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Maize production in low-yielding regions is influenced by climate variability, poor soil fertility, suboptimal agronomic practices, and biotic influences, among other limitations. Therefore, the assessment of yields to various management practices is, among others, critical for advancing site-specific measures for production enhancement. In this study, we conducted a multiseason calibration and evaluation of the DSSAT–CERES-Maize model to assess the maize yield response of two common cultivars grown in Trans Nzoia County in Kenya under various agricultural strategies, such as sowing dates, nitrogen fertilization, and water management. We then applied the Mann–Kendall (MK), and Sen’s Slope Estimator (SSE) tests to establish the yield trends and magnitudes of the different strategies. The evaluated model simulated long-term yields (1984–2021) and characterized production under various weather regimes. The model performed well in simulating the growth and development of the two cultivars, as indicated by the model evaluation results. The RMSE for yield was 333 and 239 kg ha−1 for H614 and KH600-23A, respectively, representing a relative error (RRMSE) of 8.1 and 5.1%. The management strategies assessment demonstrated significant feedback on sowing dates, nitrogen fertilization, and cultivars on maize yield. The sowing date conducted in mid-February under fertilization of 100 kg of nitrogen per hectare proved to be the best strategy for enhancing grain yields in the region. Under the optimum sowing dates and fertilization rate, the average yield for cultivar KH600-23A was 7.1% higher than that for H614. The MK and SSE tests revealed a significant (p

Soil moisture is a critical hydrological variable affecting rainfall-runoff processes, regulating net ecosystem exchange, and an essential agricultural variable that constrains food production. Soil water deficiency results in water stress to plants, causing a reduction in biomass and yield production. Thus, assessing soil moisture conditions and estimating the effects of drought or water excess are relevant and important information associated with a decline in agricultural yield. This study analyses time series data of the Cosmic Ray Neutron Sensor (CRNS) to estimate soil moisture in three different climatic zones with distinct seasonal dynamics. The CRNS allows non-invasive and continuous monitoring of soil moisture by detecting the neutrons generated by cosmic rays that mainly interact with the hydrogen atoms in soil water. This study demonstrates an improved CRNS signal processing to enhance the temporal accuracy and overall signal-to-noise ratio by observing sub-daily soil moisture changes. In particular, this study investigates the effectiveness of the Moving Average (MA), Median filter (MF), Savitzky-Golay (SG) filter, and Kalman filter (KF) to minimize errors in soil moisture estimates at distinct points in time. We anticipate the improved signal-to-noise ratio to benefit CRNS applications such as the detection of rain events at the sub-daily resolution, provision of SM at the exact time of a satellite overpass, and irrigation applications.

Water-use efficiency (WUE) is one of the major axes of ecosystem functioning and an indicator for ecosystem health, but its variability due to climate change and droughts has not yet been thoroughly understood. Here, we use remote sensing and reanalysis data to map the trends and responses to droughts of three WUE indices from 1995 – 2018 in Europe. Further, we conduct a causal network discovery analysis to identify drivers of in WUE change. We found an increasing trends of photosynthesis per canopy conductance (IWUE) in forests and grasslands. IWUE also increased during droughts over whole Europe but this was not translated into an increase of photosythesis per water evaporated (i.e. increased EWUE). We highlight that the WUE indices are predominantly explained by ecohydrological variability, which underlines the role of water demand and supply to ecosystem function in Europe.

The water-use efficiency (WUE, carbon assimilation per unit of water-use) describes a major axis of variability of ecosystems and identifies how these are coping with environmental changes. However, the response of WUE to climate change and hydrological extremes between different ecosystems remains poorly understood. Here we investigated how the WUE of ecosystems in Europe varied from 1995 - 2018, as long-term trends and in response to precipitation (P) and soil moisture (SM) droughts. We aggregated data from remote-sensing and reanalysis to calculate three different WUE indices, conducted Mann-Kendall trend analyses and determined WUE anomalies for different hydro-climates and plant functional types during P and SM deficits. Finally, we applied the Peter & Clark Momentary Conditional Independence (PCMCI) algorithm to identify causative networks of environmental variables and WUE and differences among ecosystems.We found extensive, negative long-term WUE trends in Eastern Europe, where WUE is predominantly controlled by carbon assimilation (GPP). Further, we identified soil moisture and transpiration control of GPP as drivers for the positive WUE response to droughts in arid ecosystems. In contrast, negative trends in humid ecosystems were driven mostly by temperature, which governed GPP variability.In addition, outputs from a state-of-the-art land-surface and carbon model (CLM5-BGC) will be used to compare trends, drought response and the causative relationships with the ones from satellite and reanalysis data in order to evaluate the model representation of ecosystem variability.

Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled "Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors", is shared via Forschungszentrum Julich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021)., Earth System Science Data, 14 (3), ISSN:1866-3516, ISSN:1866-3508

Reanalysis data, already used to understand terrestrial processes on the physical land surface, the carbon cycle, and the hydrologic cycle, is now being applied to terrestrial ecosystems.

Human-caused climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns and reduced snow occurrence. For example, Europe has suffered from drought events in the last decade like never since the beginning of weather recording. Here we present soil moisture data from 65 Cosmic-ray neutron sensors (CRNS) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 23 research institutions and processed using state-of-the-art methods. The harmonised processing included correction of the raw neutron counts, and a harmonised methodology for the conversion into soil moisture based on available in-situ information. In addition, information on the data uncertainty is provided with the dataset, information that is particularly useful for remote sensing and modelling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products as well as first results on how the recent drought events have been captured by the CRNS network. This harmonised European soil moisture dataset will help both hydrologists and climate scientists to study individual drought events, to understand their causes, to evaluate and improve their modelling, and to estimate the extremity of current events. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021).

Soil organic matter (SOM) is a critical ecosystem variable regulated by interacting physical, chemical and biological processes. Collaborative efforts to integrate perspectives, data, and models from interdisciplinary research and observation networks can significantly advance predictive understanding of SOM. We outline how integrating three networks – the Long‐Term Ecological Research, with a focus on ecological dynamics, the Critical Zone Observatories with strengths in landscape/geologic context, and the National Ecological Observatory Network with standardized multi‐scale measurements—can advance SOM knowledge. This integration requires improved data dissemination and sharing, coordinated data collection activities, and enhanced collaboration between empiricists and modelers within and across networks.

A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic‐abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics., Plain Language Summary: A reanalysis is a unique set of continuous variables produced by optimally merging a numerical model and observed data. The data are merged with the model using available uncertainty estimates to generate the best possible estimate of the target variables. The framework for generating a reanalysis consists of the model, the data, and the model‐data‐fusion algorithm. The very specific requirements of reanalysis frameworks have led to the development of Earth‐compartment specific reanalysis for the atmosphere, the ocean and land. Here, we review atmospheric and oceanic reanalyzes, and in more detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic, and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Based on a review of existing achievements, we identify five major steps required to develop reanalysis for terrestrial ecosystem to shed more light on biotic and abiotic interactions. In the future, terrestrial ecosystem reanalysis will deliver continuous data streams on the state and the development of terrestrial ecosystems., Key Points: Reanalyzes provide decades‐long model‐data‐driven harmonized and continuous data sets for new scientific discoveries. Novel global scale reanalyzes quantify the biogeochemical ocean cycle, terrestrial carbon cycle, land surface, and hydrologic processes. New observation technology and modeling capabilities allow in the near future production of advanced terrestrial ecosystem reanalysis., European Union's Horizon 2020 research and innovation programme, Deutsche Forschungsgemeinschaft, U.S. Department of Energy, Emory University's Halle Institute for Global Research and the Halle Foundation Collaborative Research, NSF, NASA, Natural Environment Research Council, European Union'’s Horizon 2020 research and innovation programme, NSERC Discovery program, the Ocean Frontier Institute, and MEOPAR, Research Foundation Flanders (FWO), Helmholtz Association, NASA Terrestrial Ecosystems

Hydrological extremes in Europe, such as droughts, are expected to increase in frequency and severity with advancement of climate change. The consequences for ecosystem functioning and processes, including biomass production and evapotranspiration, have not yet been thoroughly mapped. Ecosystem water use efficiency (WUE) describes the amount of carbon assimilated as biomass per unit of water. WUE was examined for various case studies and global assessments, yet disagreements in the methodologic approach and uncertainties hinder generic understanding of WUE variability. As a link between the carbon cycle, water cycle and vegetation states, disclosure of WUE courses across European ecosystems enables important estimates of past, present and future ecosystem dynamics. Here we generated a long-term and high resolution observational and reanalysis data-set of WUE over Europe by interpolation of high level observation products (GLASS, CRU TS v4) and reanalysis data sets (ERA5-Land, COSMO-REA6, ESSMRA) to a 3 x 3km grid. This European Drought and Water Use Efficiency data-set (EDWUE) contains variables for calculating WUE using three different approaches, as well as indicators of meteorological and agricultural droughts. Drought effects on WUE will be analyzed to investigate the sensitivity of ecosystem processes to extreme weather conditions at regional and local scale by comparison of WUE and drought indices time-series. Spatiotemporal analyses of the EDWUE data-set across European ecosystems will discover differences in patterns and potential trends of WUE between regions and decode the dependencies on ecosystem composition, geographical characteristics, and climate and occurring weather extremes. Intercomparisons between the different WUE calculations will allow to draw conclusions on the roots of particular WUE dynamics.

The novel method of Cosmic-ray neutron sensing (CRNS) allows non-invasive soil moisture measurements at a hectometer scaled footprint. Up to now, the conversion of soil moisture to a detectable neutron count rate relies mainly on the equation presented by Desilets et al. (2010). While in general a hyperbolic expression can be derived from theoretical considerations, their empiric parameterisation needs to be revised for two reasons. Firstly, a rigorous mathematical treatment reveals that the values of the four parameters are ambiguous because their values are not independent. We find a 3-parameter equation with unambiguous values of the parameters which is equivalent in any other respect to the 4-parameter equation. Secondly, high-resolution Monte-Carlo simulations revealed a systematic deviation of the count rate to soil moisture relation especially for extremely dry conditions as well as very humid conditions. That is a hint, that a smaller contribution to the intensity was forgotten or not adequately treated by the conventional approach. Investigating the above-ground neutron flux by a broadly based Monte-Carlo simulation campaign revealed a more detailed understanding of different contributions to this signal, especially targeting air humidity corrections. The packages MCNP and URANOS were used to derive a function able to describe the respective dependencies including the effect of different hydrogen pools and the detector-specific response function. The new relationship has been tested at three exemplary measurement sites and its remarkable performance allows for a promising prospect of more comprehensive data quality in the future.

The precise estimation and mapping of the near-surface soil moisture (~5cm, SM5cm) is key to supporting sustainable water management plans in Mediterranean agroforestry environments. In the past few years, time series of Synthetic Aperture Radar (SAR) data retrieved from Sentinel-1 (S1) enable the estimation of SM5cm at relatively high spatial and temporal resolutions. The present study focuses on developing a reliable and flexible framework to map SM5cm in a small-scale agroforestry experimental site (~30 ha) in southern Italy over the period from November 2018 to March 2019. Initially, different SAR-based polarimetric parameters from S1 (in total 62 parameters) and hydrologically meaningful topographic attributes from a 5-m Digital Elevation Model (DEM) were derived. These SAR and DEM-based parameters, and two supporting point-scale estimates of SM5cm were used to parametrize a Random Forest (RF) model. The inverse modeling module of the Hydrus-1D model enabled to simulate two supporting estimates of SM5cm by using i) sparse soil moisture data at the soil depths of 15 cm and 30 cm acquired over 20 locations comprised in a SoilNet wireless sensor network (SoilNet-based approach), and ii) field-scale soil moisture monitored by a Cosmic-Ray Neutron Probe (CRNP-based approach). In the CRNP-based approach, the field-scale SM5cm was further downscaled to obtain point-scale supporting SM5cm data over the same 20 positions by using the physical-empirical Equilibrium Moisture from Topography (EMT) model. Our results show that the CRNP-based approach can provide reasonable SM5cm retrievals with RMSE values ranging from 0.034 to 0.050 cm³ cm-3 similar to the ones based on the SoilNet approach ranging from 0.029 to 0.054 cm³ cm-3. This study highlights the effectiveness of integrating S1 SAR-based measurements, topographic attributes, and CRNP data for mapping SM5cm at the small agroforestry scale with the advantage of being non-invasive and easy to maintain.

Investigations of neutron transport through air and soil by Monte Carlo simulations led to major advancements toward a precise interpretation of measurements; they particularly improved the understanding of the cosmic-ray neutron footprint. Up to now, the conversion of soil moisture to a detectable neutron count rate has relied mainly on the equation presented by Desilets and Zreda in 2010. While in general a hyperbolic expression can be derived from theoretical considerations, their empiric parameterization needs to be revised for two reasons. Firstly, a rigorous mathematical treatment reveals that the values of the four parameters are ambiguous because their values are not independent. We found a three-parameter equation with unambiguous values of the parameters that is equivalent in any other respect to the four-parameter equation. Secondly, high-resolution Monte-Carlo simulations revealed a systematic deviation of the count rate to soil moisture relation especially for extremely dry conditions as well as very humid conditions. That is a hint that a smaller contribution to the intensity was forgotten or not adequately treated by the conventional approach. Investigating the above-ground neutron flux through a broadly based Monte-Carlo simulation campaign revealed a more detailed understanding of different contributions to this signal, especially targeting air humidity corrections. The packages MCNP and URANOS were used to derive a function able to describe the respective dependencies, including the effect of different hydrogen pools and the detector-specific response function. The new relationship has been tested at two exemplary measurement sites, and its remarkable performance allows for a promising prospect of more comprehensive data quality in the future.

Accurate near-surface soil moisture (θ; ~ 5 cm) estimation is one of the most crucial challenges in agricultural management and hydrological studies. This study aims to map θ at high spatiotemporal resolution (17 m grid size, satellite overpass of 6 days) in a small-scale agroforestry experimental site (~ 30 ha) in southern Italy. The observation period is from November 2018 until March 2019. We employed an ensemble machine-learning method based on Random Forest (RF) to map θ. This RF method is based on three input data types: i) Sentinel-1 (S1) Synthetic Aperture Radar (SAR) measurements, ii) terrain features, and iii) supporting values of sparse point-scale θ simulated in HYDRUS-1D. We propose two different approaches to obtain supporting θ simulations via inverse modeling in HYDRUS-1D. The first approach is based on θ simulated in HYDRUS-1D, which was calibrated on soil moisture data monitored at two soil depths of 15 cm and 30 cm over 20 positions belonging to the SoilNet wireless sensor network installed in the experimental site. The second approach is based on the downscaling of field-scale θ simulated in HYDRUS-1D which was calibrated on Cosmic-Ray Neutron Probe (CRNP) data. The field-scale θ was downscaled in order to obtain sparse point-scale supporting θ over the same 20 positions by using the physical-empirical Equilibrium Moisture from Topography (EMT) model. The CRNP-based approach performed similarly to the one based on SoilNet data. Therefore, this study highlights the enormous potential for modeling reliable θ maps by integrating soft data such as S1 SAR-based measurements, topographic information, and CRNP data, having the advantage of being non-invasive and easy to maintain.

Soil moisture plays an important role in the coupled water and energy cycles of the terrestrial system. However, the characterization of soil moisture at the large spatial scale is far from trivial. To cope with this challenge, the combination of data from different sources (in situ measurements by cosmic ray neutron sensors, remotely sensed soil moisture and simulated soil moisture by models) is pursued. This is done by multiscale data assimilation, to take the different resolutions of the data into account. A large number of studies on the assimilation of remotely sensed soil moisture in land surface models has been published, which show in general only a limited improvement in the characterization of root zone soil moisture, and no improvement in the characterization of evapotranspiration. In this study it was investigated whether an improved modelling of soil moisture content, using a simulation model where the interactions between the land surface, surface water and groundwater are better represented, can contribute to extracting more information from SMAP data. In this study over North-Rhine-Westphalia, the assimilation of remotely sensed soil moisture from SMAP in the coupled land surface-subsurface model TSMP was tested. Results were compared with the assimilation in the stand-alone land surface model CLM. It was also tested whether soil hydraulic parameter estimation in combination with state updating could give additional skill compared to assimilation in CLM stand-alone and without parameter updating. Results showed that modelled soil moisture by TSMP did not show a systematic bias compared to SMAP, whereas CLM was systematically wetter than TSMP. Therefore, no prior bias correction was needed in the data assimilation. The results illustrate how the difference in simulation model and parameter estimation result in significantly different estimated soil moisture contents and evapotranspiration.

The current document provides an overview of the data and Information and Communications Technology (ICT) requirements of the users emerging from the sciences cases in the eLTER PLUS project (see paragraph 3.1 and 3.2 for the description of the users identified for defining user requirements). These findings are used to derive functional (i.e. the behavior of essential features of the system) and non-functional (i.e. the general characteristics of the system) requirements for the Information Technology (IT) infrastructure and services, as well as to focus on data needs for large-scale analyses. The content will be used to design and further develop IT components in WP11. This document is the first iteration of user requirements and their implications for eLTER ICT development presenting high-level user stories collected following a user-centric approach. As development proceeds during the project and research advances, it will certainly be a living document in the sense that acquired knowledge will inform and influence further iterations of development, for instance by updating the proposed use cases in cooperation with stakeholders.

Cosmic ray probes are an emerging technology to continuously monitor soil water content at a scale significant to land surface processes. However, the application of this method is hampered by its susceptibility to the presence of aboveground biomass. Here we present a simple empirical framework to account for moderation of fast neutrons by aboveground biomass in the calibration. The method extends the N0-calibration function and was developed using an extensive data set from a network of 10 cosmic ray probes located in the Rur catchment, Germany. The results suggest a 0.9% reduction in fast neutron intensity per 1 kg of dry aboveground biomass per m2 or per 2 kg of biomass water equivalent per m2. We successfully tested the novel vegetation correction using temporary cosmic ray probe measurements along a strong gradient in biomass due to deforestation, and using the COSMIC, and the hmf method as independent soil water content retrieval algorithms. The extended N0-calibration function was able to explain 95% of the overall variability in fast neutron intensity.

Summary Soil moisture is a key variable in hydrology, meteorology and agriculture. It is influenced by many factors, such as topography, soil properties, vegetation type, management, and meteorological conditions. The role of these factors in controlling the spatial patterns and temporal dynamics is often not well known. The aim of the current study is to analyze spatio-temporal soil moisture patterns acquired across a variety of land use types, on different spatial scales (plot to meso-scale catchment) and with different methods (point measurements, remote sensing, and modeling). We apply a uniform set of tools to determine method specific effects, as well as site and scale specific controlling factors. Spatial patterns of soil moisture and their temporal development were analyzed using nine different datasets from the Rur catchment in Western Germany. For all datasets we found negative linear relationships between the coefficient of variation and the mean soil moisture, indicating lower spatial variability at higher mean soil moisture. For a forest sub-catchment compared to cropped areas, the offset of this relationship was larger, with generally larger variability at similar mean soil moisture values. Using a geostatistical analysis of the soil moisture patterns we identified three groups of datasets with similar values for sill and range of the theoretical variogram: (i) modeled and measured datasets from the forest sub-catchment (patterns mainly influenced by soil properties and topography), (ii) remotely sensed datasets from the cropped part of the Rur catchment (patterns mainly influenced by the land-use structure of the cropped area), and (iii) modeled datasets from the cropped part of the Rur catchment (patterns mainly influenced by large scale variability of soil properties). A fractal analysis revealed that all analyzed soil moisture patterns showed a multifractal behavior, with at least one scale break and generally high fractal dimensions. Corresponding scale breaks were found between different datasets. The factors causing these scale breaks are consistent with the findings of the geostatistical analysis. Furthermore, the joined analysis of the different datasets showed that small differences in soil moisture dynamics, especially at the upper and lower bounds of soil moisture (at maximum porosity and wilting point of the soils) can have a large influence on the soil moisture patterns and their autocorrelation structure. Depending on the prevalent type of land use and the time of year, vegetation causes a decrease or an increase of spatial variability in the soil moisture pattern.

[1] Soil water content is one of the key state variables in the soil-vegetation-atmosphere continuum due to its important role in the exchange of water and energy at the soil surface. A new promising method to measure integral soil water content at the field or small catchment scale is the cosmic-ray probe (CRP). Recent studies of CRP measurements have mainly presented results from test sites located in very dry areas and from agricultural fields with sandy soils. In this study, distributed continuous soil water content measurements from a wireless sensor network (SoilNet) were used to investigate the accuracy of CRP measurements for soil water content determination in a humid forest ecosystem. Such ecosystems are less favorable for CRP applications due to the presence of a litter layer. In addition, lattice water and carbohydrates of soil organic matter and belowground biomass reduce the effective sensor depth and thus were accounted for in the calibration of the CRP. The hydrogen located in the biomass decreased the level of neutron count rates and thus also decreased the sensitivity of the cosmic-ray probe, which in turn resulted in an increase of the measurement uncertainty. This uncertainty was compensated by using longer integration times (e.g., 24 h). For the Wustebach forest site, the cosmic-ray probe enabled the assessment of integral daily soil water content dynamics with a RMSE of about 0.03 cm3/cm3 without explicitly considering the litter layer. By including simulated water contents of the litter layer in the calibration, a better accuracy could be achieved.

Obtaining reliable estimates of net ecosystem exchange (NEE) for particular regions is essential for the estimation of regional carbon balances. The Community Land Model (CLM4.5-BGC) was applied to the Rur catchment in western Germany to foster the understanding of spatial carbon flux patterns and the predictability of climate-ecosystem feedbacks. We evaluated the effect estimated values of ecological key parameters had on modelled carbon fluxes and LAI compared to global default values. For three of the four most widespread PFTs in the catchment (C3-grass, coniferous forest, deciduous forest), successfully estimated and validated parameter values were adopted from a previous study. Only for C3-crop, new parameter values were estimated herein. The Markov Chain Monte Carlo (MCMC) approach DREAM(zs) was used to constrain the CLM parameters with eddy covariance (EC) NEE data. We evaluated the model performance with estimated parameter values using (i) measured NEE from four EC crop sites located inside the catchment, and (ii) LAI data from the RapidEye satellite of about 18 days. The difference between the measured and simulated NEE sum for the evaluation period was reduced ~ 40 % on average, if estimated parameters instead of default parameters were used as input. For all PFTs, estimated parameter values had a strong effect on the predicted NEE sum. This would notably alter regional carbon balance estimates, since the catchment scale NEE sum was strongly positive with default parameter values and strongly negative with the estimated values. To obtain a more comprehensive picture of model uncertainty, additional CLM ensembles were setup where perturbed meteorological input data and uncertain initial states were applied in addition to the uncertain parameters. We found that C3-grass and C3-crop were particularly sensitive to the perturbed meteorological input data, which resulted in a strong increase of the standard deviation of the NEE sum (σ∑NEE) for the different ensemble members by a factor of ~ 14 and 28. Thus, model uncertainty for these PFTs is clearly underestimated if uncertain forcings and initial states are not taken into account. The increase of σ∑NEE for needleleaf evergreen temperate tree and broadleaf deciduous temperate tree was below factor 3.5. We conclude that that the uncertainty of regional CLM NEE predictions was considerably reduced via parameter estimation. However, in order to further enhance the reliability of regional scale NEE predictions, it is considered essential to treat different crop types separately. Thus, more EC tower stations and data are needed.

Land surface models can model matter and energy fluxes between the land surface and atmosphere, and provide a lower boundary condition to atmospheric circulation models. For these applications, accurate soil moisture quantification is highly desirable but not always possible given limited observations and limited subsurface data accuracy. Cosmic-ray probes (CRPs) offer an interesting alternative to indirectly measure soil moisture and provide an observation that can be assimilated into land surface models for improved soil moisture prediction. Synthetic studies have shown the potential to estimate subsurface parameters of land surface models with the assimilation of CRP observations. In this study, the potential of a network of CRPs for estimating subsurface parameters and improved soil moisture states is tested in a real-world case scenario using the local ensemble transform Kalman filter with the Community Land Model. The potential of the CRP network was tested by assimilating CRP-data for the years 2011 and 2012 (with or without soil hydraulic parameter estimation), followed by the verification year 2013. This was done using (i) the regional soil map as input information for the simulations, and (ii) an erroneous, biased soil map. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the biased soil map, soil moisture characterization improved in both periods strongly from a ERMS of 0.11 cm3/cm3 to 0.03 cm3/cm3 (assimilation period) and from 0.12 cm3/cm3 to 0.05 cm3/cm3 (verification period) and the estimated soil hydraulic parameters were after assimilation closer to the ones of the regional soil map. Finally, the value of the CRP network was also evaluated with jackknifing data assimilation experiments. It was found that the CRP network is able to improve soil moisture estimates at locations between the assimilation sites from a ERMS of 0.12 cm3/cm3 to 0.06 cm3/cm3 (verification period), but again only if the initial soil map was biased.

Ecosystem carbon (C) fluxes in terrestrial ecosystems are affected by varying environmental conditions (e.g. soil heterogeneity and the weather) and land management. However, the interactions between soil respiration (Rs) and net ecosystem exchange (NEE) and their spatio-temporal dependence on environmental conditions and land management at field scale is not well understood. We performed repeated C flux measurement at 21 sites during the 2013 growing season in a temperate upland grassland in Germany, which was fertilized and cut three times according to the agricultural practice typical of the region. Repeated measurements included determination of NEE, Rs, leaf area index (LAI), meteorological conditions as well as physical and chemical soil properties. Temporal variability of Rs was controlled by air temperature, while LAI influenced the temporal variability of NEE. The three grass cuts reduced LAI and affected NEE markedly. More than 50% of NEE variability was explained by defoliation at field scale. Additionally, soil heterogeneity affected NEE, but to a lower extent (>30%), while Rs remained unaffected. We conclude that grassland management (i.e. repeated defoliation) and soil heterogeneity affects the spatio-temporal variability of NEE at field scale.

The Cosmic-Ray Neutron Sensor (CRNS) can provide soil moisture information at scales relevant to hydrometeorological modelling applications. Site-specific calibration is needed to translate CRNS neutron intensities into sensor footprint average soil moisture contents. We investigated temporal sampling strategies for calibration of three CRNS parameterisations (modified N0, HMF, and COSMIC) by assessing the effects of the number of sampling days and soil wetness conditions on the performance of the calibration results while investigating actual neutron intensity measurements, for three sites with distinct climate and land use: a semi-arid site, a temperate grassland, and a temperate forest. When calibrated with 1 year of data, both COSMIC and the modified N0 method performed better than HMF. The performance of COSMIC was remarkably good at the semi-arid site in the USA, while the N0mod performed best at the two temperate sites in Germany. The successful performance of COSMIC at all three sites can be attributed to the benefits of explicitly resolving individual soil layers (which is not accounted for in the other two parameterisations). To better calibrate these parameterisations, we recommend in situ soil sampled to be collected on more than a single day. However, little improvement is observed for sampling on more than 6 days. At the semi-arid site, the N0mod method was calibrated better under site-specific average wetness conditions, whereas HMF and COSMIC were calibrated better under drier conditions. Average soil wetness condition gave better calibration results at the two humid sites. The calibration results for the HMF method were better when calibrated with combinations of days with similar soil wetness conditions, opposed to N0mod and COSMIC, which profited from using days with distinct wetness conditions. Errors in actual neutron intensities were translated to average errors specifically to each site. At the semi-arid site, these errors were below the typical measurement uncertainties from in situ point-scale sensors and satellite remote sensing products. Nevertheless, at the two humid sites, reduction in uncertainty with increasing sampling days only reached typical errors associated with satellite remote sensing products. The outcomes of this study can be used by researchers as a CRNS calibration strategy guideline.