The need to develop and provide integrated observation systems to better understand and manage global and regional environmental change is one of the major challenges facing Earth system science today. In 2008, the German Helmholtz Association took up this challenge and launched the German research infrastructure TERrestrial ENvironmental Observatories (TERENO). The aim of TERENO is the establishment and maintenance of a network of observatories as a basis for an interdisciplinary and long-term research programme to investigate the effects of global environmental change on terrestrial ecosystems and their socio-economic consequences. State-of-the-art methods from the field of environmental monitoring, geophysics, remote sensing, and modelling are used to record and analyze states and fluxes in different environmental disciplines from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. Over the past 15 years we have collectively gained experience in operating a long-term observing network, thereby overcoming unexpected operational and institutional challenges, exceeding expectations, and facilitating new research. Today, the TERENO network is a key pillar for environmental modelling and forecasting in Germany, an information hub for practitioners and policy stakeholders in agriculture, forestry, and water management at regional to national levels, a nucleus for international collaboration, academic training and scientific outreach, an important anchor for large-scale experiments, and a trigger for methodological innovation and technological progress. This article describes TERENO’s key services and functions, presents the main lessons learned from this 15-year effort, and emphasises the need to continue long-term integrated environmental monitoring programmes in the future.

Here, we review in depth how soils can remember moisture anomalies across spatial and temporal scales, embedded in the concept of soil moisture memory (SMM), and we explain the mechanisms and factors that initiate and control SMM. Specifically, we explore external and internal drivers that affect SMM, including extremes, atmospheric variables, anthropogenic activities, soil and vegetation properties, soil hydrologic processes, and groundwater dynamics. We analyze how SMM considerations should affect sampling frequency and data source collection. We discuss the impact of SMM on weather variability, land surface energy balance, extreme events (drought, wildfire, and flood), water use efficiency, and biogeochemical cycles. We also discuss the effects of SMM on various land surface processes, focusing on the coupling between soil moisture, water and energy balance, vegetation dynamics, and feedback on the atmosphere. We address the spatiotemporal variability of SMM and how it is affected by seasonal variation, location, and soil depth. Regarding the representation and integration of SMM in land surface models, we provide insights on how to improve predictions and parameterizations in LSMs and address model complexity issues. The possible use of satellite observations for identifying and quantifying SMM is also explored, emphasizing the need for greater temporal frequency, spatial resolution, and coverage of measurements. We provide guidance for further research and practical applications by providing a comprehensive definition of SMM, considering its multifaceted perspective.

NASA’s Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error <0.04 m3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the ESA Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio-temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

Long Short-Term Memory (LSTM) networks are a deep learning technology to exploit long-term dependencies in the input-output relationship, which has been observed in the response of groundwater dynamics to atmospheric and land surface processes. We introduced an indirect method based on LSTM networks to estimate monthly water table depth anomalies (wtd_a) across Europe from monthly precipitation anomalies (pr_a). The network has further been optimized by including supplementary hydrometeorological variables, which are routinely measured and available at large scales. The data were obtained from daily integrated hydraulic simulation results over Europe from 1996 to 2016, with a spatial resolution of 0.11° (Furusho-Percot et al., 2019), and separated into a training set, a validation set and a test set at individual pixels. We compared test performances of the LSTM networks locally at selected pixels in eight PRUDENCE regions with random combinations of monthly pr_a, evapotranspiration anomaly, and soil moisture anomaly (θ_a) as input variables. The optimal combination of input variables was pr_a and θ_a, and the networks with this combination achieved average test R^2 between 47.88% and 91.62% in areas with simulated wtd ≤ 3 m. Moreover, we found that introducing θ_a improved the ability of the trained networks to handle new data, indicating the substantial contribution of θ_a to explain groundwater state variation. Therefore, including information about θ_a is beneficial, for instance in the estimation of groundwater drought, and the proposed optimized method may be transferred to a real-time monitoring of groundwater drought at the continental scale using remotely sensed soil moisture observations. Furusho-Percot, C., Goergen, K., Hartick, C., Kulkarni, K., Keune, J. and Kollet, S.: Pan-European groundwater to atmosphere terrestrial systems climatology from a physically consistent simulation, Sci. data, 6(1), 320, doi:10.1038/s41597-019-0328-7, 2019.