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This comment challenges the perspective of Gao et al. (2023) that is rejecting the role of soil processes in hydrology. We argue that the authors present a false dichotomy between soil-centric and ecosystem-centric views. These two views of hydrology are complementary and reflect on the inherent multiscale complexity of hydrology where soil processes dominate at certain scales but other processes may become important at the catchment scale. We recognize the need for a new scale-aware framework that reconciles the interplay between soil processes at small scales with emergent behaviors driven by vegetation, topography, and climate at large scales.

Hydro-pedotransfer functions (PTFs) relate easy-to-measure and readily available soil information to soil hydraulic properties (SHPs) for applications in a wide range of process-based and empirical models, thereby enabling the assessment of soil hydraulic effects on hydrological, biogeochemical, and ecological processes. At least more than 4 decades of research have been invested to derive such relationships. However, while models, methods, data storage capacity, and computational efficiency have advanced, there are fundamental concerns related to the scope and adequacy of current PTFs, particularly when applied to parameterise models used at the field scale and beyond. Most of the PTF development process has focused on refining and advancing the regression methods, while fundamental aspects have remained largely unconsidered. Most soil systems are not represented in PTFs, which have been built mostly for agricultural soils in temperate climates. Thus, existing PTFs largely ignore how parent material, vegetation, land use, and climate affect processes that shape SHPs. The PTFs used to parameterise the Richards–Richardson equation are mostly limited to predicting parameters of the van Genuchten–Mualem soil hydraulic functions, despite sufficient evidence demonstrating their shortcomings. Another fundamental issue relates to the diverging scales of derivation and application, whereby PTFs are derived based on laboratory measurements while often being applied at the field to regional scales. Scaling, modulation, and constraining strategies exist to alleviate some of these shortcomings in the mismatch between scales. These aspects are addressed here in a joint effort by the members of the International Soil Modelling Consortium (ISMC) Pedotransfer Functions Working Group with the aim of systematising PTF research and providing a roadmap guiding both PTF development and use. We close with a 10-point catalogue for funders and researchers to guide review processes and research.

Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs 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 13 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 sensors placed in the ground; 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 only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters.

An accurate assessment of the physical states of the Earth system is an essential component of many scientific, societal, and economical considerations. These assessments are becoming an increasingly challenging computational task since we aim to resolve models with high resolutions in space and time, to consider complex coupled partial differential equations, and to estimate uncertainties, which often requires many realizations. Machine learning methods are becoming a very popular method for the construction of surrogate models to address these computational issues. However, they also face major challenges in producing explainable, scalable, interpretable, and robust models. In this paper, we evaluate the perspectives of geoscience applications of physics-based machine learning, which combines physics-based and data-driven methods to overcome the limitations of each approach taken alone. Through three designated examples (from the fields of geothermal energy, geodynamics, and hydrology), we show that the non-intrusive reduced-basis method as a physics-based machine learning approach is able to produce highly precise surrogate models that are explainable, scalable, interpretable, and robust.

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, which 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 (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36 t ha−1 for AUS-VIC and DE-NRW respectively). 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 differences in crop yield across the AUS-VIC domain (approximately 50 % inter-annual differences in recorded yields and up to 17 % inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15 % inter-annual differences in recorded yields and up to 5 % in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, 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 overestimations and underestimations in both the forecast and 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 (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in 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.

Projections of global climate models suggest that ongoing human-induced climate change will lead to an increase in the frequency of severe droughts in many important agricultural regions of the world. Eco-hydrological models that integrate current understanding of the interacting processes governing soil water balance and plant growth may be useful tools to predict the impacts of climate change on crop production. However, the validation status of these models for making predictions under climate change is still unclear, since few suitable datasets are available for model testing. One promising approach is to test models using data obtained in “space-for-time” substitution experiments, in which samples are transferred among locations with contrasting current climates in order to mimic future climatic conditions. An important advantage of this approach is that the soil type is the same, so that differences in soil properties are not confounded with the influence of climate on water balance and crop growth. In this study, we evaluate the capability of a relatively simple eco-hydrological model to reproduce 6 years (2013–2018) of measurements of soil water contents, water balance components and grass production made in weighing lysimeters located at two sites within the TERENO-SoilCan network in Germany. Three lysimeters are located at an upland site at Rollesbroich with a cool, wet climate, while three others had been moved from Rollesbroich to a warmer and drier climate on the lower Rhine valley floodplain at Selhausen. Four of the most sensitive parameters in the model were treated as uncertain within the framework of the GLUE (generalized likelihood uncertainty estimation) methodology, while the remaining parameters in the model were set according to site measurements or data in the literature. The model satisfactorily reproduced the measurements at both sites, and some significant differences in the posterior ranges of the four uncertain parameters were found. In particular, the results indicated greater stomatal conductance as well an increase in dry-matter allocation below ground and a significantly larger maximum root depth for the three lysimeters that had been moved to Selhausen. As a consequence, the apparent water use efficiency (above-ground harvest divided by evapotranspiration) was significantly smaller at Selhausen than Rollesbroich. Data on species abundance on the lysimeters provide one possible explanation for the differences in the plant traits at the two sites derived from model calibration. These observations showed that the plant community at Selhausen had changed significantly in response to the drier climate, with a significant decrease in the abundance of herbs and an increase in the proportion of grass species. The differences in root depth and leaf conductance may also be a consequence of plasticity or acclimation at the species level. Regardless of the reason, we may conclude that such adaptations introduce significant additional uncertainties into model predictions of water balance and plant growth in response to climate change.

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

Land surface models are important for improving our understanding of the Earth system. They are continuously improving and becoming better in representing the different land surface processes, e.g., the Community Land Model version 5 (CLM5). Similarly, observational networks and remote sensing operations are increasingly providing more data, e.g., from new satellite products and new in situ measurement sites, with increasingly higher quality for a range of important variables of the Earth system. For the optimal combination of land surface models and observation data, data assimilation techniques have been developed in recent decades that incorporate observations to update modeled states and parameters. The Parallel Data Assimilation Framework (PDAF) is a software environment that enables ensemble data assimilation and simplifies the implementation of data assimilation systems in numerical models. In this study, we present the development of the new interface between PDAF and CLM5. This newly implemented coupling integrates the PDAF functionality into CLM5 by modifying the CLM5 ensemble mode to keep changes to the pre-existing parallel communication infrastructure to a minimum. Soil water content observations from an extensive in situ measurement network in the Wüstebach catchment in Germany are used to illustrate the application of the coupled CLM5-PDAF system. The results show overall reductions in root mean square error of soil water content from 7 % up to 35 % compared to simulations without data assimilation. We expect the coupled CLM5-PDAF system to provide a basis for improved regional to global land surface modeling by enabling the assimilation of globally available observational data.

Effects of climate change on the ecosystem productivity and water fluxes have been studied in various types of experiments. However, it is still largely unknown whether and how the experimental approach itself affects the results of such studies. We employed two contrasting experimental approaches, using high-precision weighable monolithic lysimeters, over a period of 4 years to identify and compare the responses of water fluxes and aboveground biomass to climate change in permanent grassland. The first, manipulative, approach is based on controlled increases of atmospheric CO2 concentration and surface temperature. The second, observational, approach uses data from a space-for-time substitution along a gradient of climatic conditions. The Budyko framework was used to identify if the soil ecosystem is energy limited or water limited. Elevated temperature reduced the amount of non-rainfall water, particularly during the growing season in both approaches. In energy-limited grassland ecosystems, elevated temperature increased the actual evapotranspiration and decreased aboveground biomass. As a consequence, elevated temperature led to decreasing seepage rates in energy-limited systems. Under water-limited conditions in dry periods, elevated temperature aggravated water stress and, thus, resulted in reduced actual evapotranspiration. The already small seepage rates of the drier soils remained almost unaffected under these conditions compared to soils under wetter conditions. Elevated atmospheric CO2 reduced both actual evapotranspiration and aboveground biomass in the manipulative experiment and, therefore, led to a clear increase and change in seasonality of seepage. As expected, the aboveground biomass productivity and ecosystem efficiency indicators of the water-limited ecosystems were negatively correlated with an increase in aridity, while the trend was unclear for the energy-limited ecosystems. In both experimental approaches, the responses of soil water fluxes and biomass production mainly depend on the ecosystems' status with respect to energy or water limitation. To thoroughly understand the ecosystem response to climate change and be able to identify tipping points, experiments need to embrace sufficiently extreme boundary conditions and explore responses to individual and multiple drivers, such as temperature, CO2 concentration, and precipitation, including non-rainfall water. In this regard, manipulative and observational climate change experiments complement one another and, thus, should be combined in the investigation of climate change effects on grassland.

Root water uptake is an important process in the terrestrial water cycle. How this process depends on soil water content, root distributions, and root properties is a soil–root hydraulic problem. We compare different approaches to implement root hydraulics in macroscopic soil water flow and land surface models. By upscaling a three-dimensional hydraulic root architecture model, we derived an exact macroscopic root hydraulic model. The macroscopic model uses the following three characteristics: the root system conductance, Krs, the standard uptake fraction, SUF, which represents the uptake from a soil profile with a uniform hydraulic head, and a compensatory matrix that describes the redistribution of water uptake in a non-uniform hydraulic head profile. The two characteristics, Krs and SUF, are sufficient to describe the total uptake as a function of the collar and soil water potential, and water uptake redistribution does not depend on the total uptake or collar water potential. We compared the exact model with two hydraulic root models that make a priori simplifications of the hydraulic root architecture, i.e., the parallel and big root model. The parallel root model uses only two characteristics, Krs and SUF, which can be calculated directly following a bottom-up approach from the 3D hydraulic root architecture. The big root model uses more parameters than the parallel root model, but these parameters cannot be obtained straightforwardly with a bottom-up approach. The big root model was parameterized using a top-down approach, i.e., directly from root segment hydraulic properties, assuming a priori a single big root architecture. This simplification of the hydraulic root architecture led to less accurate descriptions of root water uptake than by the parallel root model. To compute root water uptake in macroscopic soil water flow and land surface models, we recommend the use of the parallel root model with Krs and SUF computed in a bottom-up approach from a known 3D root hydraulic architecture.

Coupled numerical models, which simulate water and energy fluxes in the subsurface–land-surface–atmosphere system in a physically consistent way, are a prerequisite for the analysis and a better understanding of heat and matter exchange fluxes at compartmental boundaries and interdependencies of states across these boundaries. Complete state evolutions generated by such models may be regarded as a proxy of the real world, provided they are run at sufficiently high resolution and incorporate the most important processes. Such a simulated reality can be used to test hypotheses on the functioning of the coupled terrestrial system. Coupled simulation systems, however, face severe problems caused by the vastly different scales of the processes acting in and between the compartments of the terrestrial system, which also hinders comprehensive tests of their realism. We used the Terrestrial Systems Modeling Platform (TerrSysMP), which couples the meteorological Consortium for Small-scale Modeling (COSMO) model, the land-surface Community Land Model (CLM), and the subsurface ParFlow model, to generate a simulated catchment for a regional terrestrial system mimicking the Neckar catchment in southwest Germany, the virtual Neckar catchment. Simulations for this catchment are made for the period 2007–2015 and at a spatial resolution of 400 m for the land surface and subsurface and 1.1 km for the atmosphere. Among a discussion of modeling challenges, the model performance is evaluated based on observations covering several variables of the water cycle. We find that the simulated catchment behaves in many aspects quite close to observations of the real Neckar catchment, e.g., concerning atmospheric boundary-layer height, precipitation, and runoff. But also discrepancies become apparent, both in the ability of the model to correctly simulate some processes which still need improvement, such as overland flow, and in the realism of some observation operators like the satellite-based soil moisture sensors. The whole raw dataset is available for interested users. The dataset described here is available via the CERA database (Schalge et al., 2020): https://doi.org/10.26050/WDCC/Neckar_VCS_v1.

Disentangling ecosystem evapotranspiration (ET) into evaporation (E) and transpiration (T) is of high relevance for a wide range of applications, from land surface modelling to policymaking. Identifying and analysing the determinants of the ratio of T to ET (T/ET) for various land covers and uses, especially in view of climate change with an increased frequency of extreme events (e.g. heatwaves and floods), is prerequisite for forecasting the hydroclimate of the future and tackling present issues, such as agricultural and irrigation practices. One partitioning method consists of determining the water stable isotopic compositions of ET, E, and T (δET, δE, and δE, respectively) from the water retrieved from the atmosphere, the soil, and the plant vascular tissues. The present work emphasizes the challenges this particular method faces (e.g. the spatial and temporal representativeness of the T/ET estimates, the limitations of the models used, and the sensitivities to their driving parameters) and the progress that needs to be made in light of the recent methodological developments. As our review is intended for a broader audience beyond the isotopic ecohydrological and micrometeorological communities, it also attempts to provide a thorough review of the ensemble of techniques used for determining δET, δE, and δE and solving the partitioning equation for T/ET. From the current state of research, we conclude that the most promising way forward to ET partitioning and capturing the subdaily dynamics of T/ET is by making use of non-destructive online monitoring techniques of the stable isotopic composition of soil and xylem water. Effort should continue towards the application of the eddy covariance technique for high-frequency determination of δET at the field scale as well as the concomitant determination of δET, δE, and δE at high vertical resolution with field-deployable lift systems.

There is an increased demand for quantitative high-resolution soil maps that enable within-field management. Commonly available soil maps are generally not suited for this purpose, but digital soil mapping and geophysical methods in particular allow soil information to be obtained with an unprecedented level of detail. However, it is often difficult to quantify the added value of such high-resolution soil information for agricultural management and agro-ecosystem modelling. In this study, a detailed geophysics-based soil map was compared to two commonly available general-purpose soil maps. In particular, the three maps were used as input for crop growth models to simulate leaf area index (LAI) of five crops for an area of ∼ 1 km2. The simulated development of LAI for the five crops was evaluated using LAI obtained from multispectral satellite images. Overall, it was found that the geophysics-based soil map provided better LAI predictions than the two general-purpose soil maps in terms of correlation coefficient R2, model efficiency (ME), and root mean square error (RMSE). Improved performance was most apparent in the case of prolonged periods of drought and was strongly related to the combination of soil characteristics and crop type.

The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy, and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site-specific field data focusing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields, as well as water, energy, and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines following Lu et al. (2017) in CLM5; (2) implementing plant-specific parameters for sugar beet, potatoes, and winter wheat, thereby adding the two crop functional types (CFTs) for sugar beet and potatoes to the list of actively managed crops in CLM5; and (3) introducing a cover-cropping subroutine that allows multiple crop types on the same column within 1 year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is an agricultural management technique with a long history that is regaining popularity as it reduces erosion and improves soil health and carbon storage and is commonly used in the regions evaluated in this study. We compared simulation results with field data and found that both the new crop-specific parameterization and the winter wheat subroutines led to a significant simulation improvement in terms of energy fluxes (root-mean-square error, RMSE, reduction for latent and sensible heat by up to 57 % and 59 %, respectively), leaf area index (LAI), net ecosystem exchange, and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover-cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI magnitudes, seasonal cycle of LAI, and latent heat flux (reduction of wintertime RMSE for latent heat flux by 42 %). Our modifications significantly improved model simulations and should therefore be applied in future studies with CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes.

Future crop production will be affected by climatic changes. In several regions, the projected changes in total rainfall and seasonal rainfall patterns will lead to lower soil water storage (SWS), which in turn affects crop water uptake, crop yield, water use efficiency (WUE), grain quality and groundwater recharge. Effects of climate change on those variables depend on the soil properties and were often estimated based on model simulations. The objective of this study was to investigate the response of key variables in four different soils and for two different climates in Germany with a different aridity index (AI): 1.09 for the wetter (range: 0.82 to 1.29) and 1.57 for the drier (range: 1.19 to 1.77) climate. This is done by using high-precision weighable lysimeters. According to a “space-for-time” (SFT) concept, intact soil monoliths that were moved to sites with contrasting climatic conditions have been monitored from April 2011 until December 2017. Evapotranspiration (ET) was lower for the same soil under the relatively drier climate, whereas crop yield was significantly higher, without affecting grain quality. Especially “non-productive” water losses (evapotranspiration out of the main growing period) were lower, which led to a more efficient crop water use in the drier climate. A characteristic decrease of the SWS for soils with a finer texture was observed after a longer drought period under a drier climate. The reduced SWS after the drought remained until the end of the observation period which demonstrates carry-over of drought from one growing season to another and the overall long-term effects of single drought events. In the relatively drier climate, water flow at the soil profile bottom showed a small net upward flux over the entire monitoring period as compared to downward fluxes (groundwater recharge) or drainage in the relatively wetter climate and larger recharge rates in the coarser- as compared to finer-textured soils. The large variability of recharge from year to year and the long-lasting effects of drought periods on the SWS imply that long-term monitoring of soil water balance components is necessary to obtain representative estimates. Results confirmed a more efficient crop water use under less-plant-available soil moisture conditions. Long-term effects of changing climatic conditions on the SWS and ecosystem productivity should be considered when trying to develop adaptation strategies in the agricultural sector.

Abstract To unequivocally discover the actual presence of life or even of near surface liquid water on extraterrestrial planetary bodies would be a key scientific breakthrough for humankind. For this reason, studying similar environments on Earth is essential to understanding the processes shaping such extraterrestrial objects. The Yungay area in the Chilean part of the Atacama Desert is deemed to be particularly suitable as a terrestrial analog of Mars (TAM). In this study, we deployed multi‐frequency ground penetrating radar (GPR) and a six‐coil electromagnetic induction (EMI) system with a maximum depth of investigation of 1.8 m over an area of 0.66 hectares (110 x 60 m). By applying a LOWESS algorithm to the GPR envelope data, we aimed to extrapolate the strongest amplitudes indicating physical contrasts to 3D. The results were constrained with two existing pits 100 m apart. Whereas clay content was mostly responsible for GPR signal attenuation, changes in texture and stratigraphy were linked with strong amplitude reflections. EMI showed very low apparent electrical conductivity (ECa) values between 0 and 5 mS/m. The ECa variability could be linked to changes in clay content with depth. This agreed with the surface obtained from the LOWESS algorithm. Although soil samples are still necessary to constrain the measured signals, we showed the benefits of applying geophysics for large‐scale characterization and can conclude that these two methods are suitable for such hyperarid TAM environments. A similar routine if applied on the surface of Mars could deliver promising results for similar characteristics.

The time precipitation needs to travel through a catchment to its outlet is an important descriptor of a catchment's susceptibility to pollutant contamination, nutrient loss, and hydrological functioning. The fast component of total water flow can be estimated by the fraction of young water (Fyw), which is the percentage of streamflow younger than 3 months. Fyw is calculated by comparing the amplitudes of sine waves fitted to seasonal precipitation and streamflow tracer signals. This is usually done for the complete tracer time series available, neglecting annual differences in the amplitudes of longer time series. Considering inter-annual amplitude differences, we employed a moving time window of 1 year in weekly time steps over a 4.5-year δ18O tracer time series to calculate 189 Fyw estimates and their uncertainty. They were then tested against the following null hypotheses: (1) at least 90 % of Fyw results do not deviate more than ±0.04 (4 %) from the mean of all Fyw results, indicating long-term invariance. Larger deviations would indicate changes in the relative contribution of different flow paths; (2) for any 4-week window, Fyw does not change more than ±0.04, indicating short-term invariance. Larger deviations would indicate a high sensitivity of Fyw to a 1-week to 4-week shift in the start of a 1-year sampling campaign; (3) the Fyw results of 1-year sampling campaigns started in a given calendar month do not change more than ±0.04, indicating seasonal invariance. In our study, all three null hypotheses were rejected. Thus, the Fyw results were time-variable, showed variability in the chosen sampling time, and had no pronounced seasonality. We furthermore found evidence that the 2015 European heat wave and including two winters into a 1-year sampling campaign increased the uncertainty of Fyw. Based on an increase in Fyw uncertainty when the mean adjusted R2 was below 0.2, we recommend further investigations into the dependence of Fyw and its uncertainty to goodness-of-fit measures. Furthermore, while investigated individual meteorological factors did not sufficiently explain variations of Fyw, the runoff coefficient showed a moderate negative correlation of r=-0.50 with Fyw. The results of this study suggest that care must be taken when comparing Fyw of catchments that were based on different calculation periods and that the influence of extreme events and snow must be considered.

For an assessment of the roles of soil and vegetation in the climate system, a further understanding of the flux components of H2O and CO2 (e.g., transpiration, soil respiration) and their interaction with physical conditions and physiological functioning of plants and ecosystems is necessary. To obtain magnitudes of these flux components, we applied source partitioning approaches after Scanlon and Kustas (2010; SK10) and after Thomas et al. (2008; TH08) to high-frequency eddy covariance measurements of 12 study sites covering different ecosystems (croplands, grasslands, and forests) in different climatic regions. Both partitioning methods are based on higher-order statistics of the H2O and CO2 fluctuations, but proceed differently to estimate transpiration, evaporation, net primary production, and soil respiration. We compared and evaluated the partitioning results obtained with SK10 and TH08, including slight modifications of both approaches. Further, we analyzed the interrelations among the performance of the partitioning methods, turbulence characteristics, and site characteristics (such as plant cover type, canopy height, canopy density, and measurement height). We were able to identify characteristics of a data set that are prerequisites for adequate performance of the partitioning methods. SK10 had the tendency to overestimate and TH08 to underestimate soil flux components. For both methods, the partitioning of CO2 fluxes was less robust than for H2O fluxes. Results derived with SK10 showed relatively large dependencies on estimated water use efficiency (WUE) at the leaf level, which is a required input. Measurements of outgoing longwave radiation used for the estimation of foliage temperature (used in WUE) could slightly increase the quality of the partitioning results. A modification of the TH08 approach, by applying a cluster analysis for the conditional sampling of respiration–evaporation events, performed satisfactorily, but did not result in significant advantages compared to the original method versions developed by Thomas et al. (2008). The performance of each partitioning approach was dependent on meteorological conditions, plant development, canopy height, canopy density, and measurement height. Foremost, the performance of SK10 correlated negatively with the ratio between measurement height and canopy height. The performance of TH08 was more dependent on canopy height and leaf area index. In general, all site characteristics that increase dissimilarities between scalars appeared to enhance partitioning performance for SK10 and TH08.

Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union (EGU) in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals.

Abstract Agroecosystem models that simulate crop growth as a function of weather conditions and soil characteristics are among the most promising tools for improving crop yield and achieving more sustainable agricultural production systems. This study aims at using spatially distributed crop growth simulations to investigate how field‐scale patterns in soil properties obtained using geophysical mapping affect the spatial variability of soil water content dynamics and growth of crops at the square kilometer scale. For this, a geophysics‐based soil map was intersected with land use information. Soil hydraulic parameters were calculated using pedotransfer functions. Simulations of soil water content dynamics performed with the agroecosystem model AgroC were compared with soil water content measured at two locations, resulting in RMSE of 0.032 and of 0.056 cm3 cm−3, respectively. The AgroC model was then used to simulate the growth of sugar beet (Beta vulgaris L.), silage maize (Zea mays L.), potato (Solanum tuberosum L.), winter wheat (Triticum aestivum L.), winter barley (Hordeum vulgare L.), and winter rapeseed (Brassica napus L.) in the 1‐ by 1‐km study area. It was found that the simulated leaf area index (LAI) was affected by the magnitude of simulated water stress, which was a function of both the crop type and soil characteristics. Simulated LAI was generally consistent with the observed LAI calculated from normalized difference vegetation index (LAINDVI) obtained from RapidEye satellite data. Finally, maps of simulated agricultural yield were produced for four crops, and it was found that simulated yield matched well with actual harvest data and literature values. Therefore, it was concluded that the information obtained from geophysics‐based soil mapping was valuable for practical agricultural applications.

In this paper, we present and analyze a novel global database of soil infiltration measurements, the Soil Water Infiltration Global (SWIG) database. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists who performed the experiments or they were digitized from published articles. Data from 54 different countries were included in the database with major contributions from Iran, China, and the USA. In addition to its extensive geographical coverage, the collected infiltration curves cover research from 1976 to late 2017. Basic information on measurement location and method, soil properties, and land use was gathered along with the infiltration data, making the database valuable for the development of pedotransfer functions (PTFs) for estimating soil hydraulic properties, for the evaluation of infiltration measurement methods, and for developing and validating infiltration models. Soil textural information (clay, silt, and sand content) is available for 3842 out of 5023 infiltration measurements ( ∼ 76%) covering nearly all soil USDA textural classes except for the sandy clay and silt classes. Information on land use is available for 76 % of the experimental sites with agricultural land use as the dominant type ( ∼ 40%). We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models. All collected data and related soil characteristics are provided online in *.xlsx and *.csv formats for reference, and we add a disclaimer that the database is for public domain use only and can be copied freely by referencing it. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data quality assessment is strongly advised prior to any use of this database. Finally, we would like to encourage scientists to extend and update the SWIG database by uploading new data to it.

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

How much water can be taken up by roots and how this depends on the root and water distributions in the root zone are important questions that need to be answered to describe water fluxes in the soil–plant–atmosphere system. Physically based root water uptake (RWU) models that relate RWU to transpiration, root density, and water potential distributions have been developed but used or tested far less. This study aims at evaluating the simulated RWU of winter wheat using the empirical Feddes–Jarvis (FJ) model and the physically based Couvreur (C) model for different soil water conditions and soil textures compared to sap flow measurements. Soil water content (SWC), water potential, and root development were monitored noninvasively at six soil depths in two rhizotron facilities that were constructed in two soil textures: stony vs. silty, with each of three water treatments: sheltered, rainfed, and irrigated. Soil and root parameters of the two models were derived from inverse modeling and simulated RWU was compared with sap flow measurements for validation. The different soil types and water treatments resulted in different crop biomass, root densities, and root distributions with depth. The two models simulated the lowest RWU in the sheltered plot of the stony soil where RWU was also lower than the potential RWU. In the silty soil, simulated RWU was equal to the potential uptake for all treatments. The variation of simulated RWU among the different plots agreed well with measured sap flow but the C model predicted the ratios of the transpiration fluxes in the two soil types slightly better than the FJ model. The root hydraulic parameters of the C model could be constrained by the field data but not the water stress parameters of the FJ model. This was attributed to differences in root densities between the different soils and treatments which are accounted for by the C model, whereas the FJ model only considers normalized root densities. The impact of differences in root density on RWU could be accounted for directly by the physically based RWU model but not by empirical models that use normalized root density functions.

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

Land surface models (LSMs) use a large cohort of parameters and state variables to simulate the water and energy balance at the soil–atmosphere interface. Many of these model parameters cannot be measured directly in the field, and require calibration against measured fluxes of carbon dioxide, sensible and/or latent heat, and/or observations of the thermal and/or moisture state of the soil. Here, we evaluate the usefulness and applicability of four different data assimilation methods for joint parameter and state estimation of the Variable Infiltration Capacity Model (VIC-3L) and the Community Land Model (CLM) using a 5-month calibration (assimilation) period (March–July 2012) of areal-averaged SPADE soil moisture measurements at 5, 20, and 50 cm depths in the Rollesbroich experimental test site in the Eifel mountain range in western Germany. We used the EnKF with state augmentation or dual estimation, respectively, and the residual resampling PF with a simple, statistically deficient, or more sophisticated, MCMC-based parameter resampling method. The performance of the calibrated LSM models was investigated using SPADE water content measurements of a 5-month evaluation period (August–December 2012). As expected, all DA methods enhance the ability of the VIC and CLM models to describe spatiotemporal patterns of moisture storage within the vadose zone of the Rollesbroich site, particularly if the maximum baseflow velocity (VIC) or fractions of sand, clay, and organic matter of each layer (CLM) are estimated jointly with the model states of each soil layer. The differences between the soil moisture simulations of VIC-3L and CLM are much larger than the discrepancies among the four data assimilation methods. The EnKF with state augmentation or dual estimation yields the best performance of VIC-3L and CLM during the calibration and evaluation period, yet results are in close agreement with the PF using MCMC resampling. Overall, CLM demonstrated the best performance for the Rollesbroich site. The large systematic underestimation of water storage at 50 cm depth by VIC-3L during the first few months of the evaluation period questions, in part, the validity of its fixed water table depth at the bottom of the modeled soil domain.

Agroecosystem models, regional and global climate models, and numerical weather prediction models require adequate parameterization of soil hydraulic properties. These properties are fundamental for describing and predicting water and energy exchange processes at the transition zone between solid earth and atmosphere, and regulate evapotranspiration, infiltration and runoff generation. Hydraulic parameters describing the soil water retention (WRC) and hydraulic conductivity (HCC) curves are typically derived from soil texture via pedotransfer functions (PTFs). Resampling of those parameters for specific model grids is typically performed by different aggregation approaches such a spatial averaging and the use of dominant textural properties or soil classes. These aggregation approaches introduce uncertainty, bias and parameter inconsistencies throughout spatial scales due to nonlinear relationships between hydraulic parameters and soil texture. Therefore, we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the mentioned problems. The approach is based on Miller–Miller scaling in the relaxed form by Warrick, that fits the parameters of the WRC through all sub-grid WRCs to provide an effective parameterization for the grid cell at model resolution; at the same time it preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters. Based on the Mualem–van Genuchten approach we also derive the unsaturated hydraulic conductivity from the water retention functions, thereby assuming that the local parameters are also valid for this function. In addition, via the Warrick scaling parameter λ, information on global sub-grid scaling variance is given that enables modellers to improve dynamical downscaling of (regional) climate models or to perturb hydraulic parameters for model ensemble output generation. The present analysis is based on the ROSETTA PTF of Schaap et al. (2001) applied to the SoilGrids1km data set of Hengl et al. (2014). The example data set is provided at a global resolution of 0.25° at https://doi.org/10.1594/PANGAEA.870605.

In situ soil moisture sensors provide highly accurate but very local soil moisture measurements, while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moisture estimation on the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNSs installed in the 2354 km2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNSs were assimilated with the local ensemble transform Kalman filter in the Community Land Model version 4.5. Data of four, eight and nine CRNSs were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high-resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map, soil moisture predictions improved strongly to a root mean square error of 0.03 cm3 cm−3 for the assimilation period and 0.05 cm3 cm−3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm3 cm−3). The positive results obtained with data assimilation of nine CRNSs were confirmed by the jackknife experiments with four and eight CRNSs used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content on the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.

Phosphorus (P) species in colloidal and dissolved soil fractions may have different distributions. To understand which P species are potentially involved, we obtained water extracts from the surface soils of a gradient from Cambisol, Stagnic Cambisol to Stagnosol from temperate grassland in Germany. These were filtered to dissolved P (31P-nuclear magnetic resonance (NMR) spectroscopy. The total P of soil water extracts increased in the order Cambisol 43−/pyrophosphate were absent in nano-sized (1–20 nm) colloids from the Cambisol but not in the Stagnosol. The 31P-NMR results indicated that this was accompanied by elevated portions of organic P in the order Cambisol > Stagnic Cambisol > Stagnosol. Across all soil types, elevated proportions of inositol hexakisphosphate (IHP) species (e.g., myo-, scyllo- and D-chiro-IHP) were associated with soil mineral particles (i.e., bulk soil and small-sized soil colloids), whereas other orthophosphate monoesters and phosphonates were found in the dissolved P fraction. We conclude that P species composition varies among colloidal and dissolved soil fractions after characterization using advanced techniques, i.e., AF4 and NMR. Furthermore, stagnic properties affect P speciation and availability by potentially releasing dissolved inorganic and ester-bound P forms as well as nano-sized organic matter–Fe/Al–P colloids.

Biochar is considered a promising soil amendment, but an effective method to detect and characterize the spatial distribution and temporal dynamics of biochar in soil is still missing. The aim of this study is to investigate the ability of the spectral induced polarization (SIP) method for the noninvasive detection of biochar in unsaturated sandy media. In particular, a pure sand and two sand–biochar mixtures with 1 and 2% biochar made from pine ( spp.) wood by pyrolysis at 800°C were investigated. The measured SIP spectra as a function of saturation were interpreted by fitting a Cole–Cole model to the low-frequency part of the SIP measurements. The porous nature of the biochar particles strongly affected the SIP response of the partially saturated sand–biochar mixtures. Due to the high residual water content of the biochar in a dry background, the relationship between bulk electrical conductivity and water saturation was nonlinear in a log–log representation. This nonlinear behavior could adequately be explained with a dielectric mixing model that considered the drainage of the biochar particles. Both the measured phase and chargeability of the sand–biochar mixtures showed a complex dependence on water saturation. This was attributed to the decrease in polarization strength of the biochar particles with desaturation and the simultaneous increase in phase of the sand background. Overall, the results of this study suggest that field SIP measurements may be a promising tool for the characterization and monitoring of biochar amendments to agricultural soils.

The Rollesbroich headwater catchment located in western Germany is a densely instrumented hydrological observatory and part of the TERENO (Terrestrial Environmental Observatories) initiative. The measurements acquired in this observatory present a comprehensive data set that contains key hydrological fluxes in addition to important hydrological states and properties. Meteorological data (i.e., precipitation, air temperature, air humidity, radiation components, and wind speed) are continuously recorded and actual evapotranspiration is measured using the eddy covariance technique. Runoff is measured at the catchment outlet with a gauging station. In addition, spatiotemporal variations in soil water content and temperature are measured at high resolution with a wireless sensor network (SoilNet). Soil physical properties were determined using standard laboratory procedures from samples taken at a large number of locations in the catchment. This comprehensive data set can be used to validate remote sensing retrievals and hydrological models, to improve the understanding of spatial temporal dynamics of soil water content, to optimize data assimilation and inverse techniques for hydrological models, and to develop upscaling and downscaling procedures of soil water content information. The complete data set is freely available online (http://www.tereno.net, doi:10.5880/TERENO.2016.001, doi:10.5880/TERENO.2016.004, doi:10.5880/TERENO.2016.003) and additionally referenced by three persistent identifiers securing the long-term data and metadata availability.

Modelling of terrestrial systems is continuously moving towards more integrated modelling approaches, where different terrestrial compartment models are combined in order to realise a more sophisticated physical description of water, energy and carbon fluxes across compartment boundaries and to provide a more integrated view on terrestrial processes. While such models can effectively reduce certain parameterisation errors of single compartment models, model predictions are still prone to uncertainties regarding model input variables. The resulting uncertainties of model predictions can be effectively tackled by data assimilation techniques, which allow one to correct model predictions with observations taking into account both the model and measurement uncertainties. The steadily increasing availability of computational resources makes it now increasingly possible to perform data assimilation also for computationally highly demanding integrated terrestrial system models. However, as the computational burden for integrated models as well as data assimilation techniques is quite large, there is an increasing need to provide computationally efficient data assimilation frameworks for integrated models that allow one to run on and to make efficient use of massively parallel computational resources. In this paper we present a data assimilation framework for the land surface–subsurface part of the Terrestrial System Modelling Platform (TerrSysMP). TerrSysMP is connected via a memory-based coupling approach with the pre-existing parallel data assimilation library PDAF (Parallel Data Assimilation Framework). This framework provides a fully parallel modular environment for performing data assimilation for the land surface and the subsurface compartment. A simple synthetic case study for a land surface–subsurface system (0.8 million unknowns) is used to demonstrate the effects of data assimilation in the integrated model TerrSysMP and to assess the scaling behaviour of the data assimilation system. Results show that data assimilation effectively corrects model states and parameters of the integrated model towards the reference values. Scaling tests provide evidence that the data assimilation system for TerrSysMP can make efficient use of parallel computational resources for > 30 k processors. Simulations with a large problem size (20 million unknowns) for the forward model were also efficiently handled by the data assimilation system. The proposed data assimilation framework is useful in simulating and estimating uncertainties in predicted states and fluxes of the terrestrial system over large spatial scales at high resolution utilising integrated models.

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

To maximize crop productivity fertilizer P is generally applied to arable soils, a significant proportion of which becomes stabilized by mineral components and in part subsequently becomes unavailable to plants. However, little is known about the relative contributions of the different organic and inorganic P bound to Fe/Al oxides in the smaller soil particles. Alkaline (NaOH–Na2EDTA) extraction with solution 31P-nuclear magnetic resonance (31P-NMR) spectroscopy is considered a reliable method for extracting and quantifying organic P and (some) inorganic P. However, any so-called residual P after the alkaline extraction has remained unidentified. Therefore, in the present study, the amorphous (a) and crystalline (c) Fe/Al oxide minerals and related P in soil aggregate-sized fractions (> 20, 2–20, 0.45–2 and < 0.45 μm) were specifically extracted by oxalate (a-Fe/Al oxides) and dithionite–citrate–bicarbonate (DCB, both a- and c-Fe/Al oxides). These soil aggregate-sized fractions with and without the oxalate and DCB pre-treatments were then sequentially extracted by alkaline extraction prior to solution 31P-NMR spectroscopy. This was done to quantify the P associated with a- and c-Fe/Al oxides in both alkaline extraction and the residual P of different soil aggregate-sized fractions. The results showed that overall P contents increased with decreasing size of the soil aggregate-sized fractions. However, the relative distribution and speciation of varying P forms were found to be independent of soil aggregate-size. The majority of alkaline-extractable P was in the a-Fe/Al oxide fraction (42–47 % of total P), most of which was ortho-phosphate (36–41 % of total P). Furthermore, still significant amounts of particularly monoester P were bound to these oxides. Intriguingly, however, Fe/Al oxides were not the main bonding sites for pyrophosphate. Residual P contained similar amounts of total P associated with both a- (11–15 % of total P) and c-Fe oxides (7–13 % of total P) in various aggregate-sized fractions, suggesting that it was likely occluded within the a- and c-Fe oxides in soil. This implies that, with the dissolution of Fe oxides, this P may be released and thus available for plants and microbial communities.

The stable isotope compositions of soil water (δ2H and δ18O) carry important information about the prevailing soil hydrological conditions and for constraining ecosystem water budgets. However, they are highly dynamic, especially during and after precipitation events. In this study, we present an application of a method based on gas-permeable tubing and isotope-specific infrared laser absorption spectroscopy for in situ determination of soil water δ2H and δ18O. We conducted a laboratory experiment where a sand column was initially saturated, exposed to evaporation for a period of 290 days, and finally rewatered. Soil water vapor δ2H and δ18O were measured daily at each of eight available depths. Soil liquid water δ2H and δ18O were inferred from those of the vapor considering thermodynamic equilibrium between liquid and vapor phases in the soil. The experimental setup allowed for following the evolution of soil water δ2H and δ18O profiles with a daily temporal resolution. As the soil dried, we could also show for the first time the increasing influence of the isotopically depleted ambient water vapor on the isotopically enriched liquid water close to the soil surface (i.e., atmospheric invasion). Rewatering at the end of the experiment led to instantaneous resetting of the stable isotope profiles, which could be closely followed with the new method. From simple soil δ2H and δ18O gradients calculations, we showed that the gathered data allowed one to determinate the depth of the evaporation front (EF) and how it receded into the soil over time. It was inferred that after 290 days under the prevailing experimental conditions, the EF had moved down to an approximate depth of −0.06 m. Finally, data were used to calculate the slopes of the evaporation lines and test the formulation for kinetic isotope effects. A very good agreement was found between measured and simulated values (Nash and Sutcliffe efficiency, NSE = 0.92) during the first half of the experiment, i.e., until the EF reached a depth of −0.04 m. From this point, calculated kinetic effects associated with the transport of isotopologues in the soil surface air layer above the EF provided slopes lower than observed. Finally, values of kinetic isotope effects that provided the best model-to-data fit (NSE > 0.9) were obtained from inverse modeling, highlighting uncertainties associated with the determinations of isotope kinetic fractionation and soil relative humidity at the EF.

This study compares actual evapotranspiration (ETa) measurements by a set of six weighable lysimeters, ETa estimates obtained with the eddy covariance (EC) method, and evapotranspiration calculated with the full-form Penman–Monteith equation (ETPM) for the Rollesbroich site in the Eifel (western Germany). The comparison of ETa measured by EC (including correction of the energy balance deficit) and by lysimeters is rarely reported in the literature and allows more insight into the performance of both methods. An evaluation of ETa for the two methods for the year 2012 shows a good agreement with a total difference of 3.8% (19 mm) between the ETa estimates. The highest agreement and smallest relative differences (< 8%) on a monthly basis between both methods are found in summer. ETa was close to ETPM, indicating that ET was energy limited and not limited by water availability. ETa differences between lysimeter and EC were mainly related to differences in grass height caused by harvest and the EC footprint. The lysimeter data were also used to estimate precipitation amounts in combination with a filter algorithm for the high-precision lysimeters recently introduced by Peters et al. (2014). The estimated precipitation amounts from the lysimeter data differ significantly from precipitation amounts recorded with a standard rain gauge at the Rollesbroich test site. For the complete year 2012 the lysimeter records show a 16 % higher precipitation amount than the tipping bucket. After a correction of the tipping bucket measurements by the method of Richter (1995) this amount was reduced to 3%. With the help of an on-site camera the precipitation measurements of the lysimeters were analyzed in more detail. It was found that the lysimeters record more precipitation than the tipping bucket, in part related to the detection of rime and dew, which contribute 17% to the yearly difference between both methods. In addition, fog and drizzle explain an additional 5.5% of the total difference. Larger differences are also recorded for snow and sleet situations. During snowfall, the tipping bucket device underestimated precipitation severely, and these situations contributed also 7.9% to the total difference. However, 36% of the total yearly difference was associated with snow cover without apparent snowfall, and under these conditions snow bridges and snow drift seem to explain the strong overestimation of precipitation by the lysimeter. The remaining precipitation difference (about 33%) could not be explained and did not show a clear relation to wind speed. The variation of the individual lysimeters devices compared to the lysimeter mean are small, showing variations up to 3% for precipitation and 8% for evapotranspiration.

Ten Dragon 3 projects deal with hydrologic and cryosphere processes, with a focus on the Himalayas and Qinghai – Tibet Plateau, but not limited to that. At the 1st Dragon 3 Progress Symposium in 2013 a significant potential for a better and deeper integration appeared very clearly and we worked out an overview of the ten projects identifying specific issues and objectives shared by at least two projects. At the Mid Term Symposium in 2014 a joint session was held over two days. As regards cryospheric processes science highlights covered: Glacier flow velocity by optical and SAR features tracking and InSAR; Patterns in space and time of glacier flow velocity; Mass change estimated with DTM-s and altimetry; Reflectance and LST used to classify glacier surface and understand surface processes, Inventory and changes in the number and area of lakes in the Qinghai – Tibet Plateau 1970, 1990, 2000 and 2010; Deformation of permafrost along the Qinghai – Tibet railway. Highlights on hydrologic processes included: Global comparison of SMOS, ASCAT and ERA soil moisture data products; Relative deviations evaluated by climate zone; Soil moisture data products improved with ancillary data; Assimilation of FY - , TRMM and GPM precipitation data products in WRF; Improved algorithm and data products on fractional snow cover; Improvement of MODIS ET with assimilation of LST; TRMM data products evaluated in the Yangtze; Calibration of river basin models using LST; System to calibrate, correct and normalize (spatial, spectral) data collected by imaging spectral radiometers; Integration of data acquired by different sensors, e.g. ET Monitor with optical and microwave (SMOS, FY – 3) data; Hydrological data products used both for forcing and evaluation of Qinghai – Tibet Plateau hydrological model; Wetlands vulnerability assessed through changes in land cover 1987 – 2013; Multi incidence angle and multi – temporal SAR to monitor water extent. In the general session a proposal for a Dragon Water Cycle Initiative was presented.

The use of eddy covariance (EC) CO2 flux measurements in data assimilation and other applications requires an estimate of the random uncertainty. In previous studies, the (classical) two-tower approach has yielded robust uncertainty estimates, but care must be taken to meet the often competing requirements of statistical independence (non-overlapping footprints) and ecosystem homogeneity when choosing an appropriate tower distance. The role of the tower distance was investigated with help of a roving station separated between 8 m and 34 km from a permanent EC grassland station. Random uncertainty was estimated for five separation distances with the classical two-tower approach and an extended approach which removed systematic differences of CO2 fluxes measured at two EC towers. This analysis was made for a data set where (i) only similar weather conditions at the two sites were included, and (ii) an unfiltered one. The extended approach, applied to weather-filtered data for separation distances of 95 and 173 m gave uncertainty estimates in best correspondence with an independent reference method. The introduced correction for systematic flux differences considerably reduced the overestimation of the two-tower based uncertainty of net CO2 flux measurements and decreased the sensitivity of results to tower distance. We therefore conclude that corrections for systematic flux differences (e.g., caused by different environmental conditions at both EC towers) can help to apply the two-tower approach to more site pairs with less ideal conditions.

The recent development of the non-invasive cosmic-ray soil moisture sensing technique fills the gap between point-scale soil moisture measurements and regional-scale soil moisture measurements by remote sensing. A cosmic-ray probe measures soil moisture for a footprint with a diameter of ~ 600 m (at sea level) and with an effective measurement depth between 12 and 76 cm, depending on the soil humidity. In this study, it was tested whether neutron counts also allow correcting for a systematic error in the model forcings. A lack of water management data often causes systematic input errors to land surface models. Here, the assimilation procedure was tested for an irrigated corn field (Heihe Watershed Allied Telemetry Experimental Research – HiWATER, 2012) where no irrigation data were available as model input although for the area a significant amount of water was irrigated. In the study, the measured cosmic-ray neutron counts and Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products were jointly assimilated into the Community Land Model (CLM) with the local ensemble transform Kalman filter. Different data assimilation scenarios were evaluated, with assimilation of LST and/or cosmic-ray neutron counts, and possibly parameter estimation of leaf area index (LAI). The results show that the direct assimilation of cosmic-ray neutron counts can improve the soil moisture and evapotranspiration (ET) estimation significantly, correcting for lack of information on irrigation amounts. The joint assimilation of neutron counts and LST could improve further the ET estimation, but the information content of neutron counts exceeded the one of LST. Additional improvement was achieved by calibrating LAI, which after calibration was also closer to independent field measurements. It was concluded that assimilation of neutron counts was useful for ET and soil moisture estimation even if the model has a systematic bias like neglecting irrigation. However, also the assimilation of LST helped to correct the systematic model bias introduced by neglecting irrigation and LST could be used to update soil moisture with state augmentation.

We coupled a radiative transfer model and a soil hydrologic model (HYDRUS 1D) with an optimization routine to derive soil hydraulic parameters, surface roughness, and soil moisture of a tilled bare soil plot using measured brightness temperatures at 1.4 GHz (L‐band), rainfall, and potential soil evaporation. The robustness of the approach was evaluated using five 28‐d data sets representing different meteorological conditions. We considered two soil hydraulic property models: the unimodal Mualem–van Genuchten and the bimodal model of Durner. Microwave radiative transfer was modeled by three different approaches: the Fresnel equation with depth‐averaged dielectric permittivity of either 2‐ or 5‐cm‐thick surface layers and a coherent radiative transfer model (CRTM) that accounts for vertical gradients in dielectric permittivity. Brightness temperatures simulated by the CRTM and the 2‐cm‐layer Fresnel model fitted well to the measured ones. L‐band brightness temperatures are therefore related to the dielectric permittivity and soil moisture in a 2‐cm‐thick surface layer. The surface roughness parameter that was derived from brightness temperatures using inverse modeling was similar to direct estimates from laser profiler measurements. The laboratory‐derived water retention curve was bimodal and could be retrieved consistently for the different periods from brightness temperatures using inverse modeling. A unimodal soil hydraulic property function underestimated the hydraulic conductivity near saturation. Surface soil moisture contents simulated using retrieved soil hydraulic parameters were compared with in situ measurements. Depth‐specific calibration relations were essential to derive soil moisture from near‐surface installed sensors.

River–aquifer exchange fluxes influence local and regional water balances and affect groundwater and river water quality and quantity. Unfortunately, river–aquifer exchange fluxes tend to be strongly spatially variable, and it is an open research question to which degree river bed heterogeneity has to be represented in a model in order to achieve reliable estimates of river–aquifer exchange fluxes. This research question is addressed in this paper with the help of synthetic simulation experiments, which mimic the Limmat aquifer in Zurich (Switzerland), where river–aquifer exchange fluxes and groundwater management activities play an important role. The solution of the unsaturated–saturated subsurface hydrological flow problem including river–aquifer interaction is calculated for ten different synthetic realities where the strongly heterogeneous river bed hydraulic conductivities (L) are perfectly known. Hydraulic head data (100 in the default scenario) are sampled from the synthetic realities. In subsequent data assimilation experiments, where L is unknown now, the hydraulic head data are used as conditioning information, with the help of the ensemble Kalman filter (EnKF). For each of the ten synthetic realities, four different ensembles of L are tested in the experiments with EnKF; one ensemble estimates high-resolution L fields with different L values for each element, and the other three ensembles estimate effective L values for 5, 3 or 2 zones. The calibration of higher-resolution L fields (i.e. fully heterogeneous or 5 zones) gives better results than the calibration of L for only 3 or 2 zones in terms of reproduction of states, stream–aquifer exchange fluxes and parameters. Effective L for a limited number of zones cannot always reproduce the true states and fluxes well and results in biased estimates of net exchange fluxes between aquifer and stream. Also in case only 10 head data are used for conditioning, the high-resolution characterization of L fields with EnKF is still feasible. For less heterogeneous river bed hydraulic conductivities, a high-resolution characterization of L is less important. When uncertainties in the hydraulic parameters of the aquifer are also regarded in the assimilation, the errors in state and flux predictions increase, but the ensemble with a high spatial resolution for L still outperforms the ensembles with effective L values. We conclude that for strongly heterogeneous river beds the commonly applied simplified representation of the streambed, with spatially homogeneous parameters or constant parameters for a few zones, might yield significant biases in the characterization of the water balance. For strongly heterogeneous river beds, we suggest adopting a stochastic field approach to model the spatially heterogeneous river beds geostatistically. The paper illustrates that EnKF is able to calibrate such heterogeneous streambeds on the basis of hydraulic head measurements, outperforming zonation approaches.

In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

Remote sensing images deliver important information about soil moisture, but often cover only part of an area, for example due to the presence of clouds or vegetation. This paper examines the potential of incorporating the spatial horizontal correlation characteristics of surface soil moisture observations in land data assimilation in order to obtain improved estimates of soil moisture at uncovered grid cells (i.e. grid cells without observations). Observing system simulation experiments were carried out to assimilate the synthetic surface soil moisture observations into the Community Land Model for the Babaohe River Basin in northwestern China. The estimation of soil moisture at the uncovered grid cells was improved when information about surrounding observations and their spatial correlation structure was included. Including an increasing number of observations for covered and uncovered grid cells in the assimilation procedure led to a better prediction of soil moisture with an upper limit of five observations. A further increase of the number of observations did not further improve the results for this specific case. High observational coverage resulted in a better assimilation performance, depending also on the spatial distribution of observation data. In summary, the spatial horizontal correlation structure of soil moisture was found to be helpful for improving the surface soil moisture data characterization, especially for uncovered grid cells.

Knowledge of the spatial and temporal distribution of pesticide concentrations is essential for pesticide registration. In field experiments performed during the registration procedure, suctions cups are widely used to monitor the evolution of pesticide leaching over time and to calculate the mean concentration for a given drainage period. Until now, there has been no detailed information regarding whether soil water sampling by suction cups is suitable for accurately predicting the field‐scale leaching of pesticides in heterogeneous soils. To address this, we performed a numerical study to evaluate the uncertainties associated with suction cup sampling in heterogeneous soils under atmospheric boundary conditions using two pesticides with contrasting sorption and degradation properties. To address the two main operation modes of suction cup sampling, continuous and weekly sampling were analyzed. First, the variability of pesticide breakthrough concentrations was analyzed for a single pesticide application in a physically heterogeneous soil profile. Second, a 10‐yr time series was used to analyze repeated pesticide applications, and estimate variability in the leached mass fraction (LMF) and mean concentrations. In both cases, pesticide breakthrough was compared to the breakthrough of an inert tracer. The results indicate that for a single pesticide application, pore water velocities varied only slightly for all operation sampling modes and compounds. On the other hand, the total extracted mass and LMF varied greatly, suggesting that the acceleration of the breakthrough sampled by suction cups plays an important role in terms of LMF and the mean concentration, especially for degradable substances.

In situ observations of soil water state variables under natural boundary conditions are often used to estimate the soil hydraulic properties. However, many contributions to the soil hydrological literature have demonstrated that the information content of such data is insufficient to accurately and precisely estimate all the soil hydraulic parameters. In this case study, we explored to which degree prior information about the soil hydraulic parameters can help improve parameter identifiability in inverse modelling of in situ soil water dynamics under natural boundary conditions. We used percentages of sand, silt, and clay as input variables to the ROSETTA pedotransfer function that predicts the parameters in the van Genuchten-Mualem (VGM) model of the soil hydraulic functions. To derive additional information about the correlation structure of the predicted parameters, which is not readily provided by ROSETTA, we employed a Monte Carlo approach. We formulated three prior distributions that incorporate to different extents the prior information about the VGM parameters derived with ROSETTA. The inverse problem was posed in a formal Bayesian framework and solved using Markov chain Monte Carlo (MCMC) simulation with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. Synthetic and real-world soil water content data were used to illustrate the approach. The results of this study demonstrated that prior information about the soil hydraulic parameters significantly improved parameter identifiability and that this approach was effective and robust, even in case of biased prior information. To be effective and robust, however, it was essential to use a prior distribution that incorporates information about parameter correlation.

Coupled hydrogeophysical methods infer hydrological and petrophysical parameters directly from geophysical measurements. Widespread methods do not explicitly recognize uncertainty in parameter estimates. Therefore, we apply a sequential Bayesian framework that provides updates of state, parameters and their uncertainty whenever measurements become available. We have coupled a hydrological and an electrical resistivity tomography (ERT) forward code in a particle filtering framework. First, we analyze a synthetic data set of lysimeter infiltration monitored with ERT. In a second step, we apply the approach to field data measured during an infiltration event on a full-scale dike model. For the synthetic data, the water content distribution and the hydraulic conductivity are accurately estimated after a few time steps. For the field data, hydraulic parameters are successfully estimated from water content measurements made with spatial time domain reflectometry and ERT, and the development of their posterior distributions is shown.

A major drawback of current soil organic carbon (SOC) models is that their conceptually defined pools do not necessarily correspond to measurable SOC fractions in real practice. This not only impairs our ability to rigorously evaluate SOC models but also makes it difficult to derive accurate initial states of the individual carbon pools. In this study, we tested the feasibility of inverse modelling for estimating pools in the Rothamsted carbon model (ROTHC) using mineralization rates observed during incubation experiments. This inverse approach may provide an alternative to existing SOC fractionation methods. To illustrate our approach, we used a time series of synthetically generated mineralization rates using the ROTHC model. We adopted a Bayesian approach using the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm to infer probability density functions of the various carbon pools at the start of incubation. The Kullback-Leibler divergence was used to quantify the information content of the mineralization rate data. Our results indicate that measured mineralization rates generally provided sufficient information to reliably estimate all carbon pools in the ROTHC model. The incubation time necessary to appropriately constrain all pools was about 900 days. The use of prior information on microbial biomass carbon significantly reduced the uncertainty of the initial carbon pools, decreasing the required incubation time to about 600 days. Simultaneous estimation of initial carbon pools and decomposition rate constants significantly increased the uncertainty of the carbon pools. This effect was most pronounced for the intermediate and slow pools. Altogether, our results demonstrate that it is particularly difficult to derive reasonable estimates of the humified organic matter pool and the inert organic matter pool from inverse modelling of mineralization rates observed during incubation experiments.

CO2 efflux at the soil surface is the result of respiration in different depths that are subjected to variable temperatures at the same time. Therefore, the temperature measurement depth affects the apparent temperature sensitivity of field-measured soil respiration. We summarize existing literature evidence on the importance of this effect, and describe a simple model to understand and estimate the magnitude of this potential error source for heterotrophic respiration. The model is tested against field measurements. We discuss the influence of climate (annual and daily temperature amplitude), soil properties (vertical distribution of CO2 sources, thermal and gas diffusivity), and measurement schedule (frequency, study duration, and time averaging). Q10 as a commonly used parameter describing the temperature sensitivity of soil respiration is taken as an example and computed for different combinations of the above conditions. We define conditions and data acquisition and analysis strategies that lead to lower errors in field-based Q10 determination. It was found that commonly used temperature measurement depths are likely to result in an underestimation of temperature sensitivity in field experiments. Our results also apply to activation energy as an alternative temperature sensitivity parameter.

In order to address the challenges of global change, interdisciplinary research in terrestrial environmental science is of great importance. Several environmental research networks have already been established in order to monitor, analyse and predict the impact of global change on different compartments and/or matter cycles of the environment. Typically these environmental research networks have focused on specific research questions, and compartments, such as CarboEurope, FLUXNET and ILTER. In this paper, we propose the establishment of a network of terrestrial observatories, defined as a system consisting of the subsurface environment, the land surface including the biosphere, the lower atmosphere and the anthroposphere. Hydrological units will be used as the basic scaling units in a hierarchy of evolving scales and structures ranging from the local scale to the regional scale for multi-disciplinary process studies. Although terrestrial systems are extremely complex, the terrestrial component in most process-based climate and biosphere models is typically represented in a very conceptual and often rudimentary way. Remedying this deficiency is therefore one of the most important challenges in environmental and terrestrial research, and we suggest that terrestrial observatories could be an important step towards a new quality in environmental and terrestrial research.

In this paper the relation between local- and field-scale solute transport parameters in an unsaturated soil profile is investigated. At two experimental sites, local-scale steady-state solute transport was measured in-situ using 120 horizontally installed TDR probes at 5 depths. Local-scale solute transport parameters determined from BTCs were used to predict field-scale solute transport using stochastic stream tube models (STM). Local-scale solute transport was described by two transport models: (1) the convection-dispersion transport model (CDE), and (2) the stochastic convective lognormat transfer model (CLT). The parameters of the CDE-model were found to be lognormally distributed, whereas the parameters of the CLT model were normally distributed. Local-scale solute transport heterogeneity within the measurement volume of a TDR-probe was an important factor causing field-scale solute dispersion. The study of the horizontal scale-dependency revealed that the variability in the solute transport parameters contributes more to the field-scale dispersion at deeper depths than at depths near the surface. Three STMs were used to upscale the local transport parameters: (i) the stochastic piston flow STM-I assuming local piston flow transport, (ii) the convective-dispersive STM-II assuming local CDE transport, and (iii) the stochastic convective lognormal STM-III assuming local CLT. The STM-I considerably underpredicted the field-scale solute dispersion indicating that local-scale dispersion processes, which are captured within the measurement volume of the TDR-probe, are important to predict field-scale solute transport. STM-II and STM-III both described the field-scale breakthrough curves (BTC) accurately if depth dependent parameters were used. In addition, a reasonable description of the horizontal variance of the local BTCs was found. STM-III was (more) superior to STM-II if only one set of parameters from one depth is used to predict the field-scale solute BTCs at several depths. This indicates that the local-scale solute transport process, as measured with TDR in this study, is in agreement with the CLT-hypothesis.

Transport processes in heterogeneous media such as ionized plasmas, natural porous media, and turbulent atmosphere are often modeled as diffusion processes in random velocity fields. Using the Itô formalism, we decompose the second spatial moments of the concentration and the equivalent effective dispersion coefficients in terms corresponding to various physical factors which influence the transport. We explicitly define "ergodic'' dispersion coefficients, independent of the initial conditions and completely determined by local dispersion coefficients and velocity correlations. Ergodic coefficients govern an upscaled process which describes the transport at large tine-space scales. The non-ergodic behavior at finite times shown by numerical experiments for large initial plumes is explained by "memory terms'' accounting for correlations between initial positions and velocity fluctuations on the trajectories of the solute molecules.