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首页> 外文期刊>Hydrology and Earth System Sciences >Modelling the effects of climate on long-term patterns of dissolved organic carbon concentrations in the surface waters of a boreal catchment
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Modelling the effects of climate on long-term patterns of dissolved organic carbon concentrations in the surface waters of a boreal catchment

机译：模拟气候对北方流域表层水中溶解有机碳浓度的长期变化的影响

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Dissolved organic carbon concentrations ([DOC]) in surface waters are increasing in many regions of Europe and North America. These increases are likely driven by a combination of changing climate, recovery from acidification and change in severity of winter storms in coastal areas. INCA-C, a process-based model of climate effects on surface water [DOC], was used to explore the mechanisms by which changing climate controls seasonal to inter-annual patterns of [DOC] in the lake and outflow stream of a small Finnish catchment between 1990 and 2003. Both production in the catchment and mineralization in the lake controlled [DOC] in the lake. Concentrations in the catchment outflow were controlled by rates of DOC production in the surrounding organic soils. The INCA-C simulation results were compared to those obtained using artificial neural networks (ANN). In general, "black box" ANN models provide better fits to observed data but process-based models can identify the mechanism responsible for the observed pattern. A statistically significant increase was observed in both INCA-C modelled and measured annual average [DOC] in the lake. This suggests that some of the observed increase in surface water [DOC] is caused by climate-related processes operating in the lake and catchment. However, a full understanding of surface water [DOC] dynamics can only come from catchment-scale process-based models linking the effects of changing climate and deposition on aquatic and terrestrial environments.
机译：在欧洲和北美的许多地区，地表水中的溶解有机碳浓度（[DOC]）不断增加。这些增加可能是由于气候变化，酸化恢复以及沿海地区冬季风暴严重性的综合影响。 INCA-C是一种基于过程的地表水[DOC]气候影响模型，用于探索将气候控制从季节性变化到年际[DOC]模式在湖泊中以及小芬兰人的流出流中改变的机制。 1990年至2003年之间的流域。该湖的流域产量和矿化度均控制了该湖中的[DOC]。流域外流的浓度由周围有机土壤中DOC的产生速率控制。将INCA-C模拟结果与使用人工神经网络（ANN）获得的结果进行比较。通常，“黑匣子” ANN模型可以更好地拟合观察到的数据，但是基于过程的模型可以识别造成观察到的模式的机制。在INCA-C模型中和在湖中测得的年平均浓度[DOC]中均观察到统计学上的显着增加。这表明观察到的地表水[DOC]的某些增加是由湖泊和流域中与气候有关的过程引起的。但是，对地表水[DOC]动态的全面理解只能来自集水规模为基础的基于过程的模型，这些模型将气候变化和沉积物对水生和陆地环境的影响联系在一起。

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