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首页> 外文期刊>Hydrology and Earth System Sciences Discussions >Catchment classification: hydrological analysis of catchment behavior through process-based modeling along a climate gradient
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Catchment classification: hydrological analysis of catchment behavior through process-based modeling along a climate gradient

机译:集水区分类:通过沿着气候梯度的基于过程的模型进行流域行为的水文分析

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摘要

Catchment classification is an efficient method to synthesize our understanding of how climate variability and catchment characteristics interact to define hydrological response. One way to accomplish catchment classification is to empirically relate climate and catchment characteristics to hydrologic behavior and to quantify the skill of predicting hydrologic response based on the combination of climate and catchment characteristics. Here we present results using an alternative approach that uses our current level of hydrological understanding, expressed in the form of a process-based model, to interrogate how climate and catchment characteristics interact to produce observed hydrologic response. The model uses topographic, geomorphologic, soil and vegetation information at the catchment scale and conditions parameter values using readily available data on precipitation, temperature and streamflow. It is applicable to a wide range of catchments in different climate settings. We have developed a step-by-step procedure to analyze the observed hydrologic response and to assign parameter values related to specific components of the model. We applied this procedure to 12 catchments across a climate gradient east of the Rocky Mountains, USA. We show that the model is capable of reproducing the observed hydrologic behavior measured through hydrologic signatures chosen at different temporal scales. Next, we analyze the dominant time scales of catchment response and their dimensionless ratios with respect to climate and observable landscape features in an attempt to explain hydrologic partitioning. We find that only a limited number of model parameters can be related to observable landscape features. However, several climate-model time scales, and the associated dimensionless numbers, show scaling relationships with respect to the investigated hydrological signatures (runoff coefficient, baseflow index, and slope of the flow duration curve). Moreover, some dimensionless numbers vary systematically across the climate gradient, possibly as a result of systematic co-variation of climate, vegetation and soil related time scales. If such co-variation can be shown to be robust across many catchments along different climate gradients, it opens perspective for model parameterization in ungauged catchments as well as prediction of hydrologic response in a rapidly changing environment.
机译:集水区分类是一种有效的方法,可以合成我们对气候变异性和集水区特征如何相互作用以定义水文反应的理解。实现集水分类的一种方法是经验与水文行为统一地将气候和集水特性相关,并量化基于气候和集水特性的组合预测水文响应的技能。在这里,我们使用使用我们目前水平的水平理解的替代方法来显示结果,以基于过程的模型的形式表达,以询问气候和集水特性如何相互作用以产生观察到的水文反应。该模型在集水区标度和条件参数值下使用地形,地貌,土壤和植被信息,使用易于获得的降水,温度和流流量。它适用于不同气候环境中的各种集水区。我们开发了一个逐步的过程来分析观察到的水文响应,并分配与模型的特定组件相关的参数值。我们将此程序应用于美国落矶山脉的气候梯度12集水区。我们表明该模型能够再现通过在不同时间尺度所选择的水文签名测量的观察到的水文行为。接下来,我们分析了关于气候和可观察景观特征的集水区响应的主导时间尺度及其无量纲比例,以试图解释水文分区。我们发现只有有限数量的模型参数与可观察的景观功能有关。然而,几个气候模型时间尺度和相关的无量纲数,显示了相对于研究的水文签名(径流系数,基流指数和流量持续时间曲线的斜率)的缩放关系。此外,一些无量纲数在气候梯度方面系统地变化,可能是由于气候,植被和土壤相关时间尺度的系统性共变化。如果可以在不同的气候梯度横跨许多集水区中显示这种共变化,它可以在未凝固的流域中的模型参数化以及在快速变化的环境中预测水文响应的预测来打开透视。

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