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Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis

机译:将未来的气候情景缩减为精细尺度,以进行水文和生态建模与分析

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Introduction Evaluating the environmental impacts of climate change on water resources and biological components of the landscape is an integral part of hydrologic and ecological investigations, and the resultant land and resource management in the twenty-first century. Impacts of both climate and simulated hydrologic parameters on ecological processes are relevant at scales that reflect the heterogeneity and complexity of landscapes. At present, simulations of climate change available from global climate models [GCMs] require downscaling for hydrologic or ecological applications. Methods Using statistically downscaled future climate projections developed using constructed analogues, a methodology was developed to further downscale the projections spatially using a gradient-inverse-distance-squared approach for application to hydrologic modeling at 270-m spatial resolution. Results This paper illustrates a methodology to downscale and bias-correct national GCMs to subkilometer scales that are applicable to fine-scale environmental processes. Four scenarios were chosen to bracket the range of future emissions put forth by the Intergovernmental Panel on Climate Change . Fine-scale applications of downscaled datasets of ecological and hydrologic correlations to variation in climate are illustrated. Conclusions The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the downscaling while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale downscaling to analyses of ecological processes influenced by topographic complexity.
机译:引言评估气候变化对水资源和景观生物成分的环境影响是水文和生态学调查以及由此产生的21世纪土地和资源管理不可或缺的一部分。气候和模拟水文参数对生态过程的影响在反映景观异质性和复杂性的尺度上都是相关的。目前,可从全球气候模型[GCM]获得的气候变化模拟要求缩小规模以用于水文或生态应用。方法使用通过使用构造的类似物开发的统计缩减的未来气候预测,开发了一种方法,该方法使用梯度反距离平方方法进一步在空间上缩减预测,以应用于270 m空间分辨率的水文建模。结果本文说明了一种方法,可以将国家GCM缩小比例尺并进行校正,以适应适用于精细环境过程的亚千米级。政府间气候变化专门委员会提出了四种方案,以涵盖未来排放的范围。说明了缩小尺度的生态和水文相关性数据集在气候变化中的精细应用。结论该方法包括一系列严格的分析和计算,旨在减少因规模缩小而给气候数据带来的不确定性,同时提供生态分析所需的精细气候信息。它为美国的4 km,美国的西南270 m和加利福尼亚的90 m产生了新的但一致的数据集,并说明了小比例尺缩减在分析受地形复杂性影响的生态过程中的效用。

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