Environmental DNA clarifies impacts of combined sewer overflows on the bacteriology of an urban river and resulting risks to public health.

There is no reference of microbiological water quality in the European Union's Water Framework Directive, adapted into English law, and consequently microbial water quality is not routinely monitored in English rivers, except for two recently designated bathing water sites. To address this knowledge gap, we developed an innovative monitoring approach for quantitative assessment of combined sewer overflow (CSO) impacts on the bacteriology of receiving rivers. Our approach combines conventional and environmental DNA (eDNA) based methods to generate multiple lines of evidence for assessing risks to public health. We demonstrated this approach by investigating spatiotemporal variation in the bacteriology of the Ouseburn in northeast England for different weather conditions in the summer and early autumn of the year 2021 across eight sampling locations that comprised rural, urban, and recreational land use settings. We characterized pollution source attributes by collecting sewage from treatment works and CSO discharge at the peak of a storm event. CSO discharge was characterized by log10 values per 100 mL (average ± stdev) of 5.12 ± 0.03 and 4.90 ± 0.03 for faecal coliforms and faecal streptococci, and 6.00 ± 0.11 and 7.78 ± 0.04 for rodA and HF183 genetic markers, for E. coli and human host associated Bacteroides, respectively, indicating about 5 % sewage content. SourceTracker analysis of sequencing data attributed 72-77 % of bacteria in the downstream section of the river during a storm event to CSO discharge sources, versus only 4-6 % to rural upstream sources. Data from sixteen summer sampling events in a public park exceeded various guideline values for recreational water quality. Quantitative microbial risk assessment (QMRA) predicted a median and 95th percentile risk of 0.03 and 0.39, respectively, of contracting a bacterial gastrointestinal disease when wading and splashing around in the Ouseburn. We show clearly why microbial water quality should be monitored where rivers flow through public parks, irrespective of their bathing water designation.

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

Floods are a natural hazard evolving in space and time according to meteorological and river basin dynamics, so that a single flood event can affect different regions over the event duration. This physical mechanism introduces spatio-temporal relationships between flood records and losses at different locations over a given time window that should be taken into account for an effective assessment of the collective flood risk. However, since extreme floods are rare events, the limited number of historical records usually prevents a reliable frequency analysis. To overcome this limit, we move from the analysis of extreme events to the modeling of continuous stream flow records preserving spatio-temporal correlation structures of the entire process, and making a more efficient use of the information provided by continuous flow records. The approach is based on the dynamic copula framework, which allows for splitting the modeling of spatio-temporal properties by coupling suitable time series models accounting for temporal dynamics, and multivariate distributions describing spatial dependence. The model is applied to 490 stream flow sequences recorded across 10 of the largest river basins in central and eastern Europe (Danube, Rhine, Elbe, Oder, Waser, Meuse, Rhone, Seine, Loire, and Garonne). Using available proxy data to quantify local flood exposure and vulnerability, we show that the temporal dependence exerts a key role in reproducing interannual persistence, and thus magnitude and frequency of annual proxy flood losses aggregated at a basin-wide scale, while copulas allow the preservation of the spatial dependence of losses at weekly and annual time scales.

Rainfall extremes: Toward reconciliation after the battle of distributions.

[1] This study attempts to reconcile the conflicting results reported in the literature concerning the behavior of peak-over-threshold (POT) daily rainfall extremes and their distribution. By using two worldwide data sets, the impact of threshold selection and record length on the upper tail behavior of POT observations is investigated. The rainfall process is studied within the framework of generalized Pareto (GP) exceedances according to the classical extreme value theory (EVT), with particular attention paid to the study of the GP shape parameter, which controls the heaviness of the upper tail of the GP distribution. A twofold effect is recognized. First, as the threshold decreases, and nonextreme values are progressively incorporated in the POT samples, the variance of the GP shape parameter reduces and the mean converges to positive values denoting a tendency to heavy tail behavior. Simultaneously, the EVT asymptotic hypotheses are less and less realistic, and the GP asymptote tends to be replaced by the Weibull penultimate asymptote whose upper tail is exponential but apparently heavy. Second, for a fixed high threshold, the variance of the GP shape parameter reduces as the record length (number of years) increases, and the mean values tend to be positive, thus denoting again the prevalence of heavy tail behavior. In both cases, i.e., threshold selection and record length effect, the heaviness of the tail may be ascribed to mechanisms such as the blend of extreme and nonextreme values, and fluctuations of the parent distributions. It is shown how these results provide a link between previous studies and pave the way for more comprehensive analyses which merge empirical, theoretical, and operational points of view. This study also provides several ancillary results, such as a set of formulae to correct the bias of the GP shape parameter estimates due to short record lengths accounting for uncertainty, thus avoiding systematic underestimation of extremes which results from the analysis of short time series. Citation: Serinaldi, F., and C. G. Kilsby (2014), Rainfall extremes: Toward reconciliation after the battle of distributions, Water Resour. Res., 50, 336-352, doi:10.1002/2013WR014211.

The intrinsic dependence structure of peak, volume, duration, and average intensity of hyetographs and hydrographs.

[1] The information contained in hyetographs and hydrographs is often synthesized by using key properties such as the peak or maximum value Xp , volume V, duration D, and average intensity I. These variables play a fundamental role in hydrologic engineering as they are used, for instance, to define design hyetographs and hydrographs as well as to model and simulate the rainfall and streamflow processes. Given their inherent variability and the empirical evidence of the presence of a significant degree of association, such quantities have been studied as correlated random variables suitable to be modeled by multivariate joint distribution functions. The advent of copulas in geosciences simplified the inference procedures allowing for splitting the analysis of the marginal distributions and the study of the so-called dependence structure or copula. However, the attention paid to the modeling task has overlooked a more thorough study of the true nature and origin of the relationships that link [Formula: see text], and I. In this study, we apply a set of ad hoc bootstrap algorithms to investigate these aspects by analyzing the hyetographs and hydrographs extracted from 282 daily rainfall series from central eastern Europe, three 5 min rainfall series from central Italy, 80 daily streamflow series from the continental United States, and two sets of 200 simulated universal multifractal time series. Our results show that all the pairwise dependence structures between [Formula: see text], and I exhibit some key properties that can be reproduced by simple bootstrap algorithms that rely on a standard univariate resampling without resort to multivariate techniques. Therefore, the strong similarities between the observed dependence structures and the agreement between the observed and bootstrap samples suggest the existence of a numerical generating mechanism based on the superposition of the effects of sampling data at finite time steps and the process of summing realizations of independent random variables over random durations. We also show that the pairwise dependence structures are weakly dependent on the internal patterns of the hyetographs and hydrographs, meaning that the temporal evolution of the rainfall and runoff events marginally influences the mutual relationships of [Formula: see text], and I. Finally, our findings point out that subtle and often overlooked deterministic relationships between the properties of the event hyetographs and hydrographs exist. Confusing these relationships with genuine stochastic relationships can lead to an incorrect application of multivariate distributions and copulas and to misleading results.