Methods to detect outliers in network flow measurements that may be due to pipe bursts or unusual consumptions are fundamental to improve water distribution system on-line operation and management, and to ensure reliable historical data for sustainable planning and design of these systems. To detect and classify anomalous events in flow data from district metering areas a four-step methodology was adopted, implemented and tested: i) data acquisition, ii) data validation and normalization, iii) anomalous observation detection, iv) anomalous event detection and characterization. This approach is based on the renewed concept of outlier regions and depends on a reduced number of configuration parameters: the number of past observations, the true positive rate and the false positive rate. Results indicate that this approach is flexible and applicable to the detection of different types of events (e.g., pipe burst, unusual consumption) and to different flow time series (e.g., instantaneous, minimum night flow).

Large wood is often transported by rivers into reservoirs during heavy rainfall events. When a critical section like a spillway is blocked and discharge capacity reduced, an uncontrolled increase of the reservoir water level may occur. This study aims to statistically analyse the importance of repetitions for the accuracy of experimental campaigns when studying blocking probabilities at ogee crested spillways equipped with piers. Systematic and reliable estimations based on physical models are critical for developing preventive measures against large wood blockage. Two statistical methods have been described and applied to calculate confidence intervals. A minimum number of repetitions for a maximum acceptable error is recommended for blocking probabilities. The minimum number of experimental repetitions has been statistically justified in accordance with a reasonable use of resources for experimental campaigns. In addition, a maximum acceptable level of error is proposed as a common metric of accuracy in large wood studies.

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