Inverse Modeling Approach Research Articles

Evaluating the methods and influencing factors of satellite-derived estimates of NOX emissions at regional scale: A case study for Yangtze River Delta, China

The top-down estimation of NOX emissions and their influencing factors were evaluated based on the “synthetic” and real satellite observation methods at different spatial scales in eastern China. Using the “synthetic” NO2 vertical column densities (VCD) simulated from a hypothetical “true” emission inventory, the top-down estimates of NOX emissions for the Yangtze River Delta (YRD) region at 9 km resolution and the Southern Jiangsu City Cluster (SJC) at 3 km resolution were obtained using various inverse modeling approaches and the a priori emissions for January and July 2012. The normalized mean biases (NMBs) between the top-down and the hypothetical “true” emissions for all the cases were smaller than 6%, which indicates that both linear and nonlinear approaches could effectively constrain the total amount of emissions, with limited influence from spatial resolution, a priori emissions, and seasons. Larger differences for most cases were found for the normalized mean errors (NMEs), implying that the inverse modeling approach and other influencing factors played a more important role on the spatial distribution of the top-down estimates. Two NO2 VCD products from real satellite observation (Dutch OMI NO2 data product v2 (DOMINO v2) and Peking University OMI NO2 data product v2 (POMINO v2)) were then applied to emissions constraints. The NMEs between the top-down estimates derived from the two products were calculated at 182% and 99% for January and July, respectively, indicating the great importance of satellite observation in constraining emissions. With the nonlinear inverse modeling approach, the top-down estimates of NOX emissions based on POMINO v2 were 25%–60% smaller than the national bottom-up inventory for the four seasons in the YRD, which indicates overestimation by the bottom-up method due to the insufficient consideration of recent air pollution control policy. At the 9 km resolution, the simulated NO2 concentrations with air quality modeling based on the top-down estimates were much closer to available ground observation than the bottom-up ones for all seasons, which suggests improved emissions estimation from the inverse model at regional scales.

GLAM Bio-Lith RT: A Tool for Remote Sensing Reflectance Simulation and Water Components Concentration Retrieval in Glacial Lakes

A new open--source software tool, called GLAM BioLith--RT (Glacier Lakes Assisted Melting Biological Lithological Radiative Transfer), has been developed for modeling of Radiative Transfer (RT) in water bodies containing suspended lithic particles , phytoplankton, dissolved salts, and colored dissolved organic matter. Although our objective is an application to glacial lakes of High Mountain Asia, the model has potential application for the study of seawater, organic-rich lakes, rivers, etc. The tool is built on a solid foundation of an existing published open-source code called WASI, which has been reviewed and augmented with new capabilities, notably the addition of a suspended lithic particle grain size parameterization. GLAM BioLith-RT operates in both a forward modeling and inverse modeling mode. The forward mode is specifically designed to compute the reflectance spectra of glacier lakes from inherent optical water properties. Conversely, in the inverse mode, measured spectral reflectance is employed with other inputs to derive best fitting water component properties (e.g., suspended particles concentration). The inverse modeling includes Bayesian Optimization of the output which is a significant advance over the existing software. We have tested the code for sensitivity to noise, and uncertainties in input parameters. The model succeeds in nearly reproducing the hyperspectral reflectance of some glacial lakes in Nepal as observed by the EO-1 Hyperion hyperspectral imager. The inverse modeling approach, when supported up by validated forward modeling, offers a means for remote sensing characterization of suspended sediment load in glacial lakes and rivers and hence, use of suspended sediment as a proxy for glacial activity; and many other potential applications in other thematic areas.

Semi-supervised learning in multivariate calibration

Multivariate calibration has historically been associated with supervised learning where a model developed/learned from a set of labeled data is used to predict characteristics of an object by using measurements obtained from it. For example, in analytical chemistry, the characteristics are often related to composition, some form of spectroscopy is commonly used to acquire measurements, and the object is typically a sample of some material of interest. Recently in this and other contexts there has been an increased interest in semi-supervised learning where a combination of both labeled and unlabeled data are used to help produce a predictive model. The use of semi-supervised learning can be advantageous in certain situations in multivariate calibration involving both forward and inverse modeling approaches. Potential advantages of models developed via semi-supervised learning are illustrated for the two modeling approaches via a series of simulations that are derived from near-infrared reflectance spectra. The basis and conditions for benefits of a semi-supervised learning strategy for multivariate calibration are identified and discussed. In the case of both modeling approaches, the primary advantage of semi-supervised learning was found to be a reduction in conditional prediction bias. This advantage is most likely to be realized when the quantity of labeled data is small, the quantity of unlabeled data is large, and when the values of the characteristics to be predicted are distant from the centroid of associated values of the labeled data.

Using an inverse modelling approach with equifinality control to investigate the dominant controls on snowmelt nutrient export

AbstractThere is great interest in modelling the export of nitrogen (N) and phosphorus (P) from agricultural fields because of ongoing challenges of eutrophication. However, the use of existing hydrochemistry models can be problematic in cold regions because models frequently employ incomplete or conceptually incorrect representations of the dominant cold regions hydrological processes and are overparameterized, often with insufficient data for validation. Here, a process‐based N model, WINTRA, which is coupled to a physically based cold regions hydrological model, was expanded to simulate P and account for overwinter soil nutrient biochemical cycling. An inverse modelling approach, using this model with consideration of parameter equifinality, was applied to an intensively monitored agricultural basin in Manitoba, Canada, to help identify the main climate, soil, and anthropogenic controls on nutrient export. Consistent with observations, the model results suggest that snow water equivalent, melt rate, snow cover depletion rate, and contributing area for run‐off generation determine the opportunity time and surface area for run‐off–soil interaction. These physical controls have not been addressed in existing models. Results also show that the time lag between the start of snowmelt and the arrival of peak nutrient concentration in run‐off increased with decreasing antecedent soil moisture content, highlighting potential implications of frozen soils on run‐off processes and hydrochemistry. The simulations showed TDP concentration peaks generally arriving earlier than NO3 but also decreasing faster afterwards, which suggests a significant contribution of plant residue Total dissolved Phosphorus (TDP) to early snowmelt run‐off. Antecedent fall tillage and fertilizer application increased TDP concentrations in spring snowmelt run‐off but did not consistently affect NO3 run‐off. In this case, the antecedent soil moisture content seemed to have had a dominant effect on overwinter soil N biogeochemical processes such as mineralization, which are often ignored in models. This work demonstrates both the need for better representation of cold regions processes in hydrochemical models and the model improvements that are possible if these are included.

Stochastic inverse modeling and global sensitivity analysis to assist interpretation of drilling mud losses in fractured formations

This study is keyed to enhancing our ability to characterize naturally fractured reservoirs through quantification of uncertainties associated with fracture permeability estimation. These uncertainties underpin the accurate design of well drilling completion in heterogeneous fractured systems. We rely on monitored temporal evolution of drilling mud losses to propose a non-invasive and quite inexpensive method to provide estimates of fracture aperture and fracture mud invasion together with the associated uncertainty. Drilling mud is modeled as a yield power law fluid, open fractures being treated as horizontal planes intersecting perpendicularly the wellbore. Quantities such as drilling fluid rheological properties, flow rates, pore and dynamic drilling fluid pressure, or wellbore geometry, are often measured and available for modeling purposes. Due to uncertainty associated with measurement accuracy and the marked space–time variability of the investigated phenomena, we ground our study within a stochastic framework. We discuss (a) advantages and drawbacks of diverse stochastic calibration strategies and (b) the way the posterior probability densities (conditional on data) of model parameters are affected by the choice of the inverse modeling approach employed. We propose to assist stochastic model calibration through results of a moment-based global sensitivity analysis (GSA). The latter enables us to investigate the way parameter uncertainty influences key statistical moments of model outputs and can contribute to alleviate computational costs. Our results suggest that combining moment-based GSA with stochastic model calibration can lead to significant improvements of fractured reservoir characterization and uncertainty quantification.

In-situ estimation of unsaturated hydraulic conductivity in freezing soil using improved field data and inverse numerical modeling

Hydraulic property determination in freezing soils continues to be a substantial challenge. Our overall objective was to present a novel method for in-situ estimation of unsaturated hydraulic conductivity in freezing soil using a combination of improved field data and a simplified inverse modeling approach. Dielectric sensor readings in the field were corrected using knowledge of ice permittivity, temperature and water redistribution to achieve higher accuracy in determination of liquid soil water content and soil ice content. The equation describing unsaturated hydraulic conductivity in freezing soil with an added impedance parameter was inversely estimated by firstly fitting soil freezing and thawing characteristic curves using the measured liquid soil water content and soil temperature, and then minimizing differences of the measured and simulated total soil water content with a coupled heat- and water-transfer model in frozen soil. Field measurements were made over two winters between 2011 and 2013 (year-1: winter of 2011–2012; year-2: winter of 2012–2013) in Beijing, China. Results suggested that (i) the relative error of liquid soil water content measurement in freezing soil was up to 53% if the knowledge of ice permittivity, temperature and water redistribution was neglected; (ii) the estimated impedance parameter in year-1 (1.152) was an order of magnitude higher than in year-2 (0.117), possibly because a faster freezing rate generated more fine ice in year-2, resulting in reduced tortuosity; (iii) the estimated impedance parameter uncertainty likely comes from the model assumptions, the measurement accuracies of model inputs and the inverse modeling parameter estimates. Based on these results and analysis, we conclude that the impedance parameter in frozen soil is strongly related to both the soil ice content and the size of formed ice, which is affected by the freezing rate and by the size of soil pores related to the soil texture.

Toward Image Data-Driven Predictive Modeling for Guiding Thermal Ablative Therapy.

Accurate prospective modeling of microwave ablation (MWA) procedures can provide powerful planning and navigational information to physicians. However, patient-specific tissue properties are generally unavailable and can vary based on factors such as relative perfusion and state of disease. Therefore, a need exists for modeling frameworks that account for variations in tissue properties. In this study, we establish an inverse modeling approach to reconstruct a set of tissue properties that best fit the model-predicted and observed ablation zone extents in a series of phantoms of varying fat content. We then create a model of these tissue properties as a function of fat content and perform a comprehensive leave-one-out evaluation of the predictive property model. Furthermore, we validate the inverse-model predictions in a separate series of phantoms that include co-recorded temperature data. This model-based approach yielded thermal profiles in close agreement with experimental measurements in the series of validation phantoms (average root-mean-square error of 4.8°C). The model-predicted ablation zones showed compelling overlap with observed ablations in both the series of validation phantoms (93.4 ± 2.2%) and the leave-one-out cross validation study (86.6 ± 5.3%). These results demonstrate an average improvement of 17.3% in predicted ablation zone overlap when comparing the presented property-model to properties derived from phantom component volume fractions. These results demonstrate accurate model-predicted ablation estimates based on image-driven determination of tissue properties. The work demonstrates, as a proof-of-concept, that physical modeling parameters can be linked with quantitative medical imaging to improve the utility of predictive procedural modeling for MWA.

Semi-distributed parameter optimization and uncertainty assessment for an ungauged catchment of Deduru Oya Basin in Sri Lanka

ABSTRACTParameters are the key element in any hydrological model which govern the processes of water cycle. However, determining the most influential parameters and their ranges has certainly a challenging task for an ungauged river basin, where measurements related to hydrometeorological data and parameters are either too few or too poor in quality or not available at all. In this context, the aim of this research is to demonstrate a semi-distributed modelling approach and to optimize uncertainty assessment of model parameters in part of an ungauged river basin belongs to Deduru Oya Basin. Satellite retrieved weather data was used as input data and the model was run using SWAT water resources modelling tool. The parameters were optimized for the period of 2007–2011, using SUFI-2 inverse modelling approach. In local sensitivity analysis, out of the 43 parameters 14 were received as significant and out of them 4 were received as most sensitive with reference to the selected catchment. The model showed acceptable P values greater than 70% for 2007, 2008 and 2010 and lower R values that indicate lesser parameter uncertainty. The calibrated parameter ranges are also similar for the above 3 years. The key feature noticed in the final result was that the above 3 years have relatively similar seasonal rainfall pattern. Conversely, the model showed moderately acceptable P values, lesser than 60% for 2009 and 2011 where the annual rainfall pattern of the 2 years was comparatively different than its usual seasonal pattern. Moreover, the significant parameters identified by SUFI-2 have shown direct and indirect relationship with the physical properties and characteristics of the soil in the watershed.

Inverse Data-Driven Modelling and Multiomics Analysis Reveals Phgdh as a Metabolic Checkpoint of Macrophage Polarization and Proliferation

Mechanistic or mammalian target of rapamycin complex 1 (mTORC1) is an important regulator of effector functions, proliferation and cellular metabolism in macrophages. However, the biochemical processes that are controlled by mTORC1 remain ill-defined. Here, we demonstrate that multiomics in conjunction with a data-driven inverse modelling approach, termed COVRECON, identifies causal biochemical nodes that influence overall metabolic profiles and reactions of mTORC1-dependent macrophage metabolism. Using a combined approach of metabolomics, proteomics, mRNA expression analysis and enzymatic activity measurements, we demonstrate that Tsc2, a negative regulator of mTORC1 signaling, critically influences the cellular activity of macrophages by regulating the enzyme phosphoglycerate dehydrogenase (Phgdh) in an mTORC1-dependent manner. More generally, while LPS-stimulated macrophages repressed Phgdh activity, IL-4-stimulated macrophages increased the activity of the enzyme, which was required for the expression of key anti-inflammatory molecules and macrophage proliferation. Thus, we identify Phgdh as a metabolic signature of M2 macrophages, accentuating the critical role of metabolic checkpoints in immunometabolism.

Dislocation structures and the role of grain boundaries in cyclically deformed Ni micropillars

Transmission electron microscopy and finite element-based dislocation simulations were combined to study the development of dislocation microstructures after cyclic deformation of single crystal and bicrystal Ni micropillars oriented for multi-slip. A direct correlation between large accumulation of plastic strain and the presence of dislocation cell walls in the single crystal micropillars was observed, while the presence of the grain boundary hampered the formation of wall-like structures in agreement with a smaller accumulated plastic strain. Automated crystallographic orientation and nanostrain mapping using transmission electron microscopy revealed the presence of lattice heterogeneities associated to the cell walls including long range elastic strain fields. By combining the nanostrain mapping with an inverse modelling approach, information about dislocation density, line orientation and Burgers vector direction was derived, which is not accessible otherwise in such dense dislocation structures. Simulations showed that the image forces associated with the grain boundary in this specific bicrystal configuration have only a minor influence on dislocation behavior. Thus, the reduced occurrence of “mature” cell walls in the bicrystal can be attributed to the available volume, which is too small to accommodate cell structures.

An inverse modeling approach for predicting filled rubber performance

In this paper, a computational procedure combining experimental data and interphase inverse modeling is presented to predict filled rubber compound properties. The Fast Fourier Transformation (FFT) based numerical homogenization scheme is applied on the high quality filled rubber 3D Transmission Electron Microscope (TEM) image to compute its complex shear moduli. The 3D TEM filled rubber image is then compressed into a material microstructure database using a novel Reduced Order Modeling (ROM) technique, namely Self-consistent Clustering Analysis (a two-stage offline database creation from training and learning, followed by data compression via unsupervised learning, and online prediction approach), for improved efficiency and accuracy. An inverse modeling approach is formulated for quantitatively computing interphase complex shear moduli in order to understand the interphase behaviors. The two-stage SCA and the inverse modeling approach formulate a three-step prediction scheme for studying filled rubber, whose loss tangent curve can be computed in agreement with test data.

Parameter inference with deep jointly informed neural networks

AbstractA common challenge in modeling inertial confinement fusion (ICF) experiments with computer simulations is that many of the simulation inputs are unknown and cannot be directly measured. Often, parameters that are measured in the experiment are used to infer the unknown inputs by solving the inverse problem: finding the set of simulation inputs that result in outputs consistent with the experimental observations. In ICF, this process is often referred to as a “post‐shot analysis.” Post‐shot analyses are challenging as the inverse problem is often highly degenerate, the input parameter space is vast, and simulations are computationally expensive. In this work, deep neural network models equipped with model uncertainty estimates are used to train inverse models, which map directly from output to input space, to find the distribution of post‐shot simulations that are consistent with experimental observations. The inverse model approach is compared to Markov chain Monte Carlo (MCMC) sampling of the forward model, which maps from input to output space, for parameter inference tasks of varying complexity. The inverse models perform best when searching vast parameter spaces for post‐shot simulations that are consistent with a large number of observables, where MCMC sampling can be prohibitively expensive. We demonstrate how augmenting inverse models with autoencoders enable the inclusion of several dozen observables in the inverse mapping, reducing the degeneracy of the model and improving the accuracy of the post‐shot analysis.

Uncertainty-based parameter estimation for urban pollutant buildup and washoff simulation using a multiple pattern inverse modeling approach

Reliable model parameter estimation for stormwater runoff water quality modeling has always been a challenge, especially in ungauged areas. Insufficient local observations can increase parameter uncertainty, leading to modeling outputs of limited credibility. This paper proposes an event-based multiple pattern inverse modeling approach to estimate regional-scale parameters characterizing the buildup and washoff processes in urban watersheds. The approach explicitly accounts for system uncertainties by simultaneously using a wide range of storm events. A genetic algorithm was employed to solve the inverse models. The solution populations that minimize the error between the observed and predicted values were then filtered into distinct parameter groups using a K-means clustering algorithm. Each identified parameter group may be used to represent the buildup and washoff processes.The proposed approach was applied for the New England region (United States) using two study sites with impervious land cover: Massachusetts and New Hampshire. Stormwater monitoring data of total nitrogen (TN) and total phosphorus (TP) in small to medium size rainfall events were used to identify representative parameter sets for the region. Of the 21 identified candidate parameter combinations, 13 were found to have acceptable Root Mean Square Error for both TN and TP.The 13 parameter sets were grouped into four distinct patterns to represent different potential buildup and washoff mechanisms. The results demonstrate that the approach is capable of estimating robust sets of buildup and washoff parameters at a regional scale. These can be used in a continuous simulation model to predict TN and TP loads in ungauged areas in the region.

A low cost approach to estimate demographic rates using inverse modeling

Survival is a key parameter in species' management and conservation. Most methods for estimating survival require time series data, large sample sizes and, overall, costly monitoring efforts. Inverse modeling approaches can be less data hungry, however they are underused in conservation sciences. Here we present an inverse modeling approach for estimating relative survival rates of long-lived species that is mathematically straightforward and evaluate its performance under constraints common in conservation studies related to small sample sizes. Specifically, we (i) estimated the relative survival rates in a Testudo graeca population based on one-year monitoring, (ii) assessed the impact of sample size on the accuracy, and (iii) tested alternative hypotheses on the impact of fire on the survival rates. We then compared the results of our approach with capture-recapture (CRC) estimates based on long-term monitoring. Our approach (153 individuals within a single year) yielded estimates of survival rates overlapping those of CRC estimates (11 years of data and 1009 individuals) for adults and subadults, but not for juveniles. Simulation experiments showed that our method provides robust estimates if sample size is above 100 individuals. The best models describing the impact of fire on survival identified by our approach predicts a decrease in survival especially in hatchlings and juvenile individuals, similar to CRC estimates. Our work proves that inverse modeling can decrease the cost for estimating demographic rates, especially for long-lived species and as such, its use should be encouraged in conservation and management sciences.

Inverse modeling approach for transformation of propeller shaft angular deformation and velocity to propeller torque load

In full-scale trials or model-scale tests, the propeller torque load is typically measured indirectly on the propeller shaft. Due to structural flexibility and mass of the propeller and propeller shaft, the measured propeller shaft torque response includes a time delay and amplitude difference compare to the propeller load. These differences are usually small during propeller operation, but may be significant during transient states (i. e. starting of the engine, rough sea, or operation in ice-covered waters). In this paper, an inverse model is used to calculate propeller load based on the propeller shaft response. The results indicate that the inverse model can be less complex than the complete propulsion machinery model and consider only the propeller and propeller shaft. Several modeling approaches are tested based on four proposed measuring setups and three forms of system discretization. Out of five inverse modeling approaches, two approaches showed the ability to transform measured propeller shaft angular deformation and velocity responses, colored with noise, into the corresponding rule-based propeller torque load with acceptable accuracy. Furthermore, the robustness of the inverse approaches to the random ice-propeller interaction, sampling frequency, measuring location along the propeller shaft and starting time (before and during ice-propeller interaction), is discussed.

Investigating Clarinet Articulation Using a Physical Model and an Artificial Blowing Machine

A time-domain physical model is presented that is capable of simulating a variety of articulation techniques in single-reed woodwind instruments and suitable for real-time sound synthesis. Due to the nonlinear nature of the excitation mechanism, an energy-based approach is adopted for the construction of the numerical scheme in order to ensure algorithm stability. To validate the model, measurements are carried out using an artificial blowing machine. The construction of the machine, including a sensor-equipped reed and mouthpiece as well as an automated artificial tongue and lip, is described in detail. By adjusting the motion of the tongue, the blowing machine can generate audio signals corresponding to portato and staccato articulation. These signals are resynthesised following an inverse modelling approach based on the presented physical model, during which model parameters are estimated. All estimated parameters lie in a physically feasible range and may be used for sound synthesis and sound analysis applications.

A rapidly converging initialisation method to simulate the present-day Greenland ice sheet using the GRISLI ice sheet model (version 1.3)

Abstract. Providing reliable projections of the ice sheet contribution to future sea-level rise has become one of the main challenges of the ice sheet modelling community. To increase confidence in future projections, a good knowledge of the present-day state of ice flow dynamics, which is critically dependent on basal conditions, is strongly needed. The main difficulty is tied to the scarcity of observations at the ice–bed interface at the scale of the whole ice sheet, resulting in poorly constrained parameterisations in ice sheet models. To circumvent this drawback, inverse modelling approaches can be developed to infer initial conditions for ice sheet models that best reproduce available data. Most often such approaches allow for a good representation of the mean present-day state of the ice sheet but are accompanied with unphysical trends. Here, we present an initialisation method for the Greenland ice sheet using the thermo-mechanical hybrid GRISLI (GRenoble Ice Shelf and Land Ice) ice sheet model. Our approach is based on the adjustment of the basal drag coefficient that relates the sliding velocities at the ice–bed interface to basal shear stress in unfrozen bed areas. This method relies on an iterative process in which the basal drag is periodically adjusted in such a way that the simulated ice thickness matches the observed one. The quality of the method is assessed by computing the root mean square errors in ice thickness changes. Because the method is based on an adjustment of the sliding velocities only, the results are discussed in terms of varying ice flow enhancement factors that control the deformation rates. We show that this factor has a strong impact on the minimisation of ice thickness errors and has to be chosen as a function of the internal thermal state of the ice sheet (e.g. a low enhancement factor for a warm ice sheet). While the method performance slightly increases with the duration of the minimisation procedure, an ice thickness root mean square error (RMSE) of 50.3 m is obtained in only 1320 model years. This highlights a rapid convergence and demonstrates that the method can be used for computationally expensive ice sheet models.

Fiber Optics Sensors in Asphalt Pavement: State-of-the-Art Review

Pavement design is essentially and usually a structural long-term evaluation process which is needed to ensure that traffic loads are efficiently distributed at all levels of the total road structure. Furthermore, to get a complete analysis of its durability behavior, long-term monitoring should be facilitated, not only from the top by falling weight deflectometer (FWD) or core drilling but preferably from inside the structure and at exactly the same positions during a long-time interval. Considering that it is very hard to devise an efficient method to determine realistic in-situ mechanical properties of pavements, the determination of strain at the bottom of asphalt pavement layers through non-destructive tests is of a great interest. As it is known, fiber Bragg grating (FBG) sensors are the most promising candidates to effectively replace conventional strain gauges for a long-term monitoring application in a harsh environment. The main goals of this paper are to compile an overview of the recent developments worldwide in the application of fiber optics sensors (FOS) in asphalt pavement monitoring systems; to find out if those systems provide repeatable and suitable results for a long-term monitoring; if there are certain solutions to validate an inverse modelling approach based on the results of FWD and FOS.

Spatial Statistical Assessment of Groundwater PCE (Tetrachloroethylene) Diffuse Contamination in Urban Areas

Contamination by chlorinated solvents is typically associated with point sources, which are able to release high concentrations and to generate well defined plumes. Nevertheless, in urban settings (especially in functional urban areas—FUAs), multiple-point sources are frequently present, consisting of a series of unidentifiable small sources clustered within large areas, generating a diffuse, anthropogenic contamination. This situation results in the coexistence of single plumes with higher contaminant concentrations, and larger areas where the concentration is lower but still higher than the maximum admissible concentration limits. This paper proposes a methodology devised to cope with the diffuse contamination by chlorinated solvents within shallow aquifers due to multiple-point sources in FUAs. The approach is based on a Bayesian model that helps to spatially evaluate the likelihood of having active multiple-point sources, and to relate their impact on the shallow aquifer to the hydrogeological features of the area. Moreover, the approach allows testing of the efficiency of the monitoring network to properly characterize the contamination in the aquifer. The consistency of the results of the analysis was also checked for the Milan FUA (Italy) by a comparison to a previous study, performed through an inverse numerical modelling approach within a Monte Carlo statistical framework to identify the areas with the highest likelihood to host potential multiple-point sources.

Prediction of nitrate accumulation and leaching beneath groundwater irrigated corn fields in the Upper Platte basin under a future climate scenario

Understanding the impacts of future climate change on soil hydrological processes and solute transport is crucial to develop appropriate strategies to minimize the adverse impacts of agricultural activities on groundwater quality. To evaluate the direct effects of climate change on the transport and accumulation of nitrate-N, we developed an integrated modeling framework combining climatic change, nitrate-N infiltration in the unsaturated zone, and groundwater level fluctuations. The study was based on a center-pivot irrigated corn field at the Nebraska Management Systems Evaluation Area (MSEA) site. Future groundwater recharge (GR) and actual evapotranspiration (ETa) rates were predicted via an inverse vadose zone modeling approach by using climatic data generated by the Weather Research and Forecasting (WRF) climate model under the RCP 8.5 scenario, which was downscaled from the global CCSM4 model to a resolution of 24 km by 24 km. A groundwater flow model was first calibrated on the basis of historical groundwater table measurements and then applied to predict the future groundwater table in 2057–2060. Finally, the predicted future GR rate, ETa rate, and groundwater level, together with future precipitation data from the WRF climate model, were used in a three-dimensional (3D) model to predict nitrate-N concentrations in the subsurface (saturated and unsaturated parts) from 2057 to 2060. The future GR was predicted to decrease in the study area, as compared with the average GR data from the literature. Correspondingly, the groundwater level was predicted to decrease (30 to 60 cm) over the 5 years of simulation in the future. The nitrate-N mass in the simulation domain was predicted to increase but at a slower rate than in the past. Sensitivity analysis indicated that the accumulation of nitrate-N is sensitive to groundwater table elevation changes and irrigation rates.