Civil and Environmental Engineering

Editors' Note Volume 9 Issue 2

The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of weather and climate models. Here, we use functional decomposition of satellite-derived SM and cloud vertical profiles (CVP) to quantify the relationship between SM and the vertical distribution of cloud water in the central US. High-dimensional model representation is used to disentangle the contributions of SM and other land-surface and atmospheric variables to the CVP. Results show that the sign and strength of the SM-cloud-precipitation feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in antecedent 7-hr SM and land-surface temperature enhance cloud reflectivity up to 4 dBZ in the lower atmosphere about 1-3 km above the surface. Our approach presents new insights into the SM-cloud-precipitation feedback and aids in the diagnosis of land-atmosphere interactions simulated by weather and climate models.

Abstract: Huang and Merwade (2023), https://doi.org/10.1029/2023wr034947, hereafter conveniently referred to as HM23, wrongly claim improvement of their method for postprocessing multi‐model water stage predictions using Bayesian Model Averaging (BMA). Their results show all signs of a flawed implementation of the Metropolis algorithm. In this comment I will point out the many mistakes and shortcomings of the BMA methodology of HM23. Their method is deficient, inefficient and ineffective and wrongly quantifies BMA model parameter and predictive uncertainty. Furthermore, HM23 misrepresent BMA literature, articulate a poor understanding of Markov chain Monte Carlo methods and misuse the autocorrelation function for monitoring convergence of the sampled Markov chains. A proper implementation of the random walk Metropolis algorithm would have led HM23 to substantially different results and findings about their ensemble of water stage predictions. The MODELAVG toolbox of Vrugt (2018) https://www.researchgate.net/publication/299458373_MODELAVG_A_MATLAB_Toolbox_for_Postprocessing_of_Model_Ensembles satisfies all requirements of HM23 and provides robust estimates of BMA model parameter and prediction uncertainty for symmetric, skewed and truncated conditional forecast distributions of the ensemble members.

In this paper, a state-space method for double-beam systems with variable cross sections is developed, making it possible to calculate the transverse vibration of the double-beams accurately and effectively. Due to the variability in the double-beam cross sections with the viscoelastic interlayer in between, the governing equations of vibration for the systems become highly coupled partial differential equations, making the problem difficult to solve. A basic double-beam system is introduced to modify the original governing equations to two inhomogeneous differential equations. Given the separation of variables, several mode-shape coefficients and a state variable are defined to construct the state-space equations. The coupling terms and variables are transferred into the constant coefficient matrix of the state-space equations, decoupling them. Numerical procedures are presented to solve the state-space equations to obtain homogenous and inhomogeneous solutions, including the natural frequencies and mode shapes in free vibration and the dynamic responses in forced vibration, respectively. The method has substantial advantages in decoupling high-order partial differential equations and can be further extended to solve complex structural systems. Numerical results also demonstrate that the method is accurate and efficient. Finally, an engineering application with a rail-bridge with a floating slab track is discussed in detail with the method.

The relaxed eddy accumulation (REA) method is a widely-known technique that measures turbulent fluxes of scalar quantities. The REA technique has been used to measure turbulent fluxes of various compounds, such as methane, ethene, propene, butene, isoprene, nitrous oxides, ozone, and others. The REA method requires the accumulation of scalar concentrations in two separate compartments that conditionally sample updrafts and downdraft events. It is demonstrated here that the assumptions behind the conventional or two-compartment REA approach allow for one-compartment sampling, therefore called a one compartment or 1-C-REA approach, thereby expanding its operational utility. The one-compartment sampling method is tested across various land cover types and atmospheric stability conditions, and it is found that the one-compartment REA can provide results comparable to those determined from conventional two-compartment REA. This finding enables rapid expansion and practical utility of REA in studies of surface-atmosphere exchanges, interactions, and feedbacks.

Spot fires pose a major risk and add to the already complex physics, which makes fire spread so hard to predict, especially in the wildland urban interface. Firebrands can not only cross fuel breaks and thwart other suppression efforts but also directly damage infrastructure and block evacuation routes. Transport models and computational fluid dynamics tools often make simplifications when predicting spot fire risk, but there is a relative lack of experimental data to validate such parameterizations. To this end, we present a field experiment performed at the University of California Berkeley Blodgett Research Forest in California where we recorded the flame and firebrands emanating from a nighttime hand-drawn pile fire using high-frequency imaging. We used image-processing to characterize the fire intensity and turbulence as well as particle tracking velocimetry to measure ejected firebrand kinematics as they are lofted by the plume. We further collected embers that settled around the fire at varying distances and measured their size, shape, density, and settling distributions. We also examine existing physics-based time-averaged models of firebrand lofting and note discrepancies between such models, often used due to their speed and simplicity, and our experimental observations. Finally, we discuss some implications our observations could have on future modeling efforts by considering the time-dependent fire dynamics, intermittency in the plume turbulence, and in the firebrand generation rate. To the best of our knowledge, these are the first in situ observations of firebrand generation and lofting from representative fuels, addressing a major source of data gap and uncertainty in the wildland fire literature.

Urbanization and climate change are contributing to severe flooding globally, damaging infrastructure, disrupting economies, and undermining human well-being. Approaches to make cities more resilient to floods are emerging, notably with the design of flood-resilient structures, but relatively little is known about the role of urban form and its complexity in the concentration of flooding. We leverage statistical mechanics to reduce the complexity of urban flooding and develop a mean-flow theory that relates flood hazards to urban form characterized by the ground slope, urban porosity, and the Mermin order parameter which measures symmetry in building arrangements. The mean-flow theory presents a dimensionless flood depth that scales linearly with the urban porosity and the order parameter, with different scaling for disordered square- and hexagon-like forms. A universal scaling is obtained by introducing an effective mean chord length representative of the unobstructed downslope travel distance for flood water, yielding an analytical model for neighborhood-scale flood hazards globally. The proposed mean-flow theory is applied to probe city-to-city variations in flood hazards, and shows promising results linking recorded flood losses to urban form and observed rainfall extremes.

To enhance the erosion efficiency in traditional abrasive water jet processing, an abrasive water jet processing method based on self-excited fluid oscillation is proposed. Traditional abrasive water jet methods suffer from reduced jet kinetic energy due to the presence of a stagnation layer, which hinders efficient material removal. By integrating a self-oscillation chamber into the conventional abrasive water jet nozzle, the continuous jet is transformed into a pulsed jet, thereby increasing the jet velocity and enhancing the kinetic energy of the process. This modification aims to improve material removal efficiency. Using Ansys Fluent, we simulated the material removal efficiency on workpiece surfaces with varying lengths of self-oscillation chambers. The simulation results reveal that the optimal length of the self-oscillation chamber for maximum erosion is 4 mm. SiC materials were used to evaluate the impact of self-oscillation chamber length (L), jet pressure (P), abrasive flow rate (M), and abrasive grain size (D) on erosion. Experimental results show that the self-oscillation chamber increases erosion depth by 33 μm. The maximum erosion depths recorded were 167 μm when L = 4 mm, 223 μm when P = 16 MPa, 193 μm when M = 80 g/min, and 268 μm when D = 2000 μm. Overall, the self-excited oscillation effect enhances the erosion efficiency of the waterjet by 14%. This study further elucidates the factors influencing erosion behaviors in oscillating abrasive water jet processing.