Forecasting shoreline evolution for sandy coasts is important for sustainable coastal management, given the present-day increasing anthropogenic pressures and a changing future climate. Here, we evaluate eight different time-series forecasting methods for predicting future shorelines derived from historic satellite-derived shorelines. Analyzing more than 37,000 transects around the globe, we find that traditional forecast methods altogether with some of the evaluated probabilistic Machine Learning (ML) time-series forecast algorithms, outperform Ordinary Least Squares (OLS) predictions for the majority of the sites. When forecasting seven years ahead, we find that these algorithms generate better predictions than OLS for 54% of the transect sites, producing forecasts with, on average, 29% smaller Mean Squared Error (MSE). Importantly, this advantage is shown to exist over all considered forecast horizons, i.e., from 1 up to 11 years. Although the ML algorithms do not produce significantly better predictions than traditional time-series forecast methods, some proved to be significantly more efficient in terms of computation time. We further provide insight in how these ML algorithms can be improved so that they can be expected to outperform not only OLS regression, but also the traditional time-series forecast methods. These forecasting algorithms can be used by coastal engineers, managers, and scientists to generate future shoreline prediction at a global level and derive conclusions thereof.
We developed a sensitive method for determination of polyethylene glycols (PEGs) of different molecular weight (MW) in the human stratum corneum (SC) obtained by tape stripping. The analysis is based on derivatization with pentafluoropropionic anhydride (PFPA) and gas chromatography-electron capture detection (GC-ECD). The identification and quantification of PEGs was done using individual oligomers. The method showed to be suitable for studying permeability in normal and impaired skin with respect to MW in the range of 150-600 Da.
<p><span>The coastal community widely anticipates that in the next years</span> <span>data-driven studies are going to make essential contributions to bringing</span> <span>about long-term coastal adaptation and mitigation strategies at continental</span> <span>scale</span><span>. This view is also supported by CoCliCo</span><span>,</span> <span>a Horizon 2020 project, where coastal data form the fundamental</span> <span>building block for an open-web portal that</span> <span>aims to improve decision making on coastal risk management and</span> <span>adaptation. The promise of data is likely triggered by several coastal</span> <span>analyses </span><span>that showed how the </span><span>coastal zone can be be monitored at unprecedented spatial scales using geospatial cloud platforms . </span><span>However, we note that when analyses become more complex,</span> <span>i.e., require specific algorithms, pre- and post-processing or include</span> <span>data that are not hosted by the cloud provider, the cloud-native</span> <span>processing workflows are often broken, which makes analyses</span><span> at continental scale impractical.<br></span></p><p><span>We believe that the next generation of data-driven coastal models</span> <span>that target continental scales can only be built when: 1) processing</span> <span>workflows are scalable; 2) computations are run in proximity to the</span> <span>data; 3) data are available in cloud-optimized formats; 4) and, data</span> <span>are described following standardized metadata specifications. In this</span> <span>study, we introduce these practices to the coastal research community</span> <span>by showcasing the advantages of cloud-native workflows </span><span>by two case</span> <span>studies.</span></p><p><span>In the first example we map building footprints in areas prone</span> <span>to coastal flooding and estimate the assets at risk. For this</span> <span>analysis we chunk a coastal flood-risk map into several tiles and</span> <span>incorporate those into a coastal SpatioTemporal Asset Catalog</span> <span>(STAC). The second example benchmarks instantaneous shoreline</span> <span>mapping using cloud-native workflows against conventional methods.</span> <span>With data-proximate computing, processing time is reduced from the</span> <span>order of hours </span><span>to seconds per shoreline km, which means</span> <span>that a highly-specialized coastal mapping expedition can be upscaled</span> <span>from regional to global level.</span></p><p><span>The analyses mostly rely on "core-packages" from the Pangeo</span> <span>project</span><span>, with some additional support for scalable geospatial</span> <span>data analysis and cloud I/O, although they can essentially be</span> <span>run on a standard Python Planetary Computer instance. We</span> <span>publish our code, including self-explanatory Juypter notebooks, at</span> <span>https://github.com/floriscalkoen/egu2023</span><span>.</span></p><p><span>To conclude, we foresee that in next years several coastal data</span> <span>products are going to be published, of which some may be considered</span> <span>"big data". To incorporate these data products into the next generation</span> <span>of coastal models, it is urgently required to agree upon protocols for</span> <span>coastal data stewardship. With this study we do not only want to</span> <span>show the advantages of scalable coastal data analysis; we mostly want</span> <span>to encourage the coastal research community to adopt FAIR data</span> <span>management principles </span><span>and workflows in an era of exponential data</span> <span>growth.</span></p>
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