The Stability of Flood Defenses on Permeable Soils: The London Avenue Canal Failures in New Orleans

The two failures of the London Avenue Canal floodwalls contributed largely to the flooding of central New Orleans due to hurricane Katrina. In this paper, both failures are analyzed and compared to each other since the flood defenses are both located on permeable soils. Photo’s observation and calculations are used for the analysis. Both failures are caused by the permeable sand layer below the floodwall that allowed high pore water pressures to develop below the floodwall. However, the south breach seems to be caused by the piping failure mechanism and the north breach by loss of stability. At the South breach, the impermeable top layer was thicker than at the North breach, increasing the stability. The North beach was less vulnerable for piping and the lack of stability caused a large breach. The London Avenue Canal failures are a clear yet tragic example of the failure of flood defenses on permeable soils. The failures show that multiple failure mechanism may occur and since there are many flood defenses on permeable soils world wide, the lessons from Katrina can be used to prevent future catastrophes

Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes

Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity) effect), a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China. It is shown that the major cause of the ARCH effect is the seasonal variation in variance of the residual series. However, while the seasonal variation in variance can fully explain the ARCH effect for monthly streamflow, it is only a partial explanation for daily flow. It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals. The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model. Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community

ENVIRONMETRICS

Dutch case studies of the estimation of extreme quantiles and associated uncertainty by bootstrap simulation

Safety Standards of Flood Defenses

Hazard