POLLEN METHODS AND STUDIES | POLLSCAPE Model: Simulation Approach for Pollen Representation of Vegetation and Land Cover

Author(s):  
S. Sugita
2021 ◽  
Author(s):  
Saad Syed

Railroads move freight traffic on their network based on an overall operations plan that includes blocking, train formation, and train scheduling plans. The optimization of these operations over the entire network is integral to maximizing efficiency and minimizing costs. This thesis develops a simulation model for analyzing various operation plans of a railroad network along with guidelines for establishing a comprehensive operations plan. The objective is to move all freight on the network with minimal cost. With the model simulation and comparison of several operation plans can be performed to determine the 'best case' plan. The model implements a discrete state, deterministic simulation approach. The user-friendly software for implementation of the model was programmed in VBA and Excel. Application of the model is demonstrated using a hypothetical railroad network. The results show that the model is an effective tool in evaluating various scenarios and helping in determining the best plan.


2013 ◽  
Vol 805-806 ◽  
pp. 1887-1890
Author(s):  
Ting Ting Jiang ◽  
Gang Xu ◽  
Jie Hu ◽  
Lu Lu

Scene simulation technique has been widely used on the field of weapon developing. However, the problems such as technique intricacy and the difficulty of cooperating with work prevent it further developing. So the scene simulation approach to the complex system is put forward based on UML. It can be used to manage effectively the relation of scene simulation models with UML, the organization of models, and the maintenance or the modification of the simulation, to implement till the optimized project. Practical application showed that the approach is available for visualization of numerical calculation, simulation model, simulation interaction, maintenance and modification of scene simulation. It can improve actual operation efficiency considerable.


2021 ◽  
Vol 9 ◽  
Author(s):  
Preet Lal ◽  
Ankit Shekhar ◽  
Amit Kumar

The large-scale Land-Uses and Land-Cover Changes (LULCC) in India in the past several decades is primarily driven by anthropogenic factors that influence the climate from regional to global scales. Therefore, to understand the LULCC over the Indian region from 2002 to 2015 and its implications on temperature and precipitation, we performed Weather Research Forecast (WRF) model simulation using the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis data for the period 2009 to 2015 as a boundary condition with 2009 as spin-up time. The results showed moderate forest cover loss in major parts of northeast India, and the Himalayan region during 2002–2015. Such large LULC changes, primarily significant alteration of grassland and agriculture from the forest, led to increased precipitation due to increasing evapotranspiration (ET) similar to the forest-dominated regions. An increase in the precipitation patterns (>300 mm) was observed in the parts of eastern and western Himalayas, western Ghats, and the northwestern part of central India, while most parts of northeast Himalayas have an exceptional increase in precipitation (∼100–150 mm), which shows similar agreement with an increase of leaf area index (LAI) by ∼15%. The overall phenomenon leads to a greening-induced ET enhancement that increases atmospheric water vapor content and promotes downwind precipitation. In the case of temperature, warming was observed in the central to eastern parts of India, while cooling was observed in the central and western parts. The increase in vegetated areas over northwest India led to an increase in ET, which ultimately resulted in decreased temperature and increased precipitation. The study highlights the changes in temperature and precipitation in recent decades because of large LULCC and necessitates the formulation of sustainable land use-based strategies to control meteorological variability and augment ecological sustainability.


2018 ◽  
Vol 18 (3) ◽  
pp. 243-250
Author(s):  
Aijun Zhang ◽  
Xinxin Li ◽  
Gaoming Jiang ◽  
Zhijia Dong ◽  
Honglian Cong

Abstract A realistic computerized simulation of double-bar plush fabrics can result in a time-saving development process with high quality. Based on basic analysis of jacquard principles, a fast 3-D simulation method of warp-knitted plush fabrics is proposed by using a geometry shader on GPU. Firstly, pile areas and non-pile areas are identified according to the jacquard design graphs and chain notations. According to the directions of observation and raised pile, two layered chips are formed in the geometry shader with an approach of multi-layered textures. To ensure that the simulated piles resemble the real ones, the directions of the piles are randomized with the Perlin noise method. One pile is generated along its length with numerous layers in the plush fabric model. Simulation results of piles on both the technical face and technical back are obtained via the model built above, which is confirmed with practicability and efficiency. This 3D simulation approach improves the visualization appearance of piles just as they are actually raised.


2021 ◽  
Vol 9 ◽  
Author(s):  
Ke-Sheng Cheng ◽  
Jia-Yi Ling ◽  
Teng-Wei Lin ◽  
Yin-Ting Liu ◽  
You-Chen Shen ◽  
...  

In numerous applications of land-use/land-cover (LULC) classification, the classification rules are determined using a set of training data; thus, the results are inherently affected by uncertainty in the selection of those data. Few studies have assessed the accuracy of LULC classification with this consideration. In this article, we provide a general expression of various measures of classification accuracy with regard to the sample data set for classifier training and the sample data set for the evaluation of the classification results. We conducted stochastic simulations for LULC classification of a two-feature two-class case and a three-feature four-class case to show the uncertainties in the training sample and reference sample confusion matrices. A bootstrap simulation approach for establishing the 95% confidence interval of the classifier global accuracy was proposed and validated through rigorous stochastic simulation. Moreover, theoretical relationships among the producer accuracy, user accuracy, and overall accuracy were derived. The results demonstrate that the sample size of class-specific training data and the a priori probabilities of individual LULC classes must be jointly considered to ensure the correct determination of LULC classification accuracy.


2019 ◽  
Vol 176 ◽  
pp. 102655 ◽  
Author(s):  
Rogers Andrew ◽  
Jeremia Makindara ◽  
Said H. Mbaga ◽  
Roselyne Alphonce

2016 ◽  
Vol 16 (15) ◽  
pp. 9611-9628 ◽  
Author(s):  
Pawel K. Misztal ◽  
Jeremy C. Avise ◽  
Thomas Karl ◽  
Klaus Scott ◽  
Haflidi H. Jonsson ◽  
...  

Abstract. Accurately modeled biogenic volatile organic compound (BVOC) emissions are an essential input to atmospheric chemistry simulations of ozone and particle formation. BVOC emission models rely on basal emission factor (BEF) distribution maps based on emission measurements and vegetation land-cover data but these critical input components of the models as well as model simulations lack validation by regional scale measurements. We directly assess isoprene emission-factor distribution databases for BVOC emission models by deriving BEFs from direct airborne eddy covariance (AEC) fluxes (Misztal et al., 2014) scaled to the surface and normalized by the activity factor of the Guenther et al. (2006) algorithm. The available airborne BEF data from approx. 10 000 km of flight tracks over California were averaged spatially over 48 defined ecological zones called ecoregions. Consistently, BEFs used by three different emission models were averaged over the same ecoregions for quantitative evaluation. Ecoregion-averaged BEFs from the most current land cover used by the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v.2.1 resulted in the best agreement among the tested land covers and agreed within 10 % with BEFs inferred from measurement. However, the correlation was sensitive to a few discrepancies (either overestimation or underestimation) in those ecoregions where land-cover BEFs are less accurate or less representative for the flight track. The two other land covers demonstrated similar agreement (within 30 % of measurements) for total average BEF across all tested ecoregions but there were a larger number of specific ecoregions that had poor agreement with the observations. Independently, we performed evaluation of the new California Air Resources Board (CARB) hybrid model by directly comparing its simulated isoprene area emissions averaged for the same flight times and flux footprints as actual measured area emissions. The model simulation and the observed surface area emissions agreed on average within 20 %. We show that the choice of model land-cover input data has the most critical influence on model-measurement agreement and the uncertainty in meteorology inputs has a lesser impact at scales relevant to regional air quality modeling.


2021 ◽  
Vol 12 (2) ◽  
pp. 763-782
Author(s):  
Kerstin Hartung ◽  
Ana Bastos ◽  
Louise Chini ◽  
Raphael Ganzenmüller ◽  
Felix Havermann ◽  
...  

Abstract. The carbon flux due to land-use and land-cover change (net LULCC flux) historically contributed to a large fraction of anthropogenic carbon emissions while at the same time being associated with large uncertainties. This study aims to compare the contribution of several sensitivities underlying the net LULCC flux by assessing their relative importance in a bookkeeping model (Bookkeeping of Land Use Emissions, BLUE) based on a LULCC dataset including uncertainty estimates (the Land-Use Harmonization 2 (LUH2) dataset). The sensitivity experiments build upon the approach of Hurtt et al. (2011) and compare the impacts of LULCC uncertainty (a high, baseline and low land-use estimate), the starting time of the bookkeeping model simulation (850, 1700 and 1850), net area transitions versus gross area transitions (shifting cultivation) and neglecting wood harvest on estimates of the net LULCC flux. Additional factorial experiments isolate the impact of uncertainty from initial conditions and transitions on the net LULCC flux. Finally, historical simulations are extended with future land-use scenarios to assess the impact of past LULCC uncertainty in future projections. Over the period 1850–2014, baseline and low LULCC scenarios produce a comparable cumulative net LULCC flux, while the high LULCC estimate initially produces a larger net LULCC flux which decreases towards the end of the period and even becomes smaller than in the baseline estimate. LULCC uncertainty leads to slightly higher sensitivity in the cumulative net LULCC flux (up to 22 %; references are the baseline simulations) compared to the starting year of a model simulation (up to 15 %). The contribution from neglecting wood harvest activities (up to 28 % cumulative net LULCC flux) is larger than that from LULCC uncertainty, and the implementation of land-cover transitions (gross or net transitions) exhibits the smallest sensitivity (up to 13 %). At the end of the historical LULCC dataset in 2014, the LULCC uncertainty retains some impact on the net LULCC flux (±0.15 PgC yr−1 at an estimate of 1.7 PgC yr−1). Of the past uncertainties in LULCC, a small impact persists in 2099, mainly due to uncertainty of harvest remaining in 2014. However, compared to the uncertainty range of the LULCC flux estimated today, the estimates in 2099 appear to be indistinguishable. These results, albeit from a single model, are important for CMIP6 as they compare the relative importance of starting year, uncertainty of LULCC, applying gross transitions and wood harvest on the net LULCC flux. For the cumulative net LULCC flux over the industrial period, the uncertainty of LULCC is as relevant as applying wood harvest and gross transitions. However, LULCC uncertainty matters less (by about a factor of 3) than the other two factors for the net LULCC flux in 2014, and historical LULCC uncertainty is negligible for estimates of future scenarios.


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