Detecting Flood‐Rich and Flood‐Poor Periods in Annual Peak Discharges Across Europe

Abstract

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‐, 5‐, 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 2,201 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.

Causes, impacts and patterns of disastrous river floods

“…Monsoon characteristics 54 and cyclone types 55 also tend to vary over decades. Such climate−flood links lead to flood-rich and flood-poor periods [56][57][58][59][60] , which have been related to periods with above or below average flood damage [61][62][63] . For flood risk management, such variations can lead to predictability of regional floods as much as a season ahead 64,65 .…”

Understanding Heavy Tails of Flood Peak Distributions

“…Other sources of misestimation of the upper tail behavior are temporal changes in time series of flood peaks or flood-related variables. In such cases, the upper tail behavior of the flood peak distribution can vary in time, as shown for flood-rich and flood-poor periods by Lun et al (2020). When dealing with non-stationary time series, the aggregation of non-identically distributed random variables may lead to a misclassification of the underlying model and to a falsely estimated tail behavior.…”

Challenges in modeling and predicting floods and droughts: A review

“…Many studies have assessed short term (e.g., 30‐year) trends in floods (e.g., Archfield, Hirsch, Viglione, & Blöschl, 2016; Bertola et al, 2020; Delgado, Apel, & Merz, 2010; Hodgkins et al, 2017; Mangini et al, 2018; Nka, Oudin, Karambiri, Paturel, & Ribstein, 2015; Slater & Villarini, 2016) and droughts across river basins, countries, and entire continents (e.g., Ge et al, 2016; Nikbakht, Tabari, & Talaee, 2013; Stahl, Tallaksen, Hannaford, & Van Lanen, 2012). However, detection of a short‐term trend does not necessarily mean there is a long‐term trend in a hydrological record, as changes in “flood‐rich”/“drought‐rich” and “flood‐poor”/“drought‐poor“ periods often occur on time scales that are much longer then the length of the historical record (Liu & Zhang, 2017; Lun, Fischer, Viglione, & Blöschl, 2020; Mediero et al, 2015; Merz, Nguyen, & Vorogushyn, 2016). However, even in the absence of long records, signals of change may be detected using areal models that pool information across catchments (Prosdocimi et al, 2019).…”