Core Ideas: We provide an overview of the TERENO‐Rur hydrological observatory. We present information on general physical characteristics of the Rur catchment. Ongoing interdisciplinary research aims to advance understanding of complex hydrological processes. We provide an overview of the Rur hydrological observatory, which is the main observational platform of the TERENO (TERrestrial ENvironmental Observatories) Eifel/Lower Rhine Valley Observatory. The Rur catchment area exhibits distinct gradients in altitude, climate, land use, soil properties, and geology. The Eifel National Park is situated in the southern part of the Rur catchment and serves as a reference site for the hydrological observatory. We present information on general physical characteristics of the Rur catchment and describe the main features of the multi‐scale and multi‐compartment monitoring framework. In addition, we also present some examples of the ongoing interdisciplinary research that aims to advance the understanding of complex hydrological processes and interactions within the Rur catchment.

Soil moisture is an essential climate variable of the Earth system. Understanding its spatiotemporal dynamics is essential for predicting weather patterns and climate variability, monitoring and mitigating the effects and occurrence of droughts and floods, improving irrigation in agricultural areas, and sustainably managing water resources. Here we review in depth how soils can remember information on soil moisture anomalies over time, as embedded in the concept of soil moisture memory (SMM). We explain the mechanisms underlying SMM and explore its external and internal drivers; we also discuss the impacts of SMM on different land surface processes, focusing on soil‐plant‐atmosphere coupling. We explore the spatiotemporal variability, seasonality, locality, and depth‐dependence of SMM and provide insights into both improving its characterization in land surface models and using satellite observations to quantify it. Finally, we offer guidance for further research on SMM.

Core Ideas: To determine the water retention curve from inverse modeling, θ and ψ need to be monitored. δ ¹⁸ O ratios contained information to inversely estimate soil hydraulic parameters. Different observation types should be combined in a single OF to estimate parameters. Averages of local measurement could be described using effective parameters. Accurate measurement of fluxes at lysimeter boundaries improved model parameterization. Accurate estimates of soil hydraulic parameters and dispersivities are crucial to simulate water flow and solute transport in terrestrial systems, particularly in the vadose zone. However, parameters obtained from inverse modeling can be ambiguous when identifying multiple parameters simultaneously and when boundary conditions are not well known. Here, we performed an inverse modeling study in which we estimated soil hydraulic parameters and dispersivities of layered soils from soil water content, matric potential, and stable water isotope (δ ¹⁸ O) measurements in weighable lysimeter systems. We used different optimization strategies to investigate which observation types are necessary for simultaneously estimating soil hydraulic and solute transport parameters. Combining water content, matric potential, and tracer (e.g., δ ¹⁸ O) data in one objective function (OF) was found to be the best strategy for estimating parameters that can simulate all observed water flow and solute transport variables. A sequential optimization, in which first an OF with only water flow variables and subsequently an OF with transport variables was optimized, performed slightly worse indicating that transport variables contained additional information for estimating soil hydraulic parameters. Hydraulic parameters that were obtained from optimizing OFs that used either water contents or matric potential could not predict non‐measured water flow variables. When a bromide (Br ⁻ ) tracer experiment was simulated using the optimized parameters, the arrival time of the bromide pulse was underestimated. This suggested that Br ⁻ sorbed onto clay minerals and amorphous oxides under the prevailing geochemical conditions with low pH values. When accounting for anion adsorption in the simulation, Br ⁻ concentrations were well predicted, which validated the dispersivity parameterization.