Advertisement

Modeling Earth Systems and Environment

, Volume 4, Issue 2, pp 699–728 | Cite as

Assessing the impacts of urbanization on hydrological processes in a semi-arid river basin of Maharashtra, India

  • Satyavati Shukla
  • Shirishkumar Gedam
Original Article

Abstract

This study investigates the influence of changes in spatial urbanization on the hydrological processes of Upper Bhima River basin of Maharashtra state, India using Soil and Water Assessment Tool hydrological model based simulations. The simulated and observed monthly stream flow rates at the basin outlet were compared for calibration (1985–2004) and validation (2005–2014) of the SWAT model. For both the periods, Nash–Sutcliffe efficiency values were > 0.70, root mean square error observations standard deviation ratio (RSR) values were < 0.50 and percent bias values were < 10. Hence, with coefficient of determination (R2) of 0.89 and 0.96 both the periods showed good agreement between simulated and observed stream flow rates. Urbanization indicators viz. population urbanization level (U p ) and spatial urbanization level (U s ) were estimated to quantify the growth patterns in population and urban areas respectively. Further, correlation analysis and Multivariate analysis of variance were performed between U s and hydrological variables to study the influence of spatial urbanization on hydrological components for comprehending the existing relationships between them. Results reveal that from 1992 to 2014 with increase in U s of 1.44 to 6.31, the average annual surface runoff increased from 438.22 to 448.61 mm [standard deviation (σ) = 4.40; sum of squares (SS) = 58.20], whereas percolation decreased from 153.53 to 139.08 mm (σ = 6.06; SS = 110.10). These hydrological parameters are highly influenced by increase in urbanization. This study is relevant for water sources planners and policy makers for assessment of water resources for sustainable development in the urbanizing semi-arid river basins.

Keywords

Land use land cover MANOVA Remote sensing and GIS River basin Spatial urbanization level SWAT model 

Notes

Acknowledgements

Authors are also grateful to various national and international Government organizations viz. H. P. Office, Nasik; Survey of India; NBSS&LUP, Nagpur, India; Bhuvan Portal, ISRO, India; Prediction of Worldwide Energy Resource (POWER), NASA, USA; United States Geological Survey (USGS), USA for providing the necessary datasets to carry out the study.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

References

  1. Abbaspour KC, Vejdani M, Haghighat S (2007) SWAT-CUP calibration and uncertainty programs for SWAT. In: MODSIM 2007 international congress on modelling and simulation. Modelling and Simulation Society of Australia, New Zealand, pp 1596–1602Google Scholar
  2. Abbaspour KC, Rouholahnejad E, Vaghefi S, Srinivasan R, Yang H, Kløve B (2015) A continental-scale hydrology and water quality model for Europe: calibration and uncertainty of a high-resolution large-scale SWAT model. J Hydrol 524:733–752.  https://doi.org/10.1016/j.jhydrol.2015.03.027 CrossRefGoogle Scholar
  3. Adepoju MO, Millington AC, Tansey KT (2006) Land use/land cover change detection in metropolitan Lagos (Nigeria): 1984–2002. In: ASPRS 2006 Annual Conference Reno, Nevada, May 1–5, 2006. http://www.asprs.org/a/publications/proceedings/reno2006/0002.pdf. Accessed 09 July 2015
  4. Akpoti K, Antwi EO, Kabo-bah AT (2016) Impacts of rainfall variability, land use and land cover change on stream flow of the black Volta basin, West Africa. Hydrol 3(3):26.  https://doi.org/10.3390/hydrology3030026 CrossRefGoogle Scholar
  5. Alansi AW, Amin MSM, Abdul Halim G, Shafri HZM, Aimrun W (2009) Validation of SWAT model for stream flow simulation and forecasting in Upper Bernam humid tropical river basin, Malaysia. Hydrol Earth Syst Sci Discuss 6(6):7581–7609.  https://doi.org/10.5194/hessd-6-7581-2009 CrossRefGoogle Scholar
  6. Allaire MC, Vogel RM, Kroll CN (2015) The hydromorphology of an urbanizing watershed using multivariate elasticity. Adv Water Resour 86:147–154.  https://doi.org/10.1016/j.advwatres.2015.09.022 CrossRefGoogle Scholar
  7. Anderson JR (1976) A land use and land cover classification system for use with remote sensor data. US Government Printing Office, vol 964. http://pubs.er.usgs.gov/publication/pp964. Accessed 24 Mar 2017
  8. Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, Srinivasan R, Santhi C, Harmel RD, Van Griensven A, Van Liew MW, Kannan N (2012) SWAT: Model use, calibration, and validation. Trans ASABE 55(4):1491–1508CrossRefGoogle Scholar
  9. Bayramov E, Buchroithner M, Bayramov R (2016) Quantitative assessment of 2014–2015 land-cover changes in Azerbaijan using object-based classification of LANDSAT-8 timeseries. Model Earth Syst Environ 2(1):35CrossRefGoogle Scholar
  10. Beaulieu E, Lucas Y, Viville D, Chabaux F, Ackerer P, Goddéris Y, Pierret MC (2016) Hydrological and vegetation response to climate change in a forested mountainous catchment. Model Earth Syst Environ 2(4):191CrossRefGoogle Scholar
  11. Beck BF, Herring JG (2001) Geotechnical and environmental applications of Karst geology and hydrology. CRC Press, Boca Raton (ISBN: 9789058091901) Google Scholar
  12. Bhatt A, Ghosh SK, Kumar A (2016) Spectral indices based object oriented classification for change detection using satellite data. Int J Syst Assur Eng Manag 1–10.  https://doi.org/10.1007/s13198-016-0458-7
  13. Bhattacharya J (2007) Multivariate Analysis of Variance (MANOVA). Tutorial for Goldsmiths College, United Kingdom. http://homepages.gold.ac.uk/. Accessed 05 Aug 2017
  14. Bochis EC (2010) Characteristics of urban development and associated stormwater quality. Ph.D. Dissertation, The University of Alabama. http://unix.eng.ua.edu/. Accessed 11 May 2016
  15. Brice MH (2016) Impacts de l’urbanisation sur la diversité spécifique et fonctionnelle dans les forêts riveraines. M.Sc. Dissertation, Université de Montréal. http://www.biopolis.ca/. Accessed 19 Jan 2017
  16. Brown C (2012) Applied multivariate statistics in geohydrology and related sciences. Springer, Berlin (ISBN: 978-3-642-80328-4) Google Scholar
  17. Carey G (1998) Multivariate analysis of variance (MANOVA): I. theory. http://ibgwww.colorado.edu/. Accessed 10 Feb 2016
  18. Carlson TN, Arthur ST (2000) The impact of land use-land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Glob Planet Change 25(1–2):49–65CrossRefGoogle Scholar
  19. Census Department Report, Government of India (2011) Census of India 2011: provisional population totals-india-data sheet. http://pib.nic.in/prs/2011/latest31mar.pdf. Accessed 17 Oct 2017
  20. Chaemiso SE, Abebe A, Pingale SM (2016) Assessment of the impact of climate change on surface hydrological processes using SWAT: a case study of Omo-Gibe river basin, Ethiopia. Model Earth Syst Environ 2(4):205CrossRefGoogle Scholar
  21. Civco DL, Hurd JD, Wilson EH, Song M, Zhang Z (2002) A comparison of land use and land cover change detection methods. In: ASPRS-ACSM Annual Conference 2002. http://clear.uconn.edu/publications/research/tech_papers. Accessed 02 July 2015
  22. Dai Q, Peng X, Yang Z, Zhao L (2017) Runoff and erosion processes on bare slopes in the Karst Rocky desertification area. Catena 152:218–226.  https://doi.org/10.1016/j.catena.2017.01.013 CrossRefGoogle Scholar
  23. Daniel EB, Camp JV, LeBoeuf EJ, Penrod JR, Dobbins JP, Abkowitz MD (2011) Watershed modeling and its applications: a state-of-the-art review. Open Hydrol J.  https://doi.org/10.2174/1874378101105010026 Google Scholar
  24. Deng X, Xu Y, Han L, Song S, Yang L, Li G, Wang Y (2015) Impacts of urbanization on river systems in the Taihu Region, China. Water 7(4):1340–1358.  https://doi.org/10.3390/w7041340 CrossRefGoogle Scholar
  25. Devia GK, Ganasri BP, Dwarakish GS (2015) A review on hydrological models. Aquat Procedia 4:1001–1007.  https://doi.org/10.1016/j.aqpro.2015.02.126 CrossRefGoogle Scholar
  26. Dodangeh E, Shahedi K, Solaimani K, Kossieris P (2017) Usability of the BLRP model for hydrological applications in arid and semi-arid regions with limited precipitation data. Model Earth Syst Environ 3(2):539–555CrossRefGoogle Scholar
  27. Dwarakish GS, Ganasri BP (2015) Impact of land use change on hydrological systems: a review of current modeling approaches. Cogent Geosci 1(1):1115691.  https://doi.org/10.1080/23312041.2015.1115691 CrossRefGoogle Scholar
  28. Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80(1):185–201.  https://doi.org/10.1016/S0034-4257(01)00295-4 CrossRefGoogle Scholar
  29. Garg KK, Bharati L, Gaur A, George B, Acharya S, Jella K, Narasimhan B (2012) Spatial mapping of agricultural water productivity using the SWAT model in Upper Bhima Catchment, India. Irrig Drain 61(1):60–79.  https://doi.org/10.1002/ird.618 CrossRefGoogle Scholar
  30. Gassman PW, Reyes MR, Green CH, Arnold JG (2007) The soil and water assessment tool: historical development, applications, and future research directions. Trans ASABE 50(4):1211–1250.  https://doi.org/10.13031/2013.23637 CrossRefGoogle Scholar
  31. Ghosh S, Shetty A (2017) Modelling the land use system process for a pre-industrial landscape in India. Model Earth Syst Environ 3(2):703–717CrossRefGoogle Scholar
  32. Gu C, Mu X, Zhao G, Gao P, Sun W, Yu Q (2016) Changes in stream flow and their relationships with climatic variations and anthropogenic activities in the Poyang Lake Basin, China. Water 8(12):564.  https://doi.org/10.3390/w8120564 CrossRefGoogle Scholar
  33. Gumindoga W, Rientjes T, Shekede MD, Rwasoka DT, Nhapi I, Haile AT (2014) Hydrological impacts of urbanization of two catchments in Harare. Zimb Remote Sens 6(12):12544–12574.  https://doi.org/10.3390/rs61212544 CrossRefGoogle Scholar
  34. Gupta HV, Sorooshian S, Yapo PO (1999) Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. J Hydrol Eng 4(2):135–143.  https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135) CrossRefGoogle Scholar
  35. Gyamfi C, Ndambuki JM, Salim RW (2016) Hydrological responses to land use/cover changes in the Olifants basin, South Africa. Water 8(12):588.  https://doi.org/10.3390/w8120588 CrossRefGoogle Scholar
  36. Halefom A, Sisay E, Khare D, Singh L, Worku T (2017) Hydrological modeling of urban catchment using semi-distributed model. Model Earth Syst Environ 3(2):683–692CrossRefGoogle Scholar
  37. Hall SJ, Ahmed B, Ortiz P, Davies R, Sponseller RA, Grimm NB (2009) Urbanization alters soil microbial functioning in the Sonoran Desert. Ecosyst 12(4):654–671.  https://doi.org/10.1007/s10021-009-9249-1 CrossRefGoogle Scholar
  38. Helsel DR, Hirsch RM (2002) Statistical methods in water resources: US geological survey techniques of water resources investigations, book 4, chap A3. U.S. Geological Survey, p. 522. http://water.usgs.gov/pubs/twri/twri4a3/. Accessed 19 July 2017
  39. Hepp LU, Milesi SV, Biasi C, Restello RM (2010) Effects of agricultural and urban impacts on macroinvertebrates assemblages in streams (Rio Grande do Sul, Brazil). Zoologia (Curitiba) 27(1):106–113.  https://doi.org/10.1590/S1984-46702010000100016 CrossRefGoogle Scholar
  40. Hirsch RM, Slack JR, Smith RA (1982) Techniques of trend analysis for monthly water quality data. Water Resour Res 18(1):107–121.  https://doi.org/10.1029/WR018i001p00107 CrossRefGoogle Scholar
  41. Ingole AS, Shivkumar V, Thakare A, Chincholkar S, Ashwajit A (2016) Study of dam induced land use/land cover changes using GIS and RS technique; a case study of Sardar Sarovar Dam watershed basin in Narmada Dist. Gujrat, Nandurbar Dist. Maharshtra and Dhar Dist. Madhya Pradesh. In: International Conference on Science and Technology for Sustainable Development (ICSTSD) 2016, ISSN: 2348–8352, pp 18–23. http://www.internationaljournalssrg.org/IJCE/2016/Special-Issue/ICSTSD-ICEEOT/IJCE-ICSTSD-P103.pdf. Accessed 01 June 2017
  42. Jain PK (2009) Groundwater Information of Pune district, Maharashtra. Technical report 1612/DBR/2009, Central Ground Water Board (CGWB), Nagpur, IndiaGoogle Scholar
  43. Jawak SD, Devliyal P, Luis AJ (2015) A comprehensive review on pixel oriented and object oriented methods for information extraction from remotely sensed satellite images with a special emphasis on cryospheric applications. Adv Remote Sens 4(03):177.  https://doi.org/10.4236/ars.2015.43015 CrossRefGoogle Scholar
  44. Kelly M, Tuxen K (2009) Remote sensing Support for tidal wetland vegetation research and management. In: Remote sensing and geospatial technologies for coastal ecosystem assessment and management. Springer, Berlin, pp 341–363.  https://doi.org/10.1007/978-3-540-88183-4_15 CrossRefGoogle Scholar
  45. Kiros G, Shetty A, Nandagiri L (2015) Performance evaluation of SWAT model for land use and land cover changes in semi-arid climatic conditions: a review. Hydrol Curr Res 6(3):1.  https://doi.org/10.4172/2157-7587.1000216 CrossRefGoogle Scholar
  46. Kumar S, Singh A, Shrestha DP (2016) Modelling spatially distributed surface runoff generation using SWAT-VSA: a case study in a watershed of the north-west Himalayan landscape. Model Earth Syst Environ 2(4):202CrossRefGoogle Scholar
  47. Kumar N, Singh SK, Srivastava PK, Narsimlu B (2017) SWAT Model calibration and uncertainty analysis for streamflow prediction of the Tons River Basin, India, using sequential uncertainty fitting (SUFI-2) algorithm. Model Earth Syst Environ 3(1):30CrossRefGoogle Scholar
  48. Legates DR, McCabe GJ (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233–241.  https://doi.org/10.1029/1998WR900018 CrossRefGoogle Scholar
  49. Leščešen I, Pantelić M, Dolinaj D, Stojanović V, Milošević D (2015) Statistical analysis of water quality parameters of the Drina River (West Serbia), 2004-11. Pol J Environ Stud 24(2).  https://doi.org/10.15244/pjoes/29684 Google Scholar
  50. Li Z, Deng X, Wu F, Hasan SS (2015) Scenario analysis for water resources in response to land use change in the middle and upper reaches of the Heihe River Basin. Sustain 7(3):3086–3108.  https://doi.org/10.3390/su7033086 CrossRefGoogle Scholar
  51. Macfarlane C, Grigg AH, Daws MI (2017) A standardised Landsat time series (1973–2016) of forest leaf area index using pseudoinvariant features and spectral vegetation index isolines and a catchment hydrology application. Remote Sens Appl Soc Environ 6:1–14.  https://doi.org/10.1016/j.rsase.2017.01.006 Google Scholar
  52. Makwana JJ, Tiwari MK (2017) Hydrological stream flow modelling using soil and water assessment tool (SWAT) and neural networks (NNs) for the Limkheda watershed, Gujarat, India. Model Earth Syst Environ 3(2):635–645CrossRefGoogle Scholar
  53. Matthaei CD, Piggott JJ, Townsend CR (2010) Multiple stressors in agricultural streams: interactions among sediment addition, nutrient enrichment and water abstraction. J Appl Ecol 47(3):639–649.  https://doi.org/10.1111/j.1365-2664.2010.01809.x CrossRefGoogle Scholar
  54. Mertler CA, Reinhart RV (2016) Advanced and multivariate statistical methods: practical application and interpretation. Taylor & Francis, Routledge (ISBN: 978-1884585845) Google Scholar
  55. Miller SN, Kepner WG, Mehaffey MH, Hernandez M, Miller RC, Goodrich DC, Devonald K, Heggem DT, Miller WP (2002) Integrating landscape assessment and hydrologic modeling for land cover change analysis. J Am Water Resour Assoc 38(4):915–929CrossRefGoogle Scholar
  56. Ministry of Environment and Forests (MoEF) Report, Governemnt of India (2010) State of environment (SoE) report: Maharashtra, final draft. http://moef.nic.in/. Accessed 27 Mar 2015
  57. Mishra N, Aggarwal SP, Dadhwal VK (2008) Macroscale hydrological modelling and impact of land cover change on stream flows of the Mahanadi River basin. A Master thesis submitted to Andhra University, Indian Institute of Remote Sensing (National Remote Sensing Agency) Dept. of Space, Govt. of IndiaGoogle Scholar
  58. Misra D, Daanen RP, Thompson AM (2011) Base flow/groundwater flow. In: Singh VP, Singh P, Haritashya UK (eds) Encyclopedia of snow, ice and glaciers. Encyclopedia of earth sciences series, Springer, Dordrecht.  https://doi.org/10.1007/978-90-481-2642-2_36 (ISBN: 978-90-481-2642-2) Google Scholar
  59. Mohan M, Pathan SK, Narendrareddy K, Kandya A, Pandey S (2011) Dynamics of urbanization and its impact on land-use/land-cover: a case study of megacity Delhi. J Environ Prot 2(09):1274.  https://doi.org/10.4236/jep.2011.29147 CrossRefGoogle Scholar
  60. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900.  https://doi.org/10.13031/2013.23153 CrossRefGoogle Scholar
  61. Nash J, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10(3):282–290.  https://doi.org/10.1016/0022-1694(70)90255-6 CrossRefGoogle Scholar
  62. Nathan NS, Saravanane R, Sundararajan T (2017) Spatial variability of ground water quality using HCA, PCA and MANOVA at Lawspet, Puducherry in India. Comput Water Energy Environ Eng 6(03):243.  https://doi.org/10.4236/cweee.2017.63017 CrossRefGoogle Scholar
  63. National Informatics Center (NIC) - District Pune, Government of India (2009) Pune District Gazetteer Information. http://pune.nic.in/puneCollectorate/Gazette/gaz.aspx. Accessed 16 Jan 2015
  64. Niphadkar M, Nagendra H, Tarantino C, Adamo M, Blonda P (2017) Comparing pixel and object-based approaches to map an understorey invasive shrub in tropical mixed forests. Front Plant Sci 8:892.  https://doi.org/10.3389/fpls.2017.00892 CrossRefGoogle Scholar
  65. O’Driscoll M, Clinton S, Jefferson A, Manda A, McMillan S (2010) Urbanization effects on watershed hydrology and in-stream processes in the southern United States. Water 2(3):605–648.  https://doi.org/10.3390/w2030605 CrossRefGoogle Scholar
  66. Ojha CSP, Berndtsson R, Bhunya P (2008) Engineering hydrology book. Oxford University Press, Oxford, p 0195694619 (ISBN-10) Google Scholar
  67. Paul MJ, Meyer JL (2001) Streams in the urban landscape. Ann Rev Ecol Syst 32(1):333–365.  https://doi.org/10.1007/978-0-387-73412-5_12 CrossRefGoogle Scholar
  68. Pignotti G, Rathjens H, Cibin R, Chaubey I, Crawford M (2017) Comparative analysis of HRU and grid-based SWAT models. Water 9(4):272.  https://doi.org/10.3390/w9040272 CrossRefGoogle Scholar
  69. Rahman MT, Aldosary AS, Mortoja MG (2017) Modeling future land cover changes and their effects on the land surface temperatures in the Saudi Arabian eastern coastal city of Dammam. Land 6(2):36.  https://doi.org/10.3390/land6020036 CrossRefGoogle Scholar
  70. Rooijen DJV, Turral H, Biggs TW (2009) Urban and industrial water use in the Krishna basin, India. Irrig Drain 58(4):406–428.  https://doi.org/10.1002/ird.439 CrossRefGoogle Scholar
  71. Samal DR, Gedam SS (2015) Monitoring land use changes associated with urbanization: an object based image analysis approach. Eur J Remote Sens 48(1):85–99.  https://doi.org/10.5721/EuJRS20154806 CrossRefGoogle Scholar
  72. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379–1389.  https://doi.org/10.2307/2285891 CrossRefGoogle Scholar
  73. Shukla AK, Ojha CSP, Mijic A, Buytaert W, Pathak S, Garg RD, Shukla S (2017) Population growth–land use/land cover transformations–water quality nexus in Upper Ganga River basin. Hydrol Earth Syst Sci Discuss.  https://doi.org/10.5194/hess-2017-384 Google Scholar
  74. Shuster WD, Bonta J, Thurston H, Warnemuende E, Smith DR (2005) Impacts of impervious surface on watershed hydrology: a review. Urban Water J 2(4):263–275.  https://doi.org/10.1080/15730620500386529 CrossRefGoogle Scholar
  75. Singh J, Knapp HV, Arnold JG, Demissie M (2005) Hydrological modeling of the Iroquois River watershed using HSPF and SWAT. J Am Water Resour Assoc 41(2):343–360.  https://doi.org/10.1111/j.1752-1688.2005.tb03740.x CrossRefGoogle Scholar
  76. Sisay E, Halefom A, Khare D, Singh L, Worku T (2017) Hydrological modelling of ungauged urban watershed using SWAT model. Model Earth Syst Environ 3(2):693–702CrossRefGoogle Scholar
  77. Sun Y, Zhang X, Zhao Y, Xin Q (2017) Monitoring annual urbanization activities in Guangzhou using Landsat images (1987–2015). Int J Remote Sens 38(5):1258–1276.  https://doi.org/10.1080/01431161.2016.1268283 CrossRefGoogle Scholar
  78. United Nations (2008) United Nations Report, Department of Economic and Social Affairs: Population Division World urbanization prospects, (The 2008 Revision Population Database) New York. http://esa.un.org/unup/p2k0data.asp/. Accessed 19 May 2015
  79. United States Environmental Protection Agency (USEPA) (2017) Watershed Academy web: growth and water resources. https://cfpub.epa.gov/. Accessed 23 Mar 2017
  80. Van GA, Meixner T, Grunwald S, Bishop T, Diluzio M, Srinivasan R (2006) A global sensitivity analysis tool for the parameters of multi-variable catchment models. J Hydrol 324(1–4):10–23.  https://doi.org/10.1016/j.jhydrol.2005.09.008 Google Scholar
  81. Wangpimool W, Pongput K, Tangtham N, Prachansri S, Gassman PW (2016) The impact of para rubber expansion on streamflow and other water balance components of the Nam Loei River basin, Thailand. Water 9(1):1.  https://doi.org/10.3390/w9010001 CrossRefGoogle Scholar
  82. Weitzell RE, Kaushal SS, Lynch LM, Guinn SM, Elmore AJ (2016) Extent of stream burial and relationships to watershed area, topography, and impervious surface area. Water 8(11):538.  https://doi.org/10.3390/w8110538 CrossRefGoogle Scholar
  83. Wheater H, Sorooshian S, Sharma KD (2007) Hydrological modelling in arid and semi-arid areas (international hydrology series). Cambridge University Press, Cambridge.  https://doi.org/10.1017/CBO9780511535734 CrossRefGoogle Scholar
  84. Wilcox BP, Rawls WJ, Brakensiek DL, Wight JR (1990) Predicting runoff from rangeland catchments: a comparison of two models. Water Resour Res 26(10):2401–2410.  https://doi.org/10.1029/WR026i010p02401 CrossRefGoogle Scholar
  85. Woldesenbet TA, Elagib NA, Ribbe L, Heinrich J (2017) Hydrological responses to land use/cover changes in the source region of the Upper Blue Nile Basin, Ethiopia. Sci Total Environ 575:724–741.  https://doi.org/10.1016/j.scitotenv.2016.09.124 CrossRefGoogle Scholar
  86. Yan G, Mas JF, Maathuis BHP, Xiangmin Z, Dijk PMV (2006) Comparison of pixel-based and object-oriented image classification approaches-a case study in a coal fire area, Wuda, Inner Mongolia, China. Int J Remote Sens 27(18):4039–4055.  https://doi.org/10.1080/01431160600702632 (Accessed 18 May 2015) CrossRefGoogle Scholar
  87. Yin Z, Xiao H, Zou S, Zhu R, Lu Z, Lan Y, Shen Y (2014) Simulation of hydrological processes of mountainous watersheds in inland river basins: taking the Heihe Mainstream River as an example. J Arid Land 6(1):16–26.  https://doi.org/10.1007/s40333-013-0197-4 CrossRefGoogle Scholar
  88. Zhang Y, Tarrant MA, Green GT (2008) The importance of differentiating urban and rural phenomena in examining the unequal distribution of locally desirable land. J Environ Manag 88(4):1314–1319.  https://doi.org/10.1016/j.jenvman.2007.07.008 CrossRefGoogle Scholar
  89. Zhao H, Chen X (2005) Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. In: Proceedings of geoscience and remote sensing symposium, 2005. IGARSS’05, proceedings, IEEE international vol 3, pp 1666–1668Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Centre of Studies in Resources EngineeringIndian Institute of Technology BombayMumbaiIndia

Personalised recommendations