Design flood estimation at locations with no data or short records in a Bayesian framework

<p>Estimating design floods at location with no measurements or short records is a major challenge for operational hydrology. The aims of this study are to (i) develop a regional flood frequency model that consists of a regression model for the index flood and the parameters in the growth curve; (ii) assess and attribute the uncertainty to the components of the regional flood frequency model, (iii) develop flexible approaches for combining a regional flood frequency model with local data and provide recommendations for how to combine local and regional data. Annual maximum flood data from 529 gauging stations were used for the model development. We re-parametrized the Generalized Extreme Value (GEV) distribution into an index flood component and growth curve component, and we used the median flood as the index flood. The model was estimated using a MCMC algorithm within a Bayesian framework. The Bayesian approach was also used to combine local and regional data. Two approaches were used (i) combining local and regional data to estimate the index flood (ii) combining local and regional data to estimate both the index flood and the growth curve. Simulation experiments were carried out to assess the performance of these approaches. We see that in particular for data records shorter than 10 years, we can benefit from combining the local and the regional model by both approaches. We also constructed a prior for use in local analysis that complied with the distribution of the regional model for three key quantiles.  For the index flood, the regression model was successfully estimated and evaluated using a three-step cross validation approach. The most important variables for predicting the index flood were mean annual runoff, river length and lake percentage. The attribution of uncertainty showed that most of the uncertainty was found in the index flood component.</p><p> </p>

KPCA over PCA to assess urban resilience to floods

Global increases in the occurrence and frequency of flood have highlighted the need for resilience approaches to deal with future floods. The principal component analysis (PCA) has been used widely to understand the resilience of the urban system to floods. Based on feature extraction and dimensionality reduction, the PCA reduces datasets to representations consisting of principal components. Kernel PCA (KPCA) is the nonlinear form of PCA, which efficiently presents a complicated data in a lower dimensional space. In this work the KPCA techniques was applied to measure and map flood resilience across a local level. Therefore, it aims to improve the performance achieved by non-linear PCA application, compared to standard PCA. Twenty-one resilience indicators were gathered, including social, economic, physical, and natural components into a composite index (Flood resilience Index). The experimental results demonstrate the KPCA performance to get a better Flood Resilience Index, guiding q decision making to strengthen the flood resilience in our case of study of M’diq-Fnideq and martil municipalities in Northern of Morocco.

Regional Flood Frequency Analysis using Dimensionless Index Flood Method

Hydrologic designs require accurate estimation of quartiles of extreme floods. But in many developing regions, records of flood data are seldom available. A model framework using the dimensionless index flood for the transfer of Flood Frequency Curve (FFC) among stream gauging sites in a hydrologically homogeneous region is proposed. Key elements of the model framework include: (1) confirmation of the homogeneity of the region; (2) estimation of index flood-basin area relation; (3) derivation of the regional flood frequency curve (RFFC) and deduction of FFC of an ungauged catchment as a product of index flood and dimensionless RFFC. As an application, 1983 to 2004 annual extreme flood from six selected gauging sites located in Anambra-Imo River basin of southeast Nigeria, were used to demonstrate that the developed index flood model: , overestimated flood quartiles in an ungauged site of the basin. It is recommended that, for wider application, the model results can be improved by the availability and use of over 100 years length of flood data spatially distributed at critical locations of the watershed. Doi: 10.28991/cej-2020-03091627 Full Text: PDF

Comparison of two approaches for an estimation of the mean annual flood at ungauged sites in Slovakia

Regional flood frequency analysis is considered to be an important and popular method for estimating different hydrological variables at ungauged sites. The estimation of the index flood is the essential problem when this method is applied. The objective of the study is a comparison of the estimation of the mean annual flood (or index flood) by using two approaches based on the ‘so-called’ index flood method and top-kriging. The concept behind these methods permits estimating the mean annual flood at ungauged locations using information taken from gauged sites located within the same homogeneous pooling groups. The study area comprises 104 gauging stations on the whole territory of Slovakia. The observation period of the annual maximum discharges of the selected stations was from 1961-2010. The identification of the homogeneous pooling group was performed using a non-hierarchical k-means clustering algorithm. The optimal number of clusters is determined by the Silhouette method. As a result, eight homogeneous pooling group clusters were identified. Finally, the results of the estimated mean annual floods using the index flood method and top-kriging were compared with the observed data. Top-kriging provided better results than the classical index flood method for estimating the mean annual flood at ungauged sites.

An Index-Flood Statistical Model for Hydrological Drought Assessment

Modelling of hydrological extremes and drought modelling in particular has received much attention over recent decades. The main aim of this study is to apply a statistical model for drought estimation (in this case deficit volume) using extreme value theory and the index-flood method and to reduce the uncertainties in estimation of drought event return levels. Deficit volumes for 133 catchments in the Czech Republic (1901–2015) were simulated by hydrological model BILAN. The validation of severity, intensity and length of simulated drought events revealed good match with the available observed data. To estimate return levels of the deficit volumes, it is assumed (in accord with the index-flood method), that the deficit volumes within a homogeneous region are identically distributed after scaling with a site-specific factor. The parameters of the scaled regional distribution are estimated using L-moments. The goodness-of-fit of the statistical model is assessed by Anderson–Darling test. For the estimation of critical values, sampling methods allowing for handling of years without drought were used. It is shown, that the index-flood model with a Generalized Pareto distribution performs well and substantially reduces the uncertainty related to the estimation of the shape parameter and of the large deficit volume quantiles.

Extreme events Analysis of Short-Duration Rainfall and Intensity-Duration-Frequency curve using Scaling Model in North Korea

<p>Climate change has emerged as one of the defining issues of the early 21st century. Recent research confirms that the imprint of human induced climate change can be recognized in current accident events. There is a high probability of observed trends, such as increases in heat waves and heavy extreme rainfall events, intensifying over the 21st century. Extreme weather and climate events are anticipated to generate significant risks to societies and ecosystem. This paper focuses on estimation rainfall quantile using sclaling model for short duration IDF curve in North Korea. It is very important to manage the flood control facilities because of increasing the frequency and magnitude of severe rain storms. For managing flood control facilities in possibly hazardous regions, data sets such as elevation, gradient, channel, land use and soil data should be filed up. Using this information, the disaster situations can be simulated to secure evacuation routes for various rainfall scenarios. The aim of this study is to investigate and determine extreme rainfall quantile estimates in North Korea Cities using index flood method with L-moments parameter estimation. Regional frequency analysis trades space for time by using annual maximum rainfall data from nearby or similar sites to determine estimates for any given site in a homogeneous region. Regional frequency analysis based on pooled data is recommended for estimation of rainfall quantiles at sites with record lengths less than 5T, where T is return period of interest. Many variables relevant to precipitation can be used for grouping a region in regional frequency analysis. For regionalization of Han River basin, the k-means method is applied for grouping regions using variables of meteorology and geomorphology. The results from the k-means method are compared for each region using various probability distributions. In the final step of the regionalization analysis, goodness-of-fit measure is used to evaluate the accuracy of a set of candidate distributions. And rainfall quantiles by index flood method are obtained based on the appropriate distribution(GEV and GLO). Therefore, it could be possible to estimate rainfall quantiles using scale invariance and frequency analysis for Wonsan, Jangjeon, and Pyeonggang rainfall stations in North Korea. And then, rainfall quantiles based on various scenarios are used as input data for disaster simulations.</p><p>Acknowledgements</p><p>This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2019R1A2C2010854).</p><p> </p>