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

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.

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

Geoscientific Model Development ◽

10.5194/gmd-7-1211-2014 ◽

2014 ◽

Vol 7 (3) ◽

pp. 1211-1224 ◽

Cited By ~ 5

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.

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Case Study 11, Mainland China: The Impact of Pronunciation and Accents in Artificial Intelligence Speech Evaluation Systems

10.1007/978-981-16-2697-5_18 ◽

2021 ◽

pp. 223-235

Author(s):

Bin Zou ◽

Sara Liviero ◽

Kefei Wei ◽

Lu Sun ◽

Yitong Qi ◽

...

Keyword(s):

Artificial Intelligence ◽

Mainland China ◽

Evaluation Systems ◽

Speech Evaluation ◽

The Impact

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Scotland’s Named Person Scheme

The International Journal of Children s Rights ◽

10.1163/15718182-02803001 ◽

2020 ◽

Vol 28 (3) ◽

pp. 571-587

Author(s):

Gillian Black

Keyword(s):

Data Sharing ◽

Child Protection ◽

European Convention ◽

Privacy Rights ◽

Rights Of The Child ◽

Children And Parents ◽

Scottish Government ◽

Article 5 ◽

The Impact

Abstract The Scottish Government’s proposal to introduce a “Named Person” scheme was intended to improve child protection and wellbeing in Scotland, by allocating an identified Named Person to every child in Scotland. The scheme was met by considerable concern from a range of parties, and was challenged in the courts on the basis that the data sharing provisions infringed the data protection and Article 8 of the European Convention on Human Rights (echr) privacy rights of children and parents. As a result of the complexities of introducing lawful data sharing provisions, the scheme has now been scrapped, without ever being introduced. However, at no point was there any sustained analysis of the impact of Article 5 of the United Nations Convention on the Rights of the Child (uncrc) on the Named Person scheme: to what extent would the Scottish Government proposals have helped parents meet their obligations under Article 5? Or would they in fact have infringed parents’ and children’s rights? This article provides a case study of Article 5 in practice, by setting out the background to the now-defunct Named Person scheme, before going on to analyse its interaction – and compliance – with the State Party’s obligations under Article 5.

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The Impact of Vertical Resolution on Fog Forecasting in the Kilometric-Scale Model AROME: A Case Study and Statistics

Weather and Forecasting ◽

10.1175/waf-d-16-0074.1 ◽

2016 ◽

Vol 31 (5) ◽

pp. 1655-1671 ◽

Cited By ~ 13

Author(s):

A. Philip ◽

T. Bergot ◽

Y. Bouteloup ◽

F. Bouyssel

Keyword(s):

Weather Prediction ◽

Winter Season ◽

Scale Model ◽

Strong Impact ◽

Vertical Resolution ◽

Radiation Fog ◽

Fog Forecasting ◽

High Vertical Resolution ◽

The Impact

Abstract The impact of vertical resolution on numerical fog forecasting is studied in detail for a specific case and evaluated statistically over a winter season. Three vertical resolutions are tested with the kilometric-scale Applications of Research to Operations at Mesoscale (AROME) numerical weather prediction model over Paris Charles de Gaulle Airport (Paris-CDG) in Paris, France. For the case studied, the vertical resolution has a strong impact on fog onset. The nocturnal jet and the turbulence created by wind shear at the top of the nocturnal boundary layer are more pronounced with a finer vertical resolution, and the turbulence close to the ground is also stronger with high vertical resolution. Local circulations created by the terrain induce different simulated processes during the fog onset. The fog is simulated as advection–radiation fog in the finer vertical resolution run and as radiation fog in the others. The vertical resolution has little impact on the mature and dissipation phases. A statistical study over a winter season confirms the results obtained in the fog case study. High vertical resolution simulates earlier onset, as well as longer-lasting and more spatially heterogeneous fogs. The high vertical resolution configuration simulates more fog events than are found at low resolution (LR); these fog events generally form north of Paris-CDG. No observations are available in this area, leading to many simulated but no observed fog events in the fine-resolution runs. The ceiling of low clouds is not well simulated by the numerical model no matter what vertical resolution is used.

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Wind Effects on a Permeable Double Skin Façade, the ENI Head Office Case Study

Fluids ◽

10.3390/fluids6110415 ◽

2021 ◽

Vol 6 (11) ◽

pp. 415

Author(s):

Giulia Pomaranzi ◽

Ombretta Bistoni ◽

Paolo Schito ◽

Lorenzo Rosa ◽

Alberto Zasso

Keyword(s):

Energy Demand ◽

Fluid Dynamic ◽

Numerical Approach ◽

Scale Model ◽

Wind Loading ◽

Wind Effects ◽

Dynamic Simulations ◽

Double Skin ◽

The Impact

Currently, the energy and environmental efficiency of buildings has led to the development of cladding systems that may help to reduce the structure’s energy demand, using techniques such as the Permeable Double Skin Façade (PDSF). Given complex aerodynamic interactions, the presence of an external porous screen in addition to an inner skin may play a crucial role in the fluid-dynamic characterization of such buildings, making the definition of wind effects very complex. A new methodology for the quantitative assessment of the impact of wind-loading conditions on this particular type of cladding is presented. It is based on a combined experimental–numerical approach, essentially based on wind-tunnel tests on a rigid scale model and computational fluid dynamic simulations. A case study is proposed as an application of this methodology. Results include the design pressure values for the inner glazed façade and the permeable facade. An estimation of the flow rate across the porous skin is quantified using the numerical model.

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Assessment of the impact of shared data on the scientific literature

10.1101/183814 ◽

2017 ◽

Author(s):

Michael P. Milham ◽

R. Cameron Craddock ◽

Michael Fleischmann ◽

Jake Son ◽

Jon Clucas ◽

...

Keyword(s):

Data Sharing ◽

Journal Articles ◽

Transformative Power ◽

Privacy Concerns ◽

Shared Data ◽

Funding Agencies ◽

Neuroimaging Data ◽

The Impact ◽

Significant Effort

AbstractData sharing is increasingly recommended as a means of accelerating science by facilitating collaboration, transparency, and reproducibility. While few oppose data sharing philosophically, a range of barriers deter most researchers from implementing it in practice (e.g., workforce and infrastructural demands, sociocultural and privacy concerns, lack of standardization). To justify the significant effort required for sharing data (e.g., organization, curation, distribution), funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a brain imaging case study that provides direct evidence of the impact of open sharing on data use and resulting publications over a seven-year period (2010-2017). We dispel the myth that scientific findings using shared data cannot be published in high-impact journals and demonstrate rapid growth in the publication of such journal articles, scholarly theses, and conference proceedings. In contrast to commonly used ‘pay to play’ models, we demonstrate that openly shared data can increase the scale (i.e., sample size) of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings suggest the transformative power of data sharing for accelerating science and underscore the need for the scientific ecosystem to embrace the challenge of implementing data sharing universally.

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