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Environmental thresholds. tipping points and subsequent regime shifts associated with the water/climate/greenhouse gas nexus pose a genuine threat to sustainability. Both the ongoing forest dieback in Central Europe caused by the extreme droughts of the last years and the effect of global warming on ecosystem functioning have the potential to cause ecological surprise (sensu Lindenmayer et al. 2010) where ecosystems are pushed into new, unexpected and usually undesirable states.Formulating appropriate scientific and societal responses to such regime shifts requires breadth, depth, intensity and duration of environmental, ecological and socio-ecological monitoring. Broad geographic coverage to encompass relevant biophysical and societal gradients, consideration of all appropriate parameters, adequate measurement frequency and long-term, standardized observations are all needed to provide reliable early warnings of severe environmental change, test ecosystem models, avoid double counting in carbon accounting and to reduce the likelihood of undesirable ecological outcomes. This is especially true of events driven by simultaneous changes in climate, the water cycle and human activities.Well-supported, site-based research infrastructures (RIs; e.g., eLTER and ICOS) are essential tools with the necessary breadth, depth, intensity and duration for early detection and attribution of environmental change. Individually, the eLTER and ICOS RIs generate a wealth of data supporting the ecosystem and carbon research communities. Achieving synergies between the two RIs can add value to both communities and potentially offer meaningful insight into the European water-climate-greenhouse gas nexus.The unique insights into processes and mechanisms of ecosystem dynamics and functioning obtained from high intensity monitoring conducted by the ICOS RI greatly increase the likelihood of detecting signals of environmental change. These signals must be placed into the context of their long-term trajectory and potential societal and environmental drivers. The spatially extensive, long-term, multi-disciplinary monitoring conducted at LTER sites and LTSER platforms under the umbrella of the eLTER programme can provide this context.Here, we outline one potential roadmap for achieving synergies between the ICOS and eLTER RIs focussing on the value of co-location for improved understanding of the water/climate/greenhouse gas nexus. Based on data and experiences from intensively studied research sites, we highlight some of the possibilities for reducing the likelihood of ecological surprise that could result from such synergies.Lindenmayer, D.B., Likens, G.E., Krebs, C.J. and Hobbs, R.J., 2010. Improved probability of detection of ecological “surprises”. Proceedings of the National Academy of Sciences, 107(51), pp.21957-21962.

The International Soil Modeling Consortium (ISMC) was established in 2016 with the aim to integrate and advance soil systems modeling, data collection, and observational capabilities. ISMC is a community effort accepting members from all fields (https://soil-modeling.org/). Its activities are organized around three science panels: data and observation model linking (DO-LINK), soil modeling development and intercomparison (Soil-MIP), and cross cutting activities (CROSS-Connect). ISMC fosters development of new soil models for quantifying soil functions and their changes with land use, climate and degradation. Recently, a number of model intercomparison studies have been initiated, focusing on specific processes such as root growth and water uptake, soil evaporation, soil freezing, soil heat fluxes, infiltration and runoff processes, and the coupling of soil preferential flow with groundwater. ISMC is coordinating efforts to form a global soil data meta-repository that will be openly available for soil systems research. The meta-repository will be a resource to analyze past and recent model- and data requirements for continuous standardization and harmonization of soil data. ISMC also seeks to identify soil related knowledge gaps between science communities, and to share this knowledge with neighbouring disciplines to better translate soil processes into functions for assessing sustainability and ecosystem services. Finally, ISMC is involved in creating exchange platforms with other international partners like AgMip/MACSUR, GEWEX, ISCN (International Soil Carbon Network), and GSBI (Global Soil Biodiversity Initiative). In this presentation, we will highlight recent developments and activities and present future plans for engaging young scientists and communicating soil modelling with other disciplines and the public.

The International Soil Modeling Consortium (ISMC) was established in 2016 with the aim to integrate andadvance soil systems modeling, data collection, and observational capabilities. ISMC is a community effort. Itfollows a bottom-up approach, is based on scientific principles, voluntary contributions, and open to anybodywho wants to become a member of the consortium (https://soil-modeling.org/). Its activities are organized intothree science panels, organized around a broad workflow from data collection and observation (DO-LINK)to model development and intercomparison (Soil-MIP) to engagement with different scientific communities(CROSS-Connect). ISMC has an executive board and a scientific advisory board that guides ISMC in pursuing itsobjectives.The mission of the Soil-MIP science panel is to foster the further development of soil models that can predictsoil functions and their changes due to soil use and land management, as well as climate change and pollution.A number of model intercomparison studies were recently initiated, focusing on specific processes like soilhydraulic properties, soil thermal properties and related soil heat fluxes, root growth and root water uptake,soil evaporation, soil freezing, infiltration and runoff processes, and coupling between preferential flow in soilsand groundwater. From these comparisons, lessons can be learned about how process controls at small scalespropagate to larger scales and how this leads to effective process representations at larger scales. The missionof DO-Link is to assist with the development of a global soil data meta-repository that is openly availablefor soil system research. Data and data requirements are as diverse as the various disciplines developing andapplying soil models. The soil data meta-repository will be a resource to analyze past and recent model- anddata requirements for continuous standardization and harmonization of soil data. The CROSS-connect sciencepanel identifies soil related knowledge gaps between science communities, and shares this knowledge with otherISMC science panels to push forward development of new tools to better translate soil processes into functionsfor assessing sustainability and ecosystem services. CROSS-connect develops an exchange platform with otherinternational partners like AgMip/MACSUR, GEWEX, ISCN (International Soil Carbon Network), GSBI (GlobalSoil Biodiversity initiative) and CSDMS (Community Surface Dynamics Modeling System). In this presentation,we will highlight the most recent developments and activities that are currently underway as well as future projectsthat are under preparation.

Land surface models describe biogeophysical and biogeochemical processes at the land surface, and represent thelower boundary condition of atmospheric circulation models. Their key role is the quantification of mass and energyfluxes between the land surface and the atmosphere. Predictions with land surface models are affected by manyunknown parameters, uncertain meteorological forcings and incomplete process understanding. Measurement data(e.g., soil moisture) can help to improve land surface model predictions. However, soil moisture products obtainedfrom satellite remote sensing are normally available at a very coarse resolution and give information on the upperfew centimetres of the soil only. Therefore, the recently developed Cosmic Ray Neutron Sensors (CRNS) areof high interest for predictions by land surface models, because they measure neutron count intensity, which isrelated to integral soil moisture content at the field scale (about 20 hectares). High resolution land surface modelsare also able to provide solutions at the same scale. In this study, we investigated whether the assimilation ofsoil moisture retrievals from CRNS data measured by a network of nine CRNS located in the Rur catchmentin Germany (2354 km2) can improve land surface model prediction. The assimilation of soil moisture retrievalsfrom neutron count intensity data was used to update model states and parameters of the land surface modelCLM 4.5 over a two year time period, followed by a one year evaluation period without updates. This updatingwas done with the local ensemble transform Kalman filter. The real world experiment tested the value of CRNSusing jackknifing experiments and three different initial soil maps. During the assimilation period, soil moisturepredictions generally improved, for a biased soil map from an RMSE of 0.11 cm3/cm3 in the open loop run to0.03 cm3/cm3 while during the evaluation period soil moisture predictions improved from 0.12 cm3/cm3 to 0.06cm3/cm3. Improved soil parameters showed also an impact on latent heat and sensible heat flux, particularly duringthe summer period when evapotranspiration was soil moisture limited. The main conclusion from this study is thata network of CRNS is helpful to improve soil moisture predictions with a land surface model, in spite of the factthat in this study only nine CRNS were available for a catchment of size 2354 km2.

Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM(zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C3 grass and C3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ ∑ NEE) for the different ensemble members from ∼ 2 to 3 g C m−2 yr−1 (with uncertain parameters) to ∼ 45 g C m−2 yr−1 (C3 grass) and ∼ 75 g C m−2 yr−1 (C3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (σ ∑ NEE ∼ 4.0–13.5 g C m−2 yr−1 with perturbed parameters, meteorological forcings and initial states). We conclude that LAI and NEE uncertainty with CLM is clearly underestimated if uncertain meteorological forcings and initial states are not taken into account.

Advancing our understanding of Earth system dynamics (ESD) depends on the development of models and other analytical tools that apply physical, biological, and chemical data. This ambition to increase understanding and develop models of ESD based on site observations was the stimulus for creating the networks of Long-Term Ecological Research (LTER), Critical Zone Observatories (CZOs), and others. We organized a survey, the results of which identified pressing gaps in data availability from these networks, in particular for the future development and evaluation of models that represent ESD processes, and provide insights for improvement in both data collection and model integration.From this survey overview of data applications in the context of LTER and CZO research, we identified three challenges: (1) widen application of terrestrial observation network data in Earth system modelling, (2) develop integrated Earth system models that incorporate process representation and data of multiple disciplines, and (3) identify complementarity in measured variables and spatial extent, and promoting synergies in the existing observational networks. These challenges lead to perspectives and recommendations for an improved dialogue between the observation networks and the ESD modelling community, including co-location of sites in the existing networks and further formalizing these recommendations among these communities. Developing these synergies will enable cross-site and cross-network comparison and synthesis studies, which will help produce insights around organizing principles, classifications, and general rules of coupling processes with environmental conditions.

International audience; Advancing our understanding of Earth system dynamics (ESD) depends on the development of models and other analytical tools that apply physical, biological, and chemical data. This ambition to increase understanding and develop models of ESD based on site observations was the stimulus for creating the networks of Long-Term Ecological Research (LTER), Critical Zone Observatories (CZOs), and others. We organized a survey, the results of which identified pressing gaps in data availability from these networks, in particular for the future development and evaluation of models that represent ESD processes, and provide insights for improvement in both data collection and model integration. From this survey overview of data applications in the context of LTER and CZO research, we identified three challenges: (1) widen application of terrestrial observation network data in Earth system modelling, (2) develop integrated Earth system models that incorporate process representation and data of multiple disciplines, and (3) identify complementarity in measured variables and spatial extent, and promoting synergies in the existing observational networks. These challenges lead to perspectives and recommendations for an improved dialogue between the observation networks and the ESD modelling community, including co-location of sites in the existing networks and further formalizing these recommendations among these communities. Developing these synergies will enable cross-site and cross-network comparison and synthesis studies, which will help produce insights around organizing principles, classifications, and general rules of coupling processes with environmental conditions.

In-situ soil moisture sensors provide highly accurate but very local soil moisture measurements while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, Cosmic-Ray Neutron Sensors (CRNS) allow highly accurate soil moisture estimation at the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNS installed in the 2354 km2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNS were assimilated with the local ensemble transform Kalman filter in the Community Land Model v. 4.5. Data of four, eight and nine CRNS were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map soil moisture predictions improved strongly to a root mean square error of 0.03 cm3/cm3 for the assimilation period and 0.05 cm3/cm3 for the evaluation period. Improvements were limited by the measurement error of CRNS (0.03 cm3/cm3). The positive results obtained with data assimilation of nine CRNS were confirmed by the jackknife experiments with four and eight CRNS used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content at the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.

At the land surface, the hydrologic cycle is strongly linked to soil water content (SWC). Land surface models describe the mass and energy fluxes at the land surface and to the atmosphere with a spatial resolution of a few km$^{2}$ at the catchment scale. The calibration and evaluation of land surface models requests observation data, ideally at the same spatial resolution. SWC characterization by model prediction and observation remains a challenge in land surface hydrology. Recently, cosmic-ray probes (CRPs) were developed for continuous passive SWCe stimation from neutron flux measurements at a scale relevant for land surface models. One objective of this work was to set up a network of ten CRPs and to evaluate SWC estimates by CRPs. For evaluation, the footprint average SWC of the CRPs was compared to the horizontally and vertically weighted signal of two distributed networks of in-situ SWC sensors. Three different parameterization methods to estimate SWC from neutron flux were compared. Numerical complexity and potential applications distinguish the three parameterization methods. The three parameterization methods resulted in close SWC estimates at the ten study sites although the neutron flux – SWC relationships were slightly different amongst the parameterization methods. SWC estimated by the calibrated CRPs was very close to SWC measured by alternative SWC measurements. Root mean square errors (RMSEs) of the SWC were 0.031 cm$^{3}$/cm$^{3}$ at the distributed in-situ SWC sensor networks. [...]