The Reduction of Storm Surge by Vegetation Canopies: Three-Dimensional Simulations

Significant buffering of storm surges by vegetation canopies has been suggested by limited observations and simple numerical studies, particularly following recent Hurricanes Katrina, Rita, and Wilma. Here we simulate storm surge and inundation over idealized topographies using a three-dimensional vegetation-resolving storm surge model coupled to a shallow water wave model and show that a sufficiently wide and tall vegetation canopy reduces inundation on land by 5 to 40 percent, depending upon various storm and canopy parameters. Effectiveness of the vegetation in dissipating storm surge and inundation depends on the intensity and forward speed of the hurricane, as well as the density, height, and width of the vegetation canopy. Reducing the threat to coastal vegetation from development, sea level rise, and other anthropogenic factors would help to protect many coastal regions against storm surges

On the Shoaling of Solitary Waves in the Presence of Short Random Waves

Overhead video from a small number of laboratory tests conducted by Kaihatu et al. at the Tsunami Wave Basin at Oregon State University shows that the breaking point of a shoaling solitary wave shifts to deeper water if random waves are present. The analysis of the laboratory data collected confirms that solitary waves indeed tend to break earlier in the presence of random wave field, and suggests that the effect is the result of the radiation stresses gradient induced by the random wave fields. A theoretical approach based on the forced KdV equation is shown to successfully predict the shoaling process of the solitary wave. An ensemble of tests simulated using a state-of-the-art nonhydrostatic model is used to test the statistical significance of the process. The results of this study point to a potentially significant oceanographic process that has so far been ignored and suggest that systematic research into the interaction between tsunami waves and the swell background could increase the accuracy of tsunami forecasting. © 2015 American Meteorological Society

Research on the vibration damping performance of hydro-pneumatic suspension of mine dump truck

The working environment of mine dump truck is relatively harsh, thus the suspension system with poor performance will directly affect driver's driving comfort and physical and mental health. An oil cylinder is one of the key components of the hydro-pneumatic suspension system, however, the high friction levels between cylinder and piston would lead to ‘friction locking’ phenomenon in the practical application. In order to improve the vibration damping performance of hydro-pneumatic suspension system and enhance the comfortability of whole vehicle, a 110 t mine dump truck was studied by theoretical and experimental method. The results of this study show that increasing the length of cylinder guide can effectively decrease the cylinder vibration transmission rate and improve the driving comfortability of vehicle

Evaluating essential processes and forecast requirements for meteotsunami-induced coastal flooding

Meteotsunamis pose a unique threat to coastal communities and often lead to damage of coastal infrastructure, deluge of nearby property, and loss of life and injury. The Great Lakes are a known hot-spot of meteotsunami activity and serve as an important region for investigation of essential hydrodynamic processes and model forecast requirements in meteotsunami-induced coastal flooding. For this work, we developed an advanced hydrodynamic model and evaluate key model attributes and dynamic processes, including: (1) coastal model grid resolution and wetting and drying process in low-lying zones, (2) coastal infrastructure, including breakwaters and associated submerging and overtopping processes, (3) annual/seasonal (ambient) water level change, and (4) wind wave-current coupling. Numerical experiments are designed to evaluate the importance of these attributes to meteotsunami modeling, including a “representative storm” scenario in the context of regional climate change in which a meteotsunami wave is generated under high ambient lake-level conditions with a preferable wind direction and speed for wind-wave growth. Results demonstrate that accurate representation of coastal topography and fully resolving associated hydrodynamic processes are critical to forecasting the realistic hazards associated with meteotsunami events. As most of existing coastal forecast systems generally do not resolve many of these features due to insufficient model grid resolution or lack of essential model attributes, this work shows that calibrating or assessing existing forecast models against coastal water level gauges alone may result in underestimating the meteotsunami hazard, particularly when gauging stations are sparse and located behind harbor breakwaters or inside estuaries, which represent dampened or otherwise unrepresentative pictures of meteotsunami intensity. This work is the first hydrodynamic modeling of meteotsunami-induced coastal flooding for the Great Lakes, and serves as a template to guide where resources may be most beneficial in forecast system development and implementation

Sensor Selection and Integration to Improve Video Segmentation in Complex Environments

Background subtraction is often considered to be a required stage of any video surveillance system being used to detect objects in a single frame and/or track objects across multiple frames in a video sequence. Most current state-of-the-art techniques for object detection and tracking utilize some form of background subtraction that involves developing a model of the background at a pixel, region, or frame level and designating any elements that deviate from the background model as foreground. However, most existing approaches are capable of segmenting a number of distinct components but unable to distinguish between the desired object of interest and complex, dynamic background such as moving water and high reflections. In this paper, we propose a technique to integrate spatiotemporal signatures of an object of interest from different sensing modalities into a video segmentation method in order to improve object detection and tracking in dynamic, complex scenes. Our proposed algorithm utilizes the dynamic interaction information between the object of interest and background to differentiate between mistakenly segmented components and the desired component. Experimental results on two complex data sets demonstrate that our proposed technique significantly improves the accuracy and utility of state-of-the-art video segmentation technique. © 2014 Adam R. Reckley et al

Integrating Deep Learning and Hydrodynamic Modeling to Improve the Great Lakes Forecast

The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, trained with the limited data from hypothetical monitoring networks, can provide consistent and robust performance. The LSTM prediction captured the LST spatiotemporal variabilities across the five Great Lakes well, suggesting an effective and efficient way for monitoring network design in assisting the ML-based forecast. Furthermore, we employed an explainable artificial intelligence (XAI) technique named SHapley Additive exPlanations (SHAP) to uncover how the features impact the LSTM prediction. Our XAI analysis shows air temperature is the most influential feature for predicting LST in the trained LSTM. The relatively large bias in the LSTM prediction during the spring and fall was associated with substantial heterogeneity of air temperature during the two seasons. In contrast, the physics-based hydrodynamic model performed better in spring and fall yet exhibited relatively large biases during the summer stratification period. Finally, we developed a statistical integration of the hydrodynamic modeling and deep learning results based on the Best Linear Unbiased Estimator (BLUE). The integration further enhanced prediction accuracy, suggesting its potential for next-generation Great Lakes forecast systems

Integrating Deep Learning and Hydrodynamic Modeling to Improve the Great Lakes Forecast

The Laurentian Great Lakes, one of the world’s largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, trained with the limited data from hypothetical monitoring networks, can provide consistent and robust performance. The LSTM prediction captured the LST spatiotemporal variabilities across the five Great Lakes well, suggesting an effective and efficient way for monitoring network design in assisting the ML-based forecast. Furthermore, we employed an explainable artificial intelligence (XAI) technique named SHapley Additive exPlanations (SHAP) to uncover how the features impact the LSTM prediction. Our XAI analysis shows air temperature is the most influential feature for predicting LST in the trained LSTM. The relatively large bias in the LSTM prediction during the spring and fall was associated with substantial heterogeneity of air temperature during the two seasons. In contrast, the physics-based hydrodynamic model performed better in spring and fall yet exhibited relatively large biases during the summer stratification period. Finally, we developed a statistical integration of the hydrodynamic modeling and deep learning results based on the Best Linear Unbiased Estimator (BLUE). The integration further enhanced prediction accuracy, suggesting its potential for next-generation Great Lakes forecast systems

Laboratory Study of the Effects of Flexible Vegetation on Solute Diffusion in Unidirectional Flow

Background Flexible vegetation is an important part of the riverine ecosystem, which can reduce flow velocity, change turbulence structure, and affect the processes of solute transport. Compared with the flow with rigid vegetation, which has been reported in many previous studies, bending of flexible vegetation increases the complexity of the flow-vegetation-solute interactions. In this study, laboratory experiments are carried out to investigate the influence of flexible vegetation on solute transport, and methods for estimating the lateral and longitudinal diffusion coefficients in the rigid vegetated flow are examined for their applications to the flow with flexible vegetation. Results The experimental observations find that vegetation can significantly reduce flow velocity, and the Manning coefficient increases with increasing vegetation density and decreases with inflow discharge. Under all the cases, the vertical peak of the solute concentration moves towards the bottom bed along the flow, and the values of vertical peak concentration longitudinally decreases from the injection point. The lateral diffusion coefficients Dy increase with vegetation density, while the longitudinal diffusion coefficients DL are opposite. Both Dy and DL increase with the inflow discharge. To estimate the Dy and DL in the flow with flexible vegetation, an effective submerged vegetation height considering vegetation bending is incorporated in the methods proposed for flow with rigid vegetation (Lou et al. Environ Sci Eur 32:15, 2020). The modified approach can well predict the diffusion coefficients in the experiments with the relative errors in the range of 5%-12%. Conclusions The methods proposed in this study can be used to estimate the lateral and longitudinal diffusion coefficients in flows through both rigid and flexible vegetations using the effective submerged vegetation height

Heavy Metal Distribution and Groundwater Quality Assessment for a Coastal Area on a Chinese Island

Chongming Island is located in the lower Yangtze Estuary in China. Due to the Leachate from a refuse landfill and the hydrodynamics of the Yangtze Estuary, the groundwater environment is particularly complicated on Chongming Island. Field observations were carried out around the landfill disposal site. The groundwater table, temperature, pH, salinity, and dissolved oxygen were measured in the field by portable equipment, and 192 water samples were collected at eight groundwater sites and one surface water site. Through laboratory analysis we found the highest measured concentration of Cr to be 54.07 μg/L, and the measured concentration of Zn was in the range of 8 1.1 μg/L to more than 200 μg/L, which were both higher than their background values. Strong correlations were found between the heavy metal (Cr, Ni, Cu) concentrations and physico-chemical characteristics (salinity and pH), which indicated that both the landfill and the tides played an important role in the distribution of heavy metal concentrations. Both the BM and PoS Indices were greater than their critical values near the disposal site, indicating groundwater pollution by heavy metals. We show that Cr and Ni are the major heavy metals causing groundwater contamination in the study region

Numerical Study of Sediment Suspension Affected By Rigid Cylinders Under Unidirectional and Combined Wave-Current Flows

Sediment transport modeling for flows with cylinders is very challenging owing to the complicated flow–cylinder–sediment interactions, especially under the combined wave-current flows. In this paper, an improved formulation for incipient sediment suspension considering the effect of cylinder density (i.e., solid volume fraction) is employed to simulate the bottom sediment flux in the flow with cylinders. The proposed model is calibrated and validated using laboratory measurements under unidirectional and combined wave-current flows in previous studies. It is proved that the effects of cylinders on sediment suspension can be accounted for through a modified critical Shields number, and the proposed model is capable of simulating sediment suspension under both unidirectional and combined wave–current flows reasonably well with the average the coefficients of determination and model skills greater than 0.8 and 0.64