Flow monitoring in Urban Drainage Systems (UDS) is required for a successful system control and operational assessment. Commonly used methods can lead to erroneous results in partially filled pipes and hostile environmental conditions, normally encountered in UDS. Recent studies focused on the flow rate measurements in UDS revealed that the capability of acoustic Doppler velocimeters to estimate mean flow velocity is impeded by several factors. Most prominent issues are the operation under low flow depths and velocities, as well as in the case of the sedimentation at low flow velocities. This study is focused on an alternative method for the velocity measurements in the UDS, based on Electro-Magnetic Velocity (EMV) meters. The study also determines the sensor's capacity to operate when covered by a porous sediment layer, using a newly developed procedure. A brief theoretical background is given to support the idea behind the usage of EMV in UDS. Measurement uncertainties were firstly benchmarked in the laboratory flume without sediment. After local, sitespecific (re)calibration, EMV operated with combined uncertainty of only few cm/s. Furthermore, the EMV measured the flow rates with depths low as 4 cm and velocities bellow 5 cm/s. Additionally, a series of tests were performed with sediment layers above the EMV meter, varying in height from 0 to 80 mm. Observational uncertainty analysis showed that EMV meter can be used even in these conditions. Since the bias uncertainty increased with the rise of the sediment depth, a correction 2 function model was derived for the transformation of the output signal, reducing the observational uncertainties below 5 cm/s. Subsequently, practical implications of the EMV usage in the UDS are considered.

The objective of this research is to introduce a novel framework to quantify the risk of the reservoir system outside the design envelope, taking into account the risks related to flood-protection and hydro-energy generation under unfavourable reservoir element conditions (system element failures) and hazardous situations within the environment (flood event). To analyze water system behavior in adverse conditions, a system analysis approach is used, which is founded upon the system dynamics model with a causal loop. The capability of the system in performing the intended functionality can be quantified using the traditional static measures like reliability, resilience and vulnerability, or dynamic resilience. In this paper, a novel method for the assessment of a multi-parameter dynamic resilience is introduced. The multi-parameter dynamic resilience envelops the hydropower and flood-protection resilience, as two opposing demands in the reservoir operation regime. A case study of a Pirot reservoir, in the Republic of Serbia, is used. To estimate the multi -parameter dynamic resilience of the Pirot reservoir system, a hydrological model, and a system dynamic simulation model with an inner control loop, is developed. The inner control loop provides the relation between the hydropower generation and flood-protection. The hydrological model is calibrated and generated climate inputs are used to simulate the long-term flow sequences. The most severe flood event period is extracted to be used as the input for the system dynamics simulations. The system performance for five different scenarios with various multi failure events (e.g., generator failure, segment gate failure on the spillway, leakage from reservoir and water supply tunnel failure due to earthquake) are presented using the novel concept of the explicit modeling of the component failures through element functionality indicators. Based on the outputs from the system dynamics model, system performance is determined and, later, hydropower and flood protection resilience. Then, multi-parameter dynamic resilience of the Pirot reservoir system is estimated and compared with the traditional static measures (reliability). Discrepancy between the drop between multi-parameter resilience (from 0.851 to 0.935) and reliability (from 0.993 to 1) shows that static measure underestimates the risk to the water system. Thus, the results from this research show that multi-parameter dynamic resilience, as an indicator, can provide additional insight compared to the traditional static measures, leading to identification of the vulnerable elements of a complex reservoir system. Additionally, it is shown that the proposed explicit modeling of system components failure can be used to reflect the drop of the overall system functionality.

To optimize the design of a water distribution network (WDN), a large number of possible solutions need to be examined; hence computation efficiency is an important issue. To accelerate the computation, one can use more powerful computers, parallel computing systems with adapted hydraulic solvers, hybrid algorithms, more efficient hydraulic methods or any combination of these techniques. This paper explores the possibility to speed up optimization using variations of the ΔQ method to solve the network hydraulics. First, the ΔQ method was used inside the evaluation function where each tested alternative was hydraulically solved and ranked. Then, the convergence criterion was relived in order to reduce the computation time. Although the accuracy of the obtained hydraulic results was reduced, these were feasible and interesting solutions. Another modification was tested, where the ΔQ method was used just once to solve the hydraulics of the initial network, and the unknown flow corrections were added to the list of other unknown variables subject to optimization. Two case networks were used for testing and were compared to the results obtained

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Dam and reservoir systems (DRSs) are crucial aspects of the infrastructure necessary for reliable water resource management. Nowadays, DRSs are being increasingly affected by numerous natural and anthropogenic impacts (aging and outdated infrastructure, climate change, natural hazards, global crises, etc.). Hence, additional pressure on DRS management is being applied as DRSs must be operated in adverse operating conditions, outside of their design envelopes. Since there is no practical way to redesign DRSs to meet all possible adverse conditions, efficient simulation tools are necessary for various “what-if” analyses. A system dynamics (SD) approach can be used, as it has shown the capacity to comprehend the intrinsic system complexity. In this paper, an 11-step framework for the dynamic modelling of reduced functionality in a DRS and the emulation of the system operation in adverse conditions is proposed. The framework covers the system model design, input scenario generation, system simulation, and performance evaluation steps. A focus is placed on the steps related to system decomposition, the identification of failure-indicative parameters, the definition and implementation of failure functions in the subsystem dynamic models, and dynamic failure modelling. Through these steps, a novel procedure is proposed for the dynamic modelling of the DRS subsystems’ failures (reduced functionality), common in the operation of DRSs under adverse conditions. For each subsystem prone to failure, failure-indicative parameters are identified. Failure functions employing generic functionality indicators, with values spanning from 0 to 1, are suggested to modify the values of the failure-indicative parameters in simulations and emulate the component failure impacts on DRS operation. Possibilities for modelling failure modes for different subsystems, varying in nature, duration, and magnitude are discussed. Potential physical damage to the system components, increases in measurement uncertainty, and the lack of the spare parts during periods of global crisis are applied as disturbances to the Pirot DRS case study to illustrate the possibilities of the suggested framework’s application for DRS failure modelling. It was concluded that the proposed framework allowed for the detection of severe impacts on system performance, emphasizing the need for DRS dynamic failure modelling in system analysis.