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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.

Pinios River Basin in central Greece (PRB) is region of highly productive agriculture where irrigation intensification and climate change have caused a significant depletion of groundwater resources. In the framework of the EU-Horizon 2020 project ATLAS, a precision irrigation scheduling service has been developed that aims at improving irrigation water management at the field scale. Such service is intended to protect crops from water stress by keeping the soil moisture (SM) in the root zone above the maximum allowable deficit (MAD). The presented approach is developed in two highly instrumented apple orchard pilot fields (~1.2 ha extent each) located at the Pinios Hydrologic Observatory ILTER site in PRB. In each pilot field and for two consecutive cultivation periods (2021 and 2022), intensive monitoring of meteorological parameters plus SM in 12 locations and at three depths (5, 20, 50 cm) was performed. To determine the time and volume of the next irrigation event, the forecast of meteorological variables for the next six days provided by the Global Forecast Model (GFS) was included in the service. The irrigation service performance was evaluated via comparison of the model estimated crop evapotranspiration (ETc) values against the SM content distribution monitored by the cluster of the installed SM sensors. The potential service contribution to reduce irrigation water consumption was assessed via comparison of the modelled irrigation water demands against the actual water consumption monitored at the irrigation blocks that divide each field. Statistical metrics demonstrate a good agreement between modeled crop evapotranspiration (ETc) and the monitored SM dynamics as captured by the SM sensors. Comparisons between the calculated irrigation demands and the actual water consumption monitored at the irrigation blocks of the pilot fields show that irrigation water applied in the fields may be reduced from 15% up to 50% or more in some instances, without considerably impacting crop health and yield. On the contrary, significant gains may be achieved on water saving and consequently on energy consumption to abstract irrigation water, thus contributing considerably to the region’s water-energy-food nexus sustainability.

Deforestation has a wide range of effects on hydrological and geochemical processes. Dissolved organic carbon (DOC) dynamics, a sensitive environmental change indicator, is expected to be affected by deforestation, with changes in atmospheric sulfur (S) deposition compounding this. However, how precisely anthropogenic disturbance (deforestation) under a declining atmospheric S input scenario affects the underlying spatiotemporal dynamics and relationships of river DOC and sulfate with hydro-climatological variables e.g., stream water temperature, runoff, pH, total dissolved iron (Fetot), and calcium (Ca2+) remains unclear. We, therefore, examined this issue within the TERENO Wüstebach catchment (Eifel, Germany), where partial deforestation had taken place in 2013. Wavelet transform coherence (WTC) analysis was applied based on a 10-year time series (2010–2020) from three sampling stations, whose (sub) catchment areas have different proportions of deforested area (W10: 31%, W14: 25%, W17: 3%). We found that water temperature and DOC, sulfate, and Fetot concentrations showed distinct seasonal patterns, with DOC averaging concentrations ranging from 2.23 (W17) to 4.56 (W10) mg L-1 and sulfate concentration ranging from 8.04 (W10) to 10.58 (W17) mg L-1. After clear-cut, DOC significantly increased by 59, 58% in the mainstream (W10, W14), but only 26% in the reference stream. WTC results indicated that DOC was negatively correlated with runoff and sulfate, but positively correlated with temperature, Ca2+, and Fetot. The negative correlation between DOC with runoff and sulfate was apparent over the whole examined 10-year period in W17 but did end in W10 and W14 after the deforestation. Sulfate was highly correlated with stream water temperature, runoff, and Fetot in W10 and W14 and with a longer lag time than W17. Additionally, pH was stronger correlated (higher R2) with sulfate and DOC in W17 than in W10 and W14. In conclusion, WTC analysis indicates that within this low mountainous forest catchment deforestation levels over 25% (W10 and W14) affected the coupling of S and C cycling substantially more strongly than “natural” environmental changes as observed in W17.

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.

Obtaining Soil Moisture Content (SMC) over large scales is of key importance in several environmental and agricultural applications especially in the context of climate change and transition to digital farming. Remote sensing (RS) has a demonstrated capability in retrieving SMC over large areas with several operational products already available at different spatiotemporal resolutions. At the same time, cosmic-ray neutron sensing is a recently emerged approach in retrieving high temporal resolution SMC at intermediate spatial scales. The present study conducts an intercomparison between different RS-based soil moisture products, daily SMC retrievals from a cosmic-ray neutron sensor (CRNS) station and a network of in situ SoilNet wireless sensors installed at the Pinios Hydrologic Observatory ILTER site in central Greece for a time period of 2018-2019. The RS-based soil moisture products included herein are from NASA’s Soil Moisture Active Passive (SMAP) and Metop-A/B Advanced Scatterometer (ASCAT) satellite missions. The methodological workflow adopted includes standardized validation procedures employing a series of statistical measures to quantify the agreement between the different RS-based soil moisture products, CRNS-based SMC and the SoilNet ground truth data. Our study results contribute towards global efforts aiming at exploiting CRNS data in the context of soil moisture retrievals and their potential synergies with RS-based products. Furthermore, our findings provide valuable insights into assessing the capability of CRNS at retrieving more accurate SMC estimates at arid and semi-arid environments such as those found in the Mediterranean basin, while supporting also ongoing global validation efforts.Keywords: Cosmic Ray Neutron Sensors; SMAP; ASCAT; SoilNet; Soil Moisture Content

Understanding the reasons why a certain combination of precipitation event and catchment wetness condition causes a particular runoff response serves as the basis for a sustainable water resources management. Runoff responses at the catchment scale are highly variable in space and time due to numerous influencing factors, e.g., topography, land use, geology, and climatic conditions. Yet, despite previous studies having investigated these interdependencies, it remains difficult to differentiate between the various impacts on the runoff response and to determine which driving factors dominate under which conditions. Assuming that similar precipitation and catchment wetness (e.g. in terms of soil water content or groundwater level) lead to similar runoff responses, reoccurring patterns of these hydrological flux and state variables may be useful to assess the causes for similar or different runoff responses. Therefore, we compared precipitation and catchment wetness conditions and classified them as hydrologically similar if they lead to a similar runoff response. In this study, the similarity of runoff responses was assessed for the 38.5 ha, partly forested Wüstebach catchment in Western Germany using the goodness-of-fit (GOF) criteria Nash-Sutcliffe-Efficiency and Volumetric Efficiency. If the GOF exceeded a pre-defined threshold, the runoff responses were classified as similar and grouped together. Subsequently, for similar runoff responses, the corresponding rainfall and wetness conditions were compared calculating the Spearman’s rank correlation coefficient and descriptive statistics. A total of 22 similar out of 73 runoff events were identified for the Wüstebach catchment over a period of nearly 12 years and classified into seven groups with the largest group including in total eight events. Results show that for similar runoff responses in this representative group, soil water content as well as groundwater levels in the riparian zone are well correlated with r = 0.815 and r = 0.840, respectively, indicating a possible dominant control on runoff responses. However, rainfall patterns show overall weak correlations (r = 0.406), implying that the precipitation temporal pattern control on the runoff response might be limited for these types of runoff events in the Wüstebach catchment. In a next step, the here defined hydrologically similar precipitation and wetness conditions will be searched in the data of the Wüstebach catchment to compare the corresponding runoff responses, and hydro-meteorological variables will be used to explain similar or different runoff responses.

In the Upper Alento River Catchment (UARC; southern Italy), two test sites, having different physiographic and soil features, were instrumented with SoilNet wireless sensor networks controlling GS3 (METER Group, Inc. USA) capacitance sensors deployed at soil depths of 15 and 30 cm at twenty locations to monitor soil permittivity, soil temperature, and apparent electrical conductivity. The conversion of soil permittivity into soil water content is challenging given the high clay content of the soil. Therefore, the both factory and Topp’s calibration functions are compromising soil water content dynamics with underestimated dynamics during dry periods and overestimations in the wet season. In addition, the soil moisture measurements is influenced by the very strong soil temperature variations. In this study we combined a laboratory calibration with in-situ intensive validation campaigns. The laboratory calibration of GS3 electromagnetic sensors was carried out on repacked soil samples by simultaneously measuring soil permittivity and soil water content at prescribed temperature steps within the range observed in the field (from 4.5 °C and 31.9 °C). A new empirical regression-based calibration law was developed for the two study areas to relax the impact of soil temperature on the measurement of soil permittivity. The temperature-corrected approach was validated in the field by taking thermo-gravimetric measurements of soil water content using a stainless steel soil core sampler over five field campaigns at soil depths of 15 and 30 cm. Our results demonstrated that the proposed site-specific calibrations properly reflect the seasonal dynamics of observed soil water content and outperform both factory and Topp’s calibration functions.

Accurate soil moisture (SM) measurements are key in hydrological observations and subsequent applications as it can greatly improve our understanding of soil processes. Recently, Cosmic-Ray Neutron Sensors (CRNS) have been recognized as a promising tool in SM monitoring due to its large footprint of several hectares and half a meter in depth. The key characteristic feature of the method is the exceptionally high moderation strength of hydrogen, which makes it nearly independent of the soil chemistry. CRNS has a great potential for irrigation and monitoring applications as to the non-invasive nature of the method and the low-maintenance, independently operating sensors. From the initial focus on hydrological research. CRNS are increasingly used in agriculture, e.g. irrigation management and soil moisture mapping, and have been integrated LoRa or NB-IoT networks for fast data transmission. Two projects are discussed which advance CRNS technologies into monitoring networks. COSMIC-SWAMP aims to provide an open-source water monitoring platform that integrates cosmic ray sensing data with FiWare Smart Application compliant analysis routines. Extending the existing Smart Water Management Platform (swamp-project.org), COSMIC-SWAMP supports dynamic processing of multiple co-located cosmic ray sensor streams to support automated and continuous growth forecasting using Wageningen/WOFOST crop models. ADAPTER involves the development and provision of innovative simulation-based information products. Addressing weather- and climate-resilient agriculture, daily and comprehensive long-term weather and soil information are made available to the agricultural community and all interested parties as easy-to-use analyses, data products, and information interfaces (adapter-projekt.org). The hydrological model ParFlow coupled to its Common Land Model (CLM) module provides a nationwide water balance prediction with 600 m spatial resolution. The data assimilation within the product platform is supported by an independent network of CRNS stations (12 agricultural locations in North Rhine-Westphalia). This contribution provides an overview about the current state of the art in CRNS methodological integration, neutron detection technology and development of IoT interfaces with measurements and forecasts focusing on the water balance, including groundwater.

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.

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.

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.

JGR / Biogeosciences 128(2), e2022JG007197 (2023). doi:10.1029/2022JG007197, Published by Wiley, Hoboken, NJ

This study evaluate water and energy fluxes and variables in combination with parameter optimization of the state-of-the-art land surface model Community Land Model version 5 (CLM5), using six years of hourly observations of latent heat flux, sensible heat flux, groundwater recharge and soil moisture. The results show that multi-objective calibration in combination with truncated singular value decomposition and Tikhonov regularization is a powerful method to improve the current practice of using look-up tables to define parameter values in land surface models. Furthermore, reliability of the optimized model parameters can be estimated by statistical measures such as identifiability and relative error variance reduction. As in most other eddy covariance studies, closure of the land surface energy balance is not achieved on observation data. However, using direct measurement of turbulent fluxes as target variable, the parameter optimization is capable of matching simulations and observations of latent heat, especially during the summer period, while simulated sensible heat is clearly biased. The fact that CLM5 is not capable of matching sensible heat, not even with advanced parameter optimization of model parameter values, suggests that the lack of energy closure is due to biases in the sensible heat flux. The results from this study contribute to improvements in model characterization of water and energy fluxes. It is underlined that parameter calibration using available observations of hydrologic and energy fluxes and variables is necessary to obtain the optimal parameter set of a land surface model.

The integrated distributed hydrological model ParFlow-CLM was used to predict water and energy transport between subsurface, land surface, and atmosphere for the Stettbach headwater catchment, Germany. Based on this model, a global sensitivity analysis was performed using the Latin-Hypercube (LH) sampling strategy followed by the One-factor-At-a-Time (OAT) method to identify the most influential and interactive parameters affecting the main hydrologic processes. In total 12 parameters were evaluated including soil hydraulic properties, storage, Manning coefficient, leaf area index, stem area index, and aerodynamic resistance that characterize water and energy fluxes in soil and vegetation. In addition, the sensitivity analysis was also carried out for different slopes and meteorological conditions to test the transferability of the results to regions with other topographies and climates. Our results show that the simulated energy fluxes, i.e. latent heat flux and sensible heat flux are sensitive to the parameters such as wilting point, leaf area index, and stem area index, especially for steep slope and subarctic climate conditions. The simulated soil evaporation, plant transpiration, infiltration, and runoff, are most sensitive to soil porosity, the van Genuchten parameter n representing the soil pore size distribution, soil wilting point, and leaf area index. The subsurface soil water storage and groundwater storage are most sensitive to soil porosity, while the surface water storage was most sensitive to the soil roughness parameter. For the different slope and climate conditions, the rank order of input parameter sensitivity is consistent, but the magnitude of parameter sensitivity is very different. The strongest deviation in parameter sensitivity occurred for sensible heat flux under the different slope conditions as well as for transpiration under different climate conditions. Overall, this study provides an insight into the most important input parameters that control hydrological fluxes and how the simulated variables vary with the change in parameter values, which can improve our understanding of the key processes in the model and help us to reduce the computational demands of completing multiple simulations of expensive domains.

The 2018-2020 consecutive drought events in Germany resulted in impacts related with several sectors such as agriculture, forestry, water management, industry, energy production and transport. The key to increase preparedness for extreme drought events are high-resolution information systems. A major national operational drought information system is the German Drought Monitor (GDM), launched in 2014 [1]. It provides daily soil moisture (SM) simulated with the mesoscale hydrological model (mHM) and its related soil moisture index [2] at a spatial resolution of 4×4 km². The release of the new soil map BUEK200 allowed us to increase its model resolution to ≈1.2×1.2 km², which is used now for the second version of the GDM [3]. To explore the ability of the GMD-v2 to provide drought information at one-kilometer scale, we evaluated mHM soil moisture simulations against an unprecedented large sample of soil moisture observations from 40 locations across Germany. These SM observations are obtained from single profile measurements, spatially distributed sensor networks, cosmic-ray neutron stations, and lysimeters over a wide range of climatic conditions, vegetation types and soil depths. Specifically, the study aimed at answering two research questions: 1) how well do high-resolution German-wide soil moisture simulations capture the dynamics in observed soil moisture that constitute the basis for the near real-time soil moisture drought monitoring system? 2) Does the mHM simulations obtained with the high spatial resolution data set provide soil moisture estimates with greater model efficiency than those obtained in the coarser resolution? The results showed that the agreement of simulated and observed SM dynamics is especially high during the vegetation period (0.84 median Spearman correlation(r)) and lower in winter (0.59 median r). Moderate but significant improvements between the low- and high-resolution GDM versions to observed SM were found in correlations for autumn (+0.07 median r) and winter (+0.12 median r). The spatially distributed sensor networks outperformed single profile measurements with higher than average correlation values especially for the 25–60 cm depth, which supports the closer scale match of spatially distributed measurements to the simulations. The results indicate areas for potential improvement and shows limitations from both: model parameterization (e.g., improvement of local scale hydrological processes) and observations methodology (e.g., reduction of measurement errors). Finally, the results of this study underline the fact that nationwide drought information systems depend both on appropriate simulations of the water cycle and a broad, high-quality observational soil moisture database.References:[1] Zink, M. et al. doi: 10.1088/1748-9326/11/7/074002 , 2016[2] Samaniego et al. doi: 10.1175/jhm-d-12-075.1 2013[3] Boeing, et al. doi: 10.5194/hess-2021-402 2021 (in revision)

Soil moisture is an important component of the water balance despite accounting for a small volume relative to other hydrological cycle components. With the continuing evolution of land surface and global hydrological models, characterizing soil moisture dynamics at sub-kilometer scales is becoming ever important. To help with that, the cosmic-ray neutron sensing is an established technology that provides estimates of root-zone soil moisture at 200-300m radius. In simple terms, cosmic-ray neutron sensors can estimate root zone soil moisture through an indirect relationship between measured neutrons scattered from the soil and the amount of hydrogen atoms observed in the soil water.Following its development in the late 2010s and the establishment of the first COsmic-ray Soil Moisture Observing System (COSMOS) network in the USA, a continuing adoption of this technology has been observed over the years, notably with the establishment of other national scale networks in Germany, Australia, and in the UK. As the cosmic-ray neutron sensing technology matures, so does our understanding on how to better isolate the soil moisture signal from other sources of hydrogen within the sensor footprint. However, despite recent improvements in our understanding, continental and global-scale datasets from cosmic-ray stations are still inexistent, partially due to a lack of proper data harmonization. This is simply because distinct networks operate under their own data processing protocols. This poses unwanted limitations to the use of these data by the wider scientific community.Here, we introduce the initial steps towards the harmonization of cosmic-ray neutron sensors worldwide. The harmonization is performed using the state-of-the-art and recent developed Cosmic-Ray Sensor PYthon data processing tool, applied to publicly available data from more than 200 stations. We highlight examples of applications using this global harmonized dataset in hydrology, agriculture, and environmental sciences; and present an open discussion about challenges and opportunities in potentially establishing a Global COSMOS network.

An integrated parallel hydrologic model (ParFlow-CLM) was constructed to predict water and energy transport between subsurface, land surface, and atmosphere for a synthetic study using basic physical properties of the Stettbach headwater catchment, Germany. Based on this model, a global sensitivity analysis was performed using the Latin-Hypercube (LH) sampling strategy followed by the One-factor-At-a-Time (OAT) method to identify the most influential and interactive parameters affecting the main hydrologic processes. In addition, the sensitivity analysis was also carried out for assumptions of different slopes and meteorological conditions to show the transferability of the results to regions with other topographies and climates. Our results show that the simulated energy fluxes, i.e. latent heat flux, sensible heat flux and soil heat flux, are more sensitive to the parameters of wilting point, leaf area index, and stem area index, especially for steep slope and subarctic climate conditions. The simulated water fluxes, i.e. evaporation, transpiration, infiltration, and runoff, are most sensitive to soil porosity, van-Genuchen parameter n, wilting point, and leaf area index. The subsurface water storage and groundwater storage were most sensitive to soil porosity, while the surface water storage is most sensitive to the Manning’s n parameter. For the different slope and climate conditions, the rank order of in input parameter sensitivity was consistent, but the magnitude of parameter sensitivity was very different. The strongest deviation in parameter sensitivity occurred for sensible heat flux under different slope conditions and for transpiration under different climate conditions. This study provides an efficient method of the identification of the most important input parameters of the model and how the variation in the output of a numerical model can be attributed to variations of its input factors. The results help to better understand process representation of the model and reduce the computational cost of running high numbers of 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.

Soil hydrological processes (SHP) support ecosystems, modulate the impact of climate change on terrestrial systems and control feedback mechanisms between water, energy and biogeochemical cycles. However, land-use changes and extreme events are increasingly impacting these processes. In this Review, we describe SHP across scales and examine their links with soil properties, ecosystem processes and climate. Soil structure influences SHP such as infiltration, soil water redistribution and root water uptake on small scales. On local scales, SHP are driven by root water uptake, vegetation and groundwater dynamics. Regionally, SHP are impacted by extreme events such as droughts, floods, heatwaves and land-use change; however, antecedent and current SHP partially determine the broader effects of extreme events. Emerging technologies such as wireless and automated sensing, soil moisture observation through novel synthetic aperture radars satellites, big data analysis and machine learning approaches offer unique opportunities to advance soil hydrology. These advances, in tandem with the inclusion of more key soil types and properties in models, will be pivotal in predicting the role of SHP during global change.

The 2018–2020 consecutive drought events in Germany resulted in impacts related with several sectors such as agriculture, forestry, water management, industry, energy production and transport. A major national operational drought information system is the German Drought Monitor (GDM), launched in 2014. It provides daily soil moisture (SM) simulated with the mesoscale hydrological model (mHM) and its related soil moisture index at a spatial resolution of 4 × 4 km2. Key to preparedness for extreme drought events are high-resolution information systems. The release of the new soil map BUEK200 allowed to increase the model resolution to ~1.2 × 1.2 km2, which is used in the second version of the GDM. In this paper, we explore the ability to provide drought information on the one-kilometer scale in Germany. Therefore, we compare simulated SM dynamics using homogenized and deseasonalized SM observations to evaluate the high-resolution drought simulations of the GDM. These SM observations are obtained from single profile measurements, spatially distributed sensor networks, cosmic-ray neutron stations and lysimeters at 40 sites in Germany. The results show that the agreement of simulated and observed SM dynamics is especially high in the vegetation period (0.84 median correlation R) and lower in winter (0.59 median R). Lower agreement in winter results from methodological uncertainties in simulations as well as in observations. Moderate but significant improvements between the first and second GDM version to observed SM were found in correlations for autumn (+0.07 median R) and winter (+0.12 median R). The annual drought intensity ranking and the spatial structure of drought events over the past 69 years is comparable for the two GDM versions. However, the higher resolution of the second GDM version allows a much more detailed representation of the spatial variability of SM, which is particularly beneficial for local risk assessments. Furthermore, the results underline that nationwide drought information systems depend both on appropriate simulations of the water cycle and a broad, high-quality observational soil moisture database.

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.

Profile soil moisture (SM) in mountainous areas are significant in water resources management and ecohydrological studies of downstream arid watersheds. Satellite products are useful in providing spatially distributed SM information, but only have limited penetration depth (e.g. top 5 cm). In contrast, in situ observations can provide multi-depth measurements, but only with limited spatial coverage. Spatially continuous estimates of subsurface SM can be obtained from surface observations using statistical methods, but this requires sufficient coupling strength among surface and subsurface SM. This study evaluates methods to calculate subsurface SM from surface SM and an application to the satellite SM product based on a SM observation network in the Qilian Mountains (China) established since 2013. First, we used cross-correlation to analyze the coupling strength among surface (0–10 cm) and subsurface (10–20, 20–30, 30–50, 50–70 cm, and profile of 0–70 cm) SM. Our results indicated an overall strong coupling among surface and subsurface SM in this study area. Afterwards, three different methods were tested to estimate subsurface SM from in-situ surface SM: the exponential filter (ExpF), artificial neural networks (ANN) and cumulative distribution function matching (CDF) methods. The results showed that both ANN and ExpF methods were able to provide accurate estimates of subsurface soil moisture at 10–20 cm, 20–30 cm, and for the profile of 0–70 cm using surface (0–10 cm) soil moisture only. Specifically, the ANN method had the lowest estimation error (RSR) of 0.42, 0.62 and 0.49 for depths of 15 and 25 cm and profile SM, respectively, while the ExpF method best captured the temporal variation of subsurface soil moisture. Furthermore, it could be shown that the performance of the profile SM estimation was not significantly lower with using an area-generalized Topt (optimum T) compared to the station-specific Topt. In a final step, the ExpF method was applied to the satellite SM product (Soil Moisture Active Passive Level 3: SMAP_L3) to estimate profile SM, and the resulting profile SM was compared to in situ observations. The results showed that the ExpF method was able to estimate profile SM from SMAP_L3 surface products with reasonable accuracy (median R of 0.718). It was also found that the combination of ExpF method and SMAP_L3 surface product can significantly improve the estimation of profile SM in the mountainous areas in comparison to the SMAP_L4 root zone product. Overall, it was concluded that the ExpF method is able to estimate profile SM using SMAP surface products in the Qilian Mountains.

The ADAPTER project involves the development and provision of innovative simulation-based data and information products for weather- and climate-resilient agriculture. In other words, ADAPTER makes daily ("soil") weather and comprehensive long-term climate change information freely available to the agricultural community and all interested parties as easy-to-use analyses, data products, and information. A key technology is thereby cosmic-ray neutron sensing, a non-invasive method to determine environmental water on the hectar scale. Measurements and forecasts focus on the current state and development of the water balance, including groundwater. To support the agricultural community towards an optimized soil management, Forschungszentrum Jülich uses forecast simulations with the hydrological numerical model ParFlow and the regional earth system model Terrestrial Systems Modeling Platform (TSMP) in combination with observations.

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).

The upcoming launch of the L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) will enable multi-frequency SAR observations when combined with existing C-band satellite missions (e.g., Sentinel-1). Due to the different penetration depths of the SAR signals, multi-frequency SAR offers great potential for field-scale agricultural monitoring and the estimation of soil and plant parameters. The SARSense campaign, conducted between June and August 2019 at the Selhausen agricultural test site near Jülich, Germany, has yielded a comprehensive dataset that includes both air- and space-borne C- and L-band SAR data, extensive in-situ field measurements of soil and plant parameters as well as unmanned aerial systems (UAS)-based multispectral and thermal infrared measurements and cosmic neutron sensing observations. The study provides both, an insight into the strengths and limitations of the acquired dataset as well as an analysis of the different behaviour of C- and L-band backscattering on changing soil moisture and plant parameters for taproot crops and cereals.

Deforestation can considerably affect transpiration dynamics and magnitudes at the catchment scale and thereby alter the partitioning between drainage and evaporative water fluxes released from terrestrial hydrological systems. However, it has so far remained problematic to directly link reductions in transpiration to changes in the physical properties of the system and to quantify these changes in system properties at the catchment scale. As a consequence, it is difficult to quantify the effect of deforestation on parameters of catchment-scale hydrological models. This in turn leads to substantial uncertainties in predictions of the hydrological response after deforestation but also to a poor understanding of how deforestation affects principal descriptors of catchment-scale transport, such as travel time distributions and young water fractions. The objectives of this study in the Wüstebach experimental catchment are therefore to provide a mechanistic explanation of why changes in the partitioning of water fluxes can be observed after deforestation and how this further affects the storage and release dynamics of water. More specifically, we test the hypotheses that (1) post-deforestation changes in water storage dynamics and partitioning of water fluxes are largely a direct consequence of a reduction of the catchment-scale effective vegetation-accessible water storage capacity in the unsaturated root zone (SU, max) after deforestation and that (2) the deforestation-induced reduction of SU, max affects the shape of travel time distributions and results in shifts towards higher fractions of young water in the stream. Simultaneously modelling streamflow and stable water isotope dynamics using meaningfully adjusted model parameters both for the pre- and post-deforestation periods, respectively, a hydrological model with an integrated tracer routine based on the concept of storage-age selection functions is used to track fluxes through the system and to estimate the effects of deforestation on catchment travel time distributions and young water fractions Fyw. It was found that deforestation led to a significant increase in streamflow accompanied by corresponding reductions of evaporative fluxes. This is reflected by an increase in the runoff ratio from CR=0.55 to 0.68 in the post-deforestation period despite similar climatic conditions. This reduction of evaporative fluxes could be linked to a reduction of the catchment-scale water storage volume in the unsaturated soil (SU, max) that is within the reach of active roots and thus accessible for vegetation transpiration from ∼258 mm in the pre-deforestation period to ∼101 mm in the post-deforestation period. The hydrological model, reflecting the changes in the parameter SU, max, indicated that in the post-deforestation period stream water was characterized by slightly yet statistically not significantly higher mean fractions of young water (Fyw∼0.13) than in the pre-deforestation period (Fyw∼0.12). In spite of these limited effects on the overall Fyw, changes were found for wet periods, during which post-deforestation fractions of young water increased to values Fyw∼0.37 for individual storms. Deforestation also caused a significantly increased sensitivity of young water fractions to discharge under wet conditions from dFyw/dQ=0.25 to 0.36. Overall, this study provides quantitative evidence that deforestation resulted in changes in vegetation-accessible storage volumes SU, max and that these changes are not only responsible for changes in the partitioning between drainage and evaporation and thus the fundamental hydrological response characteristics of the Wüstebach catchment, but also for changes in catchment-scale tracer circulation dynamics. In particular for wet conditions, deforestation caused higher proportions of younger water to reach the stream, implying faster routing of stable isotopes and plausibly also solutes through the sub-surface.

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

Current in situ soil moisture monitoring networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical global surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g., soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100m and performed moderately well under cultivated, herbaceous, forest, and shrub soils (R2 = 0.524, RMSE = 0.07 m3 m−3). It has a relatively good transferability at the regional scale among different continental and regional networks (mean RMSE = 0.08–0.10 m3 m−3). The global model was then applied to map SSM dynamics at 30–100m across a field-scale network (TERENO-Wüstebach) in Germany and an 80-ha irrigated cropland in Wisconsin, USA. Without local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (“spiking”), the bias of the model was significantly reduced. The QRF model was also affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and high-resolution soil maps as well as assimilation with process-based water flow models.