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Impact of different types of meteorological data inputs on predicted hydrological and erosive responses to projected land use changes

  • Suman Bhattacharyya
  • Joy SanyalEmail author
Article
  • 112 Downloads

Abstract

Hydrological responses to land use/land cover (LULC) changes are complex in nature and tend to have an impact on the hydrological cycle, affecting the livelihood of the inhabitants. Rainfall–runoff models, such as the Soil and Water Assessment Tool, were used in the past to unravel the interactions between the impacts of climate and land use changes. However, the sensitivity of the model outcome, regarding the hydrological and erosive response to climatic data derived with different methods, has not been fully understood. We carried out a hydrological simulation using (a) Climate Forecast System Reanalysis data set, which synthesises outputs of global climate models along with gauged weather information and has a global coverage, and (b) purely weather station-based gridded climate data provided by Indian Meteorological Department. A possible LULC scenario for the year 2020 was created using the combined Cellular Automata–Markov model. Application of both climate data sets resulted in a modest increase in the predicted streamflow and sediment yield as a response to the probable development scenario in 2020. However, the marked variations emerged in the location and monthly pattern of significant changes in the surface runoff and sediment yield in response to the likely LULC scenario for 2020 vis-à-vis 2010.

Keywords

Land use change prediction SWAT hydrological response CFSR 

Notes

Acknowledgements

The authors gratefully acknowledge the constructive criticisms of the anonymous reviewers which helped to improve the quality of the paper.

References

  1. Abbaspour K C, Yang G, Maximov I, Siber R, Bogner K, Mieleitner J, Zobrist J and Srinivasan R 2007 Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT; J. Hydraul. Eng. 33 413–430.Google Scholar
  2. Arnold J G, Srinivasan R, Muttiah R S and Williams J R 1998 Large area hydrologic modeling and assessment part I: Model development; J. Am. Water. Resour. A 34(1) 73–89.CrossRefGoogle Scholar
  3. Arsanjani J J, Kainz W and Mousivand A J 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(4) 329–345.CrossRefGoogle Scholar
  4. Auerbach D A, Easton Z M, Walter M T, Flecker A S and Fuka D R 2016 Evaluating weather observations and the Climate Forecast System Reanalysis as inputs for hydrologic modelling in the tropics; Hydrol. Process. 30(19) 3466–3477.CrossRefGoogle Scholar
  5. Azari M, Moradi H R, Saghafian B and Faramarzi M 2016 Climate change impacts on streamflow and sediment yield in the North of Iran; Hydrol. Sci. J. 61(1) 123–133.CrossRefGoogle Scholar
  6. Baker T J and Miller S N 2013 Using the Soil and Water Assessment Tool (SWAT) to assess land use impact on water resources in an East African watershed; J. Hydrol. 486 100–111.CrossRefGoogle Scholar
  7. Blacutt L A, Herdies D L, de Gonçalves L G G, Vila D A and Andrade M 2015 Precipitation comparison for the CFSR, MERRA, TRMM3B42 and Combined Scheme datasets in Bolivia; Atmos. Res. 163 117–131.CrossRefGoogle Scholar
  8. Ciach G J 2003 Local random errors in tipping-bucket rain gauge measurements; J. Atmos. Ocean. Tech. 20(5) 752–759.CrossRefGoogle Scholar
  9. Eastman J R 2012 IDRISI selva; Clark University, Worcester, MA.Google Scholar
  10. Etemadi H, Smoak J M and Karami J 2018 Land use change assessment in coastal mangrove forests of Iran utilizing satellite imagery and CA–Markov algorithms to monitor and predict future change; Environ. Earth. Sci. 77(5) 208.CrossRefGoogle Scholar
  11. Francesconi W, Srinivasan R, Pérez-Miñana E, Willcock S P and Quintero M 2016 Using the Soil and Water Assessment Tool (SWAT) to model ecosystem services: A systematic review; J. Hydrol. 535 625–636.CrossRefGoogle Scholar
  12. Fuka D R, Walter M T, MacAlister C, Degaetano A T, Steenhuis T S and Easton M Z 2014 Using the Climate Forecast System Reanalysis as weather input data for watershed models; Hydrol. Process. 28(22) 5613–5623.CrossRefGoogle Scholar
  13. Garg K K, Bharati L, Gaur A, George B, Acharya S, Jella K and Narasimhan B 2012 Spatial mapping of agricultural water productivity using the SWAT model in Upper Bhima Catchment India; Irrig. Drain. 61(1) 60–79.CrossRefGoogle Scholar
  14. Guan D, Li H, Inohae T, Su W, Nagaie T and Hokao K 2011 Modeling urban land use change by the integration of cellular automaton and Markov model; Ecol. Model. 222(20) 3761–3772.CrossRefGoogle Scholar
  15. Iacono M, Levinson D, El-Geneidy A and Wasfi R 2015 A Markov chain model of land use change; TeMA J. Land Use Mobility Environ. 8(3) 263–276.Google Scholar
  16. Khoi D N and Suetsugi T 2014 Impact of climate and land-use changes on hydrological processes and sediment yield – A case study of the Be River catchment, Vietnam; Hydrol. Sci. J. 59(5) 1095–1108.CrossRefGoogle Scholar
  17. Li Q, Cai T, Yu M, Lu G, Xie W and Bai X 2011 Investigation into the impacts of land-use change on runoff generation characteristics in the upper Huaihe River Basin, China; J. Hydraul. Eng. 18(11) 1464–1470.Google Scholar
  18. López E, Bocco G, Mendoza M and Duhau E 2001 Predicting land-cover and land-use change in the urban fringe: A case in Morelia city, Mexico; Landscape. Urban. Plan. 55(4) 271–285.CrossRefGoogle Scholar
  19. Ma X, Xu J, Luo Y, Prasad Aggarwal S and Li J 2009 Response of hydrological processes to land cover and climate changes in Kejie watershed south-west China; Hydrol. Process. 23(8) 1179–1191.CrossRefGoogle Scholar
  20. McGrath D A, Smith C K, Gholz H L and Oliveira F D 2001 Effects of land-use change on soil nutrient dynamics in Amazonia; Ecosystems 4(7) 625–645.CrossRefGoogle Scholar
  21. Myint S W and Wang L 2006 Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach; Can. J. Remote. Sens. 32(6) 390–404.CrossRefGoogle Scholar
  22. Pontius Jr R G and Millones M 2011 Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment; Int. J. Remote. Sens. 32(15) 4407–4429.CrossRefGoogle Scholar
  23. Rajeevan M, Bhate J, Kale J D and Lal B 2006 High resolution daily gridded rainfall data for the Indian region: Analysis of break and active; Curr. Sci. 91(3) 296–306.Google Scholar
  24. Saaty T L 2003 Decision making in complex environments – The analytic hierarchy process (AHP) and the analytic network process (ANP) for decision making with dependence and feedback (superdecisions tutorial); www.superde-cisions.com.
  25. Saha S, Moorthi S, Pan H L, Wu X, Wang J, Nadiga S, Tripp P, Kistler R, Woollen J, Behringer D and Liu H 2010 The NCEP climate forecast system reanalysis; B. Am. Meteorol. Soc. 91(8) 1015–1057.CrossRefGoogle Scholar
  26. Sanyal J, Densmore A L and Carbonneau P 2014 Analysing the effect of land-use/cover changes at sub-catchment levels on downstream flood peaks: A semi-distributed modelling approach with sparse data; Catena 118 28–40.CrossRefGoogle Scholar
  27. Schilling K E, Gassman P W, Kling C L, Campbell T, Jha M K, Wolter C F and Arnold J G 2014 The potential for agricultural land use change to reduce flood risk in a large watershed; Hydrol. Process. 28(8) 3314–3325.CrossRefGoogle Scholar
  28. Serpa D, Nunes J P, Santos J, Sampaio E, Jacinto R, Veiga S, Lima J C, Moreira M, Corte-Real J, Keizer J J and Abrantes N 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.CrossRefGoogle Scholar
  29. Shah R and Mishra V 2014 Evaluation of the reanalysis products for the monsoon season droughts in India; J. Hydrometeorol. 15(4) 1575–1591.CrossRefGoogle Scholar
  30. Singh A and Gosain A K 2011 Climate-change impact assessment using GIS-based hydrological modelling; Water. Int. 36(3) 386–397.CrossRefGoogle Scholar
  31. Tarigan S D 2016 Land cover change and its impact on flooding frequency of Batanghari Watershed Jambi Province Indonesia; Procedia Environ. Sci. 33 386–392.CrossRefGoogle Scholar
  32. Tian H, Banger K, Bo T and Dadhwal V K 2014 History of land use in India during 1880–2010: Large-scale land transformations reconstructed from satellite data and historical archives; Global. Planet. Change 121 78–88.CrossRefGoogle Scholar
  33. Vemu S and Pinnamaneni U B 2011 Estimation of spatial patterns of soil erosion using remote sensing and GIS: A case study of Indravati catchment; Nat. Hazards 59(3) 1299–1315.CrossRefGoogle Scholar
  34. Wagner P D, Bhallamudi S M, Narasimhan B, Kantakumar L N, Sudheer K P, Kumar S, Schneider K and Fiener P 2016 Dynamic integration of land use changes in a hydrologic assessment of a rapidly developing Indian catchment; Sci. Total. Environ. 539 153–164.CrossRefGoogle Scholar
  35. Yan B, Fang N F, Zhang P C and Shi Z H 2013 Impacts of land use change on watershed streamflow and sediment yield: An assessment using hydrologic modelling and partial least squares regression; J. Hydrol. 484 26–37.CrossRefGoogle Scholar
  36. Yira Y, Diekkrüger B, Steup G and Bossa A Y 2016 Modeling land use change impacts on water resources in a tropical West African catchment (Dano Burkina Faso); J. Hydrol. 537 187–199.CrossRefGoogle Scholar
  37. Zare M, Panagopoulos T and Loures L 2017 Simulating the impacts of future land use change on soil erosion in the Kasilian watershed, Iran; Land Use Policy 67 558–572.CrossRefGoogle Scholar
  38. Zhang L, Nan Z, Yu W and Ge Y 2016 Hydrological responses to land-use change scenarios under constant and changed climatic conditions; Environ. Manag. 57(2) 412–431.CrossRefGoogle Scholar
  39. Zheng H W, Shen G Q, Wang H and Hong J 2015 Simulating land use change in urban renewal areas: A case study in Hong Kong; Habitat. Int. 46 23–34.CrossRefGoogle Scholar
  40. Zucca C, Canu A and Previtali F 2010 Soil degradation by land use change in an agropastoral area in Sardinia (Italy); Catena 83(1) 46–54.CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.Department of GeographyPresidency UniversityKolkataIndia

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