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Climate change impact on streamflow in large-scale river basins: projections and their uncertainties sourced from GCMs and RCP scenarios

机译:气候变化对大型河流流域流出的影响:投影及其来自GCM和RCP场景的投影及其不确定性

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Climate change impact on river runoff was investigated within the framework of the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP2) using a physically-based land surface model Soil Water - Atmosphere - Plants (SWAP) (developed in the Institute of Water Problems of the Russian Academy of Sciences) and meteorological projections (for 2006-2099) simulated by five General Circulation Models (GCMs) (including GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M) for each of four Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Eleven large-scale river basins were used in this study. First of all, SWAP was calibrated and validated against monthly values of measured river runoff with making use of forcing data from the WATCH data set and all GCMs' projections were bias-corrected to the WATCH. Then, for each basin, 20 projections of possible changes in river runoff during the 21st century were simulated by SWAP. Analysis of the obtained hydrological projections allowed us to estimate their uncertainties resulted from application of different GCMs and RCP scenarios. On the average, the contribution of different GCMs to the uncertainty of the projected river runoff is nearly twice larger than the contribution of RCP scenarios. At the same time the contribution of GCMs slightly decreases with time.
机译:大气 - - 植物(SWAP)(在发达国家对河川径流气候变化的影响是使用基于物理的地表模型土壤水分的跨部门影响模式比较计划(ISI-MIP2)的第二阶段的框架内调查俄罗斯科学院)的水问题研究所和气象预测(对2006至2099年)由五个一般环流模式(GCM)(包括GFDL-ESM2M,HadGEM2-ES,IPSL-CM5A-LR,MIROC-ESM-CHEM模拟和NorESM1-M)对于每个的四个代表性浓度途径(RCP)的场景(RCP2.6,RCP4.5,RCP6.0和RCP8.5)。十一大型江河流域在本研究中使用。首先,SWAP进行校准,并与利用从WATCH数据集强制数据和所有环流模型突起被偏压校正到手表的验证对测量河川径流的每月值。然后,对于每个流域,20所预测的21世纪期间,河川径流可能的变化是由SWAP模拟。所获得的水文预测分析,使我们估计其不确定性源于不同的大气环流模型和RCP场景的应用。平均而言,不同气候模式对投影河流径流量的不确定性的贡献大于RCP情景贡献了近两倍。同时,大气环流的贡献略有随时间而减少。

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