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An application-oriented protocol for flood frequency analysis based on botanical evidence

机译:基于植物学证据的洪水频率分析应用方案

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Flood risk assessment and management typically rely on flood frequency analysis (FFA), such that planning and countermeasures can be designed based on the discharge that has to be expected at a given location for a given return period. In mountain streams, systematic flow time series are often very short or completely missing, which significantly reduces the reliability of FFA. In fast-flowing mountain streams, the inclusion of non-systematic data obtained from botanical evidence (BE) is seen as an optimal alternative to extend systematic data back in time. However, no comprehensive protocol has been defined so far to tackle FFA using BE. On the basis of recent case studies, we present here an application-oriented protocol with guidelines on how to combine systematic and non-systematic data in FFA containing BE. This study is based on work realized in different mountain streams located in Spain, Poland and India, representing quite diverse physiographic characteristics and differing hydrological regimes. We organize the protocol along the different steps that are typically realized in BE-based FFA: i) dating of floods from BE; ii) estimation of flood flows from paleostage indicators (PSI) and hydrodynamic modelling; as well as iii) FFA using the expected moments algorithm (EMA). The ubiquity of trees growing along (mountain) streams, their longevity and the often large number of flood-affected trees makes them an almost unbeatable data source that can be employed readily and with reasonable efforts to improve the reliability of FFA, especially in data-scarce regions. In addition, the EMA represents a highly efficient tool for the collection of information contained in BE as it can be used with interval, censored and binomial-censored data and on any distributional family that can be operated with the method of moments. Accordingly, we call for more work incorporating BE into FFA in mountain streams, such that flood hazard and risk assessment can be undertaken more r
机译:洪水风险评估和管理通常依赖于洪水频率分析(FFA),因此可以根据给定重现期在给定位置的预期流量来设计规划和对策。在山间溪流中,系统流量时间序列通常很短或完全缺失,这大大降低了FFA的可靠性。在湍急的山间溪流中,纳入从植物学证据(BE)获得的非系统数据被视为将系统数据追溯到过去的最佳选择。然而,到目前为止,还没有定义全面的协议来解决使用 BE 的 FFA。在最近的案例研究的基础上,我们在这里提出了一个面向应用的协议,其中包含有关如何在含有BE的FFA中组合系统和非系统数据的指南。这项研究基于在西班牙、波兰和印度的不同山涧中完成的工作,这些溪流代表了相当不同的地貌特征和不同的水文状况。我们按照通常在基于 BE 的 FFA 中实现的不同步骤来组织协议:i) 来自 BE 的洪水测年;ii)根据古阶段指标(PSI)和水动力模型估算洪水流量;以及 iii) 使用预期矩算法 (EMA) 的 FFA。沿着(山)溪流生长的树木无处不在,它们的寿命长,而且经常有大量受洪水影响的树木,使它们成为几乎无与伦比的数据源,可以很容易地使用,并做出合理的努力来提高FFA的可靠性,特别是在数据稀缺的地区。此外,EMA 代表了一种高效的工具,用于收集 BE 中包含的信息,因为它可以与区间、删失和二项式删失数据一起使用,也可以用于任何可以使用矩量方法操作的分布族。因此,我们呼吁开展更多工作,将BE纳入山区溪流的FFA,以便可以更多地进行洪水灾害和风险评估。

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