Absence Of Calibration Data Research Articles

Generalized biomass equations for Stone pine (Pinus pinea L.) across the Mediterranean basin

Accurate estimates of tree biomass are strongly required for forest carbon budget estimates and to understand ecosystem dynamics for a sustainable management. Existing biomass equations for Mediterranean species are scarce, stand- and site-specific and therefore are not suitable for large scale application.In this study, biomass allometric equations were developed for stone pine (Pinus pinea L.), a Mediterranean tree species with relevant ecologic and economic interest. A dataset of 283 harvested trees was compiled with above- and belowground biomass from 16 sites in three countries (Italy, Spain, Portugal) representative of the species’ geographical Mediterranean distribution. A preliminary approach comparing the ordinary least squares method and the mixed model approach was performed in order to evaluate the most appropriate method for nested data in the absence of calibration data. To quantify the sources of error associated with applying biomass equations beyond the geographical range of the data used to develop them, a residual analysis was conducted.The allometric analysis showed low intra-specific variability in aboveground biomass relationships, which was relatively insensitive to the stand and site conditions. Significant differences were found for the crown components (needles and branches), which may be attributed to local geographical adaptation, site conditions and stand management. The root biomass was highly correlated with diameter at breast height irrespective of the geographical origin. Biased estimates were found when using site-specific equations outside the geographical range from where they were developed.The new biomass equations improved the accuracy of biomass estimates, particularly for the aboveground components of higher dimension trees and for the root component, being highly suitable for use in regional and national biomass forest calculations. It is, up to the present, the most complete database of harvested stone pine trees worldwide.

A Mixed-Effects Model with Different Strategies for Modeling Volume in Cunninghamia lanceolata Plantations.

A systematic evaluation of nonlinear mixed-effect taper models for volume prediction was performed. Of 21 taper equations with fewer than 5 parameters each, the best 4-parameter fixed-effect model according to fitting statistics was then modified by comparing its values for the parameters total height (H), diameter at breast height (DBH), and aboveground height (h) to modeling data. Seven alternative prediction strategies were compared using the best new equation in the absence of calibration data, which is often unavailable in forestry practice. The results of this study suggest that because calibration may sometimes be a realistic option, though it is rarely used in practical applications, one of the best strategies for improving the accuracy of volume prediction is the strategy with 7 calculated total heights of 3, 6 and 9 trees in the largest, smallest and medium-size categories, respectively. We cannot use the average trees or dominant trees for calculating the random parameter for further predictions. The method described here will allow the user to make the best choices of taper type and the best random-effect calculated strategy for each practical application and situation at tree level.

Limits of predictability in commuting flows in the absence of data for calibration.

The estimation of commuting flows at different spatial scales is a fundamental problem for different areas of study. Many current methods rely on parameters requiring calibration from empirical trip volumes. Their values are often not generalizable to cases without calibration data. To solve this problem we develop a statistical expression to calculate commuting trips with a quantitative functional form to estimate the model parameter when empirical trip data is not available. We calculate commuting trip volumes at scales from within a city to an entire country, introducing a scaling parameter α to the recently proposed parameter free radiation model. The model requires only widely available population and facility density distributions. The parameter can be interpreted as the influence of the region scale and the degree of heterogeneity in the facility distribution. We explore in detail the scaling limitations of this problem, namely under which conditions the proposed model can be applied without trip data for calibration. On the other hand, when empirical trip data is available, we show that the proposed model's estimation accuracy is as good as other existing models. We validated the model in different regions in the U.S., then successfully applied it in three different countries.

A comparison of fixed- and mixed-effects modeling in tree growth and yield prediction of an indigenous neotropical species (Centrolobium tomentosum) in a plantation system

Centrolobium tomentosum is a multipurpose pioneer tree species, indigenous in tropical South America and suitable for forest restoration, agroforestry and plantation systems. Despite its economic and ecological interest, no growth and yield models have been developed for this species so far. Fixed- and mixed-effects modeling can be used in model fitting, each technique having its pros and cons. Marginal predictions can be computed from fixed-effects models or randomized mixed-effects models. In forestry practice, models are seldom calibrated and mixed-effects models are mostly used to provide conditional predictions using only the fixed parameters, assuming that the random effects are zero. This study developed the first set of individual-tree growth and yield models for C. tomentosum and, by using the models, assessed the performance of three prediction approaches: fixed-effects models, conditional predictions of mixed-effects-models and marginal predictions of mixed-effects models. The fitted models predict maximum mean annual bole volume increments of 5.6–16.6m3/ha and optimal rotation lengths ranging from 11 to 21years, depending on site quality. Fixed-effects modeling was the best approach in growth and yield prediction, followed by conditional predictions of mixed-effects models, whereas marginal predictions based on mixed-effects models were in general the least accurate. Fixed-effects models should therefore be preferred in the absence of calibration data. However, since calibration is sometimes a feasible option, research articles should report both fixed- and mixed-effects models in order to enable the computation of the best predictions with and without the possibility of model calibration.

Evaluating marginal and conditional predictions of taper models in the absence of calibration data

A systematic evaluation of nonlinear fixed- and mixed-effects taper models in volume prediction was conducted. Among 33 taper equations, the best 1- to 10-parameter fixed-effects models according to fitting statistics were further analysed by comparing their predictions against the modelling data and an independent data set. Three alternative prediction strategies were compared using the best equation (Kozak II) in the absence of calibration data (the usual situation in forestry practice). Strategy 1 used a fixed-parameter model (marginal model), strategy 2 utilized the fixed part of a mixed-effects model (conditional model), and strategy 3 calculated a marginal prediction based on the mixed-effects model by averaging the predictions over the estimated distribution of random effects. Strategies 1 and 3 performed better than strategy 2 in model evaluation (in modelling data) and model validation (independent data). Strategy 3 was less biased than strategy 1 in model validation, and both had the same mean squared deviation. Strategy 3 shares the most advantageous features of the other prediction methods and is therefore recommended for forestry practice and for further research in different modelling disciplines within forest science.

Part-based spatio-temporal model for multi-person re-identification

In this paper we propose an adaptive part-based spatio-temporal model that characterizes person’s appearance using color and facial features. Face image selection based on low level cues is used to select usable face images to build a face model. Color features that capture the distribution of colors as well as the representative colors are used to build the color model. The model is built over a sequence of frames of an individual and hence captures the characteristic appearance as well as its variations over time. We also address the problem of multiple person re-identification in the absence of calibration data or prior knowledge about the camera layout. Multiple person re-identification is a open set matching problem with a dynamically evolving and open gallery set and an open probe set. Re-identification is posed as a rectangular assignment problem and is solved to find a bijection that minimizes the overall assignment cost. Open and closed set re-identification is tested on 30 videos collected with nine non-overlapping cameras spanning outdoor and indoor areas, with 40 subjects under observation. A false acceptance reduction scheme based on the developed model is also proposed.

Evaluation of diffuse pollution model applications in EUROHARP catchments with limited data

The application of diffuse pollution models included in EUROHARP encompassed varying levels of parameterisation and approaches to the preparation of input data depending on the model and modelling team involved. Modellers consistently faced important decisions in relation to data interpretation, especially in those catchments with unfamiliar physical or climatic characteristics, where catchment conditions were beyond the range for which a particular model was originally developed, or where only limited input data were available. In addition to a broad discussion of data issues, this paper compares the performance of the four sub-annual output models tested in EUROHARP (EveNFlow, NL-CAT, SWAT and TRK) in three test catchments without the modelling teams having sight of measured flow and nitrate concentration data. Model performance in this "blind test" indicate that the range of predictions generated by any individual models pre and post calibration exceed the differences between the estimates yielded by all four models. Comparison of Analysis of Variance (ANOVA) statistics for simulated and observed flow, concentration and loads underscores the benefits of calibration for these intermediate and complex model formulations. Interpretation of input data (e.g. rainfall interpolation method and pedotransfer functions selected) appeared equally (or more) important than process representation. In the absence of calibration data, modeller unfamiliarity with a particular catchment and its environmental processes sometimes resulted in questionable assumptions and input errors which highlight the problems facing modellers charged with implementing policies under the Water Framework Directive (2000/60/EC) in poorly monitored catchments. Catchment data owners and modellers must therefore work more closely given that the output from diffuse pollution models is clearly modeller-limited as well as model-limited.

A Multimodel Analysis, Validation, and Transferability Study of Global Soil Wetness Products

Abstract Multimodel ensemble forecasting has been shown to offer a systematic improvement in the skill of climate prediction with atmosphere and ocean circulation models. However, little such work has been done for the land surface component, an important lower boundary for weather and climate forecast models. In this study, the authors examine and evaluate several methods of combining individual global soil wetness products from uncoupled land surface model calculations and coupled land–atmosphere model reanalyses to produce an ensemble analysis. Analyses are verified against observations from the Global Soil Moisture Data Bank (GSMDB) with skill measured by correlation coefficient and root-mean-square error (RMSE). A preliminary transferability study is conducted as well for investigating the feasibility of transferring ensemble regression parameters within two specific regions (Illinois and east-central China) and between these two regions of similar climate and land use. The results show that when sufficient validation data are available, one can use a seasonally dependent linear regression to improve the skill of any individual model simulation of soil wetness. Further improvements in skill can be achieved with more sophisticated ensembling methods, such as the regression-adjusted multimodel ensemble mean analysis and regression-adjusted multimodel analysis. However, all the ensembling schemes involving regression usually do not help improve the skill scores as far as the simulation of anomalies of soil wetness is concerned. In the absence of calibration data, the simple arithmetic ensemble mean across multiple soil wetness products generally does as well or better than the best individual model at any location in the representation of both soil wetness and its anomaly. Transferability from one subset of stations from the Illinois or east-central China dataset to another gives satisfactory results. However, results are poor when transferring regression weights between different regions, even with similar climate regimes and land cover. Such an exercise helps us to understand better the virtues and limitations of various ensembling techniques and enables progress toward creating an optimum, model-independent analysis from a practical point of view.

Applicability of Sharp‐Interface Models for NAPL Transport: 2. Spreading of a LNAPL

AbstractThis paper, the second in a two‐part series, examines the applicability of the sharp‐interface approach to modeling initial accumulation and spreading of a light nonaqueous phase liquid (LNAPL) in the ground‐water aquifer. The sharp‐interface approach models NAPL transport by developing a time‐distance profile from Darcy's law by assuming a piston or plug flow. The approach is tested here against an immiscible‐phase model that includes capillarity effects in the formulation. Although significant assumptions are included in the sharp‐interface model, it may be useful for initial screening, in obtaining an order‐of‐magnitude estimate of travel times, and in describing the approximate extent of the free and dissolved NAPL contamination. In the absence of data for calibration, the paper introduces guidelines for estimating the parameters of the model. By integrating the formulation in Part 1 and this part, a practical approach for analysis of NAPL contamination is defined; the underlying assumptions and simplifications have to be recognized, however.

Applicability of Sharp‐Interface Models for NAPL Transport: 1. Infiltration

AbstractA sharp‐interface, or piston flow model is developed for nonaqueous phase liquids (NAPL) flow through the unsaturated zone. This model, which assumes saturated plug flow downward from the surface, is verified through comparison with an immiscible‐phase model that includes capillarity effects in the formulation. Although significant assumptions are included in the sharp‐interface model, it may be useful for initial screening and in obtaining an order‐of‐magnitude estimate of the time of travel of NAPL in the unsaturated zone. In the absence of data for calibration, guidelines are introduced for estimating the model's parameters.