Description: The tides are a major driver of global oceanic mixing. While global tidal elevations are very well observed by satellite altimetry, the global tidal transports are much less well known. Since twenty years, magnetic signals induced by the ocean tide are detectable in satellite magnetometer observations as Swarm and CHAMP. Here, we successfully use said satellite magnetometer observations to directly derive global ocean tidal transports.

Description: This dataset contains predictions of Earth orientation parameters (EOP) submitted during the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC). The 2nd EOP PCC has been carried out by Centrum Badań Kosmicznych Polskiej Akademii Nauk CBK PAN in Warsaw in cooperation with the GFZ German Research Centre for Geosciences in Potsdam (Germany) and under the auspices of the International Earth Rotation and Reference Systems Service (IERS) within the IERS Working Group on the 2nd EOP PCC. The purpose of the campaign was to re-assess the current capabilities of EOP forecasting and to find most reliable prediction approaches. The operational part of the campaign lasted between September 1, 2021 and December 28, 2022. Throughout the duration of the 2nd EOP PCC, registered campaign participants submitted forecasts for all EOP parameters, including dX, dY, dPsi, dEps (components of celestial pole offsets), polar motion, differences between universal time and coordinated universal time, and its time-derivative length-of-day change. These submissions were made to the EOP PCC Office every Wednesday before the 20:00 UTC deadline. The predictions were then evaluated once the geodetic final EOP observations from the forecasted period became available. Each participant could register more than one method, and each registered method was assigned an individual ID, which was used, e.g., for file naming. The dataset contains text files with predicted parameters as submitted by campaign participants and MATLAB file which is a database with all correct predictions from each participant loaded into a structure.

Description: Modern geodetic observations of the Earth's shape, external gravity field, and orientation in space reflect temporal variations of those quantities that are caused by a wide range of dynamics acting at the surface of our planet as well as deep within the Earth's interior. Global numerical models of those processes which initially are independent from geodetic observations are critically important for the utilization of geodetic data in the other branches of physical Earth sciences. By means of dedicated examples from the Earth System Modelling group at GFZ, we show how (i) a priori information about global ocean tides including minor tides reduces temporal aliasing artefacts; (ii) machine learning techniques guided by land-surface model output can help to downscale coarse resolution satellite data; and (iii) the joint consideration of expertise from both solid Earth geophysics and large-scale hydrosphere dynamics can help to discriminate between gravity field changes induced by glacial isostatic adjustment and nearby terrestrial water storage changes. We will also highlight various model-based data products specifically designed for geodetic applications that are routinely updated at GFZ and provided to the scientific community free of charge as a contribution to the international geodetic data infrastructure. Those data products include crustal deformations due to tidal and non-tidal surface loads; time-variations in gravity induced by mass redistributions in atmosphere and oceans; as well as effective angular momentum functions that characterize changes in the Earth's orientation.

Description: This dataset contains 92 estimates from individual studies for groundwater recharge rates on the Arabian Peninsula. Following information is sorted for each study: Location information: Country, Latitude*, Longitude* Estimated groundwater recharge rate: Representative value, Lower/Upper estimate range (all in mm/yr) Estimating methods** Scale of study: Aquifer scale, Study period, Study years Credibility***: Confidence, Confidence criteria (*) Location information was set as the middle point of the study area, in case that spatial coordinates are not given by the authors. (**) If more than 1 methods were used for the estimation, additional methods were written in "Method_2" (***) Confidence of estimates was evaluated by the same criteria used in another meta-study for the African continent (MacDonald et al. 2021; https://doi.org/10.1088/1748-9326/abd661) This dataset has been used to train the neural network model targeting global-scale estimation of groundwater recharge rate together with datasets used in other meta-studies. More detailed information is provided in the paper "Can eXplainable AI offer a new perspective for groundwater recharge estimation? – Global-scale modeling using neural network“.

Description: Due to the difficulties in estimating groundwater recharge and cross-boundary nature of many aquifers, estimating groundwater recharge at large scale has been called upon. Process-based models as well as data-driven models have been established to meet this need. Meanwhile, with the advent of explainable artificial intelligence (XAI) methods, data-driven machine learning models can take advantage of enhanced explainability while keeping the strength of high flexibility. In this study, an ensemble neural network model was built to check the suitability of the model to predict groundwater recharge and the possibility to gain new insights from large data set. Recent large inputs of groundwater recharge data and additional input for the Arabian Peninsula collated in this study were fed to the model with multiple predictors related to climatology considering seasonality, soil and plant characteristics, topography, and hydrogeology. The model showed higher performance (adjusted R2: 0.702, RMSE: 193.35 mm yr−1) than a recent global process-based model in predicting groundwater recharge. Using XAI methods as individual conditional expectations and Shapley Additive Explanation interaction values, the model behavior was analyzed and possible linear and non-linear relationships between the predictors and the groundwater recharge rate were found. Long-term averaged precipitation and enhanced vegetation index showed non-linear relationships with groundwater recharge rate, while slope, compound topographic index, and water table depth showed low importance to the model results. Most model behaviors followed the domain knowledge, while multi-correlation between predictors and data skewness hindered the model from learning.