Plastic After an Extreme Storm: The Typhoon-Induced Response of Micro- and Mesoplastics in Coastal Waters

Extreme storms, such as tropical cyclones, are responsible for a significant portion of the plastic debris transported from land to sea yet little is known about the storm response of microplastics and other debris in offshore and open waters. To investigate this, we conducted floating plastic surveys in the center of Sagami Bay, Japan approximately 30 km from the coastline, before and after the passage of a typhoon. The concentrations (number of particles/km2) of micro- and mesoplastics were two orders of magnitude higher 1-day after the typhoon than the values recorded pre-typhoon and the mass (g/km2) of plastic particles (sum of micro- and mesoplastics) increased 1,300 times immediately after the storm. However, the remarkably high abundance of micro- and mesoplastics found at 1-day after the typhoon returned to the pre-typhoon levels in just 2 days. Model simulations also suggested that during an extreme storm a significant amount of micro- and mesoplastics can be rapidly swept away from coastal to open waters over a short period of time. To better estimate the annual load of plastics from land to sea it is important to consider the increase in leakages of plastic debris into the ocean associated with extreme storm events.

A Robust Method for Detecting Wind-Fallen Stems from Aerial RGB Images Using a Line Segment Detection Algorithm

Increased frequencies and windspeeds of storms may cause disproportionately high increases in windthrow damage. Storm-felled trees provide a surplus of breeding material for bark beetles, often resulting in calamities in the subsequent years. Thus, the timely removal of fallen trees is regarded as a good management practice that requires strategic planning of salvage harvesting. Precise information on the number of stems and their location and orientation are needed for the efficient planning of strip roads and/or cable yarding lines. An accurate assessment of these data using conventional field-based methods is very difficult and time-consuming; remote sensing techniques may be a cost-efficient alternative. In this research, a methodology for the automatic detection of fallen stems from aerial RGB images is presented. The presented methodology was based on a line segment detection algorithm and proved to be robust regarding image quality. It was shown that the method can detect frequency, position, spatial distribution and orientation of fallen stems with high accuracy, while stem lengths were systematically underestimated. The methodology can be used for the optimized planning of salvage harvesting in the future and may thus help to reduce consequential bark beetle calamities after storm events.

Underwater Photogrammetry Captures the Initial Recovery of a Coral Reef at Lalo Atoll

Recovery of coral reefs after physical damage sustained from storm events can be affected by various factors. Here, we examined the initial recovery of a coral reef at the southern end of uninhabited Lalo Atoll of Papahānaumokuākea Marine National Monument after its complete destruction by Hurricane Walaka in 2018. While the site was still mostly (98%) covered by a mixture of rubble and sand, surveys utilizing underwater photogrammetry allowed for detailed quantitative assessments of benthic cover and confirmed colonization of coral (Pocillopora meandrina and Porites lobata), macroalgae and sponges. The proportion of sand in the rubble–sand mixture also decreased from the level observed in 2019. Visual fish surveys confirmed the presence of 35 reef fish species, a large increase from no reef fish in 2019, despite the low biotic benthic cover. Overall, the colonization of benthic organisms and the return of reef fish, which is potentially supported by the benthos and cryptofauna in the rubble bed, offer positive signs of reef recovery. The photogrammetric surveys in the present study captured the subtle changes in the benthic cover and provided us with a procedure to continue monitoring the succession of the site. Continuous monitoring of the site should reveal whether the reef returns to the original state of Acropora coral dominance or progresses towards a coral assemblage with a different composition.

Preliminary Model-Based Evaluation of Water Conservation Strategies in a Semi-Arid Urban Zone

The U.S. Environmental Protection Agency stormwater management model was applied to a semi-arid urban micro watershed. The sub-catchment’s current features were modeled as scenario A, while the insertion of a set of LID technologies (rain barrels, bioretention cells, permeable pavement, and infiltration trenches) was represented as scenario B. A third scenario (C), considering only the most feasible LID technologies, was also modeled. All the scenarios were evaluated under two representative storm events (30 and 9 mm in two consecutive days, and 39 mm of rainfall in one day) occurred during the sampling performed in this study. Water quality was also simulated for a 30-mm storm event and compared against field assessment results after a real 30-mm storm event. Through the model, the inefficiency of current evacuation methods after 30- and 39-mm storm events was demonstrated. Simulation of scenario B showed that LID technologies could satisfactorily diminish peak flows generated by the selected storm events as well as runoff-conveyed pollution, while the realistic scenario allowed a lower but satisfactory hydrological performance and almost the same runoff quality than scenario B. This preliminary study could contribute to spread awareness about the benefits of LID technologies in semi-arid urban areas of the developing world.

Comparative geomorphometric approach to understand the hydrological behaviour and identification of the Erosion prone areas of a coastal watershed using RS and GIS tools

Assessment of the geomorphometric parameters using Remote Sensing (RS) and Geographic Information System (GIS) tools forms an important part in routing the runoff and other hydrological processes. The current study uses a geospatial model based on geomorphometric parameters for the categorization of surface runoff and identification of the erosion-prone areas in the watershed of the Kuttiyadi River. The 4th order Kuttiyadi river is dominated by a dendritic to semi-dendritic drainage pattern in the subwatersheds. The linear aspect of the subwatersheds indicates towards the presence of permeable surface and subsurface materials with uniform lithology. The aerial and relief aspects of the subwatersheds shows fine drainage texture, gentle slopes, delayed peak flow, flatter hydrograph, and large concentration time which shows that subwatersheds are quite capable of managing flash floods during storm events. The estimated values of surface runoff (Q) and sediment production rate (SPR) are range from 2.13 to 32.88 km2-cm/km2 and 0.0004–0.017 Ha-m/100km2/year respectively and suggest that Subwatershed 1 (SW1) will generate more surface runoff and is prone to soil erosion followed by subwatershed 2 (SW2) in comparison to other subwatersheds. This paper aims to fill the knowledge gap regarding categorization of flow and erosion dynamics in a coastal river watershed. We believe that our work may work help in providing the crucial information for decision-makers and policymakers responsible for establishing suitable policies and sustainable land use practices for the watershed.

AVALIAÇÃO DAS CONSEQUÊNCIAS DO GALGAMENTO COSTEIRO DE 10/11 DE DEZEMBRO DE 2013 NA FRENTE URBANA DO CONCE-LHO DE SANTA CRUZ, ILHA DA MADEIRA

The expansion and consolidation of urban areas along the coast lead to the exposure of a large number of anthropic elements to sea storm events. It is with the aim of identifying and classifying the consequences of coastal overtopping that we analyse the recent storm surge of 10/11 December of 2013 which affected the urban coastal fringe of the city of Santa Cruz, on the south coast of Madeira Island. Therefore, the atmospheric conditions of wind intensity and atmospheric pressure are analysed and is characterised the direction of the waves and their maximum height, as well as the tide variation. After the oceanographic and meteorological characterization, it´s identified and evaluated the structural damage on the coastal zone and classified the functional consequences on the port structures affected by overtopping. The quantitative analysis of atmospheric and oceanographic data supports that this overtopping is a green water type resulting from an extratropical storm arising from the rapid reduction of atmospheric pressure and an average wind velocity of 30 km/h, affecting approximately 1600 meters of the coastal area of Santa Cruz. The qualitative analysis of the consequences shows that the port structures suffered severe to catastrophic consequences and dysfunction of their normal activities. The most affected areas are at the extremes of the urban front, registering reduced to insignificant consequences, in the intermediate area. Thus, it shows areas of greatest exposure, and areas that had presented seriously to severe damages, which can be highlighted in future episodes.

Detection of Windthrown Tree Stems on UAV-Orthomosaics Using U-Net Convolutional Networks

The increasing number of severe storm events is threatening European forests. Besides the primary damages directly caused by storms, there are secondary damages such as bark beetle outbreaks and tertiary damages due to negative effects on the market. These subsequent damages can be minimized if a detailed overview of the affected area and the amount of damaged wood can be obtained quickly and included in the planning of clearance measures. The present work utilizes UAV-orthophotos and an adaptation of the U-Net architecture for the semantic segmentation and localization of windthrown stems. The network was pre-trained with generic datasets, randomly combining stems and background samples in a copy–paste augmentation, and afterwards trained with a specific dataset of a particular windthrow. The models pre-trained with generic datasets containing 10, 50 and 100 augmentations per annotated windthrown stems achieved F1-scores of 73.9% (S1Mod10), 74.3% (S1Mod50) and 75.6% (S1Mod100), outperforming the baseline model (F1-score 72.6%), which was not pre-trained. These results emphasize the applicability of the method to correctly identify windthrown trees and suggest the collection of training samples from other tree species and windthrow areas to improve the ability to generalize. Further enhancements of the network architecture are considered to improve the classification performance and to minimize the calculative costs.