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The soil water retention curve (SWRC) is a key soil property required for predicting basic hydrological processes. SWRC is often obtained in laboratory with non-harmonized methods. Moreover, procedures associated to each method are not standardized. This can induce a lack of reproducibility between laboratories using different methods and procedures or using the same methods with different procedures. The goal of this study was to estimate the inter/intralaboratory variability of the measurement of the wet part (from 10 to 300 hPa) of the SWRC. An interlaboratory comparison was conducted between 14 laboratories, using artificially constructed, porous and structured samples as references. The bulk densities of samples were different at the very beginning of the experiment. This resulted in a variability of retention properties between the samples, which was estimated by a linear mixed model with a "sample" random effect. Our estimate of inter/intralaboratory variability was therefore not affected by intrinsic differences between samples. The greatest portion of the differences in the measurement of SWRCs was due to interlaboratory variability. The intralaboratory variability was highly variable depending on the laboratory. Some laboratories successfully reproduced the same SWRC on the same sample, while others did not. The mean intralaboratory variability over all laboratories was smaller than the mean interlaboratory variability. A possible explanation for these results is that all laboratories used slightly different methods and procedures. We believe that this result may be of great importance regarding the quality of SWRC databases built by pooling SWRCs obtained in different laboratories. The quality of pedotransfer functions or maps that might be derived is probably hampered by this inter-/intralaboratory variability. The way forward is that measurement procedures of the SWRC need to be harmonized and standardized.

In Poyda et al. (2019), we determined the carbon balance of six cropland sites in Southwest Germany on the basis of half-hourly net ecosystem exchange (NEE) fluxes measured with the eddy covariance (EC) method over a period of eight years from 2010 to 2017. We came up with the finding, that the sites lost on average about one ton of carbon per hectare and year. This huge loss was surprising, because the sites have been used as croplands already over several decades, and one would expect that the sites should be close to steady-state. In April 2022, we performed a soil organic carbon inventory at one of the six sites, and compared it with carbon data collected in April 2009. The soil inventory data do not give any evidence for a carbon loss in the order of one ton of carbon per hectare and year. Based on the data collected in April 2009, the carbon stock size of the plowing horizon at that time was 39.1 t C ha-1 yr-1. The carbon stock size determined in April 2022 was in the same range and amounted 39.9 t C ha-1 yr-1 with a standard error of 1.9 t C ha-1 yr-1, what means that these data indicate that the carbon budget of the cropland is indeed in or close to steady-state. In Poyda et al. (2019), the NEE flux data were gap-filled with the widely used software tool REddyProc. In REddyProc, the user has the option to apply or not to apply the friction velocity (u*) filter before gap-filling. In Poyda et al. (2019), the u* filter was applied before gap-filling. We reprocessed the data for the year 2016, the only year with no winter time gaps, because a methanol fuel cell supplied the station with additional power, without applying the u* filter. Without applying the u* filter the cumulated annual NEE was ‑2080 kg C ha-1. Applying the u* filter increased the NEE by 836 kg C ha-1 to -1244 kg C ha-1. We did the same comparison for NEE fluxes measured at the same site over the years 2019, 2020 and 2021, and we got a similar result. The application of the u* filter increased the mean NEE by +730 kg C ha-1. The cumulated NEE was -2832 kg C ha-1 without u* filter and -2102 kg C ha-1 with u* filter. This positive bias is in the range of the EC based derived carbon loss and is able to explain a major part of the suspected positive bias. We recommend all REddyProc users to check their NEE data for this phenomena simply by processing the data once with and once without enabling the u* filter and comparing both results.

Important topics in Land-Atmosphere (L-A) feedback research are water and energy balances and heterogeneities of fluxes at the land-surface and in the atmospheric booundary layer. To target these questions, the Land-Atmosphere Feedback Observatory (LAFO) has been installed in Southwest Germany. The instrumentation allows comprehensive and high-resolution measurements from the bedrock to the lower free troposphere. Grouped in three components: atmosphere, soil and land-surface and vegetation, the LAFO observation strategy aims for simultaneous measurements in all three compartments. For that the LAFO sensor synergy contains lidar systems to measure the atmospheric key variables humidity, temperature and wind. At the land-surface eddy covariance stations operated to record the energy distribution of radiation, sensible, latent and ground heat fluxes. With a water and temperature sensor network the soil water and temperature is monitored in the agricultural investigation area. The observations in LAFO are organized in operational measurements and intensive observation periods (IOPs). Operational measurements aim for long timeseries dataset to investigate statistics as we present as example the correlation between mixing layer height and surface fluxes. The potential of IOPs is demonstrated with a 24 hour case study with dynamic and thermodynamic profiles with lidar as well as a surface layer observation with the scanning differential absorption lidar to relate atmospheric humidity patterns to soil water structures. Both long-term observations and IOPs are important for improving the representation of L-A feedbacks in climate and numerical weather prediction models.

We present a comprehensive, high-quality dataset characterizing soil–vegetation and land surface processes from continuous measurements conducted in two climatically contrasting study regions in southwestern Germany: the warmer and drier Kraichgau region with a mean temperature of 9.7 ∘C and annual precipitation of 890 mm and the cooler and wetter Swabian Alb with mean temperature 7.5 ∘C and annual precipitation of 1042 mm. In each region, measurements were conducted over a time period of nine cropping seasons from 2009 to 2018. The backbone of the investigation was formed by six eddy-covariance (EC) stations which measured fluxes of water, energy and carbon dioxide between the land surface and the atmosphere at half-hourly resolution. This resulted in a dataset containing measurements from a total of 54 site years containing observations with a multitude of crops, as well as considerable variation in local growing-season climates. The presented multi-site, multi-year dataset is composed of crop-related data on phenological development stages, canopy height, leaf area index, vegetative and generative biomass, and their respective carbon and nitrogen content. Time series of soil temperature and soil water content were monitored with 30 min resolution at various points in the soil profile, including ground heat fluxes. Moreover, more than 1200 soil samples were taken to study changes of carbon and nitrogen contents. The dataset is available at https://doi.org/10.20387/bonares-a0qc-46jc (Weber et al., 2021). One field in each region is still fully set up as continuous observatories for state variables and fluxes in intensively managed agricultural fields.

In southern Xinjiang province, northwest China, farmland is undergoing rapid conversion to orchards or agroforestry. This has improved land-use efficiency but has also caused drastic ecological changes in this region. This study investigated the effects of farmland conversion to orchard or agroforestry on soil total organic carbon (TOC) and several soil labile fractions: readily oxidizable carbon (ROC), light fraction organic carbon (LFOC), and dissolved organic carbon (DOC). Soil samples were collected from seven cropping treatments: a monocultured wheat field (Mono), a 5-year-old jujube orchard (5 J), a 5-year-old jujube/wheat alley cropping system (5 JW), a 10-year-old jujube orchard (10 J), a 10-year-old jujube/wheat alley cropping system (10 JW), a 15-year-old jujube orchard (15 J), and a 15-year-old jujube/wheat alley cropping system (15 JW). The results show that the ROC concentrations varied from 0.17 ± 0.09 g/kg to 2.35 ± 0.05 g/kg across all land-use types and soil depths studied. It was higher in the 0–10 cm and 10–20 cm layers of treatment 10 JW than in other treatments and significantly greater than in the Mono treatment. The highest value of DOC was reached at 593.04 mg/kg in the 15 JW treatment at 0–10 cm. Labile organic carbon decreased with increasing depth in all treatments. The proportion of ROC and LFOC to TOC decreased with increasing soil depth. In all treatments, the ratio of DOC to TOC generally decreased initially and then increased again with increasing depth. Correlation analysis showed that ROC, LFOC, and DOC were closely correlated with TOC (p < 0.01). The ROC, LFOC, and DOC concentrations were significantly correlated with each other (p < 0.01). Following conversion of farmland to jujube orchard or agroforestry, the content and activity of soil organic carbon tended to increase due to augmentation of plant residues. Thus, jujube orchards and agroforestry systems are effective methods to restore soil organic carbon.

Methane emissions from freshwater systems, and especially from small lakes source a significant proportion of naturally produced atmospheric methane. In small temperate lakes, storage flux, i.e. the diffusive emission of methane that was stored in anoxic waters during the seasonal overturn of the water column, can contribute a large fraction of annual methane emissions. Here we use an extensive field dataset to quantify methane storage in anoxic deep water and identify as well as quantify the sources and losses of this methane. The comparison of measurements from four years in a small temperate lake (Illmensee) shows that methane storage can differ substantially between years. In 2012 and 2018, the annual maximum of the methane stored in the entire water column was around twice as high (5350 kg and 5822 kg) as in 2013 and 2017 (2722 kg and 2295 kg). A methane mass balance approach suggests that the variability of methane storage in the anoxic water between the years was not caused by the methane flux at the anoxic-oxic water interface, but rather was related to changes in the methane source from the anoxic sediments. The interannual differences in sediment flux could not be explained by sediment temperature, but rather by the differing supply of organic matter. Our findings suggest that phytoplankton blooms promote methane storage within the same year and thus cause interannual variability in emissions during autumn overturn. published

Absract Previous studies have observed a negative CO2 efflux from semi-desert and desert soil, but the carbon uptake in alkaline soils has not been measured quantitatively in laboratory. The present study aims at closing this gap considering the effect of temperature, soil pH and pCO2. CO2 uptake was studied with the help of the Barometric Process Separation (BaPS) technique. CO2 uptake was studied at 3 different temperatures viz. 15, 20 and 25 °C. CO2 uptake was observed after CO2 injection with pH values ranging from 8 to 10 in desert Solonchak soils. We found that CO2 uptake depended on pCO2 and temperature. CO2 uptake occurred rapidly within the first 10 min after the CO2 injection and gradually slowed down afterwards. Results showed that 72%–97% of injected CO2 was taken up by soils (pH 8–10). CO2 uptake increased as soil pH increased and its uptake reached equilibrium faster in soil with a lower pH level (pH 0.96) and pH. The lower temperature produced a higher CO2aq for the same pCO2 value. In conclusion, desert soils can rapidly uptake of CO2, and the main controlling process of CO2 uptake in the desert soils was decided by soil pH and temperature. The CO2 uptake by the soil solution may be an important pathway for C sequestration in semi-desert and desert regions.

The carbon (C) sequestration potential of croplands has recently become a subject of debate because it may contribute significantly to global climate change mitigation. By contrast, carbon dioxide (CO2) emissions from German croplands have continuously increased over the past decades as a result of soil organic carbon (SOC) losses. Contrasting results, however, have been obtained on the C sink or source function of European croplands based on long-term experiments and rather recent eddy covariance (EC) measurements. Over a period of eight years (2010–2017), we measured the net ecosystem exchange (NEE) of CO2 on six intensively managed cropland sites in two climatically different regions of southwest Germany (Kraichgau (KR) and Swabian Jura (SJ)) using the EC technique. Additionally, we measured aboveground crop biomass at three development stages and estimated management-related C inputs and exports. The inter-annual on-site variability of cumulated annual NEE was large, and neither the region nor the different sites significantly affected NEE budgets. Winter rapeseed showed the lowest CO2 uptake capacity among the observed crops, and the mean annual NEE in the years with winter rapeseed harvest was significantly lower compared to winter wheat, silage maize and winter barley. On average over 46 site-years, annual NEE showed a distinct CO2 uptake of −2580 kg CO2-C ha−1 yr−1. Considering management-related C fluxes, the resulting net biome productivity (NBP) indicated a C source function of the study sites with mean annual losses of 1190 kg C ha−1 yr−1. Due to high C removals after whole plant harvests, silage maize cropping resulted in significantly higher C losses of 4280 kg C ha−1 yr−1 compared to winter rapeseed, winter wheat and winter barley, with mean annual NBPs of 1430, −188 and −1340 kg C ha−1 yr−1, respectively. Consequently, a higher share of exported C in annual NBP resulted in higher C losses. We conclude that the recently increased importance of silage maize in crop rotations destabilizes SOC stocks, threatening the efforts in enhancing soil C sequestration. This calls for further investigations on the C sequestration potentials of more diverse crop rotations including perennial phases.

Turbulent fluxes at the land surface measured by the Eddy Covariance (EC) technique are typically considerably less than the difference between net radiation and ground heat flux. This is known as the energy balance closure (EBC) problem. It is crucial for validating land surface models as it provokes substantial uncertainty to the magnitude and partitioning of energy fluxes. The gap in the energy balance calls for searching for additional energy terms in the soil-plant-atmosphere system. To evaluate the contribution of these minor storage terms to the measured EBC, we conducted an experimental study to evaluate the contribution of these minor storage terms to measured EBC in the Kraichgau region in southwest Germany over two consecutive growing seasons (2015 and 2016). The measured and calculated minor storage terms comprised the enthalpy change in the plant canopy (Sc), the air enthalpy change (Sa), the energy consumption and release by photosynthesis and respiration (Sp), and the atmospheric moisture change (Sq). Furthermore, the soil heat storage (Sg) was determined at different locations within the EC footprint and compared to the single point measurements of Sg at the EC station. Calorimetric and harmonic analysis were performed to compute ground heat flux. Sp had the strongest effect in improving EBC due to the high net CO2 uptake during the productive phase of plant growth. In 2015, all minor storage terms together increased EBC by 5.0% on average, with a maximum value of 7.4% in May, while the improvement in 2016 was 6.8% on average and 8.4% in May. Ground heat flux computed with the harmonic analysis based on plate data narrowed the EBC by 3% more than the calorimetric method. In summary, a better EBC can be achieved by considering minor storage terms and applying a harmonic analysis to ground heat flux data. Regarding future research, we recommend to focus on year-round measurements of energy terms because energy stored during the growing season might be lost from the system during the rest of the year. Nonetheless, the significant contribution of minor energy terms to EBC indicates that turbulent energy fluxes are most likely overestimated when all the missing energy is assumed to be turbulent–the typical approach when fluxes are corrected by the Bowen ratio post-closure method for instance.

Soil and groundwater contamination with potentially toxic elements (PTEs) including cadmium (Cd) and copper (Cu) has become a serious problem for ecosystem functioning. Silicon (Si) may precipitate these metals as silicates, and may also form, at undersaturation of silicates, 'Si-contaminant compounds', i.e. particles of polymerized silica with PTEs incorporated or adsorbed by inner-sphere complexes. While the formation of these compounds in aqueous solution has been proven, their formation in soil remains unclear yet. Therefore, we conducted column experiments with a topsoil horizon artificially contaminated with Cd or Cu solutions (10 mM) in the presence (10 mM) and absence of monomeric Si, and monitored the elemental composition of the eluates during 12 irrigation steps with artificial rainwater by microwave-plasma atomic emission spectrometry, the size and charge of the particles eluted by dynamic light scattering and phase analysis light scattering, and determined the spatial distribution of total and exchangeable Cd and Cu in soil after the experiments. When Si was previously applied to soil, significantly larger particles (up to 200 nm) in the eluates indicated Si polymerization and formation of Si-contaminant compounds. However, Cd and Cu concentrations were very low (0.4 μM), pointing to efficient retardation in soil. In any variant, the particles formed were slightly negatively charged (-11 mV). The molar metal:Si ratios in the eluates and significant correlations between the amounts of Si and metals in soil extracted by NH

From hypothesis testing to impact assessment, soil-crop models serve a multitude of purposes in researching climate change impact on the water cycle in cropping systems. As such, model complexity undeniably differs amongst approaches and intended purpose but must include the full range of soil-water states. Climate change is predicted to increase prolonged periods of dry conditions, however, soil-crop models, often misrepresented drying of soils, inducing misquantifications in root water uptake, evapotranspiration, and concomitantly, water use efficiency and ground water recharge rates. While in soil hydrology, this has been recognised and effective solutions exist, soil-crop models typically neglect these more comprehensive approaches.Our study serves two aims: For one, we shed light into the chaotic multitude of soil hydraulic property models, by representing the models in a systematic framework, of sub-models and processes. This model is called the Brunswick Soil Hydraulic Property Model Framework. With this we hope to make soil hydrology more tangible, for non-experts. Secondly, we i) demonstrate that representing soil hydraulic properties over the entire moisture range, i.e. from fully saturated to completely dry soils, changes simulated root water uptake under dry soil conditions, which sensitively affects crop yields. We quantify these effects by simulating root water uptake using the agro-ecosystem models Expert-N and Daisy, comparing the standard as well as oversimplified approach to a comprehensive model (of the Brunswick model framework). This was possible by using new pedotransfer functions to obtain all required model parameters. The framework and pedotransfer functions are available in the R package spsh on CRAN.

We present a comprehensive, high-quality dataset characterising soil-vegetation and land-surface processes from continuous measurements conducted in two climatically contrasting study regions in South West Germany: the warmer and drier Kraichgau region with a mean temperature of 9.7 °C and annual precipitation of 890 mm, and the cooler and wetter Swabian Alp with mean temperature 7.5 °C and annual precipitation 1042 mm. In each region, measurements were conducted over a time period of nine cropping seasons from 2009 to 2018. The backbone of the investigation was formed by six eddy-covariance stations (EC) which measured fluxes of water, energy and carbon dioxide between the land surface and the atmosphere at half-hourly resolution. This resulted in a dataset containing measurements from a total of 54 site*years containing observations with a multitude of crops, as well as considerable variation in local growing season climates. The presented multi-site, multi-year data set is composed of crop-related data on phenological development stages, canopy height, leaf area index, vegetative and generative biomass and their respective carbon and nitrogen content. Time series of soil temperature and soil water content were monitored with 30-min resolution at various points in the soil profile, including ground heat fluxes. Moreover, more than 1,200 soil samples were taken to study changes of carbon and nitrogen contents. The data set was uploaded to the Pangaea database and can be accessed at https://doi.org/10.20387/bonares-a0qc-46jc (for the review process, please refer to the data availability section). One station in each region has now been set up as continuous observatories of state variables and fluxes in intensively managed agricultural fields.

Soil organic carbon (SOC) losses under a changing climate are driven by the temperature sensitivity of SOC mineralization (usually expressed as Q10, the multiplier of activity with 10 °C temperature increase). The activation energy theory (AET) suggests that, due to higher activation energies, the more complex the carbon, the higher is mineralization Q10. However, studies on Q10 have been inconsistent with regard to AET. Measurements of potential soil enzymes activity Q10 even contradicted AET: Phenoloxidase (representing complex carbon) had consistently lower Q10 than the more labile xylanase and glucosidase. This study used two approaches of examining Q10 in SOC modeling: 1) Bayesian calibration (BC) and 2) using different measured enzyme Q10 as proxies for mineralization Q10 of different SOC pools. The SOC model was DAISY (S. Hansen et al., 2012). BC informed Q10 by field measured data, while the second approach tested if directly using enzyme Q10 (of phenoloxidase, glucosidase and xylanase) for DAISY pools improved simulation results. Both approaches used the temperature sensitive measurements of CO2 evolution and soil microbial biomass. The measured enzyme Q10 were from field manipulation experiments with bare fallow and vegetated plots in the two regions of Kraichgau and Swabian Jura in Southwest Germany. The enzyme-derived Q10 were used for modelling those fields and furthermore for in‑situ litterbag decomposition experiments at 20 sites in the same region. Two further laboratory experiments with temperature manipulation were included: an incubation of the field residues into soil and an incubation of bare soil from the start and year 50 of a long duration bare fallow (from Ultuna). The BC made use of CO2 and microbial data to inform about the range of Q10 of different carbon pools for the individual experiments and combined data.The BC of the residue incubation experiment constrained Q10 for metabolic (~3) and structural litter (~2). Estimated 95% credibility intervals did not overlap. The BC for Ultuna could constrain the slow and fast SOC pool with Q10 ~2.8 and ~3, respectively, but credibility intervals of both pools overlapped. The Q10 of field experiments, which had most abundant data, could not be constrained by BC, probably because their annual temparature variability was too low. However, the model errors of the field experiment could be reduced by the second approach, when the Q10 of phenoloxidase was used for to the structural litter pool as well as for the fast and slow SOC pools. Thus regional enzyme Q10 improved the model fit but only for regional simulations. Therefore, they could be useful proxies when natural temperature range is too small to inform temperature sensitivity by BC. Any trends found in this study contradicted AET, both from measured enzymes and BC of the incubation experiments. This calls for alternative Q10 hypotheses and the need for individual Q10 values for different SOC pool rather than a general one. BC approaches would benefit from a wider temperature range of field experiments and understanding what causes variable enzyme Q10 could help to improve future SOC models.

The evaluation of the first of its kind multi-model convection permitting - regional climate model (CP-RCM) ensemble, produced within the framework of the international CORDEX - Flagship Pilot Study on Convective phenomena over the Mediterranean (CORDEX-FPS-CEM), showed the improved spatial representation, frequency and extremes of precipitation compared to coarser resolution counterparts (Ban et al., submitted 2019). It is important, though, to keep in mind that the quality of each of such high resolution simulations strongly depend on the quality of the forcing used to drive the model.In this work we investigate the impact of the land-surface forcing on the model representation of land-atmosphere (LA) feedback. For that we used the 2 Weather Research and Forecasting (WRF) settings from the CORDEX-FPS-CEM ensemble to run 4 simulations for 2 seasons (summer and fall), combining 2 sets of data for the land cover (MODIS and CORINE) and the top soil texture (FAO and HWSD). In such a way we generated two ensembles with 8 simulations for each season. Additionally we run 4 simulations with perturbed starting date for the summer season to obtain an additional 5-member ensemble to investigate the impact of the internal variability on the final results.The objective of this study is to investigate the model sensitivity to (1) land-surface static forcing, (2) season, and (3) the model configuration. To quantify the strength of LA coupling for each grid, we use coupling metrics appropriate for seasonal time scales. We applied mixing diagram approach to investigate the impact of the land-surface changes on the boundary layer evolution. We also investigate the impact of these changes on the potential for convection triggering, and we calculate correlation matrices to quantify the impact on the strength of the land-atmosphere coupling.Preliminary results show evident effects of the land surface changes on surface variables, boundary layer evolution, atmospheric stability and humidity in the lower atmosphere. Strength of the sensitivity to a specific change in the land-surface forcing depend on the model configuration: WRF with less sophisticated parameterization schemes is more sensitive to land use changes, while more sophisticated configuration shows higher sensitivity to the soil texture changes in representing boundary layer evolution. Furthermore, the WRF shows stronger coupling and therefore stronger sensitivity to the land surface changes in the summer season.Acknowledgement: This work is partially funded by the Spanish Government R+D programme through grant INSIGNIA (CGL2016-79210-R) co-funded by the ERDF/FEDER.References: Ban N., and coauthors: The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, Part I: Evaluation of precipitation, (submitted to Climate Dynamics, 2019)

Interactions between vegetation and atmosphere have a large impact on weather and climate. During the last decade, enormous efforts have been made to improve the representation of vegetation dynamics in land surface models (LSM). The present study extends the LSM Noah-MP by the dynamic crop growth model Gecros that enables simulating the development of crop stands in a weather-driven manner. This extension is a pre-requisite to simulate two-way climate-crop interactions in climate projections. Based on a comprehensive five-year dataset on energy- and water fluxes, and soil water and crop data from two different climate regions of southwest Germany, we adapted the crop growth model Gecros, integrated it with Noah-MP, calibrated the coupled model for winter wheat and maize and tested its robustness in multiple-year validation runs against independent measurements. This sound data set yielded a robust parameterization that performed well both in calibration and in validation runs over in total 16 seasons. Due to pronounced differences in phenology among maize cultivars, wheat simulations were better than maize simulations. The simulated dynamics in leaf area index of wheat and maize differed largely from the one used in standard Noah-MP simulations. The new model yielded pronounced differences in the partitioning of evapotranspiration into transpiration and soil evaporation. The added value of the improved description of vegetation dynamics needs to be evaluated in high-resolution coupled crop-climate simulations in future.

The barometric process separation (BaPS) technique is a well-established incubation method to simultaneously measure gross nitrification and respiration rates in soil. Its application, however, is still critical in soils with pH above 6.5. Here, a substantial part of microbial CO2 production is retained in soil solution (CO2,aq) due to shifts in the carbonate equilibrium. This may lead to substantial errors in gas balance calculation. Yet, utilization of the BaPS technique is only reliable if the critical term is adequately quantified. We present an easy, inexpensive, and direct method, the sterilization-CO2-injection (SCI) method, to measure CO2 retention during soil incubation. Sterilized soil was incubated in the BaPS system, and defined volumes of CO2 were injected to stepwise increase CO2 partial pressure (pCO2) inside the chamber and to analyse the physicochemical equilibration process. Five exemplary agricultural soils from Northeast China and Southwest Germany were used for method establishment, presenting pH values between 4.4 and 7.6 and carbonate contents between 0% and 3.9%. We observed that in soils with pH > 6.5, 70%–90% of the injected CO2 was taken up by the soil until the equilibrium inside the chamber was re-established. As expected, in soils with low pH (

Forecast errors with respect to wind, temperature, moisture, clouds, and precipitation largely correspond to the limited capability of current Earth system models to capture and simulate land–atmosphere feedback. To facilitate its realistic simulation in next-generation models, an improved process understanding of the related complex interactions is essential. To this end, accurate 3D observations of key variables in the land–atmosphere (L–A) system with high vertical and temporal resolution from the surface to the free troposphere are indispensable.Recently, we developed a synergy of innovative ground-based, scanning active remote sensing systems for 2D to 3D measurements of wind, temperature, and water vapor from the surface to the lower troposphere that is able to provide comprehensive datasets for characterizing L–A feedback independently of any model input. Several new applications are introduced, such as the mapping of surface momentum, sensible heat, and latent heat fluxes in heterogeneous terrain; the testing of Monin–Obukhov similarity theory and turbulence parameterizations; the direct measurement of entrainment fluxes; and the development of new flux-gradient relationships. An experimental design taking advantage of the sensors’ synergy and advanced capabilities was realized for the first time during the Land Atmosphere Feedback Experiment (LAFE), conducted at the Atmospheric Radiation Measurement Program Southern Great Plains site in August 2017. The scientific goals and the strategy of achieving them with the LAFE dataset are introduced. We envision the initiation of innovative L–A feedback studies in different climate regions to improve weather forecast, climate, and Earth system models worldwide.

Even small changes in microbial decomposition rates of soil organic carbon (SOC) at the regional scale have the potential to modify land-atmospheric feedbacks at the global scale. Limited understanding of the regulation of microbial driven processes has led to major uncertainty in global SOC estimates. Therefore, to better understand the large scale processes controlling SOC dynamics, we examined the influence of SOC quantity, quality, and soil physical and biochemical properties on soil basal respiration and of the temperature sensitivities (Q10) of soil respiration and enzymes (β-glucosidase and xylanase) at two scales: landscape (two individual areas, each approximately 27 km2) and regional (pooled data of both areas). Soil samples (0–30 cm soil depth) originated from 41 agricultural sites distributed over two areas in southwest Germany differing in climatic and geological conditions. We used a two-step data analysis procedure; variable selection through random Forest regression, followed by shortlisting of significant explanatory variables using linear mixed-effect models. Microbial biomass regulated soil basal respiration at both scales, whereas soil C:N ratio played an important role only at the regional scale based on mixed-effect models. Soil texture significantly explained temperature sensitivity (Q10) of soil respiration at both scales. Different SOC quality fractions characterized by midDRIFTS played a minor role, whereas extractable organic C related negatively to the respiration Q10. Soil properties controlling soil enzymes (Q10) were scale-specific. We found pH to be the main factor affecting β-glucosidase Q10 at the landscape scale. We argue that scale-specificity of variables may depend on homogeneity of study areas and should be considered when exploring SOC dynamics. Our study identified direct and indirect controlling factors affecting soil basal respiration and its temperature sensitivity, providing vital information for SOC dynamics at large scales.

The spatial variability of topsoil water content (SWC) is often expressed through the relationship between its spatial mean 〈θ〉 and standard deviation σθ. The present study tests the concept that a reasonably performing land surface model (LSM) should be able to produce σθ–〈θ〉 data pairs that fall into a polygon, spanned by the cloud of observed data and two anchor points: σθ at the permanent wilting point σθ–〈θwp〉 and σθ at saturation σθ–〈θs〉. A state-of-the-art LSM, Noah-MP, was driven by atmospheric forcing data obtained from eddy covariance field measurements in two regions of southwestern Germany, Kraichgau (KR) and Swabian Alb (SA). KR is characterized with deep loess soils, whereas the soils in SA are shallow, clayey, and stony. The simulations series were compared with SWC data from soil moisture networks operating in the two study regions. The results demonstrate that Noah-MP matches temporal 〈θ〉 dynamics fairly well in KR, but performs poorly in SA. The best match is achieved with the van Genuchten–Mualem representation of soil hydraulic functions and site-specific rainfall, soil texture, green vegetation fraction (GVF) and leaf area index (LAI) input data. Nevertheless, most of the simulated σθ–〈θ〉 pairs are located outside the envelope of measurements and below the lower bound, which shows that the model smooths spatial SWC variability. This can be mainly attributed to missing topography and terrain information and inadequate representation of spatial variability of soil texture and hydraulic parameters, as well as the model assumption of a uniform root distribution.

The search for approaches to a holistic sustainable agriculture requires the development of new cropping systems that provide additional ecosystem services beyond biomass supply for food, feed, material, and energy use. The reduction of chemical synthetic plant protection products is a key instrument to protect vulnerable natural resources such as groundwater and biodiversity. Together with an optimal use of mineral fertilizer, agroecological practices, and precision agriculture technologies, a complete elimination of chemical synthetic plant protection in mineral-ecological cropping systems (MECSs) may not only improve the environmental performance of agroecosystems, but also ensure their yield performance. Therefore, the development of MECSs aims to improve the overall ecosystem services of agricultural landscapes by (i) improving the provision of regulating ecosystem services compared to conventional cropping systems and (ii) improving the supply of provisioning ecosystem services compared to organic cropping systems. In the present review, all relevant research levels and aspects of this new farming concept are outlined and discussed based on a comprehensive literature review and the ongoing research project “Agriculture 4.0 without Chemical-Synthetic Plant Protection”.

Land surface models are essential parts of climate and weather models. The widely used Noah-MP land surface model requires information on the leaf area index (LAI) and green vegetation fraction (GVF) as key inputs of its evapotranspiration scheme. The model aggregates all agricultural areas into a land use class termed Cropland and Pasture. In a previous study we showed that, on a regional scale, GVF has a bimodal distribution formed by two crop groups differing in phenology and growth dynamics: early covering crops (ECC, ex.: winter wheat, winter rapeseed, winter barley) and late covering crops (LCC, ex.: corn, silage maize, sugar beet). That result can be generalized for Central Europe. The present study quantifies the effect of splitting the land use class Cropland and Pasture of Noah-MP into ECC and LCC on surface energy fluxes and temperature. We further studied the influence of increasing the LCC share, which in the study area (the Kraichgau region, southwest Germany) is mainly the result of heavily subsidized biomass production, on energy partitioning at the land surface. We used the GVF dynamics derived from high-resolution (5 m × 5 m) RapidEye satellite data and measured LAI data for the simulations. Our results confirm that GVF and LAI strongly influence the partitioning of surface energy fluxes, resulting in pronounced differences between ECC and LCC simulations. Splitting up the generic crop into ECC and LCC had the strongest effect on land surface exchange processes in July–August. During this period, ECC are at the senescence growth stage or already harvested, while LCC have a well-developed, ground-covering canopy. The generic crop resulted in humid bias, i.e. an increase of evapotranspiration by +0.5 mm d−1 (LE: 1.3 MJ m−2 d−1), decrease of H by 1.2 MJ m−2 d−1 and decrease of surface temperature by −1 °C. The bias increased as the shares of ECC and LCC became similar. The observed differences will impact the simulations of processes in the planetary boundary layer. Increasing the LCC share from 28 to 38 % in the Kraichgau region led to a decrease of LE and a heating up of the land surface in the early growing season. Over the second part of the season, LE increased and the land surface cooled down by up to 1 °C.

Re-vegetation of cropland is an important measure for carbon (C) sequestration, while the combined effects of land-use change, re-vegetation type and time on soil organic C (OC) turnover and temperature sensitivity have been less studied. Here, we investigated soil cumulative OC mineralization (Cmin), specific OC mineralization (Cmin/C0, normalized by initial OC) and temperature sensitivity (Q10) with an 84-d incubation experiment after the conversion of cropland to artificial grassland (6, 10, 15 and 20 yr), to natural grassland (6, 10 and 20 yr) and to pea shrub woodland (10, 20 and 40 yr) in the agro-pastoral ecotone of northern China. Results showed that re-vegetation significantly increased Cmin (from 38.21 to 57.79 mg kg−1 soil, +51%) at the 0–10 cm depth but not the 10–20 cm depth, with greater increases after the conversion of cropland to artificial grassland (+77%) than the other two patterns but similar among different conversion years for each pattern. Variations in Cmin among different re-vegetation patterns and times were mostly explained by changes in total nitrogen concentration (TN) in the macro-aggregate fraction. Across all treatments, 6.20–20.29 g kg−1 OC was mineralized during the incubation and Q10 was 1.16–2.40. The Cmin/C0 and Q10 changed not significantly during the post-agricultural re-vegetation chronosequences for each pattern, which were mostly due to the unchanged TN concentration in silt and clay fraction. Re-vegetation also changed the dependence of Cmin, Cmin/C0 on N availability, with significant relationships in re-vegetated artificial grassland and pea shrub woodland which may due to the influence of legumes on microbes. In conclusion, OC mineralization increased with conversion, varying by re-vegetation type, while OC decomposability and temperature sensitivity were stable regardless of re-vegetation type and time, indicating the similar biodegradability and stability in face of warming.

The direction and magnitude of change of soil organic carbon (SOC) stocks due to global warming depend strongly on the temperature sensitivity (e.g., Q10) of carbon mineralization. To date, most multi-pool SOC models assume a general Q10 of 2 despite experimental evidence suggesting different Q10 for different carbon fractions. The aim of this study was to test if the use of experimentally derived pool specific Q10 values improves the performance of SOC models. Five contrasting data sets from three field experiments and two laboratory incubations were used to study the link between carbon pool recalcitrance and Q10 using two different approaches: a) Bayesian calibration of the Daisy SOC model parameters to infer Q10 of SOC and crop-litter pools, and b) using measured Q10 values of carbon degrading enzymes as proxies for Q10 of different Daisy pools. Namely β-glucosidase (median Q10 of 1.82) was assigned to metabolic litter and phenol/peroxidase (1.35) to structural litter and both SOC pools. To partition litter-carbon and SOC into model pools, the lignin-to-nitrogen ratio and the ratio of aliphatic/aromatic-carboxylate carbon were used, respectively. Measurements included soil microbial biomass, soil carbon dioxide (CO2) evolution and remaining carbon in soils and crop-litter. In the Bayesian calibration, strong differences in inferred Q10 values of the same pools between experiments suggested that intrinsic substrate recalcitrance was not the main driver of temperature sensitivity. For field experiment simulations, both the Q10 values derived by Bayesian calibration and measured enzyme Q10 were centered around values below 2, contrasting with high Q10 values for mineralization under laboratory incubations (close to 3). Furthermore, assigning measured phenol/peroxidase Q10 values to the slow crop-litter as well as both SOC pools and β-glucosidase to the fast crop-litter pool (approach b), could significantly improve model performance compared to using the default Q10 value of 2 for all pools. Root-mean-squared-deviation reductions were between 3 and 10% for field experiments, with no change in the laboratory experiments. Thus, site specific Q10 values of soil enzymes show potential as proxies for pool specific Q10. We present a new conceptual framework to explain the observed differences in temperature sensitivities between experiments as a result of two fundamental driving factors classified in a) state variables, that fluctuate in time, and b) soil properties, that are constant over decades. Measured enzyme Q10 values were interpreted as a proxy incorporating both factors. More than intrinsic substrate recalcitrance, the state variables such as physical protection, substrate abundance and unfavorable conditions for microorganisms control temperature sensitivity of mineralization. To reduce the uncertainty in global SOC simulations under a changing climate, their relative contributions should be disentangled and then implemented into SOC models.

The mechanistic integration of microbial behavior remains a major challenge in biogeochemical modeling of organic matter turnover in soil. We recently introduced dynamic feedbacks between specific microbial groups and their micro-environment in a biogeochemical model (Pagel et al., 2014). Here, the model was applied in a case study to simulate pesticide degradation coupled to carbon (C) turnover in the detritusphere. We aimed at unravelling the effects of litter-derived substrate supply on the spatiotemporal dynamics of the microbial community and the resulting biogeochemical processes at the mm-scale in soil. We linked genetic information on abundances of bacteria, fungi and specific pesticide degraders to the biogeochemical dynamics of C and a generic model compound (MCPA, 4-chloro-2-methylphenoxyacetic acid) in soil by multiobjective calibration. We observed and simulated increased dissolved organic and microbial C as well as accelerated MCPA degradation in soil up to a 6 mm distance to litter. We found that, whereas transport and sorption processes act as extrinsic control on the encounter of microorganisms and substrates, microbial traits such as substrate preference or metabolic capabilities intrinsically determine turnover rates triggering feedback effects on physicochemical processes such as diffusion. A process analysis revealed that C cycling and pesticide degradation in the detritusphere were strongly controlled by fungal dynamics. Our study demonstrates that integrating mathematical modeling with experiments provides comprehensive insight into the microbial regulation of matter cycling in soil.

Peatlands are interesting as carbon storage option, but are also natural emitters of the greenhouse gas methane (CH4). Phragmites peatlands are particularly interesting due to the global abundancy of this wetland plant (Phragmites australis (Cav.) Trin. ex Steud.) and the highly efficient internal gas transport mechanism, which is called Humidity Induced Convection (HIC). The research aim was to (1) clarify how this plant-mediated gas transport influences the CH4 fluxes, (2) which other environmental variables influence the CO2 and CH4 fluxes, and (3) whether Phragmites peatlands are a net source or sink of greenhouse gases. CO2 and CH4 fluxes were measured with the eddy covariance technique within a Phragmites-dominated fen in Southwest Germany. One year of flux data (March 2013 to February 2014) shows very clear diurnal and seasonal patterns for both CO2 and CH4. The diurnal pattern of CH4 fluxes was only visible when living green reed was present. In August the diurnal cycle of CH4 was most distinct, with 11-times higher midday fluxes (15.7 mg CH4 m−2 h−1) than night fluxes (1.41 mg CH4 m−2 h−1). This diurnal cycle correlates the highest with global radiation, which suggest a high influence of the plants on the CH4 flux. But if the cause would be the HIC, it is expected that relative humidity would correlate stronger with CH4 flux. Therefore, we conclude that in addition to HIC, at least one additional mechanism must be involved in the creation of the convective flow within the Phragmites plants. Overall, the fen was a sink for carbon and greenhouse gases in the measured year, with a total carbon uptake of 221 g C m−2 yr−1 (26 % of the total assimilated carbon). The net uptake of greenhouse gases was 52 g CO2-eq m−2 yr−1, which is summed from an uptake of CO2 of 894 g CO2-eq m−2 yr−1 and a release of CH4 of 842 g CO2-eq m−2 yr−1.

The response of agroecosystem carbon (C) respiration fluxes to environmental changes needs to be better understood as respiration subcomponents may respond differently to management and seasonal weather dynamics, which is important for soil organic matter (SOM) modeling. Respiration measurements at two different spatial and temporal scales (eddy covariance (EC) and soil chambers) were used to ascertain the relationship between temperature and CO2 flux of different ecosystem respiration components (ecosystem (Reco), soil and root combined, and soil). Further, different model approaches (static versus dynamic reference CO2 rate (rb) and activation energy type parameter (E0) with an Arrhenius-like function) in order to partition Reco into above- and belowground autotrophic (RA_above, RA_below) and heterotrophic respiration (RH_SOM) were tested. Canopy level CO2 fluxes in winter wheat and silage maize were measured by EC stations and soil surface CO2 flux by a handheld chamber analyzer in arable fields in Southwest Germany over a period of three growing seasons (2009, 2010, and 2012). Additionally, successive bare fallow plots were installed at the beginning of each growing season to partition soil respiration between autotrophic and heterotrophic sources (including “labile” soil C (newest bare fallow) as the difference to the oldest bare fallow). Stepwise model building was tested with keeping rb and E0 constant (static method) and then by varying rb and E0 each individually or together by time period (dynamic method) over the whole growing season (15, 10 or 7 days for Reco, measurement periods for soil chamber measurements). The dynamic models were superior as measured by Aaike Information Criteria (AIC) and coefficient of determination (average R2, 0.15 for the static model and 0.50 for the dynamic model). In the best fitting model for each crop-year (lowest AIC), rb was successfully estimated in each time period (relative standard error

Six simulations with the Weather Research and Forecasting (WRF) model differing in planetary boundary layer (PBL) schemes and land surface models (LSMs) are investigated in a case study in western Germany during clear-sky weather conditions. The simulations were performed at 2 km resolution with two local and two nonlocal PBL schemes, combined with two LSMs (NOAH and NOAH-MP). Resulting convective boundary layer (CBL) features are investigated in combination with high-resolution water vapor differential absorption lidar measurements at an experimental area. Further, the simulated soil-vegetation-atmosphere feedback processes are quantified applying a mixing diagram approach. The investigation shows that the nonlocal PBL schemes simulate a deeper and drier CBL than the local schemes. Furthermore, the application of different LSMs reveals that the entrainment of dry air depends on the energy partitioning at the land surface. The study demonstrates that the impact of processes occurring at the land surface is not constrained to the lower CBL but extends up to the interfacial layer and the lower troposphere. With respect to the choice of the LSM, the discrepancies in simulating a diurnal change of the humidity profiles are even more significant at the interfacial layer than close to the land surface. This indicates that the representation of land surface processes has a significant impact on the simulation of mixing properties within the CBL.

The energy balance of eddy covariance (EC) flux data is typically not closed. The nature of the gap is usually not known, which hampers using EC data to parameterize and test models. In the present study we cross-checked the evapotranspiration data obtained with the EC method (ETEC) against ET rates measured with the soil water balance method (ETWB) at winter wheat stands in southwest Germany. During the growing seasons 2012 and 2013, we continuously measured, in a half-hourly resolution, latent heat (LE) and sensible (H) heat fluxes using the EC technique. Measured fluxes were adjusted with either the Bowen-ratio (BR), H or LE post-closure method. ETWB was estimated based on rainfall, seepage and soil water storage measurements. The soil water storage term was determined at sixteen locations within the footprint of an EC station, by measuring the soil water content down to a soil depth of 1.5 m. In the second year, the volumetric soil water content was additionally continuously measured in 15 min resolution in 10 cm intervals down to 90 cm depth with sixteen capacitance soil moisture sensors. During the 2012 growing season, the H post-closed LE flux data (ETEC = 3.4 ± 0.6 mm day−1) corresponded closest with the result of the WB method (3.3 ± 0.3 mm day−1). ETEC adjusted by the BR (4.1 ± 0.6 mm day−1) or LE (4.9 ± 0.9 mm day−1) post-closure method were higher than the ETWB by 24 and 48 %, respectively. In 2013, ETWB was in best agreement with ETEC adjusted with the H post-closure method during the periods with low amount of rain and seepage. During these periods the BR and LE post-closure methods overestimated ET by about 46 and 70 %, respectively. During a period with high and frequent rainfalls, ETWB was in-between ETEC adjusted by H and BR post-closure methods. We conclude that, at most observation periods on our site, LE is not a major component of the energy balance gap. Our results indicate that the energy balance gap is made up by other energy fluxes and unconsidered or biased energy storage terms.

Drought and heat events, such as the 2018 European drought, interact with the exchange of energy between the land surface and the atmosphere, potentially affecting albedo, sensible and latent heat fluxes, as well as CO 2 exchange. Each of these quantities may aggravate or mitigate the drought, heat, their side effects on productivity, water scarcity and global warming. We used measurements of 56 eddy covariance sites across Europe to examine the response of fluxes to extreme drought prevailing most of the year 2018 and how the response differed across various ecosystem types (forests, grasslands, croplands and peatlands). Each component of the surface radiation and energy balance observed in 2018 was compared to available data per site during a reference period 2004–2017. Based on anomalies in precipitation and reference evapotranspiration, we classified 46 sites as drought affected. These received on average 9% more solar radiation and released 32% more sensible heat to the atmosphere compared to the mean of the reference period. In general, drought decreased net CO 2 uptake by 17.8%, but did not significantly change net evapotranspiration. The response of these fluxes differed characteristically between ecosystems; in particular, the general increase in the evaporative index was strongest in peatlands and weakest in croplands. This article is part of the theme issue ‘Impacts of the 2018 severe drought and heatwave in Europe: from site to continental scale’.

The energy balance of eddy covariance (EC) measurements is typically not closed. This so-called energy balance closure (EBC) problem has been a long-standing issue in micrometeorology. It is a main challenge in evaluating and interpreting EC flux data. EBC is crucial for validating and improving regional and global climate models. To investigate the reasons behind EBC more closely for agro-ecosystems, we analysed EC measurements from two climatically contrasting regions (Kraichgau (KR) and Swabian Jura (SJ)) in southwest Germany. Data were taken at six fully equipped EC sites from 2010 to 2017. The gap in EBC was quantified by ordinary linear regression, by the energy balance ratio (EBR) between turbulent fluxes and available energy, and by the residual energy term. In order to examine potential reasons for differences in EBC, we compared the EBC under varying environmental conditions and investigated a wide range of possible controls. Statistical analyses were conducted for the whole data set to test the effects of different regions, years, sites and crops on EBC. We also investigated whether EBC was a function of buoyancy, friction velocity, or atmospheric stability. The time-variable footprints of all EC stations were estimated based on data measured in 2015, complemented by micro-topographic analyses along the prevailing wind direction. The lowest mean annual energy balance gap was 17 % in KR and 13 % in SJ. Highest EBRs were commonly measured for winds originating from the prevailing wind direction. The variation of EBC was higher in winter than in summer. The measurement site exerted a statistically significant effect on EBC, but not crop or region. The spread of EBR distinctly narrowed under unstable atmospheric conditions, strong buoyancy, and high friction velocities. Smaller footprint areas led to better EBC due to increasing homogeneity. Flow distortions of winds that first travelled past the back head of the anemometer affected EBC negatively.

A 56-day aerobic incubation experiment was performed with 15-nitrogen (N) tracer techniques after application of wheat straw to investigate nitrate-N (NO3-N) immobilization in a typical intensively managed calcareous Fluvaquent soil. The dynamics of concentration and isotopic abundance of soil N pools and nitrous oxide (N2O) emission were determined. As the amount of straw increased, the concentration and isotopic abundance of total soil organic N and newly formed labeled particulate organic matter (POM-N) increased while NO3-N decreased. When 15NO3-N was applied combined with a large amount of straw at 5000 mg carbon (C) kg−1 only 1.1 ± 0.4 mg kg−1 NO3-N remained on day 56. The soil microbial biomass N (SMBN) concentration and newly formed labeled SMBN increased significantly (P

The green vegetation fraction (GVF) is a key input variable of the evapotranspiration scheme applied in the widely used NOAH-MP land surface model (LSM). In standard applications of the NOAH-MP, the GVF is taken from a global map with a 15 km × 15 km resolution. The central objective of the present study was (a) to derive gridded GVF data of a region in Southwest Germany in a high spatial resolution (5 m × 5 m) from RapidEye satellite images, and (b) to improve the representation of the GVF dynamics of croplands in the NOAH-MP for a more accurate simulation of water and energy exchange between land surface and atmosphere. The GVF dynamics were determined based on the normalized difference vegetation index (NDVI) calculated from the red and near-infrared bands of the satellite images. The satellite GVF data were calibrated and validated against ground truth measurements. Based on the obtained calibration scheme, GVF maps were derived in a monthly resolution for the region. Our results confirm a linear relationship between GVF and NDVI and demonstrate that it is possible to determine the GVF of croplands from RapidEye images based on a simple two end-member mixing model. Our data highlight the high variability of the GVF in time and space. At the field scale, variability was mainly caused by soil heterogeneities and management differences. At the regional scale, the GVF showed a bimodal distribution formed by the different phenology of crops. We suggest to divide croplands according to their distinctly different temporal dynamics of the GVF into “early-covering” (winter rape, winter wheat, spring barley) and “late-covering” crops (sugar beet, silage maize). Based on our results, we recommend that simulations with LSM should take into account this differentiation of croplands, since it is to be expected that these two crop groups produce pronounced differences in energy partitioning at the land surface.

The energy balance of eddy covariance (EC) flux data is normally not closed. Therefore, at least if used for modeling, EC flux data are usually post-closed, i.e. the measured turbulent fluxes are adjusted so as to close the energy balance. At the current state of knowledge, however, it is not clear how to partition the missing energy in the right way. Eddy flux data therefore contain some uncertainty due to the unknown nature of the energy balance gap, which should be considered in model evaluation and the interpretation of simulation results. We propose to construct the post-closure method uncertainty band (PUB), which essentially designates the differences between non-adjusted flux data and flux data adjusted with the three post-closure methods (Bowen ratio, latent heat flux (LE) and sensible heat flux (H) method). To demonstrate this approach, simulations with the NOAH-MP land surface model were evaluated based on EC measurements conducted at a winter wheat stand in Southwest Germany in 2011, and the performance of the Jarvis and Ball–Berry stomatal resistance scheme was compared. The width of the PUB of the LE was up to 110 W m–2 (21% of net radiation). Our study shows that it is crucial to account for the uncertainty of EC flux data originating from lacking energy balance closure. Working with only a single post-closing method might result in severe misinterpretations in model-data comparisons.

Understanding in situ enzyme activities could help clarify the fate of soil organic carbon (SOC), one of the largest uncertainties in predicting future climate. Here, we explored the role of soil temperature and moisture on SOM decomposition by using, for the first time, modelled in situ enzyme activities as a proxy to explain seasonal variation in soil respiration. We measured temperature sensitivities (Q 10 ) of three enzymes (β-glucosidase, xylanase and phenoloxidase) and moisture sensitivity of β-glucosidase from agricultural soils in southwest Germany. Significant seasonal variation was found in potential activities of β-glucosidase, xylanase and phenoloxidase and in Q 10 for β-glucosidase and phenoloxidase activities but not for xylanase. We measured moisture sensitivity of β-glucosidase activity at four moisture levels (12%–32%), and fitted a saturation function reflecting increasing substrate limitation due to limited substrate diffusion at low water contents. The moisture response function of β-glucosidase activity remained stable throughout the year. Sensitivity of enzymes to temperature and moisture remains one of the greatest uncertainties in C models. We therefore used the response functions to model temperature-based and temperature and moisture-based in situ enzyme activities to characterize seasonal variation in SOC decomposition. We found temperature to be the main factor controlling in situ enzyme activities. To prove the relevance of our modelling approach, we compared the modelled in situ enzyme activities with soil respiration data measured weekly. Temperature-based in situ enzyme activities explained seasonal variability in soil respiration well, with model efficiencies between 0.35 and 0.78. Fitting an exponential response function to in situ soil temperature explained soil respiration to a lesser extent than our enzyme-based approach. Adding soil moisture as a co-factor improved model efficiencies only partly. Our results demonstrate the potential of this new approach to explain seasonal variation of enzyme related processes.

Reliable regional climate projections need improved land-surface-atmosphere models in which land-surface exchange, in particular the role of the vegetation, is well represented. To get a better understanding of the land-surface exchange and as a data basis for model development, parameterization and validation, long-term eddy-covariance measurements were performed over agricultural landscapes in two regions of Southwest Germany with different regional climate: near the northern Upper Rhine Valley with a mild climate and on the Swabian Jura, a low mountain range plateau where the climate is colder and wetter. At each study site we installed three eddy-covariance weather stations and monitored the soil water regime as well as the crop development. We describe the characteristics of the study sites, the experimental field work and data processing. The dynamics of water vapor and energy fluxes over winter wheat, winter oilseed rape and silage maize were evaluated for the time period from 2009 to 2011. The accuracy of the measured energy and matter fluxes limited by statistics and instrumental noise error was better than 10 %. Energy fluxes and albedo were closely linked to crop development. The albedo increased and decreased with LAI. Albedo values up to 0.28 were measured over flowering oilseed rape. The winter crops developed faster in the milder climate. The daily average of latent heat flux was about 10–20 Wm−2 higher and that of sensible heat flux about 5–15 Wm−2 lower there in comparison with the colder climate. Differences between both regions with regard to the turbulent fluxes are assigned to be mainly due to differences in vapor pressure deficit. Actual and prevailing weather conditions had a large impact on energy fluxes so that notable differences of flux dynamics were observed not only between the regions but also at one site for the same crop in different years. The observed long-term flux dynamics are discussed with respect to phenology, regional climate and the impact of prevailing weather conditions.

Highland agriculture is intensifying rapidly in South-East Asia, leading to alarmingly high applications of agrochemicals. Understanding the fate of these contaminants requires carefully planned monitoring programmes and, in most cases, accurate simulation of hydrological pathways into and through water bodies. We simulate run-off in a steep mountainous catchment in tropical South-East Asia. To overcome calibration difficulties related to the mountainous topography, we introduce a new calibration method, named A Nash–Sutcliffe Efficiency Likelihood Match (ANSELM), that allows the assignment of optimal parameters to different hydrological response units in simulations of stream discharge with the Soil and Water Assessment Tool (SWAT) hydrological model. ANSELM performed better than the Parasol calibration tool built into SWAT in terms of model efficiency and computation time. In our simulation, the most sensitive model parameters were those related to base flow generation, surface run-off generation, flow routing and soil moisture change. The coupling of SWAT with ANSELM yielded reasonable simulations of both wet-season and dry-season storm hydrographs. Nash–Sutcliffe model efficiencies for daily stream flow during two validation years were 0.77 and 0.87. These values are in the upper range or even higher than those reported for other SWAT model applications in temperate or tropical regions. The different flow components were realistically simulated by SWAT, and showed a similar behaviour in all the study years, despite inter-annual climatic differences. The realistic partitioning of total stream flow into its contributing components will be an important factor for using this hydrological model to simulate solute transport in the future. Copyright © 2014 John Wiley & Sons, Ltd.

Interactions between the soil, the vegetation, and the atmospheric boundary layer require close attention when predicting water fluxes in the hydrogeosystem, agricultural systems, weather, and climate. However, land-surface schemes used in large-scale models continue to show deficiencies in consistently simulating fluxes of water and energy from the subsurface through vegetation layers to the atmosphere. In this study, the multiphysics version of the Noah land-surface model (Noah-MP) was used to identify the processes, which are most crucial for a simultaneous simulation of water and heat fluxes between land surface and the lower atmosphere. Comprehensive field data sets of latent and sensible heat fluxes, ground heat flux, soil moisture, and leaf area index from two contrasting field sites in South-West Germany are used to assess the accuracy of simulations. It is shown that an adequate representation of vegetation-related processes is the most important control for a consistent simulation of energy and water fluxes in the soil-plant-atmosphere system. In particular, using a newly implemented submodule to simulate root growth dynamics has enhanced the performance of Noah-MP. We conclude that further advances in the representation of leaf area dynamics and root/soil moisture interactions are the most promising starting points for improving the simulation of feedbacks between the subsoil, land surface and atmosphere in fully coupled hydrological and atmospheric models.

Land-use change in the mountainous parts of northern Thailand is reflected by an increased application of agrochemicals, which may be lost to surface and groundwater. The close relation between flow paths and contaminant transport within hydrological systems requires recognizing and understanding the dominant hydrological processes. To date, the vast majority of studies on runoff generation have been conducted in temperate regions. Tropical regions suffer from a general lack of data, and little is known about runoff generation processes. To fill this knowledge gap, a three-component hydrograph separation based on geochemical tracers was carried out in a steep, remote and monsoon-dominated study site (7 km2) in northern Thailand. Silica and electrical conductivity (EC) were identified as useful tracers and were applied to calculate the fractions of groundwater (similar to pre-event water), shallow subsurface flow and surface runoff on stormflow. K+ was a useful indicator for surface runoff dynamics, and Ca2+ provided insights into groundwater behaviour. Nevertheless, neither measure was applicable for the quantification of runoff components. Cl- and further parameters (e.g. Na+, K+, and Mg2+) were also not helpful for flow path identification, nor were their concentrations distinguishable among the components. Groundwater contributed the largest fractions to stormflow (62–80%) throughout all events, followed by shallow subsurface flow (17–36%) and surface runoff (2–13%). Our results provide important insights into the dynamics of the runoff processes in the study area and may be used to assess the transport pattern of contaminants (i.e. agrochemicals) here.

[1] Six models with differing representation of the physical process in the coupled soil-plant system are tested to simultaneously reproduce the dynamics of soil water contents, evapotranspiration, and leaf area index during a growing season of winter wheat at two contrasting field plots in the Kraichgau and the Swabian Alb regions in South-West Germany. The main aim of the study is the assessment of the performance and the identification of structural deficits of the models LEACHN, SUCROS, CERES, GECROS, and SPASS as well as the land-surface model CLM3.5. The calibration of each model is posed in a multiobjective framework with three different objective functions that summarize the fit between model simulations and the three observation types. The AMALGAM evolutionary search algorithm is utilized to simultaneously estimate the most important soil hydraulic and plant parameters. The six models exhibit a wide variability in the trade-offs between the fitting to the data types. Mechanistic process description, particularly of the root system, reduces the trade-off considerably for the SPASS and GECROS models. These models adequately simulate the reduction of root water uptake and transpiration during senescence under nonlimited soil water supply. The SPASS model in particular shows an overall better performance as compared to the more simpler models which is related to an adequate level of structural complexity in the interplay of all model compartments combined with a relatively low parameter sensitivity to the weighting scheme in the multiobjective optimization. The dynamic consideration of the root system formation is particularly important, which is simulated quite detailed in the SPASS model as a function of nitrogen (N) and water availability in the different soil horizons. The proposed multiobjective calibration procedure proved to be very useful to identify processes that are important to adequately simulate the coupled soil-plant system. The consideration of these processes and our insights about the value of different data types for model calibration is expected to lead to more accurate, predictive land-surface models.