Abstract
Technological advancements like increase in computational power have led to high-resolution simulations of climate variables by Global Climate Models (GCMs). However, significant biases exist in GCM outputs when considered at a regional scale. Hence, bias correction has to be done before using GCM outputs for impact studies at a local/regional scale. Six bias correction methods, namely, delta change (DC) method, linear scaling (LS), empirical quantile mapping (EQM), adjusted quantile mapping (AQM), Gamma-Pareto quantile mapping (GPQM) and quantile delta mapping (QDM) were used to bias correct the high-resolution daily maximum and minimum temperature simulations by Meteorological Research Institute-Atmospheric General Circulation Model Version 3.2 (MRI-AGCM3–2-S) model which is part of Coupled Model Intercomparison Project Phase 6 (CMIP6), of Netravati basin, a tropical river basin on the south-west coast of India. The quantile-quantile (Q–Q) plots and Taylor diagrams along with performance indicators like Nash–Sutcliffe efficiency (NSE), the Root-Mean Square Error (RMSE) or Root-Mean Square Deviation (RMSD), the Mean Absolute Error (MAE), the Percentage BIAS (PBIAS) and the correlation coefficient (r) were used for the evaluation of the performance of each bias correction method in the validation period. Considerable reduction in the bias was observed for all the bias correction methods employed except for the LS method. The results of QDM method, which is a trend preserving bias correction method, was used for analysing the trend of future temperature data. The trend of historical and future temperature data revealed an increasing trend in the annual temperature. An increase of 0.051 °C and 0.046 °C is expected for maximum and minimum temperature annually during the period 2015 to 2050 as per RCP 8.5 scenario. This study demonstrates that the application of a suitable bias correction is needed before using GCM projections for climate change studies.
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Ahmed, K., Shahid, S., Sachindra, D.A., Nawaz, N., Chung, E.S.: Fidelity assessment of general circulation model simulated precipitation and temperature over Pakistan using a feature selection method. J. Hydrol. 573, 281–298 (2019). https://doi.org/10.1016/j.jhydrol.2019.03.092
Amengual, A., Homar, V., Romero, R., Alonso, S., Ramis, C.: A statistical adjustment of regional climate model outputs to local scales: application to Platja de Palma. Spain. J. Clim. 25, 939–957 (2012). https://doi.org/10.1175/JCLI-D-10-05024.1
Bhatti, H.A., Rientjes, T., Haile, A.T., Habib, E., Verhoef, W.: Evaluation of bias correction method for satellite-based rainfall data. Sensors (Switzerland). 16(884), (2016). https://doi.org/10.3390/s16060884
Bisht, D.S., Chatterjee, C., Raghuwanshi, N.S., Sridhar, V.: Spatio-temporal trends of rainfall across Indian river basins. Theor. Appl. Climatol. 132, 419–436 (2018). https://doi.org/10.1007/s00704-017-2095-8
Cannon, A.J., Sobie, S.R., Murdock, T.Q.: Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes? J. Clim. 28, 6938–6959 (2015). https://doi.org/10.1175/JCLI-D-14-00754.1
Chand, M., Bhattarai, B., Baral, P., Pradhananga, N.: Trend analysis of temperature data for Narayani River basin. Nepal. Sci. 1, 21 (2019). https://doi.org/10.3390/sci1010021
Chapman, S., Watson, J.E.M., Salazar, A., Thatcher, M., McAlpine, C.A.: The impact of urbanization and climate change on urban temperatures: a systematic review. Landsc. Ecol. 32, 1921–1935 (2017). https://doi.org/10.1007/s10980-017-0561-4
Chokkavarapu, N., Mandla, V.R.: Comparative study of GCMs, RCMs, downscaling and hydrological models: a review toward future climate change impact estimation. SN Appl. Sci. 1, 1698 (2019). https://doi.org/10.1007/s42452-019-1764-x
Déqué, M.: Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: model results and statistical correction according to observed values. Glob. Planet. Change. 57, 16–26 (2007). https://doi.org/10.1016/j.gloplacha.2006.11.030
Déqué, M., Calmanti, S., Christensen, O.B., Dell Aquila, A., Maule, C.F., Haensler, A., Nikulin, G., Teichmann, C.: A multi-model climate response over tropical Africa at +2 °C. Clim. Serv. 7, 87–95 (2017). https://doi.org/10.1016/j.cliser.2016.06.002
Ezéchiel, O., Eric, A.A., Josué, Z.E., Eliézer, B.I., Amédée, C.: Comparative study of seven bias correction methods applied to three Regional Climate Models in Mekrou catchment (Benin , West Africa). Int. J. Curr. Eng. Technol. 6, 1831–1840 (2016)
Govind, N.R., Ramesh, H.: Exploring the relationship between LST and land cover of Bengaluru by concentric ring approach. Environ. Monit. Assess. 192, 650 (2020). https://doi.org/10.1007/s10661-020-08601-x
Grimmond, S.: Urbanization and global environmental change: local effects of urban warming. Geogr. J. 173, 83–88 (2007). https://doi.org/10.1111/j.1475-4959.2007.232_3.x
Gudmundsson, L., Bremnes, J.B., Haugen, J.E., Engen-Skaugen, T.: Technical note: downscaling RCM precipitation to the station scale using statistical transformations &ndash. A comparison of methods. Hydrol. Earth Syst. Sci. 16, 3383–3390 (2012). https://doi.org/10.5194/hess-16-3383-2012
Gutjahr, O., Heinemann, G.: Comparing precipitation bias correction methods for high-resolution regional climate simulations using COSMO-CLM: effects on extreme values and climate change signal. Theor. Appl. Climatol. 114, 511–529 (2013). https://doi.org/10.1007/s00704-013-0834-z
Haarsma, R.J., Roberts, M.J., Vidale, P.L., Catherine, A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N.S., Guemas, V., Von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L.R., Lu, J., Luo, J.J., Mao, J., Mizielinski, M.S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., Von Storch, J.S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6. Geosci. Model Dev. 9, 4185–4208 (2016). https://doi.org/10.5194/gmd-9-4185-2016
Hagemann, S., Göttel, H., Jacob, D., Lorenz, P., Roeckner, E.: Improved regional scale processes reflected in projected hydrological changes over large European catchments. Clim. Dyn. 32, 767–781 (2009). https://doi.org/10.1007/s00382-008-0403-9
Hamed, K.H., Rao, A.R.: A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol. 204, 182–196 (1998). https://doi.org/10.1200/jco.2018.36.15_suppl.522
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., Piontek, F.: A trend-preserving bias correction &ndash. The ISI-MIP approach. Earth Syst. Dyn. 4, 219–236 (2013). https://doi.org/10.5194/esd-4-219-2013
Horton, R.M., Mankin, J.S., Lesk, C., Coffel, E., Raymond, C.: A review of recent advances in research on extreme heat events. Curr. Clim. Chang. Reports. 2, 242–259 (2016). https://doi.org/10.1007/s40641-016-0042-x
Hussain, M.M., Mahmud, I.: pyMannKendall: a python package for non parametric Mann Kendall family of trend tests. J. Open Source Softw. 4, 1556 (2019). https://doi.org/10.21105/joss.01556
Ilori, O.W., Ajayi, V.O.: Change detection and trend analysis of future temperature and rainfall over West Africa. Earth Syst. Environ. 4, 493–512 (2020). https://doi.org/10.1007/s41748-020-00174-6
Johnson, F., Sharma, A.: A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations. Water Resour. Res. 48, (2012). https://doi.org/10.1029/2011WR010464
Jose, D.M., Dwarakish, G.S.: Uncertainties in predicting impacts of climate change on hydrology in basin scale : a review. Arab. J. Geosci. 13, (2020). https://doi.org/10.1007/s12517-020-06071-6
Kannan, S., Ghosh, S.: Prediction of daily rainfall state in a river basin using statistical downscaling from GCM output. Stoch. Environ. Res. Risk Assess. 25, 457–474 (2011). https://doi.org/10.1007/s00477-010-0415-y
Kharin, V.V., Zwiers, F.W., Zhang, X., Wehner, M.: Changes in temperature and precipitation extremes in the CMIP5 ensemble. Clim. Chang. 119, 345–357 (2013). https://doi.org/10.1007/s10584-013-0705-8
Kumar, S., Chanda, K., Pasupuleti, S.: Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India. Theor. Appl. Climatol. 140, 343–357 (2020). https://doi.org/10.1007/s00704-020-03088-5
LaFond, K.M., Griffis, V.W., Spellman, P.: Forcing Hydrologic Models with GCM Output: Bias Correction vs. the “Delta Change” Method. World Environ. Water Resour. Congr. 2014 Water Without Borders - Proc. 2014 World Environ. Water Resour. Congr. 1, 2146–2155 (2014). https://doi.org/10.1061/9780784413548.214
Lenderink, G., Buishand, A., Van Deursen, W.: Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach. Hydrol. Earth Syst. Sci. 11, 1145–1159 (2007). https://doi.org/10.5194/hess-11-1145-2007
Lewis, S.C., King, A.D., Perkins-Kirkpatrick, S.E., Mitchell, D.M.: Regional hotspots of temperature extremes under 1.5 °C and 2 °C of global mean warming. Weather Clim. Extrem. 26, 100233 (2019). https://doi.org/10.1016/j.wace.2019.100233
Li, H., Sheffield, J., Wood, E.F.: Bias correction of monthly precipitation and temperature fields from intergovernmental panel on climate change AR4 models using equidistant quantile matching. J. Geophys. Res. Atmos. 115, (2010). https://doi.org/10.1029/2009JD012882
Lu, H., Bryant, R.B., Buda, A.R., Collick, A.S., Folmar, G.J., Kleinman, P.J.A.: Long-term trends in climate and hydrology in an agricultural headwater watershed. J. Hydrol. Reg. Stud. 4, 713–731 (2015). https://doi.org/10.13031/cc.20152124135
Luo, M., Liu, T., Meng, F., Duan, Y., Frankl, A., Bao, A., De Maeyer, P.: 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, (2018). https://doi.org/10.3390/w10081046
Maraun, D.: Bias correction, quantile mapping, and downscaling: revisiting the inflation issue. J. Clim. 26, 2137–2143 (2013). https://doi.org/10.1175/JCLI-D-12-00821.1
Marshall, E., Randhir, T.: Effect of climate change on watershed system: a regional analysis. Clim. Chang. 89, 263–280 (2008). https://doi.org/10.1007/s10584-007-9389-2
Maurer, E.P., Pierce, D.W.: Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean. Hydrol. Earth Syst. Sci. 18, 915–925 (2014). https://doi.org/10.5194/hess-18-915-2014
Mendez, M., Maathuis, B., Hein-Griggs, D., Alvarado-Gamboa, L.F.: Performance evaluation of bias correction methods for climate change monthly precipitation projections over Costa Rica. Water (Switzerland). 12(482), (2020). https://doi.org/10.3390/w12020482
Mizuta, R., Yoshimura, H., Murakami, H., Matsueda, M., Endo, H., Ose, T., Kamiguchi, K., Hosaka, M., Sugi, M., Yukimoto, S., Kusunoki, S., Kitoh, A.: Climate simulations using MRI-AGCM3.2 with 20-km grid. J. Meteorol. Soc. Japan. 90, 233–258 (2012). https://doi.org/10.2151/jmsj.2012-A12
Mizuta, R., Yoshimura, H., Ose, T., Hosaka, M., Yukimoto, S.: MRI MRI-AGCM3-2-S model output prepared for CMIP6 HighResMIP highresSST-present. Earth System Grid Federation (2019a). https://doi.org/10.22033/ESGF/CMIP6.6942. Accessed 25 May 2020
Mizuta, R., Yoshimura, H., Ose, T., Hosaka, M., Yukimoto, S.: MRI MRI-AGCM3-2-S model output prepared for CMIP6 HighResMIP highresSST-future. Earth System Grid Federation (2019b). https://doi.org/10.22033/ESGF/CMIP6.6740. Accessed 25 May 2020
Mudbhatkal, A., Mahesha, A.: Bias correction methods for hydrologic impact studies over India’s Western Ghat basins. J. Hydrol. Eng. 23, 1–13 (2018). https://doi.org/10.1061/(ASCE)HE.1943-5584.0001598
Mudbhatkal, A., Raikar, R. V., Venkatesh, B., Mahesha, A.: Impacts of climate change on Varied River-flow regimes of southern India. J. Hydrol. Eng. 22, 1–13 (2017). https://doi.org/http://ascelibrary.org/doi/10.1061/%28ASCE%29HE.1943-5584.0001556
Myers, N., Mittermeler, R.A., Mittermeler, C.G., Da Fonseca, G.A.B., Kent, J.: Biodiversity hotspots for conservation priorities. Nature. 403, 853–858 (2000). https://doi.org/10.1038/35002501
Nilawar, A.P., Waikar, M.L.: Impacts of climate change on streamflow and sediment concentration under RCP 4.5 and 8.5: A case study in Purna river basin, India. Sci. Total Environ. 650, 2685–2696 (2019). https://doi.org/10.1016/j.scitotenv.2018.09.334
Nyunt, C.T., Koike, T., Yamamoto, A.: Statistical bias correction for climate change impact on the basin scale precipitation in Sri Lanka, Philippines. Japan and Tunisia. Hydrol. Earth Syst. Sci. Discuss. (2016). https://doi.org/10.5194/hess-2016-14
Pathak, A.A., Dodamani, B.M.: Comparison of meteorological drought indices for different climatic regions of an Indian River Basin. Asia-Pacific J. Atmos. Sci. 56, 563–576 (2019). https://doi.org/10.1007/s13143-019-00162-5
Pettitt: A Non-parametric to the Approach Problem. Appl. Stat. 28, 126–135 (1979)
Raj, B., Singh, O.: Study of Impacts of Global Warming on Climate Change: Rise in Sea Level and Disaster Frequency. In: Global Warming - Impacts and Future Perspectives (2012)
Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., Rafaj, P.: RCP 8.5-A scenario of comparatively high greenhouse gas emissions. Clim. Change. 109, 33–57 (2011). https://doi.org/10.1007/s10584-011-0149-y
Sachindra, D.A., Huang, F., Barton, A., Perera, B.J.C.: Statistical downscaling of general circulation model outputs to precipitation-part 2: Bias-correction and future projections. Int. J. Climatol. 34, 3282–3303 (2014). https://doi.org/10.1002/joc.3915
Sahoo, S., Dey, S., Dhar, A., Debsarkar, A., Pradhan, B.: On projected hydrological scenarios under the influence of bias-corrected climatic variables and LULC. Ecol. Indic. 106, 105440 (2019). https://doi.org/10.1016/j.ecolind.2019.105440
Sen, P.K.: Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968). https://doi.org/10.1080/01621459.1968.10480934
Shrestha, M., Acharyaa, S.C., Shrestha, P.K.: Bias correction of climate models for hydrological modelling – are simple methods still useful ? Meteorol. Appl. 24, 531–539 (2017). https://doi.org/10.1002/met.1655
Singh, V., Goyal, M.K.: Spatio-temporal heterogeneity and changes in extreme precipitation over eastern Himalayan catchments India. Stoch. Environ. Res. Risk Assess. 31, 2527–2546 (2017). https://doi.org/10.1007/s00477-016-1350-3
Sinha, R.K., Eldho, T.I.: Effects of historical and projected land use/cover change on runoff and sediment yield in the Netravati river basin, Western Ghats. India. Environ. Earth Sci. 77, 111 (2018). https://doi.org/10.1007/s12665-018-7317-6
Smitha, P.S., Narasimhan, B., Sudheer, K.P., Annamalai, H.: An improved bias correction method of daily rainfall data using a sliding window technique for climate change impact assessment. J. Hydrol. 556, 100–118 (2018). https://doi.org/10.1016/j.jhydrol.2017.11.010
Sonali, P., Kumar, D.N.: Review of recent advances in climate change detection and attribution studies : a large-scale hydroclimatological perspective. J. Water Clim. Chang. 11, 1–29 (2020). https://doi.org/10.2166/wcc.2020.091
Srivastava, A.K., Rajeevan, M., Kshirsagar, S.R.: Development of a high resolution daily gridded temperature data set ( 1969–2005 ) for the Indian region. Atmos. Sci. Lett. 10, 249–254 (2009). https://doi.org/10.1002/asl
Switanek, B.M., Troch, A.P., Castro, L.C., Leuprecht, A., Chang, H.I., Mukherjee, R., Demaria, M.C.E.: Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes. Hydrol. Earth Syst. Sci. 21, 2649–2666 (2017). https://doi.org/10.5194/hess-21-2649-2017
Teutschbein, C., Seibert, J.: Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J. Hydrol. 456, 12–29 (2012). https://doi.org/10.1016/j.jhydrol.2012.05.052
Trzaska, S., Schnarr, E.: A Review of Downscaling Methods for Climate Change Projections. (2014)
Van de Velde, J., De Baets, B., Demuzere, M., Verhoest, N.E.C.: Comparison of occurrence-bias-adjusting methods for hydrological impact modelling. Hydrol. Earth Syst. Sci. (2020). https://doi.org/10.5194/hess-2020-83
Venkatesh, K., Ramesh, H.: Impact of land use land cover change on run off generation in tungabhadra river basin. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 4, 367–374 (2018). https://doi.org/10.5194/isprs-annals-IV-5-367-2018
Wang, L., Chen, W.: Equiratio cumulative distribution function matching as an improvement to the equidistant approach in bias correction of precipitation. Atmos. Sci. Lett. 15, 1–6 (2014). https://doi.org/10.1002/asl2.454
Yeh, N.C., Chuang, Y.C., Peng, H.S., Hsu, K.L.: Bias adjustment of satellite precipitation estimation using ground-based observation: Mei-Yu front case studies in Taiwan. Asia-Pacific J. Atmos. Sci. 56, 485–492 (2019). https://doi.org/10.1007/s13143-019-00152-7
Yu, M., Ruggieri, E.: Change point analysis of global temperature records. Int. J. Climatol. 39, 3679–3688 (2019). https://doi.org/10.1002/joc.6042
Acknowledgements
We acknowledge the India Meteorological Department (IMD) for providing daily gridded maximum and minimum temperature data. Further, we acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF. Authors would also like to thank the Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, India for providing the necessary support to carry out this research work. Last but not least, we thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.
Availability of Data and Material (Data Transparency)
The GCM data used is available online on the World Climate Research Programme (WCRP) climate data portal (https://esgf-node.llnl.gov/search/cmip6/). The gridded maximum and minimum temperature data can be accessed through IMD Pune’s website (http://www.imdpune.gov.in/Clim_Pred_LRF_New/Grided_Data_Download.html).
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Author has used an open-source MATLAB toolbox developed by Santander Meteorology Group named MeteoLab Toolbox for performing the bias correction of temperature along with other customised codes. MATLAB is also used the generation of figures and change point analysis. Trend analysis was done using pyMannKendall package in python.
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Jose, D.M., Dwarakish, G.S. Bias Correction and Trend Analysis of Temperature Data by a High-Resolution CMIP6 Model over a Tropical River Basin. Asia-Pacific J Atmos Sci 58, 97–115 (2022). https://doi.org/10.1007/s13143-021-00240-7
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DOI: https://doi.org/10.1007/s13143-021-00240-7