Sensitivity of water stress in a two-layered sandy grassland soil to variations in groundwater depth and soil hydraulic parameters

Monitoring and modelling tools may improve irrigation strategies in precision agriculture. We used non-invasive soil moisture monitoring, a crop growth and a soil hydrological model to predict soil water content fluctuations and crop yield in a heterogeneous sandy grassland soil under supplementary irrigation. The sensitivity of the soil hydrological model to hydraulic parameters, water stress, crop yield and lower boundary conditions was assessed after integrating models. Free drainage and incremental constant head conditions were implemented in a lower boundary sensitivity analysis. A time-dependent sensitivity analysis of the hydraulic parameters showed that changes in soil water content are mainly affected by the soil saturated hydraulic conductivity K-s and the Mualem-van Genuchten retention curve shape parameters n and alpha. Results further showed that different parameter optimization strategies (two-, three-, four- or six-parameter optimizations) did not affect the calculated water stress and water content as significantly as does the bottom boundary. In this case, a two-parameter scenario, where K-s was optimized for each layer under the condition of a constant groundwater depth at 135-140 cm, performed best. A larger yield reduction, and a larger number and longer duration of stress conditions occurred in the free drainage condition as compared to constant boundary conditions. Numerical results showed that optimal irrigation scheduling using the aforementioned water stress calculations can save up to 12-22 % irrigation water as compared to the current irrigation regime. This resulted in a yield increase of 4.5-6.5 %, simulated by the crop growth model

Local sensitivity analysis for compositional data with application to soil texture in hydrologic modelling

Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output with resect to a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. This method was applied to a hydrologic model and the sensitivity of the simulated soil moisture content to local changes in soil texture was assessed. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moisture retention curve

Modelling soil water conent in a tomato field: proximal gamma ray spectroscopy and soil-crop system models

Proximal soil sensors are taking hold in the understanding of soil hydrogeological processes involved in precision agriculture. In this context, permanently installed gamma ray spectroscopy stations represent one of the best space-time trade off methods at field scale. This study proved the feasibility and reliability of soil water content monitoring through a seven-month continuous acquisition of terrestrial gamma radiation in a tomato test field. By employing a 1 L sodium iodide detector placed at a height of 2.25 m, we investigated the gamma signal coming from an area having a ~25 m radius and from a depth of approximately 30 cm. Experimental values, inferred after a calibration measurement and corrected for the presence of biomass, were corroborated with gravimetric data acquired under different soil moisture conditions, giving an average absolute discrepancy of about 2%. A quantitative comparison was carried out with data simulated by AquaCrop, CRITeRIA, and IRRINET soil-crop system models. The different goodness of fit obtained in bare soil condition and during the vegetated period highlighted that CRITeRIA showed the best agreement with the experimental data over the entire data-taking period while, in presence of the tomato crop, IRRINET provided the best results.Comment: 18 pages, 9 Figures, 3 Table

Data assimilation of in situ soil moisture measurements in hydrological models: third annual doctoral progress report, work plan and achievements

Efficient water utilization and optimal water supply/distribution to increase food and fodder productivity are of utmost importance in confronting worldwide water scarcity, climate change, growing populations and increasing water demands. In this respect, irrigation efficiency, which is influenced by the type of irrigation and irrigation scheduling, is an essential issue for achieving higher productivity. To improve irrigation strategies in precision agriculture, soil water status can be more accurately described using a combination of advanced monitoring and modeling. Our study focuses on the combination of high resolution hydrological data with hydrological models that predict water flow and solute (pollutants and salts) transport and water redistribution in agricultural soils under irrigation. Field plots of a potato farmer in a sandy region in Belgium were instrumented to continuously monitor soil moisture and water potential before, during and after irrigation in dry summer periods. The aim is to optimize the irrigation process by assimilating online sensor field data into process based models. This research is part of Activity 305 ‘Precision agriculture and remote sensing’ of the VITO GWO and is also part of the strategic cooperation with UGent within the platform ‘Managing Natural Resources’. Over the past 2 years, we applied a combination of in-situ monitoring and numerical modeling -Hydrus 1D- to estimate water content fluctuations in a heterogeneous sandy grassland soil under irrigation with water table fluctuating between 80 and 155 cm. Over the last year, more sampling and analyses were carried out to further characterize the hydraulic properties over the entire field. Modeling results for the field demonstrated clearly the profound effect of the position of the GWL, and to a lesser extent, the effect of spatially variable soil hydraulic properties (Ks, n and α) on the estimated water content in the sandy two-layered soil under grass. Our results show that currently applied uniform water distribution using sprinkler irrigation seems not to be efficient since at locations with shallow groundwater, the amount of water applied will be excessive as compared to the plant requirements while in locations with a deeper GWL, requirements will not be met. To derive the optimal parameter set best describing the measured soil moisture content, 37 optimization scenarios were conducted with two to six parameters using various parameter combinations for the two soil layers. The best performing parameter optimization scenario was a 2-parameter scenario with Ks optimized for each layer. The results showed a better identifiability of the parameters (less correlations among parameters) with equal performance as compared to three, four or six parameter optimization. Model predictions using the calibrated model (with data from 2012) for an independent data set of soil moisture data in the validation period (2013) showed satisfactory performance of the model in view of irrigation management purposes. Comparing the degree of water stress for different optimization scenarios of groundwater depth, showed that grass was exposed to water stress in summer in 2013 but not for such a long period as compared to the 2012 growing season. The degree of water stress simulated with Hydrus 1D suggested to increase the irrigation amount in 2012 and 2013 and at least one or two times in the summer (June and July) and further distributing the amount of irrigation during the growing season, instead of using a huge amount of irrigation later in the season, as is common practice by the farmer. A second part of the study focused on finding a relation between measured soil hydraulic properties and apparent electrical conductivity ECa. Our measurements of hydraulic properties of the field clearly confirm that there is considerable spatial variability in the field and that this has an impact on the simulation of soil moisture content. Therefore this should be taken into account when upscaling soil hydraulic properties to the field scale in order to in understand and model flow, solute and energy fluxes in the field and develop strategies for efficient irrigation. Upscaling soil hydraulic properties to the field scale can be done by linking them to apparent electrical conductivity (ECa), which can be measured efficiently and inexpensively so a spatially dense dataset for describing within-field spatial soil variability can be generated. In this study relations between the spatial variation of soil hydraulic properties and apparent soil electrical conductivity ECa measured with EM38 and DUALEM-21S sensors at two depths of explorations (DOE) 0-50 and 0-100 cm were investigated. Two predictive modelling approaches, i.e. i) a simple regression and ii) applying Archie’s laws for saturated and unsaturated conditions in combination with MVG equations, were developed and it was compared how they were able to explain the observed values of hydraulic parameters. Results demonstrated the spatial variability and heterogeneity of ECa and soil hydraulic properties Ks, α and n. We derived a regression relationship between log Ks and ECa measured with DUALEM (r2≥0.70) and with EM38 (r2>0.46) sensors. The predicted results were tested vs measured data and confirmed that the performance of DUALEMp,100-Ks model is relatively better than that of the same sensor with lower DOE and of the EM38 sensor (RMSE = 1.31 cmh-1, R2 = 0.55). The relationships between MVG shape parameters and ECa datasets were generally poor (0.05<R2<0.26). In the second approach, we showed that the water retention curve can be translated to ECa-(h) and ECa-Se relations by combining the MVG equations and Archie’s law. Results also show that reformulating the MVG equations based on ECa-Se relationships can help to estimate unsaturated hydraulic conductivity at the field scale. In the third year, a second study site has been set up in a nearby field where potatoes are grown and has been instrumented with soil moisture sensors, tensiometers, groundwater level loggers and a weather station. Field hydraulic properties for the field will be derived using the equations developed for the first study site and the modeling approach developed for the first field will be tested here. Also quasi 3D-modelling of water flow at the field scale will be conducted. In this modeling set-up, the field will be modeled as a collection of 1D-columns representing the different field conditions (combination of soil properties, GWL, root zone depth). Combining this model with crop based models such as LINGRA-N or Aquacrop gives a direct simulation of the impact of irrigation strategies on crop yield at the field scale

An efficient surrogate model for emulation and physics extraction of large eddy simulations

In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations and statistical modeling. In this paper, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engineering applications. The novelty of the proposed method lies in the incorporation of known physical properties of the fluid flow as {simplifying assumptions} for the statistical model. In view of the massive simulation data at hand, which is on the order of hundreds of gigabytes, these assumptions allow for accurate flow predictions in around an hour of computation time. To contrast, existing flow emulators which forgo such simplications may require more computation time for training and prediction than is needed for conducting the simulation itself. Moreover, by accounting for coupling mechanisms between flow variables, the proposed model can jointly reduce prediction uncertainty and extract useful flow physics, which can then be used to guide further investigations.Comment: Submitted to JASA A&C

Measurement of atmospheric neutrino oscillations with very large volume neutrino telescopes

Neutrino oscillations have been probed during the last few decades using multiple neutrino sources and experimental set-ups. In the recent years, very large volume neutrino telescopes have started contributing to the field. First ANTARES and then IceCube have relied on large and sparsely instrumented volumes to observe atmospheric neutrinos for combinations of baselines and energies inaccessible to other experiments. Using this advantage, the latest result from IceCube starts approaching the precision of other established technologies, and is paving the way for future detectors, such as ORCA and PINGU. These new projects seek to provide better measurements of neutrino oscillation parameters, and eventually determine the neutrino mass ordering. The results from running experiments and the potential from proposed projects are discussed in this review, emphasizing the experimental challenges involved in the measurements.Comment: Review paper to appear in the special issue "Neutrino Masses and Oscillations" of Advances in High Energy Physics (accepted); 22 pages, 24 figure