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Innovative soil moisture (SM) monitoring and modelling methods are key to reduce irrigation water use in the face of expected water scarcity and increase of droughts related to climate change. A promising irrigation monitoring method is Cosmic-Ray Neutron Sensing (CRNS), which is based on the negative correlation between fast neutrons originating from Cosmic-Ray neutron intensities and SM content. The CRNS key advantage lies in its relatively large sensing volume of several hectares, which allows to use a single CRNS instead of a network of point-scale sensors. Additionally, land surface models such as the Community Land Model (CLM5) that simulate the exchange of water, energy, carbon and nitrogen at the land–atmosphere interface can be a valuable tool to study the efficiency of irrigation and effects on crop growth. In this study, novel CRNS and the newly developed CLM5-FruitTree were tested in two small (~1.2 ha) irrigated apple orchards located in the Pinios Hydrologic Observatory (Greece). In 2020, a climate station (Atmos21) and a network of 12 SoilNet nodes, each with two SM sensors at 5, 20 and 50 cm depth, were installed in each field, as well as water meters to measure irrigation timing and amounts. In addition, a CRNS was installed in each field to test the possibility of monitoring irrigation and informing irrigation models. We found that the CRNS was very sensitive to the weekly irrigation events. However, the magnitude of the SM fluctuations caused by the irrigation was underestimated by the CRNS resulting in an RMSE of up to 0.058 cm3 cm-3. This can be attributed to the fact that the CRNS has a large footprint, and the neutron counts were therefore also influenced by the surroundings of the irrigated field. Therefore, to compensate for this influence, an additional SoilNet node was installed outside one of the two irrigated fields in 2022. By combining these data with neutron transport simulations of the study area, a correction of CRNS-derived SM was developed to better capture both timing and magnitude of SM changes (RMSE reduced to 0.031 cm3 cm-3). In parallel, CLM5-FruitTree was able to reproduce the observed SM response to irrigation when the local irrigation schedule was considered (i.e., defining starting date, timing, and target soil moisture for irrigation). Interestingly, the simulated irrigation in 2021 and 2022 used 10 to 60 % less water than the amount applied by the farmer. This suggests a great water saving potential through a reduction in irrigation amounts or through improvements in irrigation efficiency by reducing losses through evaporation or deep percolation. However, existing model weaknesses in the representation of soil properties and water fluxes need to be further addressed for this modelling approach. Nevertheless, the results of this study are a further step towards the use of novel CRNS and modelling tools as a decision support system in irrigation for more efficient use of water resources.

Understanding hydrological and biogeochemical ecosystem process variability in response to a changing climate is important to improve land surface models and assess current and future states of ecosystem functioning. However, the representation of spatial heterogeneity of ecosystem processes in state-of-the-art land-surface models has not been evaluated thoroughly until today. Here we compare gross primary production (GPP) and evapotranspiration (ET) simulated by the Community Land Model version 5 (CLM5) for the period 1995-2018 over the Euro-CORDEX domain with in-situ data from eddy-covariance sites as well as remote sensing and reanalysis data. Additionally, we conducted a parameter sensitivity analysis to identify the impact of uncertainty coming from ecosystem parameters (in particular default parameters for given plant functional types) for selected sites in Europe. Our results show that GPP and ET variation across hydroclimates show in general a good agreement between CLM5 and remote sensing and reanalysis products. However, both GPP and ET simulated by CLM5 show large differences with measured in-situ data, depending on the ecosystem type. Further, we identify sensitive parameters that will be adjusted to improve ecosystem representation in CLM5 in a future study. This work is important to improve land surface models and parameterization of plant functional types to understand and improve predictions of ecosystem functioning.

Global climate change with a predicted increase in weather extremes entails vulnerability and new challenges to regional agriculture. While the general impacts of climate change on global food security are a much studied topic, the implications for regional inter-annual yield variability remain unclear. In this study, we analysed the effects of weather trends on regional crop productivity within two agriculturally managed regions in different climate zones, simulated with the latest version of the Community Land Model (version 5.0) over two decades (1999-2019). We evaluated the models’ potential to represent the inter-annual variability of crop yield in comparison to recorded yield variability and different weather indicators, e.g., drought index and growing season length and evaluated which variables (i.e., temperature, precipitation, initial soil moisture content) dominantly drive changes in CLM5-predicted yield variability. The simulation results were able to reproduce the sign of crop yield anomalies, and thus provide a basis on which to study the effects of different weather patterns on inter-annual yield variability. However, the simulations showed limitations in correctly capturing inter-annual differences of crop yield in terms of total magnitudes (up to 10 times lower than in official records). Our results indicate that these limitation arise mainly from uncertainties in the representation of the subsurface soil moisture regime and a corresponding lack of sensitivity towards drought stress. Insights from this work were used to summarize implications for future analysis of CLM5-BGC simulation results over agriculturally managed land and allowed us to discuss and investigate possible technical model improvements.

Changing environmental conditions impact ecosystem dynamics which have important implications for the land–atmosphere carbon and water exchanges. Land surface models coupled with dynamic vegetation models can be used to understand the impact of changes in terrestrial ecosystems on carbon and water cycles and their interactions with climate. However, process-based vegetation models are highly parameterized and have a large number of uncertain parameters, which lead to uncertainties in the model outputs. Here, we use a dynamic vegetation model, the Functionally Assembled Terrestrial Simulator (FATES) coupled to the Community Land Model (CLM v5) to analyze parameter sensitivities and its effects on forest growth, carbon storage and fluxes. We first calibrate allometry parameters to accurately describe plant functional types, representative of most abundant tree species across Europe (such as Norway spruce and European Beach), at three different European sites. Further, an ensemble of model simulations with perturbed parameters were performed and compared against observations to explore uncertainties in simulated vegetation structure and distributions (forest density, tree basal areas and above ground biomass) and their effects on ecosystem functioning (carbon, water and energy fluxes). Comparison with observation shows that the CLM5-FATES model is able to capture the interannual variability well for water and carbon fluxes (such as ET and GPP), but shows larger uncertainties for simulated forest structure (growth, establishment, and mortality). Future work will focus on parameter optimization to further improve model performance in simulating vegetation growth and composition for different vegetation distributions and climate conditions.

Actual evapotranspiration (ETa) is difficult to measure and limited long-term information is available about ETa. With eddy covariance systems ETa can be measured at the field scale, but the method is associated with energy balance closure issues. For measuring ETa, weighing lysimeters are considered to be the most accurate and reliable method. However, weighing lysimeters have some disadvantages like elevated costs of installation and maintenance, and a small footprint (e.g., about 1 m2). A main question is therefore whether the precise ETa-measurements by lysimeters are representative for a larger area like a field, a meso-scale catchment, or even a larger region. Our hypothesis was that a lysimeter provides information about ETa that represents a larger area than its underlying measurement area. To this end, we examined here the daily ETa measurements from lysimeter at four study sites across Germany (separation distances 10 - 500 km) for the years 2015 to 2020. The Pearson correlations of the standardized anomalies (SA) of daily ETa between different lysimeters were calculated and compared with SA of daily ETa obtained from the corresponding eddy covariance tower. The correlations were further analyzed and related to spatial correlations of SA of environmental controls like precipitation, potential evapotranspiration (ET0), and soil moisture. We found that SA of daily ETa shows high spatial correlations (>0.5) for considerable separation distances between sites of up to 50km, with similar correlations for lysimeters and eddy covariance systems. ET0 is the dominant factor for the spatial correlation of ETa, as SA of ET0 shows stronger spatial correlations than SA of ETa.

The hydrological observatory for the Rur catchment (2400 km2) in Germany is highly equipped including 15 Cosmic Ray Neutron Sensors (CRNS) to measure soil moisture content, 6 eddy covariance stations with measurement of land-atmosphere exchange fluxes and further micrometeorological observations, and additional monitoring stations for river discharge and groundwater levels, amongst others. In addition, 3 intensive research sites at representative locations have been implemented with distributed soil moisture and temperature monitoring. These measurements allow for a better local verification of terrestrial model predictions, and the improvement of model predictions by model-data fusion methods. We did a series of studies on the assimilation of observations from the Rur observatory to improve predictions with the Terrestrial Systems Modelling Platform (TSMP), which models water, energy, carbon and nitrogen cycles of the land surface and subsurface. The data assimilation algorithm was in most cases the Ensemble Kalman Filter, but also the Particle Filter and Markov Chain Monte Carlo were used. Assimilated observations included soil moisture (from FDR-probes, CRNS or remote sensing), groundwater levels and net ecosystem exchange. We found that assimilation improved the characterization of the measured variable, also at verification locations. However, states and fluxes of variables that were not assimilated, such as evapotranspiration, often were not better characterized. The results suggest the importance of the joint assimilation of measurements for different variables, including remotely sensed information and vegetation information.

Given the expected increase of droughts related to climate change, soil moisture (SM) monitoring will likely become essential for farmers as it helps to reduce water consumption while mitigating crop losses. Cosmic-Ray Neutron Sensing (CRNS) is a promising SM monitoring method that is based on the negative correlation between fast neutrons originating from cosmic radiation and SM content. As CRNS integrates SM over a large radius of ~130-210 m with a penetration depth of ~15-85 cm, it has advantages over point-scale and remote-sensing methods. However, it is yet unclear how well CRNS can monitor areas with complex SM heterogeneity, such as small irrigated fields. In this study, two CRNS equipped with a novel gadolinium oxide thermal shielding were installed in two small (~1.2 ha) irrigated apple orchards located in the Pinios Hydrologic Observatory (Greece). Each CRNS was supported by an Atmos41 all-in-one climate station, by water meters measuring irrigation timing and amounts, and by a network of 12 wireless SM measurement nodes (SoilNet) that monitored SM at 5, 20 and 50 cm depth. The results showed that the CRNS was sensitive to the weekly irrigation events, but that it showed a general underestimation of the magnitude of SM fluctuations caused by the irrigation, which resulted in a RMSE of 0.058 cm3 cm-3. To better understand these results, we used the URANOS model to simulate neutron transport for a CRNS placed in the centre of a square irrigated field of varying dimensions (0.5 to 8 ha). The simulation results showed that CRNS can be used to monitor irrigation in fields as small as 0.5 ha in certain SM conditions and that a gadolinium-based thermal shielding provides the best monitoring results due to the much-reduced detection of thermal neutrons. Nonetheless, a considerable number of detected neutrons (above 60%) can originate outside the target field if the irrigated field is small, and in such cases a CRNS may not be able to clearly distinguish irrigation from SM variations in the surroundings. In an attempt to correct for such SM variations not related to irrigation, an additional SoilNet node was installed outside one of the two irrigated apple orchards in September 2021. By combining the results of neutron transport simulations with the information provided by this additional SoilNet node, a correction of CRNS-derived SM was developed that better captures both timing and magnitude of SM changes (RMSE reduced to 0.031 cm3 cm-3). These results show that the combination of real-world studies with neutron transport simulations can help to establish CRNS as a reliable tool in irrigation management.

Cosmic-ray neutron sensors (CRNS) measure soil moisture in real-time at the field scale, bridging the gap between in situ measurements and remote sensing products. This is promising and has the potential to enhance hydrological model predictions through the assimilation of CRNS data and improve the estimation of model parameters. In this study, soil moisture measurements from a network of 13 CRNS in the Rur catchment (~2000km2, Germany) were assimilated into the integrated model Terrestrial System Modelling Platform (TSMP) by the ensemble Kalman filter (EnKF). In total 128 ensemble members were generated by perturbing atmospheric forcing variables and soil textures to account for the uncertainties. The data assimilation experiments (with and without soil hydraulic parameter estimation) were carried out in both a wet year (2016) and a dry year (2018), and later validated using an independent year (2017) without assimilation. The objectives of this study were to investigate the potential of CRNS assimilation for improving soil moisture and evapotranspiration (ET) characterization, estimation of soil hydraulic parameters at the catchment scale, and analysis of whether the data assimilation performance differs between wet and dry years. The data assimilation experiments showed that soil moisture estimation was significantly improved during the assimilation period at measurement locations, with a root mean square error (RMSE) reduction (compared to open loop simulations without assimilation) in the range of 36-60% either in the dry or wet year, and the improvements were limited by the measurement error of CRNS (0.03 cm3/cm3). The joint state-parameter estimation gives better performance than state estimation alone (more than 15% RMSE reduction), and 9% RMSE reduction in the verification period with the updated parameter. The jackknife experiments revealed that the measurement network (~1 site per 200 km2) was insufficiently dense because soil moisture characterization at independent verification locations only improved marginally with large differences between wet and dry years (with an RMSE reduction of 40% in 2016 and 16% in 2018). The improved predictions from the jackknife experiments, however, imply that the assimilation of soil moisture data from a CRNS network still has the potential to improve the soil moisture characterization on the catchment scale by updating the spatially distributed soil hydraulic parameters of the subsurface model. The comparison of simulated ET with the data from eddy covariance (EC) stations demonstrates that it is challenging to achieve great improvements in ET simulations through CRNS soil moisture assimilation (with the RMSE reduction of monthly ET ranging between 6% and 21%).

As a vital supply of water resources for human society, groundwater plays a significant part in the water cycle, and is closely linked to precipitation, surface water and soil moisture (SM). Groundwater modelling often suffers from a variety of uncertainties, including uncertain forcing data, parameters and initial conditions. To reduce the uncertainties of model predictions, data assimilation (DA) can be used to correct model predictions with observations to improve the estimation of unknown states and parameters. To investigate the effects of assimilation of groundwater data into the integrated model Terrestrial System Modelling Platform (TSMP) on groundwater table depth (WTD) simulations, groundwater assimilation experiments were conducted for the Rur catchment in Germany. 128 ensemble members were generated by perturbing atmospheric forcing variables and saturated hydraulic conductivity, and then the measured daily groundwater data from 2018 were assimilated into the model TSMP by the Localized Ensemble Kalman Filter (LEnKF). The measured data were screened rigorously before assimilation. The spatial autocorrelation analysis of the measured groundwater data and the open loop (OL) simulations showed consistency in the spatial variability of groundwater levels between measurements and simulations. Based on the results of a spatial autocorrelation analysis, three different local radii (10 km, 5 km and 2.5 km) were selected for the assimilation experiments. Comparing the results of the OL and DA experiments, the simulated WTD bias (simulated - measured) and root mean square error (RMSE) were reduced for all DA runs compared to OL. The 10km localization radius gives the smallest RMSE at assimilation locations, with 81% RMSE reduction compared to the OL. Validation with WTD data from independent verification sites shows that localized assimilation improves groundwater simulations only when the distance to assimilated sites is smaller than 2.5km. Independent WTD validation showed a reduction in RMSE of 30% and the best results were from the DA run with 10 km radius. Also soil moisture measurements from the Cosmic Ray Neutron Sensor (CRNS) were used for validation. The simulated SM reproduced the observed temporal fluctuations, with a high correlation between measured and simulated SM (from 0.70 to 0.89, except for the Wuestebach site). However, there was no RMSE reduction of SM for the DA runs compared to the OL.

Land surface models (LSM) are an important tool for advancing our knowledge of the Earth system. LSM are constantly improved to represent the various terrestrial processes in more detail. High quality data, freely available from various observation networks, are providing being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSM with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for thirteen forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in-situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point scale measurements from, however for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that state-of-the-art LSM such as CLM5 still suffer from uncertainties in the representation of soil hydrological processes in forests, e.g. deep root water uptake, or highly uncertain vegetation parameters.

Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications that usually require high resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017–2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia and the Australian state of Victoria. We found that, after pre-processing of the forecast products (temporal downscaling of precipitation and incoming shortwave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to generate a model system response very close to reference simulation results forced by reanalysis data. Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual variability in crop yield across the Australian domain (approximately 50 % inter-annual variability in recorded yields and up to 17 % in simulated yields) compared to the German domain (approximately 15 % inter-annual variability in recorded yields and up to 5 % in simulated yields). The high and low yield seasons (2020 and 2018) among the four simulated years were clearly reproduced in forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the German domain. However, the simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic over- and underestimations in both the forecast and the reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency's (ESA CCI). These observed biases of soil moisture as well as the low inter-annual variability of simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.

In the light of global climate change and growing population, the demand for freshwater is projected to increase globally by 55% between 2000 and 2050. However, the present situation of surface water and groundwater resources is grim in terms of both quality and quantity in major aquifers of the world, namely, the Ganga Basin (India), High Plains (USA), North China Plain (China), Canning Basin (Australia), and California Central Valley (USA). The reduced river discharge in critical regions has shifted the attention of stakeholders towards aquifers for water supply. Thus, the groundwater resources are facing compounding impacts of human-induced water stress and climate change. The current study explores the sensitivity of the groundwater-use regimes, namely, Natural Flow Dominated (NFD), Human Flow Dominated (HFD) and Human Withdrawal Dominated (HWD), and the associated stress levels to (a) various groundwater management strategies such as change in cropping patterns, reduction in irrigated area, reduction in irrigation application frequency, reuse of water, and (b) supply side strategies such as change in Land Use Land Cover to enhance recharge, and artificial recharge mechanisms. The present work involves conceptualisation of several groundwater management strategies and supply side strategies and running these scenarios over the Ganga Basin using a SWAT-MODFLOW model setup to simulate the impact on the groundwater system. This study helped to understand whether (i) the reduction in the pumping activity in a HWD Groundwater Regime would result in a shift in the regime type to NFD, and (ii) the positive human intervention in the form of optimum pumping would help in mitigating the adverse effects of changing precipitation and evapotranspiration characteristics. Moreover, the results of the present work also improve understanding of the conditions under which the regime shifts can occur and help answer questions such as (i) how and when would a NFD Regime with low groundwater stress level convert into a HFD Regime, (ii) will a HFD Regime convert into a NFD Regime if extraction is strongly reduced, and (iii) how can we achieve a low groundwater stress level? Thus, the present study provides a useful insight into the groundwater dynamics and its response to the existing management strategies.

The use of numerical models for real-time management of water resources is becoming increasingly popular as the increasing frequency and intensity of extreme weather events negatively affect society, agriculture and crop yields. Prolonged droughts are becoming the new normal, which, among other things, increase the need for operational, site-specific soil moisture forecasting. A model that provides accurate site-specific soil moisture forecasts can support agriculture by contributing to precision irrigation and the provision of important information for crop planning, yield maximization and the coordination of field operations. Soil moisture assimilation has proven potential to provide appropriate initial conditions for such a forecast model. However, the operational estimation of an initial condition requires model-specific protocols for continuously incorporating new observational data into models for hydrological, crop, land surface, vadose zone, or subsurface processes that are not yet widely available. In this study, we present an automated data pipeline for operational, site-specific soil moisture ensemble forecasting based on the Community Land Model Version 5.0 (CLM5) taking the TERENO agricultural research station "Selhausen" in western Germany as an example. CLM5 simulates vegetation states, carbon and nitrogen pools prognostically. We compare land surface model prediction quality (e.g., soil moisture, crop yield) with and without weather forecasts and with and without near real-time soil moisture data assimilation. Climatological mean time series and 10-day ensemble weather forecasts from the German Weather Service, aggregated to the grid cell, are the atmospheric forcings in simulating future states. Forecasts start from the states of the last simulation time step with on-site measurements of precipitation, wind speed, air temperature, air pressure, relative humidity, and global radiation as the atmospheric forcings. In parallel with forward simulations from 2011-2021 (open loop experiment), soil moisture assimilation is being performed for 2018-2021 to generate site-specific initial conditions for the land surface model with reduced uncertainty. Forecasts starting from initial conditions based on soil moisture assimilation are more reliable as model bias is reduced. Preliminary results show that the inclusion of site-specific weather forecast uncertainties in the model improves the simulation of soil moisture dynamics at the plot scale and is thus important for optimizing irrigation schedules while keeping crop productivity stable.

Water-use efficiency is the amount of carbon assimilated per water used by an ecosystem and a key indicator of ecosystem functioning, but its variability in response to climate change and droughts is not thoroughly understood. Here, we investigated trends, drought response and drivers of three water-use efficiency indices from 1995–2018 in Europe with remote sensing data that considered long-term environmental effects. We show that inherent water-use efficiency decreased by −4.2% in Central Europe, exhibiting threatened ecosystem functioning. In European grasslands it increased by +24.2%, by regulated transpiration and increased carbon assimilation. Further, we highlight modulation of water-use efficiency drought response by hydro-climate and the importance of adaptive canopy conductance on ecosystem function. Our results imply that decoupling carbon assimilation from canopy conductance and efficient water management strategies could make the difference between threatened and well-coping ecosystems with ongoing climate change, and provide important insights for land surface model development.

Although it has been shown that climate warming has steadily increased the length of the growing season (LGS) in Europe, we present new evidence that this trend reversed during last decade. Warmer European winter and spring weather combined with adequate soil moisture still results in early greening, albeit at slower rates than in the past. However, the recent (2014-2020) accelerated shift toward earlier onset of dormancy has resulted in a shortening of LGS compared to previous years. The results show that this is mainly due to higher atmospheric water demand (AWD) in summer. The higher AWD stresses the vegetation even though there is still enough water, but the vegetation cannot provide the needed water for transpiration because the water transport system is inadequate, or the root system is adapted to conditions other than the current condition. Our results have implications for future management of European ecosystems in a warmer world.

Accurate soil moisture (SM) monitoring is key in irrigation as it can greatly improve water use efficiency. Recently, cosmic-ray neutron sensors (CRNSs) have been recognized as a promising tool in SM monitoring due to their large footprint of several hectares. CRNSs also have great potential for irrigation applications, but few studies have investigated whether irrigation monitoring with CRNSs is feasible, especially for irrigated fields with a size smaller than the CRNS footprint. Therefore, the aim of this study is to use Monte Carlo simulations to investigate the feasibility of monitoring irrigation with CRNSs. This was achieved by simulating irrigation scenarios with different field dimensions (from 0.5 to 8 ha) and SM variations between 0.05 and 0.50 cm3 cm−3. Moreover, the energy-dependent response functions of eight moderators with different high-density polyethylene (HDPE) thickness or additional gadolinium thermal shielding were investigated. It was found that a considerable part of the neutrons that contribute to the CRNS footprint can originate outside an irrigated field, which is a challenge for irrigation monitoring with CRNSs. The use of thin HDPE moderators (e.g. 5 mm) generally resulted in a smaller footprint and thus stronger contributions from the irrigated area. However, a thicker 25 mm HDPE moderator with gadolinium shielding improved SM monitoring in irrigated fields due to a higher sensitivity of neutron counts with changing SM. This moderator and shielding set-up provided the highest chance of detecting irrigation events, especially when the initial SM was relatively low. However, variations in SM outside a 0.5 or 1 ha irrigated field (e.g. due to irrigation of neighbouring fields) can affect the count rate more than SM variations due to irrigation. This suggests the importance of retrieving SM data from the surrounding of a target field to obtain more meaningful information for supporting irrigation management, especially for small irrigated fields.

Integrated terrestrial system models predict the coupled water, energy and biogeochemical cycles. Simulations with these models are affected by uncertainties of model parameters, initial and boundary conditions, atmospheric forcings and the biophysical processes. Data assimilation (DA) can quantify and reduce the uncertainty. This has been tested intensively for single compartment models, but far less for integrated models with multiple compartments. We constructed a virtual reality (VR) with a coupled land surface-subsurface model under the Terrestrial Systems Modeling Platform, which mimics the Neckar catchment in southern Germany. Soil moisture and groundwater level (GWL) data extracted from the simulated VR are used as measurements to be assimilated with state-only/state-hydraulic parameter estimation. Soil moisture DA improves soil moisture characterization in the vertical profile and the neighboring grid cells, with a 40 ∼ 60% reduction of root mean square error (RMSE) over the observation points. In spite of a small ensemble size of 64 members, assimilating soil moisture data improved saturated hydraulic conductivity estimation around the measurement locations. The characterization of evapotranspiration and river discharge only show limited improvements (1% at observation points and less than 0.1% in RMSE at 3 selected gauge locations respectively). GWL DA not only improves the GWL characterization (76 ∼ 88% RMSE reduction at observation locations) but also soil moisture for some cases. In addition, a clear improvement in GWL characterization is observed up to 8 km from the observations, and updating the model states of the saturated zone only instead of the complete domain gives better performance.

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 Cosmic-Ray Neutron Sensing (CRNS) technology determines soil moisture for a few tens of hectares in a non-invasive way. These measurements, however, can be used to extend soil moisture characterization at regional scales using data assimilation. In the present study, we deployed the Ensemble Adjustment Kalman Filter (EAKF) to assimilate the CRNS neutron counts in order to update the spatial soil moisture, soil infiltration, and evapotranspiration parameters of the Noah-MP land surface model witch is also part of the WRF-Hydro modelling system. The study was conducted in the southern part of Germany, which includes the Rott and Ammer catchments within the TERENO Pre-Alpine observatory. The assimilation was carried out for both, a Noah-MP standalone scenario with observed rainfall as input and a coupled WRF-Hydro scenario with simulated rainfall to fully evaluate the added value of the assimilation. The assimilation performance was analysed at local and regional scale using independent soil moisture observations across the modelling domain. During the assimilation period, the Noah-MP standalone findings demonstrate a significant improvement in field scale soil moisture characterisation. The RMSE of simulated soil moisture was decreased by up to 66 % at field scale and up to 23 % at catchment scale. Additionally, the spatial patterns in the field scale soil moisture have showed improvement with reduction in spatial Bias by 0.025 cm3/cm3. The initial results from coupled WRF-Hydro scenario demonstrate that the soil moisture and parameter estimation experiment had a significant impact on estimated soil moisture and, humidity and evapotranspiration at regional scale. These findings support the use of the CRNS technique to improve the land surface and coupled hydro-atmospheric modelling.

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.

The largest part of the global population without reliable access to electricity lives in Africa. Here, renewable energy is a sustainable, cost efficient, and climate-friendly solution, especially given the large untapped renewable energy potential existing over the African continent. However, most renewable energy-related studies over Africa typically use input datasets at relatively coarse spatial resolutions (e.g., ERA5 at about 30km). Our objective is to produce a prototypical high-resolution dataset over southern Africa from dedicated atmospheric simulations. The data will be used with renewable energy assessment models, to eventually evaluate the renewables potentials. The hypothesis is that the high-resolution datasets provide more realistic and accurate renewable energy potential estimates. The ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON-NWP) model is run as the operational forecast model at the German Weather Service (DWD); and we employ the same model in its Limited Area Mode (ICON-LAM) in this project. The study domain over southern Africa is chosen due to its high solar and wind energy potential. ICON-LAM dynamically downscales the global deterministic ICON-NWP forecasts dataset from a spatial grid spacing of 13km to a convection-permitting resolution of 3.3km, without convection parameterization. This southern Africa ICON-LAM implementation is novel and has not been run before. Simulations cover the time span from 2017 to 2019 with contrasting meteorological conditions. The high-resolution triangulated grid cells of the 3.3km domain are exactly inscribed in the 13km global grid cells, following the sub-triangle generation rule of the ICON model mesh. To keep the ICON-LAM close to the observed atmospheric state the model atmosphere is reinitialized every 5 days, with one day spinup. The land surface and subsurface are run transient. In a very initial evaluation step, simulated 10m wind speed, global solar radiation, 2m air temperature, and precipitation from the coarser driving model, the ERA5 reanalysis as well as our ICON-LAM setup are validated using satellite data and in situ observations from the two local meteorological networks (SASSCAL and TAHMO). Initial results point to an added value of the convection-permitting simulations.

Land surface models are important tools to improve our understanding of interacting ecosystem processes and for the prediction of future risks of droughts and fires. However, such predictions are associated with uncertainties related to model forcings, parameters and process simplifications. Therefore, the increasing availability of high-quality observations should be used to improve the accuracy of land surface model predictions. In this study, we use the Ensemble Kalman Filter for the fusion of in-situ soil moisture observations from different observation networks across Europe (e.g. eLTER, FLUXNET, TERENO, ICOS) into the Community Land Model 5.0 (CLM5). The sites selected for this study cover different regional climate zones and forest types and feature in-situ soil moisture as well as evapotranspiration observations from eddy covariance towers for the period from 2009 to 2019. In this study, we specifically focus on European forested study sites where both in-situ soil moisture and evapotranspiration observations are available for the period from 2009 to 2019. CLM5 simulates a broad variety of important land surface processes including water and energy partitioning, surface runoff, subsurface runoff, photosynthesis and carbon and nitrogen storage in vegetation and soil. Here, we focus on improving the accuracy of model predictions by updating soil moisture dynamics and related soil hydraulic parameters by coupling CLM5 to the Parallel Data Assimilation Framework (PDAF) to assimilate soil moisture data into CLM5 during simulation runtime. Additionally, we implemented a new and more direct approach to update the hydraulic parameters compared to previous versions of the CLM5-PDAF coupling and show the effects of this implementation.We demonstrate the value and limitation of assimilating soil moisture data for simulating evapotranspiration focusing on recent drought events in 2018 and 2019. We found that soil moisture dynamics were better characterized by data assimilation, but this did not result in improved estimation of evapotranspiration for the different sites during both wet and dry periods.

The inclusion of perennial, woody crops in land surface models is crucial to address their role in carbon (C) sequestration, food production, and water requirements under climate change. To help quantifying the biogeochemical and biogeophysical processes associated with these agro-ecosystems, we developed and tested a new sub-model, CLM-FruitTree, for deciduous fruit orchards within the framework of the Community Land Model version 5 (CLM5). The model development included (1) a new perennial crop phenology description, (2) an adapted C and nitrogen allocation scheme, considering both storage and photosynthetic growth of annual and perennial plant organs, (3) typical management practices associated with fruit orchards, and (4) the parameterization of an apple plant functional type. CLM-FruitTree was tested using extensive field measurements from an apple orchard in South Tyrol, Italy. Growth and partitioning of biomass to the individual plant components was well represented by CLM-FruitTree and average yield was predicted within 2.3 % of the observed values despite low simulated inter-annual variability compared to observations. The simulated seasonal course of C, energy, and water fluxes was in good agreement with the EC measurements owing to the accurate representation of the prolonged growing season and typical leaf area development of the orchard. We found that gross primary production, net radiation, and latent heat flux were highly correlated (r > 0.94) with EC measurements and showed little bias (< ±5 %). Simulated respiration components, sensible heat, and ground heat flux were less consistent with observations. This was attributed to simplifications in the orchard structure and to the presence of additional management practices that are not yet represented in CLM-FruitTree. Finally, the results suggested that the representation of microbial and autotrophic respiration, and energy partitioning in complex, heterogeneous canopies in CLM5 requires further attention. The new sub-model CLM-FruitTree improved the representation of agricultural systems in CLM5 and can be used to study land surface processes in fruit orchards at the local, regional or larger scale.

Water availability is a key challenge in agriculture, especially given the expected increase of droughts related to climate change. Soil moisture (SM) sensors can be used to collect information on water availability in a reliable and accurate way. However, due to their very small measuring volume, the installation of multiple sensors is required. In addition, in-situ sensors may need to be removed during field management and connecting cables are often damaged by rodents and other wilderness animals. Hence, the demand for SM sensors that do not have such limitations will increase in the upcoming years. A promising non-invasive technique to monitor SM is cosmic-ray neutron sensing (CRNS), which is based on the negative correlation between fast neutrons originating from cosmic radiation and SM content. With its large measuring footprint of ~130-210m, CRNS can efficiently cover the field-scale. However, heterogeneous agricultural management (e.g., irrigation) can lead to abrupt SM differences, which pose a challenge for the analysis of CRNS data. Here, we investigate the effects of small-scale soil moisture patterns on the CRNS signal by using both modelling approaches and field studies. The neutron transport model URANOS was used to simulate the neutron signal of a CRNS station located in irrigated plots of different sizes (from 1 to 8 ha) with different soil moisture (from 5 and 50 Vol.%) inside and outside such a plot. A total of 400 different scenarios were simulated and the response functions of multiple detector types were further considered. In addition, two CRNS with Gadolinium shielding were installed in two irrigated apple orchards of ~1.2 ha located in the Pinios Hydrologic Observatory (Greece) in the context of the H2020 ATLAS project. Reference soil moisture was determined using 25 SoilNet stations, each with 6 SM sensors installed in pairs at 5, 20 and 50 cm depth and water potential sensors at 20 cm depth. The orchards were also equipped with two Atmos41 climate stations and eight water meters for irrigation monitoring. The CRNS were calibrated using either soil samples or the SM measured by the SoilNet network. In the URANOS simulations, the percentage of neutrons detected by the CRNS that are representative of an irrigated plot varied between 45 and 90% and was strongly influenced by both the dimension and SM of the irrigated plot. As expected, the CRNS footprint decreased considerably with increasing SM but did not appear to be influenced by the plot dimension. SM variation within the irrigated plot strongly affected the neutron energy at detection, which was not the case for SM variations outside the plot. The instrumented fields corroborated the URANOS findings and the performance of the local CRNS was dependent on a) the timing and intensity of irrigation and precipitation, b) the CRNS calibration strategy, and c) the management of the surrounding fields. These results provide novel and meaningful information on the impact of horizontal SM patterns on CRNS measurements, which will help to make CRNS more useful in irrigated agriculture.

Vegetation plays an important role in global carbon and water cycles. Long-term environmental changes modify vegetation distributions and consequently impact fluxes of carbon, water and energy. Vegetation dynamic models are useful tools to analyze terrestrial ecosystem processes and can simulate the impact of vegetation structure changes on carbon and water cycles and their interactions with climate when coupled to land surface models. Because of the complexity to represent plant growth processes, these models typically have a large number of parameters that can potentially contribute to uncertainty in model results and need to be adequately parameterized. In this study, we used the Community Land Model (CLM v5) coupled to the Functionally Assembled Terrestrial Simulator (FATES) and applied it to four forest sites from the database of European Long-Term Ecosystem Research Infrastructures (eLTER) which provides a wide range of observational data to calibrate and evaluate vegetation models. Using this database, we performed sensitivity analysis to evaluate parameter uncertainties in model results for forest growth, gross primary production, leaf area index, evapotranspiration, soil water content and soil temperature. We also explored the sensitivity of model parameters for different vegetation distributions and climate conditions. The results of this study allow us to understand the vegetation dynamics and their impact on carbon and water fluxes which will be helpful to improve model parameterization and to provide more accurate estimates of carbon and water fluxes and climate model projections.

Carbon allocation is a major driver of plant growth and plays a key role in shaping ecosystem processes and the global carbon (C) cycle. In contrast to annual crops, fruit trees store and remobilize C in their perennial plant components, have long canopy durations, relatively low respiratory costs, and remain productive for decades. To predict C dynamics in fruit tree orchards under global change, it is essential to expand the understanding of carbon allocation in fruit trees and to improve its representation in comprehensive modelling environments such as land surface models (LSMs). LSMs simulate the exchanges of matter and energy between the terrestrial biosphere and the atmosphere. They are widely used in C cycle and climate change studies, and typically include representations of various types of natural vegetation and annual crops. Despite the importance of fruit orchards in regions that are strongly affected by climate change, such as the Mediterranean, they are rarely considered in LSMs, thus leaving an important gap in the representation of C allocation and related biogeophysical and biogeochemical processes of these agro-ecosystems. In this work, we present the new fruit tree sub-model CLM-FruitTree within the Community Land Model version 5 (CLM5). Herein, a fruit tree is described by a perennial deciduous phenology with C allocation to standing woody biomass components and annual organs such as leaves, fine roots, and fruits that are either shed or harvested within the yearly cycle. Two different pools, the storage and the photosynthetic pool, contribute to tree growth while C allocation to the individual plant components is based on allocation coefficients that vary depending on the specific phenological phase. CLM-FruitTree was tested using multiple years of field measurements of above- and belowground biomass components, leaf area index (LAI), yield, soil respiration, and eddy covariance (EC) data from an apple orchard in South Tyrol, Italy. We found that biomass allocation was captured within 1-5 % of the measured values, with about half of the assimilated C allocated to fruits. Growth from C storage thereby played a significant role in shaping initial leaf development and growth of fine roots. Simulated ecosystem C fluxes showed a high correlation (r > 0.84) with the EC measurements and the seasonal dynamics were well represented. Average annual gross primary productivity was predicted within 1.5 % of the measured values while net carbon uptake was overestimated by on average 21 % mostly due to an underestimation of soil respiration in the orchard caused by necessary simplifications in the microbial respiration, orchard structure, and management practices. Overall, the new sub-model CLM-FruitTree allows the exploration of the dynamics of C allocation and fluxes in fruit orchards, and may advance C cycle and climate change studies of such agro-ecosystems at larger scale.

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.

Droughts can have important impacts on environment and economy like in the year 2018 in parts of Europe. Droughts can be analyzed in terms of meteorological drought, agricultural drought, hydrological drought and social-economic drought. In this paper, we focus on meteorological and agricultural drought and analyzed drought trends for the period 1965–2019 and assessed how extreme the drought year 2018 was in Germany and the Netherlands. The analysis was made on the basis of the following drought indices: standardized precipitation index (SPI), standardized soil moisture index (SSI), potential precipitation deficit (PPD) and ET deficit. SPI and SSI were computed at two time scales, the period April-September and a 12-months period. In order to analyze drought trends and the ranking of the year 2018, HYDRUS 1-D simulations were carried out for 31 sites with long-term meteorological observations and soil moisture, potential evapotranspiration (ET) and actual ET were determined for five soil types (clay, silt, loam, sandy loam and loamy sand). The results show that the year 2018 was severely dry, which was especially related to the highest potential ET in the time series 1965–2019, for most of the sites. For around half of the 31 sites the year 2018 had the lowest SSI, and largest PPD and ET-deficit in the 1965–2019 time series, followed by 1976 and 2003. The trend analysis reveals that meteorological drought (SPI) hardly shows significant trends over 1965–2019 over the studied domain, but agricultural droughts (SSI) are increasing, at several sites significantly, and at even more sites PPD and ET deficit show significant trends. The increasing droughts over Germany and Netherlands are mainly driven by increasing potential ET and increasing vegetation water demand.

In conventional Bayesian geostatistical inversion, specific values of hyperparameters characterizing the prior distribution of random fields are required. However, these hyperparameters are typically very uncertain in practice. Thus, it is more appropriate to consider the uncertainty of hyperparameters as well. The preconditioned Crank-Nicolson Markov chain Monte Carlo with parallel tempering (pCN-PT) has been used to efficiently solve the conventional Bayesian inversion of high-dimensional multi-Gaussian random fields. In this study, we extend pCN-PT to Bayesian inversion with uncertain hyperparameters of multi-Gaussian fields. To utilize the dimension robustness of the preconditioned Crank-Nicolson algorithm, we reconstruct the problem by decomposing the random field into hyperparameters and white noise. Then, we apply pCN-PT with a Gibbs split to this “new” problem to obtain the posterior samples of hyperparameters and white noise, and further recover the posterior samples of spatially distributed model parameters. Finally, we apply the extended pCN-PT method for estimating a finely resolved multi-Gaussian log-hydraulic conductivity field from direct data and from head data to show its effectiveness. Results indicate that the estimation of hyperparameters with hydraulic head data is very challenging and the posterior distributions of hyperparameters are only slightly narrower than the prior distributions. Direct measurements of hydraulic conductivity are needed to narrow more the posterior distribution of hyperparameters. To the best of our knowledge, this is a first accurate and fully linearization free solution to Bayesian multi-Gaussian geostatistical inversion with uncertain hyperparameters.

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