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.

Cosmic radiation on Earth responds to heliospheric, geomagnetic, atmospheric, and lithospheric changes. In order to use its signal for soil hydrological monitoring, the signal of thermal and epithermal neutron detectors needs to be corrected for external influencing factors. However, theories about the neutron response to soil water, air pressure, air humidity, and incoming cosmic radiation are still under debate. To challenge these theories, we isolated the neutron response from almost any terrestrial changes by operating bare and moderated neutron detectors in a buoy on a lake in Germany from July 15 to December 02, 2014. We found that the count rate over water has been better predicted by a recent theory compared to the traditional approach. We further found strong linear correlation parameters to air pressure and air humidity for epithermal neutrons, while thermal neutrons responded differently. Correction for incoming radiation proved to be necessary for both thermal and epithermal neutrons, for which we tested different neutron monitors and correction methods. Here, the conventional approach worked best with the Jungfraujoch monitor in Switzerland, while the approach from a recent study was able to adequately rescale data from more remote neutron monitors. However, no approach was able to sufficiently remove the signal from a major Forbush decrease event, to which thermal and epithermal neutrons showed a comparatively strong response. The buoy detector experiment provided a unique dataset for empirical testing of traditional and new theories on CRNS. It could serve as a local alternative to reference data from remote neutron monitors.

Robust estimation of average soil water content with spatial resolution of a few tens to a few hundreds of meters is essential for evaluating models or data assimilation products. Due to the high spatial variability of soil moisture at the point scale, sufficient coverage of spatial observations is required to estimate a robust field average. If sensors fail over time, averaging the remaining measurements risks the introduction of artificial shifts in the resulting time series. Here, we explore the problem of using incomplete soil moisture observations to estimate spatial averages and propose a correction accounting for temporal persistence of spatial patterns. By transforming, i.e. upscaling, each sensor measurement to the field scale using information from time periods with sufficient coverage, the dependence on full spatial coverage can be decreased. The transformed values allow to build a more robust approximation to the spatial mean, even when spatial coverage becomes sparse. We found that high temporal stability of the sensors does not necessarily guarantee that the transformed time series will provide a good estimate of the mean and therefore recommend the use of robust statistics to derive the field mean, which requires at least three estimates per observation time. The proposed protocol is applicable for observational time series with varying sample size across a given spatial extent, and it can be adopted for other variables exhibiting a temporally stable bias between the individual point observations and field scale average.

Large-scale measurements of the spatial distribution of water content in soils and snow are challenging for state-of-the-art hydrogeophysical methods. Cosmic-ray neutron sensing (CRNS) is a non-invasive technology that has the potential to bridge the scale gap between conventional in-situ sensors and remote-sensing products in both, horizontal and vertical domains. In this study we explore the feasibility and potential of estimating water content in soils and snow with neutron detectors in moving trains. Theoretical considerations quantify the stochastic measurement uncertainty as a function of water content, altitude, resolution, and detector efficiency. Numerical experiments demonstrate that the sensitivity of measured water content is almost unperturbed by train materials. And finally three distinct real world experiments provide a proof of concept on short and long-range tracks. With our results a trans-regional observational soil moisture product becomes a realistic vision within the next years.