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Modeling the influence of riparian soil air carbon dioxide concentrations on stream water alkalinity.

机译:模拟河岸土壤空气中二氧化碳浓度对溪流水碱度的影响。

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Stream water chemical composition is tightly coupled to the concentration of CO2 in soil air. However, little is known about the inorganic C cycle in soils. I describe a series of simple physically-based models that simulate soil temperature, soil tension, soil CO2 processes, and soil chemistry. I apply these models to simulate the spatial and temporal dynamics of soil CO2 concentrations throughout a small catchment in the Virginia Blue Ridge. When output from the simulations is compared with field measurements, I find that despite some model deficiencies, I am able to reasonably simulate the gross overall patterns through space and time of soil air CO2 concentration. During the growing season when soil temperature is high, I find that soil water status is the limiting control on soil respiration and CO2 concentration. I also find that soil CO2 concentration can be high despite low respiration values because of changing diffusivity of the soils with moisture content.; The ability to predict stream alkalinity values over scales shorter than monthly or annually is needed to understand the response of stream chemistry to acidic inputs which occur across short time scales (days). I apply the models described above to a nine year record of discharge and stream chemistry from a small catchment in the Shenandoah National park of Virginia. I find that I am able to reasonably predict the minimum stream alkalinity values for all years and I am able to predict the entire annual cycle for six of the nine years. The three years for which I overpredict summer stream alkalinity had summer precipitation which was much greater than normal and greater than the period for which the model was calibrated.; Models of soil and stream water and catchment acidification have typically been applied without consideration of climate change. Soil air CO2 concentrations have potential to increase as climate warms and becomes wetter. I simulate this increase by applying the coupled models described above to predicting daily stream water alkalinity values for SFBR for 60 years into the future given stochastically generated daily climate values. This is done for nine different scenarios of climate change and atmospheric deposition change. I find that stream water alkalinity continues to decline for all scenarios except when climate is gradually warming and becoming more moist. In all other scenarios, base cation removal from catchment soils is responsible for limited alkalinity change resulting from climate change. This has strong implications given the extent that models such as MAGIC are used to establish policy and legislation concerning deposition and emissions.
机译:溪水的化学成分与土壤空气中的CO2浓度紧密相关。但是,对于土壤中的无机碳循环知之甚少。我描述了一系列简单的基于物理的模型,这些模型可以模拟土壤温度,土壤张力,土壤二氧化碳过程和土壤化学性质。我应用这些模型来模拟弗吉尼亚蓝岭小流域中土壤CO2浓度的时空动态。将模拟的输出与现场测量结果进行比较后,我发现尽管模型存在一些缺陷,但我仍能够通过空间和时间来模拟土壤空气中CO2浓度的总体模式。在土壤温度高的生长期,我发现土壤水分状况是对土壤呼吸和CO2浓度的限制控制。我还发现尽管呼吸值低,但土壤中的CO2浓度仍可能很高,这是因为随着水分含量的变化,土壤的扩散性也有所变化。需要了解在短于每月或每年的尺度上预测溪流碱度值的能力,以了解溪流化学物质对酸性输入的响应。我将上述模型应用于弗吉尼亚州谢南多厄国家公园一个小流域的九年排放和水流化学记录。我发现我能够合理地预测所有年份的最低溪流碱度值,并且能够预测九年中的六年的整个年循环。我高估了夏季水流碱度的三年中,夏季降水量比正常年份大得多,并且比模型校准的时期要大。通常在不考虑气候变化的情况下采用土壤和溪流水以及流域酸化的模型。随着气候变暖和变湿,土壤空气中的二氧化碳浓度可能会增加。我通过应用上述耦合模型预测SFBR在未来60年内的每日溪流水碱度值(根据随机产生的每日气候值)来模拟这种增加。针对气候变化和大气沉积变化的九种不同情况进行了此操作。我发现,在所有情况下,溪流水的碱度持续下降,除非气候逐渐变暖并变得更加潮湿。在所有其他情况下,从集水区土壤中去除碱阳离子是由于气候变化导致有限的碱度变化的原因。考虑到使用诸如MAGIC之类的模型来建立有关沉积和排放的政策和立法的程度,这具有重大影响。

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