Uncertainty assessment of monthly precipitation using multiple GCMs and quantile mapping bias correction methods

Document Type : Research Paper

Authors

Department of Water Engineering and Management, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran.

10.22059/jwim.2024.369044.1121

Abstract

Given the complexity of the climate system and the non-linear relationships between the ocean and atmosphere within this system, it is imperative to comprehend and consider the uncertainties that stem from different sources. Understanding and accounting for uncertainties play a crucial role in predicting climatic variables and facilitating a comprehensive evaluation of greenhouse gas mitigation and adaptation policies. The objective of this study is to quantify the uncertainties in historical and future average monthly precipitation by employing various General Circulation Models (GCMs), bias correction methods, Shared Socioeconomic Pathways (SSPs) scenarios, and seven projection periods. To achieve this, the outputs of ten GCMs were adjusted using nine quantile mapping bias correction methods for the Rafsanjan study area, and a suitable method was chosen to analyze the uncertainties of SSPs and projection periods. Two statistical criteria, namely the standard deviation and interquartile range, were utilized to measure the uncertainties. The results revealed that the standard deviation and interquartile range of average monthly precipitation were lower during the historical period compared to the projection period. This difference was determined based on the selection of bias correction methods and GCMs. Furthermore, for both the historical and future periods, the STDEVs and IQRs of average monthly precipitation were lower depending on the type of bias correction methods rather than the type of GCMs. In general, the uncertainties associated with projection periods and the type of GCMs are higher during future periods compared to other sources of uncertainties such as bias correction methods and SSP scenarios. This highlights the necessity for a more accurate analysis. This study contributes to an enhanced understanding of the inherent uncertainties in climate change projections that arise from various sources.

Keywords

Main Subjects

References

  1. Abdulai, P. J., & Chung, E. S. (2019). Uncertainty assessment in drought severities for the Cheongmicheon watershed using multiple GCMs and the reliability ensemble averaging method. Sustainability (Switzerland), 11(16). https://doi.org/10.3390/su11164283
  2. Akstinas, V., Jakimavičius, D., Meilutyte-Lukauskiene, D., Kriaučiūniene, J., & Šarauskiene, D. (2020). Uncertainty of annual runoff projections in Lithuanian rivers under a future climate. Hydrology Research, 51(2), 257-271. https://doi.org/10.2166/nh.2019.004
  3. Baniasadi, A., Mazidi, A., Mozaffari, Gh. A., & Omidvar, K. (2023). Climate Change and its Effect on Agricultural Climate Indices for Pistachio Trees in Kerman Province : A case study of Rafsanjan stations. The Journal of Geographical Research on Desert Areas, 11(1), 179-191. (In Persian)
  4. Buytaert, W., Vuille, M., Dewulf, A., Urrutia, R., Karmalkar, A., & Célleri, R. (2010). Uncertainties in climate change projections and regional downscaling in the tropical Andes: Implications for water resources management. Hydrology and Earth System Sciences, 14(7), 1247-1258. https://doi.org/10.5194/hess-14-1247-2010
  5. Chen, J., Brissette, F. P., Chaumont, D., & Braun, M. (2013). Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resources Research, 49(7), 4187-4205. https://doi.org/10.1002/wrcr.20331
  6. Cook, L. M., Anderson, C. J., & Samaras, C. (2017). Framework for Incorporating Downscaled Climate Output into Existing Engineering Methods: Application to Precipitation Frequency Curves. Journal of Infrastructure Systems, 23(4), 1-28. https://doi.org/10.1061/(asce)is.1943-555x.0000382
  7. Das, J., Poonia, V., Jha, S., & Goyal, M. K. (2020). Understanding the climate change impact on crop yield over Eastern Himalayan Region: ascertaining GCM and scenario uncertainty. Theoretical and Applied Climatology, 142(1-2), 467-482. https://doi.org/10.1007/s00704-020-03332-y
  8. Diro, G. T., Rauscher, S. A., Giorgi, F., & Tompkins, A. M. (2012). Sensitivity of seasonal climate and diurnal precipitation over Central America to land and sea surface schemes in RegCM4. Climate Research, 52(1), 31-48. https://doi.org/10.3354/cr01049
  9. Ebrahimi Louyeh, A. (2009). Consequences of Groundwater Over-Exploitation (Case Study: Rafsanjan Plain). Iran-Water Resources Research, 4(3), 70-73. (In Persian)
  10. El Asri, H., Larabi, A., & Faouzi, M. (2019). Climate change projections in the Ghis-Nekkor region of Morocco and potential impact on groundwater recharge. Theoretical and Applied Climatology, 138(1-2), 713-727. https://doi.org/10.1007/s00704-019-02834-8
  11. Enayati, M., Bozorg-Haddad, O., Bazrafshan, J., Hejabi, S., & Chu, X. (2021). Bias correction capabilities of quantile mapping methods for rainfall and temperature variables. Journal of Water and Climate Change, 12(2), 401-419. https://doi.org/10.2166/wcc.2020.261
  12. Iran Ministery of Energy. (2016). extension Report on the Restricted Studey Area in Rafsanjan (Area Code 4902). (In Persian)
  13. Giorgi, F., & Mearns, L. O. (2002). Calculation of Average, Uncertainty Range, and Reliability of Regional Climate Changes from AOGCM Simulations via the “Reliability Ensemble Averaging” (REA) Method. Journal of Climate, 15(10), 1141-1158. https://doi.org/10.1175/1520-0442(2002)015<1141:COAURA>2.0.CO;2
  14. Gudmundsson, L., Bremnes, J. B., Haugen, J. E., & Engen-Skaugen, T. (2012). Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations &ndash; A comparison of methods. Hydrology and Earth System Sciences, 16(9), 3383-3390. https://doi.org/10.5194/hess-16-3383-2012
  15. Hamed, M. M., Nashwan, M. S., Shahid, S., Ismail, T., Bin, Wang, X., Jun, Dewan, A., & Asaduzzaman, M. (2022). Inconsistency in historical simulations and future projections of temperature and rainfall: A comparison of CMIP5 and CMIP6 models over Southeast Asia. Atmospheric Research, 265, 1-38. https://doi.org/10.1016/j.atmosres.2021.105927
  16. Hong, J., Javan, K., Shin, Y., & Park, J. S. (2021). Future projections and uncertainty assessment of precipitation extremes in iran from the cmip6 ensemble. Atmosphere, 12(8), 1-16. https://doi.org/10.3390/ATMOS12081052
  17. Hosseinzadehtalaei, P., Tabari, H., & Willems, P. (2017). Uncertainty assessment for climate change impact on intense precipitation: how many model runs do we need? International Journal of Climatology, 37(April), 1105-1117. https://doi.org/10.1002/joc.5069
  18. IPCC. (2014). Climate Change 2014 Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Retrieved from https://doi.org/10013/epic.45156
  19. IPCC. (2018). Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change. Retrieved from World Meteorological Organization, website: https://www.ipcc.ch/sr15/
  20. Jung, I. W., Chang, H., & Moradkhani, H. (2011). Quantifying uncertainty in urban flooding analysis considering hydro-climatic projection and urban development effects. Hydrology and Earth System Sciences, 15(2), 617-633. https://doi.org/10.5194/hess-15-617-2011
  21. Karamouz, M., Semsaryazdi, M. S., Ahmadi, B., & Ahmadi, A. (2010). Climate Change Impacts on Crop Water Requirements : A Case Study Climate Change Impacts on Crop Water Requirements: A Case Study. In 1st IWA Malaysia Young Water Professionals Conference, International Water Association, 1-11. (In Persian)
  22. Kudo, R., Yoshida, T., & Masumoto, T. (2017). Uncertainty analysis of impacts of climate change on snow processes: Case study of interactions of GCM uncertainty and an impact model. Journal of Hydrology, 548, 196-207. https://doi.org/10.1016/j.jhydrol.2017.03.007
  23. Lafon, T., Dadson, S., Buys, G., & Prudhomme, C. (2013). Bias correction of daily precipitation simulated by a regional climate model: A comparison of methods. International Journal of Climatology, 33(6), 1367-1381. https://doi.org/10.1002/joc.3518
  24. Leander, R., & Buishand, T. A. (2007). Resampling of regional climate model output for the simulation of extreme river flows. Journal of Hydrology, 332(3-4), 487-496. https://doi.org/10.1016/j.jhydrol.2006.08.006
  25. Liepert, B. G., & Previdi, M. (2012). Inter-model variability and biases of the global water cycle in CMIP3 coupled climate models. Environmental Research Letters, 7(1). https://doi.org/10.1088/1748-9326/7/1/014006
  26. Luo, M., Liu, T., Meng, F., Duan, Y., Frankl, A., Bao, A., & De Maeyer, P. (2018). Comparing bias correction methods used in downscaling precipitation and temperature from regional climate models: A case study from the Kaidu River Basin in Western China. Water (Switzerland), 10(8). https://doi.org/10.3390/w10081046
  27. Mair, L., Jönsson, M., Räty, M., Bärring, L., Strandberg, G., Lämås, T., & Snäll, T. (2018). Land use changes could modify future negative effects of climate change on old-growth forest indicator species. Diversity and Distributions, 24(10), 1416-1425. https://doi.org/10.1111/ddi.12771
  28. Mandal, S., & Simonovic, S. P. (2017). Quantification of uncertainty in the assessment of future streamflow under changing climate conditions. Hydrological Processes, 31(11), 2076-2094. https://doi.org/10.1002/hyp.11174
  29. Mendez, M., Maathuis, B., Hein-Griggs, D., & Alvarado-Gamboa, L. F. (2020). Performance evaluation of bias correction methods for climate change monthly precipitation projections over Costa Rica. Water (Switzerland), 12(2). https://doi.org/10.3390/w12020482
  30. Pardo-Igúzquiza, E., Collados-Lara, A. J., & Pulido-Velazquez, D. (2019). Potential future impact of climate change on recharge in the Sierra de las Nieves (southern Spain) high-relief karst aquifer using regional climate models and statistical corrections. Environmental Earth Sciences, 78(20), 1-12. https://doi.org/10.1007/s12665-019-8594-4
  31. Piani, C., Weedon, G. P., Best, M., Gomes, S. M., Viterbo, P., Hagemann, S., & Haerter, J. O. (2010). Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. Journal of Hydrology, 395(3-4), 199-215. https://doi.org/10.1016/j.jhydrol.2010.10.024
  32. Prudhomme, C., & Davies, H. (2009). Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: Future climate. Climatic Change, 93(1-2), 197-222. https://doi.org/10.1007/s10584-008-9461-6
  33. Räty, O., Räisänen, J., & Ylhäisi, J. S. (2014). Evaluation of delta change and bias correction methods for future daily precipitation: Intermodel cross-validation using ENSEMBLES simulations. Climate Dynamics, 42(9-10), 2287-2303. https://doi.org/10.1007/s00382-014-2130-8
  34. Reshmidevi, T. V., Nagesh Kumar, D., Mehrotra, R., & Sharma, A. (2018). Estimation of the climate change impact on a catchment water balance using an ensemble of GCMs. Journal of Hydrology, 556, 1192-1204. https://doi.org/10.1016/j.jhydrol.2017.02.016
  35. Sharma, T., Vittal, H., Chhabra, S., Salvi, K., Ghosh, S., & Karmakar, S. (2018). Understanding the cascade of GCM and downscaling uncertainties in hydro-climatic projections over India. International Journal of Climatology, 38(December 2017), e178-e190. https://doi.org/10.1002/joc.5361
  36. Shen, M., Chen, J., Zhuan, M., Chen, H., Xu, C. Y., & Xiong, L. (2018). Estimating uncertainty and its temporal variation related to global climate models in quantifying climate change impacts on hydrology. Journal of Hydrology, 556, 10–24. https://doi.org/10.1016/j.jhydrol.2017.11.004
  37. Shishehgaran, N. N., Babaeian, F., & Mianabadi, H. (2024). Comparison of CMIP6 Climate Models and Quantile Mapping Bias Correction Methods in the Simulation of Historical Precipitation. Iranian Journal of Soil and Water Research, 54(12), 1843-1862. https://doi.org/10.22059/IJSWR.2023.362445.669538. (In Persian)
  38. Sillmann, J., Kharin, V. V., Zwiers, F. W., Zhang, X., & Bronaugh, D. (2013). Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. Journal of Geophysical Research Atmospheres, 118(6), 2473–2493. https://doi.org/10.1002/jgrd.50188
  39. Song, Y. H., Chung, E. S., & Shiru, M. S. (2020). Uncertainty analysis of monthly precipitation in GCMs using multiple bias correction methods under different RCPs. Sustainability (Switzerland), 12(18). https://doi.org/10.3390/su12187508
  40. Tanveer, M. E., Lee, M. H., & Bae, D. H. (2016). Uncertainty and Reliability Analysis of CMIP5 Climate Projections in South Korea Using REA Method. Procedia Engineering, 154, 650-655. https://doi.org/10.1016/j.proeng.2016.07.565
  41. Tebaldi, C., Smith, R. L., Nychka, D., & Mearns, L. O. (2005). Quantifying uncertainty in projections of regional climate change: A Bayesian approach to the analysis of multimodel ensembles. Journal of Climate, 18(10), 1524-1540. https://doi.org/10.1175/JCLI3363.1
  42. Teutschbein, C., & Seibert, J. (2010). Regional climate models for hydrological impact studies at the catchment scale: A review of recent modeling strategies. Geography Compass, 4(7), 834-860. https://doi.org/10.1111/j.1749-8198.2010.00357.x
  43. Tong, Y., Gao, X., Han, Z., Xu, Y., Xu, Y., & Giorgi, F. (2021). Bias correction of temperature and precipitation over China for RCM simulations using the QM and QDM methods. Climate Dynamics, 57(5-6), 1425-1443. https://doi.org/10.1007/s00382-020-05447-4
  44. Vigna, I., Bigi, V., Pezzoli, A., & Besana, A. (2020). Comparison and bias-correction of satellite-derived precipitation datasets at local level in northern Kenya. Sustainability (Switzerland), 12(7). https://doi.org/10.3390/su12072896
  45. Wang, H. M., Chen, J., Xu, C. Y., Zhang, J., & Chen, H. (2020). A Framework to Quantify the Uncertainty Contribution of GCMs Over Multiple Sources in Hydrological Impacts of Climate Change. Earth’s Future, 8(8). https://doi.org/10.1029/2020EF001602
  46. Wang, J., Wang, Y., Feng, J., Chen, C., Chen, J., Long, T., Li, J., Zang, R., & Li, J. (2019). Differential responses to climate and land-use changes in threatened Chinese Taxus species. Forests, 10(9). https://doi.org/10.3390/f10090766
  47. Watanabe, S., Kanae, S., Seto, S., Yeh, P. J. F., Hirabayashi, Y., & Oki, T. (2012). Intercomparison of bias-correction methods for monthly temperature and precipitation simulated by multiple climate models. Journal of Geophysical Research Atmospheres, 117(23), 1-13. https://doi.org/10.1029/2012JD018192
  48. Woldemeskel, F. M., Sharma, A., Sivakumar, B., & Mehrotra, R. (2016). Quantification of precipitation and temperature uncertainties simulated by CMIP3 and CMIP5 models. Journal of Geophysical Research: Atmospheres, 121(1), 3-17. https://doi.org/10.1002/2015JD023719
  49. Wootten, A., Terando, A., Reich, B. J., Boyles, R. P., & Semazzi, F. (2017). Characterizing sources of uncertainty from global climate models and downscaling techniques. Journal of Applied Meteorology and Climatology, 56(12), 3245-3262. https://doi.org/10.1175/JAMC-D-17-0087.1
  50. Yazdandoost, F., Moradian, S., Izadi, A., & Aghakouchak, A. (2021). Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison. Atmospheric Research, 250(November), 105369. https://doi.org/10.1016/j.atmosres.2020.105369
  51. Zarrin, A., & Dadashi-Roudbari, A. (2021). Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theoretical and Applied Climatology, 144(1-2), 643-660. https://doi.org/10.1007/s00704-021-03568-2
  52. Zeraatkar, H., & Golkar, E. (2018). Water Consumption in the Study Area of Rafsanjan Plain from 1951 to Present. (In Persian)
  53. Zhao, L., Xu, J., Powell, A. M., & Jiang, Z. (2015). Uncertainties of the global-to-regional temperature and precipitation simulations in CMIP5 models for past and future 100 years. Theoretical and Applied Climatology, 122(1-2), 259-270. https://doi.org/10.1007/s00704-014-1293-x

 

Water and Irrigation Management
Volume 14, Issue 2
October 2024
Pages 463-486

Files

Share

How to cite

Statistics