Comparing the Performance of the Dynamic-Statistical Hybrid Method with the Dynamic Method for Downscaling of CMIP5 Precipitation Data

Document Type : Original Article

Authors

1 Ph.D. Candidate of Water Resources Engineering, Department of water Resource Engineering, College of Aburaihan, University of Tehran, Pakdasht, Iran.

2 Associate Professor, Department of Water Resources Engineering, College of Aburaihan, University of Tehran, Pakdasht, Iran.

3 Assistant Professor, Atmospheric Science & Meteorogical Research Center, Tehran, Iran.

Abstract

One of the most important challenges of hydrological studies is the projection of precipitation variability in a basin in future periods. Atmospheric Ocean General Circulation Models (AOGCMs) can project this variability but in large-scale regions. Various versions of these models have been released, one of which is the CMIP5 series with about 40 dynamic models. In this study, the CCSM4 climate model has been used. The data output of this model is in 1 × 1 (Latitude× longitude) degree. To downscale the precipitation data of this model, the hybrid of two dynamic (WRF1) and statistical methods has been proposed. The study area is the Poldokhtar subbasin in the ​​Karkheh basin and rainfall data between 1996-2005 have been used. First, the precipitation data were downscaled by the WRF model from the range of 1 × 1 degree to 9 × 9 km, and then these data were converted to the range of 3 × 3 km separately by two methods: a) hybrid and b) WRF. The results showed that the dynamic downscaling by the WRF model in the range of 9 × 9 km quite shows the precipitation fluctuations during the statistical period. In the 3 × 3 km range, the hybrid method performed better than the WRF method. Finally the results showed that downscaling of precipitation in this basin using both methods is associated with underestimation and should be accompanied by an bias correction method. As a result, by reducing the time and cost of dynamical downscaling of climate models, the findings of this paper can be of great help in making decisions about water resources management in the wet and dry years of the coming decades of a basin.

Keywords

Main Subjects

References

Altaf Y, Ahmad A, Mohd F (2017) MLR based statistical downscaling of temperature and precipitation in Lidder Basin Region of India. Environ Pollut Climate Change 1(2):1-7
Bechler A, Vrac M, Bel L (2015) A spatial hybrid approach for downscaling of extreme precipitation fields. J ournal of Geophysical Research: Atmospheres 120(10):4534–4550
Chiew F, Zheng H, Potter N, Ekstrom M (2017) Future runoff projections for Australia and science challenges in producing next generation projections. In: Proc. of International Congress on Modelling and Simulation 22-24 May, Hobart, Tasmania Australia Retrieved from www.mssanz.org.au
Clark M, Wilby R, Gutmann E (2016) Characterizing uncertainty of the hydrologic impacts of climate change. Current Climate Change Reports 2(2):55–64
Erler A, Frey S, Khader O, d’Orgeville M, Park Y (2019) Simulating climate change impacts on surface water resources within a lake-affected region using regional climate projections. Water Resources Research 55(10):130–155
Felder G, Gómez-Navarro J, Zischg A, Raible C, Röthlisberger V, Bozhinova D (2018) From global circulation to local flood loss: Coupling models across the scales. Science of the Total Environment 635:1225–1239
Georgi F, Hewitson B (2020) Regional climate information–evaluation and projections. In 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
Gorguner M, Levent Kavvas M, Ishida K (2019) Assessing the impacts of future climate change on the hydroclimatology of the Gediz Basin in Turkey by using dynamically downscaled. Science of the Total Environment 646:481–499
Jared H B, Adam J T, Vasu V (2021) High‐resolution dynamically downscaled rainfall and temperature projections for ecological life zones within Puerto Rico and for the US Virgin Islands. International Journal of Climatology 41(2):1305-1327
Joshi D, Hilaire A, Ouarda T,  Daigle A (2015) Statistical downscaling of precipitation and temperature using Sparse Bayesian Learning, Multiple Linear Regression and Genetic Programming frameworks. Canadian Water Resources Journal 4(12):1023-1036
Lei C, Vladimir A A, Christopher D A (2018) The polar WRF downscaled historical and projected twenty-first century climate for the coast and foothills of Arctic Alaska. Frontiers in Earth Science 5(111):235-247
Masoompour S J, Miri M, Porkamar F (2018) Assessment of CMIP5 climate models with observed precipitation in Iran. Iranian Journal of Geophysics 11(4):40-53
Miri M (2017) Investigation of the relationship between climate change and degradation of Zagros forests: A case study of Ilam region. Ph.D. Thesis, School of of Geography, University of Tehran (In Persian)
Neyestani A, Sarmad G, Gustafsson N, Mohebalhojeh A (2018) Inter-comparison of HARMONIE and WRF model simulations in convectivepermitting scale over western area of Iran. Iranian Journal of Geophysics 12(1):1-18
Peltier W, d’Orgeville M, Erler A, and Xie F (2018) Uncertainty in future summer precipitation in the Laurentian Great Lakes Basin Dynamical downscaling and the influence of continental scale processes on regional climate change. Journal of Climate 31(23):56-70
Piras M, Mascaro G, Deidda R, Vivoni E (2014) Quantification of hydrologic impacts of climate change in a Mediterranean basin in Sardinia, Italy, through high-resolution simulations. Hydrology and Earth System Sciences 18(12):5201-5217
Rahimi B S, Jahanbakhsh A S, Sari Sarraf B (2019) Dynamic downscaling to study climate change in the Karkheh Basin. Management and Engineering Watershed of Journal 11(3):633-649
Rasmussen R, Ikeda K, Liu C, GOCHIS D (2014) Climate change impacts on the water balance of the Colorado Headwaters: High-Resolution Regional Climate Model Simulations. American Meteorological Society 15(3):1091-1116
Rogelis M, Werner M (2018) Streamflow forecasts from WRF precipitation for flood early warning in mountain tropical areas. Hydrology and Earth System Sciences 22(1):853–870
Sharifi E, Saghafian B, Steinacker R (2019) Downscaling satellite precipitation estimates with multiple linear regression, artificial neural networks, and spline interpolation techniques. Journal of Geophysical Research Atmospheres 124(2):789–805
Sun F, Hall A, Schwartz M, Walton D, Berg N (2016) Twenty-first-century snowfall and snowpack changes over the southern California Mountains. Journal of Cimate 29:91-110
Victor O, Haishan C, Chujie G (2019) Evaluation of CMIP5 twentieth century rainfall simulation over the equatorial East Africa. Theoretical and Applied Climatology 135(3-4):893-910
Walton D, Sun F, Hall A, Capps S (2018) A hybrid dynamical–statistical downscaling technique Part I: Development and validation of the technique. Journal of Climate 28(12):4597-4617
Xingying H, Daniel L S, Alex D H (2020) Future precipitation increase from very high resolution ensemble downscaling of extreme atmospheric river storms in California. Science Advances 6(29):1-14
Yanping L, Zhenhua L (2021) High-Resolution Weather Research Forecasting (WRF) modeling and projection over western Canada, Including Mackenzie Watershed. Arctic Hydrology Permafrost and Ecosystems: 815-847. Doi: https://doi.org/ 10.1007/978-3-030-50930-9_28.

Files

History

  • Receive Date: 04 August 2021
  • Revise Date: 02 January 2022
  • Accept Date: 05 January 2022

Share

How to cite

Statistics