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.

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.

Land surface modeling as a tool to explore sustainable irrigation practices in Mediterranean fruit orchardsO. Dombrowski1, C. Brogi1, H.-J. Hendricks Franssen1, V. Pisinaras2, A. Panagopoulos2, S. Swenson3, H. Bogena11Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich 52425, Germany2Soil & Water Resources Institute, Hellenic Agricultural Organization, Gorgopotamou, Sindos, Thessaloniki 57400, Greece3Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder 80305, Colorado, United States of AmericaCorresponding author: Olga Dombrowski ([email protected])

Cosmic ray neutron sensors (CRNS) allow to determine field-scale soil moisture content non-invasively due to the dependence of aboveground measured epithermal neutrons on the amount of hydrogen. Because other pools besides soil contain hydrogen (e.g. biomass), it is necessary to consider these for accurate soil moisture content measurements, especially when they are changing dynamically (e.g., arable crops, de- and reforestation). In this study, we compare four approaches for the correction of biomass effects on soil moisture content measurements with CRNS using experiments with three crops (sugar beet, winter wheat and maize) on similar soils: I) site-specific functions based on in-situ measured biomass, II) a generic approach, III) the thermal-to-epithermal neutron ratio (Nr) and IV) the thermal neutron intensity. Calibration of the CRNS during bare soil conditions resulted in root mean square errors (RMSE) of 0.097, 0.041 and 0.019 m3/m3 between estimated and reference soil moisture content of the cropped soils, respectively. Considering in-situ measured biomass for correction reduced the RMSE to 0.015, 0.018 and 0.009 m3/m3. When thermal neutron intensity was considered for correction, similarly accurate results were obtained. Corrections based on Nr and the generic approach were less accurate. We also explored the use of CRNS for biomass estimation. The use of Nr only provided accurate biomass estimates for sugar beet. However, significant site-specific relationships between biomass and thermal neutron intensity were obtained for all three crops. It was concluded that thermal neutron intensity can be used to correct soil moisture content estimates from CRNS and to estimate biomass.

In their study, Dong and Ochsner (2018) used an extensive dataset of 18 cosmic-ray neutron rover surveys to assess the influence of precipitation and soil texture on mesoscale soil moisture patterns. Based on their analysis, they concluded that soil texture, represented by sand content, often exerts a stronger influence on mesoscale soil moisture variability than precipitation, represented by the antecedent precipitation index. However, we consider that Dong and Ochsner (2018) made a mistake in their calculation of volumetric soil moisture, such that their analysis on the influence of soil texture on soil moisture is not valid. This result does however not bring into question the paper’s conclusion on the influence of soil texture on mesoscale soil moisture patterns.

The advance of the cosmic ray neutron (CRN) sensing method for estimating field scale soil moisture relied largely on simulations of the footprint properties of epithermal neutrons (~0.5 eV - 100 keV). Commercially available CRN probes are usually additionally equipped with a thermal neutron (< 0.5 eV) detector. The potential of these measurements is rarely explored because relevant features of thermal neutrons, such as the footprint and the sensitivity to soil moisture are unknown. Here, we used neutron transport modeling and a river crossing experiment to assess the thermal neutron footprint. We found that the horizontal thermal neutron footprint ranges between 43 and 48 m distance from the probe and that the vertical footprint extends to soil depths between 10 and 65 cm depending on soil moisture. Furthermore, we derived weighting functions that quantify the footprint characteristics of thermal neutrons. These results will enable new applications of thermal neutrons.