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Hydrologic modeling using elevationally adjusted NARR and NARCCAP regional climate-model simulations: Tucannon River, Washington

机译:使用海拔高度调整的NARR和NARCCAP区域气候模型模拟进行水文模拟:华盛顿州图坎农河

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An emerging approach to downscaling the projections from General Circulation Models (GCMs) to scales relevant for basin hydrology is to use output of GCMs to force higher-resolution Regional Climate Models (RCMs). With spatial resolution often in the tens of kilometers, however, even RCM output will likely fail to resolve local topography that may be climatically significant in high-relief basins. Here we develop and apply an approach for downscaling RCM output using local topographic lapse rates (empirically-estimated spatially and seasonally variable changes in climate variables with elevation). We calculate monthly local topographic lapse rates from the 800-m Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset, which is based on regressions of observed climate against topographic variables. We then use these lapse rates to elevationally correct two sources of regional climate-model output: (1) the North American Regional Reanalysis (NARR), a retrospective dataset produced from a regional forecasting model constrained by observations, and (2) a range of baseline climate scenarios from the North American Regional Climate Change Assessment Program (NARCCAP), which is produced by a series of RCMs driven by GCMs. By running a calibrated and validated hydrologic model, the Soil and Water Assessment Tool (SWAT), using observed station data and elevationally-adjusted NARR and NARCCAP output, we are able to estimate the sensitivity of hydrologic modeling to the source of the input climate data. Topographic correction of regional climate-model data is a promising method for modeling the hydrology of mountainous basins for which noweather station datasets are available or for simulating hydrology under past or future climates.
机译:一种将通用环流模型(GCM)的预测缩减到与流域水文学相关的比例的新兴方法是使用GCM的输出来强制采用更高分辨率的区域气候模型(RCM)。但是,由于空间分辨率通常在几十公里之内,即使RCM输出也可能无法解析局部地形,而该局部地形在高浮雕盆地中可能具有重要的气候意义。在这里,我们开发并应用了一种方法,该方法使用局部地形失误率(根据气候变化随海拔高度变化的经验估计的空间和季节变量变化)来缩小RCM输出的规模。我们根据独立坡度模型(PRISM)数据集的800米参数高程回归来计算每月局部地形消失的速率,该数据基于观测到的气候对地形变量的回归。然后,我们使用这些失误率高度校正区域气候模型输出的两个来源:(1)北美区域再分析(NARR),这是由受观测约束的区域预测模型产生的回顾性数据集,以及(2)一系列来自北美区域气候变化评估计划(NARCCAP)的基准气候情景,该计划由一系列由GCM驱动的RCM产生。通过运行经过校准和验证的水文模型,即土壤和水评估工具(SWAT),使用观测站数据以及经过高度调整的NARR和NARCCAP输出,我们能够估算出水文模型对输入气候数据来源的敏感性。区域气候模型数据的地形校正是一种有前途的方法,可用于对没有noweather station数据集的山区流域进行水文建模或模拟过去或未来气候下的水文学。

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