Evaluation of RegCM4 Regional Climate Model simulations for the land surface water budget components (A case study in Toyserkan plain, Hamedan province)

Document Type : Complete scientific research article


Dept of water Engineering, College of agriculture. BASU


Background and objectives: In regional and local climate studies usually coarse-resolution outputs of global climate models are downscaled to produce necessary fine scale data. Regional climate modeling is a dynamical downscaling method. In this study HadGEM2 general circulation model outputs have been downscaled by RegCM4 Regional Climate Model coupled CLM4.5 land surface scheme. The model was run over Toyserkan plain for a reference period (1999-2005) for model evaluation and a projection period (2015-2025). In the projection period, regarding to the importance of water resources in the study area, water budget components and surface water balance equation have been evaluated.
Materials and methods: For the reference period, daily modeled precipitation, temperature and runoff were compared with observed values at available meteorological stations in the region. Modeling efficiency, correlation coefficient, bias, mean absolute error and root mean squared error statistical indices were used to evaluate model’s simulations. Modeled precipitation was compared with observations of 6 available stations in the region, Observed temperatures of the station of Hamedan- Airport were also used for modeled temperature verification; For runoff verification, the only river station at the outlet of the catchment was used. Projections are based on RCP4.5 scenario of the Fifth Assessment Report of Intergovernmental panel on climate change. For the projection period spatiotemporal variations of surface water budget components including precipitation, evapotranspiration and runoff have been studied and water balance equation in the catchment has been evaluated.
Results: Model evaluation results showed that the model has its worst performance for runoff, because of low modeling efficiency and relatively large errors while has its best performance in simulating precipitation (especially for the first five stations) and temperature. Finally, the model shows its best performance for temperature and precipitation respectively, regarding to more positive efficiency, higher correlation and smaller errors. For the first year of projection period (2015) the highest values of precipitation occur in eastern and central parts while the lowest values occur in southwestern part of the catchment. Through the next 10 years (2016-2025), precipitation decreases in most parts except small parts in east and south of the catchment. The highest values of precipitation decrease (about %12) also occur in northeastern and central parts. Since the amount of precipitation determines available moisture, spatial distribution and variation of evapotranspiration are same as those for precipitation. The highest values of runoff occur in eastern and northeastern (high elevation) parts and during 2016-2025 a %30-50 increase in eastern part and a %10-20 decrease in central part will occur. Annual water budget evaluation shows that for 7 years there is a water balance, but for the remaining 3 years (2017, 2023 and 2024) differences between two sides of the water balance equation are large.
Conclusion: Considering small area of the catchment and short time of projection period that does not allow to detect impacts of climate change, small amount of mean 10-year difference between two sides of the equation (1.3 mm) shows that the model performance in estimating water balance for the catchment is acceptable. Generally, although the water balance estimation has been improved, the land surface scheme has shortcomings in water budget parametrization especially for runoff. But regarding to improvement of mean temperature simulation, model has an appropriate performance in simulating the energy budget.


1.Akhavan, S., Ghabaei Sough, M., and Mosaedi, A. 2015. Investigation of the effect of
climate change on net irrigation requirement of main crops of Hamadan-Bahar plain using
LARS-WG downscaling model. J. Water Soil Cons. 22: 4. 25-46. (In Persian)
2.Anthes, R.A., Hsie, E.Y., and Kuo, Y.H. 1987. Description of the Penn State/NCAR
Mesoscale Model Version 4 (MM4). National Center for Atmospheric Research technical
note TN-282 + STR. Boulder-Colorado.
3.Ashofteh, P.S., and Bozorg Haddad, O. 2015. A new approach for performance evaluation of
AOGCM models in simulating runoff. J. Water Soil Cons. 22: 2. 95-110. (In Persian)
4.Bazrafshan, J., Hejabi, S., and Hashemi Nasab, A. 2015. Future Climate Change Impact on
Drought Classes Transition Probabilities in Extreme Climates of Iran (Case study: Bandar
Anzali and Bushehr Stations). J. Water Soil Cons. 22: 1. 131-150. (In Persian)
5.Beven, K.J., and Kirkby, M.J. 1979. A physically based, variable contributing area
model of basin hydrology / Un modèle à base physique de zone d'appel variable
de l'hydrologie du bassin versant. Hydrological Sciences Bulletin. 24: 1. 43-69.
doi: 10.1080/02626667909491834.
6.Collins, W.J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T.,
Hinton, T., Hughes, J., Jones, C.D., Joshi, M., Liddicoat, S., Martin, G., O'Connor, F., Rae,
J., Senior, C., Sitch, S., Totterdell, I., Wiltshire, A., and Woodward, S. 2011. Development
and evaluation of an Earth-System model – HadGEM2. Geoscientific Model Development.
4: 4. 1051-1075. doi: 10.5194/gmd-4-1051-2011.
7.Cox, P.M., Betts, R.A., Bunton, C.B., Essery, R.L.H., Rowntree, P.R., and Smith, J. 1999. The
impact of new land surface physics on the GCM simulation of climate and climate
sensitivity. Climate Dynamics. 15: 3. 183-203.
8.Dehghan, Z., Kouchakzadeh, M., and Alikhasi, M. 2014. Vulnerability of irrigation networks
under climate change with optimum cultivation in limited water resources and
implementation strategies. J. Water Soil Cons. 21: 1. 23-43. (In Persian)
9.Dickinson, R.E., Henderson-Sellers, A., and Kennedy, P.J. 1993. Biosphere-Atmosphere
Transfer Scheme (BATS) Version 1e as Coupled to the NCAR Community Climate Model.
National Center for Atmospheric Research Technical Note. Boulder-Colorado.
1- AMIP II, phase II of the Atmospheric Model Intercomparison Project
10.Diro, G.T., Rauscher, S.A., Giorgi, F., and Tompkins, A.M. 2012. Sensitivity of seasonal
climate and diurnal precipitation over Central America to land and sea surface schemes in
RegCM4. Climate Research, 31p.
11.Emanuel, K.A. 1991. A scheme for representing cumulus convection in large-scale models.
J. Atm. Sci. 48: 21. 2313-2335.
12.Fuentes-Franco, R., Coppola, E., Giorgi, F., Graef, F., and Pavia, E.G. 2014. Assessment of
RegCM4 simulated inter-annual variability and daily-scale statistics of temperature and
precipitation over Mexico. Climate Dynamics. 42: 3-4. 629-647.
13.Garratt, J.R. 1993. Sensitivity of climate simulations to land-surface and atmospheric
boundary-layer treatments-A review. J. Clim. 6: 3. 419-448.
14.Giorgi, F., Marinucci, M.R., Bates, G.T., and De Canio, G. 1993. Development of a SecondGeneration Regional Climate Model (RegCM2). Part II: Convective Processes and
Assimilation of Lateral Boundary Conditions. Monthly Weather Review. 121: 10. 2814-2832.
15.Giorgi, F., Bi, X., and Pal, J. 2004. Mean interannual variability and trends in a regional
climate change experiment over Europe. II: climate change scenarios (2071–2100). Climate
Dynamics. 23: 7-8. 839-58.
16.Giorgi, F., Coppola, E., Solmon, F., Mariotti, L., Sylla, M.B., Bi, X., Elguindi, N., Diro,
G.T., Nair, V., Giuliani, G., Turuncoglu, U.U., Cozzini, S., Güttler, I., O’Brien, T.A.,
Tawfik, A.B., Shalaby, A., Zakey, A.S., Steiner, A.L., Stordal, F., Sloan, L.C., and
Brankovic, C. 2012. RegCM4: model description and preliminary tests over multiple
CORDEX domains. Climate Research. 52: 7-29. doi: 10.3354/cr01018.
17.Grell, G.A. 1993. Prognostic evaluation of assumptions used by cumulus parameterizations.
Mon. Wea. Rev. 121: 764-787.
18.Grell, G.A., Dudhia, J., and Stauffer, D.R. 1994. Description of the fifth generation
Penn State/NCAR Mesoscale Model(MM5), National Center for Atmospheric Research.
19.Halder, S., Dirmeyer, P.A., and Saha, S.K. 2015. Sensitivity of the mean and variability
of Indian summer monsoon to land surface schemes in RegCM4: Understanding coupled
landatmosphere feedbacks. J. Geophys. Res. Atm. 120: 18. 9437-9458.
20.Holtslag, A.A.M., de Bruijn, E.I.F., and Pan, H.L. 1990. A high resolution air mass
transformation model for short-range weather forecasting. Mon. Wea. Rev. 118: 1561-1575.
21.IPCC. 2013. Summary for Policymakers. In: 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 and New York, NY, USA.
22.Irannejad, P., and Henderson-Sellers, A. 2007. Evaluation of AMIP II Global Climate Model
Simulations of the Land Surface Water Budget and Its Components over the GEWEX-CEOP
Regions. J. Hydrometeorol. 8: 3. 304-326.
23.Kang, S., Im, E.S., and Ahn, J.B. 2014. The impact of two landsurface schemes on
the characteristics of summer precipitation over East Asia from the RegCM4 simulations.
Inter. J. Climatol. 34: 15. 3986-3997.
24.Ke, Y., Leung, L.R., Huang, M., Coleman, A.M., Li, H., and Wigmosta, M.S. 2012.
Development of high resolution land surface parameters for the Community Land Model.
Geoscientific Model Development. 5: 6. 1341-1362.
25.Kiehl, J., Hack, J., Bonan, G., Boville, B., Breigleb, B., Williamson, D., and Rasch, P. 1996.
Description of the NCAR Community Climate Model (CCM3). National Center for
Atmospheric Research technote NCAR/TN-420 + STR. Boulder-Colorado.
26.Malmir, M., Mohamadrezapour, O., Sharifazari, S., and Ghandehari, Gh. 2016. The effect of
climate change on stream flow used Statistical downscaling of HADCM3 model and
Artificial Neural Networks. J. Water Soil Cons. 23: 3. 317-326. (In Persian)
27.Nash, J.E., and Sutcliffe, J.V. 1970. River flow forecasting through conceptual models
part I - A discussion of principles. J. Hydrol. 10: 3. 282-290.
28.Niu, G.Y., Yang, Z.L., Dickinson, R.E., and Gulden, L.E. 2005. A simple TOPMODELbased runoff parameterization (SIMTOP) for use in global climate models. J. Geophys. Res.
110: D21. doi: 10.1029/2005jd006111.
29.Office of Water Resources Research. 2009. Justificative report on extending the ban on
development of water resources in Toyserkan plain. Ministry of Energy, Iranian Water
Resources Management Company, Regional Water Company of Hamedan. (In Persian)
30.Oh, S.G., Park, J.H., Lee, S.H., and Suh, M.S. 2014. Assessment of the RegCM4 over East
Asia and future precipitation change adapted to the RCP scenarios. J. Geophys. Res. Atm.
119: 6. 2913-2927. doi: 10.1002/2013jd020693.
31.Oleson, K.W., Niu, G.Y., Yang, Z.L., Lawrence, D.M., Thornton, P.E., Lawrence, P.J.,
Stöckli, R., Dickinson, R.E., Bonan, G.B., Levis, S., Dai, A., and Qian, T. 2008.
Improvements to the Community Land Model and their impact on the hydrological cycle.
J. Geophys. Res. 113: G1. doi: 10.1029/2007jg000563.
32.Oleson, K.W., Lawrence, D.M., Bonan, G.B., Flanner, M.G., Kluzek, E., Lawrence, P.J.,
Levis, S., Swenson, S.C., and Thornton, P.E. 2010. Technical Description of version 4.0 of
the Community Land Model (CLM). National Center for Atmospheric Research Technical
Note. Boulder-Colorado.
33.Oleson, K.W., Lawrence, D.M., Bonan, G.B., Drewniak, B., Huang, M., Koven, C.D., Levis,
S., Li, F., Riley, W.J., Subin, Z.M., Swenson, S.C., and Thornton, P.E. 2013. Technical
Description of version 4.5 of the Community Land Model (CLM). National Center for
Atmospheric Research Technical Note. Boulder-Colorado.
34.Pal, J.S., Small, E.E., and Eltahir, E.A.B. 2000. Simulation of regional scale water and
energy budgets: representation of subgrid cloud and precipitation processes within RegCM.
J. Geophys. Res. 105: D24. 29579-29594. doi:10.1029/2000JD900415.
35.Swenson, S.C., and Lawrence, D.M. 2012. A new fractional snow-covered area
parameterization for the Community Land Model and its effect on the surface energy
balance. J. Geophys. Res. Atm. 117: D21. D21107. doi: 10.1029/2012JD018178.
36.Tiedtke, M. 1989. A comprehensive mass-flux scheme for cumulus parameterization in
large-scale models. Mon. Wea. Rev. 117: 1779-1800.
37.Tiwari, P.R., Kar, S.C., Mohanty, U.C., Dey, S., Sinha, P., Raju, P.V.S., and Shekhar, M.S.
2015. The role of land surface schemes in the regional climate model (RegCM) for seasonal
scale simulations over Western Himalaya. Atmósfera. 28: 2. 129-142.
38.Torma, C., Coppola, E., Giorgi, F., Bartholy, J., and Pongrácz, R. 2011. Validation of a
High-Resolution Version of the Regional Climate Model RegCM3 over the Carpathian
Basin. J. Hydrometeorol. 12: 1. 84-100. doi: 10.1175/2010jhm1234.1.
39.Roosmalen, L., Christensen, J.H., Butts, M.B., Jensen, K.H., and Refsgaard, J.C. 2010. An
intercomparison of regional climate model data for hydrological impact studies in Denmark.
J. Hydrol. 380: 3-4. 406-419. doi: 10.1016/j.jhydrol.2009.11.014.
40.Von Storch, H. 1995. Inconsistencies at the interface of climate impact studies and global
climate research. Meteorologische Zeitschrift. 4: 2. 72-80.
41.Wang, X., Yang, M., and Pang, G. 2015. Influences of Two Land-Surface Schemes
on RegCM4 Precipitation Simulations over the Tibetan Plateau. Advances in Meteorology.
1-12. doi: 10.1155/2015/106891.
42.Xue-Jie, G., Mei-Li, W., and Giorgi, F. 2013. Climate change over China in the 21st
century as simulated by BCC_CSM1. 1-RegCM4.0. Atmospheric and Oceanic Science
Letters. 6: 5. 381-6.
43.Xue, Y., Janjic, Z., Dudhia, J., Vasic, R., and De Sales, F. 2014. A review on
regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and
major factors that affect downscaling ability. Atmospheric Research. 147-148. 68-85. doi:
44.Yu, Y., Xie, Z., and Zeng, X. 2014. Impacts of modified Richards equation on RegCM4
regional climate modeling over East Asia. J. Geophys. Res. Atm. 119: 22. 12642-12659.
45.Zahabioun, B. 2002. Climate change impacts on water resources. Ministry of Energy- Iranian
National Committee on Large Dams, No. 49. (In Persian)
46.Zorita, E., and Von Storch, H. 1999. The analog method as a simple statistical downscaling
technique: comparison with more complicated methods. J. Clim. 12: 8. 2474-2489.