Stormwater reservoirs are used to control the runoff volume and to attenuate the peak flow rate from urban catchments. The required capacity of a stormwater reservoir and its dimensions are calculated with the help of rainfall-runoff models, using a time series of observed rainfall or a design rainfall. However, many studies have revealed that the reliability of models of urbanized basins is far from perfect. This study presents an optimization of the capacity of a stormwater reservoir in a small urbanized basin, taking into account the predictive uncertainty of the rainfall-runoff model. An analysis of the model uncertainty using the generalized likelihood uncertainty estimation (GLUE) framework allowed for an assessment of the optimal capacity of the stormwater reservoir required to ensure a given safety level. Such a probabilistic approach is compared with a deterministic approach, which is commonly used in practice, where the effect of uncertainty of the rainfall-runoff model is usually neglected. The numerical experiment showed that stochastic and deterministic approaches are not equivalent as the calculated capacities of the reservoirs were different. The deterministic solution leads to an overestimation of the reservoir capacity from 20 to 150%, compared to the mean capacity obtained using a stochastic formulation. The results indicate that the uncertainty of the runoff model might potentially be a significant factor in stormwater reservoir design.

Despite the development of advanced process-based methods for estimating the discharge capacity of vegetated river channels, most of the practical one-dimensional modeling is based on a relatively simple divided channel method (DCM) with the Manning flow resistance formula. This study is motivated by the need to improve the reliability of modeling in practical applications while acknowledging the limitations on the availability of data on vegetation properties and related parameters required by the process-based methods. We investigate whether the advanced methods can be applied to modeling of vegetated compound channels by identifying the missing characteristics as parameters through the formulation of an inverse problem. Six models of channel discharge capacity are compared in respect of their uncertainty using a probabilistic approach. The model with the lowest estimated uncertainty in explaining differences between computed and observed values is considered the most favorable. Calculations were performed for flume and field settings varying in floodplain vegetation submergence, density, and flexibility, and in hydraulic conditions. The output uncertainty, estimated on the basis of a Bayes approach, was analyzed for a varying number of observation points, demonstrating the significance of the parameter equifinality. The results showed that very reliable predictions with low uncertainties can be obtained for process-based methods with a large number of parameters. The equifinality affects the parameter identification but not the uncertainty of a model. The best performance for sparse, emergent, rigid vegetation was obtained with the Mertens method and for dense, flexible vegetation with a simplified two-layer method, while a generalized two-layer model with a description of the plant flexibility was the most universally applicable to different vegetative conditions. In many cases, the Manning-based DCM performed satisfactorily but could not be reliably extrapolated to higher flows.

Water flow in channels with a compound cross-section involves an exchange of water mass and momentum between the slower flowing water in the floodplains and the faster water in the main channel. This process is called the streams interaction. As a result, the water velocity in the main channel decreases, and at the same time the velocity and depth of flow increase in the part of the floodplains adjacent to the main channel. Diversification of the surface roughness of the main channel and floodplains significantly affects the form of interactions. The results of laboratory experiments were used to characterize the influence of interactions on the discharge capacity of the channel with diversified roughness. The reduction in velocity of the main channel caused by the stream interactions is described with the apparent friction coefficients introduced at the boundary between the main channel and the floodplain. The obtained values of resistance coefficients, supplemented with the values from experiments reported in the literature, were used to establish a relationship useful in assessing the discharge capacity of such channels.

The study presents experimental investigations of spatial turbulence intensity and scales of turbulent eddies (macroeddies) in a rectangular channel and the impact of the hydraulic jump on their vertical and streamwise distributions over a flat and scoured bed. The results of four tests and two different discharge rates are presented. Intensive mixing caused by the hydraulic jump has an impact on the instantaneous velocity, turbulence intensity and sizes of macroeddies, as well as their vertical and longitudinal distributions along the channel. The largest differences in turbulence characteristics were reported directly after the hydraulic jump, above the eroded bed. The interaction between the stream of the increased turbulence and the bed is a direct cause of formation of scour downstream water structures, which has a great effect on overall flow characteristics. The scour hole that arose downstream the jump moderated, in a small degree, the turbulence intensity at its end. Just next to the hydraulic jump only the small longitudinal relative sizes of macroeddies were present, while at the end of the analyzed reach, downstream of the scour, the relative scale reached around 1.5 depth of the stream.

Soil moisture content simulation models have continuously been an important research objective. In particular, the comparisons of the performance of different model types deserve proper attention. Therefore, the quality of selected physically-based and statistical models was analyzed utilizing the data from the Time Domain Reflectometry technique. An E-Test measurement system was applied with the reflectogram interpreted into soil volumetric moisture content by proper calibration equations. The gathered data facilitated to calibrate the physical model of Deardorff and establish parameters of: support vector machines, multivariate adaptive regression spline, and boosted trees model. The general likelihood uncertainty estimation revealed the sensitivity of individual model parameters. As it was assumed, a simple structure of statistical models was achieved but no direct physical interpretation of their parameters, contrary to a physically-based method. The TDR technique proved useful for the calibration of different soil moisture models and a satisfactory quality for their future exploitation.