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Including effects of watershed heterogeneity in the curve number method using variable initial abstraction

机译:在使用可变初始抽象的曲线数方法中包括分水岭异质性的影响

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The curve number?(CN) method was developed more than half a century ago and is still used in many watershed and water-quality models to estimate direct runoff from a rainfall event. Despite its popularity, the method is plagued by a conceptual problem where CN is assumed to be constant for a given set of watershed conditions, but many field observations show that CN decreases with event rainfall?(P). Recent studies indicate that heterogeneity within the watershed is the cause of this behavior, but the governing mechanism remains poorly understood. This study shows that heterogeneity in initial abstraction, Isuba/sub, can be used to explain how CN varies with P. By conventional definition, Isuba/sub is equal to the cumulative rainfall before the onset of runoff and is assumed to be constant for a given set of watershed conditions. Our analysis shows that the total storage in Isuba/sub (IsubaT/sub) is constant, but the effective Isuba/sub varies with P, and is equal to the filled portion ofIsubaT/sub, which we call IsubaF/sub. CN calculated using IsubaF/sub varies with P similar to published field observations. This motivated modifications to the CN method, called variable Isuba/sub models?(VIMs), which replace Isuba/sub with IsubaF/sub. VIMs were evaluated against conventional models CM0.2 (λ = 0.2) and CMλ (calibrated?λ) in their ability to predict runoff data generated using a distributed parameter CN model. The performance of CM0.2 was the poorest, whereas those of the VIMs were the best in predicting overall runoff and watershed heterogeneity. VIMs also predicted the runoff from smaller events better than the CMs and eliminated the false prediction of zero-runoffs, which is a common shortcoming of the CMs. We conclude that including variable Isuba/sub accounts for heterogeneity and improves the performance of the CN method while retaining its simplicity.
机译:曲线数法(CN)是半个多世纪前开发的,至今仍在许多流域和水质模型中用于估计降雨事件的直接径流。尽管该方法很受欢迎,但仍存在一个概念问题,其中假设CN在给定的集水区条件下是恒定的,但是许多现场观察表明,CN随着事件降雨而减少(P)。最近的研究表明,分水岭内的异质性是造成这种现象的原因,但其控制机制仍知之甚少。这项研究表明,初始抽象I a 中的异质性可以用来解释CN随着P的变化。按照常规定义,I a 等于之前的累积降雨在给定的分水岭条件下,径流的开始是恒定的。我们的分析表明,I a (I aT )中的总存储量是恒定的,但是有效I a 随P的变化而变化,并且等于到I aT 的填充部分,我们称为I aF 。使用I aF 计算的CN随P的变化而变化,这与已发布的现场观测结果相似。这促使对CN方法进行了修改,称为变量I a 模型?(VIM),该模型将I a 替换为I aF 。根据常规模型CM0.2(λ= 0.2)和CMλ(校准Δλ)对VIM进行预测,以预测使用分布式参数CN模型生成的径流数据的能力。在预测总体径流和流域异质性方面,CM0.2的性能最差,而VIM的性能最佳。 VIM还比CM更好地预测了较小事件的径流,并消除了零径流的错误预测,这是CM的普遍缺点。我们得出的结论是,包括变量I a 可以解决异构问题,并改善了CN方法的性能,同时保留了其简单性。

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