The purpose of this article is to analyse and describe the extreme characteristics of the water levels and illustrate them as the topography of the sea surface along the whole Baltic Sea coast. The general pattern is to show the maxima and minima of Baltic Sea water levels and the extent of their variations in the period from 1960 to 2010. A probability analysis is carried out on the annual sea level maxima and minima for 31 water level gauges in order to define the probability of occurrence of theoretical sea levels once in a specific number of years. The spatial distribution of sea levels for hundred-year maximum and minimum water levels is illustrated. Then, the number of storm surges for the accepted criteria are presented: these numbers increased in the 50-year period analysed. The final part of the work analyses some extreme storm events and calculates the static value and dynamic deformation of the sea surface by mesoscale, deep low-pressure systems

Cliff coasts are dynamic environments that can retreat very quickly. However, the short-term changes and factors contributing to cliff coast erosion have not received as much attention as dune coasts. In this study, three soft-cliff systems in the southern Baltic Sea were monitored with the use of terrestrial laser scanner technology over a period of almost two years to generate a time series of thirteen topographic surveys. Digital elevation models constructed for those surveys allowed the extraction of several geomorphological indicators describing coastal dynamics. Combined with observational and modeled datasets on hydrological and meteorological conditions, descriptive and statistical analyses were performed to evaluate cliff coast erosion. A new statistical model of short-term cliff erosion was developed by using a non-parametric Bayesian network approach. The results revealed the complexity and diversity of the physical processes influencing both beach and cliff erosion. Wind, waves, sea levels, and precipitation were shown to have different impacts on each part of the coastal profile. At each level, different indicators were useful for describing the conditional dependency between storm conditions and erosion. These results are an important step toward a predictive model of cliff erosion.

Terefenko, P.; Giza, A.; Paprotny, D.; Kubicki, A., and Winowski, M., 2018. Cliff Retreat Induced by Series of Storms at Międzyzdroje (Poland). In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 181–185. Coconut Creek (Florida), ISSN 0749-0208. In Międzyzdroje, a coastal town in Poland, significant cliff retreat has been observed in recent times. It used to be considered mainly a response to storm events with particularly high water levels and wave energy. However, morphology of cliff coasts is shaped not only by the most extreme storm surges or by a number of accompanying processes such as precipitation. Much wider effects are now being linked to the occurrence of series of subsequent storms. This research uses a set of five terrestrial LiDAR surveys carried out between November 2016 and April 2017 to determine short-term cliff erosion associated with t...

A prerequisite for solving issues associated with surf zone variability, which affect human activity in coastal zones, is an accurate estimation of the effects of coastal protection methods. Therefore, performing frequent monitoring activities, especially when applying new nature-friendly coastal defense methods, is a major challenge. In this manuscript, we propose a pipeline for performing low-cost monitoring using RGB images, accessed by an unmanned aerial vehicle (UAV) and a four-level analysis architecture of an underwater object detection methodology. First, several color-based pre-processing activities were applied. Second, contrast-limited adaptive histogram equalization and the Hough transform methodology were used to automatically detect the underwater, circle-shaped elements of a hybrid coastal defense construction. An alternative pipeline was used to detect holes in the circle-shaped elements with an adaptive thresholding method; this pipeline was subsequently applied to the normalized images. Finally, the concatenation of the results from both the methods and the validation processes were performed. The results indicate that our automated monitoring tool works for RGB images captured by a low-cost consumer UAV. The experimental results showed that our pipeline achieved an average error of four pixels in the test set.

The burgeoning demand for critical metals used in high-tech and green technology industries has turned attention toward the vast resources of polymetallic nodules on the ocean floor. Traditional methods for estimating the abundance of these nodules, such as direct sampling or acoustic imagery are time and labour-intensive or often insufficient for large-scale or accurate assessment. This paper advocates for the automatization of polymetallic nodules detection and abundance estimation using deep learning algorithms applied to seabed photographs. We propose UNET convolutional neural network framework specifically trained to process the unique features of seabed imagery, which can reliably detect and estimate the abundance of polymetallic nodules based on thousands of seabed photographs in significantly reduced time (below 10 h for 30 thousand photographs). Our approach addresses the challenges of data preparation, variable image quality, coverage-abundance transition model and sediments presence. We indicated the utilization of this approach can substantially increase the efficiency and accuracy of resource estimation, dramatically reducing the time and cost currently required for manual assessment. Furthermore, we discuss the potential of this method to be integrated into large-scale systems for sustainable exploitation of these undersea resources.