Articles | Volume 24, issue 9
https://doi.org/10.5194/nhess-24-3207-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-24-3207-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Sep 2024
Research article |
| 23 Sep 2024
| 23 Sep 2024
More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh–Joshimath (NH-7) highway, Uttarakhand, India
Institute of Environmental Science and Geography, University of Potsdam, 14476 Potsdam-Golm, Germany
Ravi Kumar Guntu
Department of Hydrology, IIT Roorkee, Roorkee, 247667, India
now at: GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
Alexander Plakias
Institute of Environmental Science and Geography, University of Potsdam, 14476 Potsdam-Golm, Germany
now at: Urban Ecosystem Science, Institute of Ecology, Technische Universität Berlin, 10587 Berlin, Germany
Igo Silva de Almeida
Institute of Environmental Science and Geography, University of Potsdam, 14476 Potsdam-Golm, Germany
Wolfgang Schwanghart
Institute of Environmental Science and Geography, University of Potsdam, 14476 Potsdam-Golm, Germany
Related authors
Anna-Maartje de Boer, Wolfgang Schwanghart, Jürgen Mey, Basanta Raj Adhikari, and Tony Reimann
Geochronology, 6, 53–70, https://doi.org/10.5194/gchron-6-53-2024, https://doi.org/10.5194/gchron-6-53-2024, 2024
Short summary
Short summary
This study tested the application of single-grain feldspar luminescence for dating and reconstructing sediment dynamics of an extreme mass movement event in the Himalayan mountain range. Our analysis revealed that feldspar signals can be used to estimate the age range of the deposits if the youngest subpopulation from a sample is retrieved. The absence of clear spatial relationships with our bleaching proxies suggests that sediments were transported under extremely limited light exposure.
Jürgen Mey, Wolfgang Schwanghart, Anna-Maartje de Boer, and Tony Reimann
Geochronology, 5, 377–389, https://doi.org/10.5194/gchron-5-377-2023, https://doi.org/10.5194/gchron-5-377-2023, 2023
Short summary
Short summary
This study presents the results of an outdoor flume experiment to evaluate the effect of turbidity on the bleaching of fluvially transported sediment. Our main conclusions are that even small amounts of sediment lead to a substantial change in the intensity and frequency distribution of light within the suspension and that flow turbulence is an important prerequisite for bleaching grains during transport.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-295, https://doi.org/10.5194/nhess-2022-295, 2023
Manuscript not accepted for further review
Short summary
Short summary
The current socioeconomic development in the Himalayan region leads to a rapid expansion of the road network and an increase in the exposure to landslides. Our study along the NH-7 demonstrates the scale of this challenge as we detect more than one partially or fully road-blocking landslide per road kilometer. We identify the main controlling variables, i.e. slope angle, rainfall amount and lithology. As our approach uses a minimum of data, it can be extended to more complicated road networks.
Nazaré Suziane Soares, Carlos Alexandre Gomes Costa, Till Francke, Christian Mohr, Wolfgang Schwanghart, and Pedro Henrique Augusto Medeiros
EGUsphere, https://doi.org/10.5194/egusphere-2025-884, https://doi.org/10.5194/egusphere-2025-884, 2025
Short summary
Short summary
We use drone surveys to map river intermittency in reaches and classify them into "Wet", "Transition", "Dry" or "Not Determined". We train Random Forest models with 40 candidate predictors, and select altitude, drainage area, distance from dams and dynamic predictors. We separate different models based on dynamic predictors: satellite indices (a) and (b); or (c) accumulated precipitation (30 days). Model (a) is the most successful in simulating intermittency both temporally and spatially.
Boris Gailleton, Philippe Steer, Philippe Davy, Wolfgang Schwanghart, and Thomas Bernard
Earth Surf. Dynam., 12, 1295–1313, https://doi.org/10.5194/esurf-12-1295-2024, https://doi.org/10.5194/esurf-12-1295-2024, 2024
Short summary
Short summary
We use cutting-edge algorithms and conceptual simplifications to solve the equations that describe surface water flow. Using quantitative data on rainfall and elevation, GraphFlood calculates river width and depth and approximates erosive power, making it a suitable tool for large-scale hazard management and understanding the relationship between rivers and mountains.
Wolfgang Schwanghart, Ankit Agarwal, Kristen Cook, Ugur Ozturk, Roopam Shukla, and Sven Fuchs
Nat. Hazards Earth Syst. Sci., 24, 3291–3297, https://doi.org/10.5194/nhess-24-3291-2024, https://doi.org/10.5194/nhess-24-3291-2024, 2024
Short summary
Short summary
The Himalayan landscape is particularly susceptible to extreme events, which interfere with increasing populations and the expansion of settlements and infrastructure. This preface introduces and summarizes the nine papers that are part of the special issue,
Estimating and predicting natural hazards and vulnerabilities in the Himalayan region.
Sebastian Vogel, Katja Emmerich, Ingmar Schröter, Eric Bönecke, Wolfgang Schwanghart, Jörg Rühlmann, Eckart Kramer, and Robin Gebbers
SOIL, 10, 321–333, https://doi.org/10.5194/soil-10-321-2024, https://doi.org/10.5194/soil-10-321-2024, 2024
Short summary
Short summary
To rapidly obtain high-resolution soil pH data, pH sensors can measure the pH value directly in the field under the current soil moisture (SM) conditions. The influence of SM on pH and on its measurement quality was studied. An SM increase causes a maximum pH increase of 1.5 units. With increasing SM, the sensor pH value approached the standard pH value measured in the laboratory. Thus, at high soil moisture, calibration of the sensor pH values to the standard pH value is negligible.
Anna-Maartje de Boer, Wolfgang Schwanghart, Jürgen Mey, Basanta Raj Adhikari, and Tony Reimann
Geochronology, 6, 53–70, https://doi.org/10.5194/gchron-6-53-2024, https://doi.org/10.5194/gchron-6-53-2024, 2024
Short summary
Short summary
This study tested the application of single-grain feldspar luminescence for dating and reconstructing sediment dynamics of an extreme mass movement event in the Himalayan mountain range. Our analysis revealed that feldspar signals can be used to estimate the age range of the deposits if the youngest subpopulation from a sample is retrieved. The absence of clear spatial relationships with our bleaching proxies suggests that sediments were transported under extremely limited light exposure.
Jürgen Mey, Wolfgang Schwanghart, Anna-Maartje de Boer, and Tony Reimann
Geochronology, 5, 377–389, https://doi.org/10.5194/gchron-5-377-2023, https://doi.org/10.5194/gchron-5-377-2023, 2023
Short summary
Short summary
This study presents the results of an outdoor flume experiment to evaluate the effect of turbidity on the bleaching of fluvially transported sediment. Our main conclusions are that even small amounts of sediment lead to a substantial change in the intensity and frequency distribution of light within the suspension and that flow turbulence is an important prerequisite for bleaching grains during transport.
Monika Pfau, Georg Veh, and Wolfgang Schwanghart
The Cryosphere, 17, 3535–3551, https://doi.org/10.5194/tc-17-3535-2023, https://doi.org/10.5194/tc-17-3535-2023, 2023
Short summary
Short summary
Cast shadows have been a recurring problem in remote sensing of glaciers. We show that the length of shadows from surrounding mountains can be used to detect gains or losses in glacier elevation.
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-295, https://doi.org/10.5194/nhess-2022-295, 2023
Manuscript not accepted for further review
Short summary
Short summary
The current socioeconomic development in the Himalayan region leads to a rapid expansion of the road network and an increase in the exposure to landslides. Our study along the NH-7 demonstrates the scale of this challenge as we detect more than one partially or fully road-blocking landslide per road kilometer. We identify the main controlling variables, i.e. slope angle, rainfall amount and lithology. As our approach uses a minimum of data, it can be extended to more complicated road networks.
Cited articles
Adhikari, D., Tiwary, R., Singh, P. P., Suchiang, B. R., Nonghuloo, I. M., and Barik, S. K.: Trees, Shrubs and Herbs for Slope Stabilization in Landslide Prone Areas of Eastern Himalaya BT, in: Nature-based Solutions for Resilient Ecosystems and Societies, edited by: Dhyani, S., Gupta, A. K., and Karki, M., Springer Singapore, Singapore, 307–326, https://doi.org/10.1007/978-981-15-4712-6_18, 2020.
Akaike, H.: A new look at the statistical model identification, IEEE T. Automat. Contr., 19, 716–723, 1974.
Andermann, C., Bonnet, S., and Gloaguen, R.: Evaluation of precipitation data sets along the Himalayan front, Geochem. Geophy. Geosy., 12, Q07023, https://doi.org/10.1029/2011GC003513, 2011.
Ang, Q., Baddeley, A., and Nair, G.: Geometrically Corrected Second Order Analysis of Events on a Linear Network, with Applications to Ecology and Criminology, Scand. J. Stat., 39, 591–617, https://doi.org/10.1111/j.1467-9469.2011.00752.x, 2012.
Asthana, B. N. and Khare, D.: Hill Slope Stabilization at Dam and Power Projects in Himalayas BT, in: Recent Advances in Dam Engineering, edited by: Asthana, B. N. and Khare, D., Springer International Publishing, Cham, 239–264, https://doi.org/10.1007/978-3-030-32278-6_11, 2022.
Azad, M. A., Singh, S. K., Alok, A., Meenakshi, Shekhar, S., and Kumar, P.: Geotechnical and geological studies of Adit-6 of the railway tunnel between Rishikesh and Karnprayag in India focusing on the excavation methods and design of support analysis: a case study, Arab. J. Geosci., 15, 129, https://doi.org/10.1007/s12517-021-09355-7, 2022.
Baddeley, A., Berman, M., Fisher, N. I., Hardegen, A., Milne, R. K., Schuhmacher, D., Shah, R., and Turner, R.: Spatial logistic regression and change-of-support in Poisson point processes, Electron. J. Stat., 4, 1151–1201, https://doi.org/10.1214/10-EJS581, 2010.
Baddeley, A., Rubak, E., and Turner, R.: Spatial point patterns: methodology and applications with R, CRC press, ISBN 9781482210217, 2015.
Baddeley, A., Nair, G., Rakshit, S., McSwiggan, G., and Davies, T. M.: Analysing point patterns on networks – A review, Spat. Stat., 42, 100435, https://doi.org/10.1016/J.SPASTA.2020.100435, 2021.
Barnard, P. L., Owen, L. A., Sharma, M. C., and Finkel, R. C.: Natural and human-induced landsliding in the Garhwal Himalaya of northern India, Geomorphology, 40, 21–35, https://doi.org/10.1016/S0169-555X(01)00035-6, 2001.
Bartarya, S. K. and Valdiya, K. S.: Landslides and Erosion in the Catchment of the Gaula River, Kumaun Lesser Himalaya, India, Mt. Res. Dev., 9, 405, https://doi.org/10.2307/3673588, 1989.
Basistha, A., Arya, D. S., and Goel, N. K.: Spatial Distribution of Rainfall in Indian Himalayas – A Case Study of Uttarakhand Region, Water Resour. Manag., 22, 1325–1346, https://doi.org/10.1007/s11269-007-9228-2, 2008.
Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I. J. M., Weedon, G. P., Brocca, L., Pappenberger, F., Huffman, G. J., and Wood, E. F.: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling, Hydrol. Earth Syst. Sci., 21, 6201–6217, https://doi.org/10.5194/hess-21-6201-2017, 2017.
Bíl, M., Kubeček, J., and Andrášik, R.: An epidemiological approach to determining the risk of road damage due to landslides, Nat. Hazards, 73, 1323–1335, https://doi.org/10.1007/s11069-014-1141-4, 2014.
Bollinger, L., Sapkota, S. N., Tapponnier, P., Klinger, Y., Rizza, M., Van der Woerd, J., Tiwari, D. R., Pandey, R., Bitri, A., and Bes de Berc, S.: Estimating the return times of great Himalayan earthquakes in eastern Nepal: Evidence from the Patu and Bardibas strands of the Main Frontal Thrust, J. Geophys. Res.-Sol. Ea., 119, 7123–7163, https://doi.org/10.1002/2014JB010970, 2014.
Bookhagen, B.: Appearance of extreme monsoonal rainfall events and their impact on erosion in the Himalaya, Geomatics, Nat. Hazards Risk, 1, 37–50, https://doi.org/10.1080/19475701003625737, 2010.
Boora, S. and Karakunnel, M. T.: The SDG conundrum in India: navigating economic development and environmental preservation, Int. J. Environ. Stud., 81, 961–976, https://doi.org/10.1080/00207233.2024.2323321, 2024.
Buchhorn, M., Smets, B., Bertels, L., Roo, B. De, Lesiv, M., Tsendbazar, N.-E., Li, L., and Tarko, A.: Copernicus Global Land Service: Land Cover 100 m: version 3 Globe 2015–2019, Product User Manual, https://doi.org/10.5281/zenodo.3938963, 2020.
Ching, J., Liao, H. J., and Lee, J. Y.: Predicting rainfall-induced landslide potential along a mountain road in Taiwan, Geotechnique, 61, 153–166, 2011.
Chouhan, S., Thieken, A. H., Bubeck, P., and Mukherjee, M.: Role of tourism on disaster recovery: A case study of Uttarakhand, India, Int. J. Disast. Risk Re., 95, 103878, https://doi.org/10.1016/j.ijdrr.2023.103878, 2023.
Das, I., Stein, A., Kerle, N., and Dadhwal, V. K.: Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models, Geomorphology, 179, 116–125, https://doi.org/10.1016/j.geomorph.2012.08.004, 2012.
Devkota, K. C., Regmi, A. D., Pourghasemi, H. R., Yoshida, K., Pradhan, B., Ryu, I. C., Dhital, M. R., and Althuwaynee, O. F.: Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya, Nat. Hazards, 65, 135–165, https://doi.org/10.1007/s11069-012-0347-6, 2013.
European Space Agency: Copernicus Global Digital Elevation Model, Distributed by OpenTopography, https://doi.org/10.5069/G9028PQB, 2021.
Evans, I. S.: Geomorphometry and landform mapping: What is a landform?, Geomorphology, 137, 94–106, https://doi.org/10.1016/j.geomorph.2010.09.029, 2012.
Fort, M., Cossart, E., and Arnaud-Fassetta, G.: Hillslope-channel coupling in the Nepal Himalayas and threat to man-made structures: The middle Kali Gandaki valley, Geomorphology, 124, 178–199, https://doi.org/10.1016/j.geomorph.2010.09.010, 2010.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes, Sci. Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66, 2015.
Gariano, S. L. and Guzzetti, F.: Landslides in a changing climate, Earth-Sci. Rev., 162, 227–252, https://doi.org/10.1016/j.earscirev.2016.08.011, 2016.
Geological Survey of India: Uttarakhand lithology, Government of India, Kolkata, India, https://bhukosh.gsi.gov.in, last access: 10 August 2022.
Gerrard, J.: The landslide hazard in the Himalayas: geological control and human action, edited by: Morisawa, M., Elsevier, Amsterdam, 221–230, https://doi.org/10.1016/B978-0-444-82012-9.50019-0, 1994.
Guthrie, R. H.: The effects of logging on frequency and distribution of landslides in three watersheds on Vancouver Island, British Columbia, Geomorphology, 43, 273–292, 2002.
Haigh, M. and Rawat, J. S.: Landslide causes: Human impacts on a Himalayan landslide swarm, Belg. J. Geogr., 3–4, 201–220, https://doi.org/10.4000/belgeo.6311, 2011.
Haigh, M. J.: Landslide prediction and highway maintenance in the Lesser Himalaya, India, Z. Geomorphol. Supp., 51, 17–38, 1984.
Hanley, J. A. and McNeil, B. J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143, 29–36, 1982.
Hu, Z., Hu, Q., Zhang, C., Chen, X., and Li, Q.: Evaluation of reanalysis, spatially interpolated and satellite remotely sensed precipitation data sets in central Asia, J. Geophys. Res.-Atmos., 121, 5648–5663, https://doi.org/10.1002/2016JD024781, 2016.
Huat, B. B. and Jamaludin, S.: Evaluation of slope assessment system in predicting landslides along roads underlain by granitic formation, American Journal of Environmental Sciences, 1, 90–96, 2005.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.: GPM IMERG Late Precipitation L3 1 day 0.1 degree × 0.1 degree V06, edited by: Savtchenko, A., Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/GPM/IMERGDL/DAY/06, 2019.
Jaquet, S., Schwilch, G., Hartung-Hofmann, F., Adhikari, A., Sudmeier-Rieux, K., Shrestha, G., Liniger, H. P., and Kohler, T.: Does outmigration lead to land degradation? Labour shortage and land management in a western Nepal watershed, Appl. Geogr., 62, 157–170, https://doi.org/10.1016/j.apgeog.2015.04.013, 2015.
Jena, P., Garg, S., and Azad, S.: Performance Analysis of IMD High-Resolution Gridded Rainfall (0.25°×0.25°) and Satellite Estimates for Detecting Cloudburst Events over the Northwest Himalayas, J. Hydrometeorol., 21, 1549–1569, https://doi.org/10.1175/JHM-D-19-0287.1, 2020.
Joshi, V. and Kumar, K.: Extreme rainfall events and associated natural hazards in Alaknanda valley, Indian Himalayan region, J. Mt. Sci., 3, 228–236, https://doi.org/10.1007/s11629-006-0228-0, 2006.
Kanungo, D. P., Pain, A., and Sharma, S.: Finite element modeling approach to assess the stability of debris and rock slopes: a case study from the Indian Himalayas, Nat. Hazards, 69, 1–24, https://doi.org/10.1007/s11069-013-0680-4, 2013.
Kayal, J. R., Ram, S., Singh, O. P., Chakraborty, P. K., and Karunakar, G.: Aftershocks of the March 1999 Chamoli Earthquake and Seismotectonic Structure of the Garhwal Himalaya, B. Seismol. Soc. Am., 93, 109–117, 2003.
Koushik, P., Shantanu, S., Manojit, S., and Mahesh, S.: Stability analysis and design of slope reinforcement techniques for a Himalayan landslide BT, in: Proceedings of the Conference on Recent Advances in Rock Engineering (RARE 2016), Bengaluru, India, 16–18 November 2016, 97–104, https://doi.org/10.2991/rare-16.2016.16, 2016.
Krishnan, R., Shrestha, A. B., Ren, G., Rajbhandari, R., Saeed, S., Sanjay, J., Syed, M. A., Vellore, R., Xu, Y., You, Q., and Ren, Y.: Unravelling Climate Change in the Hindu Kush Himalaya: Rapid Warming in the Mountains and Increasing Extremes, in: The Hindu Kush Himalaya Assessment: Mountains, Climate Change, Sustainability and People, edited by: Wester, P., Mishra, A., Mukherji, A., and Shrestha, A. B., Springer International Publishing, Cham, 57–97, https://doi.org/10.1007/978-3-319-92288-1_3, 2019.
Kumar, M., Hodnebrog, Ø., Sophie Daloz, A., Sen, S., Badiger, S., and Krishnaswamy, J.: Measuring precipitation in Eastern Himalaya: Ground validation of eleven satellite, model and gauge interpolated gridded products, J. Hydrol., 599, 126252, https://doi.org/10.1016/j.jhydrol.2021.126252, 2021.
Kundu, J., Sarkar, K., and Singh, A. K.: Integrating structural and numerical solutions for road cut slope stability analysis-a case study, India, in: ISRM 2nd International Conference on Rock Dynamics, Suzhou, China, 18–19 May 2016, ISRM-ROCDYN-2016-64, 2016.
Laimer, H. J.: Anthropogenically induced landslides – A challenge for railway infrastructure in mountainous regions, Eng. Geol., 222, 92–101, https://doi.org/10.1016/j.enggeo.2017.03.015, 2017.
Laurance, W. F., Clements, G. R., Sloan, S., O’Connell, C. S., Mueller, N. D., Goosem, M., Venter, O., Edwards, D. P., Phalan, B., Balmford, A., Van Der Ree, R., and Arrea, I. B.: A global strategy for road building, Nature, 513, 229–232, 2014.
Lavé, J. and Avouac, J. P.: Active folding of fluvial terraces across the Siwaliks Hills, Himalayas of central Nepal, J. Geophys. Res., 105, 5735–5770, https://doi.org/10.1029/1999JB900292, 2000.
Li, Y., Wang, X., and Mao, H.: Influence of human activity on landslide susceptibility development in the Three Gorges area, Nat. Hazards, 104, 2115–2151, https://doi.org/10.1007/s11069-020-04264-6, 2020.
Lombardo, L., Opitz, T., and Huser, R.: Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster, Stoch. Env. Res. Risk A., 32, 2179–2198, https://doi.org/10.1007/s00477-018-1518-0, 2018.
Lombardo, L., Bakka, H., Tanyas, H., van Westen, C., Mai, P. M., and Huser, R.: Geostatistical Modeling to Capture Seismic-Shaking Patterns From Earthquake-Induced Landslides, J. Geophys. Res.-Earth, 124, 1958–1980, https://doi.org/10.1029/2019JF005056, 2019.
Makalic, E. and Schmidt, D. F.: A Simple Bayesian Algorithm for Feature Ranking in High Dimensional Regression Problems, in: Advances in Artificial Intelligence. AI 2011. Lecture Notes in Computer Science, edited by: Wang, D. and Reynolds, M., vol. 7106, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-25832-9_23, 2011.
Makalic, E. and Schmidt, D. F.: High-Dimensional Bayesian Regularised Regression with the BayesReg Package, arXiv [preprint], https://doi.org/10.48550/arXiv.1611.06649, 2016.
Mauri, L., Straffelini, E., and Tarolli, P.: Multi-temporal modeling of road-induced overland flow alterations in a terraced landscape characterized by shallow landslides, Int. Soil Water Conserv. Res., 10, 240–253, https://doi.org/10.1016/j.iswcr.2021.07.004, 2022.
McAdoo, B. G., Quak, M., Gnyawali, K. R., Adhikari, B. R., Devkota, S., Rajbhandari, P. L., and Sudmeier-Rieux, K.: Roads and landslides in Nepal: how development affects environmental risk, Nat. Hazards Earth Syst. Sci., 18, 3203–3210, https://doi.org/10.5194/nhess-18-3203-2018, 2018.
Meyer, N. K., Schwanghart, W., Korup, O., and Nadim, F.: Roads at risk: traffic detours from debris flows in southern Norway, Nat. Hazards Earth Syst. Sci., 15, 985–995, https://doi.org/10.5194/nhess-15-985-2015, 2015.
Mitra, A. K., Bohra, A. K., Rajeevan, M. N., and Krishnamurti, T. N.: Daily Indian Precipitation Analysis Formed from a Merge of Rain-Gauge Data with the TRMM TMPA Satellite-Derived Rainfall Estimates, J. Meteorol. Soc. Jpn. Ser. II, 87A, 265–279, https://doi.org/10.2151/jmsj.87A.265, 2009.
Muenchow, J., Brenning, A., and Richter, M.: Geomorphic process rates of landslides along a humidity gradient in the tropical Andes, Geomorphology, 139, 271–284, 2012.
National Crime Records Bureau: Accidental Deaths & Suicides in India 2021, https://www.data.gov.in/catalog/accidental-deaths-suicides-india-adsi-2021 (last access: 1 December 2022), 2022.
Okabe, A. and Sugihara, K.: Spatial analysis along networks: statistical and computational methods, John Wiley & Sons, ISBN 9781119967767, 2012.
Okabe, A., Okunuki, K., and Shiode, S.: SANET: A Toolbox for Spatial Analysis on a Network, Geogr. Anal., 38, 57–66, https://doi.org/10.1111/j.0016-7363.2005.00674.x, 2006.
Ozturk, U., Saito, H., Matsushi, Y., Crisologo, I., and Schwanghart, W.: Can global rainfall estimates (satellite and reanalysis) aid landslide hindcasting?, Landslides, 18, 3119–3133, https://doi.org/10.1007/s10346-021-01689-3, 2021.
Ozturk, U., Bozzolan, E., Holcombe, E. A., Shukla, R., Pianosi, F., and Wagener, T.: How climate change and unplanned urban sprawl bring more landslides, Nature, 608, 262–265, https://doi.org/10.1038/d41586-022-02141-9, 2022.
Pai, D. S., Sridhar, L., Rajeevan, M., Sreejith, O. P., Satbhai, N. S., and Mukhopadhyay, B.: Development of a new high spatial resolution (0.25°×0.25°) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region, MAUSAM, 1, 1–18, https://doi.org/10.54302/mausam.v65i1.851, 2014.
Park, T. and Casella, G.: The Bayesian lasso, J. Am. Stat. Assoc., 103, 681–686, 2008.
Penna, D., Borga, M., Aronica, G. T., Brigandì, G., and Tarolli, P.: The influence of grid resolution on the prediction of natural and road-related shallow landslides, Hydrol. Earth Syst. Sci., 18, 2127–2139, https://doi.org/10.5194/hess-18-2127-2014, 2014.
Petley, D. N., Hearn, G. J., Hart, A., Rosser, N. J., Dunning, S. A., Oven, K., and Mitchell, W. A.: Trends in landslide occurrence in Nepal, Nat. Hazards, 43, 23–44, https://doi.org/10.1007/s11069-006-9100-3, 2007.
Prasad, S. and Siddique, T.: Stability assessment of landslide-prone road cut rock slopes in Himalayan terrain: A finite element method based approach, J. Rock Mech. Geotech. Eng., 12, 59–73, https://doi.org/10.1016/j.jrmge.2018.12.018, 2020.
Rajendran, K., Parameswaran, R. M., and Rajendran, C. P.: Seismotectonic perspectives on the Himalayan arc and contiguous areas: Inferences from past and recent earthquakes, Earth-Sci. Rev., 173, 1–30, https://doi.org/10.1016/j.earscirev.2017.08.003, 2017.
Rawat, M. S., Joshi, V., Uniyal, D. P., and Rawat, B. S.: Investigation of hill slope stability and mitigation measures in Sikkim Himalaya, Int. J. Landslide Environ., 3, 8–15, 2016.
Sarkar, S. and Kanungo, D. P.: Landslides in the Alaknanda Valley of Garhwal Himalaya, India, Q. J. Eng. Geol. Hydroge., 39, 79–82, https://doi.org/10.1144/1470-9236/05-020, 2006.
Sati, S. P., Sundriyal, Y., Rana, N., and Dangwal, S.: Recent landslides in Uttarakhand: nature's fury or human folly, Curr. Sci. India, 100, 1617–1620, 2011.
Schwanghart, W. and Scherler, D.: Short Communication: TopoToolbox 2 – MATLAB-based software for topographic analysis and modeling in Earth surface sciences, Earth Surf. Dynam., 2, 1–7, https://doi.org/10.5194/esurf-2-1-2014, 2014.
Schwanghart, W. and Scherler, D.: Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques, Earth Surf. Dynam., 5, 821–839, https://doi.org/10.5194/esurf-5-821-2017, 2017.
Schwanghart, W., Molkenthin, C., and Scherler, D.: A systematic approach and software for the analysis of point patterns on river networks, Earth Surf. Proc. Land., 46, 1847–1862, https://doi.org/10.1002/esp.5127, 2021.
Sharma, A. K., Parkash, S., and Roy, T. S.: Response to Uttarakhand disaster 2013, International Journal of Scientific and Engineering Research, 5, 1251–1256, 2014.
Shugar, D. H., Jacquemart, M., Shean, D., Bhushan, S., Upadhyay, K., Sattar, A., Schwanghart, W., McBride, S., van Wyk de Vries, M., Mergili, M., Emmer, A., Deschamps-Berger, C., McDonnell, M., Bhambri, R., Allen, S., Berthier, E., Carrivick, J. L., Clague, J. J., Dokukin, M., Dunning, S. A., Frey, H., Gascoin, S., Haritashya, U. K., Huggel, C., Kääb, A., Kargel, J. S., Kavanaugh, J. L., Lacroix, P., Petley, D., Rupper, S., Azam, M. F., Cook, S. J., Dimri, A. P., Eriksson, M., Farinotti, D., Fiddes, J., Gnyawali, K. R., Harrison, S., Jha, M., Koppes, M., Kumar, A., Leinss, S., Majeed, U., Mal, S., Muhuri, A., Noetzli, J., Paul, F., Rashid, I., Sain, K., Steiner, J., Ugalde, F., Watson, C. S., and Westoby, M. J.: A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya, Science, 373, 300–306, https://doi.org/10.1126/science.abh4455, 2021.
Siddique, T. and Pradhan, S. P.: Stability and sensitivity analysis of Himalayan road cut debris slopes: an investigation along NH-58, India, Nat. Hazards, 93, 577–600, https://doi.org/10.1007/s11069-018-3317-9, 2018.
Siddique, T., Pradhan, S. P., Vishal, V., Mondal, M. E. A., and Singh, T. N.: Stability assessment of Himalayan road cut slopes along National Highway 58, India, Environ. Earth Sci., 76, 1–18, 2017.
Singh, R., Umrao, R. K., and Singh, T. N.: Stability evaluation of road-cut slopes in the Lesser Himalaya of Uttarakhand, India: conventional and numerical approaches, B. Eng. Geol. Environ., 73, 845–857, 2014.
Stanley, T. and Kirschbaum, D. B.: A heuristic approach to global landslide susceptibility mapping, Nat. Hazards, 87, 145–164, https://doi.org/10.1007/s11069-017-2757-y, 2017.
Stead, D.: The Influence of Shales on Slope Instability, Rock Mech. Rock Eng., 49, 635–651, https://doi.org/10.1007/s00603-015-0865-0, 2016.
Sur, U., Singh, P., and Meena, S. R.: Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data, Geomatics, Nat. Hazards Risk, 11, 2176–2209, https://doi.org/10.1080/19475705.2020.1836038, 2020.
Swarnkar, S., Mujumdar, P., and Sinha, R.: Modified hydrologic regime of upper Ganga basin induced by natural and anthropogenic stressors, Sci. Rep.-UK, 11, 1–11, 19491, https://doi.org/10.1038/s41598-021-98827-7, 2021.
Tanyaş, H., Görüm, T., Kirschbaum, D., and Lombardo, L.: Could road constructions be more hazardous than an earthquake in terms of mass movement?, Nat. Hazards, 112, 639–663, https://doi.org/10.1007/s11069-021-05199-2, 2022.
The Tribune, India: https://www.tribuneindia.com/news/nation/10-969-km-roads-in-himalayan-states-461738, last access: 29 December 2022.
United States Geological Survey (USGS): Earthquake Catalog, https://earthquake.usgs.gov/earthquakes/search/, last access: 27 November 2022.
Uniyal, A.: Infra Development Vision for Himalaya in the Aftermath of Rishiganga Tragedy, in: Disaster & Development, Journal of the National Institute of Disaster Management, New Delhi, vol. 10, edited by: Bindal, M. K., Parkash, S., Thapa, R., Kaur, H., and Kathait, A., New Delhi, 149–159, 2021.
van Erp, S., Oberski, D. L., and Mulder, J.: Shrinkage priors for Bayesian penalized regression, J. Math. Psychol., 89, 31–50, https://doi.org/10.1016/j.jmp.2018.12.004, 2019.
van Westen, C. J., Castellanos, E., and Kuriakose, S. L.: Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview, Eng. Geol., 102, 112–131, https://doi.org/10.1016/j.enggeo.2008.03.010, 2008.
Vergani, C., Giadrossich, F., Buckley, P., Conedera, M., Pividori, M., Salbitano, F., Rauch, H., Lovreglio, R., and Schwarz, M.: Root reinforcement dynamics of European coppice woodlands and their effect on shallow landslides: A review, Earth-Sci. Rev., 167, 88–102, https://doi.org/10.1016/j.earscirev.2017.02.002, 2017.
Wadhawan, S. K., Singh, B., and Ramesh, M. V.: Causative factors of landslides 2019: case study in Malappuram and Wayanad districts of Kerala, India, Landslides, 17, 2689–2697, 2020.
Wang, Q., Zhang, P.-Z., Freymueller, J. T., Bilham, R., Larson, K. M., Lai, X., You, X., Niu, Z., Wu, J., Li, Y., Liu, J., Yang, Z., and Chen, Q.: Present-Day Crustal Deformation in China Constrained by Global Positioning System Measurements, Science, 294, 574–577, https://doi.org/10.1126/science.1063647, 2001.
Short summary
The Himalayan road network links remote areas, but fragile terrain and poor construction lead to frequent landslides. This study on the NH-7 in India's Uttarakhand region analyzed 300 landslides after heavy rainfall in 2022 . Factors like slope, rainfall, rock type and road work influence landslides. The study's model predicts landslide locations for better road maintenance planning, highlighting the risk from climate change and increased road use.
The Himalayan road network links remote areas, but fragile terrain and poor construction lead to...
Altmetrics
Final-revised paper
Preprint