A combined risk analysis approach for complex dam-levee systems

[EN] In many areas of the world, dams and levees are built to reduce the likelihood of flooding. However, if they fail, the result can be catastrophic flooding beyond what would happen if they did not exist. Therefore, understanding the risk reduced by the dam or levee, as well as any risk imposed by these flood defences is of high importance when determining the appropriate risk reduction investment strategy. This paper describes an approach for quantifying and analysing risk for complex dam-levee systems, and its application to a real case study. The basis behind such approach rely on the potential of event tree modelling to analyse risk from multiple combinations of load-system response-consequence' events, tested by the authors for a real case study. The combined approach shows how the contribution to system risk of each sub-system can be assessed. It also describes how decisions on risk mitigation measures, at the individual asset scale, can and should be informed in terms of how they impact the overall system risk.This work was supported by Spanish Ministry of Economy and Competitiveness (MINECO) [BIA 2013-48157-C2-1-R].Castillo-Rodríguez, J.; Needham, J.; Morales Torres, A.; Escuder Bueno, I. (2017). A combined risk analysis approach for complex dam-levee systems. Structure and Infrastructure Engineering. 13(12):1624-1638. https://doi.org/10.1080/15732479.2017.1314514S16241638131

Prediction of Hydrologic Characteristics for Ungauged Catchments to Support Hydroecological Modeling

Hydrologic variability is a fundamental driver of ecological processes and species distribution patterns within river systems, yet the paucity of gauges in many catchments means that streamflow data are often unavailable for ecological survey sites. Filling this data gap is an important challenge in hydroecological research. To address this gap, we first test the ability to spatially extrapolate hydrologic metrics calculated from gauged streamflow data to ungauged sites as a function of stream distance and catchment area. Second, we examine the ability of statistical models to predict flow regime metrics based on climate and catchment physiographic variables. Our assessment focused on Australia's largest catchment, the Murray‐Darling Basin (MDB). We found that hydrologic metrics were predictable only between sites within ∼25 km of one another. Beyond this, correlations between sites declined quickly. We found less than 40% of fish survey sites from a recent basin‐wide monitoring program (n = 777 sites) to fall within this 25 km range, thereby greatly limiting the ability to utilize gauge data for direct spatial transposition of hydrologic metrics to biological survey sites. In contrast, statistical model‐based transposition proved effective in predicting ecologically relevant aspects of the flow regime (including metrics describing central tendency, high‐ and low‐flows intermittency, seasonality, and variability) across the entire gauge network (median R2 ∼ 0.54, range 0.39–0.94). Modeled hydrologic metrics thus offer a useful alternative to empirical data when examining biological survey data from ungauged sites. More widespread use of these statistical tools and modeled metrics could expand our understanding of flow‐ecology relationships. Supporting InformationFull Tex