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Model Parameterization (model + parameterization)
Selected AbstractsAddressing non-uniqueness in linearized multichannel surface wave inversionGEOPHYSICAL PROSPECTING, Issue 1 2009Michele Cercato ABSTRACT The multichannel analysis of the surface waves method is based on the inversion of observed Rayleigh-wave phase-velocity dispersion curves to estimate the shear-wave velocity profile of the site under investigation. This inverse problem is nonlinear and it is often solved using ,local' or linearized inversion strategies. Among linearized inversion algorithms, least-squares methods are widely used in research and prevailing in commercial software; the main drawback of this class of methods is their limited capability to explore the model parameter space. The possibility for the estimated solution to be trapped in local minima of the objective function strongly depends on the degree of nonuniqueness of the problem, which can be reduced by an adequate model parameterization and/or imposing constraints on the solution. In this article, a linearized algorithm based on inequality constraints is introduced for the inversion of observed dispersion curves; this provides a flexible way to insert a priori information as well as physical constraints into the inversion process. As linearized inversion methods are strongly dependent on the choice of the initial model and on the accuracy of partial derivative calculations, these factors are carefully reviewed. Attention is also focused on the appraisal of the inverted solution, using resolution analysis and uncertainty estimation together with a posteriori effective-velocity modelling. Efficiency and stability of the proposed approach are demonstrated using both synthetic and real data; in the latter case, cross-hole S-wave velocity measurements are blind-compared with the results of the inversion process. [source] Genetic data in population viability analysis: case studies with ambystomatid salamandersANIMAL CONSERVATION, Issue 2 2010K. R. Greenwald Abstract Parameterization of population viability models is a complicated task for most types of animals, as knowledge of population demography, abundance and connectivity can be incomplete or unattainable. Here I illustrate several ways in which genetic data can be used to inform population viability analysis, via the parameterization of both initial abundance and dispersal matrices. As case studies, I use three ambysomatid salamander datasets to address the following question: how do population viability predictions change when dispersal estimates are based on genetic assignment test data versus a general dispersal,distance function? Model results showed that no local population was large enough to ensure long-term persistence in the absence of immigration, suggesting a metapopulation structure. Models parameterized with a dispersal,distance function resulted in much more optimistic predictions than those incorporating genetic data in the dispersal estimates. Under the dispersal,distance function scenario all local populations persisted; however, using genetic assignments to infer dispersal revealed local populations at risk of extinction. Viability estimates based on dispersal,distance functions should be interpreted with caution, especially in heterogeneous landscapes. In these situations I promote the idea of model parameterization using genetic assignment tests for a more accurate portrayal of real-world dispersal patterns. [source] On the use of logarithmic scales for analysis of diffraction dataACTA CRYSTALLOGRAPHICA SECTION D, Issue 12 2009Alexandre Urzhumtsev Predictions of the possible model parameterization and of the values of model characteristics such as R factors are important for macromolecular refinement and validation protocols. One of the key parameters defining these and other values is the resolution of the experimentally measured diffraction data. The higher the resolution, the larger the number of diffraction data Nref, the larger its ratio to the number Nat of non-H atoms, the more parameters per atom can be used for modelling and the more precise and detailed a model can be obtained. The ratio Nref/Nat was calculated for models deposited in the Protein Data Bank as a function of the resolution at which the structures were reported. The most frequent values for this distribution depend essentially linearly on resolution when the latter is expressed on a uniform logarithmic scale. This defines simple analytic formulae for the typical Matthews coefficient and for the typically allowed number of parameters per atom for crystals diffracting to a given resolution. This simple dependence makes it possible in many cases to estimate the expected resolution of the experimental data for a crystal with a given Matthews coefficient. When expressed using the same logarithmic scale, the most frequent values for R and Rfree factors and for their difference are also essentially linear across a large resolution range. The minimal R -factor values are practically constant at resolutions better than 3,Å, below which they begin to grow sharply. This simple dependence on the resolution allows the prediction of expected R -factor values for unknown structures and may be used to guide model refinement and validation. [source] Transit time distributions of a conceptual model: their characteristics and sensitivitiesHYDROLOGICAL PROCESSES, Issue 12 2010S. M. Dunn Abstract The internal behaviour of a conceptual hydrological and tracer transport model, STREAM, has been examined through generation of transit time distributions for the model. The model has been applied to a small sub-catchment of the Lunan Water in the east of Scotland where daily precipitation and stream water samples have been analysed for isotope content. Transit time distributions are generated by numerically tracking pulse inputs of tracer to the model and evaluating the simulated stream outputs. A set of baseline simulations was first established through calibration to time series of stream flow. A series of model experiments was then undertaken to assess the sensitivity of the simulated transit time distributions to different model parameterizations, flow paths and mixing assumptions. The results of the analysis show that the model transit time distributions do not conform to any simple statistical function and that their characteristics can be significantly altered depending on how the model is set up. The analysis provided valuable insight into the functioning of the model and could be usefully applied to other model codes. Comparison of the transit time distributions generated by conceptual models with data-based empirical evidence of distributions gives the potential to close the gap in understanding the physical explanation for why catchment systems behave as they do. Copyright © 2010 John Wiley & Sons, Ltd. [source] Modeling Postfire Response and Recovery using the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS),JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 3 2009Kristina Cydzik Abstract:, This paper investigates application of the Army Corps of Engineers' Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) to a burned watershed in San Bernardino County, California. We evaluate the HEC-HMS' ability to simulate discharge in prefire and postfire conditions in a semi arid watershed and the necessary parameterizations for modeling hydrologic response during the immediate, and subsequent recovery, period after a wildfire. The model is applied to City Creek watershed, which was 90% burned during the Old Fire of October 2003. An optimal spatial resolution for the HEC-HMS model was chosen based on an initial sensitivity analysis of subbasin configurations and related model performance. Five prefire storms were calibrated for the selected model resolution, defining a set of parameters that reasonably simulate prefire conditions. Six postfire storms, two from each of the following rainy (winter) seasons were then selected to simulate postfire response and evaluate relative changes in parameter values and model behavior. There were clear trends in the postfire parameters [initial abstractions (Ia), curve number (CN), and lag time] that reveal significant (and expected) changes in watershed behavior. CN returns to prefire (baseline) values by the end of Year 2, while Ia approaches baseline by the end of the third rainy season. However, lag time remains significantly lower than prefire values throughout the three-year study period. Our results indicate that recovery of soil conditions and related runoff response is not entirely evidenced by the end of the study period (three rainy seasons postfire). Understanding the evolution of the land surface and related hydrologic properties during the highly dynamic postfire period, and accounting for these changes in model parameterizations, will allow for more accurate and reliable discharge simulations in both the immediate, and subsequent, rainy seasons following fire. [source] | ||||||||||