ABSTRACT

Spatial cross-correlation among flood sequences impacts the accuracy of regional predictors. Our study investigates this impact for two regionalization procedures, generalized least squares (GLS) regression and top-kriging (TK), which deal with cross-correlation in two fundamentally different ways and therefore might be associated with different accuracy and uncertainty of predicted flood quantiles. We perform a Monte Carlo experiment based on a dataset of annual maximum flood series for 20 catchments in a hydrologically homogeneous region. Based on a log-Pearson type III parent distribution, we generate 3000 realizations of the region with different degrees of cross-correlation. For each realization, GLS and TK are applied in leave-one-out cross-validation to predict at-site flood quantiles. Our study shows that (a) TK outperforms GLS when catchment area is the only catchment descriptor used for predicting “true” population (theoretical) flood quantiles, regardless of the level of cross-correlation, and (b) GLS and TK perform similarly when multiple catchment descriptors are used.

摘要

洪水序列之间的空间互相关影响区域预报器的准确性。我们的研究调查了这两种区域化程序的影响，即广义最小二乘（GLS）回归和top-kriging（TK），它们以两种根本不同的方式处理互相关，因此可能与预测洪水分位数的准确性和不确定性有关。我们基于水文均质地区20个集水区的年度最大洪水序列数据集进行了蒙特卡洛实验。基于对数-皮尔逊III型父分布，我们生成了具有不同互相关度的区域的3000个实现。对于每一种实现，GLS和TK都被应用在留一法交叉验证中，以预测现场洪水量。