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Estimation of instantaneous peak flow from maximum mean daily flow by regionalization of catchment model parameters

机译：根据流域模型参数的区域划分，从最大平均日流量估算瞬时峰值流量

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Regionalization methods have been effectively used in many hydrological studies, such as regional flood frequency analysis and low flows. However, there is no study to estimate the instantaneous peak flow (IPF) from maximum mean daily flow (MDF) using hydrological models with regionalized parameters. In this paper, the semidistributed conceptual hydrological model Hydrologiska Byrans Vattenbalansavdelning is operated on a daily time step for 18 catchments in the Aller-Leine basin, Germany. The model is calibrated on four different flow statistics, including winter/summer extremes distribution and flow duration curves. The model parameter values are predefined with the associated catchment descriptors by a transfer function. Two different regionalization schemes are investigated: one is carried out for all the catchments in the study area; the other one is only performed for several catchments within a cluster. The k-means algorithm is used to 12 different catchment characteristics from all 18 catchments as the partitional clustering algorithm. Subsequently, the general extreme value distributions are fitted to the modeled MDFs, which are then transferred into IPF quantiles using a multiple regression model. The results show that (a) the uncertainty resulted from model parameter regionalization for the estimation of IPFs is much smaller than the error when using MDFs instead of IPFs; (b) the hydrological responses of the clustered catchments located in the flat areas are, in general, not as homogeneous as the ones in high elevated regions; and (c) the model with the parameters derived from the same regionalization coefficients within a cluster performs better using the corresponding parameters estimated through all the catchments.
机译：区域化方法已有效地用于许多水文学研究中，例如区域洪水频率分析和低流量。但是，尚无研究使用具有区域参数的水文模型从最大平均日流量（MDF）估算瞬时峰值流量（IPF）。在本文中，半分布式概念水文模型Hydrologiska Byrans Vattenbalansavdelning在德国Aller-Leine盆地的18个集水区每天运行。该模型已根据四种不同的流量统计数据进行了校准，包括冬季/夏季的极端分布和流量持续时间曲线。模型参数值通过传递函数与关联的流域描述符进行了预定义。研究了两种不同的分区方案：一种是针对研究区域的所有流域进行的；另一种是针对研究区域的所有流域。另一个仅对集群中的几个集水区执行。 k-均值算法用于划分所有18个流域的12个不​​同的流域特征，作为分区聚类算法。随后，将一般极值分布拟合到建模的MDF，然后使用多元回归模型将其转换为IPF分位数。结果表明：（a）模型参数区域化对IPF估计的不确定性远小于使用MDF代替IPF时的误差； （b）位于平坦地区的集水集水区的水文响应通常不如高海拔地区的集水区均匀； （c）具有从集群中相同的区域化系数得出的参数的模型使用通过所有流域估算的相应参数表现更好。

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