FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1937370143> ?p ?o ?g. }

• W1937370143 abstract "[1] Large simulation models of eutrophication processes are commonly used to aid scientific understanding and to guide management decisions. Confidence in models for these purposes depends on uncertainty in model equations (structural uncertainty) and on effects of input uncertainties (model parameters, initial conditions, and forcing functions) on model outputs. Our objective herein is to illustrate two strategies, a generalized likelihood uncertainty estimation (GLUE) approach combined with a simple Monte Carlo sampling scheme and a Bayesian methodological framework along with Markov Chain Monte Carlo (MCMC) simulations, for elucidating the propagation of uncertainty in the high-dimensional parameter spaces of mechanistic eutrophication models. We examine the ability of the two approaches to offer insights into the degree of information about model inputs that the data contain, to quantify the correlation structure among parameter estimates, and to obtain predictions along with uncertainty bounds for modeled output variables. Our analysis is based on a four-state-variable (phosphate-detritus-phytoplankton-zooplankton) model and the mesotrophic Lake Washington (Washington State, United States) as a case study. Scientific knowledge, expert judgment, and observational data were used to formulate prior probability distributions and characterize the uncertainty pertaining to 14 model parameters. Despite the conceptual differences for addressing model equifinality, that is, wide ranges of parameter values subject to complex multivariate relationships that result in plausible observed behaviors and produce equivalently accurate predictions, we found that the two strategies provided fairly consistent estimates of the posterior parameter correlation structure and output uncertainty. Nonetheless, our analysis also shows that MCMC can more efficiently quantify the joint probability distribution of model parameters and make inference about this distribution. The latter finding can be explained by the basic idea underlying the MCMC methodology, that is, the configuration of a Markov process whose stationary distribution approximates the joint posterior distribution of all the stochastic model nodes; as a result, Monte Carlo samples are not drawn from the prior parameter space, and problems of wide or highly correlated prior distributions can be overcome. Finally, our study stresses the lack of perfect simulators of natural system dynamics and introduces two statistical formulations that can explicitly account for the discrepancy between mathematical models and environmental systems." @default.
• W1937370143 created "2016-06-24" @default.
• W1937370143 creator A5003465845 @default.
• W1937370143 creator A5006172745 @default.
• W1937370143 creator A5034239310 @default.
• W1937370143 creator A5042911844 @default.
• W1937370143 creator A5055346939 @default.
• W1937370143 creator A5062587170 @default.
• W1937370143 date "2008-01-01" @default.
• W1937370143 modified "2023-10-02" @default.
• W1937370143 title "Addressing equifinality and uncertainty in eutrophication models" @default.
• W1937370143 cites W1528989240 @default.
• W1937370143 cites W1594703822 @default.
• W1937370143 cites W1752868210 @default.
• W1937370143 cites W1965776712 @default.
• W1937370143 cites W1973333099 @default.
• W1937370143 cites W1974473926 @default.
• W1937370143 cites W1975932397 @default.
• W1937370143 cites W1990861384 @default.
• W1937370143 cites W1991211136 @default.
• W1937370143 cites W1995737178 @default.
• W1937370143 cites W1998523300 @default.
• W1937370143 cites W1998540980 @default.
• W1937370143 cites W2000297972 @default.
• W1937370143 cites W2002065526 @default.
• W1937370143 cites W2019165048 @default.
• W1937370143 cites W2019333049 @default.
• W1937370143 cites W2026170388 @default.
• W1937370143 cites W2029302045 @default.
• W1937370143 cites W2029376597 @default.
• W1937370143 cites W2033725672 @default.
• W1937370143 cites W2035892982 @default.
• W1937370143 cites W2038653611 @default.
• W1937370143 cites W2040586388 @default.
• W1937370143 cites W2043371662 @default.
• W1937370143 cites W2046614181 @default.
• W1937370143 cites W2048330891 @default.
• W1937370143 cites W2056329779 @default.
• W1937370143 cites W2056760934 @default.
• W1937370143 cites W2056901072 @default.
• W1937370143 cites W2069993596 @default.
• W1937370143 cites W2071098465 @default.
• W1937370143 cites W2077545453 @default.
• W1937370143 cites W2078261434 @default.
• W1937370143 cites W2078689532 @default.
• W1937370143 cites W2081346522 @default.
• W1937370143 cites W2083875149 @default.
• W1937370143 cites W2084653679 @default.
• W1937370143 cites W2092612581 @default.
• W1937370143 cites W2096350564 @default.
• W1937370143 cites W2097002356 @default.
• W1937370143 cites W2110929766 @default.
• W1937370143 cites W2113987830 @default.
• W1937370143 cites W2121032760 @default.
• W1937370143 cites W2123538678 @default.
• W1937370143 cites W2124738823 @default.
• W1937370143 cites W2129393831 @default.
• W1937370143 cites W2138309709 @default.
• W1937370143 cites W2142245349 @default.
• W1937370143 cites W2146283576 @default.
• W1937370143 cites W2168087399 @default.
• W1937370143 cites W2185697453 @default.
• W1937370143 cites W4211177544 @default.
• W1937370143 cites W4247690662 @default.
• W1937370143 cites W4253132626 @default.
• W1937370143 doi "https://doi.org/10.1029/2007wr005862" @default.
• W1937370143 hasPublicationYear "2008" @default.
• W1937370143 type Work @default.
• W1937370143 sameAs 1937370143 @default.
• W1937370143 citedByCount "69" @default.
• W1937370143 countsByYear W19373701432012 @default.
• W1937370143 countsByYear W19373701432013 @default.
• W1937370143 countsByYear W19373701432014 @default.
• W1937370143 countsByYear W19373701432015 @default.
• W1937370143 countsByYear W19373701432016 @default.
• W1937370143 countsByYear W19373701432017 @default.
• W1937370143 countsByYear W19373701432018 @default.
• W1937370143 countsByYear W19373701432019 @default.
• W1937370143 countsByYear W19373701432020 @default.
• W1937370143 countsByYear W19373701432021 @default.
• W1937370143 countsByYear W19373701432022 @default.
• W1937370143 countsByYear W19373701432023 @default.
• W1937370143 crossrefType "journal-article" @default.
• W1937370143 hasAuthorship W1937370143A5003465845 @default.
• W1937370143 hasAuthorship W1937370143A5006172745 @default.
• W1937370143 hasAuthorship W1937370143A5034239310 @default.
• W1937370143 hasAuthorship W1937370143A5042911844 @default.
• W1937370143 hasAuthorship W1937370143A5055346939 @default.
• W1937370143 hasAuthorship W1937370143A5062587170 @default.
• W1937370143 hasConcept C127413603 @default.
• W1937370143 hasConcept C142796444 @default.
• W1937370143 hasConcept C149782125 @default.
• W1937370143 hasConcept C154945302 @default.
• W1937370143 hasConcept C162324750 @default.
• W1937370143 hasConcept C183030095 @default.
• W1937370143 hasConcept C186699998 @default.
• W1937370143 hasConcept C187320778 @default.
• W1937370143 hasConcept C18903297 @default.
• W1937370143 hasConcept C39432304 @default.
• W1937370143 hasConcept C41008148 @default.

• next