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Theoretical and Applied Climatology

, Volume 134, Issue 1–2, pp 309–323 | Cite as

The impact of urbanization during half a century on surface meteorology based on WRF model simulations over National Capital Region, India

  • Ankur Prabhat Sati
  • Manju Mohan
Original Paper

Abstract

An estimated 50% of the global population lives in the urban areas, and this percentage is projected to reach around 69% by the year 2050 (World Urbanization Prospects 2009). There is a considerable growth of urban and built-up area during the recent decades over National Capital Region (NCR) of India (17-fold increase in the urban extent). The proposed study estimates the land use land cover changes particularly changes to urban class from other land use types such as croplands, shrubland, open areas, and water bodies and quantify these changes for a span of about five decades. Further, the impact of these land use/land cover changes is examined on spatial and temporal variations of meteorological parameters using the Weather Research and Forecast (WRF) Model. The urbanized areas appear to be one of the regions with highest changes in the values of the fluxes and temperatures where during daytime, the surface sensible heat flux values show a noticeable increase of 60–70 W m−2 which commensurate with increase in urbanization. Similarly, the nighttime LST and T2m show an increase of 3–5 and 2–3 K, respectively. The diurnal temperature range (DTR) of LST and surface temperature also shows a decrease of about 5 and 2–3 K, respectively, with increasing urbanization. Significant decrease in the magnitude of surface winds and relative humidity is also observed over the areas converted to urban form over a period of half a century. The impacts shown here have serious implications on human health, energy consumption, ventilation, and atmospheric pollution.

Notes

Acknowledgements

The authors thank NCEP/NCAR for FNL analysis dataset and WRF modeling system, Central Pollution control Board (CPCB), India, and Indian Meteorological Department India for meteorological data used in the present study.

Funding information

Authors acknowledge High Performance Computational (HPC) facility provided under DST FIST ('Fund for Improvement of Science and Technology Infrastructure) 2014 Projects at Centre for Atmospheric Sciences, IIT Delhi.

References

  1. Anderson JR, Hardy EE, Roach JT, Witmer RE (1976) A land use and land cover classification system for use with remote sensor data. US Geol Surv Prof Pap 964: 28Google Scholar
  2. Bhati S, Mohan M (2015) WRF model evaluation for the urban heat island assessment under varying land use/land cover and reference site conditions. Theor Appl Climatol 126(1):385–400.  https://doi.org/10.1007/s00704-015-1589-5 CrossRefGoogle Scholar
  3. Bornstein R, Johnson DS (1977) Urban-rural wind velocity differences. Atmos Environ 11:597–604CrossRefGoogle Scholar
  4. Chen F, Dudhia J (2001) Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: model description and implementation. Mon Weather Rev 129:569–585CrossRefGoogle Scholar
  5. Cheng FY, Byun DW (2008) Application of high resolution land use and land cover data for atmospheric modeling in the Houston–Galveston metropolitan area, part I: meteorological simulation results. Atmos Environ 42:7795–7811CrossRefGoogle Scholar
  6. Cheng FY, Hsu YC, Lin PL, Lin TH (2013) Investigation of the effects of different land use and land cover patterns on mesoscale meteorological simulations in the Taiwan area. J Appl Meteorol Climatol 52:870–887CrossRefGoogle Scholar
  7. Clarke SG, Zehnder JA, Loridan T, Grimmond CSB (2010) Contribution of land use changes to near-surface air temperatures during recent summer extreme heat events in the phoenix metropolitan area. J Appl Meteorol Climatol 49:1649–1664CrossRefGoogle Scholar
  8. De UK, Chauhan K (2015) Degradation of forest and biodiversity in Sariska National Park, India and the responsible factors. Int J Environ Sustain Dev 14(4).  https://doi.org/10.1504/IJESD.2015.072104 CrossRefGoogle Scholar
  9. Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107CrossRefGoogle Scholar
  10. Ellis JT, Spruce JP, Swann RA, Smoot JC, Hilber KW (2011) An assessment of coastal land-use and land-cover change from 1974–2008 in the vicinity of Mobile Bay, Alabama. J Coast Conserv 15:139–149CrossRefGoogle Scholar
  11. Emery C, Tai E, Yarwood G (2001) Enhanced meteorological modeling and performance evaluation for two Texas ozone episodes. Prepared for The Texas Natural Resource Conservation CommissionGoogle Scholar
  12. Fameli KM, Assimakopoulos VD, Kotroni V (2013) On the effect of land use change on the meteorological parameters above the Greater Athens Area. Advances in meteorology, climatology and atmospheric physics. Springer Atmospheric Sciences, Springer Berlin, Heidelberg, pp 73–78.  https://doi.org/10.1007/978-3-642-29172-2_11 CrossRefGoogle Scholar
  13. Gallo KP, Easterling DR, Peterson TC (1996) The influence of land use/land cover on climatological values of the diurnal temperature range. J Clim 9:2941–2944CrossRefGoogle Scholar
  14. Grimm NB, Faeth SH, Golubiewski NE, Redman CL, Wu J, Bai X, Briggs JM (2008) Global change and the ecology of cities. Science 319(5864):756–760.  https://doi.org/10.1126/science.1150195 CrossRefGoogle Scholar
  15. Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models: the Kain-Fritcsh scheme, The representation of cumulus convection in numerical models. Am Meteorol Soc 165–170Google Scholar
  16. Kalnay E, Cai M (2003) Impact of urbanization and land use on climate change. Nature 423:528–531CrossRefGoogle Scholar
  17. Kawy ORA, Rod JK, Ismail HA, Suliman AS (2011) Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Appl Geogr 31:483–494CrossRefGoogle Scholar
  18. Lam JSL, Lau AKH, Fung JCH (2006) Application of refined land-use categories for high resolution mesoscale atmospheric modelling. Bound-Layer Meteorol 119:263–288CrossRefGoogle Scholar
  19. Lambin EF, Turner BL, Geista HJ, Agbol SB et al (2001) The causes of land-use and land-cover change: moving beyond the myths. Glob Environ Chang 11:261–269.  https://doi.org/10.1016/S0959-3780(01)00007-3 CrossRefGoogle Scholar
  20. Lee SH, Kim SW, Angevine WM, Bianco L, McKeen SA, Senff CJ, Trainer M, Tucker SC, Zamora RJ (2011) Evaluation of urban surface parameterizations in the WRF model using measurements during the Texas Air Quality Study 2006 field campaign. Atmos Chem Phys 11:2127–2143.  https://doi.org/10.5194/acp-11-2127-2011 CrossRefGoogle Scholar
  21. Li Z, Zhou Q (2011) Utility of landsat image in the study of land cover and land surface temperature change. Procedia Environ Sci 10:1287–1292CrossRefGoogle Scholar
  22. Lin YL, Farley RD, Orville HD (1983) Bulk parameterization of the snow field in a cloud model. J Clim Appl Meteorol 22:1065–1092CrossRefGoogle Scholar
  23. Lo CP, Yang X (2002) Drivers of land-use/land-cover changes and dynamic modeling for the Atlanta, Georgia metropolitan area. Photogramm Eng Remote Sens 68(10):1073–1082Google Scholar
  24. Mahmood R, Pielke RA Sr, Hubbard KG et al (2010) Impacts of land use/land cover change on climate and future research priorities. Am Meteorol Soc.  https://doi.org/10.1175/2009BAMS2769.1 CrossRefGoogle Scholar
  25. Mellor GL, Yamada T (1982) Development of a turbulence closure model for geophysical fluid problems. Rev Geophys Space Phys 20:851–875CrossRefGoogle Scholar
  26. Meyer WB, Turner BL (1992) Human population growth and global land-use/cover change. Annu Rev Ecol Syst 23:39–61CrossRefGoogle Scholar
  27. Michalakes J, Dudhia J, Gill D, Henderson T, Klemp J, Skamarock W, Wang W (2004) The weather research and forecast model: software architecture and performance. Proceeding of the Eleventh ECMWF Workshop on the Use of High Performance Computing in Meteorology. Reading, U.K., Ed. George MozdzynskiGoogle Scholar
  28. Misra A, Balaji R (2015) Decadal changes in the land use/land cover and shoreline along the coastal districts of southern Gujarat, India. Environ Monit Assess 187:461.  https://doi.org/10.1007/s10661-015-4684-2 CrossRefGoogle Scholar
  29. Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102(D14):16663–16682CrossRefGoogle Scholar
  30. Mohan M, Bhati S (2011) Analysis of WRF model performance over subtropical region of Delhi, India. Adv Meteorol 2011: Article ID 621235,  https://doi.org/10.1155/2011/621235 CrossRefGoogle Scholar
  31. Mohan M, Bhati S (2012) Wind flow conditions as an indicator to assimilative capacities of urban airsheds towards atmospheric pollution potential. J Civil Environ Eng S1:003.  https://doi.org/10.4172/2165-784X.S1-003 CrossRefGoogle Scholar
  32. Mohan M, Kandya A (2015) Impact of urbanization and land-use/land-cover change n diurnal temperature range: a case study of tropical urban airshed of India using remote sensing data. Sci Total Environ 506-507:453–465CrossRefGoogle Scholar
  33. Mohan M, Sati AP (2016) WRF model performance analysis for a suite of simulation design. Atmos Res 169:280–291CrossRefGoogle Scholar
  34. Mohan M, Siddiqui TA (1997) An evaluation of dispersion coefficients for use in air quality models. Bound.-Layer Meteorol 84:177–206CrossRefGoogle Scholar
  35. Mohan M, Pathan SK, Narendrareddy K, Kandya A, Pandey S (2011a) Dynamics of urbanization and its impact on land-use/land-cover: a case study of megacity Delhi. J Environ Prot 2:1274–1283CrossRefGoogle Scholar
  36. Mohan M, Kandya A, Battiprolu A (2011b) Urban heat island effect over National Capital Region of India: a study using the temperature trends. J Environ Prot 2:465–472CrossRefGoogle Scholar
  37. Monin AS, Obukhov AM (1954) Basic laws of turbulent mixing in the surface layer of the atmosphere. Contrib Geophys Inst Slovak Acad Sci 24(151):163–187Google Scholar
  38. Mustafa G, Yomralıoglu MT, Reis S (2007) Using landsat data to determine land use/land cover changes in Samsun, Turkey. Environ Monit Assess 127:155–167CrossRefGoogle Scholar
  39. National Centre for Atmospheric Research (NCAR) (2014) ARW version 3 modeling system’s user’s guide. NCAR, BoulderGoogle Scholar
  40. Patwardhan S, Harit AK (2007) Workshop on climate change and health in South-East and East Asian countries, Kuala Lumpur, MalaysiaGoogle Scholar
  41. Payra S, Mohan M (2014, 2014) Multirule based diagnostic approach for the fog predictions using WRF modelling tool. Adv Meteorol.  https://doi.org/10.1155/2014/456065 CrossRefGoogle Scholar
  42. Pielke RA, Walko RL, Steyaert LT, Vidale PL, Liston GE, Lyons WA, Chase TN (1999) The influence of anthropogenic landscape changes on weather in south Florida. Mon Weather Rev 127:1663–1673CrossRefGoogle Scholar
  43. Punia M, Singh L (2012) Entropy approach for assesment of urban growth: a case study of Jaipur, India. J Indian Soc Remote Sens 40(2):231–244.  https://doi.org/10.1007/s12524-011-0141-z CrossRefGoogle Scholar
  44. Rahman A, Kumar S, Fazal S, Siddiqui MA (2012) Assessment of land use/land cover change in the North-West District of Delhi using remote sensing and GIS techniques. J Indian Soc Remote Sens 40(4):689–697CrossRefGoogle Scholar
  45. Richards JA (1993) Remote sensing digital image analysis: an introduction. Springer-Verlag, New YorkCrossRefGoogle Scholar
  46. Ryu YH, Baik JJ, Kwak KH, Kim S, Moon N (2013) Impacts of urban land-surface forcing on ozone air quality in the Seoul metropolitan area. Atmos Chem Phys 13:2177–2194CrossRefGoogle Scholar
  47. Shahabuddin G, Verma A, Kumar R (2004) Birds, forests and conservation: critical issues in Sariska Tiger Reserve, Rajasthan, India. Newsl Ornithol 1(6):82–83Google Scholar
  48. Sleeter BM, Sohl TL, Loveland TR, Auch RF, Acevedo W, Drummond MA, Sayler KL, Stehman SV (2013) Land cover change in the conterminous United States from 1973 to 2000. Glob Environ Chang 23:733–748CrossRefGoogle Scholar
  49. Sulieman HM, Elagi NA (2012) Implications of climate, land-use and land-cover changes for pastoralism in eastern Sudan. J Arid Environ 85:132–141CrossRefGoogle Scholar
  50. Theeuwes NE, Steeneveld G, Ronada RJ, Rotach MW, Holtslag AAM (2015) Cool city mornings by urban heat. Environ Res Lett 10:114022.  https://doi.org/10.1088/1748-9326/10/11/114022 CrossRefGoogle Scholar
  51. Wang X, Chen F, Wu Z, Tewari M, Guenther A, Wiedinmyer C (2009) Impacts of weather conditions modified by urban expansion on surface ozone: comparison between the Pearl River Delta and Yangtze River Delta regions. Adv Atmos Sci 26(5):962–972CrossRefGoogle Scholar
  52. Wentz EA, Nelson D, Rahman A, Stefanov WL, Roy SS (2008) Expert system classification of urban land use/cover for Delhi, India. Int J Remote Sens 29(15):4405–4427.  https://doi.org/10.1080/01431160801905497 CrossRefGoogle Scholar
  53. Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 11:1309–1313CrossRefGoogle Scholar
  54. Willmott CJ, Ackleson SG, Davis RE, Feddema JJ, Klink KM, Legates DR, O’Donnell J, Rowe CM (1985) Statistics for the evaluation and comparison of models. J Geophys Res 90:8995–9005CrossRefGoogle Scholar
  55. World Urbanization Prospects The 2009 Theol Rev 2010Google Scholar
  56. Xiao J, Shen Y, Ge J, Tateishi R, Tang C, Liang Y, Huang Z (2006) Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landsc Urban Plan 75:69–80.  https://doi.org/10.1016/j.landurbplan.2004.12.005 CrossRefGoogle Scholar
  57. Xiao RB, Ouyang ZY, Zheng H, Li EF, Schienke EW, Wang XK (2007) Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. J Environ Sci 19(2):250–256.  https://doi.org/10.1016/S1001-0742(07)60041-2 CrossRefGoogle Scholar
  58. Yao X, Wang Z, Wang H (2015) Impact of urbanization and land use change on surface climate in the middle and lower reaches of the Yangtze river, 1988–2008. Adv Meteorol 2015: Article ID 395094, 10 pages, doi:  https://doi.org/10.1155/2015/395094 Google Scholar

Copyright information

© Springer-Verlag GmbH Austria 2017

Authors and Affiliations

  1. 1.Centre for Atmospheric SciencesIndian Institute of Technology DelhiHauz KhasIndia

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