Abstract
Climate change most likely to increase the soil erosion (SE) and sediment yield (SY) rate by changing the rainfall–runoff erosivity in many locations worldwide. Global climate model output is commonly used to appraise the impact of climate change on SE and SY. However, determining the suitable climate model is challenging for the accurate simulation of future SE and SY. Therefore, The Revised Universal Soil Loss Equation (RUSLE) coupled with Sediment Delivery Ratio (SDR) model was used to estimate the SE and SY in the humid sub-tropical catchment. We also evaluated the high-resolution regional climate models (RCMs) using five statistical bias correction methods (linear scaling, empirical quantile mapping, quantile mapping with linear transformation, quantile mapping with smoothing spline, and quantile mapping with gamma distribution) for future simulation of the SE and SY. Our result suggests that quantile mapping with gamma distribution-based bias correction method and ICHEC-EC-EARTH-RCA model best project the changes in precipitation and consequently, the rainfall–runoff erosivity factor in the catchment. It is estimated that a 3.67–11.08% increase in rainfall–runoff erosivity may result in a 3.7–11.76% increase in SE and SY in the catchment under the representative concentration pathway (RCP) 4.5 and RCP 8.5 emission scenarios. In addition to this 1.6-thousand-hectare area will be included in high (10–20 t ha−1 year−1) to very severe (> 80 t ha−1 year−1) SE intensity class during the near (2020–2049) and far future (2070–2099) period. The result also revealed that the increasing rate of SE and SY will be observed in the sub-catchment numbers 10, 18, 20, 23, 24, 27, 29 located in the middle part of the catchment under the RCP 8.5 scenario. The result may help the policymakers to strengthen the conservation strategy and more informed decisions for the management of catchment in and around the area.
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References
Asharaf S, Ahrens B (2015) Indian summer monsoon rainfall processes in climate change scenarios. J Clim 28:5414–5429. https://doi.org/10.1175/JCLI-D-14-00233.1
Azari M, Moradi HR, Saghafian B, Faramarzi M (2016) Climate change impacts on streamflow and sediment yield in the North of Iran. Hydrol Sci J 61:123–133. https://doi.org/10.1080/02626667.2014.967695
Bagwan WA, Gavali RS (2020) Delineating changes in soil erosion risk zones using RUSLE model based on confusion matrix for the Urmodi river watershed. Model Earth Syst Environ, Maharashtra, India. https://doi.org/10.1007/s40808-020-00965-w
Balasubramani K, Veena M, Kumaraswamy K, Saravanabavan V (2015) Estimation of soil erosion in a semi-arid watershed of Tamil Nadu (India) using revised universal soil loss equation (RUSLE) model through GIS. Model Earth Syst Environ. https://doi.org/10.1007/s40808-015-0015-4
Basin C-A, Khusen U, Gafforov S et al (1981) The Assessment of Climate Change on Rainfall–runoff Erosivity in the Chirchik–Akhangaran Basin, Uzbekistan. Sustainability. https://doi.org/10.3390/su12083369
Bayramov E, Schlager P, Kada M et al (2019) Quantitative assessment of climate change impacts onto predicted erosion risks and their spatial distribution within the landcover classes of the Southern Caucasus using GIS and remote sensing. Model Earth Syst Environ 5:659–667. https://doi.org/10.1007/s40808-018-0557-3
Benavidez, R. et al. (2018) A review of the ( Revised ) Universal Soil Loss Equation (( R ) USLE ): with a view to increasing its global applicability and improving soil loss estimates, (1995), pp. 6059–6086.
Bennett JC, Grose MR, Corney SP et al (2014) Performance of an empirical bias-correction of a high-resolution climate dataset. Int J Climatol 34:2189–2204. https://doi.org/10.1002/joc.3830
Borrelli P, Robinson DA, Panagos P et al (2020) Land use and climate change impacts on global soil erosion by water (2015–2070). Proc Natl Acad Sci USA 117:21994–22001. https://doi.org/10.1073/pnas.2001403117
Borselli L, Cassi P, Torri D (2008) Catena Prolegomena to sediment and flow connectivity in the landscape: a GIS and field numerical assessment 75:268–277. https://doi.org/10.1016/j.catena.2008.07.006
Bussi G, Francés F, Horel E et al (2014) Modelling the impact of climate change on sediment yield in a highly erodible Mediterranean catchment. J Soils Sediments 14:1921–1937. https://doi.org/10.1007/s11368-014-0956-7
Caroletti GN, Coscarelli R, Caloiero T (2019) Validation of satellite, reanalysis and RCM data of monthly rainfall in Calabria (Southern Italy). Remote Sens. https://doi.org/10.3390/rs11131625
Casanueva A, Herrera S, Iturbide M et al (2020) Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch. Atmos Sci Lett 21:1–12. https://doi.org/10.1002/asl.978
Chatterjee N (2020) Soil erosion assessment in a humid, Eastern Himalayan watershed undergoing rapid land use changes, using RUSLE, GIS and high-resolution satellite imagery. Model Earth Syst Environ 6:533–543. https://doi.org/10.1007/s40808-019-00700-0
Chen J, Brissette FP, Chaumont D, Braun M (2013) Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North American river basins. J Hydrol 479:200–214. https://doi.org/10.1016/j.jhydrol.2012.11.062
Choudhary A, Dimri AP (2018) Assessment of CORDEX-South Asia experiments for monsoonal precipitation over Himalayan region for future climate. Clim Dyn 50:3009–3030. https://doi.org/10.1007/s00382-017-3789-4
Choukri F, Raclot D, Naimi M et al (2020) Distinct and combined impacts of climate and land use scenarios on water availability and sediment loads for a water supply reservoir in northern Morocco. Int Soil Water Conserv Res. https://doi.org/10.1016/j.iswcr.2020.03.003
Dabral PP, Baithuri N, Pandey A (2008) Soil erosion assessment in a hilly catchment of North Eastern India using USLE, GIS and remote sensing. Water Resour Manag 22(12):1783–1798
de Oliveira VA, de Mello CR, Beskow S et al (2019) Modeling the effects of climate change on hydrology and sediment load in a headwater basin in the Brazilian Cerrado biome. Ecol Eng 133:20–31. https://doi.org/10.1016/j.ecoleng.2019.04.021
de Vente J, Poesen J (2005) Predicting soil erosion and sediment yield at the basin scale: Scale issues and semi-quantitative models. Earth-Science Rev 71:95–125. https://doi.org/10.1016/j.earscirev.2005.02.002
Dissanayake D, Morimoto T, Ranagalage M (2019) Accessing the soil erosion rate based on RUSLE model for sustainable land use management: a case study of the Kotmale watershed, Sri Lanka. Model Earth Syst Environ 5:291–306. https://doi.org/10.1007/s40808-018-0534-x
Eekhout JPC, de Vente J (2019) The implications of bias correction methods and climate model ensembles on soil erosion projections under climate change. Earth Surf Process Landforms 44:1137–1147. https://doi.org/10.1002/esp.4563
Eekhout JPC, De Vente J (2020) How soil erosion model conceptualization affects soil loss projections under climate change. Prog Phys Geogr 44:212–232. https://doi.org/10.1177/0309133319871937
Endris HS, Omondi P, Jain S et al (2013) Assessment of the performance of CORDEX regional climate models in simulating East African rainfall. J Clim 26:8453–8475. https://doi.org/10.1175/JCLI-D-12-00708.1
Falco M, Carril AF, Li LZX et al (2020) The potential added value of Regional Climate Models in South America using a multiresolution approach. Clim Dyn 54:1553–1569. https://doi.org/10.1007/s00382-019-05073-9
Fang GH, Yang J, Chen YN, Zammit C (2015) Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China. Hydrol Earth Syst Sci 19:2547–2559. https://doi.org/10.5194/hess-19-2547-2015
Fistikoglu O, Harmancioglu NB (2003) Integration of GIS with USLE in Assessment of
Soil Erosion 447–467.
García-Ruiz JM, Beguería S, Nadal-Romero E et al (2015) A meta-analysis of soil erosion rates across the world. Geomorphology 239:160–173. https://doi.org/10.1016/j.geomorph.2015.03.008
Giorgi F, Mearns LO (1999) Introduction to special section—Regional climate modeling revisited in the issue illustrate a wide range of applications. J Geophys Res Atmos 104:6335–6352
Gudmundsson L, Bremnes JB, Haugen JE, Engen-Skaugen T (2012) Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – A comparison of methods. Hydrol Earth Syst Sci 16:3383–3390. https://doi.org/10.5194/hess-16-3383-2012
Gupta S, Kumar S (2017) Simulating climate change impact on soil erosion using RUSLE model − A case study in a watershed of mid-Himalayan landscape. https://doi.org/https://doi.org/10.1007/s12040-017-0823-1
Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009) Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J Hydrol 377:80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003
Haregeweyn N, Poesen J, Verstraeten G et al (2013) Assessing the performance of a spatially distributed soil erosion and sediment delivery model (WATEM/SEDEM) in northern ethiopia. L Degrad Dev 24:188–204. https://doi.org/10.1002/ldr.1121
Hastie T, Tibshirani R, Friedman JH (2001) The Elements of Statistical Learning. Springer 6190:6191
Henry O, Elias E (2020) Effect of land use land cover changes on the rate of soil erosion in the Upper Eyiohia river catchment of Afikpo North Area, Nigeria, Environmental Challenges. Elsevier B.V., 1(November), p 100002. https://doi.org/10.1016/j.envc.2020.100002.
Hoomehr S, Schwartz JS, Yoder DC (2016) Potential changes in rainfall erosivity under GCM climate change scenarios for the southern Appalachian region, USA. CATENA 136:141–151. https://doi.org/10.1016/j.catena.2015.01.012
Kalognomou EA, Lennard C, Shongwe M et al (2013) A diagnostic evaluation of precipitation in CORDEX models over Southern Africa. J Clim 26:9477–9506. https://doi.org/10.1175/JCLI-D-12-00703.1
Kattsov V, Federation R, Reason C, et al (2013) Evaluation of climate models. Clim Chang 2013 Phys Sci Basis Work Gr I Contrib to Fifth Assess Rep Intergov Panel Clim Chang 9781107057:741–866. https://doi.org/10.1017/CBO9781107415324.020
Kendall MG (1975) Rank correlation methods. Charles Griffin, London
Khademalrasoul A, Amerikhah H (2020) Assessment of soil erosion patterns using RUSLE model and GIS tools (case study: the border of Khuzestan and Chaharmahal Province. Model Earth Syst Environ, Iran. https://doi.org/10.1007/s40808-020-00931-6
Kim KB, Bray M, Han D (2015) An improved bias correction scheme based on comparative precipitation characteristics. Hydrol Process 29:2258–2266. https://doi.org/10.1002/hyp.10366
Konapala G, Mishra AK, Wada Y, Mann ME (2020) Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat Commun 11:1–10. https://doi.org/10.1038/s41467-020-16757-w
Kripalani S, LeFevre F, Phillips CO et al (2007) Deficits in communication and information transfer between hospital-based and primary care physicians: Implications for patient safety and continuity of care. J Am Med Assoc 297:831–841. https://doi.org/10.1001/jama.297.8.831
Kumar N, Tischbein B, Kusche J et al (2017) Impact of climate change on water resources of upper Kharun catchment in Chhattisgarh, India. J Hydrol Reg Stud 13:189–207. https://doi.org/10.1016/j.ejrh.2017.07.008
Lafon T, Dadson S, Buys G, Prudhomme C (2013) Bias correction of daily precipitation simulated by a regional climate model: A comparison of methods. Int J Climatol 33:1367–1381. https://doi.org/10.1002/joc.3518
Latombe G, Burke A, Vrac M et al (2018) Comparison of spatial downscaling methods of general circulation model results to study climate variability during the Last Glacial Maximum. Geosci Model Dev 11:2563–2579. https://doi.org/10.5194/gmd-11-2563-2018
Lazoglou G, Zittis G, Anagnostopoulou C et al (2020) Bias correction of RCM precipitation by TIN-copula method: a case study for historical and future simulations in Cyprus. Climate. https://doi.org/10.3390/CLI8070085
Li Z, Fang H (2016) Impacts of climate change on water erosion: a review. Earth Sci Rev 163:94–117. https://doi.org/10.1016/j.earscirev.2016.10.004
Li Z, Liu W, zhao, Zhang X chang, Zheng F li, (2009) Impacts of land use change and climate variability on hydrology in an agricultural catchment on the Loess Plateau of China. J Hydrol 377:35–42. https://doi.org/10.1016/j.jhydrol.2009.08.007
Luo M, Liu T, Meng F et al (2018) Comparing bias correction methods used in downscaling precipitation and temperature from regional climate models: A case study from the Kaidu River Basin in Western China. Water (Switzerland). https://doi.org/10.3390/w10081046
Mann HB (1945) Non-parametric test against trend Econometrika 13:245–259
Manatsa D, Chingombe W, Matarira CH (2008) The impact of the positive Indian Ocean dipole on Zimbabwe droughts Tropical climate is understood to be dominated by. Int J Climatol 2029:2011–2029. https://doi.org/10.1002/joc
Maurya S et al (2020) Soil erosion in future scenario using CMIP5 models and earth observation datasets, Journal of Hydrology. Elsevier B.V., p 125851. https://doi.org/10.1016/j.jhydrol.2020.125851.
Meseret D, Gangadhara MH (2017) Integration of geospatial technologies with RUSLE for analysis of land use/cover change impact on soil erosion: case study in Rib watershed, northwestern highland Ethiopia, Environmental Earth Sciences. Springer, Berlin Heidelberg 76(22):1–14. https://doi.org/10.1007/s12665-017-7109-4
Mullan D, Favis-Mortlock D, Fealy R (2012) Addressing key limitations associated with modelling soil erosion under the impacts of future climate change. Agric For Meteorol 156:18–30. https://doi.org/10.1016/j.agrformet.2011.12.004
Nyesheja EM et al (2018) Soil erosion assessment using RUSLE model in the Congo Nile Ridge region of Rwanda Soil erosion assessment using RUSLE model in the Congo Nile Ridge region of Rwanda. Phys Geogr Taylor Francis 00(00):1–22. https://doi.org/10.1080/02723646.2018.1541706
Pan S, Tian H, Dangal SRS et al (2015) Responses of global terrestrial evapotranspiration to climate change and increasing atmospheric CO2 in the 21st century. Earth’s Futur 3:15–35. https://doi.org/10.1002/2014EF000263
Pheerawat P, Udmale P (2017) Impacts of climate change on rainfall erosivity in the Huai Luang watershed. Thailand Atmosphere (Basel). https://doi.org/10.3390/atmos8080143
Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192. https://doi.org/10.1007/s00704-009-0134-9
Pruski FF, Nearing MA (2002) Climate-induced changes in erosion during the 21st century for eight U.S. locations. Water Resour Res 38:34–1–34–11. https://doi.org/https://doi.org/10.1029/2001wr000493
Rajbanshi J, Bhattacharya S (2020) Assessment of soil erosion, sediment yield and basin specific controlling factors using RUSLE-SDR and PLSR approach in Konar river basin. India J Hydrol 587:124935. https://doi.org/10.1016/j.jhydrol.2020.124935
Renard, K. . and J.R, F. (1994) ‘Using monthly precipitation data to estimate the R-factor in the revised USLE’, 157(1994), pp. 287–306.
Routschek A, Schmidt J, Kreienkamp F (2014) Impact of climate change on soil erosion - A high-resolution projection on catchment scale until 2100 in Saxony/Germany. CATENA 121:99–109. https://doi.org/10.1016/j.catena.2014.04.019
Sachindra D, Huang F, Barton A, Perera B (2012) Issues associated with statistical downscaling of general circulation model outputs: A discussion. Water Clim Policy Implement Challenges; Proc 2nd Pract Responses to Clim Chang Conf 98
Salvi K, Ghosh S (2013) High-resolution multisite daily rainfall projections in India with statistical downscaling for climate change impacts assessment. J Geophys Res Atmos 118:3557–3578. https://doi.org/10.1002/jgrd.50280
Sayadi A et al (2019) Investigation into the effects of climatic change on temperature, rainfall, and runoff of the Doroudzan Catchment, Iran, Using the Ensemble Approach of CMIP3 Climate Models. Adv Meteorol. https://doi.org/10.1155/2019/6357912
Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 39:1379–1389
Serpa D, Nunes JP, Santos J et al (2015) Impacts of climate and land use changes on the hydrological and erosion processes of two contrasting Mediterranean catchments. Sci Total Environ 538:64–77. https://doi.org/10.1016/j.scitotenv.2015.08.033
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 15:79–85. https://doi.org/10.1002/asl2.466
Shiferaw M, Abebe R (2020) A spatial analysis and modeling study of sedimentation impacts on dams found in south Gondar zone. Model Earth Syst Environ, Ethiopia. https://doi.org/10.1007/s40808-020-01003-5
Shrestha B, Babel MS, Maskey S et al (2012) Impact of climate change on sediment yield in the Mekong River Basin: a case study of the Nam Ou Basin, Lao PDR. Hydrol Earth Syst Sci Discuss 9:3339–3384. https://doi.org/10.5194/hessd-9-3339-2012
Shrestha M, Acharya SC, Shrestha PK (2017) Bias correction of climate models for hydrological modelling – are simple methods still useful? Meteorol Appl 24:531–539. https://doi.org/10.1002/met.1655
Song F, Zhou T (2014) Interannual variability of East Asian summer monsoon simulated by CMIP3 and CMIP5 AGCMs: Skill dependence on Indian Ocean-western pacific anticyclone teleconnection. J Clim 27:1679–1697. https://doi.org/10.1175/JCLI-D-13-00248.1
Sørland SL, Schär C, Lüthi D, Kjellström E (2018) Bias patterns and climate change signals in GCM-RCM model chains. Environ Res Lett. https://doi.org/10.1088/1748-9326/aacc77
Sperber KR, Annamalai H, Kang IS, et al (2013) The Asian summer monsoon: An intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century
Su H, Jiang JH, Zhai C et al (2014) Weakening and strengthening structures in the Hadley circulation and the implications for climate sensitivity. J Geophys Res Atmos 119:5787–5805. https://doi.org/10.1002/2014JD021642.Received
Switanek BM, Troch AP, Castro LC et al (2017) Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes. Hydrol Earth Syst Sci 21:2649–2666. https://doi.org/10.5194/hess-21-2649-2017
Talchabhadel R, Nakagawa H, Kawaike K, Prajapati R (2020) Evaluating the rainfall erosivity (R-factor) from daily rainfall data: an application for assessing climate change impact on soil loss in Westrapti River basin. Nepal Model Earth Syst Environ 6:1741–1762. https://doi.org/10.1007/s40808-020-00787-w
Taylor KE (2001) In a Single Diagram. J Geophys Res 106:7183–7192. https://doi.org/10.1029/2000JD900719
Teng H, Liang Z, Chen S et al (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. https://doi.org/10.1016/j.scitotenv.2018.04.146
Teutschbein C, Seibert J (2013) Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions. Hydrol Earth Syst Sci 17:5061–5077. https://doi.org/10.5194/hess-17-5061-2013
Themeßl MJ, Gobiet A, Heinrich G (2012) Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Clim Change 112:449–468. https://doi.org/10.1007/s10584-011-0224-4
Thomas, J., Joseph, S. and Thrivikramji, K. P. (2017) AC SC, Geoscience Frontiers. China University of Geosciences (Beijing). doi: https://doi.org/10.1016/j.gsf.2017.05.011.
Trenberth KE (2011) Changes in precipitation with climate change. Clim Res 47:123–138. https://doi.org/10.3354/cr00953
Trinh-Tuan L, Matsumoto J, Tangang FT et al (2019) Application of Quantile Mapping bias correction for mid-future precipitation projections over Vietnam. Sci Online Lett Atmos 15:1–6. https://doi.org/10.2151/SOLA.2019-001
Tsitsagi M, Berdzenishvili A, Gugeshashvili M (2018) Spatial and temporal variations of rainfall–runoff erosivity (R) factor in Kakheti, Georgia. Ann Agrar Sci 16:226–235. https://doi.org/10.1016/j.aasci.2018.03.010
Vigiak O, Borselli L, Newham LTH, McInnes J, Roberts AM (2012) Comparison of conceptual landscape metrics to define hillslope-scale sediment delivery ratio. Geomorphology 138:74–88. https://doi.org/10.1016/j.geomorph.2011.08.026
Vrac M, Naveau P (2007) Stochastic downscaling of precipitation: from dry events to heavy rainfalls. Water Resour Res 43:1–13. https://doi.org/10.1029/2006WR005308
Wang L, Wang Y, Saskia K et al (2018) Effect of soil management on soil erosion on sloping farmland during crop growth stages under a large-scale rainfall simulation experiment. J Arid Land 10:921–931. https://doi.org/10.1007/s40333-018-0016-z
Wang X, Quine TA, Zhang H et al (2019) Redistribution of Soil Organic Carbon Induced by Soil Erosion in the Nine River Basins of China
Wen L, Zheng F, Shen H et al (2015) Rainfall intensity and inflow rate effects on hillslope soil erosion in the Mollisol region of Northeast China. Nat Hazards 79:381–395. https://doi.org/10.1007/s11069-015-1847-y
Wilks, D.S. (2006) Statistical methods in the atmospheric sciences. In: International Geophysics Series, Vol. 59, 2nd edition. Burlington, MA: Elsevier Academic Press, 627
Wu L, Liu X, Ma X (2016) Spatiotemporal distribution of rainfall erosivity in the Yanhe River watershed of hilly and gully region, Chinese Loess Plateau. Environmental Earth Sciences 75(4):1–13. https://doi.org/10.1007/s12665-015-5136-6
Yang D, Kanae S, Oki T et al (2003) Global potential soil erosion with reference to land use and climate changes. Hydrol Process 17:2913–2928. https://doi.org/10.1002/hyp.1441
Zhang R, Liu X, Heathman GC et al (2013) Assessment of soil erosion sensitivity and analysis of sensitivity factors in the Tongbai-Dabie mountainous area of China. CATENA 101:92–98. https://doi.org/10.1016/j.catena.2012.10.008
Zhang S, Li Z, Lin X, Zhang C (2019) Assessment of climate change and associated vegetation cover change on watershed-scale runoff and sediment yield. Water (Switzerland). https://doi.org/10.3390/w11071373
Zhang XC, Nearing MA (2005) Impact of climate change on soil erosion, runoff, and wheat productivity in central Oklahoma. CATENA 61:185–195. https://doi.org/10.1016/j.catena.2005.03.009
Acknowledgements
This PhD research is funded by University Grant Commission under Junior Research Fellowship (UGC-JRF) scheme (F.15-6(DEC.2013)/2014(NET)). The authors thankful to Indian Meteorological Department (IMD), for providing relevant data needed for the study. We also acknowledge the Earth System Grid Federation (ESGF) and the Climate Data Portal at Centre for Climate Change Research (CCCR), Indian Institute of Tropical Meteorology, India, for providing CORDEX-SA data.
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Rajbanshi, J., Bhattacharya, S. Modelling the impact of climate change on soil erosion and sediment yield: a case study in a sub-tropical catchment, India. Model. Earth Syst. Environ. 8, 689–711 (2022). https://doi.org/10.1007/s40808-021-01117-4
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DOI: https://doi.org/10.1007/s40808-021-01117-4