Skip to main content

Advertisement

Log in

Modeling of land use change under the recent climate projections of CMIP6: a case study of Indian river basin

  • Environmental Impacts and Consequences of Urban Sprawl
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

The aim of the study was to investigate the land use change dynamics under CMIP6 projections using Land Change Modeler (LCM). The Global Sensitivity Analysis (GSA) techniques was applied to quantify the sensitivity of single parameter and combination of parameters. Land use and land cover (LULC) transitions of the baseline period (2006–2016) was assessed with a model performance accuracy of 80%. Receiver operating characteristic (ROC) analysis shows that the model has performed well for all the LULC classes except builtup land. Prediction under the SSP245 scenario depicts that areal extent of agricultural, forest, and snow, and glacier will decrease by the mid-century (2045). However, the grassland and barren land area will increase from the baseline period. A similar change pattern with a higher magnitude has also been predicted under SSP585 scenario. The CMIP6 forcing index considers socio-economic effects and LCM predicted an expansion in barren land which may be attributed to changes in cryosphere in the studied area.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

There is no associated data with this manuscript. Raw data will be made available as per request.

References

  • Adhikari BR, Gautam S, Paudel B (2022) Landslide land cover and land use changes and its impacts in Nepal. Impact of climate change, land use and land cover, and socio-economic dynamics on landslides. Springer, Singapore, pp 149–164

    Chapter  Google Scholar 

  • Akyol Alay M, Tunçay HE, Clarke KC (2021) SLEUTH modeling informed by landscape ecology principles: case study using scenario-based planning in Sariyer, Istanbul, Turkey. J Urban Plann Dev 147(4):05021043

    Article  Google Scholar 

  • Anees MM, Sharma R, Joshi PK (2022) Urbanization in Himalaya—an interregional perspective to land use and urban growth dynamics. Mountain landscapes in transition. Springer, Cham, pp 517–538

    Chapter  Google Scholar 

  • Anselm N, Brokamp G, Schütt B (2018) Assessment of land cover change in peri-urban high andean environments South of Bogotá, Colombia. Land 7(2):75

    Article  Google Scholar 

  • Ayele GT, Tebeje AK, Demissie SS, Belete MA, Jemberrie MA, Teshome WM, Mengistu DT, Teshale EZ (2018) Time series land cover mapping and change detection analysis using geographic information system and remote sensing, Northern Ethiopia. Air Soil Water Res 11:1178622117751603

  • Badar B, Romshoo SA, Khan MA (2013) Modelling catchment hydrological responses in a Himalayan Lake as a function of changing land use and land cover. J Earth Syst Sci 122(2):433–449

    Article  Google Scholar 

  • Bandyopadhyay S (2021) Land-use/land-cover change and vulnerability to landslide disasters in Kurseong (Darjeeling Himalayas), India (Doctoral dissertation, Oklahoma State University). https://hdl.handle.net/11244/330890

  • Behera DK, Saxena MR, Ravi Shankar G (2017) Decadal landuse and landcover change dynamics in east coast of india-case study on chilika lake. Indian Geogr J 92(1):73–82

    Google Scholar 

  • Behera MD, Tripathi P, Das P, Srivastava SK, Roy PS, Joshi C, Behera PR, Deka J, Kumar P, Khan ML et al (2018) Remote sensing based deforestation analysis in Mahanadi and Brahmaputra river basin in India since 1985. J Environ Manage 206:1192–1203

    Article  CAS  Google Scholar 

  • Behera DK, Kumari A, Kumar R, Modi M, Singh SK (2023) Assessment of site suitability of wastelands for solar power plants installation in Rangareddy District, Telangana, India. Ecological Footprints of Climate Change: Adaptive Approaches and Sustainability. Springer International Publishing, Cham, pp 559–576. https://doi.org/10.1007/978-3-031-15501-7_22

    Chapter  Google Scholar 

  • Bhasin A, Dolker P, Raina P, Ghosal S (2022) Land use and land cover change detection using remote sensing in the Trans Himalayan Region of Ladakh, India. ECS Trans 107(1):2985

    Article  Google Scholar 

  • Bony S, Stevens B, Frierson DMW, Jakob C, Kageyama M, Pincus R, Shepherd TG, Sherwood SC, Siebesma AP, Sobel AH et al (2015) Clouds, circulation and climate sensitivity. Nat Geosci 8(4):261–268

    Article  CAS  Google Scholar 

  • Chakrabortty R, Pradhan B, Mondal P, Pal SC (2020) The use of RUSLE and GCMs to predict potential soil erosion associated with climate change in a monsoon-dominated region of eastern India. Arab J Geosci 13(20):1–20

    Article  Google Scholar 

  • Chaturvedi RK, Joshi J, Jayaraman M, Ravindranath Bala G, NH, (2012) Multi-model climate change projections for India under representative concentration pathways. Curr Sci 103(7):791–802

    Google Scholar 

  • Chu-Agor ML, Muñoz-Carpena R, Kiker G, Emanuelsson A, Linkov I (2011) Exploring vulnerability of coastal habitats to sea level rise through global sensitivity and uncertainty analyses. Environ Model Softw 26(5):593–604

    Article  Google Scholar 

  • Clarke KC (2018) Land use change modeling with SLEUTH: improving calibration with a genetic algorithm. Geomatic approaches for modeling land change scenarios. Springer, Cham, pp 139–161

    Chapter  Google Scholar 

  • Cox PM, Huntingford C, Williamson MS (2018) Emergentconstraint on equilibrium climate sensitivity from global temperaturevariability. Nat 553(7688):319–322

    Article  CAS  Google Scholar 

  • De Palma A, Abrahamczyk S, Aizen MA, Albrecht M, Basset Y, Bates A, Blake RJ, Boutin C, Bugter R, Connop S et al (2016) Predicting bee community responses to land-use changes: effects of geographic and taxonomic biases. Sci Rep 6(1):1–14

    Article  Google Scholar 

  • Déri A (2014) Maps, knowledge and resilience: application of ArcGIS in building small islands’ resilience to climate change. In: Sundaresan J, Santosh K, Déri A, Roggema R, Singh R (eds) Geospatial Technologies and Climate Change. Geotechnologies and the Environment, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-01689-4_9

  • Eastman JR (2015) TerrSet manual. Accessed in TerrSet version 18:1–390

  • Eastman JR, Van Fossen ME, Solarzano LA (2005) Transition potential modeling for land cover change. GIS Spat Anal Model 17:357–386

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Geist HJ, Lambin EF (2001) What drives tropical deforestation. LUCC Rep Series 4:116

    Google Scholar 

  • Gemitzi A (2021) Predicting land cover changes using a CA Markov model under different shared socioeconomic pathways in Greece. GIScience Remote Sens 58(3):425–441

    Article  Google Scholar 

  • Gogoi PP, Vinoj V, Swain D, Roberts G, Dash J, Tripathy S (2019) Land use and land cover change effect on surface temperature over Eastern India. Sci Rep 9(1):1–10

    Article  Google Scholar 

  • Hoque MZ, Cui S, Islam I, Xu L, Ding S (2021) Dynamics of plantation forest development and ecosystem carbon storage change in coastal Bangladesh. Ecol Ind 130:107954

    Article  CAS  Google Scholar 

  • Iooss B, Veiga SD, Janon A, Pujol G (2023) Package ‘sensitivity’, global sensitivity analysis of model outputs v1.27.1. R, CRAN. https://cran.r-project.org/web/packages/sensitivity/sensitivity.pdf

  • Kamel Boulos MN, Wilson JP (2023) Geospatial techniques for monitoring and mitigating climate change and its effects on human health. Int J Health Geogr 22(1):1–7. https://doi.org/10.1186/s12942-023-00324-9

    Article  Google Scholar 

  • Khatiwada KR, Panthi J, Shrestha ML, Nepal S (2016) Hydro-Climatic Variability in the Karnali River Basin of Nepal Himalaya. Climate 4(2):17. https://doi.org/10.3390/cli4020017

    Article  Google Scholar 

  • Kumar N, Singh SK (2021) Soil erosion assessment using earth observation data in a trans-boundary river basin. Nat Hazards 107:1–34. https://doi.org/10.1007/s11069-021-04571-6

  • Kumar N, Singh SK, Dubey AK, Ray RL, Mustak S, Rawat KS (2021) Prediction of soil erosion risk using earth observation data under recent emission scenarios of CMIP6. Geocarto Int 1–24. https://doi.org/10.1080/10106049.2021.1973116

  • Kumar N, Dubey AK, Goswami UP and Singh S.K (2022). Modeling of hydrological and environmental flow dynamics over a central Himalayan River basin through satellite altimetry and recent climate projections. Int J Climatol. Accepted Author Manuscript https://doi.org/10.1002/joc.7734

  • Kushwaha K, Singh MM, Singh SK, Patel A (2021) Urban growth modeling using earth observation datasets, Cellular Automata-Markov Chain model and urban metrics to measure urban footprints. Remote Sens Appl Soc Environ 22:100479. https://doi.org/10.1016/j.rsase.2021.100479

  • Lambin EF, Turner BL, Geist HJ, Agbola SB, Angelsen A, Bruce JW, Xu J (2001) The causes of land-use and land-cover change: moving beyond the myths. Global Environ Change 11(4):261–269

    Article  Google Scholar 

  • Liang X, Guan Q, Clarke KC, Chen G, Guo S, Yao Y (2021) Mixed-cell cellular automata: a new approach for simulating the spatio-temporal dynamics of mixed land use structures. Landsc Urban Plan 205:103960

    Article  Google Scholar 

  • Luo P, He B, Duan W, Takara K, Nover D (2018) Impact assessment of rainfall scenarios and land-use change on hydrologic response using synthetic Area IDF curves. J Flood Risk Manage 11:S84–S97

    Article  Google Scholar 

  • Lutz AF, ter Maat HW, Biemans H, Shrestha AB, Wester P, Immerzeel WW (2016) Selecting representative climatemodels for climate change impact studies: an advanced envelope-based selection approach. Int J Climatol 36(12):3988–4005

    Article  Google Scholar 

  • Maithani S (2015) Neural networks-based simulation of land cover scenarios in Doon valley, India. Geocarto Int 30(2):163–185

    Google Scholar 

  • Makula EK, Zhou B (2022) Coupled Model Intercomparison Project phase 6 evaluation and projection of East African precipitation. Int J Climatol 42(4):2398–2412

    Article  Google Scholar 

  • Meehl GA, Senior CA, Eyring V, Flato G, Lamarque JF, Stouffer RJ, Taylor KE, Schlund M (2020) Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models. Sci Adv 6(26):eaba1981

  • Meshesha TW, Tripathi SK, Khare D (2016) Analyses of land use and land cover change dynamics using GIS and remote sensing during 1984 and 2015 in the Beressa Watershed Northern Central Highland of Ethiopia. Model Earth Syst Environ 2(4):1–12

    Article  Google Scholar 

  • Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30

    Article  Google Scholar 

  • Munsi M, Malaviya S, Oinam G, Joshi PK (2010) A landscape approach for quantifying land-use and land-cover change (1976–2006) in middle Himalaya. Reg Environ Change 10(2):145–155

    Article  Google Scholar 

  • Munsi M, Areendran G, Joshi PK (2012) Modeling spatio-temporal change patterns of forest cover: a case study from the Himalayan foothills (India). Reg Environ Change 12(3):619–632

    Article  Google Scholar 

  • Munthali MG, Mustak S, Adeola A, Botai J, Singh SK, Davis N (2020) Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid Cellular Automata and Markov model. Remote Sens Appl Soc Environ 17:100276. https://doi.org/10.1016/j.rsase.2019.100276

  • Mustak S, Baghmar NK, Singh SK, Srivastava PK (2022) Multi-scenario based urban growth modeling and prediction using earth observation datasets towards urban policy improvement. Geocarto Int 1–29. https://doi.org/10.1080/10106049.2022.2138983

  • NRSC L (2014) Land use/land cover database on 1: 50,000 scale. Natural Resources Census Project, LUCMD, LRUMG, RSAA, National Remote Sensing Centre, ISRO, Hyderabad. Nat Resource Census-Land Use Land Cover Ver 1:1–11

    Google Scholar 

  • Oñate-Valdivieso F, Sendra JB (2010) Application of GIS and remote sensing techniques in generation of land use scenarios for hydrological modeling. J Hydrol 395(3–4):256–263

    Article  Google Scholar 

  • Pai DS, Rajeevan M, Sreejith OP, Mukhopadhyay B, Satbha NS (2014) 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 65(1):1–18

    Article  Google Scholar 

  • Pal I (2015) Land use and land cover change analysis in Uttarakhand Himalaya and its impact on environmental risks. Mountain Hazards and Disaster Risk Reduction. Springer, Tokyo, pp 125–137

    Chapter  Google Scholar 

  • Pal SC, Chakrabortty R (2019) Simulating the impact of climate change on soil erosion in sub-tropical monsoon dominated watershed based on RUSLE, SCS runoff and MIROC5 climatic model. Adv Space Res 64(2):352–377

    Article  Google Scholar 

  • Pontius RG, Chen H (2006) GEOMOD modeling. Idrisi 15: The Andes Edition

  • Pontius RG, Schneider LC (2001) Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agric Ecosyst Environ 85(1–3):239–248

    Article  Google Scholar 

  • Pontius RG, Boersma W, Castella JC, Clarke K, de Nijs T, Dietzel C, ... and Verburg PH (2008) Comparing the input, output, and validation maps for several models of land change. Ann Reg Sci 42(1):11–37

  • Rai R, Zhang Y, Paudel B, Acharya BK, Basnet L (2018) Land use and land cover dynamics and assessing the ecosystem service values in the trans-boundary Gandaki River Basin, Central Himalayas. Sustainability 10(9):3052

    Article  Google Scholar 

  • Rajeevan M, Bhate J, Jaswal AK (2008) Analysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data. Geophys Res Lett 35(18):L18707. https://doi.org/10.1029/2008GL035143

  • Rasool R, Fayaz A, ulShafiq, M., Singh, H., and Ahmed, P. (2021) Land use land cover change in Kashmir Himalaya: linking remote sensing with an indicator based DPSIR approach. Ecol Ind 125:107447

    Article  Google Scholar 

  • Rawat JS, Kumar M (2015) Monitoring land use/cover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India. Egypt J Remote Sens Space Sci 18(1):77–84

    Google Scholar 

  • Rijal S, Rimal B, Acharya RP, Stork NE (2021) Land use/land cover change and ecosystem services in the Bagmati River Basin, Nepal. Environ Monit Assess 193(10):1–17

    Article  Google Scholar 

  • Ritse V, Basumatary H, Kulnu AS, Dutta G, Phukan MM, Hazarika N (2020) Monitoring land use land cover changes in the Eastern Himalayan landscape of Nagaland, Northeast India. Environ Monit Assess 192(11):1–17

    Article  Google Scholar 

  • Saltelli A (2002) Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun 145(2):280–297

    Article  CAS  Google Scholar 

  • Sangermano F, Eastman JR, Zhu H (2010) Similarity weighted instance-based learning for the generation of transition potentials in land use change modeling. Trans GIS 14(5):569–580

    Article  Google Scholar 

  • Shafizadeh-Moghadam H, Minaei M, Pontius RG Jr, Asghari A, Dadashpoor H (2021) Integrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj Region of Iran. Comput Environ Urban Syst 87:101595

    Article  Google Scholar 

  • Shashikanth K, Salvi K, Ghosh S, Rajendran K (2014) Do CMIP5 simulations of Indian summer monsoon rainfall differ from those of CMIP3? Atmos Sci Lett 15(2):79–85

    Article  Google Scholar 

  • Shrestha B, Ye Q, Khadka N (2019) Assessment of ecosystem services value based on land use and land cover changes in the transboundary Karnali River basin, central Himalayas. Sustainability 11(11):3183

    Article  Google Scholar 

  • Singh G, Pandey A (2021) Evaluation of classification algorithms for land use land cover mapping in the snow-fed Alaknanda River Basin of the Northwest Himalayan Region. Appl Geomatics 13(4):863–875

    Article  Google Scholar 

  • Singh SK, Mustak S, Srivastava PK, Szabó S, Islam T (2015) Predicting spatial and decadal LULC changes through cellular automata markov chain models using Earth observation datasets and Geo-information. Environ Process 2(1):61–78. https://doi.org/10.1007/s40710-015-0062-x

    Article  Google Scholar 

  • Singh H, Singh D, Singh SK, Shukla DN (2017) Assessment of river water quality and ecological diversity through multivariate statistical techniques, and earth observation dataset of rivers Ghaghara and Gandak, India. Int J River Basin Manag 15(3):347–360

    Article  Google Scholar 

  • Singh SK, Laari PB, Mustak SK, Srivastava PK, Szabó S (2018) Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh. India. Geocarto Int 33(11):1202–1222. https://doi.org/10.1080/10106049.2017.1343390

    Article  Google Scholar 

  • Singh VG, Singh SK, Kumar N, Singh RP (2022) Simulation of land use/land cover change at a basin scale using satellite data and markov chain model. Geocarto Int 37(26):11339–11364. https://doi.org/10.1080/10106049.2022.2052976

    Article  Google Scholar 

  • Sohl TL, Wimberly MC, Radeloff VC, Theobald DM, Sleeter BM (2016) Divergent projections of future land use in the United States arising from different models and scenarios. Ecol Modell 337:281–297

    Article  Google Scholar 

  • Srivastava AK, Rajeevan M, Kshirsagar SR (2009) Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmos Sci Lett 10(4):249–254

    Article  Google Scholar 

  • Sulla-Menashe D, Friedl MA (2018) User guide to collection 6 MODIS land cover (MCD12Q1 and MCD12C1) product. USGS, Reston, pp 1–18

    Google Scholar 

  • Taylor KE, Juckes M, Balaji V, Cinquini L, Denvil S, Durack PJ, Elkington M, Guilyardi E, Kharin S, Lautenschlager M et al (2018) CMIP6 global attributes, DRS, filenames, directory structure, and CV’s. PCMDI Q17 document. http://cerfacs.fr/∼coquart/data/uploads/cmip6_global_attributes_filenames_cvs_v6.2.6.pdf. Accessed 04/05/2021

  • Teng H, Liang Z, Chen S, Liu Y, ViscarraRossel RA, Chappell A, Yu W, Shi Z (2018) Current and future assessments of soil erosion by water on the Tibetan Plateau based on RUSLE and CMIP5 climate models. Sci Total Environ 635:673–686

    Article  CAS  Google Scholar 

  • Thenkabail PS, Schull M, Turral H (2005) Ganges and Indus river basin land use/land cover (LULC) and irrigated area mapping using continuous streams of MODIS data. Remote Sens Environ 95(3):317–341

    Article  Google Scholar 

  • Tiwari PC (2000) Land-use changes in Himalaya and their impact on the plains ecosystem: need for sustainable land use. Land use Policy 17(2):101–111

    Article  Google Scholar 

  • Trenberth KE, Asrar GR (2012) Challenges and opportunities in water cycle research: WCRP Contributions. In: Bengtsson L et al (eds) The Earth’s Hydrological Cycle. Space Sciences Series of ISSI, vol 46. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8789-5_3

    Chapter  Google Scholar 

  • Turner KG, Anderson S, Gonzales-Chang M, Costanza R, Courville S, Dalgaard T, Dominati E, Kubiszewski I, Ogilvy S, Porfirio L et al (2016) A review of methods, data, and models to assess changes in the value of ecosystem services from land degradation and restoration. Ecol Modell 319:190–207

    Article  Google Scholar 

  • Varga OG, Pontius RG, Singh SK, Szabó S (2019) Intensity analysis and the figure of merit’s components for assessment of a cellular automata – Markov simulation model. Ecol Indic 101:933–942. https://doi.org/10.1016/j.ecolind.2019.01.057

    Article  Google Scholar 

  • Verburg PH, Van De Steeg J, Veldkamp A, Willemen L (2009) From land cover change to land function dynamics: a major challenge to improve land characterization. J Environ Manage 90(3):1327–1335

    Article  Google Scholar 

  • Voight C, Hernandez-Aguilar K, Garcia C, Gutierrez S (2019) Predictive modeling of future forest cover change patterns in southern Belize. Remote Sens 11(7):823

    Article  Google Scholar 

  • Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004) Guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting Material of the Intergovernmental Panel on Climate Change, Available from the DDC of IPCC TGCIA. 27. https://www.ipcc-data.org/guidelines/dgm_no2_v1_09_2004.pdf. Accessed 5 May 2021

  • Xie FD, Wu X, Liu LS, Zhang YL, Paudel B (2021) Land use and land cover change within the Koshi River Basin of the central Himalayas since 1990. J Mt Sci 18(1):159–177

    Article  Google Scholar 

  • Zang S, Wu C, Liu H, Na X (2011) Impact of urbanization on natural ecosystem service values: a comparative study. Environ Monit Assess 179:575–588

    Article  Google Scholar 

  • Zelinka MD, Myers TA, McCoy DT, Po-Chedley S, Caldwell PM, Ceppi P, Klein SA, Taylor KE (2020) Causes of higher climate sensitivity in CMIP6 models. Geophys Res Lett 47(1):e2019GL085782

Download references

Acknowledgements

The authors NK and SKS thank the Coordinator, KBCAOS, University of Allahabad, and DST-FIST, Government of India.

Funding

DST-INSPIRE fellowship (No. DST/INSPIRE Fellowship/2016/IF160401), New Delhi, for financial help. SERB-DST, New Delhi, India (Grant no. CRG/2019/003551).

Author information

Authors and Affiliations

Authors

Contributions

Conceptualization: Nirmal Kumar (N.K.), Sudhir Kumar Singh (S.K.S) and Mateo Gašparović (M.G.); methodology: N.K., Vikram Gaurav Singh (V.G.S.) and S.K.S; formal analysis: N.K., and V.G.S.; investigation, VGS and SKS; data curation: NK and Dhiroj Kumar Bahera (D.K.B.); visualization, NK and VGS; writing—original draft preparation, NK, VGS, S.K.S., D.K.B, and M.G.; writing—review and editing, NK, VGS, SKS, D.K.B., & M.G.;All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Sudhir Kumar Singh.

Ethics declarations

Ethics approval

We follow the ethical guidelines and no ethical approval was required in this work.

Consent to participate

All the authors agree to participate.

Consent for publication

We all agree to publish the content.

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: Marcus Schulz

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 261 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, N., Singh, V.G., Singh, S.K. et al. Modeling of land use change under the recent climate projections of CMIP6: a case study of Indian river basin. Environ Sci Pollut Res 30, 107219–107235 (2023). https://doi.org/10.1007/s11356-023-26960-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-023-26960-z

Keywords

Navigation