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A new time-space accounting scheme to predict stream water residence time and hydrograph source components at the watershed scale

机译:一种新的时空核算方案,可预测流域尺度上的溪流水滞留时间和水位图要素

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摘要

Hydrograph source components and stream water residence time are fundamental behavioral descriptors of watersheds but, as yet, are poorly represented in most rainfall-runoff models. We present a new time-space accounting scheme (T-SAS) to simulate the pre-event and event water fractions, mean residence time, and spatial source of streamflow at the watershed scale. We use a physically based hydrologic model together with field data from the well-studied Maimai M8 watershed and HJ Andrews WS10 watershed to explore how catchment properties, particularly soil depth, controls the age and source of streamflow. Our model simulates unsaturated, saturated subsurface, and surface rainfall-runoff processes. We first demonstrate the ability of the model to capture hydrograph dynamics and compare the model flow component and age simulations against measured values at the two sites. We show that the T-SAS approach can capture flow and transport dynamics for the right dominant process reasons. We then conduct a series of virtual experiments by switching soil depths between the two watersheds to understand how soil depth and its distribution control water age and source. Results suggest that thicker soils increase mean residence time and damp its temporal dynamics in response to rainfall inputs. Soil depth influenced the geographic source of streamflow, whereas pre-event water sources became more concentrated to near stream zones as soil depth increased. Our T-SAS approach provides a learning tool for linking the dynamics of residence time and time-space sources of flow at the watershed scale and may be a useful framework for other distributed rainfall-runoff models.
机译:水位图源分量和溪流水停留时间是流域的基本行为描述,但到目前为止,在大多数降雨径流模型中,水位图的成分都很少。我们提出了一种新的时空核算方案(T-SAS),以模拟流域尺度上的事件前和事件水分量,平均停留时间以及水流的空间来源。我们使用基于物理的水文模型,以及来自经过深入研究的Maimai M8流域和HJ Andrews WS10流域的现场数据,来探索集水性质(尤其是土壤深度)如何控制河流的年龄和水流来源。我们的模型模拟了非饱和,饱和地下和地表降雨-径流过程。我们首先展示了该模型捕获水文动力学的能力,并将模型流量分量和年龄模拟与两个站点的测量值进行比较。我们表明,T-SAS方法可以出于正确的主要流程原因捕获流量和运输动态。然后,我们通过在两个流域之间切换土壤深度来进行一系列虚拟实验,以了解土壤深度及其分布如何控制水龄和水源。结果表明,较厚的土壤会增加平均停留时间,并响应降雨输入而减弱其时间动态。土壤深度影响着河流的地理来源,而随着土壤深度的增加,事前水源变得更加集中到近河带。我们的T-SAS方法提供了一个学习工具,用于将流域尺度上的停留时间动态和时空流源联系起来,并且可能是其他分布式降雨-径流模型的有用框架。

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  • 来源
    《Water resources research》 |2009年第7期|7-20|共14页
  • 作者单位

    Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, Oregon, USA Disaster Prevention Research Institute, Kyoto University, Gokasho Uji, Kyoto, Japan;

    Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, Oregon, USA;

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