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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. [ABSTRACT FROM AUTHOR]

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

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

Fast and accurate large‐scale localization and quantification of harmfully compacted soils in recultivated post‐mining landscapes are of particular importance for mining companies and the following farmers. The use of heavy machinery during recultivation imposes soil stress and can cause irreversible subsoil compaction limiting crop growth in the long term. To overcome or guide classical point‐scale methods to determine compaction, fast methods covering large areas are required. In our study, a recultivated field of the Garzweiler mine in North Rhine‐Westphalia, Germany, with known variability in crop performance was intensively studied using non‐invasive electromagnetic induction (EMI) and electrode‐based electrical resistivity tomography (ERT). Additionally, soil bulk density, volumetric soil water content and soil textures were analysed along two transects covering different compaction levels. The results showed that the measured EMI apparent electrical conductivity (ECa) along the transects was highly correlated (R2 >.7 for different dates and depths below 0.3 m) to subsoil bulk density. Finally, the correlations established along the transects were used to predict harmful subsoil compaction within the field, whereby a spatial probabilistic map of zones of harmful compaction was developed. In general, the results revealed the feasibility of using the EMI derived ECa to predict harmful compaction. They can be the basis for quick monitoring of the recultivation process and implementation of necessary melioration to return a well‐structured soil with good water and nutrient accessibility, and rooting depths for increased crop yields to the farmers. [ABSTRACT FROM AUTHOR]

Purpose: Pesticide contamination of river waters is a global problem, and therefore, authorities regularly monitor the water quality status. Especially, flood events might transport large pesticide loads downstream and impact adjacent areas such as sensible floodplain environments by deposing particle bound pesticides or by contaminating the environments by dissolved substances directly. Unfortunately, only little attempts were made to quantify the pesticide pollution of such environments, as the monitoring of soil and sediment contamination is by far more time consuming and complicated compared to the analysis of water samples. Materials and methods: In the study presented, we therefore used a holistic approach starting with the reconstruction of the inundation characteristics of a floodplain located in the upper part of the river Elbe, Germany, by using nearby gauging data, screened databases for pesticide concentrations and calculated pesticide loads, and finally, sampled the floodplain soils along an elevation (inundation) gradient. Results: As expected, the reconstructed inundation characteristics showed that the low-lying areas of the backwater inflow of the floodplain were flooded more frequently compared to the areas at higher elevation, whereby even the highest elevations sampled were at least flooded during each decade. Furthermore, pesticide concentrations of the river waters and calculated pesticide loads revealed that 13 pesticides can be found regularly, whereby atrazine, terbuthylazine, metazachlor, metolachlor, isoproturon, and chlorotoluron are the main contaminants. Conclusion: Concluding, a spatial pattern in the contamination could be detected. High residues of simazine and ethofumesate were associated with areas of less and more frequent inundations, respectively. The transformation products of atrazine and terbuthylazine (2-hydroxy-atrazine and 2-hydroxy-terbuthylazine) were detected, whereby the quantity could be well explained by the inundation characteristics and pesticide loads of the river water. [ABSTRACT FROM AUTHOR]

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 virtual 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 model COSMO, the land-surface model CLM, and the subsurface model ParFlow, to generate a virtual catchment for a regional terrestrial system mimicking the Neckar catchment in southwest Germany. 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 modelling challenges, the model performance is evaluated based on real observations covering several variables of the water cycle. We find that the simulated (virtual) 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%5FVCS%5Fv1. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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

The dissolved organic matter (DOM) quality or quantity of stream waters reflects catchment functioning. Dissolved organic matter features, pH, and Fe content in the main stream and tributaries of the forested Wüstebach catchment in Germany were examined for a 4-yr weekly sampling period (2009-2013). The catchment outlet (Wu14) discharge (Q) ranged from 0.22 to 72.5 L s-1, with minima and maxima around the summer and winter seasons, respectively. Discharge clearly followed precipitation in the late autumn to early spring period. Dissolved organic C (DOC) values at Wu14 ranged between 0.8 and 7.4 mg L-1, with a mean of 2.7 mg L-1. Annual periodicities in DOC and temperature (T), and DOC and Fe concentrations were positively correlated, reflecting DOM transport via organo-mineral complexes. The watershed tributaries dominated by surficial water (Wu01, Wu08, and Wu09) exhibited relatively high DOC, Fe, pH, and specific DOC UV absorbance (SUVA) and high variability. Groundwater-dominated tributaries (Wu03, Wu12, and Wu15) had lower, less variable contents. The extremely dry 2011 spring had limited effect on stream water chemistry. Outlet (Wu14) stream water DOC reflected seasonally variable contributions of surficial water and groundwater, annually 65 vs. 35%. Critically, a 4-yr lowering of SUVA (reduction of the DOM aromaticity and increased biodegradability) was observed in headwater tributaries (Wu01 and Wu08) but not at Wu14. Our long-term spatial-temporal explicit data set, covering both tributaries and main stream water DOM quality and quantity, was critical in apportionment of contributing sources and drivers of sub- and whole-catchment stream water DOM dynamics. [ABSTRACT FROM AUTHOR]

We examined 3 years of measured daily values of all major water budget components (precipitation P, potential evapotranspiration PET, actual evapotranspiration ET, and runoff R) and volumetric soil water content θ of a small, forested catchment located in the west of Germany. The spatial distribution of θ was determined from a wireless sensor network of 109 points with 3 measurement depths each; ET was calculated from eddy-covariance tower measurements. The water budget was dominantly energy limited, with ET amounting to approximately 90% of PET, and a runoff ratio R/P of 56%. P, ET, and R closed the long-term water budget with a residual of 2% of precipitation. On the daily time scale, the residual of the water budget was larger than on the annual time scale, and explained to a moderate extent by θ ( R2 = 0.40). Wavelet analysis revealed subweekly time scales, presumably dominated by unaccounted fast-turnover storage terms such as interception, as a major source of uncertainty in water balance closure. At weekly resolution, soil water content explained more than half ( R2 = 0.62) of the residual. By means of combined empirical orthogonal function and cluster analysis, two slightly different spatial patterns of θ could be identified that were associated with mean θ values below and above 0.35 cm3/cm3, respectively. The timing of these patterns as well as the varying coherence between PET, ET, and soil water content responded to changes in water availability, including a moderate response to the European drought in spring 2011. [ABSTRACT FROM AUTHOR]

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite was launched in November 2009 and delivers now brightness temperature and soil moisture products over terrestrial areas on a regular three-day basis. In 2010, several airborne campaigns were conducted to validate the SMOS products with microwave emission radiometers at L-band (1.4 GHz). In this paper, we present results from measurements performed in the Rur and Erft catchments in May and June 2010. The measurement sites were situated in the very west of Germany close to the borders to Belgium and The Netherlands. We developed an approach to validate spatial and temporal SMOS brightness temperature products. An area-wide brightness temperature reference was generated by using an area-wide modeling of top soil moisture and soil temperature with the WaSiM-ETH model and radiative transfer calculation based on the L-band Microwave Emission of the Biosphere model. Measurements of the airborne L-band sensors EMIRAD and HUT-2D on-board a Skyvan aircraft as well as ground-based mobile measurements performed with the truck mounted JÜLBARA L-band radiometer were analyzed for calibration of the simulated brightness temperature reference. Radiative transfer parameters were estimated by a data assimilation approach. By this versatile reference data set, it is possible to validate the spaceborne brightness temperature and soil moisture data obtained from SMOS. However, comparisons with SMOS observations for the campaign period indicate severe differences between simulated and observed SMOS data. [ABSTRACT FROM PUBLISHER]

The main goal of the project was to assess nitrogen pollution of surface waters and groundwater in the Federal State of North Rhine Westphalia (NRW), Germany. For this purpose the hydro(geo-)logical models GROWA-DENUZ/WEKU were coupled to the agro-economic model RAUMIS in order to assess the diffuse nitrogen loads and to approaches to determine the nitrogen loads from point sources. In this way the complex socio-economic interrelations and hydrological/hydrogeological interdependencies were simultaneously. The model network was applied consistently across the whole territory of NRW. At first the actual N inputs into groundwater and surface waters resulting from diffuse sources and point sources were assessed. For the relevant diffuse input pathways (groundwater runoff, drainage runoff and natural interflow) this was done in a spatial resolution of 100 m ∙ 100 m. In the case of point source inputs information from municipal waste water treatment plants, industrial effluents, rainwater sewers and combined sewer overflows has been considered. For NRW an actual total N input into surface waters of ca. 117.000 t ∙ a N has been quantified. As the inputs via natural interflow (ca. 30 %), groundwater runoff (ca. 26 %) and drainage systems (ca. 18 %) hold the largest portion, it is evident that measures to control nitrate pollution have to focus on the inputs from diffuse sources. For this purpose, initially the development of the agrarian sector according to the Common Agricultural Policy, CAP until 2015 including supplementary measures and other impact factors has been analysed. The impact of this so-called baseline scenario 2015 was predicted for both, the diffuse N surpluses and the N pollution of groundwater and surface waters. It could be shown that the baseline projections for the agricultural sector through 2015 may lead to decrease of the diffuse N inputs into groundwater by ca. 13.500 t ∙ a N and an overall decrease of the diffuse N inputs into surface waters by ca. 25.000 kg ∙ ha ∙ a N. Based on the baseline scenario 2015 the additional N reduction to guarantee nitrate concentrations in groundwater below the EU-threshold value of 50 mg ∙ l NO was determined by means of a backward model calculation. This was done using the predicted nitrate concentrations in the leachate 2015 for the individual 100 m ∙ 100 m grids as starting points. In this way for the whole territory of NRW an additional N reduction beyond the baseline scenario 2015 of ca. 12.000 t ∙ a N has been assessed. Model results indicate that additional N reduction measures don't have to be implemented area-covering in order to be efficient, but in certain subareas only. It is suggested that in these subareas the available financial resources for the implementation of N reduction measures shall be used for individual, i.e. regionally adapted nitrate reduction measures. [ABSTRACT FROM AUTHOR]

An area-differentiated model approach (MEPhos) for the quantification of mean annual P-inputs from point and diffuse sources is presented. The following pathways are considered: artificial drainage, wash-off, groundwater outflow, soil erosion, rainwater sewers, combined sewer overflows, municipal waste water treatment plants and industrial effluents. Two retention functions for rivers and reservoirs are included in order to model P-sinks within a river basin. This allows a complete record of P-loads in heterogeneous meso- and macroscale river basins and enables validation of modeling results with water quality data on a load basis. The model is applied to the River Ruhr basin (4,485 km2) in Germany, which includes contrasting natural conditions, land use patterns as well as population and industry densities. Based on validated modelling results sub-areas of high P-loads are localized and management options for the reduction of P-inputs to surface waters are proposed taking into account the site conditions of the sub-areas relevant for high P-inputs into surface waters. [ABSTRACT FROM AUTHOR]

We jointly invert field data of flowmeter and multiple pumping tests in fully screened wells to estimate hydraulic conductivity using a geostatistical method. We use the steady-state drawdowns of pumping tests and the discharge profiles of flowmeter tests as our data in the inference. The discharge profiles need not be converted to absolute hydraulic conductivities. Consequently, we do not need measurements of depth-averaged hydraulic conductivity at well locations. The flowmeter profiles contain information about relative vertical distributions of hydraulic conductivity, while drawdown measurements of pumping tests provide information about horizontal fluctuation of the depth-averaged hydraulic conductivity. We apply the method to data obtained at the Krauthausen test site of the Forschungszentrum Jülich, Germany. The resulting estimate of our joint three-dimensional (3D) geostatistical inversion shows an improved 3D structure in comparison to the inversion of pumping test data only. [ABSTRACT FROM AUTHOR]

Land surface models (LSMs) simulate water and energy cycles at the atmosphere–soil interface, however, the physical processes in the subsurface are typically oversimplified and lateral water movement is neglected. Here, a cross-evaluation of land surface model results (with and without lateral flow processes), the National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) mission soil moisture product, and cosmic-ray neutron sensor (CRNS) measurements is carried out over a temperate climate region with cropland and forests over western Germany. Besides a traditional land surface model (the Community Land Model (CLM) version 3.5), a coupled land surface-subsurface model (CLM-ParFlow) is applied. Compared to CLM stand-alone simulations, the coupled CLM-ParFlow model considered both vertical and lateral water movement. In addition to standard validation metrics, a triple collocation (TC) analysis has been performed to help understanding the random error variances of different soil moisture datasets. In this study, it is found that the three soil moisture datasets are consistent. The coupled and uncoupled model simulations were evaluated at CRNS sites and the coupled model simulations showed less bias than the CLM-standalone model (−0.02 cm3 cm−3 vs. 0.07 cm3 cm−3), similar random errors, but a slightly smaller correlation with the measurements (0.67 vs. 0.71). The TC-analysis showed that CLM-ParFlow reproduced better soil moisture dynamics than CLM stand alone and with a higher signal-to-noise ratio. This suggests that the representation of subsurface physics is of major importance in land surface modeling and that coupled land surface-subsurface modeling is of high interest. [ABSTRACT FROM AUTHOR]

We assess the usefulness of electrical resistivity tomography (ERT) in imaging and characterising subsurface solute transport in heterogeneous unconfined aquifers. A field tracer experiment was conducted at the Krauthausen test site, Germany. The spatial and temporal evolution of the injected NaBr solute plume was monitored in a 2D ERT image plane located downstream of the injection well for 90 days. Since ERT maps changes in bulk electrical conductivity, the reconstructed images at selected time intervals are first converted to solute concentration maps by postulating a linear relation. The concentration maps are then analysed using an equivalent convection–dispersion model (CDM), which conceptualises the aquifer as a homogeneous medium with a uniform mean flow velocity. As demonstrated by associated synthetic model studies, ERT resolution in terms of recovered equivalent dispersivities is limited due to spatial smoothing inherent to the imaging algorithm. Since for heterogeneous media, local concentrations within the plume deviate from those predicted by the equivalent CDM, we also interpret the ERT-derived pixel breakthrough curves in terms of an equivalent stream-tube model (STM). The STM represents transport in the aquifer by a set of 1D convection–dispersion processes, allowing the degree of mixing and the heterogeneity of transport within the plume to be quantified. We believe that the observed tracer plume is satisfactorily described by the equivalent CDM, probably because the tracer plume was small relative to the heterogeneity scale of the aquifer. Even though application of the STM revealed some deviation from the ideal homogeneous case, the equivalent dispersivity in the STM matches the longitudinal dispersivity of the CDM closely, consistent with predominantly homogeneous mixing. However, the STM analysis illustrates how ERT results can be used to quantify the variability of parameters relevant to flow and transport in heterogeneous aquifers. [Copyright &y& Elsevier]

Highlights • A novel RWU scheme based on the root hydraulic architecture is integrated in a LSM. • Root hydraulic properties control transpiration during prolonged dry conditions. • Soil parameterization uncertainty influences transpiration and soil water content. • Roots distribution induce larger variability in the hydraulic model response. Abstract A detailed representation of plant hydraulic traits and stomatal closure in land surface models (LSMs) is a prerequisite for improved predictions of ecosystem drought response. This work presents the integration of a macroscopic root water uptake (RWU) model based on the hydraulic architecture approach in the LSM of the Terrestrial Systems Modeling Platform. The novel RWU approach is based on three parameters derived from first principles that describe the root system equivalent conductance, the compensatory RWU conductance, and the leaf water potential at stomatal closure, which defines the water stress condition for the plants. The developed RWU model intrinsically accounts for changes in the root density as well as for the simulation of the hydraulic lift process. The standard and the new RWU approach are compared by performing point-scale simulations for cropland over a sheltered minirhizotron facility in Selhausen, Germany, and validated against transpiration fluxes estimated from sap flow and soil water content measurements at different depths. Numerical sensitivity experiments are carried out using different soil textures and root distributions in order to evaluate the interplay between soil hydrodynamics and plant characteristics, and the impact of assuming time-constant plant physiological properties. Results show a good agreement between simulated and observed transpiration fluxes for both RWU models, with a more distinct response under water stress conditions and with uncertainty in the soil parameterization prevailing to the differences due to changes in the model formulation. The hydraulic RWU model exhibits also a lower sensitivity to the root distributions when simulating the onset of the water stress period. Finally, an analysis of variability across the soil and root scenarios indicates that differences in soil water content are mainly influenced by the root distribution, while the transpiration flux in both RWU models is additionally determined by the soil characteristics. [ABSTRACT FROM AUTHOR]

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

Abstract: Atrazine was banned in Germany in 1991 due to findings of atrazine concentrations in ground- and drinking waters exceeding threshold values. Monitoring of atrazine concentrations in the groundwater since then provides information about the resilience of the groundwater quality to changing agricultural practices. In this study, we present results of a monitoring campaign of atrazine concentrations in the Zwischenscholle aquifer. This phreatic aquifer is exposed to intensive agricultural land use and susceptible to contaminants due to a shallow water table. In total 60 observation wells (OWs) have been monitored since 1991, of which 15 are sampled monthly today. Descriptive statistics of monitoring data were derived using the “regression on order statistics” (ROS) data censoring approach, estimating values for nondetects. The monitoring data shows that even 20 years after the ban of atrazine, the groundwater concentrations of sampled OWs remain on a level close to the threshold value of 0.1 μg l−1 without any considerable decrease. The spatial distribution of atrazine concentrations is highly heterogeneous with OWs exhibiting permanently concentrations above the regulatory threshold on the one hand and OWs were concentrations are mostly below the limit of quantification (LOQ) on the other hand. A deethylatrazine-to-atrazine ratio (DAR) was used to distinguish between diffuse – and point-source contamination, with a global mean value of 0.84 indicating mainly diffuse contamination. Principle Component Analysis (PCA) of the monitoring dataset demonstrated relationships between the metabolite desisopropylatrazine, which was found to be exclusively associated with the parent compound simazine but not with atrazine, and between deethylatrazine, atrazine, nitrate, and the specific electrical conductivity. These parameters indicate agricultural impacts on groundwater quality. The findings presented in this study point at the difficulty to estimate mean concentrations of contamination for entire aquifers and to evaluate groundwater quality based on average parameters. However, analytical data of monthly sampled single observation wells provide adequate information to characterize local contamination and evolutionary trends of pollutant concentration. [Copyright &y& Elsevier]

Abstract: Soil erosion on arable land and on steep vineyards is a major problem in the state of Hesse (21,115km2) in central Germany. The aim of a joint study between the Research Centre Jülich and the Hessian Agency for the Environment and Geology was to identify parcels which are severely affected by water erosion and to identify areas at risk for sediment input to surface waters. For this purpose, the ABAG, an adaption of the USLE approach to German conditions, has been employed with the best available data sets on K-, C-, R- and LS-factors. Model results show that soil losses in Hesse vary between <0.5 and >15tonnes/(ha·year). The mean loss amounts to ca. 4.3tonnes/(ha·year). The sediment delivery ratios for 450 sub-catchments range between 0.5 and 78% with a mean of 18%. An uncertainty analysis based on Gaussian error propagation and Monte Carlo simulation showed that the uncertainty of model results induced by input data is ±1.7tonnes/(ha·year) or ±34%. The model results are employed for further use in a soil erosion atlas and internet-based soil data viewer. [Copyright &y& Elsevier]

The advantages of using multispectral remotely sensed data instead of CORINE Land Cover for the modelling of nitrate concentrations in the leachate of the Rur catchment are presented and discussed in this paper. In this context it has been shown that the identification of main crops and annual crop rotation in the Rur catchment by SPOT, LANDSAT and ASTER imagery provides the key for a spatial and thematic enhancement of the model results. The spatial resolution of the nitrogen surplus data set which denotes the linkage between RAUMIS and GROWA is enhanced from district level to field/pixel level. In parallel, the empirical water balance model GROWA is enhanced to differentiate between agricultural crops in the real evapotranspiration calculation. It is calibrated by runoff data measured at gauging stations. Results indicate, e.g., an average nitrate concentration in the leachate of 42mg NO3/L in the relatively wet year of 2002 and almost 62mg NO3/L in the dry year of 2003. There is a 20mg NO3/L weather-induced difference which can be modelled in a more detailed way using self-processed remotely sensed data. The model results were compared to nitrate concentrations observed in the top parts of multi-level wells. In this way the related coefficient of determination has been improved from a value (R) of −0.50 using CORINE to 0.59 by using self-processed remotely sensed data, thus demonstrating the potential of the enhanced model system. [Copyright &y& Elsevier]

In 2008 the TERENO project was initiated with the general aim to conduct integrated and long-term observation studies of climate change and global change impacts on the terrestrial system across Germany. Central elements of the TERENO network are "terrestrial observatories" at the catchment scale which were selected in climate sensitive regions of Germany. Within these observatories small scale research facilities and test areas were placed in order to accomplish energy, water, carbon and nutrient process studies across the different compartments of the terrestrial environment. Following a hierarchical scaling approach (point-plot-field) these detailed information and the knowledge gained is transferred to the regional scale using integrated modelling approaches. In addition, mobile ground-based and airborne measurement platforms are used to monitor spatial patterns of dynamic processes at the regional scale. By combining the observatories within Germany, large-scale atmospheric feedbacks and impacts can be investigated, and thus a more pronounced general link to the atmospheric research community can be established. A data policy statement was developed in a common approach by all TERENO partners. As a rule, all TERENO data are freely accessible for the scientific community and a data portal facilitates the online data provision. In the ten years since its foundation, TERENO has become a key infrastructure in the German and international research landscape and has made a significant contribution to the development of integrated approaches in long-term environmental research. In this contribution the concept of TERENO will be illustrated with example key findings of the ongoing interdisciplinary research work aiming to advance the understanding of complex hydrological and biogeochemical processes and interactions. [ABSTRACT FROM AUTHOR]

The integrated land surface-subsurface model TerrSysMP was set up with a lateral resolution of 10m for the TERENO-site Rollesbroich in the Eifel mountains in Germany. A soil moisture sensor network is installed at this site and operational since 2011; soil moisture at 5, 20 and 50cm depth is recorded. In this work, it was evaluated whether the assimilation of soil moisture data recorded by the network into the integrated land surface-subsurface model was able to improve the characterization of the water and energy balances for the site. The data were assimilated with the Ensemble Kalman Filter (EnKF) in the integrated model and different data assimilation scenarios were evaluated: (i) for the real-world case and a synthetic case which mimics the real-world case; (ii) for updating states only and updating both states and parameters; (iii) for 128 or 256 ensemble members; (iv) for saturated hydraulic conductivity as only unknown soil hydraulic parameter, or for additional uncertain Mualem-van Genuchten soil hydraulic parameters. For the real-world case it was found that the RMSE of soil moisture at the sensor network locations was at most reduced by 14 % while stream discharge estimation was not improved significantly by assimilation. Simulation results were much more favourable for the synthetic case with a RMSE reduction up to 55 % at independent verification locations in case of daily soil moisture assimilation and also stream discharge estimation improved significantly with a NSE-increase from -0.04 (open loop) to 0.61. These results show that data assimilation in combination with integrated, high resolution land surface-subsurface models can potentially improve soil moisture and discharge estimation at the hillslope scale. The worse performance for the real-world case remains nevertheless puzzling and is explained by model structural errors, like the representation of the drainage network at the site. [ABSTRACT FROM AUTHOR]