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Water-induced erosion potentiality and vulnerability assessment in Kangsabati river basin, eastern India

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Abstract

The large-scale water-induced erosion is one of the most determining elements on land degradation in subtropical monsoon-dominated region. From this large-scale erosion, the fertility of the agricultural land has been decline consequently. So, estimation of the amount of erosion and its accurate prediction is necessary for escaping from this hazardous situation. In this study, the application of evidential belief function (EBF), spatial logistic regression (SLR) and ensemble of EBF and SLR to estimate the erosion potentiality with the help of ArcGIS and Soil and water assessment tool (SWAT). The average annual soil erosion has been estimated with the help of revised universal soil loss equation (RUSLE) and geographical information system (GIS). Apart from this to evaluate the importance of morphotectonic parameters on soil erosion, the correlation between erosion potentiality and average annual soil erosion has been quantified. In large-scale erosion, there is a direct impact of storm rainfall event in monsoon period in the entire subtropical region. Here, in erosion potentiality assessment, the optimal capacity of ensemble EBF-SLR is higher than the single alone methods, i.e., EBF and SLR. So, the mentioned approaches can be applied in soil erosion research in subtropical environment with considering the erosion causal parameters. This type of information can be helpful to the decision-maker and stakeholders to take proper initiative to reducing the rate of erosion. The main task of the future researcher is to implement this method more accurate ways with considering more reliable variables and slight modifications of the approaches in keeping in the view of regional environment.

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References

  • Aher, P. D., Adinarayana, J., & Gorantiwar, S. D. (2014). Quantification of morphometric characterization and prioritization for management planning in semi-arid tropics of India: A remote sensing and GIS approach. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2014.02.028

    Article  Google Scholar 

  • Alejandro, M., & Omasa, K. (2007). Estimation of vegetation parameter for modeling soil erosion using linear spectral mixture analysis of landsat ETM data. ISPRS Journal of Photogrammetry and Remote Sensing, 62, 309–324.

    Article  Google Scholar 

  • Arabameri, A., Asadi Nalivan, O., Saha, S., et al. (2020). Novel ensemble approaches of machine learning techniques in modeling the gully erosion susceptibility. Remote Sensing, 12, 1890. https://doi.org/10.3390/rs12111890

    Article  Google Scholar 

  • Arabameri, A., Chandra Pal, S., Costache, R., Saha, A., Rezaie, F., Seyed Danesh, A., & Hoang, N. D. (2021). Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms. Geomatics, Natural Hazards and Risk, 12(1), 469–498.

    Article  Google Scholar 

  • Avinash, K., Jayappa, K. S., & Deepika, B. (2011). Prioritization of sub-basins based on geomorphology and morphometricanalysis using remote sensing and geographic informationsystem (GIS) techniques. Geocarto International. https://doi.org/10.1080/10106049.2011.606925

    Article  Google Scholar 

  • Band, S. S., Janizadeh, S., Chandra Pal, S., et al. (2020a). Novel ensemble approach of deep learning neural network (DLNN) model and particle swarm optimization (PSO) algorithm for prediction of gully erosion susceptibility. Sensors, 20, 5609.

    Article  CAS  Google Scholar 

  • Band, S. S., Janizadeh, S., Chandra Pal, S., et al. (2020b). Flash flood susceptibility modeling using new approaches of hybrid and ensemble tree-based machine learning algorithms. Remote Sensing, 12, 3568.

    Article  Google Scholar 

  • Baskan, O. (2021). Analysis of spatial and temporal changes of RUSLE-K soil erodibility factor in semi-arid areas in two different periods by conditional simulation. Archives of Agronomy and Soil Science. https://doi.org/10.1080/03650340.2021.1922673

    Article  Google Scholar 

  • Bauwe, A., Kahle, P., & Lennartz, B. (2016). Hydrologic evaluation of the curve number and green and ampt infiltration methods by applying hooghoudt and kirkham tile drain equations using SWAT. Journal of Hydrology, 537, 311–321.

    Article  Google Scholar 

  • Bhave, A. G., Mishra, A., & Raghuwanshi, N. S. (2014). A combined bottom-up and top-down approach for assessment of climate change adaptation options. Journal of Hydrology, 518, 150–161. https://doi.org/10.1016/j.jhydrol.2013.08.039

    Article  Google Scholar 

  • Blaikie, P., & Brookfield, H. (2015). Land degradation and society. Routledge.

    Book  Google Scholar 

  • Cetin, M. (2015a). Consideration of permeable pavement in landscape architecture. Journal of Environmental Protection and Ecology, 16, 385–392.

    Google Scholar 

  • Cetin, M. (2015b). Using GIS analysis to assess urban green space in terms of accessibility: Case study in Kutahya. International Journal of Sustainable Development & World Ecology, 22, 420–424.

    Google Scholar 

  • Cetin, M. (2015c). Determining the bioclimatic comfort in Kastamonu City. Environmental Monitoring and Assessment, 187, 640.

    Article  Google Scholar 

  • Cetin, M., & Sevik, H. (2016a). Evaluating the recreation potential of Ilgaz mountain national park in Turkey. Environmental Monitoring and Assessment, 188, 52.

    Article  Google Scholar 

  • Cetin M, Sevik H (2016b) Assessing potential areas of ecotourism through a case study in Ilgaz Mountain National Park. Tourism-from empirical research towards practical application 81–110

  • Cetin, M., Sevik, H., Canturk, U., & Cakir, C. (2018a). Evaluation of the recreational potential of Kutahya Urban Forest. Fresenius Environmental Bulletin, 27, 2629–2634.

    Google Scholar 

  • Cetin, M., Zeren, I., Sevik, H., et al. (2018b). A study on the determination of the natural park’s sustainable tourism potential. Environmental Monitoring and Assessment, 190, 167.

    Article  Google Scholar 

  • Cetin, M., Adiguzel, F., Gungor, S., et al. (2019). Evaluation of thermal climatic region areas in terms of building density in urban management and planning for Burdur, Turkey. Air Quality, Atmosphere & Health, 12, 1103–1112.

    Article  CAS  Google Scholar 

  • Chakrabortty, R., Pal, S. C., Chowdhuri, I., et al. (2020a). Assessing the importance of static and dynamic causative factors on erosion potentiality using SWAT, EBF with uncertainty and plausibility, logistic regression and novel ensemble model in a sub-tropical environment. Journal of the Indian Society of Remote Sensing, 48, 765–789. https://doi.org/10.1007/s12524-020-01110-x

    Article  Google Scholar 

  • Chakrabortty, R., Pal, S. C., Sahana, M., et al. (2020b). Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India. Natural Hazards, 104, 1259–1294. https://doi.org/10.1007/s11069-020-04213-3

    Article  Google Scholar 

  • Chakrabortty, R., Pradhan, B., Mondal, P., & Pal, S. C. (2020c). The use of RUSLE and GCMs to predict potential soil erosion associated with climate change in a monsoon-dominated region of eastern India. Arabian Journal of Geosciences, 13, 1–20.

    Article  Google Scholar 

  • Chen, J. (2007). Rapid urbanization in China: A real challenge to soil protection and food security. CATENA, 69, 1–15.

    Article  Google Scholar 

  • Chen, W., Lei, X., Chakrabortty, R., et al. (2021). Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility. Journal of Environmental Management, 284, 112015.

    Article  Google Scholar 

  • Chowdary, V. M., Ramakrishnan, D., Srivastava, Y. K., et al. (2009). Integrated water resource development plan for sustainable management of mayurakshi watershed India using remote sensing and GIS. Water Resources Management. https://doi.org/10.1007/s11269-008-9342-9

    Article  Google Scholar 

  • Chowdhuri, I., Pal, S. C., Arabameri, A., et al. (2020). Implementation of artificial intelligence based ensemble models for gully erosion susceptibility assessment. Remote Sensing, 12, 3620. https://doi.org/10.3390/rs12213620

    Article  Google Scholar 

  • Clarke, J. (1966). Morphometry from maps. Heinmann, London: Essays in geomorphology.

    Google Scholar 

  • Dempster, A. P. (1968). Upper and lower probabilities generated by a random closed interval. The Annals of Mathematical Statistics, 39, 957–966.

    Article  Google Scholar 

  • Dou, J., Yunus, A. P., Tien Bui, D., et al. (2019). Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island Japan. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2019.01.221

    Article  Google Scholar 

  • Enters, T. (1998). Methods for the economic assessment of the on-and off-site impacts of soil erosion. IBSRAM Bangkok.

    Google Scholar 

  • Erener, A., & Düzgün, H. (2012). Landslide susceptibility assessment: What are the effects of mapping unit and mapping method? Environmental Earth Sciences, 66, 859–877.

    Article  Google Scholar 

  • Fadil, A., Rhinane, H., Kaoukaya, A., et al. (2011). Hydrologic modeling of the bouregreg watershed (Morocco) using GIS and SWAT model. Journal of Geographic Information System. https://doi.org/10.4236/jgis.2011.34024

    Article  Google Scholar 

  • Feizizadeh, B., Blaschke, T., & Nazmfar, H. (2014). GIS-based ordered weighted averaging and Dempster-Shafer methods for landslide susceptibility mapping in the Urmia Lake Basin Iran. International Journal of Digital Earth, 7, 688–708.

    Article  Google Scholar 

  • Foster, G. (1986). Understanding Ephemeral Gully Erosion. Soil Conservation, 2, 90–125.

    Google Scholar 

  • Full article: Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms. Accessed 27 May 2021 https://www.tandfonline.com/doi/full/https://doi.org/10.1080/19475705.2021.1880977

  • Gajbhiye, S., Mishra, S. K., & Pandey, A. (2014). Prioritizing erosion-prone area through morphometric analysis: An RS and GIS perspective. Applied Water Science. https://doi.org/10.1007/s13201-013-0129-7

    Article  Google Scholar 

  • Garosi, Y., Sheklabadi, M., Conoscenti, C., et al. (2019). Assessing the performance of GIS-based machine learning models with different accuracy measures for determining susceptibility to gully erosion. Science of the Total Environment, 664, 1117–1132.

    Article  CAS  Google Scholar 

  • Gayen, A., Pourghasemi, H. R., Saha, S., et al. (2019). Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Science of the Total Environment, 668, 124–138.

    Article  CAS  Google Scholar 

  • Gelagay, H. S., & Minale, A. S. (2016). Soil loss estimation using GIS and Remote sensing techniques: A case of Koga watershed Northwestern Ethiopia. International Soil and Water Conservation Research. https://doi.org/10.1016/j.iswcr.2016.01.002

    Article  Google Scholar 

  • Ghosh, S., & Guchhait, S. K. (2016). Geomorphic threshold estimation for gully erosion in the lateritic soil of birbhum West Bengal India. SOIL Discussions. https://doi.org/10.5194/soil-2016-48

  • Gorsevski, P. V., Gessler, P. E., Foltz, R. B., & Elliot, W. J. (2006). Spatial prediction of landslide hazard using logistic regression and ROC analysis. Transactions in GIS, 10, 395–415.

    Article  Google Scholar 

  • Hembram, T. K., & Saha, S. (2020). Prioritization of sub-watersheds for soil erosion based on morphometric attributes using fuzzy AHP and compound factor in Jainti river basin, Jharkhand, Eastern India. Environment, Development and Sustainability, 22, 1241–1268. https://doi.org/10.1007/s10668-018-0247-3

    Article  Google Scholar 

  • Horton, R. E. (1932). Drainage-Basin Characteristics. Transactions AGU, 13, 350. https://doi.org/10.1029/TR013i001p00350

    Article  Google Scholar 

  • Horton, R. E. (1945). Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Geol Soc America Bull, 56, 275. https://doi.org/10.1130/0016-7606(1945)56[275:EDOSAT]2.0.CO;2

    Article  Google Scholar 

  • Kanth, T., & Hassan, Z. (2012). Morphometric analysis and prioritization of watersheds for soil and water resource management in Wular catchment using geo-spatial tools. International Journal of Geology, Earth and Environmental Sciences, 2, 30–41.

    Google Scholar 

  • Kaya, E., Agca, M., Adiguzel, F., & Cetin, M. (2019). Spatial data analysis with R programming for environment. Human and Ecological Risk Assessment: An International Journal, 25, 1521–1530.

    Article  CAS  Google Scholar 

  • Kelson, K. I., & Wells, S. G. (1989). Geologic influences on fluvial hydrology and bedload transport in small mountainous watersheds Northern New Mexico USA. Earth Surface Processes and Landforms. https://doi.org/10.1002/esp.3290140803

    Article  Google Scholar 

  • Korb, K. B., & Nicholson, A. E. (2010). Bayesian artificial intelligence. CRC Press.

    Book  Google Scholar 

  • Koskivaara, E. (2004). Artificial neural networks in analytical review procedures. Managerial Auditing Journal.

    Book  Google Scholar 

  • Kottagoda, S., & Abeysingha, N. (2017). Morphometric analysis of watersheds in Kelani river basin for soil and water conservation. Journal of the National Science Foundation of Sri Lanka, 45, 6.

    Article  Google Scholar 

  • Kouli, M., Soupios, P., & Vallianatos, F. (2009). Soil erosion prediction using the revised universal soil loss equation (RUSLE) in a GIS framework Chania Northwestern Crete Greece. Environmental Geology. https://doi.org/10.1007/s00254-008-1318-9

    Article  Google Scholar 

  • Lal, R. (2003). Soil erosion and the global carbon budget. Environment International, 29(4), 437–450.

    Article  CAS  Google Scholar 

  • Lal, R. (2014). Soil conservation and ecosystem services. International Soil and Water Conservation Research. https://doi.org/10.1016/S2095-6339(15)30021-6

    Article  Google Scholar 

  • Lee, S. (2007). Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surface Processes and Landforms: THe Journal of the British Geomorphological Research Group, 32, 2133–2148.

    Article  Google Scholar 

  • Li, Y., Jiao, J., Wang, Z., et al. (2016). Effects of revegetation on soil organic carbon storage and erosion-induced carbon loss under extreme rainstorms in the hill and gully region of the loess plateau. IJERPH, 13, 456. https://doi.org/10.3390/ijerph13050456

    Article  CAS  Google Scholar 

  • Lin, C. Y., Lin, W. T., & Chou, W. C. (2002). Soil erosion prediction and sediment yield estimation: The Taiwan experience. Soil and Tillage Research. https://doi.org/10.1016/S0167-1987(02)00114-9

    Article  Google Scholar 

  • Liu, X., Jia, G., Yu, X. (2021). Effects of the undecomposed layer and semi-decomposed layer of Quercus variabilis litter on the soil erosion process and the eroded sediment particle size distribution. Hydrological Processes. https://doi.org/10.1002/hyp.14195

  • Malik, S., Pal, S. C., Das, B., & Chakrabortty, R. (2019). Assessment of vegetation status of Sali River basin a tributary of Damodar river in Bankura West Bengal using satellite data (pp. 1–35). Development and Sustainability: Environment.

    Google Scholar 

  • Malpica, J. A., Alonso, M. C., & Sanz, M. A. (2007). Dempster-Shafer Theory in geographic information systems: A survey. Expert Systems with Applications, 32, 47–55.

    Article  Google Scholar 

  • Martınez, A., Dimitriadis, Y., Rubia, B., et al. (2003). Combining qualitative evaluation and social network analysis for the study of classroom social interactions. Computers & Education, 41, 353–368.

    Article  Google Scholar 

  • Meshram, S. G., & Sharma, S. K. (2017). Prioritization of watershed through morphometric parameters: A PCA-based approach. Applied Water Science. https://doi.org/10.1007/s13201-015-0332-9

    Article  Google Scholar 

  • Miller VC (1953). Quantitative geomorphic study of drainage basin characteristics in the Clinch Mountain area, Virginia and Tennessee. Technical report (Columbia University Department of Geology); no 3

  • Mittal, N., Mishra, A., Singh, R., et al. (2014). Flow regime alteration due to anthropogenic and climatic changes in the Kangsabati River, India. Ecohydrology & Hydrobiology, 14, 182–191.

    Article  Google Scholar 

  • Moglen, G. E., Eltahir, E. A. B., & Bras, R. L. (1998). On the sensitivity of drainage density to climate change. Water Resources Research. https://doi.org/10.1029/97WR02709

    Article  Google Scholar 

  • Mohammadi, S., Balouei, F., Haji, K., et al. (2021). Country-scale spatio-temporal monitoring of soil erosion in Iran using the G2 model. International Journal of Digital Earth. https://doi.org/10.1080/17538947.2021.1919230

    Article  Google Scholar 

  • Mostaghimi, S., Brannan, K. M., Dillaha III, T. A., & Bruggeman, A. C. (2000). Best management practices for nonpoint source pollution control: Selection and assessment. In Agricultural Nonpoint Source Pollution; Water Management and Hydrology, (Ch. 10, pp. 91–109).

  • Nasir Ahmad, N. S. B., Mustafa, F. B., Yusoff, M., S, Y, & Didams, G. (2020). A systematic review of soil erosion control practices on the agricultural land in Asia. International Soil and Water Conservation Research, 8, 103–115. https://doi.org/10.1016/j.iswcr.2020.04.001

    Article  Google Scholar 

  • Nearing MA (2013). Soil Erosion and Conservation. In: Environmental Modelling: Finding Simplicity in Complexity: Second Edition

  • Oguchi, T. (1997). Drainage density and relative relief in humid steep mountains with frequent slope failure. Earth Surface Processes and Landforms. https://doi.org/10.1002/(SICI)1096-9837(199702)22:2%3c107::AID-ESP680%3e3.0.CO;2-U

    Article  Google Scholar 

  • Oldeman LR (1992) Global extent of soil degradation. In: Bi-Annual Report 1991–1992/ISRIC. ISRIC, pp 19–36

  • Oliver, M. A., & Gregory, P. (2015). Soil, food security and human health: A review. European Journal of Soil Science, 66, 257–276.

    Article  Google Scholar 

  • Ozdemir, H., & Bird, D. (2009). Evaluation of morphometric parameters of drainage networks derived from topographic maps and DEM in point of floods. Environmental Geology, 56, 1405–1415.

    Article  Google Scholar 

  • Pal, S. C., & Chakrabortty, R. (2019a). Simulating the impact of climate change on soil erosion in sub-tropical monsoon dominated watershed based on RUSLE, SCS runoff and MIROC5 climatic model. Advances in Space Research, 64, 352–377.

    Article  Google Scholar 

  • Pal, S. C., & Chakrabortty, R. (2019b). Modeling of water induced surface soil erosion and the potential risk zone prediction in a sub-tropical watershed of Eastern India. Modeling Earth Systems and Environment, 5, 369–393.

    Article  Google Scholar 

  • Pal, S. C., & Shit, M. (2017). Application of RUSLE model for soil loss estimation of Jaipanda watershed, West Bengal. Spatial Information Research, 25, 399–409.

    Article  Google Scholar 

  • Pal, S. C., Chakrabortty, R., Malik, S., & Das, B. (2018). Application of forest canopy density model for forest cover mapping using LISS-IV satellite data: A case study of Sali watershed, West Bengal. Model Earth Syst Environ, 4, 853–865. https://doi.org/10.1007/s40808-018-0445-x

    Article  Google Scholar 

  • Pal, S. C., Arabameri, A., Blaschke, T., et al. (2020). Ensemble of machine-learning methods for predicting gully erosion susceptibility. Remote Sensing, 12, 3675. https://doi.org/10.3390/rs12223675

    Article  Google Scholar 

  • Pal, S. C., Chakrabortty, R., Roy, P., et al. (2021). Changing climate and land use of 21st century influences soil erosion in India. Gondwana Research, 94, 164–185. https://doi.org/10.1016/j.gr.2021.02.021

    Article  Google Scholar 

  • Pareta, K., & Pareta, U. (2011). Quantitative Morphometric Analysis of a Watershed of Yamuna Basin, India using ASTER DEM Data and GIS. International Journal of Geomatics and Geosciences., 2(1), 248.

    Google Scholar 

  • Patel, D. P., Gajjar, C. A., & Srivastava, P. K. (2013). Prioritization of Malesari mini-watersheds through morphometric analysis: A remote sensing and GIS perspective. Environment and Earth Science, 69, 2643–2656. https://doi.org/10.1007/s12665-012-2086-0

    Article  Google Scholar 

  • Pimentel, D. (2006). Soil erosion: A food and environmental Threat. Environment, Development and Sustainability, 8, 119–137. https://doi.org/10.1007/s10668-005-1262-8

    Article  Google Scholar 

  • Poesen J (1996) Contribution of gully erosion to sediment production. IAHS, p 251

  • Pradhan, B. (2010). Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. Journal of the Indian Society of Remote Sensing, 38, 301–320.

    Article  Google Scholar 

  • Rahmati, O., Haghizadeh, A., & Stefanidis, S. (2016). Assessing the accuracy of GIS-based analytical hierarchy process for watershed prioritization Gorganrood river basin Iran. Water Resources Management. https://doi.org/10.1007/s11269-015-1215-4

    Article  Google Scholar 

  • Rai, P. K., Chandel, R. S., Mishra, V. N., & Singh, P. (2018). Hydrological inferences through morphometric analysis of lower Kosi river basin of India for water resource management based on remote sensing data. Applied Water Science, 8, 15.

    Article  Google Scholar 

  • Renard KG, Yoder DC, Lightle DT, Dabney SM (2011). Universal Soil Loss Equation and Revised Universal Soil Loss Equation. In: Handbook of Erosion Modelling

  • Richard SM (1968) Unclassified ad number

  • Roy, P., Chakrabortty, R., Chowdhuri, I., et al. (2020a). Development of Different Machine Learning Ensemble Classifier for Gully Erosion Susceptibility in Gandheswari Watershed of West Bengal, India. In J. K. Rout, M. Rout, & H. Das (Eds.), Machine Learning for Intelligent Decision Science. Singapore: Springer Singapore.

    Google Scholar 

  • Roy, P., Chandra Pal, S., Arabameri, A., et al. (2020b). Novel ensemble of multivariate adaptive regression spline with spatial logistic regression and boosted regression tree for gully erosion susceptibility. Remote Sensing, 12, 3284.

    Article  Google Scholar 

  • Roy, P., Chandra Pal, S., Chakrabortty, R., et al. (2020c). Threats of climate and land use change on future flood susceptibility. Journal of Cleaner Production, 272, 122757. https://doi.org/10.1016/j.jclepro.2020.122757

    Article  Google Scholar 

  • Saha, A., Ghosh, M., & Pal, S. C. (2020). Understanding the Morphology and Development of a Rill-Gully: An Empirical Study of Khoai Badland, West Bengal, India. In P. K. Shit, H. R. Pourghasemi, & G. S. Bhunia (Eds.), Gully Erosion Studies from India and Surrounding Regions (pp. 147–161). Springer International Publishing.

    Chapter  Google Scholar 

  • Saha, A., Pal, S. C., Arabameri, A., et al. (2021). Optimization modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements. Journal of Environmental Management, 287, 112284. https://doi.org/10.1016/j.jenvman.2021.112284

    Article  Google Scholar 

  • Sahour, H., Gholami, V., Vazifedan, M., & Saeedi, S. (2021). Machine learning applications for water-induced soil erosion modeling and mapping. Soil and Tillage Research, 211, 105032. https://doi.org/10.1016/j.still.2021.105032

    Article  Google Scholar 

  • Schumm, S. A. (1956). Evolution of drainage systems and slopes in badlands at Perth Amboy. Bulletin of the Geological Society of America. https://doi.org/10.1130/0016-7606(1956)67[597:EODSAS]2.0.CO;2

    Book  Google Scholar 

  • Sepuru, T. K., & Dube, T. (2018). An appraisal on the progress of remote sensing applications in soil erosion mapping and monitoring. Remote Sensing Applications: Society and Environment, 9, 1–9. https://doi.org/10.1016/j.rsase.2017.10.005

    Article  Google Scholar 

  • Sreedevi, P., Owais, S., Khan, H., & Ahmed, S. (2009). Morphometric analysis of a watershed of South India using SRTM data and GIS. Journal of the Geological Society of India, 73, 543–552.

    Article  Google Scholar 

  • Strahler, A. N. (1957). Quantitative analysis of watershed geomorphology. Eos, Transactions American Geophysical Union. https://doi.org/10.1029/TR038i006p00913

    Article  Google Scholar 

  • Strahler, A. N. (1964). Part II Quantitative geomorphology of drainage basins and channel networks (pp. 4–39). Handbook of Applied Hydrology: McGraw-Hill, New York.

    Google Scholar 

  • Tehrany, M. S., Shabani, F., Javier, D. N., & Kumar, L. (2017). Soil erosion susceptibility mapping for current and 2100 climate conditions using evidential belief function and frequency ratio. Geomatics, Natural Hazards and Risk, 8, 1695–1714.

    Article  Google Scholar 

  • 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. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2018.04.146

    Article  Google Scholar 

  • Thomas, A., Snyder, W., Mills, W., & Dillard, A. (1991). Erosion risk assessment for soil conservation planning. Soil Technology, 4, 373–389.

    Article  Google Scholar 

  • Tian, P., Zhu, Z., Yue, Q., et al. (2021). Soil erosion assessment by RUSLE with improved P factor and its validation: Case study on mountainous and hilly areas of Hubei Province China. International Soil and Water Conservation Research. https://doi.org/10.1016/j.iswcr.2021.04.007

    Article  Google Scholar 

  • B. Wg V., Thornbury WD. (2006). Principles of geomorphology. The Geographical Journal. https://doi.org/10.2307/1791828

    Article  Google Scholar 

  • Wischmeier WH, Smith DD (1978). Predicting Rainfall Erosion Losses : a guide to conservation planning. Agriculture Handbook

  • Yadav, S. K., Singh, S. K., Gupta, M., & Srivastava, P. K. (2014). Morphometric analysis of upper tons basin from Northern Foreland of peninsular India using CARTOSAT satellite and GIS. Geocarto International. https://doi.org/10.1080/10106049.2013.868043

    Article  Google Scholar 

  • Yang, J., Song, C., Yang, Y., et al. (2019). New method for landslide susceptibility mapping supported by spatial logistic regression and GeoDetector: A case study of Duwen Highway Basin, Sichuan Province, China. Geomorphology, 324, 62–71.

    Article  Google Scholar 

  • Yilmaz, I. (2009). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey). Computers & Geosciences, 35, 1125–1138.

    Article  Google Scholar 

  • Zgłobicki, W., Baran-Zgłobicka, B., Gawrysiak, L., & Telecka, M. (2015). The impact of permanent gullies on present-day land use and agriculture in loess areas (E. Poland). CATENA, 126, 28–36. https://doi.org/10.1016/j.catena.2014.10.022

    Article  Google Scholar 

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Chakrabortty, R., Pal, S.C., Arabameri, A. et al. Water-induced erosion potentiality and vulnerability assessment in Kangsabati river basin, eastern India. Environ Dev Sustain 24, 3518–3557 (2022). https://doi.org/10.1007/s10668-021-01576-w

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  • DOI: https://doi.org/10.1007/s10668-021-01576-w

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