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Quantifying the relationship between streamflow and climate change in a small basin under future scenarios

机译:在未来情景下,量化小盆地流出与气候变化之间的关系

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Previous studies have identified the importance of simulating and quantifying the relationship between hydrologic variation and climate change under historical scenarios at regional and continental scales. However, robust demonstration of the potential of combining consistent land use/cover change (LUCC) and climate change to simulate future hydrologic processes is still lacking. Furthermore, investigating the future connections between hydrologic characteristics and climate variables demands exploration of these phenomena at small (basin) scale. To fill this gap, this research simulates land use/cover patterns in 2030 based on the logistic Cellular Automata-Markov model. Then the Soil Water Assessment Tool (SWAT) simulates change in streamflow within the Ashuelot River basin in New England between 2002-2009 and 2032-2039. Projected climate data are obtained from two general circulation models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5. We also quantify relationships between the rates of change (RC) of streamflow, precipitation and potential evapotranspiration (PET) among 29 subbasins at a monthly scale between the two time periods under different climate scenarios by implementing a panel data approach. Results show greatest changes in forestland (-21.07 km(2)) and intensive urban land (+5.4 km(2)) by 2030. Comparisons between the two periods show a negative overall trend in runoff under RCPs 4.5 and 8.5 for both selected GCMs. Panel data analysis indicates that precipitation may contribute more to the RC of streamflow when change in streamflow is significantly influenced by both PET and precipitation over the study period. Therefore, this study provides an important insight into quantifying and comparing the relationship of basin-scale change between streamflow and future climate.
机译:以前的研究已经确定了在区域和大陆尺度的历史情景下模拟和量化水文变异与气候变化之间关系的重要性。然而,仍然缺乏缺乏组合一致的土地使用/覆盖变化(LUCC)和气候变化的潜力的强大演示仍然缺乏。此外,研究了水文特征与气候变量之间的未来联系要求在小(盆地)规模的情况下探索这些现象。为了填补这一差距,本研究基于Logistic Cellular Automata-Markov模型模拟2030年的土地使用/覆盖图案。然后土壤水分评估工具(SWAT)模拟了2002-2009和2032-2039之间的新英格兰的Ashuelot River盆地内的流出变化。预计的气候数据是从代表性浓度途径(RCPS)4.5和8.5的两种通用循环模型(GCMS)获得。我们还通过实施面板数据方法,在不同气候情景下的两个时间段之间的每月比例中量化流出,降水和潜在蒸发(PET)的变化,降水和潜在蒸发(PET)之间的关系。结果显示林地的最大变化(-21.07公里(2))和强化城市土地(+5.4公里(2))到2030年。两个时期之间的比较显示了RCPS 4.5和8.5下的径流下的负面整体趋势,适用于选定的GCM。 。面板数据分析表明,当通过研究时期的宠物流量的流出的变化显着影响时,降水量可能导致流流的RC。因此,本研究提供了对量化和比较流流量和未来气候之间的盆地变化关系的重要洞察。

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