Analyzing SCADA to Understand the Contribution of Hydraulic Pressures to Trunk-main Failure

AbstractWater distribution networks throughout the world are ageing, which increasingly leads to sudden pipe failure. About 108 trunk-main pipe failures in an urban sub-network were investigated using a pipe failure data base and SCADA (Supervisory Control and Data Acquisition) data to understand the contribution of hydraulic pressure to pipe failure using multiple lines of evidence. The forensic investigation revealed a dominant system-wide failure mode which was characterized by predominately off-peak high speed pumping with limited pressure relief from downstream reservoirs. A frequency analysis was conducted for greater understanding of the dominant failure mode

Pitfalls and improvements in the joint inference of heteroscedasticity and autocorrelation in hydrological model calibration

Residual errors of hydrological models are usually both heteroscedastic and autocorrelated. However, only a few studies have attempted to explicitly include these two statistical properties into the residual error model and jointly infer them with the hydrological model parameters. This technical note shows that applying autoregressive error models to raw heteroscedastic residuals, as done in some recent studies, can lead to unstable error models with poor predictive performance. This instability can be avoided by applying the autoregressive process to standardized residuals. The theoretical analysis is supported by empirical findings in three hydrologically distinct catchments. The case studies also highlight strong interactions between the parameters of autoregressive residual error models and the water balance parameters of the hydrological model. ©2013. American Geophysical Union. All Rights Reserved.Guillaume Evin, Dmitri Kavetski, Mark Thyer, and George Kuczer

Using paleoclimate reconstructions to analyse hydrological epochs associated with Pacific decadal variability

The duration of dry or wet hydrological epochs (run lengths) associated with positive or negative Inter-decadal Pacific Oscillation (IPO) or Pacific Decadal Oscillation (PDO) phases, termed Pacific decadal variability (PDV), is an essential statistical property for understanding, assessing and managing hydroclimatic risk. Numerous IPO and PDO paleoclimate reconstructions provide a valuable opportunity to study the statistical signatures of PDV, including run lengths. However, disparities exist between these reconstructions, making it problematic to determine which reconstruction(s) to use to investigate pre-instrumental PDV and run length. Variability and persistence on centennial scales are also present in some millennium-long reconstructions, making consistent run length extraction difficult. Thus, a robust method to extract meaningful and consistent run lengths from multiple reconstructions is required. In this study, a dynamic threshold framework to account for centennial trends in PDV reconstructions is proposed. The dynamic threshold framework is shown to extract meaningful run length information from multiple reconstructions. Two hydrologically important aspects of the statistical signatures associated with the PDV are explored: (i) whether persistence (i.e. run lengths) during positive epochs is different to persistence during negative epochs and (ii) whether the reconstructed run lengths have been stationary during the past millennium. Results suggest that there is no significant difference between run lengths in positive and negative phases of PDV and that it is more likely than not that the PDV run length has been non-stationary in the past millennium. This raises concerns about whether variability seen in the instrumental record (the last ∼100 years), or even in the shorter 300–400-year paleoclimate reconstructions, is representative of the full range of variability.</p

Relationships between the El-Niño Southern Oscillation and spate flows in southern Africa and Australia

International audienceThe flow records of arid zone rivers are characterised by a high degree of seasonal variability, being dominated by long periods of very low or zero flow. Discrete flow events in these rivers are influenced by aseasonal factors such as global climate forcings. The atmospheric circulations of the El-Niño Southern Oscillation (ENSO) have been shown to influence climate regimes across many parts of the world. Strong teleconnections between changing ENSO regimes and discharges are likely to be observed in highly variable arid zones. In this paper, the influence of ENSO mechanisms on the flow records of two arid zone rivers in each of Australia and Southern Africa are identified. ENSO signals, together with multi-decadal variability in their impact as identified through seasonal values of the Interdecadal Pacific Oscillation (IPO) index, are shown to influence both the rate of occurrence and the size of discrete flow episodes in these rivers. Keywords: arid zones, streamflow, spates, climate variability, ENSO, Interdecadal Pacific Oscillation, IP

Probabilistic streamflow prediction and uncertainty estimation in ephemeral catchments

Conference theme 'Digital Water.'Probabilistic streamflow predictions at the daily scale are of major practical interest for environmental management and planning, including risk assessment as part of reservoir management operations. Ephemeral catchments, where streamflow is frequently zero or negligible, pose particularly stark challenges in this context, due to asymmetry of the error distribution and the discrete (rather than continuous) nature of zero flows. In this work, our focus is on two practical error modelling approaches where predictive uncertainty is approximated by a (transformed) Gaussian error model. The first approach, termed "pragmatic", does not distinguish between zero and positive flows during calibration, but sets negative flows to zero when making predictions. The second approach, termed "explicit", applies a "censored" Gaussian assumption in both calibration and prediction. We report a comparison of these two approaches over 74 Australian catchments with diverse hydroclimatology, using multiple performance metrics. The performance of the approaches depended on the catchment type as follows: (1) "mid-ephemeral" catchments, where 5-50% of days have zero flows, are best modelled using the "explicit" approach in combination with the Box-Cox streamflow transformation with a power parameter of 0.2; (2) "low-ephemeral" catchments, with fewer than 5% zero flow days, can be modelled using the pragmatic approach with (relatively) little loss of predictive performance; (3) "high-ephemeral" catchments, with more than 50% zero flow days, prove challenging to both approaches, and require more specialised techniques. The findings provide practical guidance towards improving probabilistic streamflow predictions in ephemeral catchments. Previous chapter Next chapterDmitri Kavetski, David McInerney, Mark Thyer, Julien Lerat and George Kuczer

An efficient causative event-based approach for deriving the annual flood frequency distribution

In ungauged catchments or catchments without sufficient streamflow data, derived flood frequency methods are often applied to provide the basis for flood risk assessment. The most commonly used event-based methods, such as design storm and joint probability approaches are able to give fast estimation, but can also lead to prediction bias and uncertainties due to the limitations of inherent assumptions and difficulties in obtaining input information (rainfall and catchment wetness) related to events that cause extreme floods. An alternative method is a long continuous simulation which produces more accurate predictions, but at the cost of massive computational time. In this study a hybrid method was developed to make the best use of both event-based and continuous approaches. The method uses a short continuous simulation to provide inputs for a rainfall-runoff model running in an event-based fashion. The total probability theorem is then combined with the peak over threshold method to estimate annual flood distribution. A synthetic case study demonstrates the efficacy of this procedure compared with existing methods of estimating annual flood distribution. The main advantage of the hybrid method is that it provides estimates of the flood frequency distribution with an accuracy similar to the continuous simulation approach, but with dramatically reduced computation time. This paper presents the method at the proof-of-concept stage of development and future work is required to extend the method to more realistic catchments. © 2014 Elsevier B.V.Jing Li, Mark Thyer, Martin Lambert, George Kuczera, Andrew Metcalf

Preliminary analysis of trends in Australian flood data

In recent years, the potential impacts of climate variability and change on the hydrologic regime have received a great deal of attention from researchers. Review of hydrological data recorded in different parts of the world has provided evidence of regime-like or quasiperiodic climate behaviour and of systematic trends in key climate variables due to climate change and/or climate variability. It has been established that a changing climate will have notable impacts on the rainfall runoff process, and thus hydrologic time series (e.g., flood data) can no longer be assumed to be stationary. A failure to take such change/variability into account can lead to underestimation/overestimation of the design flood estimate, which in turn will have important implications on the design and operation of water infrastructures. This paper presents preliminary results from a study aimed to identify the nature of time trends in flood data in the Australian continent with the final objective of assessing the impacts of climatic change on regional floods in Australia. This research is being carried out as a part of the on-going revision of Australian Rainfall and Runoff – the national guide of design flood estimation in Australia. For this study, 491 suitable stations with flood data in the range of 30 to 97 years have been selected across the Australian continent. Two trend tests are applied: Mann-Kendall test and Spearman’s Rho test to the data set. Preliminary trend analysis results show that about 30% of the selected stations show trends in annual maxima flood series data, with downward trends in the southern part of Australia and upward trends in the northern part. Further investigation is needed before any firm conclusion can be made about the trends in Australian flood data. Future work aims to address the influence of spatial correlation and autocorrelation on the ability to detect trend in annual maximum flood series data in Australia and assess the relationship between the observed trends in annual maximum flood data and other meteorological variables.E.H. Ishak, A. Rahman, S. Westra, A. Sharma, G. Kuczer

The MuTHRE Model for High Quality Sub-seasonal Streamflow Forecasts

Conference theme 'Digital Water.'Sub-seasonal streamflow forecasts, with lead times up to 30 days, can provide valuable information for water management, including reservoir operation to meet environmental flow, irrigation demands, and managing flood protection storage. A key aim is to produce “seamless” probabilistic forecasts, with high quality performance across the full range of lead times (1-30 days) and time scales (daily to monthly). This paper demonstrates that the Multi-Temporal Hydrological Residual Error (MuTHRE) model can address the challenge of “seamless” sub-seasonal forecasting. The MuTHRE model is designed to capture key features of hydrological errors, namely seasonality, dynamic biases due to hydrological non-stationarity, and extreme errors poorly represented by the common Gaussian distribution. The MuTHRE model is evaluated comprehensively over 11 catchments in the MurrayDarling Basin using multiple performance metrics, across a range of lead times, months and years, and at daily and monthly time scales. It is shown to provide “high” improvements, in terms of reliability for short lead times (up to 10 days), in dry months, and dry years. Forecast performance also improved in terms of sharpness. Importantly, improvements are consistent across multiple time scales (daily and monthly). This study highlights the benefits of modelling multiple temporal characteristics of hydrological errors, and demonstrates the power of the MuTHRE model for producing seamless sub-seasonal streamflow forecasts that can be utilized for a wide range of applications.David McInerney, Mark Thyer, Dmitri Kavetski, Richard Laugesen, Narendra Tuteja, and George Kuczer