Offshore wind farms are widely adopted by coastal countries to obtain clean and green energy; their environmental impact has gained an increasing amount of attention. Although offshore wind farm datasets are commercially available via energy industries, records of the exact spatial distribution of individual wind turbines and their construction trajectories are rather incomplete, especially at the global level. Here, we construct a global remote sensing-based offshore wind turbine (OWT) database derived from Sentinel-1 synthetic aperture radar (SAR) time-series images from 2015 to 2019. We developed a percentile-based yearly SAR image collection reduction and autoadaptive threshold algorithm in the Google Earth Engine platform to identify the spatiotemporal distribution of global OWTs. By 2019, 6,924 wind turbines were constructed in 14 coastal nations. An algorithm performance analysis and validation were performed, and the extraction accuracies exceeded 99% using an independent validation dataset. This dataset could further our understanding of the environmental impact of OWTs and support effective marine spatial planning for sustainable development.
This paper analyses the spatio-temporal trends and variability in annual, seasonal, and monthly rainfall with corresponding rainy days in Bhilangana river basin, Uttarakhand Himalaya, based on stations and two gridded products. Station-based monthly rainfall and rainy days data were obtained from the India Meteorological Department (IMD) for the period from 1983 to 2008 and applied, along with two daily rainfall gridded products to establish temporal changes and spatial associations in the study area. Due to the lack of more recent ground station rainfall measurements for the basin, gridded data were then used to establish monthly rainfall spatio-temporal trends for the period 2009 to 2018. The study shows all surface observatories in the catchment experienced an annual decreasing trend in rainfall over the 1983 to 2008 period, averaging 15.75 mm per decade. Analysis of at the monthly and seasonal trend showed reduced rainfall for August and during monsoon season as a whole (10.13 and 11.38 mm per decade, respectively); maximum changes were observed in both monsoon and winter months. Gridded rainfall data were obtained from the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). By combining the big data analytical potential of Google Earth Engine (GEE), we compare spatial patterns and temporal trends in observational and modelled precipitation and demonstrate that remote sensing products can reliably be used in inaccessible areas where observational data are scarce and/or temporally incomplete. CHIRPS reanalysis data indicate that there are in fact three significantly distinct annual rainfall periods in the basin, viz. phase 1: 1983 to 1997 (relatively high annual rainfall); phase 2: 1998 to 2008 (drought); phase 3: 2009 to 2018 (return to relatively high annual rainfall again). By comparison, PERSIANN-CDR data show reduced annual and winter precipitation, but no significant changes during the monsoon and pre-monsoon seasons from 1983 to 2008. The major conclusions of this study are that rainfall modelled using CHIRPS corresponds well with the observational record in confirming the decreased annual and seasonal rainfall, averaging 10.9 and 7.9 mm per decade respectively between 1983 and 2008, although there is a trend (albeit not statistically significant) to higher rainfall after the marked dry period between 1998 and 2008. Long-term variability in rainfall in the Bhilangana river basin has had critical impacts on the environment arising from water scarcity in this mountainous region.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.