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首页> 外文期刊>Journal of hydro-environment research >Data- and model-driven determination of flow pathways in the Piako catchment, New Zealand
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Data- and model-driven determination of flow pathways in the Piako catchment, New Zealand

机译：新西兰Piako集水区流动路径的数据和模型驱动的确定

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Quantifying flow pathways within a larger catchment can help improve diffuse pollution management strategies across subcatchments. But, spatial quantification of flow pathway contributions to catchment stream flow is very limited, since it is challenging to physically separate water from different paths and very expensive to measure, especially for larger areas. To overcome this problem, a novel, combined data and modelling approach was employed to partition stream flow in the Piako catchment, New Zealand, which is a predominantly agricultural catchment with medium to high groundwater recharge potential. The approach comprised a digital filtering technique to separate baseflow from total stream flow, machine learning to predict a baseflow index (BFI) for all streams with Strahler 1st order and higher, and hydrological modelling to partition the flow into five flow components: surface runoff, interflow, tile drainage, shallow groundwater, and deep groundwater. The baseflow index scores corroborated the spatial distributions of the flow pathways modelled in 1st order catchments. Average depth to groundwater data matched well with BFI and Hydrological Predictions for the Environment (HYPE) modeled flow pathway partitioning results, with deeper water tables in areas of the catchment predicted to have greater baseflow or shallow and deep groundwater contributions to stream flow. Since direct quantification of flow pathways at catchment-scale is scarce, it is recommended to use soft data and expert knowledge to inform model parameterization and to constrain the model results. The approach developed here is applicable as a screening method in ungauged catchments.
机译：在较大集水区内的量化流动途径可以帮助改善跨子割径的漫反射管理策略。但是，流动通路的空间量化对集水流流程的贡献非常有限，因为它对从不同的路径分离的水有挑战，并且非常昂贵地测量，特别是对于较大的区域。为了克服这个问题，采用了一种新颖的，组合的数据和建模方法来分配新西兰Piako集水区的流流，这是一种与中型到高地下水充电潜力的主要农业集水区。该方法包括数字滤波技术，将Basfflow与总流流程分开，机器学习预测与Strahler第1顺序和更高的所有流的基础流指数（BFI），以及水文建模以将流量分成五个流量组件：表面径流，交织，瓷砖排水，浅地下水和深层地下水。基流指数评分证实了在第1阶集中区中建模的流动通路的空间分布。地下水数据的平均深度与BFI和环境的水文预测匹配良好，对环境建模的流动途径分区结果，在集水区的区域内具有更深的水表，预测具有更大的基流或浅层和深层地下水的流程。由于集水量表中的流动通路的直接量化是稀缺的，因此建议使用软数据和专家知识来告知模型参数化并限制模型结果。这里开发的方法适用于未凝固的集水区中的筛选方法。

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