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.

Reliable water resources management requires decision support tools to successfully forecast hydraulic data (stage and flow hydrographs). Even though data-driven methods are nowadays trendy to apply, they still fail to provide reliable forecasts during extreme periods due to a lack of training data. Therefore, model-driven forecasting is still needed. However, the model-driven forecasting approach is affected by numerous uncertainties in initial and boundary conditions. To improve the real-time model's operation, it can be regularly updated using measured data in the data assimilation (DA) procedure. Widely used DA techniques are computationally expensive, which reduce their real-time applications. Previous research shows that tailor-made, time-efficient DA methods based on the control theory could be used instead. This paper presents further insights into the control theory-based DA for 1D hydraulic models. This method uses Proportional–Integrative–Derivative (PID) controllers to assimilate computed water levels and observed data. This paper describes the two-stage PID controllers’ tuning procedure. Multi-objective optimization by Nondominated Sorting Genetic Algorithm II (NSGA-II) was used to determine optimal parameters for PID controllers. The proposed tuning procedure is tested on a hydraulic model used as a decision support tool for the transboundary Iron Gate 1 hydropower system on the Danube River, showing that the average discrepancy between modeled and observed water levels can be less than 0.05 m for more than 97% of assimilation window.

HIGHLIGHTS. Uncertainty of the boundary and initial conditions affects model-driven forecasting.Data Assimilation is used to overcome these problems.Research pr

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.