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Possible Impacts of Climate Change on Daily Streamflow and Extremes at Local Scale in Ontario, Canada. Part II: Future Projection

机译:在加拿大安大略省,气候变化可能对当地规模的日流量和极端值产生影响。第二部分:未来的预测

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The paper forms the second part of an introduction to possible impacts of climate change on daily streamflow and extremes in the Province of Ontario, Canada. Daily streamflow simulation models developed in the companion paper (Part I) were used to project changes in frequency of future daily streamflow events. To achieve this goal, future climate information (including rainfall) at a local scale is needed. A regression-based downscaling method was employed to downscale eight global climate model (GCM) simulations (scenarios A2 and B1) to selected weather stations for various meteorological variables (except rainfall). Future daily rainfall quantities were projected using daily rainfall simulation models with downscaled future climate information. Following these projections, future daily streamflow volumes can be projected by applying daily streamflow simulation models. The frequency of future daily high-streamflow events in the warm season (May–November) was projected to increase by about 45%-55% late this century from the current condition, on average of eight-GCM A2 projections and four selected river basins. The corresponding increases for future daily low-streamflow events and future daily mean streamflow volume could be about 25%-90% and 10%-20%, respectively. In addition, the return values of annual one-day maximum streamflow volume for various return periods were projected to increase by 20%-40%, 20%-50%, and 30%-80%, respectively for the periods 2001-50, 2026-75, and 2051-2100. Inter-GCM and interscenario uncertainties of future streamflow projections were quantitatively assessed. On average, the projected percentage increases in frequency of future daily high-streamflow events are about 1.4-2.2 times greater than inter-GCM and interscenario uncertainties.
机译:本文构成了第二部分,介绍了气候变化可能对加拿大安大略省每日流量和极端天气的影响。随行论文(第一部分)开发的每日流量模拟模型用于预测未来每日流量事件发生频率的变化。为了实现这一目标,需要将来在当地范围内获得气候信息(包括降雨)。使用基于回归的缩减方法将八种全球气候模型(GCM)模拟(场景A2和B1)缩减为针对各种气象变量(降雨除外)的选定气象站。使用每日降雨模拟模型和缩小的未来气候信息来预测未来的每日降雨量。按照这些预测,可以通过应用每日流量模拟模型来预测将来的每日流量。预计到本世纪末,温暖季节(5月至11月)未来每天的高流量事件的发生频率将比当前状况在本世纪末增加约45%-55%,平均为8个GCM A2预测和四个选定的流域。未来每日低流量事件和未来每日平均流量的相应增加分别约为25%-90%和10%-20%。此外,在2001-50年期间,各个回归期的年度一日最大流量流量的回归值预计分别增加20%-40%,20%-50%和30%-80%, 2026-75和2051-2100。 GCM间和情景间不确定性的未来流量预测进行了定量评估。平均而言,未来日高流量事件发生频率的预计百分比增长约为GCM间和场景间不确定性的1.4-2.2倍。

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