资源环境科技发展态势分析平台

This paper proposes a method from Scan statistics for identifying flood‐rich and flood‐poor periods (i.e. anomalies) in flood discharge records. Exceedances of quantiles with 2‐year, 5‐year and 10‐year return periods are used to identify periods with unusually many (or few) threshold exceedances with respect to the reference condition of independent and identically distributed random variables. For the case of flood+‐rich periods, multiple window lengths are used in the identification process. The method is applied to 2201 annual flood peak series in Europe between 1960 and 2010. Results indicate evidence for the existence of flood‐rich and flood‐poor periods, as about 2 to 3 times more anomalies are detected than what would be expected by chance. The frequency of the anomalies tends to decrease with an increasing threshold return period which is consistent with previous studies, but this may be partly related to the method and the record length of about 50 years. In the Northwest of Europe, the frequency of stations with flood‐rich periods tends to increase over time and the frequency of stations with flood‐poor periods tends to decrease. In the East and South of Europe, the opposite is the case. There appears to exist a turning point around 1970 when the frequencies of anomalies start to change most clearly. This turning point occurs at the same time as a turning point of the North Atlantic Oscillation index. The method is also suitable for Peak‐over‐threshold series and can be generalized to higher dimensions, such as space and space‐time.