Derek Karssenberg

Contact areas and pressure distribution on the proximal articular surface of the proximal phalanx under sagittal plane loading.

The objective of this study was to map topographically contact areas and pressure distributions on the proximal articular surface (PAS) of the proximal phalanx (PI) under various clinically relevant loading conditions. Left and right forelimbs of 13 mature horses were transected halfway down the radius and loaded in a position mimicking the weightbearing attitude close to the midstance phase. Five loads were used which corresponded with loads that can be expected in different gaits or during athletic performance (stance: 1800 N, walk: 3600 N, trot: 5400 N, gallop: 10,500 N and jumping: 12,000 N). Contact areas and pressure distributions at the PAS of PI were determined using a methylene blue dye staining technique and 2 pressure sensitive films (low pressure: range 2.5-10 MPa and medium pressure: range 10-50 MPa). The contact area of PI was positively correlated (r = 0.86; P<0.01) with the applied load. The contact area increased from 63% at 1800 N to 95% at 12,000 N and gradually shifted to include more of the edges of the articular surface, but especially the dorsal articular margin of PI. Pressure distribution patterns were similar under the different loading conditions. Pressure was less at the palmar margin and in the central depression and highest at the dorsal articular margin. With increasing load, the highest peak pressures were measured at sites of the dorsal articular margin that are not loaded in the standing or walking horse. The results of this study suggest that the frequent occurrence of osteochondral lesions at the dorsal articular margin of PI is caused by the combination of the intermittent character and the high absolute values of loads at this site as they occur during athletic performance.

Evaluation of the Surface Energy Balance System (SEBS) applied to ASTER imagery with flux-measurements at the SPARC 2004 site (Barrax, Spain)

Accurate quantification of the amount and spatial variation of evapotranspiration is important in a wide range of disciplines. Remote sensing based surface energy balance models have been developed to estimate turbulent surface energy fluxes at different scales. The objective of this study is to evaluate the Surface Energy Balance System (SEBS) model on a landscape scale, using tower-based flux measurements at different land cover units during an overpass of the ASTER sensor over the SPARC 2004 experimental site in Barrax (Spain). A sensitivity analysis has been performed in order to investigate to which variable the sensible heat flux is most sensitive. Taking into account their estimation errors, the aerodynamic parameters ( hc, z0M andd0) can cause large deviations in the modelling of sensible heat flux. The effect of replacement of empirical derivation of these aerodynamic parameters in the model by field estimates or literature values is investigated by testing two scenarios: the Empirical Scenario in which empirical equations are used to derive aerodynamic parameters and the Field Scenario in which values from field measurements or literature are used to replace the empirical calculations of the Empirical Scenario. In the case of a homogeneous land cover in the footprints of the measurements, the Field Scenario only resulted in a small improvement, compared to the Empirical Scenario. The Field Scenario can even worsen the result in the case of heterogeneous footprints, by creating sharp borders related to the land cover map. In both scenarios modelled fluxes correspond Correspondence to: J. van der Kwast (hans.vanderkwast@vito.be) better with flux measurements over uniform land cover compared to cases where different land covers are mixed in the measurement footprint. Furthermore SEBS underestimates sensible heat flux especially over dry and sparsely vegetated areas, which is common in single-source models.

Topographical mapping of biochemical properties of articular cartilage in the equine fetlock joint

The aim of this study was to evaluate topographical differences in the biochemical composition of the extracellular matrix of articular cartilage of the normal equine fetlock joint. Water content, DNA content, glycosaminoglycan (GAG) content and a number of characteristics of the collagen network (total collagen content, levels of hydroxylysine- (Hyl) and the crosslink hydroxylysylpyridinoline, (HP) of articular cartilage in the proximal 1st phalanx (P1), distal 3rd metacarpal bone (MC), and proximal sesamoid bones (PSB) were determined in the left and right fetlock joint of 6 mature horses (age 5-9 years). Twenty-eight sites were sampled per joint, which included the clinically important areas often associated with pathology. Biochemical differences were evaluated between sampling sites and related with the predisposition for osteochondral injury and type of loading. Significant regional differences in the composition of the extracellular matrix existed within the joint. Furthermore, left and right joints exhibited biochemical differences. Typical topographic distribution patterns were observed for each parameter. In P1 the dorsal and palmar articular margin showed a significantly lower GAG content than the more centrally located sites. Collagen content and HP crosslinks were higher at the joint margins than in the central area. Also, in the MC, GAG content was significantly lower at the (dorsal) articular margin compared with the central area. Consistent with findings in P1, collagen and HP crosslinks were significantly lower in the central area compared to the (dorsal) articular margin. Biochemical and biomechanical heterogeneity of articular cartilage is supposed to reflect the different functional demands made at different sites. In the present study, GAG content was highest in the constantly loaded central areas of the joint surfaces. In contrast, collagen content and HP crosslinks were higher in areas intermittently subjected to peak loading which suggests that the response to a certain type of loading of the various components of the extracellular matrix of articular cartilage are different. The differences in biochemical characteristics between the various sites may help to explain the site specificity of osteochondral lesions commonly found in the equine fetlock joint. Finally, these findings emphasise that the choice of sampling sites may profoundly influence the outcome of biochemical studies of articular cartilage.

The benefits of using remotely sensed soil moisture in parameter identification of large‐scale hydrological models

Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system, in particular the unsaturated zone, remains uncalibrated. Soil moisture observations from satellites have the potential to fill this gap. Here we evaluate the added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: (1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? (2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to calibration based only on discharge observations, such that this leads to improved simulations of soil moisture content and discharge? A dual state and parameter Ensemble Kalman Filter is used to calibrate the hydrological model LISFLOOD for the Upper Danube. Calibration is done using discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS, and ASCAT. Calibration with discharge data improves the estimation of groundwater and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate identification of parameters related to land-surface processes. For the Upper Danube upstream area up to 40,000 km2, calibration on both discharge and soil moisture results in a reduction by 10–30% in the RMSE for discharge simulations, compared to calibration on discharge alone. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models, leading to a better simulation of soil moisture content throughout the catchment and a better simulation of discharge in upstream areas.

The PCRaster Software and Course Materials for Teaching Numerical Modelling in the Environmental Sciences

Teaching numerical modelling in the environmental sciences not only needs good software and course material but also an understanding of how to program the models in the computer. Conventional environmental modelling procedures require computer science and programming skills, which may detract from the important understanding of the environmental processes involved. An alternative strategy is to build a generic toolkit or modelling language that operates with concepts and operations that are familiar to the environmental scientist. PCRaster is such a spatio-temporal environmental modelling language developed at Utrecht University, the Netherlands. It is used for teaching modelling in classrooms and over the Web (distance learning) at three levels: (1) explaining environmental processes and models, where models with a fixed structure of model equations are evaluated by changing model parameters, (2) teaching model construction, where students learn to program spatial and temporal models with the language, and (3) teaching all phases of scientific modelling related to field research. So far, we have received positive responses to these courses, largely because the software provides a set of easily learned functions matching the conceptual thought processes of a geoscientist that can be used at all levels of teaching.

Conditioning a Process-Based Model of Sedimentary Architecture to Well Data

ABSTRACT Prediction of sedimentary architecture for modeling of fluid flow in hydrocarbon reservoirs and aquifers is accomplished mainly using stochastic, structure-imitating models, because these can be conditioned to data from wells, seismic profiles, and outcrop analogs. Conditioning implies that modeled architecture fits all available observations. However, the sedimentary architecture simulated by such models is commonly unrealistic. Process-based (forward) models potentially provide more realistic prediction and understanding of sedimentary architecture, but these models are not widely used because conditioning to well, seismic, or outcrop data is considered to be very difficult. We show here that conditioning of process-based models to well data is possible in principle, using a 3D alluvial-architecture model as an example. This model considers the formation of alluvial deposits as a predominantly deterministic process, with a single channel belt moving by avulsion over an aggrading floodplain. However, the initial floodplain topography is simulated by a random field, thus yielding different model output for each run. Monte Carlo simulation was used to produce model realizations that fit five hypothetical vertical wells within predetermined tolerance bands. Such simulation allows calculation of the probability of occurrence of channel-belt deposits for each 3D cell in the 3D block of sediments generated by the model, as well as the probability distributions of volumes of channel-belt deposits and connectedness ratios. Adding more conditioning wells increases the precision of model predictions. Application of this approach in practice will require a major effort, particularly in overcoming the anticipated large amounts of computing time.

Dynamic environmental modelling in GIS: 2. Modelling error propagation

Environmental modelling languages provide the possibility to construct models in two or three spatial dimensions. These models can be static models, without a time component, or dynamic models. Dynamic models are simulations run forward in time, where the state of the model at time t is defined as a function of its state in a period or time step preceding t. Since inputs and parameters of environmental models are associated with errors, environmental modelling languages need to provide techniques to calculate how these errors propagate to the output(s) of the model. Since these techniques are not yet available, the paper describes concepts for extending an environmental‐modelling language with functionality for error‐propagation modelling. The approach models errors in inputs and parameters as stochastic variables, while the error in the model outputs is approximated with a Monte Carlo simulation. A modelling language is proposed which combines standard functions in a structured script (program) for building environmental models, and calculation of error propagation in these models. A prototype implementation of the language is used in three example models to illustrate the concepts.

Linking external components to a spatio-temporal modelling framework: Coupling MODFLOW and PCRaster

An important step in the procedure of building an environmental model is the transformation of a conceptual model into a numerical simulation. To simplify model construction a framework is required that relieves the model developer from software engineering concerns. In addition, as the demand for a holistic understanding of environmental systems increases, access to external model components is necessary in order to construct integrated models. We present a modelling framework that provides two- and three-dimensional building blocks for construction of spatio-temporal models. Two different modelling languages available in the framework, the first tailored and the second an enhanced Python scripting language, allow the development and modification of models. We explain for both languages the interfaces allowing to link specialised model components and thus extending the functionality of the framework. We demonstrate the coupling of external components in order to create multicomponent models by the development of the link to the groundwater model MODFLOW and provide results of an integrated catchment model. The approach described is appropriate for constructing integrated models that include a coupling of a small number of components.

Spatio-temporal uncertainty in Spatial Decision Support Systems: A case study of changing land availability for bioenergy crops in Mozambique

Abstract Spatial Decision Support Systems (SDSSs) often include models that can be used to assess the impact of possible decisions. These models usually simulate complex spatio-temporal phenomena, with input variables and parameters that are often hard to measure. The resulting model uncertainty is, however, rarely communicated to the user, so that current SDSSs yield clear, but therefore sometimes deceptively precise outputs. Inclusion of uncertainty in SDSSs requires modeling methods to calculate uncertainty and tools to visualize indicators of uncertainty that can be understood by its users, having mostly limited knowledge of spatial statistics. This research makes an important step towards a solution of this issue. It illustrates the construction of the PCRaster Land Use Change model (PLUC) that integrates simulation, uncertainty analysis and visualization. It uses the PCRaster Python framework, which comprises both a spatio-temporal modeling framework and a Monte Carlo analysis framework that together produce stochastic maps, which can be visualized with the Aguila software, included in the PCRaster Python distribution package. This is illustrated by a case study for Mozambique in which it is evaluated where bioenergy crops can be cultivated without endangering nature areas and food production now and in the near future, when population and food intake per capita will increase and thus arable land and pasture areas are likely to expand. It is shown how the uncertainty of the input variables and model parameters effects the model outcomes. Evaluation of spatio-temporal uncertainty patterns has provided new insights in the modeled land use system about, e.g., the shape of concentric rings around cities. In addition, the visualization modes give uncertainty information in an comprehensible way for users without specialist knowledge of statistics, for example by means of confidence intervals for potential bioenergy crop yields. The coupling of spatio-temporal uncertainty analysis to the simulation model is considered a major step forward in the exposure of uncertainty in SDSSs.

Modelling landscape dynamics with Python

A new tool for construction of models is presented that allows earth scientists without specialist knowledge in programming to convert theories to numerical computer models simulating landscape change through time. This tool, referred to as the PCRaster Python library, consists of: (1) the standard Python programming language, which is a generic, interpreted scripting language, supporting object oriented programming; (2) a large set of spatial and temporal functions on raster maps that are embedded in the Python language as an extension; (3) a framework provided as a Python class to construct and run iterative temporal models and to calculate error propagation with Monte Carlo simulation; and (4) visualization routines to display spatio‐temporal data read and written by this framework. Python is a high‐level programming language, and users of the tool do not have to be specialist computer programmers. Users of the PCRaster Python library can take advantage of several other Python libraries, such as extensions for matrix algebra and for modelling in three spatial dimensions.