Understanding Persistence to Avoid Underestimation of Collective Flood Risk

Abstract

Abstract:The assessment of collective risk for flood risk management requires a better understanding of the space-time characteristics of flood magnitude and occurrence. In particular, classic formulation of collective risk implies hypotheses concerning the independence of intensity and number of events over fixed time windows that are unlikely to be tenable in real-world hydroclimatic processes exhibiting persistence. In this study, we investigate the links between the serial correlation properties of 473 daily stream flow time series across the major river basins in Europe, and the characteristics of over-threshold events which are used as proxies for the estimation of collective risk. The aim is to understand if some key features of the daily stream flow data can be used to infer properties of extreme events making a more efficient and effective use of the available data. Using benchmark theoretical processes such as Hurst-Kolmogorov (HK), generalized HK (gHK), autoregressive fractionally integrated moving average (ARFIMA) models, and Fourier surrogate data preserving second order linear moments, our findings confirm and expand some results previously reported in the literature, namely: (1) the interplay between short range dependence (SRD) and long range dependence (LRD) can explain the majority of the serial dependence structure of deseasonalized data, but losing information on nonlinear dynamics; (2) the standardized return intervals between over-threshold values exhibit a sub-exponential Weibull-like distribution, implying a higher frequency of return intervals longer than expected under independence, and expected return intervals depending on the previous return intervals; this results in a tendency to observe short (long) inter-arrival times after short (long) inter-arrival times; (3) as the average intensity and the number of events over one-year time windows are not independent, years with larger events are also the more active in terms of number of events; and (4) persistence influences the distribution of the collective risk producing a spike of probability at zero, which describes the probability of years with no events, and a heavier upper tail, suggesting a probability of more extreme annual losses higher than expected under independence. These results provide new insights into the clustering of stream flow extremes, paving the way for more reliable simulation procedures of flood event sets to be used in flood risk management strategies.

Unsurprising Surprises: The Frequency of Record‐breaking and Overthreshold Hydrological Extremes Under Spatial and Temporal Dependence

“…To mitigate this effect, we did not apply monthly or seasonal stratification in the analysis of CONUS temperature and precipitation, and spatiotemporal dependence structures were estimated on the parent process X and then suitably deflated by equation to obtain those of Y . Moreover, it is known that correlation can influence the statistics of extreme values yielding, for instance, spatiotemporal clustering of RB, POT, or block maxima (e.g., Bogachev & Bunde, ; Eichner et al, ; Serinaldi & Kilsby, ) . On the other hand, it is generally difficult to retrieve the underlying correlation structures only from extreme events, which often appear to be approximately independent because of downsampling effects of data selection and consequent removal of nonextreme data providing information on correlation (e.g., Serinaldi et al, ).…”

“…This property can result in observed sequences of low and high summary statistics (e.g., block minima, block maxima, and POT values) that appear, however, approximately uncorrelated because these statistics do not provide enough information to assess the actual dependence of the underlying process. Lack of apparent dependence can lead to interpret low and high regimes as lack of stationarity, while they can be explained by the underlying dependence, which is however concealed by the sampling procedure (Serinaldi & Kilsby, ; Serinaldi et al, ). In this respect, focusing on monthly, seasonal, and annual values, as done in the literature mentioned above, and using resampling methods that preserve only approximately a fraction of the actual correlation can invalidate preliminary analysis and modeling efforts.…”

On return period and probability of failure in hydrology

“…In hydrology, but also in many other geophysical fields of application, time-dependence has been recognized to be the rule rather than the exception since a long time (see, e.g., the examples provided by Eichner, Kantelhardt, Bunde, & Havlin, 2011); this important characteristic of natural processes has stimulated many scientists to investigate the properties of return period when the independence condition is omitted (Douglas et al, 2002;Fernández & Salas, 1999b;Leadbetter, 1983;Lloyd, 1970;Rosbjerg, 1977;Sen, 1999;Serinaldi & Kilsby, 2016;Volpi et al, 2015).…”

“…Besides, when dealing with observed time series that shows correlation in time, data could be preliminary selected to allow for events to be independent from one another; common methods for independent data selection in hydrological applications are those based on the selection of annual maxima or independent peaks exceeding a given threshold (Coles, 2001). Note that even annual maxima are usually selected to fulfill the independence assumption, flood annual extremes may exhibit a nonnegligible temporal correlation in systems dominated by a large water storage; at smaller sampling time intervals (i.e., subannual scale) the effects of persistence usually become more and more important (the reader is referred to the article by Serinaldi and Kilsby (2016) for an interesting discussion on this issue).…”

A Blueprint for Full Collective Flood Risk Estimation: Demonstration for European River Flooding

“…Such policies also imply a shift toward more integrated flood risk management strategies comprising portfolios of structural flood protection assets such as dikes, levees, resilience‐improved residences, and upstream retention areas, and nonstructural solutions such as property‐level protection, land‐use planning, and insurance arrangements . An effective implementation of evolving management strategies must account for key flood characteristics such as their inherent spread over many administrative/physical regions, causing simultaneous collective losses, and their temporal clustering, resulting in flood‐rich and flood‐poor periods . In particular, modeling flood clustering is paramount to set up mitigation strategies that are not affected by the flood risk perception related to the alternation of flood‐rich and flood‐poor periods.…”