Abstract. Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation. The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021).

Abstract. Heavy Precipitation Events (HPE) are the result of enormous quantities of water vapour being transported to a limited area. HPE rainfall rates and volumes cannot not be fully stored on and below the land surface, often leading to floods with short forecast lead times that may cause damage to humans, properties, and infrastructure. Towards an improved scientific understanding of the entire process chain from HPE formation to flooding at the catchment scale, we propose an elaborated event-triggered observation concept. It combines flexible mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics. As part of the Helmholtz Research Infrastructure MOSES (Modular Observation Solutions for Earth Systems), the added value of our observation strategy is exemplarily shown by its first implementation in the Mueglitz river basin (210 km2), a headwater catchment of the Elbe in the Eastern Ore Mountains with historical and recent extreme flood events. Punctual radiosonde observations combined with continuous microwave radiometer measurements and back trajectory calculations deliver information about the moisture sources, initiation and development of HPE X-Band radar observations calibrated by ground based disdrometers and rain gauges deliver precipitation information with high spatial resolution. Runoff measurements in small sub-catchments complement the discharge times series of the operational network of gauging stations. Closing the catchment water balance at the HPE scale, however, is still challenging. While evapotranspiration is of less importance when studying short term convective HPE, information on the spatial distribution and on temporal variations of soil moisture and total water storage by stationary and roving cosmic ray measurements and by hybrid terrestrial gravimetry offer prospects for improved quantification of the storage term of the water balance equation. Overall, the cross-disciplinary observation strategy presented here opens up new ways towards an integrative and scale-bridging understanding of event dynamics.

<p>Climate change, urbanization, and growing population have led to the rapid increase in the use of groundwater. Therefore, monitoring the groundwater (GW) changes is essential for water management and decision-makers. Due to frequent lack of reliable and sufficient in-situ information, remote sensing and hydrological models can be counted as the alternative sources for assessing GW storage changes on a regional and global scale. Here, we test such an approach for Qazvin Plain in Iran, one of the regions that recently have been facing severe drought conditions. The main purpose of this study is to downscale GW storage anomaly (GWSA) of the WaterGAP Global Hydrology Model (WGHM) from a coarse (0.5-degree) to a finer spatial resolution (0.1-degree) using fine spatial resolution auxiliary datasets (0.1-degree) such as the evaporation, surface and subsurface runoff, snow depth, volumetric soil water, and soil temperature from the ERA5-Land model and precipitation from integrated multi-satellite retrievals for global precipitation measurement (IMERG). Different regression models were tested for the GWSA downscaling. Moreover, since different water budget components such as precipitation or storage are known to have temporal lead or lag relative to each other, the approach also includes a time shift factor among the components. The most suitable regression model with the highest skill score during the validation test was selected and applied to predict the 0.1-degree GWSA. The downscaled results showed a high agreement with the in-situ groundwater levels for Qazvin Plain in both interannual and monthly scales, with a correlation coefficient of 0.99 and 0.65, respectively. Moreover, the downscaled product clearly proves that the developed downscaling technique is able to learn from high-resolution auxiliary data to capture GWSA features at higher spatial resolution. The major benefit of this method is in utilizing only the auxiliary data that are available with global coverage and free of cost, and that this method does not need in-situ GW records for training. Therefore, the proposed downscaling technique can potentially be applied to a global scale, other geographical regions, or aquifers.</p><p>This study has received funding from the European Union’s Horizon 2020 research and innovation programme for G3P (Global Gravity-based Groundwater Product) under grant agreement nº 870353.</p>

<p>The Global Gravity-based Groundwater Product (G3P) aims at developing a satellite-based groundwater storage (GW) data set as a new product for the EU Copernicus Climate Change Service. As the world’s largest distributed freshwater storage, GW is a key resource for mankind, industrial, and agricultural demands. In Copernicus, there is no service available yet to deliver data on this fundamental resource, nor is there any other data source worldwide that operationally provides information on changing groundwater resources in a consistent way, observation-based, and with global coverage. Therefore, G3P develops an operational global groundwater service as a cross-cutting extension of the existing Copernicus portfolio. G3P capitalizes from the unique capability of GRACE and GRACE-FO satellite gravimetry as the only remote sensing technology to monitor subsurface mass variations, and from other satellite-based water storage products to provide a data set of groundwater storage change for large areas with global coverage. G3P is obtained by using a mass balance approach, i.e., by subtracting satellite-based water storage compartments (WSCs) such as snow water equivalent, root-zone soil moisture, glacier mass, and surface water storage from GRACE/GRACE-FO monthly terrestrial water storage anomalies (TWSA). For a consistent subtraction of all individual WSCs from GRACE-TWSA, the individual WSCs are filtered in a similar way as GRACE-TWSA, where optimal filter types were derived by analyses of spatial correlation patterns. G3P groundwater variations are provided for almost two decades (from 2002 to the present), with the monthly resolution, and at a 0.5-degree spatial resolution globally. In this contribution, we also illustrate preliminary results of the G3P data set and of its uncertainties, as well as its evaluation by independent groundwater data.</p><p>This study has received funding from the European Union’s Horizon 2020 research and innovation programme for G3P (Global Gravity-based Groundwater Product) under grant agreement nº 870353.</p>