Cited by 142Hydrology and Watershed Management StudiesEnvironmental and Agricultural SciencesHydrology and Drought Analysis

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Individual and combined impacts of future climate and land use changes on the water balance

Kundu, Sananda, Khare, Deepak, Mondal, Arun·2017·Ecological Engineering

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Research works printed but not linked: 60

  1. Anandhi, 2008, Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine, Int. J. Climatol., 28, 401, 10.1002/joc.1529 · DOI
  2. Andréassian, 2004, Waters and forests: from historical controversy to scientific debate, J. Hydrol., 291, 1, 10.1016/j.jhydrol.2003.12.015 · DOI
  3. Arsanjani, 2011, Tracking dynamic land-use change using spatially explicit Markov Chain based on cellular automata: the case of Tehran, Int. J. Image Data Fusion, 2, 329, 10.1080/19479832.2011.605397 · DOI
  4. Baker, 2013, Using the Soil and Water Assessment Tool (SWAT) to assess land use impact on water resources in an East African watershed, J. Hydrol., 486, 100, 10.1016/j.jhydrol.2013.01.041 · DOI
  5. Baldyga, 2008, Assessing land cover change in Kenya's Mau Forest region using remotely sensed data, Afr. J. Ecol., 46, 46, 10.1111/j.1365-2028.2007.00806.x · DOI
  6. Bosch, 1982, A review of catchment experiments to determine the effect of vegetation changes on water yield and evapotranspiration, J. Hydrol., 55, 3, 10.1016/0022-1694(82)90117-2 · DOI · Paper page
  7. Brown, 2005, A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation, J. Hydrol., 310, 28, 10.1016/j.jhydrol.2004.12.010 · DOI
  8. Ceola, 2014, Hydrologic controls on basin-scale distribution of benthic invertebrates, Water Resour. Res., 50, 2903, 10.1002/2013WR015112 · DOI
  9. Chawla, 2015, Isolating the impacts of land use and climate change on streamflow, Hydrol. Earth Syst. Sci., 19, 3633, 10.5194/hess-19-3633-2015 · DOI
  10. Cuo, 2011, Effects of mid-twenty-first century climate and land cover change on the hydrology of the Puget Sound basin, Washington, Hydrol. Process., 25, 1729, 10.1002/hyp.7932 · DOI
  11. Dias, 2015, Effects of land cover change on evapotranspiration and streamflow of small catchments in the Upper Xingu River Basin, Central Brazil, J. Hydrol. Reg. Stud., 4, 108, 10.1016/j.ejrh.2015.05.010 · DOI
  12. Du, 2012, Assessing the effects of urbanization on annual runoff and flood events using an integrated hydrological modeling system for Qinhuai River basin, China, J. Hydrol., 464, 127, 10.1016/j.jhydrol.2012.06.057 · DOI
  13. Duhan, 2015, Statistical downscaling of temperature using three techniques in the Tons River basin in Central India, Theor. Appl. Climatol., 121, 605, 10.1007/s00704-014-1253-5 · DOI
  14. El-Khoury, 2015, Combined impacts of future climate and land use changes on discharge, nitrogen and phosphorus loads for a Canadian river basin, J. Environ. Manage., 151, 76, 10.1016/j.jenvman.2014.12.012 · DOI
  15. Gupta, 1999, Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration, J. Hydrol. Eng., 4, 135, 10.1061/(ASCE)1084-0699(1999)4:2(135) · DOI
  16. Guse, 2015, Eco-hydrologic model cascades: simulating land use and climate change impacts on hydrology, hydraulics and habitats for fish and macroinvertebrates, Sci. Total Environ., 533, 542, 10.1016/j.scitotenv.2015.05.078 · DOI
  17. Hosseini, 2012, Effects of land use changes on water balance in Taleghan Catchment, Iran, J. Agric. Sci. Technol., 14, 1161
  18. Jeganathan, 2010, Markov model for predicting the land cover changes in Shimla district, Indian For., 136, 667
  19. Kalnay, 1996, The NCEP/NCAR 40-year reanalysis project, Bull. Am. Meteorol. Soc., 77, 437, 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2 · DOI
  20. Karan, 2016, Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas, Environ. Monit. Assess., 188, 486, 10.1007/s10661-016-5494-x · DOI
  21. Kim, 2014, Assessment of future water resources and water scarcity considering the factors of climate change and social–environmental change in Han River basin, Korea, Stoch. Environ. Res. Risk Assess., 28, 1999, 10.1007/s00477-014-0924-1 · DOI
  22. Kundu, 2016, Interrelationship of rainfall, temperature and reference evapotranspiration trends and their net response to the climate change in Central India, Theor. Appl. Climatol., 1
  23. Kundu, 2016, Future changes in rainfall, temperature and reference evapotranspiration in the central India by Least Square Support Vector Machine, Geosci. Front.
  24. Kundu, 2014, Shifting shoreline of Sagar Island Delta, India, J. Maps, 10, 612, 10.1080/17445647.2014.922131 · DOI
  25. Landis, 1977, The measurement of observer agreement for categorical data, Biometrics, 159, 10.2307/2529310 · DOI · Paper page
  26. Leander, 2007, Resampling of regional climate model output for the simulation of extreme river flows, J. Hydrol., 332, 487, 10.1016/j.jhydrol.2006.08.006 · DOI
  27. Legates, 1999, Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation, Water Resour. Res., 35, 233, 10.1029/1998WR900018 · DOI
  28. Leite, 2011, Historical reconstruction of land use in the Brazilian Amazon (1940–1995), J. Land Use Sci., 6, 33, 10.1080/1747423X.2010.501157 · DOI
  29. 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, 10.1016/j.jhydrol.2009.08.007 · DOI
  30. Macedo, 2012, Decoupling of deforestation and soy production in the southern Amazon during the late 2000s, Proc. Natl. Acad. Sci. U. S. A., 109, 1341, 10.1073/pnas.1111374109 · DOI · Paper page
  31. Memarian, 2014, SWAT-based hydrological modelling of tropical land-use scenarios, Hydrol. Sci. J., 59, 1808, 10.1080/02626667.2014.892598 · DOI
  32. Mondal, 2014, Impact of climate change on future soil erosion in different slope, land use, and soil-type conditions in a part of the Narmada River Basin, India, J. Hydrol. Eng., 20, C5014003, 10.1061/(ASCE)HE.1943-5584.0001065 · DOI
  33. Mondal, 2012, Comparison of support vector machine and maximum likelihood classification technique using satellite imagery, Int. J. Remote Sens. GIS, 1, 116
  34. Mondal, 2013, Modeling of spatio-temporal dynamics of land use and land cover in a part of Brahmaputra River basin using Geoinformatic techniques, Geocarto Int., 28, 632, 10.1080/10106049.2013.776641 · DOI
  35. Mukhopadhyay, 2014, Forest cover change prediction using hybrid methodology of geoinformatics and Markov chain model: a case study on sub-Himalayan town Gangtok, India, J. Earth Syst. Sci., 123, 1349, 10.1007/s12040-014-0476-2 · DOI
  36. Narsimlu, 2013, Assessment of future climate change impacts on water resources of upper Sind river basin, India using SWAT model, Water Resour. Manage., 27, 3647, 10.1007/s11269-013-0371-7 · DOI
  37. Nash, 1970, River flow forecasting through conceptual models part I—a discussion of principles, J. Hydrol., 10, 282, 10.1016/0022-1694(70)90255-6 · DOI
  38. Obeysekera, 2011, Climate change and its implications for water resources management in south Florida, Stoch. Environ. Res. Risk Assess., 25, 495, 10.1007/s00477-010-0418-8 · DOI
  39. Rahman, 2014, Streamflow response to regional climate model output in the mountainous watershed: a case study from the Swiss Alps, Environ. Earth Sci., 72, 4357, 10.1007/s12665-014-3336-0 · DOI
  40. Sajikumar, 2015, Impact of land cover and land use change on runoff characteristics, J. Environ. Manage., 161, 460, 10.1016/j.jenvman.2014.12.041 · DOI
  41. Santos, 2014, The impact of climate change, human interference, scale and modeling uncertainties on the estimation of aquifer properties and river flow components, J. Hydrol., 519, 1297, 10.1016/j.jhydrol.2014.09.001 · DOI
  42. Setegn, 2011, Impact of climate change on the hydroclimatology of Lake Tana Basin, Ethiopia, Water Resour. Res., 47, 10.1029/2010WR009248 · DOI
  43. Sivakumar, 2011, Global climate change and its impacts on water resources planning and management: assessment and challenges, Stoch. Environ. Res. Risk Assess., 25, 583, 10.1007/s00477-010-0423-y · DOI
  44. Srinivasan, 2010, SWAT ungauged: hydrological budget and crop yield predictions in the Upper Mississippi River Basin, Trans. ASABE, 53, 1533, 10.13031/2013.34903 · DOI
  45. Suykens, 1999, Least squares support vector machine classifiers, Neural Process. Lett., 9, 293, 10.1023/A:1018628609742 · DOI
  46. Tavakoli, 2014, Impact of climate change and urban development on extreme flows in the Grote Nete watershed, Belgium, Nat. Hazards, 71, 2127, 10.1007/s11069-013-1001-7 · DOI
  47. Tripathi, 2006, Downscaling of precipitation for climate change scenarios: a support vector machine approach, J. Hydrol., 330, 621, 10.1016/j.jhydrol.2006.04.030 · DOI
  48. Van Griensven, 2006, A global sensitivity analysis tool for the parameters of multi-variable catchment models, J. Hydrol., 324, 10, 10.1016/j.jhydrol.2005.09.008 · DOI
  49. Verma, 2017, Sugarcane crop identification from LISS IV data using ISODATA, MLC, and indices based decision tree approach, Arab. J. Geosci., 10, 16, 10.1007/s12517-016-2815-x · DOI
  50. Wada, 2011, Modelling global water stress of the recent past: on the relative importance of trends in water demand and climate variability, Hydrol. Earth Syst. Sci., 15, 3785, 10.5194/hess-15-3785-2011 · DOI
  51. Wagner, 2013, An assessment of land use change impacts on the water resources of the Mula and Mutha Rivers catchment upstream of Pune, India, Hydrol. Earth Syst. Sci., 17, 2233, 10.5194/hess-17-2233-2013 · DOI
  52. Wagner, 2016, Dynamic integration of land use changes in a hydrologic assessment of a rapidly developing Indian catchment, Sci. Total Environ., 539, 153, 10.1016/j.scitotenv.2015.08.148 · DOI
  53. Wilby, 2002, SDSM—a decision support tool for the assessment of regional climate change impacts, Environ. Model. Softw., 17, 145, 10.1016/S1364-8152(01)00060-3 · DOI
  54. Wingate, 2016, Mapping decadal land cover changes in the woodlands of north eastern Namibia from 1975 to 2014 using the Landsat satellite archived data, Remote Sens., 8, 681, 10.3390/rs8080681 · DOI
  55. Wu, 2013, Projecting the land cover change and its environmental impacts in the Cedar River Basin in the Midwestern United States, Environ. Res. Lett., 8, 024025, 10.1088/1748-9326/8/2/024025 · DOI
  56. Yang, 2012, A spatiotemporal model of land use change based on ant colony optimization. Markov chain and cellular automata, Ecol. Model., 233, 11, 10.1016/j.ecolmodel.2012.03.011 · DOI
  57. Ye, 2013, Modelling potential impacts of climate change on water and nitrate export from a mid-sized, semiarid watershed in the US Southwest, Clim. Change, 120, 419, 10.1007/s10584-013-0827-z · DOI
  58. Zhang, 2012, Assessments of impacts of climate change and human activities on runoff with SWAT for the Huifa River Basin, Northeast China, Water Resour. Manage., 26, 2199, 10.1007/s11269-012-0010-8 · DOI
  59. Zhao, 2016, Impacts of land use change and climate variability on green and blue water resources in the Weihe River Basin of northwest China, Catena, 137, 318, 10.1016/j.catena.2015.09.018 · DOI
  60. Zhou, 2013, Hydrological response to urbanization at different spatio-temporal scales simulated by coupling of CLUE-S and the SWAT model in the Yangtze River Delta region, J. Hydrol., 485, 113, 10.1016/j.jhydrol.2012.12.040 · DOI

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Metadata

Authors
Kundu, S., Khare, D., & Mondal, A.
Affiliations
  • University of South Carolina (US)
  • Indian Institute of Technology Roorkee (IN)
Corresponding author
Mondal, Arun
Venue
Ecological Engineering
Publisher
Elsevier BV
Published
2017-08
Volume
105
Pages
42-57
ISSN
0925-8574 (ISSN-L)
OpenAlex
W2612574867
Cited by
142
References
2
Citations by year
2026: 7 · 2025: 8 · 2024: 21 · 2023: 22 · 2022: 25 · 2021: 26 · 2020: 14 · 2019: 7 · 2018: 7 · 2017: 5
FWCI
4.77
Type
journal article
Language
en
Open access
closed
Locations
Ecological Engineering (publishedVersion)
Funders
University Grants Commission
Topics
  • Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
  • Physical Sciences › Environmental Science › Water Science and Technology › Environmental and Agricultural Sciences
  • Physical Sciences › Environmental Science › Global and Planetary Change › Hydrology and Drought Analysis
Keywords
water balance, climate change impacts, land use change, Narmada basin, water yield, surface runoff, evapotranspiration, rainfall projections, temperature projections, climate downscaling, sub-watersheds, SWAT model

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