Download or read book Global Sensitivity Analysis Probabilistic Calibration and Predictive Assessment for the Data Assimilation Linked Ecosystem Carbon Model written by and published by . This book was released on 2015 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we propose a probabilistic framework for an uncertainty quantification (UQ) study of a carbon cycle model and focus on the comparison between steady-state and transient simulation setups. A global sensitivity analysis (GSA) study indicates the parameters and parameter couplings that are important at different times of the year for quantities of interest (QoIs) obtained with the data assimilation linked ecosystem carbon (DALEC) model. We then employ a Bayesian approach and a statistical model error term to calibrate the parameters of DALEC using net ecosystem exchange (NEE) observations at the Harvard Forest site. The calibration results are employed in the second part of the paper to assess the predictive skill of the model via posterior predictive checks.
Download or read book Ecological Forecasting written by Michael C. Dietze and published by Princeton University Press. This book was released on 2017-05-30 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative and accessible introduction to the concepts and tools needed to make ecology a more predictive science Ecologists are being asked to respond to unprecedented environmental challenges. How can they provide the best available scientific information about what will happen in the future? Ecological Forecasting is the first book to bring together the concepts and tools needed to make ecology a more predictive science. Ecological Forecasting presents a new way of doing ecology. A closer connection between data and models can help us to project our current understanding of ecological processes into new places and times. This accessible and comprehensive book covers a wealth of topics, including Bayesian calibration and the complexities of real-world data; uncertainty quantification, partitioning, propagation, and analysis; feedbacks from models to measurements; state-space models and data fusion; iterative forecasting and the forecast cycle; and decision support. Features case studies that highlight the advances and opportunities in forecasting across a range of ecological subdisciplines, such as epidemiology, fisheries, endangered species, biodiversity, and the carbon cycle Presents a probabilistic approach to prediction and iteratively updating forecasts based on new data Describes statistical and informatics tools for bringing models and data together, with emphasis on: Quantifying and partitioning uncertainties Dealing with the complexities of real-world data Feedbacks to identifying data needs, improving models, and decision support Numerous hands-on activities in R available online
Download or read book Global Sensitivity Analysis and Parameter Calibration for an Ecosystem Carbon Model written by and published by . This book was released on 2013 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Land Carbon Cycle Modeling written by Yiqi Luo and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Carbon moves through the atmosphere, through the oceans, onto land, and into and between various ecosystems. This cycling has a large effect on climate - changing geographic patterns of rainfall and the frequency of extreme weather. The impact of changes in global carbon cycling are altered as the use of fossil fuels add carbon to the cycle. This book addresses the crucial question of how to assess, evaluate, and estimate the potential impact of the additional carbon to the global carbon cycle. The contributors describe a set of models for exploring ecological questions regarding changes in carbon cycling; provide background for developing new models; employs data assimilation techniques for model improvement; and do real- or near-time ecological forecasting for decision support. This book strives to balance theoretical considerations, technical details, and applications of ecosystem modeling for research, assessment, and crucial decision making. Key Features Helps readers understand, implement and criticize carbon cycling models Does not require computer programming skills or deep knowledge of mathematics Describes a suite of modeling skills - modeling questions, building models, data assimilation Combines modeling with statistical analysis of models Introduces data assimilation, statistical analysis, Markov chain Mote Carlo methods, and decision supporting systems
Download or read book Sensitivity Analysis written by Emanuele Borgonovo and published by Springer. This book was released on 2017-04-19 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an expository introduction to the methodology of sensitivity analysis of model output. It is primarily intended for investigators, students and researchers that are familiar with mathematical models but are less familiar with the techniques for performing their sensitivity analysis. A variety of sensitivity methods have been developed over the years. This monograph helps the analyst in her/his first exploration of this world. The main goal is to foster the recognition of the crucial role of sensitivity analysis methods as the techniques that allow us to gain insights from quantitative models. Also, exercising rigor in performing sensitivity analysis becomes increasingly relevant both to decision makers and modelers. The book helps the analyst in structuring her/his sensitivity analysis quest properly, so as to obtain the correct answer to the corresponding managerial question. The first part of the book covers Deterministic Methods, including Tornado Diagrams; One-Way Sensitivity Analysis; Differentiation-Based Methods and Local Sensitivity Analysis with Constraints. The second part looks at Probabilistic Methods, including Regression-Based methods, Variance-Based Methods, and Distribution-Based methods. The final section looks at Applications, including capital budgeting, sensitivity analysis in climate change modelling and in the risk assessment of a lunar space mission.
Download or read book Global Sensitivity Analysis written by Andrea Saltelli and published by John Wiley & Sons. This book was released on 2008-02-28 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex mathematical and computational models are used in all areas of society and technology and yet model based science is increasingly contested or refuted, especially when models are applied to controversial themes in domains such as health, the environment or the economy. More stringent standards of proofs are demanded from model-based numbers, especially when these numbers represent potential financial losses, threats to human health or the state of the environment. Quantitative sensitivity analysis is generally agreed to be one such standard. Mathematical models are good at mapping assumptions into inferences. A modeller makes assumptions about laws pertaining to the system, about its status and a plethora of other, often arcane, system variables and internal model settings. To what extent can we rely on the model-based inference when most of these assumptions are fraught with uncertainties? Global Sensitivity Analysis offers an accessible treatment of such problems via quantitative sensitivity analysis, beginning with the first principles and guiding the reader through the full range of recommended practices with a rich set of solved exercises. The text explains the motivation for sensitivity analysis, reviews the required statistical concepts, and provides a guide to potential applications. The book: Provides a self-contained treatment of the subject, allowing readers to learn and practice global sensitivity analysis without further materials. Presents ways to frame the analysis, interpret its results, and avoid potential pitfalls. Features numerous exercises and solved problems to help illustrate the applications. Is authored by leading sensitivity analysis practitioners, combining a range of disciplinary backgrounds. Postgraduate students and practitioners in a wide range of subjects, including statistics, mathematics, engineering, physics, chemistry, environmental sciences, biology, toxicology, actuarial sciences, and econometrics will find much of use here. This book will prove equally valuable to engineers working on risk analysis and to financial analysts concerned with pricing and hedging.
Download or read book Sensitivity Analysis written by Andrea Saltelli and published by John Wiley & Sons. This book was released on 2000-10-03 with total page 515 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensitivity analysis is used to ascertain how a given model output depends upon the input parameters. This is an important method for checking the quality of a given model, as well as a powerful tool for checking the robustness and reliability of its analysis. The topic is acknowledged as essential for good modelling practice, and is an implicit part of any modelling field. · Offers an accessible introduction to sensitivity analysis · Covers all the latest research · Illustrates concepts with numerous examples, applications and case studies · Includes contributions form the leading researchers active in developing strategies for sensitivity analysis The principles of sensitivity analysis area carefully described, and suitable methods for approaching many types of problems are given. The book introduces the modeller to the entire causal assessment chain, from data to predictions, whilst explaining the impact of source uncertainties and framing assumptions. A 'hitch-hiker's guide' is included to allow the more experienced reader to readily access specific applications. Modellers from a wide range of disciplines, including biostatistics, economics, environmental impact assessment, chemistry and engineering will benefit greatly form the numerous examples and applications.
Download or read book Description Calibration and Sensitivity Analysis of the Local Ecosystem Submodel of a Global Model of Carbon and Nitrogen Cycling and the Water Balance of the Terrestrial Biosphere written by and published by . This book was released on 1995 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Description Calibration and Sensitivity Analysis of the Local Ecosystem Submodel of a Global Model of Carbon and Nitrogen Cycling and the Water Balance in the Terrestrial Biosphere written by and published by . This book was released on 1995 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: We have developed a geographically-distributed ecosystem model for the carbon, nitrogen, and water dynamics of the terrestrial biosphere TERRA. The local ecosystem model of TERRA consists of coupled, modified versions of TEM and DAYTRANS. The ecosystem model in each grid cell calculates water fluxes of evaporation, transpiration, and runoff; carbon fluxes of gross primary productivity, litterfall, and plant and soil respiration; and nitrogen fluxes of vegetation uptake, litterfall, mineralization, immobilization, and system loss. The state variables are soil water content; carbon in live vegetation; carbon in soil; nitrogen in live vegetation; organic nitrogen in soil and fitter; available inorganic nitrogen aggregating nitrites, nitrates, and ammonia; and a variable for allocation. Carbon and nitrogen dynamics are calibrated to specific sites in 17 vegetation types. Eight parameters are determined during calibration for each of the 17 vegetation types. At calibration, the annual average values of carbon in vegetation C, show site differences that derive from the vegetation-type specific parameters and intersite variation in climate and soils. From calibration, we recover the average C{sub v} of forests, woodlands, savannas, grasslands, shrublands, and tundra that were used to develop the model initially. The timing of the phases of the annual variation is driven by temperature and light in the high latitude and moist temperate zones. The dry temperate zones are driven by temperature, precipitation, and light. In the tropics, precipitation is the key variable in annual variation. The seasonal responses are even more clearly demonstrated in net primary production and show the same controlling factors.
Download or read book A Qualitative Analysis of the Data Assimilation Linked Ecosystem Carbon Model DALEC written by Ann M. Chuter and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Sensitivity Analysis in Earth Observation Modelling written by George P. Petropoulos and published by Elsevier. This book was released on 2016-10-07 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensitivity Analysis in Earth Observation Modeling highlights the state-of-the-art in ongoing research investigations and new applications of sensitivity analysis in earth observation modeling. In this framework, original works concerned with the development or exploitation of diverse methods applied to different types of earth observation data or earth observation-based modeling approaches are included. An overview of sensitivity analysis methods and principles is provided first, followed by examples of applications and case studies of different sensitivity/uncertainty analysis implementation methods, covering the full spectrum of sensitivity analysis techniques, including operational products. Finally, the book outlines challenges and future prospects for implementation in earth observation modeling. Information provided in this book is of practical value to readers looking to understand the principles of sensitivity analysis in earth observation modeling, the level of scientific maturity in the field, and where the main limitations or challenges are in terms of improving our ability to implement such approaches in a wide range of applications. Readers will also be informed on the implementation of sensitivity/uncertainty analysis on operational products available at present, on global and continental scales. All of this information is vital in the selection process of the most appropriate sensitivity analysis method to implement. Outlines challenges and future prospects of sensitivity analysis implementation in earth observation modeling Provides readers with a roadmap for directing future efforts Includes case studies with applications from different regions around the globe, helping readers to explore strengths and weaknesses of the different methods in earth observation modeling Presents a step-by-step guide, providing the principles of each method followed by the application of variants, making the reference easy to use and follow
Download or read book Sensitivity Analysis in Practice written by Andrea Saltelli and published by John Wiley & Sons. This book was released on 2004-07-16 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensitivity analysis should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. For a non-expert, choosing the method of analysis for their model is complex, and depends on a number of factors. This book guides the non-expert through their problem in order to enable them to choose and apply the most appropriate method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It is focussed on the use of SIMLAB – a widely distributed freely-available sensitivity analysis software package developed by the authors – for solving problems in sensitivity analysis of statistical models. Other key features: Provides an accessible overview of the current most widely used methods for sensitivity analysis. Opens with a detailed worked example to explain the motivation behind the book. Includes a range of examples to help illustrate the concepts discussed. Focuses on implementation of the methods in the software SIMLAB - a freely-available sensitivity analysis software package developed by the authors. Contains a large number of references to sources for further reading. Authored by the leading authorities on sensitivity analysis.
Download or read book Uncertainty Quantification and Sensitivity Analysis of Geoscientific Predictions with Data driven Approaches written by Jihoon Park and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty quantification in the Earth Sciences forms an integral component in decision making. Such decision has different objectives depending on the subsurface system. For example, the goals include maximizing profits in exploitation of resources or minimizing the effects on the environment. It is often the case that the decision has to balance between multiple conflicting objectives. Because the decision is made on prediction uncertainty, it is crucial to quantify realistic uncertainty which necessitates identification of a variety of sources of model uncertainty. The sources of model uncertainty include different interpretations on subsurface structures and depositional scenarios, unknown spatial distributions of properties, uncertainty in boundary conditions, hydrological/hydraulic properties and errors in measurements. The subsurface system is parameterized to represent model uncertainty. The model variable can be either global (takes scalar value) or spatially distributed. With limited available data, a large number of uncertain model variables exists. One of key tasks is to quantify how each model variable contribute to response uncertainty, which can be achieved by means of sensitivity analysis. Sensitivity analysis plays an important role in geoscientific computer experiments, whether for forecasting, data assimilation or model calibration. Some methods of sensitivity analysis have been used in Earth Sciences but they have clear limitations -- they cannot efficiently deal with multivariate responses, excessive calculations are required, and it is hard to take into account categorical input uncertainty. Overcoming these limitations, we revisit the idea of regionalized sensitivity analysis. In particular, we focus on distance-based global sensitivity analysis to estimate sensitivities of multivariate responses with limited number of samples. We demonstrate how the results from sensitivity analysis can be utilized to reduce model uncertainty with minimal impact on response uncertainty. The results can be used to design second Monte Carlo or building a surrogate model. Uncertainty needs to be updated as more data are required from different sources. In a Bayesian framework, this requires sampling from a posterior density of model and prediction variables. The key components of the workflow are dimensionality reduction of data variables and building of a statistical surrogate model to replace full forward models. It is demonstrated that the methodology successfully performs model inversions with limited number of full forward model runs. In many geoscientific applications, both global and spatial variables are uncertain. For convenience in computations, spatial variables are often converted to a few global variables. Even if the approach is efficient, the inversion results may not be consistent with the stated geological prior which leads to unrealistic uncertainty. In this dissertation, we propose to extend direct forecasting to predict model variables themselves. It is shown that successful inversion can be performed with both global and spatial variables characterizing a field-scale subsurface system. All the methodologies are demonstrated with the case studies. The first case deals with an oil reservoir in Libya. The case is used to study the proposed methods for global sensitivity analysis and approaches for model inversions to integrate dynamic data. The second case deals with the groundwater reservoir in Denmark. The case is used to integrate different sources of data to offer the inputs of decision models for groundwater management.
Download or read book Predictive Modeling of Environmental Systems written by Elias C. Massoud and published by . This book was released on 2017 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: In 2013, the World Meteorological Organization (WMO) urged the global community for coordinated international action against accelerating and potentially devastating climate change. Preliminary data indicated that CO2 levels increased more between 2012 and 2013 than during any other year since 1984, and this was possibly related to reduced uptake by the Earth's biosphere in addition to the steadily increasing emissions from the Earth's surface. In the upcoming decades, it will be critical for scientists and policy makers to not only resolve the problem of carbon emissions by assessing human behavior, but also to understand as thoroughly as possible the underlying coupled processes of the Earth's atmosphere and biosphere in order to adequately measure and estimate the fluxes of carbon, water, and energy that are dictating the climatic trends we observe today. Fortunately, our ability to understand Earth's processes and predict climate change is improving.This thesis covers a suite of environmental models and numerical methods to disentangle information found both in observed data as well as model simulations. Various methods are applied such as parameter estimation with Markov Chain Monte Carlo (MCMC), state estimation with data assimilation using the Ensemble Kalman Filter (EnKF), and sensitivity analysis of model parameters using the Fourier Amplitude Sensitivity Test (FAST), which all in one way or another offer treatments to predictive uncertainty. Furthermore, applying these methods on more sophisticated and complex models can be impossible sometimes due to their high CPU costs; in this thesis model emulators are built using Polynomial Chaos Expansion (PCE) to reduce the computational burden for various environmental models. Overall, our goal in this dissertation is to present what tools are currently available for making predictions of environmental systems, with emphasis on maintaining accuracy of model simulations when compared to observed data, optimizing the efficiency of computationally heavy models to minimize their run time costs, and obtaining fidelity of model structures to properly represent the underlying hydrologic, biophysical, and biogeochemical processes occurring on our Earth.
Download or read book Data Assimilation in the Ecosystem Carbon Pool Model Using the Forward Sensitivity Method written by Dung Tuan Phan and published by . This book was released on 2011 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book PREDICTIVE GLOBAL SENSITIVITY ANALYSIS written by CHARLES L.. LUO MUNSON (LAN. HUANG, XIAOHUI.) and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Pricing and Forecasting Carbon Markets written by Bangzhu Zhu and published by Springer. This book was released on 2017-05-09 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book applies the multidisciplinary approaches of econometrics, statistics, finance and artificial intelligence for pricing and forecasting the carbon market in the context of managerial issues. It explores the related issues of pricing and forecasting the carbon market using theoretical models and empirical analyses, demonstrating how the carbon market, as a policy-based artificial market, is complex and influenced by both the market mechanisms and the external heterogeneous environments. By integrating the features of analytical systems, it offers insights to further our scientific understanding of the pricing mechanism and the variable laws governing the carbon market. Moreover, it lays a foundation for dealing with climate change in China and constructing a national carbon market there. Ultimately, it actively contributes to the energy saving and CO2 emission reduction promoted by the carbon market. The carbon market, represented by the European Union Emissions Trading System (EU ETS), is a cost-effective measure for tackling climate change. Furthermore, pricing and forecasting carbon market has been one of the research focuses in the fields of energy and climate change. As a policy tool of the trading mechanism, the carbon market offers a great institutional innovation for coping with climate change. Due to its multiple advantages including saving costs and environment protection, and political feasibility, more and more countries including China have applied the carbon market for carbon dioxide (CO2) emission reduction. Accurately understanding the pricing mechanism and mastering the fluctuating law of carbon market is essential to build a national carbon market for China.
