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Abstract Interdisciplinary research is considered a source of innovativeness and creativity, serving as a key mechanism for creating recombination necessary for the evolution of science systems. The aim of this study is to quantitatively establish the connection between interdisciplinary research and the research fronts that have recently emerged in civil engineering. The degree of interdisciplinarity of the research fronts was measured by developing metrics from bibliographic analyses. As indicated by the consistent increase in the metrics of interdisciplinarity over time, research fronts tend to emerge in studies with increasing diversity in the disciplines involved. The active disciplines involved in the fronts vary over time. The most active disciplines are no longer fundamental but those associated with energy, environment, and sustainable development, focusing on solutions to climate change and integrating intelligence technologies.

Abstract Tunnels constructed in gas-bearing strata are affected by the potential leakage of harmful gases, such as methane gas. Based on the basic principles of computational fluid dynamics, a numerical analysis was performed to simulate the ventilation and diffusion of harmful gases in a shield tunnel, and the effect of ventilation airflow speed on the diffusion of harmful gases was evaluated. As the airflow speed increased from 1.8 to 5.4 m/s, the methane emission was diluted, and the methane accumulation was only observed in the area near the methane leakage channels. The influence of increased ventilation airflow velocity was dominant for the ventilation modes with two and four fans. In addition, laboratory tests on methane leakage through segment joints were performed. The results show that the leakage process can be divided into “rapid leakage” and “slight leakage”, depending on the leakage pressure and the state of joint deformation. Based on the numerical and experimental analysis results, a relationship between the safety level and the joint deformation is established, which can be used as guidelines for maintaining utility tunnels.

Extreme precipitation events can lead to disastrous floods, which are the most significant natural hazards in the Mediterranean regions. Therefore, a proper characterization of these events is crucial. Extreme events defined as annual maxima can be modeled with the generalized extreme value (GEV) distribution. Owing to spatial heterogeneity, the distribution of extremes is non-stationary in space. To take non-stationarity into account, the parameters of the GEV distribution can be viewed as functions of covariates that convey spatial information. Such functions may be implemented as a generalized linear model (GLM) or with a more flexible non-parametric non-linear model such as an artificial neural network (ANN). In this work, we evaluate several statistical models that combine the GEV distribution with a GLM or with an ANN for a spatial interpolation of the GEV parameters. Key issues are the proper selection of the complexity level of the ANN (i.e., the number of hidden units) and the proper selection of spatial covariates. Three sites are included in our study: a region in the French Mediterranean, the Cap Bon area in northeast Tunisia, and the Merguellil catchment in central Tunisia. The comparative analysis aim at assessing the genericity of state-of-the-art approaches to interpolate the distribution of extreme precipitation events.

Abstract Collecting data on the dynamic breakup of a river's ice cover is a notoriously difficult task. However, such data are necessary to reconstruct the events leading to the formation of ice jams and calibrate numerical ice jam models. Photogrammetry using images from remotely piloted aircraft (RPA) is a cost-effective and rapid technique to produce large-scale orthomosaics and digital elevation maps (DEMs) of an ice jam. Herein, we apply RPA photogrammetry to document an ice jam that formed on a river in southern Quebec in the winter of 2022. Composite orthomosaics of the 2-km ice jam provided evidence of overbanking flow, hinge cracks near the banks and lengthy longitudinal stress cracks in the ice jam caused by sagging as the flow abated. DEMs helped identify zones where the ice rubble was grounded to the bed, thus allowing ice jam thickness estimates to be made in these locations. The datasets were then used to calibrate a one-dimensional numerical model of the ice jam. The model will be used in subsequent work to assess the risk of ice interacting with the superstructure of a low-level bridge in the reach and assess the likelihood of ice jam flooding of nearby residences.

In agricultural fields, tile drains represent potential pathways for the migration of solutes, such as nitrates, in groundwater and surface water bodies. Tile drain flow is controlled by the temporal and spatial dynamics of the shallow groundwater table, which results from complex interactions between climate, topography and soil heterogeneity. Studies on the effect of topsoil heterogeneity on shallow water and drainage dynamics by fully 3D surface water and groundwater flow modeling are limited. The objective of our study is to demonstrate the use of depth specific electrical conductivity (EC) estimates to improve hydrological simulations in a tile-drained field. The model was applied to a field site in Denmark where times series of drainage discharge and water table elevations are available. Clay-rich soil zones were identified in a tile-drained field using depth specific electrical conductivity estimates generated by the inversion of apparent electrical conductivity data measured using an electromagnetic induction instrument. One model that included the low-permeability clayey zones in the soil layers down to a depth of 1.2 m was compared to a simpler model that assumed homogeneous soil layers. Both models simulate drainage discharge that compares well to the observations. However, including the clayey zones improves the simulation of hydraulic heads, and water table fluctuations, and generates flooded areas that are more representative of those observed during the wet seasons. Our results suggest that the simulation of water table fluctuations can be improved when the soil heterogeneity determined from depth specific EC estimates is included in integrated hydrological models. A better representation of the subsurface flow dynamics will also improve subsequent simulations of the transport and fate of agrochemical substances leaching from fields such as nitrate, which may deteriorate the quality of groundwater and surface water bodies.

Abstract The Canadian Precipitation Analysis (CaPA) system provides near-real-time precipitation analyses over Canada by combining observations with short-term numerical weather prediction forecasts. CaPA’s snowfall estimates suffer from the lack of accurate solid precipitation measurements to correct the first-guess estimate. Weather radars have the potential to add precipitation measurements to CaPA in all seasons but are not assimilated in winter due to radar snowfall estimate imprecision and lack of precipitation gauges for calibration. The main objective of this study is to assess the impact of assimilating Canadian dual-polarized radar-based snowfall data in CaPA to improve precipitation estimates. Two sets of experiments were conducted to evaluate the impact of including radar snowfall retrievals, one set using the high-resolution CaPA (HRDPA) with the currently operational quality control configuration and another increasing the number of assimilated surface observations by relaxing quality control. Experiments spanned two winter seasons (2021 and 2022) in central Canada, covering part of the entire CaPA domain. The results showed that the assimilation of radar-based snowfall data improved CaPA’s precipitation estimates 81.75% of the time for 0.5-mm precipitation thresholds. An increase in the probability of detection together with a decrease in the false alarm ratio suggested an improvement of the precipitation spatial distribution and estimation accuracy. Additionally, the results showed improvements for both precipitation mass and frequency biases for low precipitation amounts. For larger thresholds, the frequency bias was degraded. The results also indicated that the assimilation of dual-polarization radar data is beneficial for the two CaPA configurations tested in this study.