Simulation Output Research Articles

Atmospheric CO2 fertilization effect on cereal yields in Morocco using the CARAIB dynamic vegetation model

Climate change and rising atmospheric CO2 levels are critical factors influencing agricultural productivity, particularly in Morocco, where cereal crops are essential for food security. The primary objective of this study is to evaluate the combined effects of atmospheric CO2 variations and climatic changes on cereal yields up to 2099 using the CARAIB dynamic vegetation model. This evaluation is driven by four future scenarios based on the Euro-CORDEX initiative's regional climate models under the Representative Concentration Pathway 8.5. As part of this evaluation, the study also validates the CARAIB model for Morocco’s major cereal crops: soft wheat, durum wheat, and barley, in order to ensure the model's accuracy in simulating crop responses under projected environmental conditions. The CARAIB model effectively simulated historical cereal yield trajectories across major farming regions in Morocco from 2000 to 2016, demonstrating its robust predictive capability. Our future projections suggest that elevated CO2 levels might initially sustain cereal yields at approximately 90 % of current levels until 2050. This trend indicates that the increase in atmospheric CO2 may exert a moderating influence on the negative impacts of other environmental stressors on crop yields. However, despite this initial buffering effect, the overall yield trend from the present until 2099 indicates a decrease for most combinations of crop, zone, and climate model, even with the CO2 fertilization effect, except in some cases, the model exhibits slight increases or stabilization in yields. Additionally, the CARAIB model predicts potential yield shocks of 10–35 % below current levels from the 2080 s onwards, primarily due to periodic droughts. This variation underscores the complexity of the interplay between CO2 fertilization and climatic changes, emphasizing the urgent need for Morocco to develop adaptive agricultural strategies for long-term sustainability in the face of climatic challenges.

Extrusion Benchmark 2023: Effect of Die Design on Profile Speed, Seam Weld Quality and Microstructure of Hollow Tubes

The Extrusion Benchmark 2023 was focused on the evaluation of different die design strategies for the manufacturing of AA6082 hollow tubes (40 mm external diameter and 4 mm thickness) through a porthole die with 3 openings. The extrusion process was monitored in industrial environment in terms of press load, profiles speed, profiles exit temperature, and die temperatures under different processing conditions (air quenching, water quenching, nitrogen die cooling). Extruded profiles were then analyzed in terms of seam weld quality, charge weld extension and microstructure evolution for both air/water quench and presence/absence of nitrogen cooling. The results of the study are aimed at validating FEM simulation outputs in the context of the International Conference on Extrusion and Benchmark (ICEB).

Fatigue damage mechanics approach to predict the end of incubation and breakthrough of leading edge protection coatings for wind turbine blades

The state of the surface of the leading edge of wind turbine blades is important for optimal aerodynamic performance. Rain erosion leads to damage and roughness on the leading edge. In this study, we present an approach to predict the level of roughness of the leading edge based on fatigue damage accumulation due to impacts of rain droplets. Impact simulations of droplets on the protective coating layer are coupled with rain erosion test data and fatigue S-N curves. Fatigue damage values are then related to surface roughness levels. An approach to extract S-N curve parameters from a rain erosion test and then make predictions for other coatings based only on the output of impact simulations is presented and tested against test data. Both the end of incubation and coating breakthrough times are predicted with this approach. Stress, strain and energy density values were used as fatigue damage indicators and the respective predictions were compared to each other. It was found that the maximum principal strain gave the best predictions and matched the experimental trends.

Process simulation and optimization of process parameters of single-pulse femtosecond laser processing Invar 36 alloy

Invar 36 alloy has an extremely low coefficient of thermal expansion and is suitable for preparing a fine metal mask (FMM) for the evaporation of silicon-based organic light emitting display microdisplays. The higher the resolution and the greater the number of pixels, the finer and more delicate the holes required. Femtosecond laser processing technology is an effective technical means for processing Invar 36 alloy FMM due to its characteristics of extremely short pulse width, extremely high pulse energy density, smaller heat-affected zone, and more regular machining edges. The femtosecond laser processing parameters directly affect temperature distribution and then affect the manufacturing quality and efficiency of Invar 36 alloy FMM. At present, femtosecond laser processing of Invar 36 alloy FMM is still in the exploration stage, and the processing mechanism and technology are not clear. In this paper, a simulation model of single-pulse femtosecond laser processing Invar 36 alloy is established based on the two-temperature equation. By comparing the ablation morphology of the simulation output with the experimental measurement results, the validity of the simulation model is verified and the processing mechanism is preliminarily explored. Based on the simulation model, the effects of femtosecond laser energy density, pulse duration, and spot diameter on the electron temperature field, lattice temperature field, and ablation morphology of the craters are explored. The process window of laser processing parameters is determined. A full-factor test is conducted. Take the minimum length of the heat-affected zone, the maximum depth of the ablation crater, and the minimum diameter of the ablation crater as three optimization objectives and obtain Pareto optimal solutions based on genetic algorithm. The optimization of single-pulse femtosecond laser drilling Invar 36 alloy is realized.

Metamaterial inspired superstrate loaded miniaturized quad port MIMO antenna for 5G C-band applications

This paper proposes a new quad-port Multiple-Input-Multiple-Output antenna with a unique Maze structure Epsilon Negative (ENG) Metamaterial superstrate for 5G NR C-band applications. The suggested 2x2 MIMO antenna has four microstrip antennas with a C-shaped stub inside the slot arranged orthogonally on a FR4 substrate. The overall dimension of the proposed 2x2 MIMO antenna is 0.62λ0 x 0.62λ0 x 0.02λ0, where λ0 is the free space wavelength at 3.7 GHz. The proposed MIMO antenna has excellent impedance matching over a bandwidth of 32% (3.2 GHz–4.4 GHz). The proposed antenna incorporates a superstrate layer composed of modified maze-structured metamaterial elements with a unit cell size of 0.16λ0 x 0.16λ0 x 0.02λ0 on a single-layer FR4 substrate to enhance the antenna's gain, improve isolation properties, and achieve size reduction. By strategically placing the metamaterial superstrate, it reduces the mutual coupling between the antennas and enhances peak isolation of < −30 dB. In order to accomplish the goals of isolation and gain improvement, the metamaterial superstrate is positioned 10.5 mm (0.13λ0) above the MIMO components. Furthermore, it attains a 3.54 dB peak realized gain, which is enhanced by 162% across the frequency ranges, and the overall size of the MIMO antenna is reduced to approximately 17%. Adequate values for factors such as the diversity gain, envelope correlation coefficient, channel capacity loss, and total active reflection coefficient are also determined for the MIMO antenna performance. The design prototype of the proposed antenna is built and verified by measurements that reveal a strong correlation with the simulation's output.

A Python toolkit for integrating geographic information system into regulatory dispersion models for refined pollution modeling

AERMOD is designated as U.S. Environmental Protection Agency (EPA)'s preferred air dispersion model for refined transportation project hot-spot analyses beginning in 2020. One of the key challenges in its modeling process is spatially encoding roadway geometry, especially when simulating highways with complex geometric designs. This research proposed an open-source Python package, GTA, which enables conversion of publicly available roadway Geographic Information System (GIS) layers into defined sources, and source-based emission rates from MOtor Vehicle Emissions Simulator (MOVES) output for AERMOD modeling. The research selected a suburban area in Atlanta, and conducted a comprehensive analysis in terms of annual PM2.5 concentration results and the speed of preparing AERMOD input files for highway network modeling both manually and using software developed based on the proposed methodology. The results prove that the proposed methodology significantly expedites the AERMOD input preparation process, and facilitates convenient testing of multiple modeling configurations for multi-scenario or sensitivity analysis.

Advancing building energy modeling with large language models: Exploration and case studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

Subfield-level crop yield mapping without ground truth data: A scale transfer framework

Ongoing advances in satellite remote sensing data and machine learning methods have enabled crop yield estimation at various spatial and temporal resolutions. While yield mapping at broader scales (e.g., state or county level) has become common, mapping at finer scales (e.g., field or subfield) has been limited by the lack of ground truth data for model training and evaluation. Here we present a scale transfer framework, named Quantile loss Domain Adversarial Neural Networks (QDANN), that leverages knowledge from county-level datasets to map crop yields at the subfield level. Based on the strategy of unsupervised domain adaptation, QDANN is trained on labeled county-level data and unlabeled subfield-level data, with no requirement for yield information at the subfield level. We evaluate the proposed method applied to Landsat imagery and Gridmet weather data for maize, soybean, and winter wheat fields in the United States, using as reference data yield monitor records from roughly one million field-year observations. The model is compared with several process-based and machine learning-based benchmark approaches that train on simulated yield records or county-level data. QDANN-estimated yields achieved an R2 score (RMSE) of 48 % (2.29 t/ha), 32 % (0.85 t/ha), and 39 % (1.40 t/ha) for maize, soybean, and winter wheat in comparison with the ground-based yield measures, respectively. These performances are higher than benchmark approaches and are nearly as good as models trained on field-level data. When aggregated to the county level, the improvement achieved by QDANN is more pronounced and the R2 scores (RMSE) improved to 78 % (0.98 t/ha), 62 % (0.37 t/ha), and 53 % (1.00 t/ha) for maize, soybean, and winter wheat, respectively. This study demonstrates that the proposed scale transfer framework can serve as a reliable approach for yield mapping at the subfield level when there is no access to fine-scale yield information. Based on the QDANN model, we have generated and made publicly available 30-m annual yield maps for major crop-producing states in the U.S. since 2008.

Stochastic Modeling of Two-Phase Transport in Fractured Porous Media Under Geological Uncertainty Using an Improved Probabilistic Collocation Method

Summary Simulation of multiphase transport through fractured porous media is highly affected by the uncertainty in fracture distribution and matrix block size that arises from inherent heterogeneity. To quantify the effect of such uncertainties on displacement performance in porous media, the probabilistic collocation method (PCM) has been applied as a feasible and accurate approach. However, propagation of uncertainty during the simulation of unsteady-state transport through porous media could not be computed by this method or even by the direct-sampling Monte Carlo (MC) approach. Therefore, with this research, we implement a novel numerical modeling workflow that improves PCM on sparse grids and combines it with the Smolyak algorithm for selection of collocation points sets, Karhunen-Loeve (KL) decomposition, and polynomial chaos expansion (PCE) to compute the uncertainty propagation in oil-gas flow through fractured porous media in which gravity drainage force is enabled. The effect of uncertainty in the vertical dimension of matrix blocks, which are frequently an uncertain and history-matching parameter, on simulation results of randomly synthetic 3D fractured media is explored. The developed numerical model is innovatively coupled with solving governing deterministic partial differential equations (PDEs) to compute uncertainty propagation from the first timestep to the last timestep of the simulation. The uncertainty interval and aggregation of uncertainty in ultimate recovery are quantified, and statistical moments for simulation outputs are presented at each timestep. The results reveal that the model properly quantifies uncertainty and extremely reduces central processing unit (or CPU) time in comparison with MC simulation.

Utilizing fractional derivatives and sensitivity analysis in a random framework: a model-based approach to the investigation of random dynamics of malware spread

In this study, an ordinary-deterministic equation system modeling the spread dynamics of malware under mutation is analyzed with fractional derivatives and random variables. The original model is transformed into a system of fractional-random differential equations (FRDEs) using Caputo fractional derivatives. Normally distributed random variables are defined for the parameters of the original system that are related to the mutations and infections of the nodes in the network. The resulting system of FRDEs is simulated using the predictor-corrector method based fde12 algorithm and the forward fractional Euler method (ffEm) for various values of the model components such as the standard deviations, orders of derivation, and repetition numbers. Additionally, the sensitivity analysis of the original model is investigated in relation to the random nature of the components and the basic reproduction number (R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$R_{0}$\\end{document}) to underline the correspondence of random dynamics and sensitivity indices. Both the normalized forward sensitivity indices (NFSI) and the standard deviation of R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$R_{0}$\\end{document} with random components give matching results for analyzing the changes in the spread rate. Theoretical results are backed by the simulation outputs on the numerical characteristics of the fractional-random model for the expected number of infections and mutations, expected timing of the removal of mutations from the network, and measurement of the variability in the results such as the coefficients of variation. Comparison of the results from the original model and the fractional-random model shows that the fractional-random analysis provides a more generalized perspective while facilitating a versatile investigation with ease and can be used on different models as well.

Multiscale modelling and characterisation of fused filament fabricated neat and graphene nanoplatelet reinforced G-polymers

AbstractIn this study, the fused filament fabrication of novel biodegradable G-polymer is investigated, including also filaments reinforced by graphene nanoplatelets. A combined framework of computational multiscale modelling and experimental studies is applied to characterise and model the mechanical behaviour of composite and nanocomposite materials, fabricated using a 3D-printer by depositing G-polymer filaments in +45$$^\circ$$ ∘ and $$-45^\circ$$ - 45 ∘ angles. The investigation is performed on both hot-extruded single filaments and 3D-printed specimens. Experimental results of hot-extruded single filaments are utilized on the microscale to extract the macroscale constitutive models. At the microscale level, representative volume elements with different percentages of penetration are analyzed to compute the effective orthotropic elastic material properties. A comprehensive comparison study is conducted using different micromechanical models, multiscale simulation outputs, and the mechanical properties resulting from experiments. The accuracy of the results obtained from the homogenization technique is validated against realistic finite element microstructural models and experimental measurements. The results demonstrate that computational homogenization, when coupled with accurate property characterisation, serves as a reliable tool for predicting the elastic response of 3D-printed parts. Furthermore, the proposed framework reduces the need for extensive experimental replication and lowers manufacturing costs.

Coronal energy release by MHD avalanches

Context. Magnetohydrodynamic (MHD) instabilities, such as the kink instability, can trigger the chaotic fragmentation of a twisted magnetic flux tube into small-scale current sheets that dissipate as aperiodic impulsive heating events. In turn, the instability could propagate as an avalanche to nearby flux tubes and lead to a nanoflare storm. Our previous work was devoted to related 3D MHD numerical modeling, which included a stratified atmosphere from the solar chromosphere to the corona, tapering magnetic field, and solar gravity for curved loops with the thermal structure modelled by plasma thermal conduction, along with optically thin radiation and anomalous resistivity for 50 Mm flux tubes. Aims. Using 3D MHD modeling, this work addresses predictions for the extreme-ultraviolet (EUV) imaging spectroscopy of such structure and evolution of a loop, with an average temperature of 2–2.5 MK in the solar corona. We set a particular focus on the forthcoming MUSE mission, as derived from the 3D MHD modeling. Methods. From the output of the numerical simulations, we synthesized the intensities, Doppler shifts, and non-thermal line broadening in 3 EUV spectral lines in the MUSE passbands: Fe IX 171 Å, Fe XV 284 Å, and Fe XIX 108 Å, emitted by ∼1 MK, ∼2 MK, and ∼10 MK plasma, respectively. These data were detectable by MUSE, according to the MUSE expected pixel size, temporal resolution, and temperature response functions. We provide maps showing different view angles (front and top) and realistic spectra. Finally, we discuss the relevant evolutionary processes from the perspective of possible observations. Reults. We find that the MUSE observations might be able to detect the fine structure determined by tube fragmentation. In particular, the Fe IX line is mostly emitted at the loop footpoints, where we might be able to track the motions that drive the magnetic stressing and detect the upward motion of evaporating plasma from the chromosphere. In Fe XV, we might see the bulk of the loop with increasing intensity, with alternating filamentary Doppler and non-thermal components in the front view, along with more defined spots in the topward view. The Fe XIX line is very faint within the chosen simulation parameters; thus, any transient brightening around the loop apex may possibly be emphasized by the folding of sheet-like structures, mainly at the boundary of unstable tubes. Conclusions. In conclusion, we show that coronal loop observations with MUSE can pinpoint some crucial features of MHD-modeled ignition processes, such as the related dynamics, helping to identify the heating processes.

Relationship between NDVI, LST and simulated wheat yield with district wise reported yield: a case study of Bathinda, Punjab

Relationship between NDVI, LST and simulated wheat yield with district wise reported yield: a case study of Bathinda, Punjab

Design and modeling of indirectly driven magnetized implosions on the NIF

The use of magnetic fields to improve the performance of hohlraum-driven implosions on the National Ignition Facility (NIF) is discussed. The focus is on magnetically insulated inertial confinement fusion, where the primary field effect is to reduce electron-thermal and alpha-particle loss from the compressed hotspot (magnetic pressure is of secondary importance). We summarize the requirements to achieve this state. The design of recent NIF magnetized hohlraum experiments is presented. These are close to earlier shots in the three-shock, high-adiabat (BigFoot) campaign, subject to the constraints that magnetized NIF targets must be fielded at room-temperature, and use ≲1 MJ of laser energy to avoid the risk of optics damage from stimulated Brillouin scattering. We present results from the original magnetized hohlraum platform, as well as a later variant that gives a higher hotspot temperature. In both platforms, imposed fields (at the capsule center) of up to 28 T increase the fusion yield and hotspot temperature. Integrated radiation-magneto-hydrodynamic modeling with the Lasnex code of these shots is shown, where laser power multipliers and a saturation clamp on cross-beam energy transfer are developed to match the time of peak capsule emission and the P2 Legendre moment of the hotspot x-ray image. The resulting fusion yield and ion temperature agree decently with the measured relative effects of the field, although the absolute simulated yields are higher than the data by 2.0−2.7×. The tuned parameters and yield discrepancy are comparable for experiments with and without an imposed field, indicating the model adequately captures the field effects. Self-generated and imposed fields are added sequentially to simulations of one BigFoot NIF shot to understand how they alter target dynamics.

A switching norm based least mean Square/Fourth adaptive technique for sparse channel estimation and echo cancellation

To realize rapid data transmission, the broadband transmission technique is being extensively explored and applied in existing wireless communication systems. The multi-path channel in broadband wireless communication systems is sparse and this sparsity can be used as prior knowledge to estimate the channel. To make use of sparsity, this paper recommends a switching norm-based least mean square/fourth (SN-LMS/F) adaptive approach for sparse channel estimation and echo cancellation. The suggested SN-LMS/F is implemented by adding a soft parameter adjustment function (SPF) into the conventional LMS/F adaptive method's cost function and utilizes both the l0 and l1 norm to exploit system sparsity with reduced complexity. The simulated output indicates that the suggested SN-LMS/F adaptive technique provides a more desirable performance for sparse channel estimation and echo cancellation with reduced execution time.

Modeling of cotton yield responses to different irrigation strategies in Southern Xinjiang Region,China

Southern Xinjiang region represents a prototypical oasis agricultural area, and cotton is the primary cash crop. The current cotton water productivity for this area remains room for improvement. In order to refine cotton drip irrigation strategy in Southern Xinjiang, this study combined field experiment with crop model simulation and prediction. In this experiment, two irrigation methods, furrow irrigation and drip irrigation, and different degrees of deficit irrigation were set up. The impacts of soil moisture on cotton yield and quality were assessed, with the additional utilization of the Root Zone Water Quality Model (RZWQM2) for simulating irrigation scenarios and predicting cotton yield field data showed that during the 2021–2022 growing season, drip irrigation significantly increased boll weight, yield, and Water Productivity compared to furrow irrigation. The deficit irrigation with 90 % of full irrigation during the flowering period resulted in the highest yield among all drip irrigation treatments, while simultaneously improving the fiber quality. The RZWQM2 model was calibrated and validated using 2021 and 2022 cotton field experimental data, respectively. The calibrated and verified RZWQM2 model shows good performance. In terms of soil water storage, cotton yield and evapotranspiration. The simulated value output by the model is close to the measured value in the field. Model prediction data suggest that there is still room for improvement in cotton yield, accompanied by an increase in evapotranspiration. But deficit irrigation below 80 % of the full irrigation quotas poses a notable risk of yield reduction. Targeting higher yields, the predicted scenario suggests an increase in water application (irrigation quota:314.8 mm). Additionally, this study proposes irrigation regimes with water savings of 10 % and 20 %, with irrigation quotas of 290.5 mm and 266.5 mm during the growth period, respectively. Based on this study, we provide a more detailed and reliable water-saving scheme for cotton drip irrigation in southern Xinjiang region.

Modelling the impacts of future climate change on mixed farming system in southeastern Australia

Mixed farming systems play a crucial role in Australian agriculture, offering economic, social, and environmental advantages. However, these systems are vulnerable to climate change, characterized by rising temperatures and increased rainfall variability. We utilized the pre-calibrated AusFarm model, forced with daily climate data downscaled from 27 Global Climate Models, to simulate how climate change would affect mixed-farming systems at two sites, Condobolin and Wagga Wagga located in southeastern Australia. The results indicated that climate change had diverse effects on crop yields. The simulated yield for some crops, such as canola, was projected to decrease, while others, like field peas, were expected to increase. Elevated atmospheric CO2 levels were anticipated to boost pasture production, but the overall outcome would depend on how these changes interact with rising temperatures and changed rainfall patterns. The increase in pastures was associated with higher live sheep weights and increased fleece growth, with a more significant impact observed at the drier Condobolin site. Furthermore, we found that the gross margin was projected to rise at both sites, with Condobolin experiencing more variability under the influence of climate change. These modelling findings highlight the capacity of mixed-farming systems, which integrate both crops and livestock, to uphold or even improve farm profitability in the context of impending climate change. This underscores the crucial significance of mixed-farming systems in southeastern Australia.

Assessing Soybean Yield Potential and Yield Gap in Different Agroecological Regions of India Using the DSSAT Model

The study used the DSSAT model to assess potential soybean yields in different regions of India and validated it under diverse agroecological conditions. The average simulated yield under irrigated conditions was 3794 kg ha−1 relative to the simulated average rainfed yield of 2446 kg ha−1, showing a 35.52% reduction in grain yield due to adverse moisture conditions under rainfed conditions. Relative to simulated yield, the average observed (actual) rainfed yield across 43 districts of India was 1025 kg ha−1, which was 2769 and 1421 kg ha−1 lower than irrigated and rainfed potential yield, respectively. A significant positive correlation was observed between simulated water non-limited yield and solar radiation (R2 = 0.55, p ≤ 0.05). The simulated rainfed grain yield (R2 = 0.66, p ≤ 0.05) had a significant, positive, and curvilinear relationship with growing season rainfall. On the other hand, the actual yield (R2 = 0.008) showed a non-significant relationship with mean crop seasonal rainfall across locations. The gap between simulated yield under irrigated and rainfed conditions is huge at locations with low seasonal rainfall and narrows with increasing rainfall. In addition, the gap between actual yield and simulated yield under rainfed conditions was larger, even in high seasonal rainfall areas. The yield gap under rainfed conditions is due to the non-adoption of improved crop management practices and could be reduced with proper interventions. This includes adapting drought-resistant varieties, conserving rainwater, changing land configuration, and adopting waterlogging-tolerant varieties using improved technology to reduce the soybean yield gap.

Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model

ABSTRACT Modelling land-use/landcover (LULC) change is vital for addressing global environmental and sustainability issues and evaluating various land system scenarios. However, existing geosimulation methodologies for global LULC change fail to account for spatial distortions caused by the Earth’s curvature and do not consider multiple LULC change processes. Thus, this research study proposes an enhanced spherical geosimulation modelling approach that integrates deep learning (DL) to simulate change of multiple classes of LULC process under the shared socioeconomic pathways (SSP) at the global level. Based on the simulation results, the frontiers of urbanization, cropland expansion, and deforestation are indicated to be in developing countries particularly in Asia and Africa. The simulation outputs also reveal 42.5%–63.2% of new urban development would occur on croplands. The proposed modelling approach can serve as a valuable tool for spatial decision-making and environmental policy formulation at the global level.

Simulating water-limited potato yields across the Netherlands with (SWAP-)WOFOST: Experimentation, model improvement and evaluation

Water availability explains a large part of the spatial and temporal yield variability of ware potato in the Netherlands. Climate change is projected to lead to greater variability in water availability. Therefore, an accurate simulation of crop yield as a function of water availability is of high importance. Many crop models can simulate water-limited potato yields, but as detailed experimental data on drought and oxygen stress are scarce, models are often not well calibrated and evaluated. We set up a large experiment and an on-farm observational trial to calibrate and evaluate the simulation of water-limited yield of different potato cultivars across the Netherlands. In addition, we investigated the required model complexity to accurately simulate water-limited potato yields. The crop growth model WOFOST was used for simulations either as a single model or coupled with SWAP. Yields were simulated applying either only drought stress or by including both drought and oxygen stress. We employed two methods for simulating oxygen stress in SWAP-WOFOST. The calibration improved accuracy of simulated water-limited yields as demonstrated in our model evaluation. Both on sandy and clayey soils, water-limited yields were best simulated using SWAP-WOFOST while simulating oxygen stress using a process-based method in combination with a rooting density that decreases linearly with rooting depth. On sandy soils, the bottom boundary condition of free drainage was applicable. On clayey soils measured groundwater levels (or soil water pressure heads when measurements are not available) should be used as bottom boundary condition. On sandy soils both WOFOST and SWAP-WOFOST may be used, as results were similar. On clayey soils, WOFOST is less suitable, as it cannot simulate capillary rise. SWAP-WOFOST however has many options and parameters, which can result in large differences in results. Hence, a careful model set up and evaluation is required for each application.