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The authors study small disturbances to the periodic, plane Couette flow in the 3D incompressible Navier-Stokes equations at high Reynolds number Re. They prove that for sufficiently regular initial data of size $\\epsilon \\leq c_0\\mathbf {Re}^-1$ for some universal $c_0 > 0$, the solution is global, remains within $O(c_0)$ of the Couette flow in $L^2$, and returns to the Couette flow as $t \\rightarrow \\infty $. For times $t \\gtrsim \\mathbf {Re}^1/3$, the streamwise dependence is damped by a mixing-enhanced dissipation effect and the solution is rapidly attracted to the class of \"2.5 dimensional\" streamwise-independent solutions referred to as streaks.

Catchment‐scale hydrological models encountered dichotomies with the numerical hydrodynamic models when describing surface routing process. We propose a new modeling framework, the so‐called “Runoff‐On‐Grid” approach, for embedding distributed process‐based hydrological modeling into shallow water models, as an alternative to the traditional Fully Hydrodynamic Approach (also known as Rain‐On‐Grid). Antecedent Soil Moisture, subsurface dynamics, and other topsoil hydrological processes are implicitly integrated in the governing hydrodynamic equations via the proposed methodology. The resulting hydrological‐hydrodynamic coupling, based on the DREAM distributed hydrological model and the Iber+ shallow water model, enhances the capabilities of both reference models. Through introducing non‐negligible runoff generation sources, the Runoff‐On‐Grid approach extends the surface hydrodynamic modeling to medium‐sized vegetated and/or (semi)humid catchments, bypassing the limitations of the widespread hydrological losses' empirical formulations. Employed in an event‐based analysis within a High‐Performance Computing framework, the DREAM‐Iber model provides an efficient and reliable reconstruction of the November 2020 flood that occurred in Crotone (Italy), envisaging consequences of similar future scenarios. We show that the proposed modeling technique, nested within emerging environmental technologies and robust on‐site data, details the flood hazard inducing processes merging physical hydrology with advanced hydrodynamics. Plain Language Summary In this scientific contribution, the potential of combining two different operational tools, namely distributed rainfall‐runoff and flood models, is investigated. An hindcast procedure has been used as reference to assess both the hydrological processes and the inundations at the catchment‐scale. In this context, were exploited cutting edge computational and environmental technologies, which significantly quickened the simulations and enabled a high‐fidelity reconstruction of the extreme meteorological event. According to our findings, there is merit of the proposed approach for bridging the dichotomies between the hydrological and hydrodynamic simulators. This can favor of a more comprehensive method to reduce the limitation of the standalone models. Key Points The Runoff‐On‐Grid approach integrates subsurface hydrological processes, antecedent soil moisture and soil physics in shallow water models The Runoff‐On‐Grid approach expands the capabilities of the Rain‐On‐Grid approach introducing non‐negligible runoff generation sources The DREAM‐Iber model supported by enabling technologies provides a high‐fidelty reconstruction of the 2020 Esaro flood

Strongly interacting electrons can move in a neatly coordinated way, reminiscent of the movement of viscous fluids. Here, we show that in viscous flows, interactions facilitate transport, allowing conductance to exceed the fundamental Landauer’s ballistic limit G ball. The effect is particularly striking for the flow through a viscous point contact, a constriction exhibiting the quantum mechanical ballistic transport at T = 0 but governed by electron hydrodynamics at elevated temperatures. We develop a theory of the ballistic-to-viscous crossover using an approach based on quasi-hydrodynamic variables. Conductance is found to obey an additive relation G = G ball + G vis, where the viscous contribution G vis dominates over Gball in the hydrodynamic limit. The superballistic, low-dissipation transport is a generic feature of viscous electronics.

Fluid-structure interaction (FSI) is a class of mechanics-related problems with mutual dependence between the fluid and structure parts and it is observable nearly everywhere, in natural phenomena to many engineering systems. The primary challenges in developing numerical models with conventional grid-based methods are the inherent nonlinearity and time-dependent nature of FSI, together with possible large deformations and moving interfaces. Smoothed particle hydrodynamics (SPH) method is a truly Lagrangian and meshfree particle method that conveniently treats large deformations and naturally captures rapidly moving interfaces and free surfaces. Since its invention, the SPH method has been widely applied to study different problems in engineering and sciences, including FSI problems. This article presents a review of the recent developments in SPH based modeling techniques for solving FSI-related problems. The basic concepts of SPH along with conventional and higher order particle approximation schemes are first introduced. Then, the implementation of FSI in a pure SPH framework and the hybrid approaches of SPH with other grid-based or particle-based methods are discussed. The SPH models of FSI problems with rigid, elastic and flexible structures, with granular materials, and with extremely intensive loadings are demonstrated. Some discussions on several key techniques in SPH including the balance of accuracy, stability and efficiency, the treatment of material interface, the coupling of SPH with other methods, and the particle regularization and adaptive particle resolution are provided as concluding marks.

Satellite remote sensing enhances model predictions by providing insights into terrestrial and hydrological processes. While data assimilation techniques have proven promising, there is a lack of standardized and effective approaches for integrating multiple observations simultaneously. This study presents a novel assimilation framework, the multi‐observation local ensemble‐Kalman‐filter (MoLEnKF), designed to effectively integrate multiple variables, even at scales different than the model. Evaluation of MoLEnKF in the Amazon River basin includes assimilation experiments with remote sensing data only, including water surface elevation (WSE), terrestrial water storage (TWS), flood extent (FE), and soil moisture (SM). MoLEnKF demonstrates improvements in a scenario where regions lack in‐situ hydroclimatic records and when assuming uncertainties of large‐scale hydrologic‐hydrodynamic models. Assimilating WSE outperforms daily discharge and water‐level estimations, achieving 38% and 36% error reduction, respectively. However, the monthly evapotranspiration estimate achieves the greatest error reduction by assimilating SM with 11%. MoLEnKF always remains in second position in a ranking of error and uncertainty reduction, providing an intermediate condition, being able to holistically outperform univariate experiments. MoLEnKF also outperform state‐of‐the‐art models in many cases. This study suggests potential improvements, urging exploration of correlations between assimilated variables and adaptive localization methods based on seasonality. The flexibility and the elegant way of expressing the LEnKF equations by MoLEnKF facilitates their application with different types of variables, compatible with large‐scale hydrologic‐hydrodynamic models and missions such as SWOT. Its robustness ensures easy replicability worldwide, facilitating hydrological reanalysis and improved forecasting, establishing MoLEnKF as a valuable tool for the scientific community in hydrological research.