A new time‐space accounting scheme to predict stream water residence time and hydrograph source components at the watershed scale
A new time‐space accounting scheme to predict stream water residence time and hydrograph source components at the watershed scale
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一种新的时空核算方案，用于预测流域尺度的河流水停留时间和水文源成分
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
J. McDonnell
中科院分区：
文献类型：
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作者：
T. Sayama;J. McDonnell
过程线源分量和溪流水停留时间是流域的基本行为描述符，但到目前为止，在大多数降雨径流模型中表现不佳。我们提出了一个新的时空核算方案（T-SAS），以模拟事件前和事件的水分数，平均停留时间，并在流域尺度上的径流空间来源。我们使用一个基于物理的水文模型，结合来自经过充分研究的Maimai M8流域和HJ Andrews WS 10流域的实地数据，探讨集水特性，特别是土壤深度，如何控制径流的年龄和来源。我们的模型模拟了非饱和、饱和地下和地表降雨径流过程。我们首先证明了该模型的能力，捕捉水文动态和比较模型的流量组件和年龄模拟对测量值在两个网站。我们表明，T-SAS方法可以捕捉流和输运动力学的正确的主导过程的原因。然后，我们通过切换两个流域之间的土壤深度进行了一系列虚拟实验，以了解土壤深度及其分布如何控制水的年龄和来源。结果表明，较厚的土壤增加平均停留时间和阻尼的时间动态响应降雨输入。土壤深度影响了径流的地理来源，而随着土壤深度的增加，事件前的水源变得更加集中在附近的河流区域。我们的T-SAS方法提供了一个学习工具，用于在流域尺度上连接停留时间和时空流源的动态，并可能成为其他分布式降雨径流模型的有用框架。
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.