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A full introduction to geodetic data and systems written by well-known experts in their respective fields, this book is an ideal text for courses in geodesy and geomatics covering everything from coordinate and gravimetry data to geodetic systems of all types.

The primary objective of the 1-cm geoid experiment in Colorado (USA) is to compare the numerous geoid computation methods used by different groups around the world. This is intended to lay the foundations for tuning computation methods to achieve the sought after 1-cm accuracy, and also evaluate how this accuracy may be robustly assessed. In this experiment, (quasi)geoid models were computed using the same input data provided by the US National Geodetic Survey (NGS), but using different methodologies. The rugged mountainous study area (730 km × 560 km) in Colorado was chosen so as to accentuate any differences between the methodologies, and to take advantage of newly collected GPS/leveling data of the Geoid Slope Validation Survey 2017 (GSVS17) which are now available to be used as an accurate and independent test dataset. Fourteen groups from fourteen countries submitted a gravimetric geoid and a quasigeoid model in a 1′ × 1′ grid for the study area, as well as geoid heights, height anomalies, and geopotential values at the 223 GSVS17 marks. This paper concentrates on the quasigeoid model comparison and evaluation, while the geopotential value investigations are presented as a separate paper (Sánchez et al. in J Geodesy 95(3):1. https://doi.org/10.1007/s00190-021-01481-0 , 2021). Three comparisons are performed: the area comparison to show the model precision, the comparison with the GSVS17 data to estimate the relative accuracy of the models, and the differential quasigeoid (slope) comparison with GSVS17 to assess the relative accuracy of the height anomalies at different baseline lengths. The results show that the precision of the 1′ × 1′ models over the complete area is about 2 cm, while the accuracy estimates along the GSVS17 profile range from 1.2 cm to 3.4 cm. Considering that the GSVS17 does not pass the roughest terrain, we estimate that the quasigeoid can be computed with an accuracy of ~ 2 cm in Colorado. The slope comparisons show that RMS values of the differences vary from 2 to 8 cm in all baseline lengths. Although the 2-cm precision and 2-cm relative accuracy have been estimated in such a rugged region, the experiment has not reached the 1-cm accuracy goal. At this point, the different accuracy estimates are not a proof of the superiority of one methodology over another because the model precision and accuracy of the GSVS17-derived height anomalies are at a similar level. It appears that the differences are not primarily caused by differences in theory, but that they originate mostly from numerical computations and/or data processing techniques. Consequently, recommendations to improve the model precision toward the 1-cm accuracy are also given in this paper.

The Exploration of energization and Radiation in Geospace (ERG) Science Center serves as a hub of the ERG project, providing data files in a common format and developing the space physics environment data analysis software and plug-ins for data analysis. The Science Center also develops observation plans for the ERG (Arase) satellite according to the science strategy of the project. Conjugate observations with other satellites and ground-based observations are also planned. These tasks contribute to the ERG project by achieving quick analysis and well-organized conjugate ERG satellite and ground-based observations.

Per- and polyfluoroalkyl substances (PFAS) are a class of fluorinated chemicals used widely in consumer and industrial products. Their human toxicity and ecosystem impacts have received extensive public, scientific and regulatory attention. Regulatory PFAS guidance is rapidly evolving, with the inclusion of a wider range of PFAS included in advisories and a continued decrease in what is deemed safe PFAS concentrations. In this study we collated PFAS concentration data for over 45,000 surface and groundwater samples from around the world to assess the global extent of PFAS contamination and their potential future environmental burden. Here we show that a substantial fraction of sampled waters exceeds PFAS drinking water guidance values, with the extent of exceedance depending on the jurisdiction and PFAS source. Additionally, current monitoring practices probably underestimate PFAS in the environment given the limited suite of PFAS that are typically quantified but deemed of regulatory concern. An improved understanding of the range of PFAS embodied in consumer and industrial products is required to assess the environmental burden and develop mitigation measures. While PFAS is the focus of this study, it also highlights society’s need to better understand the use, fate and impacts of anthropogenic chemicals. A global data analysis suggests that a large fraction of surface waters and groundwaters globally have concentrations of per- and polyfluoroalkyl substances (PFAS) that exceed international advisories or national regulations.

Vertical land movements can cause regional relative sea-level changes to differ substantially from climate-driven absolute sea-level changes. Whereas absolute sea level has been accurately monitored by satellite altimetry since 1992, there are limited observations of vertical land motion. Vertical land motion is generally modelled as a linear process, despite some evidence of nonlinear motion associated with tectonic activity, changes in surface loading or groundwater extraction. As a result, the temporal evolution of vertical land motion, and its contribution to projected sea-level rise and its uncertainty, remains unresolved. Here we generate a probabilistic vertical land motion reconstruction from 1995 to 2020 to determine the impact of regional-scale and nonlinear vertical land motion on relative sea-level projections up to 2150. We show that regional variations in projected coastal sea-level changes are equally influenced by vertical land motion and climate-driven processes, with vertical land motion driving relative sea-level changes of up to 50 cm by 2150. Accounting for nonlinear vertical land motion increases the uncertainty in projections by up to 1 m on a regional scale. Our results highlight the uncertainty in future coastal impacts and demonstrate the importance of including nonlinear vertical land motions in sea-level change projections. A probabilistic reconstruction of vertical land motion reveals regional variations in relative sea-level changes and large uncertainties in sea-level projections due to nonlinear effects.

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

Effective land-based solutions to climate change mitigation require actions that maximize soil carbon storage without generating surplus nitrogen. Land management for carbon sequestration is most often informed by bulk soil carbon inventories, without considering the form in which carbon is stored, its capacity, persistency and nitrogen demand. Here, we present coupling of European-wide databases with soil organic matter physical fractionation to determine continental-scale forest and grassland topsoil carbon and nitrogen stocks and their distribution between mineral-associated and particulate organic matter pools. Grasslands and arbuscular mycorrhizal forests store more soil carbon in mineral-associated organic carbon, which is more persistent but has a higher nitrogen demand and saturates. Ectomycorrhizal forests store more carbon in particulate organic matter, which is more vulnerable to disturbance but has a lower nitrogen demand and can potentially accumulate indefinitely. The share of carbon between mineral-associated and particulate organic matter and the ratio between carbon and nitrogen affect soil carbon stocks and mediate the effects of other variables on soil carbon stocks. Understanding the physical distribution of organic matter in pools of mineral-associated versus particulate organic matter can inform land management for nitrogen-efficient carbon sequestration, which should be driven by the inherent soil carbon capacity and nitrogen availability in ecosystems.

Monitoring and predicting fault slip behaviors in subduction zones is essential for understanding earthquake cycles and assessing future earthquake potential. We developed a data assimilation method for fault slip monitoring and the short-term prediction of slow slip events, and applied to the 2010 Bungo Channel slow slip event in southwest Japan. The observed geodetic data were quantitatively explained using a physics-based model with data assimilation. We investigated short-term predictability by assimilating observation data within limited periods. Without prior constraints on fault slip style, observations solely during slip acceleration predicted the occurrence of a fast slip; however, the inclusion of slip deceleration data successfully predicted a slow transient slip. With prior constraints to exclude unstable slip, the assimilation of data after slow slip event occurrence also predicted a slow transient slip. This study provides a tool using data assimilation for fault slip monitoring and prediction based on real observation data. Graphical Abstract

Karst regions offer a variety of natural resources such as freshwater and biodiversity, and many cultural resources. The World Karst Aquifer Map (WOKAM) is the first detailed and complete global geodatabase concerning the distribution of karstifiable rocks (carbonates and evaporites) representing potential karst aquifers. This study presents a statistical evaluation of WOKAM, focusing entirely on karst in carbonate rocks and addressing four main aspects: (1) global occurrence and geographic distribution of karst; (2) karst in various topographic settings and coastal areas; (3) karst in different climatic zones; and (4) populations living on karst. According to the analysis, 15.2% of the global ice-free continental surface is characterized by the presence of karstifiable carbonate rock. The largest percentage is in Europe (21.8%); the largest absolute area occurs in Asia (8.35 million km2). Globally, 31.1% of all surface exposures of carbonate rocks occur in plains, 28.1% in hills and 40.8% in mountains, and 151,400 km or 15.7% of marine coastlines are characterized by carbonate rocks. About 34.2% of all carbonate rocks occur in arid climates, followed by 28.2% in cold and 15.9% in temperate climates, whereas only 13.1 and 8.6% occur in tropical and polar climates, respectively. Globally, 1.18 billion people (16.5% of the global population) live on karst. The highest absolute number occurs in Asia (661.7 million), whereas the highest percentages are in Europe (25.3%) and North America (23.5%). These results demonstrate the global importance of karst and serve as a basis for further research and international water management strategies.