scholarly journals The influence of canopy radiation parameter uncertainty on model projections of terrestrial carbon and energy cycling

2019 ◽  
Author(s):  
T. Viskari ◽  
A. Shiklomanov ◽  
M.C. Dietze ◽  
S.P. Serbin
Keyword(s):  
Parameter Uncertainty ◽  
Canopy Structure ◽  
Radiative Properties ◽  
Surface Model ◽  
List Type ◽  
Terrestrial Carbon ◽  
Parameter Uncertainties ◽  
Near Infrared Reflectance ◽  
Radiative Parameter ◽  
Model Predictions

AbstractReducing uncertainties in Earth System Model predictions requires carefully evaluating core model processes. Here we examined how canopy radiative transfer model (RTM) parameter uncertainties, in combination with canopy structure, affect terrestrial carbon and energy projections in a demographic land-surface model, the Ecosystem Demography model (ED2). Our analyses focused on temperate deciduous forests and tested canopies of varying structural complexity. The results showed a strong sensitivity of tree productivity, albedo, and energy balance projections to RTM parameters. Impacts of radiative parameter uncertainty on stand-level canopy net primary productivity ranged from ~2 to > 20% and was most sensitive to canopy clumping and leaf reflectance/transmittance in the visible spectrum (~400 – 750 nm). ED2 canopy albedo varied by ~1 to ~10% and was most sensitive to near-infrared reflectance (~800 – 1200 nm). Bowen ratio, in turn, was most sensitive to wood optical properties parameterization; this was much larger than expected based on literature, suggesting model instabilities. In vertically and spatially complex canopies the model response to RTM parameterization may show an apparent reduced sensitivity when compared to simpler canopies, masking much larger changes occurring within the canopy. Our findings highlight both the importance of constraining canopy RTM parameters in models and valuating how the canopy structure responds to those parameter values. Finally, we advocate for more model evaluation, similar to this study, to highlight possible issues with model behavior or process representations, particularly models with demographic representations, and identify potential ways to inform and constrain model predictions.Key pointsUncertainties in vegetation radiative parameter affect carbon, water and energy balances in ecosystem models.Radiative properties can lead to significant changes in demography and canopy structure even if aggregate model responses appear unchanged

scholarly journals Data-Driven Uncertainty Quantification for Cardiac Electrophysiological Models: Impact of Physiological Variability on Action Potential and Spiral Wave Dynamics

2020 ◽  
Vol 11 ◽  
Author(s):  
Pras Pathmanathan ◽  
Suran K. Galappaththige ◽  
Jonathan M. Cordeiro ◽  
Abouzar Kaboudian ◽  
Flavio H. Fenton ◽  
...  
Keyword(s):  
Action Potential ◽  
Uncertainty Quantification ◽  
Parameter Uncertainty ◽  
Spiral Wave ◽  
Data Driven ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Wave Dynamics ◽  
Potential Shape ◽  
Model Predictions

Computational modeling of cardiac electrophysiology (EP) has recently transitioned from a scientific research tool to clinical applications. To ensure reliability of clinical or regulatory decisions made using cardiac EP models, it is vital to evaluate the uncertainty in model predictions. Model predictions are uncertain because there is typically substantial uncertainty in model input parameters, due to measurement error or natural variability. While there has been much recent uncertainty quantification (UQ) research for cardiac EP models, all previous work has been limited by either: (i) considering uncertainty in only a subset of the full set of parameters; and/or (ii) assigning arbitrary variation to parameters (e.g., ±10 or 50% around mean value) rather than basing the parameter uncertainty on experimental data. In our recent work we overcame the first limitation by performing UQ and sensitivity analysis using a novel canine action potential model, allowing all parameters to be uncertain, but with arbitrary variation. Here, we address the second limitation by extending our previous work to use data-driven estimates of parameter uncertainty. Overall, we estimated uncertainty due to population variability in all parameters in five currents active during repolarization: inward potassium rectifier, transient outward potassium, L-type calcium, rapidly and slowly activating delayed potassium rectifier; 25 parameters in total (all model parameters except fast sodium current parameters). A variety of methods was used to estimate the variability in these parameters. We then propagated the uncertainties through the model to determine their impact on predictions of action potential shape, action potential duration (APD) prolongation due to drug block, and spiral wave dynamics. Parameter uncertainty had a significant effect on model predictions, especially L-type calcium current parameters. Correlation between physiological parameters was determined to play a role in physiological realism of action potentials. Surprisingly, even model outputs that were relative differences, specifically drug-induced APD prolongation, were heavily impacted by the underlying uncertainty. This is the first data-driven end-to-end UQ analysis in cardiac EP accounting for uncertainty in the vast majority of parameters, including first in tissue, and demonstrates how future UQ could be used to ensure model-based decisions are robust to all underlying parameter uncertainties.

scholarly journals Detection and Analysis of Degree of Maize Lodging Using UAV-RGB Image Multi-Feature Factors and Various Classification Methods

2021 ◽  
Vol 10 (5) ◽  
pp. 309
Author(s):  
Zixu Wang ◽  
Chenwei Nie ◽  
Hongwu Wang ◽  
Yong Ao ◽  
Xiuliang Jin ◽  
...  
Keyword(s):  
Crop Production ◽  
Accurate Determination ◽  
Management Strategies ◽  
Canopy Structure ◽  
Object Oriented ◽  
Texture Features ◽  
Sowing Date ◽  
Kappa Coefficient ◽  
Surface Model ◽  
Field Management

Maize (Zea mays L.), one of the most important agricultural crops in the world, which can be devastated by lodging, which can strike maize during its growing season. Maize lodging affects not only the yield but also the quality of its kernels. The identification of lodging is helpful to evaluate losses due to natural disasters, to screen lodging-resistant crop varieties, and to optimize field-management strategies. The accurate detection of crop lodging is inseparable from the accurate determination of the degree of lodging, which helps improve field management in the crop-production process. An approach was developed that fuses supervised and object-oriented classifications on spectrum, texture, and canopy structure data to determine the degree of lodging with high precision. The results showed that, combined with the original image, the change of the digital surface model, and texture features, the overall accuracy of the object-oriented classification method using random forest classifier was the best, which was 86.96% (kappa coefficient was 0.79). The best pixel-level supervised classification of the degree of maize lodging was 78.26% (kappa coefficient was 0.6). Based on the spatial distribution of degree of lodging as a function of crop variety, sowing date, densities, and different nitrogen treatments, this work determines how feature factors affect the degree of lodging. These results allow us to rapidly determine the degree of lodging of field maize, determine the optimal sowing date, optimal density and optimal fertilization method in field production.

Gas pipeline leakage detection in the presence of parameter uncertainty using robust extended Kalman filter

2021 ◽  
pp. 014233122198911
Author(s):  
Mohadese Jahanian ◽  
Amin Ramezani ◽  
Ali Moarefianpour ◽  
Mahdi Aliari Shouredeli
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Parameter Uncertainty ◽  
Oil And Gas ◽  
Estimation Error ◽  
Real Data ◽  
Gas Pipeline ◽  
Pipeline System ◽  
Parameter Uncertainties ◽  
Simulation Results

One of the most significant systems that can be expressed by partial differential equations (PDEs) is the transmission pipeline system. To avoid the accidents that originated from oil and gas pipeline leakage, the exact location and quantity of leakage are required to be recognized. The designed goal is a leakage diagnosis based on the system model and the use of real data provided by transmission line systems. Nonlinear equations of the system have been extracted employing continuity and momentum equations. In this paper, the extended Kalman filter (EKF) is used to detect and locate the leakage and to attenuate the negative effects of measurement and process noises. Besides, a robust extended Kalman filter (REKF) is applied to compensate for the effect of parameter uncertainty. The quantity and the location of the occurred leakage are estimated along the pipeline. Simulation results show that REKF has better estimations of the leak and its location as compared with that of EKF. This filter is robust against process noise, measurement noise, parameter uncertainties, and guarantees a higher limit for the covariance of state estimation error as well. It is remarkable that simulation results are evaluated by OLGA software.

scholarly journals Interannual variability in Australia's terrestrial carbon cycle constrained by multiple observation types

2016 ◽  
Vol 13 (23) ◽  
pp. 6363-6383 ◽  
Author(s):  
Cathy M. Trudinger ◽  
Vanessa Haverd ◽  
Peter R. Briggs ◽  
Josep G. Canadell
Keyword(s):  
Interannual Variability ◽  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Net Primary Production ◽  
Land Surface Model ◽  
Heterotrophic Respiration ◽  
Arid Ecosystems ◽  
Surface Model ◽  
Calibration Data ◽  
Terrestrial Carbon

Abstract. Recent studies have shown that semi-arid ecosystems in Australia may be responsible for a significant part of the interannual variability in the global concentration of atmospheric carbon dioxide. Here we use a multiple constraints approach to calibrate a land surface model of Australian terrestrial carbon and water cycles, with a focus on interannual variability. We use observations of carbon and water fluxes at 14 OzFlux sites, as well as data on carbon pools, litterfall and streamflow. We include calibration of the function describing the response of heterotrophic respiration to soil moisture. We also explore the effect on modelled interannual variability of parameter equifinality, whereby multiple combinations of parameters can give an equally acceptable fit to the calibration data. We estimate interannual variability of Australian net ecosystem production (NEP) of 0.12–0.21 PgC yr−1 (1σ) over 1982–2013, with a high anomaly of 0.43–0.67 PgC yr−1 in 2011 relative to this period associated with exceptionally wet conditions following a prolonged drought. The ranges are due to the effect on calculated NEP anomaly of parameter equifinality, with similar contributions from equifinality in parameters associated with net primary production (NPP) and heterotrophic respiration. Our range of results due to parameter equifinality demonstrates how errors can be underestimated when a single parameter set is used.

Response Surface Model Predictions of Wake-Up Time During Scoliosis Surgery

2014 ◽  
Vol 118 (6) ◽  
pp. 1396-1403 ◽  
Author(s):  
Chien-Kun Ting ◽  
Ken B. Johnson ◽  
Wei-Nung Teng ◽  
Noah D. Synoid ◽  
Cris LaPierre ◽  
...  
Keyword(s):  
Response Surface ◽  
Response Surface Model ◽  
Surface Model ◽  
Scoliosis Surgery ◽  
Model Predictions

Infrared Radiative Properties of Heavily Doped Silicon at Room Temperature

2007 ◽  
Author(s):  
S. Basu ◽  
B. J. Lee ◽  
Z. M. Zhang
Keyword(s):  
Carrier Mobility ◽  
Room Temperature ◽  
Radiative Properties ◽  
Multilayer Thin Films ◽  
Mems Devices ◽  
Si Substrate ◽  
Model Predictions ◽  
Doped Silicon ◽  
Infrared Spectrometer ◽  
Heavily Doped

This paper describes an experimental investigation on the infrared radiative properties of heavily-doped silicon (Si) at room temperature. Lightly-doped Si wafers were ion implanted with boron and phosphorus atoms to doping concentrations of 1×1020 and 1×1021 cm−3. Rapid thermal annealing was performed to activate the implanted dopants. A Fourier-transform infrared spectrometer was employed to measure the normal transmittance as well as reflectance of the samples in the spectral region from 2 to 20 μm. Accurate carrier mobility and ionization models were identified after carefully reviewing the available literature, and then incorporated into Drude model to predict the dielectric function of doped Si. The radiative properties of doped Si samples were calculated by treating the doped region as multilayer thin films of different doping concentrations on a thick Si substrate. The measured spectral transmittance and reflectance agree well with the model predictions. The results obtained from this study will facilitate the future applications of heavily-doped Si in semiconductor as well as MEMS devices.

Robust Optimization With Mixed Interval and Probabilistic Parameter Uncertainties, Model Uncertainty, and Metamodeling Uncertainty

2019 ◽  
Author(s):  
Yanjun Zhang ◽  
Tingting Xia ◽  
Mian Li
Keyword(s):  
Robust Optimization ◽  
Real World ◽  
Parameter Uncertainty ◽  
Model Uncertainty ◽  
Optimization Problems ◽  
Probability Distributions ◽  
Parameter Uncertainties ◽  
True Value ◽  
Uncertainty Model ◽  
The Difference

Abstract Various types of uncertainties, such as parameter uncertainty, model uncertainty, metamodeling uncertainty may lead to low robustness. Parameter uncertainty can be either epistemic or aleatory in physical systems, which have been widely represented by intervals and probability distributions respectively. Model uncertainty is formally defined as the difference between the true value of the real-world process and the code output of the simulation model at the same value of inputs. Additionally, metamodeling uncertainty is introduced due to the usage of metamodels. To reduce the effects of uncertainties, robust optimization (RO) algorithms have been developed to obtain solutions being not only optimal but also less sensitive to uncertainties. Based on how parameter uncertainty is modeled, there are two categories of RO approaches: interval-based and probability-based. In real-world engineering problems, both interval and probabilistic parameter uncertainties are likely to exist simultaneously in a single problem. However, few works have considered mixed interval and probabilistic parameter uncertainties together with other types of uncertainties. In this work, a general RO framework is proposed to deal with mixed interval and probabilistic parameter uncertainties, model uncertainty, and metamodeling uncertainty simultaneously in design optimization problems using the intervals-of-statistics approaches. The consideration of multiple types of uncertainties will improve the robustness of optimal designs and reduce the risk of inappropriate decision-making, low robustness and low reliability in engineering design. Two test examples are utilized to demonstrate the applicability and effectiveness of the proposed RO approach.

Safety and Uncertainty Analyses for the Shallow-Land Disposal of Radioactive Waste With Uranium Decay Chain

2001 ◽  
Author(s):  
Seiji Takdea ◽  
Mitsuhiro Kanno ◽  
Naofumi Minase ◽  
Hideo Kimura
Keyword(s):  
Radioactive Waste ◽  
Uncertainty Analysis ◽  
Parameter Uncertainty ◽  
Safety Assessment ◽  
Dose Calculation ◽  
Decay Chain ◽  
Statistical Methodology ◽  
Parameter Uncertainties ◽  
Land Disposal ◽  
Uncertainty Analyses

Abstract Safety and uncertainty analyses for the shallow-land disposal of radioactive wastes with uranium decay chain were performed using the deterministic and probabilistic safety assessment models. The deterministic analyses show that the dose calculation in residence scenario is of great importance owing to the influence of daughters built up by uranium decay chain. The parameter uncertainties for the important pathways in residence scenario are estimated from the probabilistic analyses using the statistical methodology. The uncertainty analysis indicates that the influence of parameter uncertainty is the most remarkable in the estimation for the inhalation of radon gas with residence.

scholarly journals Implementation of a soil albedo scheme in the CABLEv1.4b land surface model and evaluation against MODIS estimates over Australia

2014 ◽  
Vol 7 (2) ◽  
pp. 1671-1707
Author(s):  
J. Kala ◽  
J. P. Evans ◽  
A. J. Pitman ◽  
C. B. Schaaf ◽  
M. Decker ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Albedo ◽  
Land Surface Model ◽  
Surface Model ◽  
Parameter Uncertainties ◽  
Australian Continent ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Land surface albedo, the fraction of incoming solar radiation reflected by the land surface, is a key component of the earth system. This study evaluates snow-free surface albedo simulations by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model with the Moderate Resolution Imaging Spectroradiometer (MODIS) albedo. We compare results from two offline simulations over the Australian continent, one with prescribed background snow-free and vegetation-free soil albedo derived from MODIS (the control), and the other with a simple parameterisation based on soil moisture and colour. The control simulation shows that CABLE simulates albedo over Australia reasonably well, with differences with MODIS within an acceptable range. Inclusion of the parameterisation for soil albedo however introduced large errors for the near infra red albedo, especially for desert regions of central Australia. These large errors were not fully explained by errors in soil moisture or parameter uncertainties, but are similar to errors in albedo in other land surface models which use the same soil albedo scheme. Although this new parameterisation has introduced larger errors as compared to prescribing soil albedo, dynamic soil moisture-albedo feedbacks are now enabled in CABLE. Future directions for albedo parameterisations development in CABLE are discussed.

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