•Botanical evidence is often the only non-systematic data source in mountain rivers.•Flood flows from botanical evidence allow extending flow time series of mountain rivers.•Scars on trees provide ...paleostage indicators to characterize the extent of flooding.•The expected moments algorithm is efficient integrating flows from botanical evidence.•A protocol is provided for flood frequency analysis from botanical evidence.
Flood risk assessment and management typically rely on flood frequency analysis (FFA), such that planning and countermeasures can be designed based on the discharge that has to be expected at a given location for a given return period. In mountain streams, systematic flow time series are often very short or completely missing, which significantly reduces the reliability of FFA. In fast-flowing mountain streams, the inclusion of non-systematic data obtained from botanical evidence (BE) is seen as an optimal alternative to extend systematic data back in time. However, no comprehensive protocol has been defined so far to tackle FFA using BE. On the basis of recent case studies, we present here an application-oriented protocol with guidelines on how to combine systematic and non-systematic data in FFA containing BE. This study is based on work realized in different mountain streams located in Spain, Poland and India, representing quite diverse physiographic characteristics and differing hydrological regimes. We organize the protocol along the different steps that are typically realized in BE-based FFA: i) dating of floods from BE; ii) estimation of flood flows from paleostage indicators (PSI) and hydrodynamic modelling; as well as iii) FFA using the expected moments algorithm (EMA). The ubiquity of trees growing along (mountain) streams, their longevity and the often large number of flood-affected trees makes them an almost unbeatable data source that can be employed readily and with reasonable efforts to improve the reliability of FFA, especially in data-scarce regions. In addition, the EMA represents a highly efficient tool for the collection of information contained in BE as it can be used with interval, censored and binomial-censored data and on any distributional family that can be operated with the method of moments. Accordingly, we call for more work incorporating BE into FFA in mountain streams, such that flood hazard and risk assessment can be undertaken more robustly and, therefore, more effective risk mitigation measures can be envisaged.
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Floods are natural disasters that cause extreme economic damage and therefore have a significant impact on society. Understanding the spatial and temporal characteristics exhibited by floods is one ...of the crucial parts of effective flood management. The Danube River with its basin is an important region in Europe and floods have occurred in the Danube River basin throughout history. Flood frequency analysis (FFA) and seasonality analysis were performed in this study using the annual maximum discharge series data from 86 gauging stations in order to form a comprehensive characterisation of floods in the Danube River basin. The results of the study demonstrate that some noticeable clusters of stations can be identified based on the best-fitting distribution regarding FFA. Furthermore, the best-fitting distributions regarding FFA for the stations in the Danube River basin are generalized extreme values (GEV) and log Pearson type 3 (LP3) distributions as among 86 considered gauging stations, 76 stations have one of these two distributions among their two best fits. Moreover, seasonality analysis demonstrates that large floods in the Danube River basin mainly occur in the spring, and flood seasonality in the basin is highly clustered.
AbstractThe accuracy of flood control models depends on the set of annual maximum discharge used to estimate design flood via statistical flood frequency analysis (FFA). The uncertainties associated ...with the discharge time series from stage records are often ignored. Indeed, the uncertainty associated with discharge estimation is not addressed in many of the previous hydraulic risk analyses. In this study, we provide a quantitative approach to rigorously explore the effect that the rating curve uncertainty has on the design flood estimation and the flood hazard mapping. The town of Vieux-Ténès, Algeria, located near the mouth of the Allala River, was used as a case study. Despite the presence of concrete flood protection walls, several floods caused severe damage over the last decades in the town. Multisegment Bayesian rating curve, based on the Bayesian rating curve (BaRatin) method, was used to compute the rating curve uncertainty of the Allala hydrometric station, allowing for the creation of a new time series of annual maximum discharge for the 1973–2017 time period and the estimation of the design flood for different return periods by FFA. The Hydrologic Engineering Center’s river analysis system (HEC-RAS) was used to model the water levels for different locations based on steady flow analysis, using them to define flood-prone areas and an effective protection system. We found that estimations of the flooded area varied between −18% and 15% when assessing rating curve uncertainties. Results highlighted that the existing flood control system is not sufficient to protect the inner city against flood risks, especially in the lower-lying areas of the flooded area.
Flood Frequency Analysis (FFA) in Spanish catchments Mateo Lázaro, Jesús; Sánchez Navarro, José Ángel; García Gil, Alejandro ...
Journal of hydrology (Amsterdam),
July 2016, 2016-07-00, 20160701, Volume:
538
Journal Article
Peer reviewed
•A flood frequency analysis with various models is performed in two Spanish catchments.•The EHVE software is used to adjust various statistical distribution functions.•The SHEE software has been used ...to simulate complete hydrometeorological events.•The results are compared with CAUMAX data, the Spanish system for floods estimation.•It is concluded that the exclusive use (without other models) of CAUMAX data is risky.
A frequency analysis of rainfall and flow from the available data and applications in Spain takes place. In the case of streamflow, various methods that can be grouped into two categories are used, (1) the gauged method which consist in the analysis of maximum flow rate annual series, and (2) the hydro-meteorological method which take into account processes with rainfall–runoff transformation models. The results are compared with observed data in historical series. Finally, six episodes with actual rainfall and flow record are analyzed. These episodes are also classified according to their frequency domain and results obtained from models are contrasted. To make this work we have used two applications launched in the University of Zaragoza: the SHEE program, which provides a simple and flexible working environment which allows the simultaneous management of the most actual and important databases from a hydrological point of view, highlighting the digital terrain models, the rainfall coverage and the curve number coverage, and that is suitable for the application of hydro-meteorological models; and the EHVE software, which is a hydrological statistical program for analysis of time series of extreme values, suitable for application in models of gauged data.
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Flood frequency analysis is concerned with fitting a probability distribution to observed data to make predictions about the occurrence of floods in the future. Under conditions of climate change, or ...other changes to the water cycle that impact flood runoff, the flood series is likely to exhibit non-stationarity, in which case the return period of a flood event of a certain magnitude would change over time. In non-stationary flood frequency analysis, it is customary to examine only the non-stationarity of annual maximum flood data. We developed a way of considering the effect of non-stationarity in the annual daily flow series on the non-stationarity in the annual maximum flood series, which we termed the norming constants method (NCM) of non-stationary flood frequency analysis (FFA). After developing and explaining a framework for application of the method, we tested it using data from the Wei River, China. After detecting significant non-stationarity in both the annual maximum daily flood series and the annual daily flow series, application of the method revealed superior model performance compared to modelling the annual maximum daily flood series under the assumption of stationarity, and the result was further improved if explanatory climatic variables were considered. We conclude that the NCM of non-stationary FFA has potential for widespread application due to the now generally accepted weakness of the assumption of stationarity of flood time series.
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This work aims to analyze the yearly most extreme release of the Nekor River monitoring station Tamellaht between 1973 and 2011 and to predict possible future events using the Flood Frequency ...Analysis Method (FFA). We use the four most estimated distributions that are accessible for prediction of hydrological risk: the three Log Normal, LogPerson Type III, Weibull and GAMMA distributions, and conclude that the Weibull distribution is the suitable statistical model that describe well into our data series, even though the other distributions show data adjustment. Given the Weibull dispersion, the upsides of 580.3 m3/s, 1339 m3/s and 2146.7 m3/s are for the time of return of 10, 50 and 100 years, individually, still high relying upon the semi-dry environment that wins around this region. In fact, the period of extreme returns of the 10th period which can cause dangerous flooding especially considering the mountainous characteristics of the region. The magnitude of the floods is greater because the return period is greater, which explains the semi-arid climate of this region. In addition, a simple statistical description shows that the maximum flow trend has declined over the years, reflecting a possible impact of climate change phenomena.
AbstractEstimations of low-probability flood events are frequently used to plan infrastructure and to determine the dimensions of flood protection measures. Several well-established methods exist for ...estimating low-probability floods. However, a global assessment of the consistency of these methods is difficult to achieve because the “true value” of an extreme flood is not observable. A detailed comparison performed on a given case study brings useful information about the statistical and hydrological processes involved in different methods. In the present study, the following three methods of estimating low-probability floods are compared: a purely statistical method (ordinary extreme value statistics), a statistical method based on stochastic rainfall-runoff simulation (SCHADEX method), and a deterministic method (physically based estimation of the probable maximum flood, PMF). These methods are tested for two different Swiss catchments; the results show that the 10,000-year return level flood estimations exceed the PMF estimations by 3% and 18%. The analysis shows that the plausibility of an extreme flood estimation does not only depend on the applied method but also on its ability to represent flood-triggering processes, including precipitation input, spatio-temporal precipitation distribution, and runoff.
Study region: Narew River in Northeastern Poland. Study focus: Three methods for frequency analysis of snowmelt floods were compared. Two dimensional (2D) normal distribution and copula-based 2D ...probability distributions were applied to statistically describe floods with two parameters (flood peak Qmax,f and flood volume Vf). Two copula functions from different classes – the elliptical Gaussian copula and Archimedean 1-parameter Gumbel–Hougaard copula – were evaluated based on measurements. New hydrological insights for the region: The results indicated that the 2D normal probability distribution model gives a better probabilistic description of snowmelt floods characterized by the 2-dimensional random variable (Qmax,f, Vf) compared to the elliptical Gaussian copula and Archimedean 1-parameter Gumbel–Hougaard copula models, in particular from the view point of probability of exceedance as well as complexity and time of computation. Nevertheless, the copula approach offers a new perspective in estimating the 2D probability distribution for multidimensional random variables. Results showed that the 2D model for snowmelt floods built using the Gumbel–Hougaard copula is much better than the model built using the Gaussian copula.
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