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Prediction of the future landslide susceptibility scenario based on LULC and climate projections

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A Correction to this article was published on 07 June 2023

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Abstract

Worldwide, landslides are the most frequently occurring disaster that is very destructive and unpredictable in nature. A total of 850 landslide events were detected during 2005–2020 in the Tehri region of the Indian Himalayas. Many researchers have conducted landslide susceptibility mapping (LSM) studies for this region using different static landslide-causing factors. However, studies considering dynamic factors in predicting future landslide susceptibility scenarios are inadequate. Hence in this study, both dynamic and static factors were utilized in predicting future landslide susceptibility maps for the year 2050. The paper’s main objective is the future prediction of LSM, considering future projections of land use land cover (LULC) and climate variables (precipitation and temperature). To achieve this objective, first, the geospatial database in three temporal categories, 2005–2010, 2010–2015, and 2015–2020, was prepared for the historical landslide events. Second, the landslide-causing factors were optimized and utilized in LSM for 2010, 2015, and 2020. Third, projected LULC map was generated for the year 2050 using the Artificial Neural Network-Cellular Automata (ANN-CA) model. Fourth, CMIP6 climate projection maps were prepared using the Indian Institute of Tropical Meteorology Earth system model (IITM ESM) under four shared socioeconomic pathway (SSP) scenarios. Finally, the projected maps were used as the driving parameter for the future prediction of LSM. The results reveal a high increase in the built-up area (5%) and agriculture land (4%) with a decrease in forest area (10%) in future LULC projections. The results of future LSM prediction under SSP 1–2.6, SSP 2–4.5, SSP 3–7.0, and SSP 5–8.5 climate scenarios show an increase in very high landslide susceptibility class by 2%, 4%, 7%, and 9% respectively. The predicted maps were validated utilizing the Kappa coefficient verifies the reliability of the simulated future results.

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References

  • Abancó C, Hürlimann M (2014) Estimate of the debris-flow entrainment using field and topographical data. Nat Hazards 71(1):363–383. https://doi.org/10.1007/s11069-013-0930-5

    Article  Google Scholar 

  • Almazroui M, Saeed S, Saeed F, Islam MN, Ismail M (2020) Projections of precipitation and temperature over the South Asian countries in CMIP6. Earth Systems and Environment 4(2):297–320. https://doi.org/10.1007/s41748-020-00157-7

    Article  Google Scholar 

  • Al-Najjar HA, Kalantar B, Pradhan B, Saeidi V (2019) Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms. In Earth resources and environmental remote sensing/GIS applications X 11156:97–107. SPIE. https://doi.org/10.1117/12.2532687

  • Andersson-Sköld Y, Bergman R, Johansson M, Persson E, Nyberg L (2013) Landslide risk management—a brief overview and example from Sweden of current situation and climate change. Int J Disaster Risk Reduct 3:44–61.https://doi.org/10.1016/j.ijdrr.2012.11.002

  • Arora A, Arabameri A, Pandey M, Siddiqui MA, Shukla UK, Bui DT, Bhardwaj A (2021) Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India. Sci Total Environ 750:141565. https://doi.org/10.1016/j.scitotenv.2020.141565

  • Bayat M, Ghorbanpour M, Zare R, Jaafari A, Pham BT (2019) Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran. Comput Electron Agric 164:104929. https://doi.org/10.1016/j.compag.2019.104929

  • Bernardie S, Vandromme R, Thiery Y, Houet T, Grémont M, Masson F, ... Bouroullec I (2021) Modelling landslide hazards under global changes: the case of a Pyrenean valley. Natural Hazards and Earth System Sciences 21(1):147–169

  • Caniani D, Pascale S, Sdao F, Sole A (2008) Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Nat Hazards 45(1):55–72. https://doi.org/10.1007/s11069-007-9169-3

    Article  Google Scholar 

  • Chen W, Xie X, Peng J, Wang J, Duan Z, Hong H (2017) GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomat Nat Haz Risk 8(2):950–973. https://doi.org/10.1080/19475705.2017.1289250

    Article  Google Scholar 

  • Collison A, Wade S, Griffiths J, Dehn M (2000) Modelling the impact of predicted climate change on landslide frequency and magnitude in SE England. Eng Geol 55(3):205–218. https://doi.org/10.1016/S0013-7952(99)00121-0

    Article  Google Scholar 

  • Comegna L, Picarelli L, Bucchignani E, Mercogliano P (2013) Potential effects of incoming climate changes on the behaviour of slow active landslides in clay. Landslides 10(4):373–391. https://doi.org/10.1007/s10346-012-0339-3

    Article  Google Scholar 

  • Crozier MJ (2010) Deciphering the effect of climate change on landslide activity: a review. Geomorphology 124(3–4):260–267.https://doi.org/10.1016/j.geomorph.2010.04.009

  • Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42(3-4):213–228

  • Dixon N, Brook E (2007) Impact of predicted climate change on landslide reactivation: case study of Mam Tor. UK Landslides 4(2):137–147. https://doi.org/10.1007/s10346-006-0071-y

    Article  Google Scholar 

  • Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Pham BT (2019) Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ 662:332–346. https://doi.org/10.1016/j.scitotenv.2019.01.221

  • Ebi KL (2014) Health in the new scenarios for climate change research. Int J Environ Res Public Health 11(1):30–46. https://doi.org/10.3390/ijerph110100030

    Article  Google Scholar 

  • ESRI FAQ (2016) What is the Jenks optimization method? https://support.esri.com/en/technical-article/000006743

  • Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9(5):1937-1958. https://doi.org/10.5194/gmd-9-1937-2016

  • Feizizadeh B, Blaschke T (2014) An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping. Int J Geogr Inf Sci 28(3):610–638. https://doi.org/10.1080/13658816.2013.869821

    Article  Google Scholar 

  • Galve JP, Cevasco A, Brandolini P, Soldati M (2015) Assessment of shallow landslide risk mitigation measures based on land use planning through probabilistic modelling. Landslides 12(1):101–114. https://doi.org/10.1007/s10346-014-0478-9

    Article  Google Scholar 

  • Gidden MJ, Riahi K, Smith SJ, Fujimori S, Luderer G, Kriegler E, Van Vuuren DP, Van Den Berg M, Feng L, Klein D, Calvin K (2019) Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century. Geoscientific Model Development 12(4):1443–1475. https://doi.org/10.5194/gmd-12-1443-2019

    Article  Google Scholar 

  • Ghosh JK, Bhattacharya D (2010) Knowledge-based landslide susceptibility zonation system. J Comput Civ Eng 24(4):325–334. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000034

  • Guillard C, Zezere J (2012) Landslide susceptibility assessment and validation in the framework of municipal planning in Portugal: the case of Loures Municipality. Environ Manag 50(4):721–735. https://doi.org/10.1007/s00267-012-9921-7

  • Guo Z, Shi Y, Huang F, Fan X, Huang J (2021) Landslide susceptibility zonation method based on C5. 0 decision tree and K-means cluster algorithms to improve the efficiency of risk management. Geosci Front 12(6):101249.https://doi.org/10.1016/j.gsf.2021.101249

  • Gupta P, Anbalagan R (1997) Slope stability of Tehri Dam Reservoir Area, India, using landslide hazard zonation (LHZ) mapping. Q J Eng GeolHydrogeol 30(1):27–36. https://doi.org/10.1144/GSL.QJEGH.1997.030.P1.03

    Article  Google Scholar 

  • Hansen J, Sato M, Ruedy R, Lo K, Lea DW, Medina-Elizade M (2006) Global temperature change. Proc Natl Acad Sci USA 103:14288–14293. https://doi.org/10.1073/pnas.0606291103

    Article  Google Scholar 

  • Haroun A, Adam M (2013) Accuracy assessment of land use & land cover classification (LU/LC) “Case study of Shomadi area-Renk County-Upper Nile State, South Sudan”. https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.414.8771

  • Hess DM, Leshchinsky BA, Bunn M, Benjamin Mason H, Olsen MJ (2017) A simplified three-dimensional shallow landslide susceptibility framework considering topography and seismicity. Landslides 14(5):1677–1697. https://doi.org/10.1007/s10346-017-0810-2

    Article  Google Scholar 

  • Houghton RA (1994) The worldwide extent of land-use change. Bioscience 44(5):305–313. https://doi.org/10.2307/1312380

    Article  Google Scholar 

  • Huffman GJ, Stocker EF, Bolvin DT, Nelkin EJ, Tan J (2019) GPM IMERG Early precipitation L3 half hourly 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [20/09/2022]. https://doi.org/10.5067/GPM/IMERG/3B-HH-E/06

  • Huggel C, Clague JJ, Korup O (2012) Is climate change responsible for changing landslide activity in high mountains? Earth Surf Proc Land 37(1):77–91. https://doi.org/10.1002/esp.2223

    Article  Google Scholar 

  • Hürlimann M, Guo Z, Puig-Polo C, Medina V (2022) Impacts of future climate and land cover changes on landslide susceptibility: regional scale modelling in the Val d’Aran region (Pyrenees, Spain). Landslides 19(1):99–118. https://doi.org/10.1007/s10346-021-01775-6

    Article  Google Scholar 

  • Ilia I, Tsangaratos P (2016) Applying weight of evidence method and sensitivity analysis to produce a landslide susceptibility map. Landslides 13(2):379–397. https://doi.org/10.1007/s10346-015-0576-3

    Article  Google Scholar 

  • Jacinth Jennifer J, Saravanan S (2022) Artificial neural network and sensitivity analysis in the landslide susceptibility mapping of Idukki district, India. Geocarto Int 37(19):5693–5715. https://doi.org/10.1080/10106049.2021.1923831

  • James N, Sitharam TG (2014) Assessment of seismically induced landslide hazard for the State of Karnataka using GIS technique. J Indian Soc Remote Sens 42:73–89

  • Jat MK, Garg PK, Khare D (2008) Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. Int J Appl Earth Obs Geoinf 10(1):26–43. https://doi.org/10.1016/j.jag.2007.04.002

  • Jakob M, Lambert S (2009) Climate change effects on landslides along the southwest coast of British Columbia. Geomorphology 107(3–4):275–284. https://doi.org/10.1016/j.geomorph.2008.12.009

    Article  Google Scholar 

  • Jenks GF (1967) The data model concept in statistical mapping. International Yearbook of Cartography 7:186–190

    Google Scholar 

  • Joshi V, Murthy TVR, Arya AS, Narayana A, Naithani AK, Garg JK (2003) Landslide hazard zonation of Dharasu-Tehri-Ghansali area of Garhwal Himalaya, India using remote sensing and GIS techniques. Journal of Nepal Geological Society 28:85–94. https://doi.org/10.3126/jngs.v28i0.31727

    Article  Google Scholar 

  • Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11:425–439. https://doi.org/10.1007/s10346-013-0391-7

  • Kim HG, Lee DK, Park C, Kil S, Son Y, Park JH (2015) Evaluating landslide hazards using RCP 4.5 and 8.5 scenarios. Environ Earth Sci 73(3):1385–1400. https://doi.org/10.1007/s12665-014-3775-7

  • Kim D, Im S, Lee C, Woo C (2013) Modeling the contribution of trees to shallow landslide development in a steep, forested watershed. Ecol Eng 61:658–668

  • Kim YH, Min SK, Zhang X, Sillmann J, Sandstad M (2020) Evaluation of the CMIP6 multi-model ensemble for climate extreme indices. Weather Clim Extremes 29:100269.  https://doi.org/10.1016/j.wace.2020.100269

  • König T, Kux HJ, Mendes RM (2019) Shalstab mathematical model and WorldView-2 satellite images to identification of landslide-susceptible areas. Nat Hazards 97(3):1127–1149. https://doi.org/10.1007/s11069-019-03691-4

  • Krishnan R, Swapna P, Vellore R, Narayanasetti S, Prajeesh AG, Choudhury AD, Singh M, Sabin TP, Sanjay J (2019) The IITM earth system model (ESM): development and future roadmap. In Current trends in the Representation of physical processes in weather and climate models (pp. 183–195). Springer, Singapore. https://doi.org/10.1007/978-981-13-3396-5_9

  • Krishnan R, Gnanaseelan C, Sanjay J, Swapna P, Dhara C, Sabin TP, Jadhav J, Sandeep N, Choudhury AD, Singh M, Mujumdar M (2020a) Introduction to climate change over the Indian region. In Assessment of climate change over the Indian region (pp. 1–20). Springer, Singapore. https://doi.org/10.1007/978-981-15-4327-2_1

  • Krishnan R, Sanjay J, Gnanaseelan C, Mujumdar M, Kulkarni A, Chakraborty S (2020b) Assessment of climate change over the Indian region: a report of the ministry of earth sciences (MOES), government of India (p. 226). Springer Nature. https://doi.org/10.1007/978-981-15-4327-2

  • Krishnan R, Swapna P, Choudhury AD, Narayansetti S, Prajeesh AG, Singh M, Modi A, Mathew R, Vellore R, Jyoti J, Sabin TP (2021) The IITM earth system model (IITM ESM). arXiv preprint arXiv:2101.03410. https://doi.org/10.48550/arXiv.2101.03410

  • Kumar R, Anbalagan R (2015a) Landslide susceptibility zonation of Tehri reservoir rim region using binary logistic regression model. Curr Sci 1662–1672. https://www.jstor.org/stable/24905532

  • Kumar R, Anbalagan R (2015b) Landslide susceptibility zonation in part of Tehri reservoir region using frequency ratio, fuzzy logic and GIS. J Earth Syst Sci 124(2):431–448. https://doi.org/10.1007/s12040-015-0536-2

    Article  Google Scholar 

  • Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. J Geol Soc India 87(3):271–286. https://doi.org/10.1007/s12594-016-0395-8

    Article  Google Scholar 

  • Kumar A, Sharma MP (2016) Assessment of risk of GHG emissions from Tehri hydropower reservoir, India. Hum Ecol Risk Assess Int J 22(1):71–85. https://doi.org/10.1080/10807039.2015.1055708

    Article  Google Scholar 

  • Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl M, Venema VKC (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48(3)

  • Maraun D (2013) Bias correction, quantile mapping, and downscaling: revisiting the inflation issue. J Clim 26(6):2137–2143

    Article  Google Scholar 

  • Maraun D (2016) Bias correcting climate change simulations-a critical review. Current Climate Change Reports 2(4):211–220

    Article  Google Scholar 

  • Márquez AM, Guevara E, Rey D (2019) Hybrid model for forecasting of changes in land use and land cover using satellite techniques. IEEE J Sel Top Appl Earth Obs Remote Sens 12(1):252–273. https://doi.org/10.1109/JSTARS.2018.2885612

    Article  Google Scholar 

  • McNally A (2018) FLDAS noah land surface model L4 global monthly 0.1× 0.1 degree (MERRA-2 and CHIRPS). Atmos. Compos. Water Energy Cycles Clim. Var

  • Mendes RM, de Andrade MRM, Graminha CA, Prieto CC, de Ávila FF, Camarinha PIM (2018) Stability analysis on urban slopes: case study of an anthropogenic-induced landslide in São José dos Campos, Brazil. Geotech Geol Eng 36(1):599–610. https://doi.org/10.1007/s10706-017-0303-z

  • Meneses BM, Pereira S, Reis E (2019) Effects of different land use and land cover data on the landslide susceptibility zonation of road networks. Nat Hazard 19(3):471–487. https://doi.org/10.5194/nhess-19-471-2019

    Article  Google Scholar 

  • Moung-Jin L, Won-Kyong S, Joong-Sun W, Inhye P, Saro L (2014) Spatial and temporal change in landslide hazard by future climate change scenarios using probabilistic-based frequency ratio model. Geocarto Int 29(6):639–662. https://doi.org/10.1080/10106049.2013.826739

    Article  Google Scholar 

  • Mukherjee F, Singh D (2020) Assessing land use–land cover change and its impact on land surface temperature using LANDSAT data: a comparison of two urban areas in India. Earth Systems and Environment 4(2):385–407. https://doi.org/10.1007/s41748-020-00155-9

    Article  Google Scholar 

  • Nicu IC (2018) Application of analytic hierarchy process, frequency ratio, and statistical index to landslide susceptibility: an approach to endangered cultural heritage. Environmental Earth Sciences 77(3):1–16. https://doi.org/10.1007/s12665-018-7261-5

    Article  Google Scholar 

  • Olsson J, Yang W, Graham LP, Rosberg JR, Andr´ Easson J (2011) Using an ensemble of climate projections for simulating recent and near-future hydrological change to lake V¨ anern in Sweden. Tellus A: Dyn Meteorol Oceanogr 63(1):126–137. https://doi.org/10.1111/j.1600-0870.2010.00476.x

  • O’Neill BC, Kriegler E, Riahi K, Ebi KL, Hallegatte S, Carter TR, Mathur R, van Vuuren DP (2014) A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Clim Change 122(3):387–400. https://doi.org/10.1007/s10584-013-0905-2

    Article  Google Scholar 

  • O’Neill BC, Tebaldi C, Van Vuuren DP, Eyring V, Friedlingstein P, Hurtt G, Knutti R, Kriegler E, Lamarque JF, Lowe J, Meehl GA (2016) The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development 9(9):3461–3482. https://doi.org/10.5194/gmd-9-3461-2016

    Article  Google Scholar 

  • O’Neill BC, Kriegler E, Ebi KL, Kemp-Benedict E, Riahi K, Rothman DS, van Ruijven BJ, van Vuuren DP, Birkmann J, Kok K, Levy M (2017) The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob Environ Change 42:169–180. https://doi.org/10.1016/j.gloenvcha.2015.01.004

  • Pandey R, Aretano R, Gupta AK, Meena D, Kumar B, Alatalo JM (2017) Agroecology as a climate change adaptation strategy for smallholders of Tehri-Garhwal in the Indian Himalayan region. Small-Scale Forestry 16(1):53–63. https://doi.org/10.1007/s11842-016-9342-1

    Article  Google Scholar 

  • Pandey VK, Pourghasemi HR, Sharma MC (2020) Landslide susceptibility mapping using maximum entropy and support vector machine models along the Highway Corridor, Garhwal Himalaya. Geocarto Int 35(2):168–187. https://doi.org/10.1080/10106049.2018.1510038

  • Parry ML, Canziani O, Palutikof J, Van der Linden P, Hanson C (eds.) (2007) Climate change 2007-impacts, adaptation and vulnerability: working group II contribution to the fourth assessment report of the IPCC (Vol. 4). Cambridge University Press

  • Persichillo MG, Bordoni M, Meisina C (2017) The role of land use changes in the distribution of shallow landslides. Sci Total Environ 574:924–937. https://doi.org/10.1016/j.scitotenv.2016.09.125

    Article  Google Scholar 

  • Pham QB, Chandra Pal S, Chakrabortty R, Saha A, Janizadeh S, Ahmadi K, Khedher KM, Anh DT, Tiefenbacher JP, Bannari A (2021) Predicting landslide susceptibility based on decision tree machine learning models under climate and land use changes. Geocarto Int pp.1–27. https://doi.org/10.1080/10106049.2021.1986579

  • Pinyol NM, Alonso EE, Corominas J, Moya J (2012) Canelles landslide: modelling rapid drawdown and fast potential sliding. Landslides 9(1):33–51. https://doi.org/10.1007/s10346-011-0264-x

  • Pisano L, Zumpano V, Malek Ž, Rosskopf CM, Parise M (2017) Variations in the susceptibility to landslides, as a consequence of land cover changes: a look to the past, and another towards the future. Sci Total Environ 601:1147–1159. https://doi.org/10.1016/j.scitotenv.2017.05.231

    Article  Google Scholar 

  • Popp A, Calvin K, Fujimori S, Havlik P, Humpenöder F, Stehfest E, Bodirsky BL, Dietrich JP, Doelmann JC, Gusti M, Hasegawa T (2017) Land-use futures in the shared socioeconomic pathways. Glob Environ Chang 42:331–345. https://doi.org/10.1016/j.gloenvcha.2016.10.002

    Article  Google Scholar 

  • Pradhan B, Seeni MI, Kalantar B (2017) Performance evaluation and sensitivity analysis of expert-based, statistical, machine learning, and hybrid models for producing landslide susceptibility maps. In Laser scanning applications in landslide assessment (pp. 193–232). Springer, Cham. https://doi.org/10.1007/978-3-319-55342-9_11

  • Rajeevan M, Bhate J, Kale JD, Lal B (2006) High resolution daily gridded rainfall data for the Indian region: analysis of break and active monsoon spells. Curr Sci 91(3):296–306. https://doi.org/10.1007/s12040-007-0019-1

    Article  Google Scholar 

  • Reichenbach P, Mondini AC, Rossi M (2014) The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy). Environ Manage 54(6):1372–1384. https://doi.org/10.1007/s00267-014-0357-0

    Article  Google Scholar 

  • Rianna G, Zollo A, Tommasi P, Paciucci M, Comegna L, Mercogliano P (2014) Evaluation of the effects of climate changes on landslide activity of Orvieto clayey slope. Procedia Earth and Planetary Science 9:54–63. https://doi.org/10.1016/j.proeps.2014.06.017

    Article  Google Scholar 

  • Roy A, Inamdar AB (2019) Multi-temporal land use land cover (LULC) change analysis of a dry semi-arid river basin in western India following a robust multi-sensor satellite image calibration strategy. Heliyon 5(4):e01478. https://doi.org/10.1016/j.heliyon.2019.e01478

  • Saini V, Tiwari RK (2020) A systematic review of urban sprawl studies in India: a geospatial data perspective. Arab J Geosci 13(17):1–21. https://doi.org/10.1007/s12517-020-05843-4

    Article  Google Scholar 

  • Saputra MH, Lee HS (2019) Prediction of land use and land cover changes for north sumatra, indonesia, using an artificial-neural-network-based cellular automaton. Sustainability 11(11):3024. https://doi.org/10.3390/su11113024

    Article  Google Scholar 

  • Schuster RL (1996) Landslides: investigation and mitigation. Chapter 2-socioeconomic significance of landslides (No. 247). http://worldcat.org/isbn/030906208X

  • Shou KJ, Yang CM (2015) Predictive analysis of landslide susceptibility under climate change conditions—a study on the Chingshui River Watershed of Taiwan. Eng Geol 192:46–62. https://doi.org/10.1016/j.enggeo.2015.03.012

    Article  Google Scholar 

  • Shu H, Hürlimann M, Molowny-Horas R, González M, Pinyol J, Abancó C, Ma J (2019) Relation between land cover and landslide susceptibility in Val d'Aran, Pyrenees (Spain): historical aspects, present situation and forward prediction. Sci Total Environ 693:133557. https://doi.org/10.1016/j.scitotenv.2019.07.363

  • Sidle RC, Bogaard TA (2016) Dynamic earth system and ecological controls of rainfall-initiated landslides. Earth Sci Rev 159:275–291. https://doi.org/10.1016/j.earscirev.2016.05.013

  • Srivastava A, Grotjahn R, Ullrich PA (2020) Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions. Weather Clim Extremes 29:100268. https://doi.org/10.1016/j.wace.2020.100268

  • Sun D, Shi S, Wen H, Xu J, Zhou X, Wu J (2021) A hybrid optimization method of factor screening predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping. Geomorphology 379:107623. https://doi.org/10.1016/j.geomorph.2021.107623

  • Sur U, Singh P, Rai PK, Thakur JK (2021) Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand, India. Environ Dev Sustain 23(9):13526–13554. https://doi.org/10.1007/s10668-021-01226-1

    Article  Google Scholar 

  • Swapna P, Roxy MK, Aparna K, Kulkarni K, Prajeesh AG, Ashok K, Krishnan R, Moorthi S, Kumar A, Goswami BN (2015) The IITM earth system model: transformation of a seasonal prediction model to a long-term climate model. Bull Am Meteorol Soc 96(8):1351–1367. https://doi.org/10.1175/BAMS-D-13-00276.1

  • Swapna P, Krishnan R, Sandeep N, Prajeesh AG, Ayantika DC, Manmeet S, Vellore R (2018) Long‐term climate simulations using the IITM earth system model (IITM‐ESMv2) with focus on the South Asian monsoon. J Adv Model Earth Syst 10(5):1127–1149. https://doi.org/10.1029/2017MS001262

  • Tiwari PC, Tiwari A, Joshi B (2018) Urban growth in Himalaya: understanding the process and options for sustainable development. J Urban Reg Stud Contemp India 4(2):15–27. https://core.ac.uk/download/pdf/197310112.pdf

  • Tyagi A, Tiwari RK, James N (2021) GIS-based landslide hazard zonation and risk studies using MCDM. In Local Site Effects and Ground Failures (pp. 251–266). Springer, Singapore. https://doi.org/10.1007/978-981-15-9984-2_22

  • Tyagi A, Tiwari RK, James N (2022) A review on spatial, temporal and magnitude prediction of landslide hazard. J Asian Earth Sci X p.100099. https://doi.org/10.1016/j.jaesx.2022.100099

  • Tyagi A, Tiwari RK, James N (2023) Mapping the landslide susceptibility considering future land-use land-cover scenario. Landslides 20(1):65–76. https://doi.org/10.1007/s10346-022-01968-7

    Article  Google Scholar 

  • Zeshan MT, Mustafa MRU, Baig MF (2021) Monitoring land use changes and their future prospects using GIS and ANN-CA for Perak River basin. Malaysia Water 13(16):2286. https://doi.org/10.3390/w13162286

    Article  Google Scholar 

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Acknowledgements

We want to acknowledge free access to geospatial data on the BHUVAN platform provided by the Indian Space Research Organization (ISRO), the United States Geological Survey (USGS) for providing the temporal LANDSAT satellite data, and the Climate Data Store (CDS) for providing CMIP6 climate projections. This study was supported by the Department of Civil Engineering IIT Ropar.

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Correspondence to Reet Kamal Tiwari.

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The original online version of this article was revised: The authors regret that the Figure 7 that appears in the article is incorrect. The correct Figure 7 is shown below. The original article has been corrected.

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Tyagi, A., Tiwari, R.K. & James, N. Prediction of the future landslide susceptibility scenario based on LULC and climate projections. Landslides 20, 1837–1852 (2023). https://doi.org/10.1007/s10346-023-02088-6

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