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首页> 外文期刊>Hydrological Processes >Testing a spatially distributed tracer‐aided runoff model in a snow ‐influenced catchment: Effects of multicriteria calibration on streamwater ages
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Testing a spatially distributed tracer‐aided runoff model in a snow ‐influenced catchment: Effects of multicriteria calibration on streamwater ages

机译：在受雪影响的流域中测试空间分布的示踪剂辅助径流模型：多标准校准对溪流年龄的影响

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Integrating stable isotope tracers into rainfall-runoff models allows investigation of water partitioning and direct estimation of travel times and water ages. Tracer data have valuable information content that can be used to constrain models and, in integration with hydrometric observations, test the conceptualization of catchment processes in model structure and parameterization. There is great potential in using tracer-aided modelling in snow-influenced catchments to improve understanding of these catchments' dynamics and sensitivity to environmental change. We used the spatially distributed tracer-aided rainfall-runoff (STARR) model to simulate the interactions between water storage, flux, and isotope dynamics in a snow-influenced, long-term monitored catchment in Ontario, Canada. Multiple realizations of the model were achieved using a combination of single and multiple objectives as calibration targets. Although good simulations of hydrometric targets such as discharge and snow water equivalent could be achieved by local calibration alone, adequate capture of the stream isotope dynamics was predicated on the inclusion of isotope data in the calibration. Parameter sensitivity was highest, and most local, for single calibration targets. With multiple calibration targets, key sensitive parameters were still identifiable in snow and runoff generation routines. Water ages derived from flux tracking subroutines in the model indicated a catchment where runoff is dominated by younger waters, particularly during spring snowmelt. However, resulting water ages were most sensitive to the partitioning of runoff sources from soil and groundwater sources, which was most realistically achieved when isotopes were included in the calibration. Given the paucity of studies where hydrological models explicitly incorporate tracers in snow-influenced regions, this study using STARR is an important contribution to satisfactorily simulating snowpack dynamics and runoff generation processes, while simultaneously capturing stable isotope variability in snow-influenced catchments.
机译：将稳定的同位素示踪剂整合到降雨径流模型中，可以研究水分配并直接估算旅行时间和水龄。示踪剂数据具有有价值的信息内容，可用于约束模型，并与水文观测结合，测试模型结构和参数化过程中集水过程的概念化。在受雪影响的流域中使用示踪剂辅助建模有很大的潜力，以增进对这些流域动态和对环境变化敏感性的了解。我们使用空间分布的示踪剂辅助降雨径流（STARR）模型来模拟加拿大安大略省受雪影响的长期监测集水区中储水，通量和同位素动力学之间的相互作用。使用单个和多个目标作为校准目标，可以实现模型的多个实现。尽管仅通过局部校准就可以很好地模拟水文指标，例如排放量和雪水当量，但是在校准中包括同位素数据的基础上，可以充分捕获流态同位素动力学。对于单个校准目标，参数灵敏度是最高的，并且大多数是局部的。通过多个校准目标，关键敏感参数仍可在积雪和径流生成程序中识别。该模型中的通量跟踪子程序推导出的水龄表明流域的径流主要由较年轻的水域主导，特别是在春季融雪期间。但是，最终的水龄对径流源与土壤和地下水源的分配最敏感，而当将同位素包括在标定中时，这是最现实的。鉴于缺乏水文模型明确将示踪剂纳入受雪影响地区的研究，因此使用STARR进行的这项研究对于令人满意地模拟积雪动力学和径流产生过程，同时捕获受雪影响流域的稳定同位素变异性做出了重要贡献。

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