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首页> 外文期刊>Journal of hydrometeorology >A Global Approach to Assess the Potential Impact of Climate Change on Stream Water Temperatures and Related In-Stream First-Order Decay Rates
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A Global Approach to Assess the Potential Impact of Climate Change on Stream Water Temperatures and Related In-Stream First-Order Decay Rates

机译:评估气候变化对溪水温度和相关溪流一阶衰减率的潜在影响的全球方法

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Stream water temperature is an important factor used in water quality modeling. To estimate monthly stream temperature on a global scale, a simple nonlinear regression model was developed. It was applied to stream temperatures recorded over a 36-yr period (1965–2001) at 1659 globally distributed gauging stations. Representative monthly air temperatures were obtained from the nearest grid cell included in the new global meteorological forcing dataset—the Water and Global Change (WATCH) Forcing Data. The regression model reproduced monthly stream temperatures with an efficiency of fit of 0.87. In addition, the regression model was applied for different climate zones (polar, snow, warm temperate arid, and equatorial climates) based on the Ko¨ppen–Geiger climate classification. For snow, warm temperate, and arid climates the efficiency of fit was larger than 0.82 including more than 1504 stations (90% of all records used). Analyses of heatstorage effects (seasonal hysteresis) did not show noticeable differences between the warming/cooling and global regression curves, respectively. The maximum difference between both limbs of the hysteresis curves was 1.68C and thus neglected in the further analysis of the study. For validation purposes time series of stream temperatures for five individual river basins were computed applying the global regression equation. The accuracy of the global regression equation could be confirmed. About 77% of the predicted values differed by 38C or less from measured stream temperatures. To examine the impact of climate change on stream water temperatures, gridded global monthly stream temperatures for the climate normal period (1961–90) were calculated as well as stream temperatures for the A2 and B1 climate change emission scenarios for the 2050s (2041–70). On average, there will be an increase of 18–48C in monthly stream temperature under the two climate scenarios. It was also found that in the months December, January, and February a noticeable warming predominantly occurs along the equatorial zone, while during the months June, July, and August large-scale or large increases can be observed in the northern and southern temperate zones. Consequently, projections of decay rates show a similar seasonal and spatial pattern as the corresponding stream temperatures. Aregional increase up to ;25% could be observed. Thus, to ensure sufficient water quality for human purposes, but also for freshwater ecosystems, sustainable management strategies are required.
机译:溪水温度是水质建模中使用的重要因素。为了在全球范围内估算月流温度,开发了一个简单的非线性回归模型。它应用于在1659个全球分布的测量站的36年(1965-2001年)中记录的河流温度。有代表性的每月气温是从新的全球气象强迫数据集中所包含的最近的网格单元获得的,即水和全球变化(WATCH)强迫数据。回归模型重现了每月溪流温度,拟合效率为0.87。此外,基于柯本–盖格气候分类,回归模型适用于不同的气候区(极地,降雪,温带温带干旱和赤道气候)。对于下雪,温暖的温带和干旱气候,拟合效率大于0.82,包括1504个站(占所有使用记录的90%)。储热效应(季节滞后)的分析未显示出变暖/降温曲线和全局回归曲线之间的显着差异。磁滞曲线的两个分支之间的最大差异为1.68°C,因此在研究的进一步分析中被忽略。为了验证目的,使用全局回归方程计算了五个单独流域的河流温度时间序列。可以确定整体回归方程的准确性。约77%的预测值与测得的物流温度相差38°C或更小。为了检查气候变化对溪流水温的影响,计算了气候正常时期(1961–90)的网格化全球月度溪流温度,以及2050年代(2041-70)A2和B1气候变化排放情景的溪流温度。 )。在两种气候情景下,平均每月河流温度平均会升高18-48C。还发现在12月,1月和2月这两个月期间,赤道带主要发生了明显的变暖,而在6月,7月和8月这两个月,北部和南部的温带地区出现了大规模或大幅度的增加。 。因此,衰减率的预测显示出与相应的河流温度相似的季节和空间格局。可以观察到高达25%的区域增长。因此,为了确保足够的水质既可用于人类目的,也可用于淡水生态系统,则需要可持续的管理策略。

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