1,721,034 research outputs found
Uncertainty in flood frequency analysis and its implications for infrastructure design
Full text linkWorking with environmental data means dealing with complex processes, limited data (in space and/or time) and the impossibility of setting up controlled experiments, leading to uncertain predictions of system behaviour.
In the field of statistical hydrology, many efforts have been made during the last decades to provide methods to quantify uncertainty, but the common practice of infrastructure design has not yet incorporated them. This may be due to several reasons, including the complexity of the methods, which are often difficult to apply in most everyday cases, and regulations that "favour" well-established requirements.
Here we present the "uncertainty compliant design flood estimator" (UNCODE) method, which accounts for aleatory uncertainty in the estimation of the design flood value. The method provides a corrected design value and is easy to use for practical purposes as simplified formulae are provided to quantify the correction factor. However, in addition to its practical application, it can also be used to compare different models with different levels of uncertainty and to highlight the "cost" of uncertainty.
Finally, its mathematical formulation allows a direct link to be made between the classical approach to hydrological design, based on a fixed hazard level (or return period), and a risk-based design approach, which is widely recognised as a more flexible method but is not usually included in regulations
Hydrological Applications of the Burr Distribution: Practical Method for Parameter Estimation
No full textThe three-parameter Burr XII distribution has been seldom used in hydrological applications, although it is particularly appealing
because its range covers positive values only, which is convenient when modeling streamflows or rainfall data. Moreover, it has two shape
parameters, allowing it to be quite adaptable to different samples because it covers a wide range of skewness and kurtosis values. Parameter
estimation methods currently available in the literature require the numerical solution of two joint nonlinear equations to estimate the shape
parameters of the distribution. This work proposes a simplified, although accurate, method to analytically compute the two shape parameters
starting from the dimensionless L-moments ratios representing the distribution’s variability (L-CV) and skewness (L-skewness). The obtained
parameters can be directly used in practical applications or otherwise may be useful to properly initialize the algorithms to obtain a numerical
solution for the shape parameters. A detailed analysis of the accuracy of the approximated solution is performed, showing that the errors in the
estimation of the distribution quantiles are negligible compared with the sample variability typically affecting hydrological samples. An
extensive data set of empirical flow duration curves from stations located in northwestern Italy is considered to demonstrate the suitability
of the extended Burr XII distribution to represent flow duration curves in a wide range of situations
Mapping precipitation extremes for pluvial flood risk management in the Sirba river basin, Burkina Faso.
Full text linkSahelian Africa is increasingly exposed to extreme hydrological events. Both fluvial and pluvial floods are becoming more severe and frequent, posing significant new threats to the livelihoods of local communities. To enhance resilience to floods, the development of effective operational tools for assessing risk and supporting decision-making is crucial. When it comes to pluvial floods, the first step towards this goal is to improve the understanding of extreme daily and sub-daily precipitation events and their spatial patterns in the target areas. Within the SLAPIS Project framework, this work does so for the Sirba river basin (Burkina Faso and Niger) proposing a methodology to address the challenges posed by the scarcity of hydrological data typical of the Sahel region. First, it was assessed how well gridded precipitation products (ERA5, TRMM, TAMSAT) match observed rainfall records. Then, bias correction of selected datasets was performed and tested to evaluate its reliability when spatially interpolated through the whole basin. The Metastatistical Extreme Value Distribution was finally applied to the corrected datasets to investigate the precipitation extremes exploiting the bulk of the available data, unlike classical extreme value analysis, which relies on only a small subset of the data. This procedure resulted in the production of extreme daily and sub-daily precipitation maps with enhanced accuracy and robustness, providing novel information on events that can cause pluvial flooding at the settlement scale. The methodology adopted in this study could be applied to other Sahelian basins where enhanced knowledge of extreme precipitation magnitudes and patterns is needed
An approach to propagate streamflow statistics along the river network
Full text linkStreamflow at ungauged sites is often predicted by means of regional statistical procedures. The standard regional approaches do not preserve the information related to the hierarchy among gauged stations deriving from their location along the river network. However, this information is important when estimating runoff at a site located immediately upstream or downstream of a gauging station. We propose here a novel approach, referred to as the Along-Stream Estimation (ASE) method, to improve runoff estimation at ungauged sites. The ASE approach starts from the regional estimate at an ungauged (target) site, and corrects it based on regional and sample estimates of the same variable at a donor site, where sample data are available. A criterion to define the domain of application around each donor site of the ASE approach is proposed, and the uncertainty inherent in the estimates obtained is evaluated. This allows one to compare the variance of the along-stream estimates to that of other models that eventually become available for application (e.g. regional models), and thus to choose the most accurate method (or to combine different estimates). The ASE model was applied in the northwest of Italy in connection with an existing regional model for flood frequency analysis. The analysed variables are the first L-moments of the annual discharge maxima. The application demonstrates that the ASE approach can be used effectively to improve the regional estimates for the L-moment of order one (the index flood), particularly when the area ratio of a pair of donor-target basins is less than or equal to ten. However, in this case study, the method does not provide significant improvements to the estimation of higher-order L-moment
A comparison of regional flood frequency analysis approaches in a simulation framework
Full text linkRegional frequency analysis (RFA) is a well-established methodology to provide an estimate of the flood frequency curve at ungauged (or scarcely gauged) sites. Different RFA approaches exist, depending on the way the information is transferred to the site of interest, but it is not clear in the literature if a specific method systematically outperforms the others. The aim of this study is to provide a framework wherein carrying out the intercomparison by building up a virtual environment based on synthetically generated data. The considered regional approaches include: (i) a unique regional curve for the whole region; (ii) a multiple-region model where homogeneous subregions are determined through cluster analysis; (iii) a Region-of-Influence model which defines a homogeneous subregion for each site; (iv) a spatially smooth estimation procedure where the parameters of the regional model vary continuously along the space. Virtual environments are generated considering different patterns of heterogeneity, including step change and smooth variations. If the region is heterogeneous, with the parent distribution changing continuously within the region, the spatially smooth regional approach outperforms the others, with overall errors 10-50% lower than the other methods. In the case of a step-change, the spatially smooth and clustering procedures perform similarly if the heterogeneity is moderate, while clustering procedures work better when the stepchange is severe. To extend our findings, an extensive sensitivity analysis has been performed to investigate the effect of sample length, number of virtual stations, return period of the predicted quantile, variability of the scale parameter of the parent distribution, number of predictor variables and different parent distribution. Overall, the spatially smooth approach appears as the most robust approach as its performances are more stable across different patterns of heterogeneity, especially when short records are considered
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