org/10.5194/hess-26-3921-2022, 2022
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This work shows advances in high-resolution satellite data for hydrology. We performed hydrological simulations for the Po River basin using various satellite products, including precipitation, evaporation, soil moisture, and snow depth. Evaporation and snow depth improved a simulation based on high-quality ground observations. Interestingly, a model calibration relying on satellite data skillfully reproduces observed discharges, paving the way to satellite-driven hydrological applications.
Paolo Filippucci, Luca Brocca, Raphael Quast, Luca Ciabatta, Carla Saltalippi, Wolfgang Wagner, and Angelica Tarpanelli
Hydrol. Earth Syst. Sci., 26, 2481–2497, https://doi.org/10.5194/hess-26-2481-2022, https://doi.org/10.5194/hess-26-2481-2022, 2022
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A high-resolution (1 km) rainfall product with 10–30 d temporal resolution was obtained starting from SM data from Sentinel-1. Good performances are achieved using observed data (gauge and radar) over the Po River Valley, Italy, as a benchmark. The comparison with a product characterized by lower spatial resolution (25 km) highlights areas where the high spatial resolution of Sentinel-1 has great benefits. Possible applications include water management, agriculture and index-based insurances.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
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The International Soil Moisture Network (ISMN) is a community-based open-access data portal for soil water measurements taken at the ground and is accessible at https://ismn.earth. Over 1000 scientific publications and thousands of users have made use of the ISMN. The scope of this paper is to inform readers about the data and functionality of the ISMN and to provide a review of the scientific progress facilitated through the ISMN with the scope to shape future research and operations.
Daniele Masseroni, Stefania Camici, Alessio Cislaghi, Giorgio Vacchiano, Christian Massari, and Luca Brocca
Hydrol. Earth Syst. Sci., 25, 5589–5601, https://doi.org/10.5194/hess-25-5589-2021, https://doi.org/10.5194/hess-25-5589-2021, 2021
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We evaluate 63 years of changes in annual streamflow volume across Europe, using a data set of more than 3000 stations, with a special focus on the Mediterranean basin. The results show decreasing (increasing) volumes in the southern (northern) regions. These trends are strongly consistent with the changes in temperature and precipitation.
Louise Mimeau, Yves Tramblay, Luca Brocca, Christian Massari, Stefania Camici, and Pascal Finaud-Guyot
Hydrol. Earth Syst. Sci., 25, 653–669, https://doi.org/10.5194/hess-25-653-2021, https://doi.org/10.5194/hess-25-653-2021, 2021
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Soil moisture is a key variable related to droughts and flood genesis, but little is known about the evolution of soil moisture under climate change. Here, using a simulation approach, we show that changes in soil moisture are driven by changes in precipitation intermittence rather than changes in precipitation intensity or in temperature.
Stefania Camici, Christian Massari, Luca Ciabatta, Ivan Marchesini, and Luca Brocca
Hydrol. Earth Syst. Sci., 24, 4869–4885, https://doi.org/10.5194/hess-24-4869-2020, https://doi.org/10.5194/hess-24-4869-2020, 2020
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The paper performs the most comprehensive European-scale evaluation to date of satellite rainfall products for river flow prediction. In doing so, how errors transfer from satellite-based rainfall products into flood simulation is investigated in depth and, for the first time, quantitative guidelines on the use of these products for hydrological applications are provided. This result can represent a keystone in the use of satellite rainfall products, especially in data-scarce regions.
El Mahdi El Khalki, Yves Tramblay, Christian Massari, Luca Brocca, Vincent Simonneaux, Simon Gascoin, and Mohamed El Mehdi Saidi
Nat. Hazards Earth Syst. Sci., 20, 2591–2607, https://doi.org/10.5194/nhess-20-2591-2020, https://doi.org/10.5194/nhess-20-2591-2020, 2020
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In North Africa, the vulnerability to floods is high, and there is a need to improve the flood-forecasting systems. Remote-sensing and reanalysis data can palliate the lack of in situ measurements, in particular for soil moisture, which is a crucial parameter to consider when modeling floods. In this study we provide an evaluation of recent globally available soil moisture products for flood modeling in Morocco.
Cited articles
Adamson, G. C. D. and Nash, D. J.: Long-term variability in the date of monsoon onset over western India, Clim. Dynam., 40, 2589–2603, https://doi.org/10.1007/s00382-012-1494-x, 2013.
Annamalai, H., Slingo, J. M., Sperber, K. R., and Hodges, K.: The mean evolution and variability of the Asian summer monsoon: comparison of ECMWF and NCEP–NCAR reanalyses, Mon. Weather Rev., 127, 1157–1186,
https://doi.org/10.1175/1520-0493(1999)127<1157:TMEAVO>2.0.CO;2, 1999.
Berti, M., Martina, M. L. V., Franceschini, S., Pignone, S., Simoni, A., and
Pizziolo, M.: Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach, J. Geophys. Res., 117, F04006, https://doi.org/10.1029/2012JF002367, 2012.
Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer,
S., Kidd, R., Dorigo, W., Wagner, W., and Levizzani, V.: Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data, J.
Geophys. Res., 119, 5128–5141, https://doi.org/10.1002/2014JD021489, 2014.
Brocca, L., Filippucci, P., Hahn, S., Ciabatta, L., Massari, C., Camici, S., Schüller, L., Bojkov, B., and Wagner, W.: SM2RAIN-ASCAT (2007–June 2020): global daily satellite rainfall from ASCAT soil moisture (Version 1.3) [Data set], Zenodo, https://doi.org/10.5281/zenodo.3972958, 2019.
Brunetti, M. T., Peruccacci, S., Rossi, M., Luciani, S., Valigi, D., and
Guzzetti, F.: Rainfall thresholds for the possible occurrence of landslides in Italy, Nat. Hazards Earth Syst. Sci., 10, 447–458, https://doi.org/10.5194/nhess-10-447-2010, 2010.
Brunetti, M. T., Melillo, M., Peruccacci, S., Ciabatta, L., and Brocca, L.: How far are we from the use of satellite rainfall products in landslide
forecasting?, Remote Sens. Environ., 210, 65–75, https://doi.org/10.1016/j.rse.2018.03.016, 2018a.
Brunetti, M. T., Peruccacci, S., Palladino, M. R., Viero, A., and Guzzetti, F.: TXT-tool 2.039-1.2: Rainfall Thresholds for the Possible Initiation of
Landslides in the Italian Alps, in: Landslide Dynamics: ISDR-ICL Landslide
Interactive Teaching Tools, Vol. 1: Fundamentals, Mapping and Monitoring,
edited by: Sassa, K., Guzzetti, F., Yamagishi, H., Arbanas, Ž., and Casagli, N., Springer, Cham, 361–369, https://doi.org/10.1007/978-3-319-57774-6_26, 2018b.
Camici, S., Massari, C., Ciabatta, L., Marchesini, I., and Brocca, L.: Which
rainfall metric is more informative about the flood simulation performance?
A comprehensive assessment on 1318 basins over Europe, Hydrol. Earth Syst.
Sci., 24, 4869–4885, https://doi.org/10.5194/hess-24-4869-2020, 2020.
Cepeda, J., Höeg, K., and Nadim, F.: Landslide-triggering rainfall
thresholds: a conceptual framework, Q. J. Eng. Geol. Hydroge., 43, 69–84,
https://doi.org/10.1144/1470-9236/08-066, 2010.
Ciabatta, L., Marra, A. C., Panegrossi, G., Casella, D., Sanò, P., Dietrich, S., Massari, C., and Brocca, L.: Daily precipitation estimation
through different microwave sensors: Verification study over Italy, J. Hydrol., 545, 436–450, https://doi.org/10.1016/j.jhydrol.2016.12.057, 2017.
Dikshit, A. and Satym, N.: Probabilistic rainfall thresholds in Chibo, India: estimation and validation using monitoring system, J. Mt. Sci., 16, 870–883, https://doi.org/10.1007/s11629-018-5189-6, 2019.
Dikshit, A., Satym, N., Pradhan, B., and Kushal, S.: Estimating rainfall
threshold and temporal probability for landslide occurrences in Darjeeling
Himalayas, Geosci. J., 24, 225–233, https://doi.org/10.1007/s12303-020-0001-3, 2020a.
Dikshit, A., Sarkar, R., Pradhan, B., Segoni, S., and Alamri, A. M.: Rainfall
induced landslide studies in Indian Himalayan region: a critical review, Appl. Sci., 10, 2466, https://doi.org/10.3390/app10072466, 2020b.
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006.
Froude, M. J. and Petley, D. N.: Global fatal landslide occurrence from 2004
to 2016, Nat. Hazards Earth Syst. Sci., 18, 2161–2181,
https://doi.org/10.5194/nhess-18-2161-2018, 2018.
Gariano, S. L., Brunetti, M. T., Iovine, G., Melillo, M., Peruccacci, S.,
Terranova, O., Vennari, C., and Guzzetti, F.: Calibration and validation of
rainfall thresholds for shallow landslide forecasting in Sicily, southern
Italy, Geomorphology, 228, 653–665, https://doi.org/10.1016/j.geomorph.2014.10.019,
2015.
Gariano, S. L., Sarkar, R., Dikshit, A., Dorji, K., Brunetti, M. T., Peruccacci, S., and Melillo, M.: Automatic calculation of rainfall thresholds for landslide occurrence in Chukha Dzongkhag, Bhutan, Bull. Eng. Geol. Environ., 78, 4325–4332, https://doi.org/10.1007/s10064-018-1415-2, 2019.
Gariano, S. L., Melillo, M., Peruccacci, S., and Brunetti, M. T.: How much does the rainfall temporal resolution affect rainfall thresholds for landslide triggering?, Nat. Hazards, 100, 655–670, https://doi.org/10.1007/s11069-019-03830-x, 2020.
Geethu, T. H., Madhu, D., Ramesh, M. V., and Pullarkatt, D.: Towards establishing rainfall thresholds for a real-time landslide early warning
system in Sikkim, India, Landslides, 16, 2395–2408,
https://doi.org/10.1007/s10346-019-01244-1, 2019.
Guha-Sapir, D., Below, R., and Hoyois, P. H.: EM-DAT: International Disaster
Database, Université Catholique de Louvain, Brussels, Belgium, available
at: http://www.emdat.be, last access: 19 April 2021.
Gupta, V., Jain, M. K., Singh, P. K., and Singh, V.: An assessment of global
satellite-based precipitation datasets in capturing precipitation extremes:
A comparison with observed precipitation dataset in India, Int. J. Climatol., 40, 3667–3688, https://doi.org/10.1002/joc.6419, 2020.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: Rainfall thresholds for the initiation of landslides in central and southern Europe, Meteorol. Atmos. Phys., 98, 239–267, https://doi.org/10.1007/s00703-007-0262-7, 2007.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: The rainfall
intensity–duration control of shallow landslides and debris flows: an update, Landslides, 5, 3–17, https://doi.org/10.1007/s10346-007-0112-1, 2008.
Guzzetti, F., Gariano, S. L., Peruccacci, S., Brunetti, M. T., Marchesini, I., Rossi, M., and Melillo, M.: Geographical landslide early warning systems,
Earth-Sci. Rev., 200, 102973, https://doi.org/10.1016/j.earscirev.2019.102973, 2020.
He, S., Wang, J., and Liu, S.: Rainfall Event–Duration Thresholds for Landslide Occurrences in China, Water, 12, 494, https://doi.org/10.3390/w12020494, 2020.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R.J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global
Reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hong, Y., Adler, R. F., and Huffman, G. J.: Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment, Geophys. Res. Lett., 33, L22402, https://doi.org/10.1029/2006GL028010, 2006.
Huffman, G., Bolvin, D., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C.,
Nelkin, E., and Xie, P.: Algorithm Theoretical Basis Document (ATBD) Version 4.5, NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), NASA, Greenbelt, MD, USA, 2018.
Jaiswal, P. and van Westen, C. J.: Estimating temporal probability for landslide initiation along transportation routes based on rainfall thresholds, Geomorphology, 112, 96–10, https://doi.org/10.1016/j.geomorph.2009.05.008, 2009.
Jordanova, G., Gariano, S. L., Melillo, M., Peruccacci, S., Brunetti, M. T.,
and Jemec Auflič, M.: Determination of empirical rainfall thresholds for
shallow landslides in Slovenia using an automatic tool, Water, 12, 1449,
https://doi.org/10.3390/w12051449, 2020.
Kanungo, D. and Sharma, S.: Rainfall thresholds for prediction of shallow
landslides around Chamoli-Joshimath region, Garhwal Himalayas, India,
Landslides, 11, 629–638, https://doi.org/10.1007/s10346-013-0438-9, 2014.
Kim, S., Parinussa, R. M., Liu, Y. Y., Johnson, F. M., and Sharma, A.: A framework for combining multiple soil moisture retrievals based on maximizing temporal correlation, Geophys. Res. Lett., 42, 6662–6670, https://doi.org/10.1002/2015GL064981, 2015.
Kirschbaum, D. and Stanley, T.: Satellite-based assessment of rainfall-triggered landslide hazard for situational awareness, Earth's Future, 6, 505–523, https://doi.org/10.1002/2017EF000715, 2018.
Leonarduzzi, E. and Molnar, P.: Deriving rainfall thresholds for landsliding
at the regional scale: daily and hourly resolutions, normalisation, and
antecedent rainfall, Nat. Hazards Earth Syst. Sci., 20, 2905–2919,
https://doi.org/10.5194/nhess-20-2905-2020, 2020.
Mandal, P. and Sarkar, S.: Estimation of rainfall threshold for the early warning of shallow landslides along National Highway-10 in Darjeeling Himalayas, Nat. Hazards, 105, 2455–2480, https://doi.org/10.1007/s11069-020-04407-9, 2021.
Marra, F.: Rainfall thresholds for landslide occurrence: systematic underestimation using coarse temporal resolution data, Nat. Hazards, 95, 83–890, https://doi.org/10.1007/s11069-018-3508-4, 2019.
Massari, C., Brocca, L., Pellarin, T., Abramowitz, G., Filippucci, P., Ciabatta, L., Maggioni, V., Kerr, Y., and Fernández-Prieto, D: A daily/25 km short-latency rainfall product for data scarce regions based on the integration of the GPM IMERG Early Run with multiple satellite soil moisture products, Hydrol. Earth Syst. Sci., 24, 2687–2710,
https://doi.org/10.5194/hess-24-2687-2020, 2020.
Mathew, J., Giri Babu, D., Kundu, S., Vinod Kumar, K., and Pant, C. C.:
Integrating intensity–duration-based rainfall threshold and antecedent
rainfall-based probability estimate towards generating early warning for
rainfall-induced landslides in parts of the Garhwal Himalaya, India,
Landslides, 11, 575–588, https://doi.org/10.1007/s10346-013-0408-2, 2014.
Melillo, M., Brunetti, M. T., Peruccacci, S., Gariano, S. L., and Guzzetti, F.: An algorithm for the objective reconstruction of rainfall events responsible for landslides, Landslides, 12, 311–320, https://doi.org/10.1007/s10346-014-0471-3, 2015.
Melillo, M., Brunetti, M. T., Peruccacci, S., Gariano, S. L., Roccati, A.,
and Guzzetti, F.: A tool for the automatic calculation of rainfall thresholds
for landslide occurrence, Environ. Model. Softw., 105, 230–243,
https://doi.org/10.1016/j.envsoft.2018.03.024, 2018a.
Melillo, M., Brunetti, M. T., Peruccacci, S., Gariano, S. L., and Guzzetti, F.: CTRL–T (Calculation of Thresholds for Rainfall-induced Landslides – Tool), Zenodo, https://doi.org/10.5281/zenodo.4533719, 2018b.
Monsieurs, E., Dewitte, O., and Demoulin, A.: A susceptibility-based rainfall
threshold approach for landslide occurrence, Nat. Hazards Earth Syst. Sci.,
19, 775–789, https://doi.org/10.5194/nhess-19-775-2019, 2019.
Mooley, D. A. and Shukla, J.: Variability and forecasting of the summer monsoon rainfall over India, in: Monsoon Meteorology, edited by: Chang, C.-P. and Krishnamurti, T. N., Clarendon Press, Oxford, UK, 26–59, 1987.
Naidu, S., Sajinkumar, K. S., Oommen, T., Anuja, V. J., Samual, R. A., and
Muraleedharan, C.: Early warning system for shallow landslides using rainfall threshold and slope stability analysis, Geosci. Front., 9, 1871–1882, https://doi.org/10.1016/j.gsf.2017.10.008, 2018.
NASA: GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree × 0.1 degree V06 (GPM_3IMERGHHE) at GES DISC, available at: https://search.earthdata.nasa.gov/search?q=GPM_3IMERGHHE_06, last access: 20 January 2021.
Neal, R., Robbins, J., Dankers, R., Mitra, A., Jayakumar, A., Rajagopal, E. N., and Adamson, G.: Deriving optimal weather pattern definitions for the
representation of precipitation variability over India, Int. J. Climatol., 40, 342–360, https://doi.org/10.1002/joc.6215, 2019.
Pai, D. S., Sridhar, L., Rajeevan, M., Sreejith, O. P., Satbhai, N. S., and
Mukhopadyay, B.: Development of a new high spatial resolution
( ) Long Period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region, Q. J. Meteorol. Hydrol. Geophys., 65, 1–18, 2014.
Peruccacci, S., Brunetti, M. T., Luciani, S., Vennari, C., and Guzzetti, F.:
Lithological and seasonal control of rainfall thresholds for the possible
initiation of landslides in central Italy, Geomorphology, 139–140, 79–90,
https://doi.org/10.1016/j.geomorph.2011.10.005, 2012.
Peruccacci, S., Brunetti, M. T., Gariano, S. L., Melillo, M., Rossi, M., and
Guzzetti, F.: Rainfall thresholds for possible landslide occurrence in Italy, Geomorphology, 290, 39–57, https://doi.org/10.1016/j.geomorph.2017.03.031, 2017.
Rao, P. L. S., Mohanty, U. C., and Ramesh, K. J.: The evolution and retreat features of the summer monsoon over India, Meteorol. Appl., 12, 241–255, 2005.
Robbins, J. C.: A probabilistic approach for assessing landslide triggering
event rainfall in Papua New Guinea, using TRMM satellite precipitation estimates, J. Hydrol., 541, 296–309, https://doi.org/10.1016/j.jhydrol.2016.06.052,
2016.
Rossi, M., Marchesini, I., Tonelli, G., Peruccacci, S., Brunetti, M. T.,
Luciani, S., Ardizzone, F., Balducci, V., Bianchi, C., Cardinali, M., Fiorucci, F., Mondini, A. C., Reichenbach, P., Salvati, P., Santangelo, M.,
and Guzzetti, F.: TXT-tool 2.039-1.1 Italian National Early Warning System, in: Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools, edited by: Sassa, K., Guzzetti, F., Yamagishi, H., Arbanas, Ž., and Casagli, N.,
Springer, Cham, 341–349, https://doi.org/10.1007/978-3-319-57774-6_24, 2018.
Salinas-Jasso, J. A., Velasco-Tapia, F., Navarro De León, I., Salinas-Jasso, R. A., and Alva-Niño, E.: Estimation of rainfall thresholds for shallow landslides in the Sierra Madre Oriental, northeastern Mexico, J. Mt. Sci., 17, 1565–1580, https://doi.org/10.1007/s11629-020-6050-2, 2020.
Segoni, S., Piciullo, L., and Gariano, S. L.: A review of the recent literature on rainfall thresholds for landslide occurrence, Landslides, 15, 1483–1501, https://doi.org/10.1007/s10346-018-0966-4, 2018.
Sengupta, A., Gupta, S., and Anbarasu, K.: Rainfall thresholds for the initiation of landslide at Lanta Khola in north Sikkim, India, Nat. Hazards,
52, 31–42, https://doi.org/10.1007/s11069-009-9352-9, 2010.
Staley, D. M., Kean, J. W., Cannon, S. H., Schmidt, K. M., and Laber, J. L.:
Objective definition of rainfall intensity–duration thresholds for the initiation of post-fire debris flows in southern California, Landslides, 10,
547–562, https://doi.org/10.1007/s10346-012-0341-9, 2013.
Tang, G. Q., Clark, M. P., Papalexiou, S. M., Ma, Z. Q., and Hong, Y.: Have
satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis
datasets, Remote Sens. Environ., 240, 111697, https://doi.org/10.1016/j.rse.2020.111697, 2020.
Thakur, M. K., Lakshmi Kumar, T. V., Koteswara Rao, K., Barbosa, H., and Rao,
V. B.: A new perspective in understanding rainfall from satellites over a
complex topographic region of India, Sci. Rep., 9, 15610,
https://doi.org/10.1038/s41598-019-52075-y, 2019.
Thakur, M. K., Lakshmi Kumar, T. V., Narayanan, M. S., Kundeti, K. R., and
Barbosa, H.: Analytical study of the performance of the IMERG over the Indian landmass, Meteorol. Appl., 27, 1908, https://doi.org/10.1002/met.1908, 2020.
Thomas, M. A., Collins, B. D., and Mirus, B. B.: Assessing the feasibility of
satellite-based thresholds for hydrologically driven landsliding, Water Resour. Res., 55, 9006–9023, https://doi.org/10.1029/2019WR025577, 2019.
Tyagi, A., Mazumdar, A. B., Khole, M., Gaonkar, S. B., and Devi, S.:
Re-determination of normal dates of onset of southwest monsoon over India,
Mausam, 62, 321–328, 2011.
Uwihirwe, J., Hrachowitz, M., and Bogaard, T. A.: Landslide precipitation
thresholds in Rwanda, Landslides, 17, 2469–2481, https://doi.org/10.1007/s10346-020-01457-9, 2020.
Valenzuela, P., Zêzere, J. L., Domínguez-Cuesta, M. J., and Mora García, M. A.: Empirical rainfall thresholds for the triggering of
landslides in Asturias (NW Spain), Landslides, 16, 1285–1300,
https://doi.org/10.1007/s10346-019-01170-2, 2019.
Wagner, W., Hahn, S., Kidd, R., Melzer, T., Bartalis, Z., Hasenauer, S., Figa, J., de Rosnay, P., Jann, A., Schneider, S., Komma, J., Kubu, G., Brugger, K., Aubrecht, C., Zuger, J., Gangkofner, U., Kienberger, S., Brocca, L., Wang, Y., Bloeschl, G., Eitzinger, J., Steinnocher, K., Zeil, P., and Rubel, F.: The ASCAT soil moisture product: a review of its specifications, validation results, and emerging applications, Meteorol. Z., 22, 5–33, https://doi.org/10.1127/0941-2948/2013/0399, 2013.
Zêzere, J. L., Vaz, T., Pereira, S., Oliveira, S. C., Marques, R., and Garcia, R. A. C.: Rainfall thresholds for landslide activity in Portugal: a state of the art, Environ. Earth Sci., 73, 2917–2936,
https://doi.org/10.1007/s12665-014-3672-0, 2015.
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...