Landmines and explosive remnants of war are still present in the Western Balkans and remain a deadly legacy of the hostilities at the end of the 20th century. Over the years, several incidents have occurred in Bosnia and Herzegovina, in Serbia, and in Croatia where intact ordnance has caused injuries and fatalities. Floods, torrential flows, and gravitational mass movements pose a particular threat. Landmines and explosive remnants of war are mobilized and displaced into previously uncontaminated areas. We first discuss the historical and technical background of this hazardous situation. We then show which hydro-morphological processes are responsible for the mobilization and displacement. We then illustrate how a prediction of the likely contaminated areas can be obtained. We show that the problem can only be tackled using a stochastic-deterministic model. However, for the eventual development of risk-hazard maps, preliminary work using laboratory experiments and field surveys is required. The article, therefore, proposes a novel approach to the problem in an international research project. The aim would be to produce risk-hazard maps that can be used by elected decision-makers, administrative authorities, and emergency personnel in affected municipalities.

Wall deflection in supercritical flow induces standing waves which significantly influence the flow field close to the wall. This paper analyses the flow in the converging stepped spillway, using two scale-models with different step heights and convergence angles. Results show that the height and the width of the standing wave increase with the increase of the convergence angle. Air concentration decreases while the air–water mixture velocity and residual energy head increase in the vicinity of the converging wall and gradually attain the values for the undisturbed flow outside the standing wave. Compared to the prismatic chutes of equal upstream width, converging spillways are less efficient energy dissipators. Equations for predicting the maximum flow depth and the width-averaged residual energy are proposed.

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.

The performance of flat stilling basins can be inadequate for conditions when the tailwater depth is insufficient for hydraulic jump stabilization. In such cases, adverse-slope stilling basins can be used because they reduce the necessary tailwater depth. Sloped basins combined with smooth chutes have been the subject of many studies. However, limited research has been done for basins with stepped chutes, which are characterized by intensive flow aeration and high energy dissipation. Based on our scale-model experimental measurements of depth, velocity, and air concentration, we present a momentum-based method to characterize such hydraulic jump: the sequent depth ratio, the length of hydraulic jump roller, and energy dissipation effectiveness. The proposed method provides better agreement with experimental data when compared to existing methods and can be used for preliminary design.

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