Earth Sciences

| Peer-Reviewed |

Prediction of Temperature and Precipitation in Damavand Catchment in Iran by Using LARS –WG in Future

Received: 26 April 2015    Accepted: 11 May 2015    Published: 21 May 2015
Views:       Downloads:

Share This Article

Abstract

In recent years the issue of climate change and its effects on various aspects of the environment has become one of the challenges facing planners. It is desirable to analyze and predict the change of critical climatic variables, such as temperature and precipitation, which will provide valuable reference results for future water resources planning and management in the region. The aims of this study are to test the applicability of the Long Ashton Research Station Weather Generator (LARS-WG) model in downscaling daily precipitation and daily maximum (Tmax) and daily minimum (Tmin) temperatures in Damavand catchment in Iran and use it to predict future changes of precipitation and temperature. Future climate of the Damavand catchment is predicted by statistical downscaling outputs from General Circulation Models (GCMs) (HADCM3 for SRES A2 and B2 and A1B scenarios) for the period of 2046–2065.The results showed that the LARS-WG model produces excellent performance in downscaling Tmax and Tmin in the study region but compared to temperature, the model showed more error in downscaling daily precipitation. This issue was confirmed by examining the performance indicators including coefficient of determination, mean absolute error and root-mean square error. Also results showed that precipitation will decrease in future under these scenarios but temperature will increase. Findings of this study will serve as a reference for further studies and planning of future water management strategies in the Damavand catchment.

DOI 10.11648/j.earth.20150403.12
Published in Earth Sciences (Volume 4, Issue 3, June 2015)
Page(s) 95-100
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Climate Change, Prediction, LARS-WG, Statistical Downscaling

References
[1] Babaeian,I , Najafi Nik, LARS-WG introduce and evaluate models for modeling of meteorological parameters province (1961-2003). Nivar journal ,63,Tehran,(2006), 66-49. (In Persian).
[2] Chen,H., Guo,J., Zhang,C. and Xu,C, Prediction of temperature and precipitation in Sudan and South Sudan by using LARS-WG in future, Springer, 113, (2013), 363-375.
[3] Chu, J. T., Xia, J., Xu, C.Y., Singh, V.P , Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China. Theor. Appl. Climatol, 99, (2010), 149–161.
[4] [4] Eslamian,s.s , Handbook of Engineering Hydrology Modeling,Climate Change and Variability, Francis and Taylor,CRC Group,USA, 2, (2014), 646 .
[5] Forests,Range & Watershed Management Organization of Iran. Watershed feasibility studies and natural resources, (2011).
[6] Giorgi, F, Climate change hot-spots. Geophys. Res. Lett, 33, (2006), 8.
[7] Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Dai, X., Maskell, K., Johnson, C.A , Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK/New York, USA, (2001).
[8] IPCC. Summary for Policymakers. In: Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom; New York, New York, USA, (2013).
[9] IPCC. The Scientific Basis of Climate Change. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. Cambridge, (2001), 67-68.
[10] Karamouz, M., Nazif, S., Fallahi, M., Rainfall downscaling using statistical downscaling model and canonical correlation analysis: a case study. In: Palmer, R.N. (Ed.), World Environmental and Water Resources Congress 2010: Challenges of Change—Proceedings of the World Environmental and Water Resources Congress 2010. American Society of Civil Engineers, Reston, (2010), 4579–4587.
[11] Khadka,D.,Babel,M.,Shrestha,S.,Tripathi,N, Climate change impact on glacier and snow melt and runoff in Tamakoshi basin in the Hindu Kush Himalayan (HKH) region .Journal of Hydrology 511, (2014), 49-60.
[12] Khalili & et al. LARS-WG ability to predict the atmospheric parameters of Sanandaj .Journal of Research of Soil and Water Conservation, 4, (2012), 85-102.(In Persian)
[13] Mavromatis, Th., and Hansen, J.W , Inter annual variability characteristics and simulated crop response of four stochastic weather generators. Agricultural and forest meteorology, 109, (2001), 283-296.
[14] Massah Bavani & et al, Detection of changes in temperature and precipitation in the previous periods, the ratio of the gases Glkhanh¬Ay (The case of West Azarbaijan province), Journal of the Earth and Space Physics, 3, (2013), 111-128. (In Persian)
[15] Nakicenovic N, Swart, R, Emissions scenarios. Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, (2000).
[16] Pervez,M.S.,Henebry,G.M, Projection of the Ganges-Brahmaputra precipitation – Downscaled from GCM predictors. Journal of Hydrology,517, (2014), 120-134.
[17] Sayemuzzaman,M.,Manjo,K, Seasonal and annual precipitation time series trend analysis in North Carolina,United States. Atmospheric Research, 137, (2014), 183-149.
[18] Rajabi A. Sedghi H. Eslamian S. and Musavi H, Comparison of LARS-WG and SDSM Downscaling Models in Kermanshah (Iran). Ecology, Environment and Conservation ,16, (2010),465-474.
[19] Racsko P, Szeidl L, Semenov. M , A serial approach to local stochastic weather models. Ecol Model, 57, (1991), 27–41.
[20] Semenov, M.A., Barrow, E.M, LARS-WG: a stochastic weather generator for use in climate impact studies, (2002) http://www.rothamsted.ac.uk/masmodels/ larswg.php User Manual: 1–27.
[21] Semenov, M.A, Simulation of extreme weather events by a stochastic weather generator. Climate Research, 35, (2008), 203-212.
[22] Semenov, MA, Barrow EM , Use of a stochastic weather generator in the development of climate change scenarios. Clim Chang, 35,(1997), 397–414.
[23] Semenov, MA, Stratonovitch,P , Use of multi-model ensembles from global climate models for assessment of climate change impacts. Clim Res, 41, (2010), 1–14.
[24] Taxak,A.K.,Murumkar,A.R.,Arya,D.S , Long term spatial and temporal rainfall trends and homogeneity analysis in Wainganga basin, Central India, Weather and Climate Extremes, 4, (2014), 50-61.
[25] Wilby, R.L.,& et al., Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods. Supporting material of the Intergovermental Panel on Climate Change, available from the DDC of IPPC TGCIA 27, (2004).
[26] Xu,c. climate change and Hydrological Models: A Review of Existing Gaps and Recent Research Development. Water Resources Management, 13, (1999), 369-382.
Author Information
  • Department of Environmental Education, Management & Planning, Faculty of Environment, University of Tehran, Tehran, Iran

  • Department of Environmental Education, Management & Planning, Faculty of Environment, University of Tehran, Tehran, Iran

  • Department of Environmental Education, Management & Planning, Faculty of Environment, University of Tehran, Tehran, Iran

  • Department of Environmental Engineering, Faculty of Environment, University of Tehran, Tehran, Iran

Cite This Article
  • APA Style

    Sepideh Karimi, Saeed Karimi, Ahmad Reza Yavari, Mohamad Hosein Niksokhan. (2015). Prediction of Temperature and Precipitation in Damavand Catchment in Iran by Using LARS –WG in Future. Earth Sciences, 4(3), 95-100. https://doi.org/10.11648/j.earth.20150403.12

    Copy | Download

    ACS Style

    Sepideh Karimi; Saeed Karimi; Ahmad Reza Yavari; Mohamad Hosein Niksokhan. Prediction of Temperature and Precipitation in Damavand Catchment in Iran by Using LARS –WG in Future. Earth Sci. 2015, 4(3), 95-100. doi: 10.11648/j.earth.20150403.12

    Copy | Download

    AMA Style

    Sepideh Karimi, Saeed Karimi, Ahmad Reza Yavari, Mohamad Hosein Niksokhan. Prediction of Temperature and Precipitation in Damavand Catchment in Iran by Using LARS –WG in Future. Earth Sci. 2015;4(3):95-100. doi: 10.11648/j.earth.20150403.12

    Copy | Download

  • @article{10.11648/j.earth.20150403.12,
      author = {Sepideh Karimi and Saeed Karimi and Ahmad Reza Yavari and Mohamad Hosein Niksokhan},
      title = {Prediction of Temperature and Precipitation in Damavand Catchment in Iran by Using LARS –WG in Future},
      journal = {Earth Sciences},
      volume = {4},
      number = {3},
      pages = {95-100},
      doi = {10.11648/j.earth.20150403.12},
      url = {https://doi.org/10.11648/j.earth.20150403.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.earth.20150403.12},
      abstract = {In recent years the issue of climate change and its effects on various aspects of the environment has become one of the challenges facing planners. It is desirable to analyze and predict the change of critical climatic variables, such as temperature and precipitation, which will provide valuable reference results for future water resources planning and management in the region. The aims of this study are to test the applicability of the Long Ashton Research Station Weather Generator (LARS-WG) model in downscaling daily precipitation and daily maximum (Tmax) and daily minimum (Tmin) temperatures in Damavand catchment in Iran and use it to predict future changes of precipitation and temperature. Future climate of the Damavand catchment is predicted by statistical downscaling outputs from General Circulation Models (GCMs) (HADCM3 for SRES A2 and B2 and A1B scenarios) for the period of 2046–2065.The results showed that the LARS-WG model produces excellent performance in downscaling Tmax and Tmin in the study region but compared to temperature, the model showed more error in downscaling daily precipitation. This issue was confirmed by examining the performance indicators including coefficient of determination, mean absolute error and root-mean square error. Also results showed that precipitation will decrease in future under these scenarios but temperature will increase. Findings of this study will serve as a reference for further studies and planning of future water management strategies in the Damavand catchment.},
     year = {2015}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Prediction of Temperature and Precipitation in Damavand Catchment in Iran by Using LARS –WG in Future
    AU  - Sepideh Karimi
    AU  - Saeed Karimi
    AU  - Ahmad Reza Yavari
    AU  - Mohamad Hosein Niksokhan
    Y1  - 2015/05/21
    PY  - 2015
    N1  - https://doi.org/10.11648/j.earth.20150403.12
    DO  - 10.11648/j.earth.20150403.12
    T2  - Earth Sciences
    JF  - Earth Sciences
    JO  - Earth Sciences
    SP  - 95
    EP  - 100
    PB  - Science Publishing Group
    SN  - 2328-5982
    UR  - https://doi.org/10.11648/j.earth.20150403.12
    AB  - In recent years the issue of climate change and its effects on various aspects of the environment has become one of the challenges facing planners. It is desirable to analyze and predict the change of critical climatic variables, such as temperature and precipitation, which will provide valuable reference results for future water resources planning and management in the region. The aims of this study are to test the applicability of the Long Ashton Research Station Weather Generator (LARS-WG) model in downscaling daily precipitation and daily maximum (Tmax) and daily minimum (Tmin) temperatures in Damavand catchment in Iran and use it to predict future changes of precipitation and temperature. Future climate of the Damavand catchment is predicted by statistical downscaling outputs from General Circulation Models (GCMs) (HADCM3 for SRES A2 and B2 and A1B scenarios) for the period of 2046–2065.The results showed that the LARS-WG model produces excellent performance in downscaling Tmax and Tmin in the study region but compared to temperature, the model showed more error in downscaling daily precipitation. This issue was confirmed by examining the performance indicators including coefficient of determination, mean absolute error and root-mean square error. Also results showed that precipitation will decrease in future under these scenarios but temperature will increase. Findings of this study will serve as a reference for further studies and planning of future water management strategies in the Damavand catchment.
    VL  - 4
    IS  - 3
    ER  - 

    Copy | Download

  • Sections