Abstract
The simulations from climate models require bias correction prior to use in impact assessments or when used as predictors in statistical or dynamic downscaling models. Recent works have sought to address each of these limitations and the results are the Multivariate Recursive Nesting Bias Correction (MRNBC) and Multivariate recursive Quantile-matching Nested Bias Correction (MRQNBC) methods. The model was applied to a mountain region of Heihe River. A comparison of the historical and generated statistics shows that the model preserves all the important characteristics of meteorological variables at daily, monthly, seasonally and annual time scales. This study has documented the performance of Multivariate Recursive Nesting Bias Correction to remove the discrepancy between the predictors in the simulated GCM and the reanalysis NCEP data and assess the projected future precipitation accuracy in the headwater region of Heihe River. A relatively high spatial resolution GCM outputs—ACCESS1-3—from the CMIP5 Earth System Models (ESMs) was employed to downscale for the historical 1960–2005 and the future period 2010–2100 under the scenarios of Representative Concentration Pathways RCP4.5 and RCP8.5. The MRNBC method can dramatically increase the performance of the simulated precipitation data. Verified by statistical score metrics applied for evaluation of the results, the developed method appears to be an important statistical tool in the correction of the bias between the GCM output and the reanalysis data, leading to significant improvements in the predictive performance accuracy of the precipitation projections. The projected precipitation under RCP8.5 appeared to exhibit the significant increasing trend relative to the RCP4.5 scenario in the headwater region of Heihe River. Future precipitation will increasing by 8% and 20% for near and long term period under RCP4.5 and increasing 14% and 37% for near and long term period, under RCP8.5, respectively.
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References
Arnell NW, Reynard NS (1996) The effects of climate change due to global warming on river flows in Great Britain. J Hydrol 183(3–4):397–424
Cayan DR, Maurer EP, Dettinger MD, Tyree M, Hayhoe K (2008) Climate change scenarios for the California region. Clim Change 87(Suppl. 1):21–42. https://doi.org/10.1007/s10584-007-9377-6
Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250
Chen X, Wang D, Chopra M (2013) Constructing comprehensive datasets for understanding human and climate change impacts on hydrologic cycle. Irrig Drain Syst Eng 2:106. https://doi.org/10.4172/2168-9768.1000106
Cheng G et al (2014) Integrated study of the water–ecosystem–economy in the Heihe River Basin. Natl Sci Rev 1:413–428
Chiew FHS, McMahon TA (2002) Modelling the impacts of climate change on Australian streamflow. Hydrol Process 16(6):1235–1245
Colette A, Vautard R, Vrac M (2012) Regional climate downscaling with prior statistical correction of the global climate forcing. Geophys Res Lett. https://doi.org/10.1029/2012GL052258
Deo RC, Kisi O, Singh VP (2017) Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmos Res 184:149–175
Draper N, Smith H (1981) Applied regression analysis. Wiley, New York, p 709
Ines AVM, Hansen JW (2006) Bias correction of daily GCM rainfall for crop simulation studies. Agric For Meteorol 138:44–53
Maraun D (2012) Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums. Geophys Res Lett 39:L06706
Mehrotra R, Sharma A (2015) Correcting for systematic biases in multiple raw GCM variables across a range of timescales. J Hydrol 520:214–223
Mehrotra R, Johnson F, Sharma A (2018) A software toolkit for correcting systematic biases in climate model simulations. Environ Model Softw 104:130–152
Montgomery DC, Peck EA, Vining GG (2012) Introduction to linear regression analysis. Wiley, London
Nash J, Sutcliffe J (1970) River flow forecasting through conceptual models part I: a discussion of principles. J Hydrol 10:282–290
Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192. https://doi.org/10.1007/s00704-009-0134-9
Salas JD (1980) Applied modeling of hydrologic time series. Water Resources Publication, Littleton
Salas JD, Tabios GQ, Bartolini P (1985) Approaches to multivariate modeling of water resources time series. J Am Water Resour Assoc 21(4):683–708
Sarhadi A, Burn DH, Johnson F, Mehrotra R, Sharma A (2016) Water resources climate change projections using supervised nonlinear and multivariate soft computing techniques. J Hydrol 536:119–132
Srikanthan R, Pegram GGS (2009) A nested multisite daily rainfall stochastic generation model. J Hydrol 371(1–4):142–153
Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO (2004) Guidelines for use of climate scenarios developed from statistical downscaling methods. In: IPCC task group on scenarios for climate and impact assessement, Geneva, Switzerland
Yang L et al (2017a) Separation of the climatic and land cover impacts on the flow regime changes in two watersheds of northeastern Tibetan Plateau. Adv Meteorol 2017:15
Yang L et al (2017b) Identifying separate impacts of climate and land use/cover change on hydrological processes in upper stream of Heihe River, northwest China. Hydrol Process 31:1100–1112
Acknowledgement
This study was supported by the national social sciences foundation: water resources assessment and management system research based on water account in the typical desert oasis of the Silk Road economic belt (17CGL032), Gansu Provincial key research and development foundation: water resource assessment and model demonstration in the typical desert oasis of the Silk Road economic belt (17YF1FA134). The authors also would like to thank the editors and anonymous reviewers for their detailed and constructive comments, which helped to significantly improve the manuscript. The reanalysis data is obtained from the National Center for Environmental Prediction (NCEP) reanalysis provided by the NOAA-CIRES Climate Diagnostics Centre, Boulder, Colorado, USA, from their web site at http://www.cdc.noaa.gov/.
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Handling Editor: Pierre Dutilleul.
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Zhu, Q., Zhao, W. Correcting climate model simulations in Heihe River using the multivariate bias correction package. Environ Ecol Stat 25, 387–403 (2018). https://doi.org/10.1007/s10651-018-0410-x
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DOI: https://doi.org/10.1007/s10651-018-0410-x