Frontiers in Earth Science
About: Frontiers in Earth Science is an academic journal published by Frontiers Media. The journal publishes majorly in the area(s): Geology & Environmental science. It has an ISSN identifier of 2296-6463. It is also open access. Over the lifetime, 7011 publications have been published receiving 47800 citations.
TL;DR: In this paper, the authors review the state of citizen science in a hydrological context and explore the potential for citizen science to complement more traditional ways of scientific data collection and knowledge generation.
Abstract: The participation of the general public in the research design, data collection and interpretation process together with scientists is often referred to as citizen science. While citizen science itself has existed since the start of scientific practice, developments in sensing technology, data processing and visualisation, and communication of ideas and results, are creating a wide range of new opportunities for public participation in scientific research. This paper reviews the state of citizen science in a hydrological context and explores the potential of citizen science to complement more traditional ways of scientific data collection and knowledge generation for hydrological sciences and water resources management. Although hydrological data collection often involves advanced technology, the advent of robust, cheap and low-maintenance sensing equipment provides unprecedented opportunities for data collection in a citizen science context. These data have a significant potential to create new hydrological knowledge, especially in relation to the characterisation of process heterogeneity, remote regions, and human impacts on the water cycle. However, the nature and quality of data collected in citizen science experiments is potentially very different from those of traditional monitoring networks. This poses challenges in terms of their processing, interpretation, and use, especially with regard to assimilation of traditional knowledge, the quantification of uncertainties, and their role in decision support. It also requires care in designing citizen science projects such that the generated data complement optimally other available knowledge. Lastly, we reflect on the challenges and opportunities in the integration of hydrologically-oriented citizen science in water resources management, the role of scientific knowledge in the decision-making process, and the potential contestation to established community institutions posed by co-generation of new knowledge.
TL;DR: In this paper, the Global Glacier Evolution Model (GloGEM) is proposed for calculating the 21st century mass changes of all glaciers on Earth outside the ice sheets. But the model is not suitable for the analysis of large-scale glaciers.
Abstract: The anticipated retreat of glaciers around the globe will pose far-reaching challenges to the management of fresh water resources and significantly contribute to sea-level rise within the coming decades. Here, we present a new model for calculating the 21st century mass changes of all glaciers on Earth outside the ice sheets. The Global Glacier Evolution Model (GloGEM) includes mass loss due to frontal ablation at marine-terminating glacier fronts and accounts for glacier advance/retreat and surface Elevation changes. Simulations are driven with monthly near-surface air temperature and precipitation from 14 Global Circulation Models forced by the RCP2.6, RCP4.5 and RCP8.5 emission scenarios. Depending on the scenario, the model yields a global glacier volume loss of 25-48% between 2010 and 2100. For calculating glacier contribution to sea-level rise, we account for ice located below sea-level presently displacing ocean water. This effect reduces glacier contribution by 11-14%, so that our model predicts a sea-level equivalent (multi-model mean +-1 standard deviation) of 79+-24 mm (RCP2.6), 108+-28 mm (RCP4.5) and 157+-31 mm (RCP8.5). Mass losses by frontal ablation account for 10% of total ablation globally, and up to 30% regionally. Regional equilibrium line altitudes are projected to rise by 100-800 m until 2100, but the effect on ice wastage depends on initial glacier hypsometries.
TL;DR: Efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale and in terms of classification accuracy, the neural network based approach outperformed support vector machine, decision tree and random forest classifiers available in GEE.
Abstract: Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g. country level) and multiple sensors (e.g. Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory (~28,100 km2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.
TL;DR: In this article, high-resolution digital elevation models (DEMs) from available sub-meter commercial stereo imagery (DigitalGlobe WorldView-1/2/3 and GeoEye-1) acquired over HMA glaciers from 2007-2018 (primarily 2013-2017).
Abstract: High-mountain Asia (HMA) constitutes the largest glacierized region outside of the Earth's polar regions. Although available observations are limited, long-term records indicate sustained HMA glacier mass loss since ~1850, with accelerated loss in recent decades. Recent satellite data capture the spatial variability of this mass loss, but spatial resolution is coarse and some estimates for regional and HMA-wide mass loss disagree. To address these issues, we generated 5797 high-resolution digital elevation models (DEMs) from available sub-meter commercial stereo imagery (DigitalGlobe WorldView-1/2/3 and GeoEye-1) acquired over HMA glaciers from 2007–2018 (primarily 2013–2017). We also reprocessed 28278 ASTER DEMs over HMA from 2000–2018. We combined these observations to generate robust elevation change trend maps and geodetic mass balance estimates for 99% of HMA glaciers between 2000 and 2018. We estimate total HMA glacier mass change of -19.0±2.5 Gt yr-1 (-0.19±0.03 m w.e. yr-1). We document the spatial pattern of HMA glacier mass change with unprecedented detail, and present aggregated estimates for HMA glacierized sub-regions and hydrologic basins. Our results offer improved estimates for the HMA contribution to global sea level rise in recent decades with total cumulative sea-level rise contribution of ~0.7 mm from exorheic basins between 2000 and 2018. We estimate that the range of excess glacier meltwater runoff due to negative glacier mass balance in each basin constitutes ~12-53% of the total basin-specific glacier meltwater runoff. These results can be used for calibration and validation of glacier mass balance models, satellite gravimetry observations, and hydrologic models needed for present and future water resource management.
TL;DR: In this paper, a review of up-to-date information on climate and hydrological variability, and on warming trends in Amazonia, with 2016 as the warmest year since at least 1950 (0.9 °C + 0.3°C).
Abstract: This paper shows recent progress in our understanding of climate variability and trends in the Amazon region, and how these interact with land use change. The review includes an overview of up-to-date information on climate and hydrological variability, and on warming trends in Amazonia, which reached 0.6-0.7 °C over the last 40 years, with 2016 as the warmest year since at least 1950 (0.9 °C +0.3°C). We focus on local and remote drivers of climate variability and change. We review the impacts of these drivers on the length of dry season, the role of the forest in climate and carbon cycles, the resilience of the forest, the risk of fires and biomass burning, and the potential “die back” of the Amazon forests if surpassing a “tipping point”. The role of the Amazon in moisture recycling and transport is also investigated, and a review of model development for climate change projections in the region is included. In sum, future sustainability of the Amazonian forests and its many services requires management strategies that consider the likelihood of multi-year droughts superimposed on a continued warming trend. Science has assembled enough knowledge to underline the global and regional importance of an intact Amazon region that can support policymaking and to keep this sensitive ecosystem functioning. This major challenge requires substantial resources and strategic cross-national planning, and a unique blend of expertise and capacities established in Amazon countries and from international collaboration. This also highlights the role of deforestation control in in support of policy for mitigation options as established in the Paris Agreement of 2015.