Implications of heterogeneous flood‐frequency distributions on traditional stream‐discharge prediction techniques

Abstract

Abstract:Traditional flood-frequency analysis involves the assumption of homogeneity of the flood distribution. However, floods are often generated by heterogeneous distributions composed of a mixture of two or more populations. Differences between the populations may be the result of a number of factors, including seasonal variations in the flood-producing mechanisms, changes in weather patterns resulting from low-frequency climate shifts and/or El Niño/La Nina oscillations, changes in channel routing owing to the dominance of within-channel or floodplain flow, and basin variability resulting from changes in antecedent soil moisture. Not recognizing these physical processes in conventional flood-frequency analysis probably is the main reason that many frequency distributions do not provide an acceptable fit to flood data. In this paper, we use long-term hydroclimatic records from the Gila River basin of south-east and central Arizona in the USA to explore the extent and significance of mixed populations. First, we discuss the probable causes of heterogeneity in the frequency distribution of annual flood and present evidence of its occurrence. Second, we investigate the implications of using various popular homogeneous distributions for predicting peak flows for basins that exhibit mixed population characteristics. Third, we demonstrate how alternative frequency models that explicitly account for floods generated by a mixture of two or more populations are both hydrologically and statistically more appropriate. We illustrate how the selection of the most plausible distribution for flood-frequency analysis also should be based on hydrological reasoning as opposed to the sole application of the traditional statistical goodness-of-fit tests.

Probability distribution of flood flows in Tunisia

“…He claimed that L-moments artificially impose a structure upon a data set and de-emphasize the importance of observed extremes, which leads to the underestimation of extreme design events. However, Alila and Mtiraoui (2002) argued that if the annual floods in a sample are distributed identically and the outliers are caused by sampling variability (for instance, a 100-year event in a 10-year sample) they should not be given an undue weight. If any historic information can be found for any high outlier, a reasonably wellestablished method, referred to as "flood frequency analysis with historic information", could be used (Pilon and Harvey, 1994).…”

Flood-initiating catchment conditions: a spatio-temporal analysis of large-scale soil moisture patterns in the Elbe River basin

“…In order to capture the variety of large-scale flood generation mechanisms, flood events have been classified and analyzed according to their hydrometeorological conditions along with their interactions between catchment state and meteorological conditions (e.g. Alila and Mtiraoui, 2002;Apipattanavis et al, 2010;Merz and Blöschl, 2003). These interactions vary from decade to decade (Alila and Mtiraoui, 2002), seasonally (Sivapalan et al, 2005;Merz and Blöschl, 2003;Parajka et al, 2010), and from event to event as well as from catchment to catchment (Merz and Blöschl, 2003).…”

“…Alila and Mtiraoui, 2002;Apipattanavis et al, 2010;Merz and Blöschl, 2003). These interactions vary from decade to decade (Alila and Mtiraoui, 2002), seasonally (Sivapalan et al, 2005;Merz and Blöschl, 2003;Parajka et al, 2010), and from event to event as well as from catchment to catchment (Merz and Blöschl, 2003). In addition to the occurrence and interaction of the hydro-meteorological conditions, their spatial patterns related to flooding need to be taken into account (Merz and Blöschl, 2003), which can be especially important in larger catchments (Merz and Blöschl, 2008).…”

Best Fit and Selection of Theoretical Flood Frequency Distributions Based on Different Runoff Generation Mechanisms

“…The first frequently involve the use of distributions that are characterized by many parameters (e.g., [25][26][27][28][29]) and most of them totally lack physical interpretation. On the other hand, advanced knowledge of real processes has driven the construction of several hydrological models used to derive the flood frequency curve based on Monte-Carlo simulations (e.g., [30][31][32]).…”