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Historical and future changes in global flood magnitude – evidence from a model–observation investigation

机译:全球洪水幅度的历史和未来变化 - 来自模型观测调查的证据

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To improve the understanding of trends in extreme flows related to flood events at the global scale, historical and future changes of annual maxima of 7 d streamflow are investigated, using a comprehensive streamflow archive and six global hydrological models. The models' capacity to characterise trends in annual maxima of 7 d streamflow at the continental and global scale is evaluated across 3666?river gauge locations over the period from?1971 to?2005, focusing on four aspects of trends: (i)?mean, (ii)?standard deviation, (iii)?percentage of locations showing significant trends and (iv)?spatial pattern. Compared to observed trends, simulated trends driven by observed climate forcing generally have a higher mean, lower spread and a similar percentage of locations showing significant trends. Models show a low to moderate capacity to simulate spatial patterns of historical trends, with approximately only from 12 % to 25 % of the spatial variance of observed trends across all gauge stations accounted for by the simulations. Interestingly, there are statistically significant differences between trends simulated by global hydrological models?(GHMs) forced with observational climate and by those forced by bias-corrected climate model output during the historical period, suggesting the important role of the stochastic natural (decadal, inter-annual) climate variability. Significant differences were found in simulated flood trends when averaged only at gauged locations compared to those averaged across all simulated grid cells, highlighting the potential for bias toward well-observed regions in our understanding of changes in floods. Future climate projections (simulated under the RCP2.6 and RCP6.0 greenhouse gas concentration scenarios) suggest a potentially high level of change in individual regions, with up to 35 % of cells showing a statistically significant trend (increase or decrease; at 10 % significance level) and greater changes indicated for the higher concentration pathway. Importantly, the observed streamflow database under-samples the percentage of locations consistently projected with increased flood hazards under the RCP6.0 greenhouse gas concentration scenario by more than an order of magnitude (0.9 % compared to 11.7 %). This finding indicates a highly uncertain future for both flood-prone communities and decision makers in the context of climate change.
机译:为了改善对全球规模洪水事件相关的极端流量趋势的理解,研究了7 D流出的年度最大值的历史和未来变化,采用了全面的流流程档案和六个全球水文模型。在大陆和全球范围内为7 d流出的年度最大值的趋势的趋势的能力在3666年的时间内进行评估,从?1971年到2005年的时间,重点关注趋势的四个方面:(i)?卑鄙,(ii)?标准偏差,(iii)?显示显着趋势和(iv)的位置的百分比。空间模式。与观察到的趋势相比,观察到气候迫使推动的模拟趋势通常具有更高的平均值,较低的差异和类似百分比的位置,显示出显着趋势。模型显示出低于中等的能力来模拟历史趋势的空间模式,只有大约只有12%到25%的观察到趋势的空间方差,跨越模拟的所有仪表。有趣的是,全球水文模型模拟的趋势之间存在统计学意义的差异?(GHM)强迫观察气候,并通过历史时期偏正纠正的气候模型产出的人,表明随机自然(Decadal,Conso)的重要作用造型)气候变异性。当与所有模拟网格细胞的平均值相比仅在测量位置进行平均时,模拟洪水趋势中发现了显着差异,突出了我们对洪水变化的理解,突出了对观察到的地区的偏见的可能性。未来的气候预测(在RCP2.6和RCP6.0温室气体集中方案下模拟)表明各个地区的潜在高水平的变化,高达35%的细胞显示出统计上显着的趋势(增加或减少; 10%显着性水平)和更高浓度途径所示的更大变化。重要的是,观察到的Stream流程数据库在RCP6.0温室气体浓度方案下持续增加了洪水危险的百分比持续预测的位置持续增加了超过一个数量级(0.9%,而11.7%)。这一发现表示,在气候变化的背景下,洪水易受洪水社区和决策者的高度不确定的未来。

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