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Climate Change Trend Analysis on Extreme Precipitation over the Shasta Dam Watershed Based on 159-Year Long-Term Dynamic Downscaling

机译:基于159年长期动态降尺度的沙斯塔水库极端降水气候变化趋势分析。

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California's interconnected water system is one of the most advanced water management systems in the world, and understanding of long-term trends in atmospheric and hydrologic behavior has increasingly being seen as vital to its future well-being. Knowledge of such trends is hampered by the lack of long-period observation data and the uncertainty surrounding future projections of atmospheric models. This study examines historical precipitation trends over the Shasta Dam Watershed (SDW), which lies downstream of one of the most important aspects of California's water system, Shasta Dam, using a dynamic downscaling methodology that can obtain reconstructed climate data at fine time-space scales. The methodology for reconstructing historical precipitation data over SDW started with the coarse-resolution historical 2.5 degrees Twentieth Century Reanalysis Version 2c (20CRv2c) dataset. The weather research and forecasting (WRF) model was then used to produce 159 years of long-term reconstructed hourly precipitation data at a 3 km spatial resolution over SDW. Trend analysis on this data indicated an increase in total precipitation over the study period as well as a growing intensity of extreme events such as 6, 12, 24, 48, and 72-hour storms. These results both inform long-term planning decisions regarding the future of Shasta Dam and California's water system and validate a methodology that can be used in data-sparse basins around the world.
机译:加利福尼亚州的互连水系统是世界上最先进的水管理系统之一,对于大气和水文行为的长期趋势的理解越来越被视为对其未来福祉至关重要。由于缺乏长期的观测数据以及围绕大气模型未来预测的不确定性,阻碍了对这种趋势的了解。这项研究使用动态降尺度方法可以在精细的时空尺度上获得重建的气候数据,研究了位于加利福尼亚水系统最重要方面之一的沙斯塔大坝下游的沙斯塔大坝流域(SDW)的历史降水趋势。 。在SDW上重建历史降水数据的方法是从20世纪vv的2c粗分辨率历史数据集(20CRv2c)开始的。然后使用天气研究和预报(WRF)模型在SDW上以3 km的空间分辨率生成159年的长期重建每小时降水数据。对这些数据的趋势分析表明,在研究期间总降水量增加,并且诸如6、12、24、48和72小时风暴等极端事件的强度也在增加。这些结果既可为有关Shasta大坝和加利福尼亚州供水系统未来的长期规划决策提供依据,也可验证可用于全球数据稀疏流域的方法。

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