scholarly journals Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

2014 ◽  
Vol 7 (3) ◽  
pp. 1211-1224 ◽  
Author(s):  
W. Zhang ◽  
Q. Zhang ◽  
Y. Huang ◽  
T. T. Li ◽  
J. Y. Bian ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Standard Deviation ◽  
Rice Variety ◽  
Scale Up ◽  
Methane Emissions ◽  
Data Availability ◽  
Scale Model ◽  
Anthropogenic Source ◽  
Rice Paddies ◽  
Data Scarcity

Abstract. Rice paddies are a major anthropogenic source of the atmospheric methane. However, because of the high spatial heterogeneity, making accurate estimations of the methane emission from rice paddies is still a big challenge, even with complicated models. Data scarcity is one of the substantial causes of the uncertainties in estimating the methane emissions on regional scales. In the present study, we discussed how data scarcity affected the uncertainties in model estimations of rice paddy methane emissions, from county/provincial scale up to national scale. The uncertainties in methane emissions from the rice paddies of China was calculated with a local-scale model and the Monte Carlo simulation. The data scarcities in five of the most sensitive model variables, field irrigation, organic matter application, soil properties, rice variety and production were included in the analysis. The result showed that in each individual county, the within-cell standard deviation of methane flux, as calculated via Monte Carlo methods, was 13.5–89.3% of the statistical mean. After spatial aggregation, the national total methane emissions were estimated at 6.44–7.32 Tg, depending on the base scale of the modeling and the reliability of the input data. And with the given data availability, the overall aggregated standard deviation was 16.3% of the total emissions, ranging from 18.3–28.0% for early, late and middle rice ecosystems. The 95% confidence interval of the estimation was 4.5–8.7 Tg by assuming a gamma distribution. Improving the data availability of the model input variables is expected to reduce the uncertainties significantly, especially of those factors with high model sensitivities.

scholarly journals Estimation of uncertainties due to data scarcity in model upscaling: a case study of methane emissions from rice paddies in China

2014 ◽  
Vol 7 (1) ◽  
pp. 181-216 ◽  
Author(s):  
W. Zhang ◽  
T. Li ◽  
Y. Huang ◽  
Q. Zhang ◽  
J. Bian ◽  
...  
Keyword(s):  
Data Sharing ◽  
Spatial Resolution ◽  
Mainland China ◽  
Methane Emissions ◽  
Scale Model ◽  
Rice Paddies ◽  
Data Scarcity ◽  
Estimation Of Uncertainties ◽  
The Impact

Abstract. Data scarcity is a major cause of substantial uncertainties in regional estimations conducted with model upscaling. To evaluate the impact of data scarcity on model upscaling, we introduce an approach for aggregating uncertainties in model estimations. A data sharing matrix was developed to aggregate the modeled uncertainties in divisions of a subject region. In a case study, the uncertainty in methane emissions from rice paddies on mainland China was calculated with a local-scale model CH4MOD. The data scarcities in five of the most sensitive model variables were included in the analysis. The national total methane emissions were 6.44–7.32 Tg, depending on the spatial resolution used for modeling, with a 95% confidence interval of 4.5–8.7 Tg. Based on the data sharing matrix, two numeral indices, IR and Ids, were also introduced to suggest the proper spatial resolution in model upscaling.

scholarly journals Uncertainties in the national inventory of methane emissions from rice cultivation: field measurements and modeling approaches

2017 ◽  
Vol 14 (1) ◽  
pp. 163-176 ◽  
Author(s):  
Wen Zhang ◽  
Wenjuan Sun ◽  
Tingting Li
Keyword(s):  
Methane Emission ◽  
Regression Models ◽  
Model Performance ◽  
Field Measurements ◽  
Methane Emissions ◽  
Data Availability ◽  
Rice Paddies ◽  
Total Uncertainty ◽  
Modeling Approach ◽  
National Inventory

Abstract. Uncertainties in national inventories originate from a variety of sources, including methodological failures, errors, and insufficiency of supporting data. In this study, we analyzed these sources and their contribution to uncertainty in the national inventory of rice paddy methane emissions in China and compared the differences in the approaches used (e.g., direct measurements, simple regressions, and more complicated models). For the 495 field measurements we collected from the scientific literature, the area-weighted 95 % CI (confidence interval) ranged from 13.7 to 1115.4 kg CH4 ha−1, and the histogram distribution of the measurements agreed well with parameterized gamma distributions. For the models, we compared the performance of methods of different complexity (i.e., the CH4MOD model, representing a complicated method, and two less complex statistical regression models taken from literature) to evaluate the uncertainties associated with model performance as well as the quality and accessibility of the regional datasets. Comparisons revealed that the CH4MOD model may perform worse than the comparatively simple regression models when no sufficient input data for the model is available. As simulated by CH4MOD with data of irrigation, organic matter incorporation, and soil properties of rice paddies, the modeling methane fluxes varied from 17.2 to 708.3 kg CH4 ha−1, covering 63 % of the range of the field measurements. When applying the modeling approach to the 10 km  ×  10 km gridded dataset of the model input variables, the within-grid variations, made via the Monte Carlo method, were found to be 81.2–95.5 % of the grid means. Upscaling the grid estimates to the national inventory, the total methane emission from the rice paddies was 6.43 (3.79–9.77) Tg. The fallacy of CH4MOD contributed 56.6 % of the total uncertainty, with the remaining 43.4 % being attributed to errors and the scarcity of the spatial datasets of the model inputs. Our analysis reveals the dilemma between model performance and data availability when using a modeling approach: a model with better performance may help in reducing uncertainty caused by model fallacy but increases the uncertainty caused by data scarcity since greater levels of input are needed to improve performance. Reducing the total uncertainty in the national methane inventory depends on a better understanding of both the complexity of the mechanisms of methane emission and the spatial correlations of the factors that influence methane emissions from rice paddies.

Rigorous time-domain analysis of statistically oriented graphene sheet fluctuations

2017 ◽  
Vol 36 (5) ◽  
pp. 1351-1363 ◽  
Author(s):  
Athanasios N. Papadimopoulos ◽  
Stamatios A. Amanatiadis ◽  
Nikolaos V. Kantartzis ◽  
Theodoros T. Zygiridis ◽  
Theodoros D. Tsiboukis
Keyword(s):  
Monte Carlo ◽  
Standard Deviation ◽  
Finite Difference ◽  
Surface Waves ◽  
Time Domain ◽  
Finite Difference Time Domain ◽  
Mean Value ◽  
Content Type ◽  
The Mean ◽  
Difference Time

Purpose Important statistical variations are likely to appear in the propagation of surface plasmon polariton waves atop the surface of graphene sheets, degrading the expected performance of real-life THz applications. This paper aims to introduce an efficient numerical algorithm that is able to accurately and rapidly predict the influence of material-based uncertainties for diverse graphene configurations. Design/methodology/approach Initially, the surface conductivity of graphene is described at the far infrared spectrum and the uncertainties of its main parameters, namely, the chemical potential and the relaxation time, on the propagation properties of the surface waves are investigated, unveiling a considerable impact. Furthermore, the demanding two-dimensional material is numerically modeled as a surface boundary through a frequency-dependent finite-difference time-domain scheme, while a robust stochastic realization is accordingly developed. Findings The mean value and standard deviation of the propagating surface waves are extracted through a single-pass simulation in contrast to the laborious Monte Carlo technique, proving the accomplished high efficiency. Moreover, numerical results, including graphene’s surface current density and electric field distribution, indicate the notable precision, stability and convergence of the new graphene-based stochastic time-domain method in terms of the mean value and the order of magnitude of the standard deviation. Originality/value The combined uncertainties of the main parameters in graphene layers are modeled through a high-performance stochastic numerical algorithm, based on the finite-difference time-domain method. The significant accuracy of the numerical results, compared to the cumbersome Monte Carlo analysis, renders the featured technique a flexible computational tool that is able to enhance the design of graphene THz devices due to the uncertainty prediction.

Benchmarking of Truss Spar Vortex Induced Motions Derived From CFD With Experiments

2005 ◽  
Author(s):  
John Halkyard ◽  
Senu Sirnivas ◽  
Samuel Holmes ◽  
Yiannis Constantinides ◽  
Owen H. Oakley ◽  
...  
Keyword(s):  
Scale Up ◽  
Vertical Cylinder ◽  
Reynolds Numbers ◽  
Full Scale ◽  
Scale Model ◽  
Current Direction ◽  
Test Results ◽  
Helical Strakes ◽  
Truss Spar ◽  
Scale Data

Floating spar platforms are widely used in the Gulf of Mexico for oil production. The spar is a bluff, vertical cylinder which is subject to Vortex Induced Motions (VIM) when current velocities exceed a few knots. All spars to date have been constructed with helical strakes to mitigate VIM in order to reduce the loads on the risers and moorings. Model tests have indicated that the effectiveness of these strakes is influenced greatly by details of their design, by appurtenances placed on the outside of the hull and by current direction. At this time there is limited full scale data to validate the model test results and little understanding of the mechanisms at work in strake performance. The authors have been investigating the use of CFD as a means for predicting full scale VIM performance and for facilitating the design of spars for reduced VIM. This paper reports on the results of a study to benchmark the CFD results for a truss spar with a set of model experiments carried out in a towing tank. The focus is on the effect of current direction, reduced velocity and strake pitch on the VIM response. The tests were carried out on a 1:40 scale model of an actual truss spar design, and all computations were carried out at model scale. Future study will consider the effect of external appurtenances on the hull and scale-up to full scale Reynolds’ numbers on the results.

scholarly journals Sensitivity of wetland methane emissions to model assumptions: application and model testing against site observations

2012 ◽  
Vol 9 (7) ◽  
pp. 2793-2819 ◽  
Author(s):  
L. Meng ◽  
P. G. M. Hess ◽  
N. M. Mahowald ◽  
J. B. Yavitt ◽  
W. J. Riley ◽  
...  
Keyword(s):  
Soil Ph ◽  
High Latitude ◽  
Large Range ◽  
Rice Paddy ◽  
Temporal Variations ◽  
Methane Emissions ◽  
Biogeochemical Model ◽  
Atmospheric Methane ◽  
Rice Paddies ◽  
Community Land Model

Abstract. Methane emissions from natural wetlands and rice paddies constitute a large proportion of atmospheric methane, but the magnitude and year-to-year variation of these methane sources are still unpredictable. Here we describe and evaluate the integration of a methane biogeochemical model (CLM4Me; Riley et al., 2011) into the Community Land Model 4.0 (CLM4CN) in order to better explain spatial and temporal variations in methane emissions. We test new functions for soil pH and redox potential that impact microbial methane production in soils. We also constrain aerenchyma in plants in always-inundated areas in order to better represent wetland vegetation. Satellite inundated fraction is explicitly prescribed in the model, because there are large differences between simulated fractional inundation and satellite observations, and thus we do not use CLM4-simulated hydrology to predict inundated areas. A rice paddy module is also incorporated into the model, where the fraction of land used for rice production is explicitly prescribed. The model is evaluated at the site level with vegetation cover and water table prescribed from measurements. Explicit site level evaluations of simulated methane emissions are quite different than evaluating the grid-cell averaged emissions against available measurements. Using a baseline set of parameter values, our model-estimated average global wetland emissions for the period 1993–2004 were 256 Tg CH4 yr−1 (including the soil sink) and rice paddy emissions in the year 2000 were 42 Tg CH4 yr−1. Tropical wetlands contributed 201 Tg CH4 yr−1, or 78% of the global wetland flux. Northern latitude (>50 N) systems contributed 12 Tg CH4 yr−1. However, sensitivity studies show a large range (150–346 Tg CH4 yr−1) in predicted global methane emissions (excluding emissions from rice paddies). The large range is sensitive to (1) the amount of methane transported through aerenchyma, (2) soil pH (±100 Tg CH4 yr−1), and (3) redox inhibition (±45 Tg CH4 yr−1). Results are sensitive to biases in the CLMCN and to errors in the satellite inundation fraction. In particular, the high latitude methane emission estimate may be biased low due to both underestimates in the high-latitude inundated area captured by satellites and unrealistically low high-latitude productivity and soil carbon predicted by CLM4.

VS30 in the NGA GMPEs: Regional Differences and Suggested Practice

2016 ◽  
Vol 32 (4) ◽  
pp. 2083-2108 ◽  
Author(s):  
Ronnie Kamai ◽  
Norman A. Abrahamson ◽  
Walter J. Silva
Keyword(s):  
Standard Deviation ◽  
Velocity Profile ◽  
Shear Wave Velocity ◽  
Regional Differences ◽  
Depositional Environment ◽  
Parametric Model ◽  
Data Availability ◽  
Profile Data ◽  
Site Specific ◽  
Geographical Association

Shear-wave velocity profiles from California, Taiwan, and Japan are used to evaluate the regionalized linear V S30 scaling in the recent NGA-West2 GMPEs. Profiles in the same V S30 range are compared, and their differences and similarities are discussed. A simple parametric model for the median velocity profile and its standard deviation is provided for California and Japan. The model should only be used for 250 ≤ V S30 ≤ 850 m/s, representing the range of profile data availability. We make the following recommendation: for site-specific evaluations, knowledge of V S30 alone is insufficient. A representative velocity profile should be constructed either from site-specific measurements or from measured profiles in a similar geological settings and depositional environment. The representative velocity profile should be consistent with one of the proposed profiles in terms of both its median and standard deviation, for the corresponding V S30 model in the GMPE to be used, regardless of true geographical association.

scholarly journals ANALISIS PENJADWALAN PROYEK GEDUNG BERTINGKAT DENGAN METODE PERT DAN M-PERT MENGGUNAKAN SIMULASI MONTE CARLO

2020 ◽  
Vol 3 (3) ◽  
pp. 533
Author(s):  
Josua Guntur Putra ◽  
Jane Sekarsari
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Standard Deviation ◽  
Research Method ◽  
Probabilistic Approach ◽  
Deterministic Scheduling ◽  
Work Duration ◽  
The Mean ◽  
Building Project ◽  
Story Building

One of the keys to success in construction execution is timeliness. In fact, construction is often late than originally planned. It’s caused by project scheduling uncertainty. Deterministic scheduling methods use data from previous projects to determine work duration. However, not every project has same work duration. The PERT method provides a probabilistic approach that can overcome these uncertainties, but it doesn’t account for the increase in duration due to parallel activities. In 2017, the PERT method was developed into the M-PERT method. The purpose of this study is to compare the mean duration and standard deviation of the overall project between PERT and M-PERT methods and compare them in Monte Carlo simulation. The research method used is to calculate the mean duration of the project with the PERT, M-PERT, and Monte Carlo simulation. The study was applied to a three-story building project. From the results of the study, the standard deviation obtained was 5.079 for the M-PERT method, 8.915 for the PERT method, and 5.25 for the Monte Carlo simulation. These results show the M-PERT method can provide closer results to computer simulation result than the PERT method. Small standard deviation value indicates the M-PERT method gives more accurate results.ABSTRAKSalah satu kunci keberhasilan dalam suatu pelaksanaan konstruksi adalah ketepatan waktu. Kenyataannya, pelaksanaan konstruksi sering mengalami keterlambatan waktu dari yang direncanakan. Hal ini disebabkan oleh ketidakpastian dalam merencanakan penjadwalan proyek. Metode penjadwalan yang bersifat deterministik menggunakan data dari proyek sebelumnya untuk menentukan durasi pekerjaan. Akan tetapi, tidak setiap proyek memiliki durasi pekerjaan yang sama. Metode PERT memberikan pendekatan probabilistik yang dapat mengatasi ketidakpastian tersebut, tetapi metode ini tidak memperhitungkan pertambahan durasi akibat adanya kegiatan yang berbentuk paralel. Pada tahun 2017, metode PERT dikembangkan menjadi metode M-PERT. Tujuan dari penelitian ini adalah membandingkan mean durasi dan standar deviasi proyek secara keseluruhan antara metode PERT dan M-PERT dan membandingkan kedua metode tersebut dalam simulasi Monte Carlo. Metode penelitian yang dilakukan adalah menghitung mean durasi proyek dengan metode PERT, M-PERT, dan simulasi Monte Carlo. Penelitian diterapkan pada proyek gedung bertingkat tiga. Dari hasil penelitian, nilai standar deviasi diperoleh sebesar 5,079 untuk metode M-PERT, 8,915 untuk metode PERT, dan 5,25 untuk simulasi Monte Carlo. Hasil ini menunjukan metode M-PERT dapat memberikan hasil yang lebih mendekati hasil simulasi komputer daripada metode PERT. Nilai standar deviasi yang kecil menunjukan metode M-PERT memberikan hasil yang lebih akurat.

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