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In the context of the repeated droughts that have affected central Europe over the last years (2018–2020, 2022), climate-resilient management of water resources, based on timely information about the current state of the terrestrial water cycle and forecasts of its evolution, has gained an increasing importance. To achieve this, we propose a new setup for simulations of the terrestrial water cycle using the integrated hydrological model ParFlow/CLM at high spatial and temporal resolution (i.e., 0.611 km, hourly time step) over Germany and the neighboring regions. We show that this setup can be used as a basis for a monitoring and forecasting system that aims to provide stakeholders from many sectors, but especially agriculture, with diagnostics and indicators highlighting different aspects of subsurface water states and fluxes, such as subsurface water storage, seepage water, capillary rise, or fraction of plant available water for different (root-)depths. The validation of the new simulation setup with observation-based data monthly over the period 2011–2020 yields good results for all major components of the terrestrial water cycle analyzed here, i.e., volumetric soil moisture, evapotranspiration, water table depth, and river discharge. As this setup relies on a standardized grid definition and recent globally available static fields and parameters (e.g., topography, soil hydraulic properties, land cover), the workflow could easily be transferred to many regions of the Earth, including sparsely gauged regions, since ParFlow/CLM does not require calibration.

Despite previous reports on European growing seasons lengthening due to global warming, evidence shows that this trend has been reversing in the past decade due to increased transpiration needs. To asses this, we used an innovative method along with space-based observations to determine the timing of greening and dormancy and then to determine existing trends of them and causes. Early greening still occurs, albeit at slower rates than before. However, a recent (2011–2020) shift in the timing of dormancy has caused the season length to decrease back to 1980s levels. This shortening of season length is attributed primarily to higher atmospheric water demand in summer that suppresses transpiration even for soil moisture levels as of previous years. Transpiration suppression implies that vegetation is unable to meet the high transpiration needs. Our results have implications for future management of European ecosystems (e.g., net carbon balance and water and energy exchange with atmosphere) in a warmer world., Communications Earth & Environment, 4 (1), ISSN:2662-4435

Functional structural root architecture models have evolved as tools for the design of improved agricultural management practices and for the selection of optimal root traits. In order to test their accuracy and reliability, we present the first benchmarking of root water uptake from soil using five well established functional structural root architecture models: DuMux, CPlantBox, RSWMS, OpenSimRoot and SRI. The benchmark scenarios include basic tests for water flow in soil and roots as well as advanced tests for the coupled soil root system. The reference solutions and the solutions of the different simulators are available through Jupyter Notebooks on a GitHub repository. All of the simulators were able to pass the basic tests and continued to perform well in the benchmarks for the coupled soil-plant system. For the advanced tests, we created an overview of the different ways of coupling the soil and the root domains as well as the different methods used to account for rhizosphere resistance to water flow. Although the methods used for coupling and modelling rhizosphere resistance were quite different, all simulators were in reasonably good agreement with the reference solution. During this benchmarking effort, individual simulators were able to learn about their strengths and challenges, while some were even able to improve their code. Some now include the benchmarks as standard tests within their codes. Additional model results may be added to the GitHub repository at any point in the future and will be automatically included in the comparison.

The link between soil water content (SWC) and evapotranspiration (ET) is of great importance in ecohydrology, agroecosystem management, and land-atmosphere interaction of earth-system analysis. There is still a need to understand the key processes linking SWC and ET in different vegetated ecosystems, especially at larger scales. Therefore, in this work, we used wavelet coherence analysis to explore the long-term relationship between SWC and ET among the predominant land use types (cropland, evergreen needleleaf forest, mixed forest, open shrubland, wooden tundra, grassland, and mixed tundra) in Europe during the last four decades (1980-2020). To this end, first a principal component analysis was performed among the SWC and ET data from GLDAS, GLEAM, and ERA5-land, and the first component was then used for further analyses when the target variable was in demand. Using the first component, then, we averaged SWC and ET data over the pixels covered by each land use type. Then, for each land-use types, we averaged the data daily for each decade to account for a representative decadal year of daily data. Then, wavelet coherence analysis was conducted between those averaged data of SWC and ET for each land-use type. The results showed a negative correlation between ET and SWC for all land use types, with ET lagging behind SWC, with an average phase shift value of 134 days for grassland (the minimum) and 168 days for mixed tundra (the maximum). Converting the phase shift values to a time lag [lag = n/2 - phase shift for the case phase shift < -n/4 where n represents the period (1/frequency) of the signals] shows (Fig. 1) that ET controls SWC with a lag of 15 days in mixed tundra (the minimum) and 48 days in grassland (the maximum). Moreover, we applied Mann-Kendall trend analysis test and found that the lag between SWC and ET decreases in mixed and wooden tundras with a slope value of -2.4 days/year, while it increases in cropland and grassland with a slope value of 1.6 days/year. Although there is a significant downward trend in evergreen needleleaf forests (with a slope value of -0.3 days/year) and an increasing trend in mixed forests and open shrublands (with a slope value of 0.4 days/year), the lower slope values in these land use types indicate that the change is slower compared to grasslands, croplands, and tundras. Figure 1- Temporal evolution of the lag between soil water content and evapotranspiration in the annual cycle in different land use types in Europe

Predicting the impact of land use and climate change on the Earth system hinges on credible representation of soil hydrological processes (SHP), adequate availability of parameters and hydrological states and inclusion of key soil properties. There is increasing evidence that extreme events such as droughts and high intensity precipitation, and land use changes, affect fundamental hydrological processes such as infiltration and runoff generation. In this review, we analyse the influence of soil structure on SHP, critically evaluate the parameterization of soil hydrologic properties and their importance in representing the terrestrial water cycle and highlight the key role of soil hydrology in the functioning of carbon-rich soils and in linking the water and carbon cycles. It emerges that linking soil hydrology and pedology will lead to better understanding critical zone processes, especially in tropical regions. Further, we discuss the role of local scale hydrological processes in understanding root water uptake, vegetation and groundwater dynamics and feedbacks. These processes control and modulate the impact of extreme events such as droughts, floods and heatwaves and they are essential to assess drought and flooding. Finally, new emerging technologies such as wireless and automated sensing approaches, soil moisture observation through novel synthetic aperture radars satellites, big data analysis and machine learning approaches offer unique opportunities to advance soil hydrology.

In his seminal paper on the solution of the infiltration equation, Philip (1969), https://doi.org/10.1016/b978-1-4831-9936-8.50010-6 proposed a gravity time, tgrav, to estimate practical convergence time and the time domain validity of his infinite time series expansion, TSE, for describing the transient state. The parameter tgrav refers to a point in time where infiltration is dominated equally by capillarity and gravity as derived from the first two (dominant) terms of the TSE. Evidence suggests that applicability of the truncated two-term equation of Philip has a time limit requiring higher-order TSE terms to better describe the infiltration process for times exceeding that limit. Since the conceptual definition of tgrav is valid regardless of the infiltration model used, we opted to reformulate tgrav using the analytic implicit model proposed by Parlange et al. (1982), https://doi.org/10.1097/00010694-198206000-00001 valid for all times and related TSE. Our derived gravity times ensure a given accuracy of the approximations describing transient states, while also providing insight about the times needed to reach steady state. In addition to the roles of soil sorptivity (S) and the saturated (Ks) and initial (Ki) hydraulic conductivities, we explored the effects of a soil specific shape parameter β, involved in Parlange's model and related to the type of soil, on the behavior of tgrav. We show that the reformulated tgrav (notably (Formula presented.) where F(β) is a β-dependent function) is about three times larger than the classical tgrav given by (Formula presented.). The differences between the classical tgrav,Philip and the reformulated tgrav increase for fine-textured soils, attributed to the time needed to attain steady-state infiltration and thus i + nfiltration for inferring soil hydraulic properties. Results show that the proposed tgrav is a better indicator of time domain validity than tgrav,Philip. For the attainment of steady-state infiltration, the reformulated tgrav is suitable for coarse-textured soils. Still neither the reformulated tgrav nor the classical tgrav,Philip are suitable for fine-textured soils for which tgrav is too conservative and tgrav,Philip too short. Using tgrav will improve predictions of the soil hydraulic parameters (particularly Ks) from infiltration data compared to tgrav,Philip., Water Resources Research, 58 (5), ISSN:0043-1397, ISSN:1944-7973

Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNov\(^{SAR}\)) or the combination of 43 SAR and 10 terrain parameters (θNov\(^{SAR+Terrain}\)). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNov\(^{SAR+Terrain}\) with 0.025 and 0.020 m3 m\(^{−3}\), and 89% compared to θNov\(^{SAR}\) with 0.028 and 0.022 m\(^3\) m\(^{−3}\, and 86% in terms of RMSE, MAE, and R2). The higher explanatory power for θNov\(^{SAR+Terrain}\) is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon.

ispartof: VADOSE ZONE JOURNAL vol:21 issue:1 status: published

Deforestation has a wide range of effects on hydrological and geochemical processes. Dissolved organic carbon (DOC) dynamics, a sensitive environmental change indicator, is expected to be affected by deforestation, with changes in atmospheric sulfur (S) deposition compounding this. However, how precisely anthropogenic disturbance (deforestation) under a declining atmospheric S input scenario affects the underlying spatiotemporal dynamics and relationships of river DOC and sulfate with hydro-climatological variables e.g., stream water temperature, runoff, pH, total dissolved iron (Fetot), and calcium (Ca2+) remains unclear. We, therefore, examined this issue within the TERENO Wüstebach catchment (Eifel, Germany), where partial deforestation had taken place in 2013. Wavelet transform coherence (WTC) analysis was applied based on a 10-year time series (2010–2020) from three sampling stations, whose (sub) catchment areas have different proportions of deforested area (W10: 31%, W14: 25%, W17: 3%). We found that water temperature and DOC, sulfate, and Fetot concentrations showed distinct seasonal patterns, with DOC averaging concentrations ranging from 2.23 (W17) to 4.56 (W10) mg L–1 and sulfate concentration ranging from 8.04 (W10) to 10.58 (W17) mg L–1. After clear-cut, DOC significantly increased by 59, 58% in the mainstream (W10, W14), but only 26% in the reference stream. WTC results indicated that DOC was negatively correlated with runoff and sulfate, but positively correlated with temperature, Ca2+, and Fetot. The negative correlation between DOC with runoff and sulfate was apparent over the whole examined 10-year period in W17 but did end in W10 and W14 after the deforestation. Sulfate (SO4) was highly correlated with stream water temperature, runoff, and Fetot in W10 and W14 and with a longer lag time than W17. Additionally, pH was stronger correlated (higher R2) with sulfate and DOC in W17 than in W10 and W14. In conclusion, WTC analysis indicates that within this low mountainous forest catchment deforestation levels over 25% (W10 and W14) affected the coupling of S and C cycling substantially more strongly than “natural” environmental changes as observed in W17.

Many different equations have been proposed to describe quantitatively one-dimensional soil water infiltration. The unknown coefficients of these equations characterize soil hydraulic properties and may be estimated from a n record, (Formula presented.), of cumulative infiltration measurements using curve fitting techniques. The two-term infiltration equation, (Formula presented.), of Philip has been widely used to describe measured infiltration data. This function enjoys a solid mathematical–physical underpinning and admits a closed-form solution for the soil sorptivity, S [L T−1/2], and multiple, c [−], of the saturated hydraulic conductivity, Ks [L T−1]. However, Philip's two-term equation has a limited time validity, tvalid [T], and thus cumulative infiltration data, (Formula presented.), beyond (Formula presented.) will corrupt the estimates of S and Ks. This paper introduces a novel method for estimating S, c, Ks, and tvalid of Philip's two-term infiltration equation. This method, coined parasite inversion, use as vehicle Parlange's three-parameter infiltration equation. As prerequisite to our method, we present as secondary contribution an exact, robust and efficient numerical solution of Parlange's infiltration equation. This solution admits Bayesian parameter estimation with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm and yields as byproduct the marginal distribution of Parlange's β parameter. We evaluate our method for 12 USDA soil types using synthetic infiltration data simulated with HYDRUS-1D. An excellent match is observed between the inferred values of S and Ks and their “true” values known beforehand. Furthermore, our estimates of c and tvalid correlate well with soil texture, corroborate linearity of the (Formula presented.) relationship for (Formula presented.), and fall within reported ranges. A cumulative vertical infiltration of about 2.5cm may serve as guideline for the time-validity of Philip's two-term infiltration equation.

Remotely-sensed Evapotranspiration (ET) estimates can effectively contribute to agricultural water stress detection. Fully operational, high temporal, and moderate spatial resolution ET products derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the Meteosat Second Generation (MSG) satellites make it a suitable candidate for water stress monitoring. However, dedicated efforts are still required to develop simple workflows, preferably executable on cloud-based platforms, to evaluate the potentials, exploit the information content, and demonstrate the capability of ‎SEVIRI ET-products for water stress monitoring at larger scales. In this study, an extensive assessment of daily actual and reference ET (SEVIRI-ETa and SEVIRI-‎ET0) products were conducted against in situ measurements collected at 54 Eddy Covariance (EC) sites across Europe ‎distributed in various terrestrial ecosystems (i.e., forest, grassland, cropland, and peatland) between 2011-2018. The evaluated SEVIRI-ET products were then utilized mainly for two purposes: i) providing inputs to run a proposed water stress detection workflow, based on monthly evaporative stress index (ESI) anomaly, and implemented in cloud-based Virtual Earth Laboratory (VLab) platform to monitor one decade (2011 to 2020) of spatio-temporal water stress variations for entire Europe, and ii) investigating the mean terrestrial ecosystems response to water stress. The direct comparison of in situ ET with their corresponding SEVIRI-ET products resulted in a fair agreement in various ecosystems, albeit with expected inter-site variability. At the monthly time step, the mean R2 of 0.78, 0.76, 0.88, 0.85 and RMSE of 0.51, 0.60, 0.41, 0.59 mm day-1 were observed between SEVIRI-ETa and in situ ETa for forest, cropland, peatland and grassland, respectively. Further, mean R2 of 0.95, 0.98, 0.96, 0.97 and RMSE of 0.48, 0.36, 0.31, 0.35 mm day-1 were obtained between SEVIRI-ET0 and in situ ET0 for these land cover types. The SEVIRI-ESI-based monthly water stress workflow implemented on the online VLab platform provides spatio-temporal variations of water stress in Europe for the last decade (i.e., 2011 – 2020) that can be further utilized in scientific research and terrestrial applications. The analysis of various ecosystems' responses to water stress revealed that general water stress effects on vegetated ecosystems are “visible” in the SEVIRI-ESI-based water stress values and anomalies. The results from this study highlight the value, support the potentials, and unlock the full capacity of SEVIRI-ET products and the VLab platform for water stress detection at various terrestrial ecosystems at larger domains.

Observations of ecosystem processes inform about states of their function and health. However, discrepancies between site-scale and spatiotemporal estimations still pose a challenge for robust interpretations. Here we present causal networks of ecosystem processes on different scales to improve the understanding of the underlying representation of the ecosystem.

Groundwater sustainable yield (availability) and abstraction cost (accessibility) are two suitable indicators for assessing groundwater sustainability, but have not yet been quantified across Africa. In this study, we quantified the long-term average (LTA) spatial distribution of groundwater sustainable yield (for the period 1984 – 2013), the abstraction gross power, and energy demand. The groundwater sustainable yield is calculated as a function of groundwater recharge, environmental flow, and sectoral water use. The two formers are simulated by the Community Land Model version 5 (CLM5) [1], while the latter is obtained from the literature [2]. Moreover, the groundwater abstraction power and energy demand are calculated based on available groundwater table depth [3] and aquifer productivity [4] maps. We found that the African continent has the potential of 43.27 and 209.71 [km3 year-1] annual sustainable yield under conservative and optimum scenarios, respectively. Moreover, on average 2.086 kW gross power and 0.146 kWh/m3 energy demand are required for groundwater abstraction across the whole continent. This study provides a first model-based estimation of groundwater availability and accessibility across Africa that can inspire further moving toward more sustainable pathways of groundwater use in Africa.

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., Forschungszentrum Jülich GmbH (4205)

The limitation of global warming to +1.5°C compared to preindustrial levels requires net-zero CO2 emissions globally by mid-century and substantial removal of CO2 thereafter. Carbon sequestration in agricultural soils has been proposed as a potential mitigation strategy. Aim of this study is to quantify current carbon storage and emission reduction potential in agricultural soils, and assess the impact of mitigation measures in a prognostic modeling approach. The land surface model Community Land Model 5.0 (CLM) is used to assess soil carbon changes in agricultural soils in Germany. The simulation domain was set up with an 8 x 8 km grid across Germany using recent land use and soil texture maps, and parameters for major field crops. The model was spun up for ~1500 years with a 30-year climate dataset. Preliminary results show that spinup-derived organic carbon density (OCD, 0-188 cm) was significantly related to Soil Grid v2 OCD (R2 = 0.82), but only weakly related to field-measured OCD (R2 = 0.21). The simulated OCD values in the upper 32 cm soil layer were lower in Northwestern Germany compared to Soil Grids. This is probably due to the intensive use of organic amendment application in the region, and CLM5 lacks a subroutine for simulating organic carbon application. In a next step, carbon storage for different climate projections (regional EUR11 RCP2.6 and RCP8.5 scenarios) and management systems from 2020 - 2100 will be investigated. We will present preliminary results and discuss improvements of CLM5 to better represent agricultural soils.

Environmental thresholds. tipping points and subsequent regime shifts associated with the water/climate/greenhouse gas nexus pose a genuine threat to sustainability. Both the ongoing forest dieback in Central Europe caused by the extreme droughts of the last years and the effect of global warming on ecosystem functioning have the potential to cause ecological surprise (sensu Lindenmayer et al. 2010) where ecosystems are pushed into new, unexpected and usually undesirable states.Formulating appropriate scientific and societal responses to such regime shifts requires breadth, depth, intensity and duration of environmental, ecological and socio-ecological monitoring. Broad geographic coverage to encompass relevant biophysical and societal gradients, consideration of all appropriate parameters, adequate measurement frequency and long-term, standardized observations are all needed to provide reliable early warnings of severe environmental change, test ecosystem models, avoid double counting in carbon accounting and to reduce the likelihood of undesirable ecological outcomes. This is especially true of events driven by simultaneous changes in climate, the water cycle and human activities.Well-supported, site-based research infrastructures (RIs; e.g., eLTER and ICOS) are essential tools with the necessary breadth, depth, intensity and duration for early detection and attribution of environmental change. Individually, the eLTER and ICOS RIs generate a wealth of data supporting the ecosystem and carbon research communities. Achieving synergies between the two RIs can add value to both communities and potentially offer meaningful insight into the European water-climate-greenhouse gas nexus.The unique insights into processes and mechanisms of ecosystem dynamics and functioning obtained from high intensity monitoring conducted by the ICOS RI greatly increase the likelihood of detecting signals of environmental change. These signals must be placed into the context of their long-term trajectory and potential societal and environmental drivers. The spatially extensive, long-term, multi-disciplinary monitoring conducted at LTER sites and LTSER platforms under the umbrella of the eLTER programme can provide this context.Here, we outline one potential roadmap for achieving synergies between the ICOS and eLTER RIs focussing on the value of co-location for improved understanding of the water/climate/greenhouse gas nexus. Based on data and experiences from intensively studied research sites, we highlight some of the possibilities for reducing the likelihood of ecological surprise that could result from such synergies.Lindenmayer, D.B., Likens, G.E., Krebs, C.J. and Hobbs, R.J., 2010. Improved probability of detection of ecological “surprises”. Proceedings of the National Academy of Sciences, 107(51), pp.21957-21962.

Detailed knowledge of the intra-field variability of soil properties and crop characteristics is indispensable for the establishment of sustainable precision agriculture. We present an approach that combines ground-based agrogeophysical soil and aerial crop data to delineate field-specific management zones that we interpret with soil attribute measurements of texture, bulk density, and soil moisture, as well as yield and nitrate residue in the soil after potato (Solanum tuberosum L.) cultivation. To delineate the management zones, we use aerial drone-based normalized difference vegetation index (NDVI), spatial electromagnetic induction (EMI) soil scanning, and the EMI–NDVI data combination as input in a machine learning clustering technique. We tested this approach in three successive years on six agricultural fields (two per year). The field-scale EMI data included spatial soil information of the upper 0–50 cm, to approximately match the soil depth sampled for attribute measurements. The NDVI measurements over the growing season provide information on crop development. The management zones delineated from EMI data outperformed the management zones derived from NDVI in terms of spatial coherence and showed differences in properties relevant for agricultural management: texture, soil moisture deficit, yield, and nitrate residue. The combined EMI–NDVI analysis provided no extra benefit. This underpins the importance of including spatially distributed soil information in crop data interpretation, while emphasizing that high-resolution soil information is essential for variable rate applications and agronomic modeling. ispartof: Vadose Zone Journal vol:20 issue:4 pages:1539-1663 status: published

With the upcoming L‐band Synthetic Aperture Radar (SAR) satellite mission Radar Ob-serving System for Europe L‐band SAR (ROSE‐L) and its integration into existing C‐band satellite missions such as Sentinel‐1, multi‐frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between June and August 2019 to investigate the potential for estimating soil and plant parameters at the agricultural test site in Selhausen (Germany). It included C‐ and L‐band air‐ and space‐borne observations accompanied by extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multi-spectral and thermal infrared measurements. In this regard, we introduce a new publicly available SAR data set and present the first analysis of C‐ and L‐band co‐ and cross‐polarized backscattering signals regarding their sensitivity to soil and plant parameters. Results indicate that a multi‐fre-quency approach is relevant to disentangle soil and plant contributions to the SAR signal and to identify specific scattering mechanisms associated with the characteristics of different crop type, especially for root crops and cereals.

With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Ob-serving System for Europe L-band SAR (ROSE-L) and its integration into existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. The SARSense campaign was conducted between June and August 2019 to investigate the potential for estimating soil and plant parameters at the agricultural test site in Selhausen (Germany). It included C- and L-band air- and space-borne observations accompanied by extensive in situ soil and plant sampling as well as unmanned aerial system (UAS) based multi-spectral and thermal infrared measurements. In this regard, we introduce a new publicly available SAR data set and present the first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters. Results indicate that a multi-fre-quency approach is relevant to disentangle soil and plant contributions to the SAR signal and to identify specific scattering mechanisms associated with the characteristics of different crop type, especially for root crops and cereals.

Multiannual data (2016–2018) from 12 weighed lysimeters (four soil types with textures ranging from sandy loam to silt loam, three replicates) of the TERENO SOILCan network were used to evaluate if evaporation (E) rates could be predicted from weather data using the FAO Penman–Monteith (PM) method combined with soil water flow simulations using the Richards equation. Soil hydraulic properties (SHPs) were estimated either from soil texture using the ROSETTA pedotransfer functions, from in situ measured water retention curves, or from soil surface water contents using inverse modeling. In all years, E was water limited and the measured evaporation rates (Em) surprisingly did not vary significantly among the four different soil types. When SHPs derived from pedotransfer functions were used, simulated evaporation rates of the finer textured soils overestimated the measured ones considerably. Better agreement was obtained when simulations were based on in situ measured or inversely estimated SHPs. The SHPs estimated from pedotransfer functions represented unrealistically large characteristic lengths of evaporation (Lc), and Lc was found to be a useful characteristic to constrain estimates of SHPs. Also, when soil evaporation was water limited and Em rates were below Epot (PM evaporation scaled by an empirical coefficient), the diurnal dynamics of Em followed those of Epot. The Richards equation that considers only isothermal liquid water flow did not reproduce these dynamics caused by temperature dependent vapor transport in the soil.

With the upcoming L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe at L-band (ROSE-L) and its combination with existing C-band satellite missions such as Sentinel-1, multi-frequency SAR observations with high temporal and spatial resolution will become available. To investigate the potential for estimating soil and plant parameters, the SARSense campaign was conducted between June and August 2019 at the agricultural test site Selhausen in Germany. In this regard, we introduce a new publicly available, extensive SAR dataset and present a first analysis of C- and L-band co- and cross-polarized backscattering signals regarding their sensitivity to soil and plant parameters. The analysis includes C- and L-band airborne recordings as well as Senitnel-1 and ALOS-2 acquisitions, accompanied by in-situ soil moisture measurements and plant samplings. In addition, soil moisture was measured using cosmic-ray neutron sensing as well as unmanned aerial system (UAS) based multispectral and temperature measurements were taken during the campaign period. First analysis of the dataset revealed, that due to misalignments of corner reflectors during the SAR acquisition, temporal consistency of airborne SAR data is not given. In this regard, a scene-based, spatial analysis of backscatter behaviour from airborne SAR data was conducted, while the spaceborne SAR data enabled the analysis of temporal changes in backscatter behaviour. Focusing on root crops with radial canopy structure (sugar beet and potato) and cereal crops with elongated canopy structure (wheat, barley), the lowest correlations can be observed between backscattering signal and soil moisture, with R² values ranging below 0.35 at C-band and below 0.36 at L-band. Higher correlations can be observed focusing on vegetation water content, with R² values ranging between 0.12 and 0.64 at C-band and 0.06 and 0.64 at L-band. Regarding plant height, at C-band higher correlations with R² up to 0.55 can be seen compared to R² up to 0.36 at L-band. Looking at the individual agricultural corps in more detail, in almost all cases, the backscatter signals of C- and L-band contain a different amount of information about the soil and plant parameters, indicating that a multi-frequency approach is envisaged to disentangle soil and plant contributions to the signal and to identify specific scattering mechanisms related to the crop type, especially related to the different characteristics of root crops and cereals.

Crop model inter-comparisons have mostly been carried out to test the predictive ability under the past range of climatic conditions and for soils of the same site. Unknown is, however, the ability of individual crop models to predict effects of changes in climatic conditions on soil ecosystems beyond the range of site-specific variability. The objective of this study was to test the predictive ability of agro ecosystem models using weighable lysimeter data for the same soil under changed climatic conditions and to compare the simulated crop growth and soil-ecosystem response to climate change between these models. To achieve the objective, data were analyzed from the network of TERENO SOILCan lysimeters for a soil-ecosystem at the original site (Dedelow) and data from the lysimeters containing Dedelow soil monoliths that were transferred to Bad Lauchstädt and Selhausen. For Bad Lauchstädt, this transfer was to a drier and a warmer and for Selhausen to a warmer and wetter site as compared to the original location of the soils at Dedelow. Data time series from the cropped arable soil lysimeters included drier and wetter years and a site-specific crop rotation under comparable management conditions. Identical soil properties and crop growth and boundary conditions were provided for all models after a calibration for the original site at Dedelow, predictions were made for sites Selhausen and Bad Lauchstädt using boundary conditions from those sites. The overall simulation performance of the models was separated in a crop-related part, ecosystem productivity (grain yield, biomass, leaf area index) and in an environmental part, ecosystem fluxes (evapotranspiration, net drainage, soil moisture). When moving soil to a drier region, the crop models’ agronomic and environmental part were well predicted, when moving to wetter regions, only the environmental part of the models seemed to be well predicted. The results suggest considering climate change scenarios, more attention to soil properties and testing of model performance for conditions beyond the calibrated range and site-specific variability will help improving the models.

Different land surface modelling studies over Africa have been performed in the recent past at both the continental and subcontinental scales. The spatial resolution of these simulations is often very coarse leading to lack of locally specific information. We hypothesize that using CLM5, a land surface model with multi-level sub-grid hierarchy alongside high resolution soil texture information at 3km resolution over the CORDEX Africa domain, can provide land surface model results that provide locally specific information. We assessed the impact of different meteorological forcings and soil information over the African domain for the period between January 2013 and December 2014. The reanalysis products CRUNCEPv7, GSWPv3, and WFDE5 were used to force CLM5 simulations, and these have different temporal resolutions of 6 hours, 3 hours and 1 hour, respectively. Two different inputs for soil texture were used, namely from the FAO and SoilGrids250m. In addition, four different upscaling methods to upscale the SoilGrids information, available at 250m resolution to the model grid resolution of 3km, were evaluated. We found that 1) variation in the meteorological forcing product has a higher impact on the variability of evapotranspiration than variation in the soil texture input; 2) the soil texture information source (FAO or SoilGrids) impacted results stronger than the soil texture upscaling method; and 3) higher temporal resolution WFDE5 forcings result in higher surface runoff and also differences in simulated ET compared to lower temporal resolution atmospheric forcings, and in this case also differences appear between different soil information upscaling methods.

Land surface models are important tools to improve our understanding of interacting ecosystem processes and for the prediction of future risk ofdroughts and fires, but their predictions are associated with uncertainties related to model forcings, parameters and process simplifications. Theincreasing availability of high-quality observations can be used to improve the accuracy of land surface model predictions. In this study, we use the EnsembleKalman Filter for the fusion of in-situ observations into the Community Land Model 5.0 (CLM5). CLM5 simulates a broad variety of important land surfaceprocesses including water and energy partitioning, surface runoff, subsurface runoff, photosynthesis and carbon and nitrogen storage in vegetation and soil.Here, we focus on simulation of soil water content dynamics and related soil hydraulic parameters. We use CLM5 coupled to the Parallel Data AssimilationFramework (PDAF) to assimilate soil moisture data into CLM5 during simulation runtime. We perform simultaneous state and parameter updates to improvethe estimate of model states and parameters. In this study, we specifically focus on European forested study sites where both in-situ soil moisture andevapotranspiration observations are available for the period from 2009 to 2019. We demonstrate the value and limitation of assimilating soil moisturedata for assessing drought stress using eddy covariance data from multiple sites of different observation networks across Europe (e.g. eLTER, FLUXNET,TERENO, ICOS) covering different regional climates and forest types and focusing on recent drought events in 2018 and 2019.

Crop model comparisons have mostly been carried out to test predictive ability under previous climate conditions and for soils of the same location. However, the ability of individual agricultural models to predict the effects of changes in climatic conditions on soil-ecosystems beyond the range of site-specific variability is unknown. The objective of this study was to test the predictive ability of agroecosystem models using weighable lysimeter data for the same soil under changing climatic conditions and to compare simulated plant growth and soil-ecosystem response to climate change between these models. To achieve this, data from the TERENO-SOILCan lysimeters-network for a soil-ecosystem at the original site (Dedelow) and data from the lysimeters with Dedelow soil monoliths transferred to Bad Lauchstädt and Selhausen were analysed. The transfer of the soils took place to a drier and warmer location (Bad Lauchstädt) and to a warmer and wetter location (Selhausen) compared to the original location of the soils in Dedelow with the same crop rotation. After model calibration for data from the original Dedelow site, crop growth and soil water balances of transferred Dedelow soil monoliths were predicted using the site-specific boundary conditions and compared with the observations at Selhausen and Bad Lauchstädt. The overall simulation output of the models was separated into a plant-related part, ecosystem-productivity (grain yield, biomass, LAI) and an environmental part, ecosystem-fluxes (evapotranspiration, net-drainage, soil moisture). The results showed that when the soil was transferred to a drier region, the agronomic part of the crop models predicted well, and when the soil was moved to wetter regions, the environmental flow part of the models seemed to predict better. The results suggest that accounting for climate change scenarios, more consideration of soil properties and testing model performance for conditions outside the calibrated range and site-specific variability will help improve the models.

Soil salinization in irrigated croplands is a key factor in soil degradation and directly affects plant growth and soil hydrological processes such as evaporation and infiltration. In order to support the development of appropriate irrigation strategies, it is important to understand the impact of salt crusts that form during evaporation from saline soils on water flow. The determination of the effective hydraulic properties of salt crusts that control evaporation is still a challenge due to the lack of suitable measurement techniques. In this study, we propose an approach using gas flow to determine the permeability of salt crusts obtained from evaporation of unsaturated saline solutions of three different salt types and investigate the impact of the crust permeability on evaporation. For this, sand columns saturated with initial solutions of sodium chloride (NaCl), magnesium sulfate (MgSO4), and sodium sulfate (Na2SO4) at concentrations corresponding to 33% of the solubility limit were prepared and allowed to evaporate in order to induce crust formation. The results demonstrated that the intrinsic permeability of the dry salt crusts was similar for the different types of salts (&asymp, 4&times, 10&minus, 12m2), whereas the evaporation of the prepared columns differed significantly. We conclude that the intrinsic crust permeability only partly explains the impact of the crust on evaporation. Other effective crust properties such as porosity or unsaturated hydraulic properties may provide additional information on how evaporation is affected by salt crust formation.

The relatively poor simulation of the below-ground processes is a severe drawback for many ecosystem models, especially when predicting responses to climate change and management. For a meaningful estimation of ecosystem production and the cycling of water, energy, nutrients and carbon, the integration of soil processes and the exchanges at the surface is crucial. It is increasingly recognized that soil biota play an important role in soil organic carbon and nutrient cycling, shaping soil structure and hydrological properties through their activity, and in water and nutrient uptake by plants through mycorrhizal processes. In this article, we review the main soil biological actors (microbiota, fauna and roots) and their effects on soil functioning. We review to what extent they have been included in soil models and propose which of them could be included in ecosystem models. We show that the model representation of the soil food web, the impact of soil ecosystem engineers on soil structure and the related effects on hydrology and soil organic matter (SOM) stabilization are key issues in improving ecosystem-scale soil representation in models. Finally, we describe a new core model concept (KEYLINK) that integrates insights from SOM models, structural models and food web models to simulate the living soil at an ecosystem scale. Nasl. z nasl. zaslona. Opis vira z dne 22. 9. 2020. Avtor iz Slovenije: Tine Grebenc. Bibliografija: str. 43-69. Abstract.

Root water uptake (RWU) is a key ecohydrological process for which a physically-based understanding has been developed in the past decades. Isotopic analysis of soil and sap xylem water allows for a quantification of RWU profiles relative to plant transpiration at the whole-plant scale and provides independent means for constraining these models. However, due to methodological constraints, mainly having to do with destructive sampling and subsequent extraction of water under vacuum, significant knowledge gaps remain about the plastic response of a whole plant root system to a rapidly changing environment. We designed a laboratory experimental setup consisting of a plant chamber coupled to a 60-cm long acrylic soil column equipped with gas-permeable tubing at eight different depths for the non-destructive monitoring of the stable isotopic compositions in plant transpiration of an herbaceous species (Centaurea jacea) and of water across the soil profile, respectively. In addition, soil water content was monitored at five different depths in the soil column. Isotopically labeled water was brought to the plant alternatively from the top and the bottom of the soil column to simulate a rain event and capillary rise of groundwater. Finally, root length density (RLD) was measured destructively at the end of the experimental period. Fast shifts in the isotopic composition of both soil and transpiration water could be observed with the setup and translated into dynamic and pronounced shifts of RWU profile by means of a statistical Bayesian multi-source mixing model. The incorporation of plant physiological and soil physical information into statistical modelling improved the model output. A simple exercise of water balance closure underlined the non-unique relationship between RWU profile on the one hand, and water content and RLD profiles on the other, illustrating the continuous adaptation of the plant RWU as a function of its root hydraulic architecture and soil water availability during the experiment.

Agro-ecosystem models have been developed to study effects of agricultural management on crop production, mostly from an agronomic point of view. Based on a biophysical process representation, their most prominent advantage is the coupled modelling of crop development and yield formation, as well as water and nutrients fluxes in the plant-soil system. Crop models have previously been calibrated based on experimental data with a focus on plant observations. Less attention has been given to soil water and solute dynamics despite the importance of plant nutrient availability and chemical leaching, particularly for arable soils often affected by erosion. The question was whether the description of soil processes and properties play an important role in the crop simulations.The aim of this study was to compare the ability of agro-ecosystem models to predict crop development and water fluxes under changing environmental conditions. Observations on crop growth and soil water dynamics were obtained from four weighable lysimeter of the TERENO-SOILCan lysimeter network in the northeast of Germany (Dedelow). The intact soil monoliths are representative for the spatial soil variability of erosion-affected hummocky agricultural landscape. Twelve agro-ecosystem models (AgroC; DailyDayCent; Daisy; HERMES; MONICA; Theseus, Theseus-HydroGeoSphere; Theseus-Hydrus-1D; Expert-N coupled to CERES, GECROS, SPASS, and SUCROS) were tested. Crop development stages were used to calibrate the agro-ecosystem models. The model performance was tested against observed grain yield, aboveground biomass, leaf area index, actual evapotranspiration, drainage, and soil water content.Model descriptions were highly diverse for both crop development and water fluxes. Crop growth and soil water fluxes were better predicted by the Multi Model Mean simulations than by any individual model. Results demonstrate that i) the hydraulic properties of erosion-affected soil profiles controlled the observed interactions between crop yield, plant development, and water fluxes, ii) data on phenological stages contained insufficient information content to calibrated agro-ecosystem models for soils affected by erosion, and iii) neither an individual model nor the Multi Model Mean could describe the observation on crop development and water dynamics, when using phenological stages only for model calibration. The results suggest that soil does matter in agro-ecosystem models and that weighable lysimeter can provide such soil related observation.

Soil degradation is a worsening global phenomenon driven by socio-economic pressures, poor land management practices and climate change. A deterioration of soil structure at timescales ranging from seconds to centuries is implicated in most forms of soil degradation including the depletion of nutrients and organic matter, erosion and compaction. New soil-crop models that could account for soil structure dynamics at decadal to centennial timescales would provide insights into the relative importance of the various underlying physical (e.g. tillage, traffic compaction, swell/shrink and freeze/thaw) and biological (e.g. plant root growth, soil microbial and faunal activity) mechanisms, their impacts on soil hydrological processes and plant growth, as well as the relevant timescales of soil degradation and recovery. However, the development of such a model remains a challenge due to the enormous complexity of the interactions in the soil-plant system. In this paper, we focus on the impacts of biological processes on soil structure dynamics, especially the growth of plant roots and the activity of soil fauna and microorganisms. We first define what we mean by soil structure and then review current understanding of how these biological agents impact soil structure. We then develop a new framework for modelling soil structure dynamics, which is designed to be compatible with soil-crop models that operate at the soil profile scale and for long temporal scales (i.e. decades, centuries). We illustrate the modelling concept with a case study on the role of root growth and earthworm bioturbation in restoring the structure of a severely compacted soil.

Crosshole GPR full-waveform inversion (FWI) has shown a high potential to characterize the near surface at a decimeter scale which is crucial for flow and transport. GPR FWI provide high-resolution tomograms of dielectric permittivity and electrical conductivity, which can be linked lithological properties. This study tests the potential of time-lapse GPR FWI to monitor tracers of different geophysical properties (salt, heat, ethanol). Synthetic and preliminary field results show that both properties can resolve major transport processes.

Most soil hydraulic information used in Earth System Models (ESMs) is derived from pedo-transfer functions that use easy-to-measure soil attributes to estimate hydraulic parameters. This parameterization relies heavily on soil texture, but overlooks the critical role of soil structure originated by soil biophysical activity. Soil structure omission is pervasive also in sampling and measurement methods used to train pedotransfer functions. Here we show how systematic inclusion of salient soil structural features of biophysical origin affect local and global hydrologic and climatic responses. Locally, including soil structure in models significantly alters infiltration-runoff partitioning and recharge in wet and vegetated regions. Globally, the coarse spatial resolution of ESMs and their inability to simulate intense and short rainfall events mask effects of soil structure on surface fluxes and climate. Results suggest that although soil structure affects local hydrologic response, its implications on global-scale climate remains elusive in current ESMs.

Terrestrial evapotranspiration (ET) is the second largest water flux in the global water cycle. It can be measured with different techniques; weighable lysimeters can provide very accurate measurements, and some very long‐term time series exist. However, these lysimeter time series are affected by data gaps that must be filled to estimate actual ET totals and long‐term trends. In this paper, we explore four different gap‐filling methods: the potential ET‐method, the ratio method, the FAO‐based water balance method, and HYDRUS modeling. These gap‐filling methods were evaluated for three time series of actual ET measured by lysimeters and meteorological data of three European sites. Separate evaluations were made for the five driest and five wettest April–October periods to investigate whether the performance of the gap‐filling methods was affected by hydrological conditions. Series of random gaps were artificially created for the three time series, including gaps of four different lengths. Actual ET was estimated for these gaps with the gap‐filling methods, which were evaluated based on RMSE and mean bias error. The results show that the ratio method outperformed other methods for gap filling of lysimeter data for Basel (Switzerland), whereas the HYDRUS method outperformed other methods for Rheindahlen (Germany). For Rietholzbach (Switzerland), the different methods performed very similarly, except that the FAO method gives slightly larger RMSEs. The gap‐filling methods do not perform very differently for dry and wet conditions. The ratio method is recommended for filling smaller gaps, and the HYDRUS method is recommended for longer gaps of 30 d., Vadose Zone Journal, 19 (1), ISSN:1539-1663

Terrestrial Essential Climate Variables, known as terrestrial ECVs, are key sources of information for both application- and scientific- oriented research. A large number of global terrestrial ECV products have been derived from satellite observations, and more are forthcoming. To unlock the full potential of these products, end-users need to know their uncertainties and error magnitudes. Due to the lack of conformity among validation strategies, a wide range of validation approaches have been employed to assess the quality of these products, and have resulted in reduced comparability even for the same terrestrial ECV. Addressing this challenge in validation practices requires the use of unified, standard, publicly available, traceable and objective validation procedures that are operational for all products of a specific terrestrial ECV, and preferably also applicable for all ECVs at the global scale. This can allow end-users to perform comparative assessments. To this end, the current study aims to investigate the readiness status of a selected group of seven global long-term satellite-based terrestrial ECVs for operational validation. Selected variables are Leaf Area Index (LAI), Land Surface Temperature (LST), Evapotranspiration (ET), Soil Moisture (SM), Albedo, the fraction of Absorbed Photosynthetically Active Radiation (fAPAR), and Land Cover (LC). For each of these terrestrial ECVs, we reviewed key prerequisites and primary tools [notably, long term global product availability, globally distributed in situ measurement availability, a validation good practice protocol, and an online validation platform] required for developing an operational validation system. With respect to the “readiness level”, the investigation results demonstrate that LAI, SM, and LC are at the highest level of readiness for moving toward a full operational validation at the global scale. However, ET is at the lowest level of readiness, mainly due to the lack of standard validation good practice protocol and lack of a pilot online validation platform. The remainder of the selected terrestrial ECVs are identified to be at mid-level readiness, mainly because either a validation platform (i.e., LST and albedo) or good practice protocol (i.e., fAPAR) still needs to be developed. This review can pave the way for open-access, traceable, transparent, and operational validation procedures of satellite-based global terrestrial ECVs.

Cross-hole GPR full-waveform inversion (FWI) has shown a high potential to characterize the near surface at a decimeter scale which is crucial for flow and transport. GPR FWI provide high-resolution tomograms of dielectric permittivity (ε) and electrical conductivity (EC), which can be linked lithological properties. This study tests the potential of time-lapse GPR FWI to monitor tracers of different geophysical properties (salt, heat, ethanol). Synthetic and field results show that both properties can resolve the plume at decimeter- channelized transport- scale.

International audience; Induced calcite precipitation is used in geotechnics to modify the mechanical and hydrological properties of the underground. It also is an interesting model system to investigate mixing-induced geochemical reactions occurring in a range of hydrological systems. Laboratory experiments have shown that spectral induced polarization (SIP) measurements can detect calcite precipitation. However, most previous studies have only shown qualitatively how the SIP response changes due to calcite precipitation. This study aims to quantify the relationship between SIP response and calcite precipitation. For this, a 2D cell with a synthetic porous medium was developed so as to allow visualizing the dynamics of calcite precipitation while making SIP measurements. Here, calcite precipitation was generated by injecting Na2CO3 and CaCl2 solutions through two different inlets for 40 hours. During injection, the development of precipitation in the region where the two injected fluids mix, was monitored with a camera. Calcite crystal growth was observed with a microscope at selected locations. The SIP response was measured in three regions of the cell. The imaging results showed that calcite precipitation mainly occurred in the first 15 hours, likely because formation of the precipitate at the location of the mixing front inhibited further reactions. The SIP response near the injection started to increase after 5 hours, while the SIP response far from the injection started to increase after 15 hours. Although there was almost no visual change in the precipitation after 15 hours, the SIP response continued to increase for a much longer time, up to high values of 65 to 100 mrad depending on the position in the cell. These results suggest that the mass of calcite precipitation does not necessarily control the SIP response of calcite, but that other factors such as kinetic changes in the electric double layer of the precipitates may be important. Additionally, the peak phase was higher for the region near the injection, which is in agreement with the smaller size of the crystals determined from microscope images in this region. Our results highlight that the novel setup that combines a cell containing a 2D porous medium with SIP measurements can be successfully used to infer how calcite precipitation affects the SIP response in a dynamic system.

There has been much effort to improve the performance of pedotransfer functions (PTFs) using intelligent algorithms, but the issue of covariate shift, i.e., different probability distributions in training and testing datasets, and its impact on prediction uncertainty of PTFs has been rarely addressed. The common practice in PTF generation is to randomly separate the dataset into training and testing subsets, and the outcomes of this random selection may be different if the process is subject to covariate shift. We evaluated the impact of covariate shift generated by data shuffling and detected by Kolmogorov-Smirnov test for the prediction of water contents using soil databases from Denmark and Brazil. The soil water contents at different pressure heads were predicted by developing linear and stepwise regression besides machine learning based PTFs including Gaussian process regression and ensemble method. Regression based PTFs for the Brazilian dataset resulted in better predictions compared with machine learning methods, which in their turn estimated high water contents in Danish soils more accurately. One hundred PTFs were developed for water content at specific pressure heads by data shuffling. From these, 100 sets of fitted van Genuchten parameters were obtained representing the generated uncertainty. Data shuffling led to covariate shift, resulting in uncertainty in water content prediction by the PTFs. Inherent variability of data may lead to increased prediction uncertainty. For correlated data, simple regression models performed as good as sophisticated machine learning methods. Using PTF-predicted water contents for van Genuchten retention parameter fitting may lead to a high uncertainty.

Horizontally stratified media are commonly used to represent naturally occurring and manmade structures, such as soils, roads, and pavements, when probed by ground-penetrating radar (GPR). Electromagnetic (EM) wave scattering from such multilayered media is dependent on the roughness of the interfaces. In this study, we developed a closed-form asymptotic EM model taking into account random rough layers based on the scalar Kirchhoff-tangent plane approximation (SKA) model, which we combined with planar multilayered media Green’s functions. In order to validate our extended SKA model, we conducted simulations using a numerical EM solver based on the finite-difference time-domain (FDTD) method. We modeled a medium with three layers – a base layer of perfect electric conductor (PEC), overlaid by two layers of different materials with rough interfaces. The reflections at the first and at the second interface were both well reproduced by the SKA model for each roughness condition. For the reflection at the PEC surface, the extended SKA model slightly overestimated the reflection, and this overestimation increased with the roughness amplitude. A good agreement was also obtained between the FDTD simulation input values and inverted root mean square (RMS) height estimates of the top interface, while the inverted RMS heights of the second interface were slightly overestimated. The accuracy and the performances of our asymptotic forward model demonstrate promising perspectives for simulating rough multilayered media, and hence, for the full waveform inversion of GPR data to non-invasively characterize soils and materials.

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

Accurate characterization of forest litter is of high interest for land surface modeling and for interpreting remote sensing observations over forested areas. Due to the large spatial heterogeneity of forest litter, scattering from litter layers has to be considered when sensed using microwave techniques. Here, we apply a full-waveform radar model combined with a surface roughness model to ultrawideband ground-penetrating radar (GPR) data acquired above forest litter during controlled and in situ experiments. For both experiments, the proposed modeling approach successfully described the radar data, with improvements compared to a previous study in which roughness was not directly accounted for. Inversion of the GPR data also provided reliable estimates of the relative dielectric permittivity of the recently fallen litter (OL layer) and of the fragmented litter in partial decomposition (OF layer) with, respectively, averaged values of 1.35 and 3.8 for the controlled experiment and of 3.9 and 7.5 for the in situ experiment. These results show the promising potentialities of GPR for efficient and non-invasive characterization of forest organic layers.