Flood Generation Mechanisms Research Articles

A multi-level approach to flood frequency regionalisation

Abstract. A multi-level approach to flood frequency regionalisation is given. Based on observed flood data, it combines physical and statistical criteria to cluster homogeneous groups in a geographical area. Seasonality analysis helps identify catchments with a common flood generation mechanism. Scale invariance of annual maximum flood, as parameterised by basin area, is used to check the regional homogeneity of flood peaks. Homogeneity tests are used to assess the statistical robustness of the regions. The approach is based on the appropriate use of the index flood method (Dalrymple, 1960) in regions with complex climate and topography controls. An application to north-western Italy is presented. Keywords: homogeneity, multi-level approach, regionalisation, seasonality, scale invariance, similarity, tests

Regional analysis of annual maximum and partial duration flood data by nonparametric and L-moment methods

The analysis of annual maximum (AM) flood series has revealed unimodal and multimodal probability density functions for floods in the Provinces of Ontario and Quebec, Canada. Based on density function shapes and the timing of floods, Ontario and Quebec have been divided into nine homogeneous regions reflecting similar flood generating mechanisms. A similar analysis of peak over threshold or partial duration (PD) data also revealed unimodality and bimodality linked to flood-generating mechanisms. These results point out deficiencies in currently used parametric approaches for both AM and PD series, since traditional regional flood frequency analysis procedures assume that all floods within a homogeneous region are generated by the same, often unimodal distribution. The analysis of AM and PD series using L-moments revealed that while the overall series did not appear to be unimodal, separate rainfall and snowmelt PD series extracted from the same data set could each be described by unimodal distributions. L-moment tests confirmed that separate rainfall and snowmelt PD series statistics are homogeneous but the overall series is heterogeneous. A regional relationship is developed using nonparametric analysis on the AM and PD flood series. Using the Geographic Information System methodology, mapping was performed for the site-specific L_coefficient of variation and the average snowpack depth. This analysis revealed an apparent correspondence between these variables.

Regional flood frequency analysis using GIS, L-moment and geostatistical methods

Advances in space–time tools and techniques offer new possibilities to improve methods for exploratory data analysis and parameter estimation in regional flood frequency analysis (FFA). A general framework and methodological approach are proposed which integrate concepts and techniques of regional FFA, geostatistical theory and analytical geographical information systems (GIS) using data on climate, vegetation, geography and flood timing and magnitude statistics. Non-parametric methods are used to screen data and to delineate homogeneous regions. Simulations are used to identify discordant sites, diagnose each region using the signal-to-noise ratio and test regions for homogeneity based on L-moment ratios. Geostatistical measures of spatial autocorrelation are used to diagnose hierarchical spatial models for each L-moment ratio and to obtain map estimates of parameters using spatially explicit kriging techniques (analogous to regression). In addition to storing and displaying the spatio-temporal information accurately, the GIS is used to quantify spatial associations between dependent and independent variables and to diagnose homogeneous regions for further refinement using a simple spatial contrast measure. Analysis of data from central and eastern Canada (except the eastern parts of Newfoundland), encompassing a large area with significant random and systematic variability, demonstrates that: (i) map sets of L-mean and L-CV (coefficient of variation) for flood timing and magnitude can serve as indicators of climatic influences on the flood-generating mechanisms; (ii) models of spatial autocorrelation can be used to map point variables and their geostatistical spatial variance, which indicates whether maps are significant; and (iii) associations between L-CV and snow or vegetation could support improved mapping using co-kriging or geostatistical simulations. The map-based method provides parameter values at ungauged sites and maps of spatial variance that could support decisions to add or remove gauges from a hydrometric network. Copyright © 2000 John Wiley & Sons, Ltd.

Homogeneous region delineation based on annual flood generation mechanisms

Flood distributions can have unimodal or multimodal densities due to different flood generation mechanisms such as snowmelt and rainfall in the annual flood series. When applying nonparametric frequency analysis to annual flood data from the province of New Brunswick in Canada, unimodal, bimodal and heavy-tailed distribution shapes were found. By grouping basins with similarly-shaped densities on a geographical basis, homogeneous regions were delineated. Regional equations derived for a homogeneous region gave lower integral square errors than those of province-wide equations.

A cluster model for flood analysis

A two‐level and two‐dimensional nonhomogeneous point stochastic process is developed to model the flood peaks of a hydrograph. The model is a cluster process of the Neyman‐Scott type and presents the occurrences of flood generating mechanisms (FGM) at the precipitation level as the triggers for clusters of flood peaks at the runoff level. The FGMs are studied in terms of their times of occurrence τ and their volumes ν. The volume of a FGM corresponds to the volume of precipitation in the precipitation cluster that originates at the occurrence time of the FGM. Thus, each precipitation cluster at the precipitation level is represented by its origin time and by its volume as a point in a time volume plane, τ = (τ, ν). The FGM points in the time volume plane of the precipitation level form a two‐dimensional process, Nc(τ), with a nonhomogeneous rate of occurrence λ(τ). It is assumed that Nc(τ) is a Poisson process. The FGMs, in turn, generate a two‐dimensional subsidiary process, Ns(t |τ), in the runoff level. This process represents the occurrences of flood peaks at time t with magnitude m as a point in the time magnitude plane t = (t, m), determined by the conditional rate of occurrence μ(t|τ), and is also assumed to be a Poisson process. The statistical properties of the flood cluster process N(t), defined as the total number of flood peaks, are found in terms of the probability generating functional of the process. Using a nonparametric methodology, the two‐dimensional parameters λ(τ) and μ(t|τ) are estimated. The theoretical rate of occurrence, the theoretical covariance density, and the theoretical probability mass function of the process are compared with the respective empirical functions obtained for several stations located in the Ohio River Basin. A good fit is found for the analyzed stations as the first two moments and the two‐dimensional probability mass function of the flood peak process are preserved.