Environmental Earth Sciences manuscript No.(will be inserted by the editor)
Visualisation Strategies for Environmental ModellingData
Karsten Rink · Lars Bilke · Olaf Kolditz
The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-013-2970-2
Abstract We present a framework that allows users
to apply a number of strategies to view and modify
a wide range of environmental data sets for the mod-
elling of natural phenomena. These data sets can be
concurrently visualised to find inconsistencies or arte-
facts. This ensures at an early stage that models set up
for the simulation of hydrological or thermal processes
will not give implausible results due to complications
based on input data. A number of generally applica-
ble visualisation techniques are provided by our frame-
work to help researchers detect potential problems. We
also propose a number mapping algorithms for the in-
tegration of multiple data sets to resolve some of the
most common issues. Techniques for the presentation of
input- and modelling data in combination with simula-
tion results are proposed with respect to the benefits ofvisualisation of environmental data within specialised
environments. The complete workflow from input data
to presentation is demonstrated based on a case study
in Central Germany. We identify typical problems, pro-
pose approaches for a suitable data integration for this
case study and compare results of the original and mod-
ified data sets.
Keywords Computer Graphics · Visualisation ·Virtual Reality · Hydrology · OpenGeoSys Data
Explorer
K. Rink and L. Bilke and O. KolditzDepartment of Environmental Informatics,Helmholtz Centre for Environmental Research,Leipzig, Germany,E-mail: [email protected]
O. KolditzApplied Environmental System Analysis,Technische Universitat Dresden,Dresden, Germany
1 Introduction
Simulation of natural phenomena is becoming increas-
ingly important as we are faced with problems such as
climate change, overpopulation and the need to change
from fossil fuels to green energy. For instance, compre-
hending hydrological processes is necessary to predict
floods [23, 30], develop water management schemes for
arid regions [12, 15, 41] or simulate the contamination
of drinking water [4, 31]. Likewise, an understanding
of thermal processes is needed for geothermal applica-
tions such as extraction of geothermal energy [16, 38]
or the safety of nuclear waste deposits [35,42]. In order
to run simulations for a better understanding of such
phenomena in general or specific predictions for certain
regions, it is necessary to set up models based on a
large number of data sets. While basic simulations can
be calculated with only a few data sets, additional infor-
mation is often required to account for as many of the
natural characteristics for a given process as possible.
These data sets often differ significantly in spatial and
temporal resolution, structure, area of influence and re-
liability. A possible classification based on the structure
of input data sets is the following:
– Time series data: Sensors measuring temperature,
precipitation, soil moisture, etc. at a specific loca-
tion.
– Geometric/Vector data: Courses of streams acquired
via GPS, boundaries (e.g. of model regions, river
catchments, etc.) or borehole data.
– Raster Data: Maps, climate data such as weather
surveillance radar, remote sensing data acquired via
satellite or airplane (e.g. digital elevation models
(DEM), hyperspectral imagery, etc.), or data ac-
quired using geophysical monitoring techniques such
as ground-penetrating radar, gamma spectroscopy
or electromagnetic induction.
For hydrological case studies, the model is often de-
fined by a DEM in combination with (interpolated)
subsurface information gathered from borehole strati-
graphies, with model boundaries that are either de-
fined, measured or calculated (such as the boundary
of a river catchment). Additional information might be
integrated from land use maps to include water bod-
ies, cities or agricultural areas. Based on this data,
the domain is discretised into a finite element mesh
for a subsequent simulation. Boundary conditions are
often based on time series data gathered from obser-
vation sites such as precipitation- or gauging-stations.
Using adequate process equations, models predict one
or more simulated parameters within the domain over
time. Typical results include groundwater recharge, tem-
perature, or the distribution of chemicals in the envi-
ronment.
A concurrent visualisation of all the specified data
sets is a crucial part of the workflow prior to as well as
after the actual simulation. It supports researchers in
identifying potential problems while creating the model
and will show the plausibility of the results in compar-
ison to the input data after running the simulation.
In section 2 we give an overview over existing soft-
ware and approaches for the visualisation of complex
geoscientific data. Section 3 gives an overview on how
data is visualised, modified and presented using our
framework. The application of the proposed methods
is demonstrated in section 4 for a case study in Central
Germany.
2 Related Work
Despite the complexity of geoscientific data, surpris-
ingly few tools offer an integrated visualisation of a
large number of heterogeneous data sets. A number
of Geographic Information Systems (GIS) such as Arc-
GIS [20] or GRASS [24] offer a wide range of function-
ality for 2D visualisation of raster- and vector data. 3D
GIS approaches also exist, such as ArcGIS 3D Analyst,
but do not support typical data from exploration or
modelling software. A number of approaches use a 2D
GIS context and employ the third dimension for visu-
alisation of statistical or climate data [8, 14, 34]. Simu-
lation codes and libraries only rarely offer visualisation
functionality. Aside from our own framework, one of the
exceptions is FEFLOW [5] which provides an extensive
graphical user interface for model setup and displaying
simulation results. Due to the lack of more specialised
software, visualisation is often limited to multi-purpose
software such as ParaView [2] or VisIt [21]. These sup-
port the visualisation of a large number of graphics
file formats for 3D data as well as time discretisation.
For most cases these tools cannot be employed directly
for geoscientific or simulation data and require external
data conversion first. Visualisation techniques for com-
plex 3D or 4D data sets found in literature are mostly
concerned with applications for climate data [17,25,39].
Examples for the use of visualisation of hydrogeological
data are few. Jones et al. [18] propose an approach for
multi-scale geological models that allows handling data
sets of varying scales; Ling and Chen [22] present tech-
niques for the visualisation of contaminant distribution
in geological data; and Schuchardt et al. [33] propose
a framework for numerical model setup using hydroge-
ological data integrating VisIt for 3D visualisation. In
addition, there exist a number of commercial products
developed for use in the petroleum industry such as
GoCAD, Petrel or LeapFrog Geothermal [3], support-
ing mostly their own proprietary data formats.
3 Data Visualisation and Presentation
An adequate visualisation presents a tremendous bene-
fit for researchers as it provides a better understanding
of the data itself as well as the correlations between
variables within one or across multiple data sets. Fur-
thermore, it supports researchers in finding potential
problems for the subsequent modelling and simulation
process. Data visualisation is also an effective means
for presenting progress or results of a research study to
stakeholders or the interested public.
We have developed the OpenGeoSys Data Explorer
framework [27] for import and basic visualisation of geo-
scientific data as well as model information and simu-
lation results (Fig. 1). The Data Explorer is a platform-
independent graphical user interface for the OpenGeoSys
simulation code [19] for coupled thermal, hydrological,
mechanical and chemical processes. Display, interaction
and modification of data within the framework is im-
plemented using the Visualization Toolkit (VTK) [32],
an established library for 3D computer graphics and vi-
sualisation. For subsequent presentations, the Data Ex-
plorer also allows to export single geoscientific data sets
as well as complete scenes consisting of multiple data
sets into popular graphics formats, such as the VTK-,
VRML-, OpenSG [26] or Autodesk-format. This allows
for professional postprocessing using software such as
ParaView or Unity [9]. Additional VTK functionality
for performing transformations or emphasising certain
aspects of the data can be either applied within the
framework or after export. Likewise, after conversion
The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-013-2970-2
Fig. 1: The OGS Data Explorer framework displaying various input data sets from the Rappbode reservoir system,
including DEM [37], water bodies [1], observation sites, rivers, and time series data. While all spatial data sets are
visualised in 3D, the time series (in this case water temperature, water level and spectral absorption) are displayed
in a diagram. (Time series data kindly provided by Karsten Rinke)
to either OpenSG or Unity, advanced rendering meth-
ods can be applied to objects or scenes within the re-
spective frameworks. This workflow for preparation and
visualisation of models as well as simulation results has
already been applied successfully in cooperation with
experts for multiple hydrogeological studies, including
model regions in Germany [27], China [36], Brazil [10]
and the Middle East [11]. While these publications fo-
cus on the data integration and modelling aspects using
the OpenGeoSys Data Explorer, this paper is centred on
visualisation and presentation aspects.
3.1 Typical Data Integration Problems
Difficulties often arise when attempting to concurrently
visualise multiple data sets acquired under different cir-
cumstances or using different devices. Data sets might
include artefacts due to errors during data acquisition
or transfer. More frequently, problems arise because of
inconsistencies between data sets. Typical examples for
inconsistencies include differences in scale, resolution,
coordinate systems and measurement techniques. Of-
ten these issues are interconnected: if two data sets
have large differences in scale, the data set with the
smaller extent often has a finer resolution. This might
be caused by the data being acquired using a different
device (satellite imagery vs GPS) or using a different
parameterisation. Also, different acquisition techniques
often have different offsets for measured parameters or
implicitly employ certain preprocessing algorithms or
coordinate systems. Likewise, if data is generated based
on other data sets, some of the original information
might be lost. For example, geometric data created us-
ing GIS is frequently missing elevation information be-
cause this is often considered insignificant in a 2D envi-
ronment using a sequence of layers. Other errors might
be caused by artefacts, such as cloud cover in satellite
imagery, stained sensors at observation sites or extreme
weather events. Errors might even be introduced into
the data after the actual measurement process, for ex-
ample due to inaccuracy during meter-reading, using
a wrong projection when harmonising the coordinate
systems of multiple data sets, transposing digits when
The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-013-2970-2
manually copying or transcribing data, or OCR-errors
when digitalising data. Finally, problems might arise
in the model creation process itself when choosing a
parameterisation of the finite element mesh, merging
similar soil types, or interpolating the subsurface based
on borehole information.
Even though several of the problems described above
occur quite frequently, most are too complex to check or
fix automatically. While automated data modification
algorithms are employed for some common issues where
necessary modifications are unambiguous (see [27] for
details), for the most part we take advantage of a con-
current visualisation of relevant data sets. Experts can
often easily detect potential problems in a 3D environ-
ment either instantly or after some inspection of the
data given a set of basic tools.
3.2 Visualisation
As previously mentioned, visualisation of input-, model-
and simulation-data is a valuable help during every step
in the modelling workflow. It is an important means for
initially detecting potential problems, evaluating modi-
fied data sets and comparing them to the original data,
and verifying the plausibility of simulation results. With
some additional preparation, such an integrated scene
consisting of multiple data sets can then also be used
for presenting the progress or results of this particular
study to other scientists or stakeholders.
The OGS Data Explorer framework supports a large
number of file formats commonly used in the creation
of a simulation model, including ESRI shape files [6],
various raster- and mesh file formats as well as files
containing borehole stratigraphies or other geological
information. After loading, every data set (except time
series information provided by data loggers) is rendered
in 3D space.
The two basic classes of input data sets are vector-
and raster-data. Vector data typically consists of in-
formation given in the form of points and (poly-)lines.
While the data can be displayed in exactly this form,
it is often hard to actually see such structures in a
complex scene consisting of many data sets. To ensure
that this data (e.g. boreholes, logger positions or even
streams) remains visible, we typically represent point
data using glyphs (typically spheres or arrows) while
geometric lines (i.e. boreholes or streams) are rendered
as tubes. While a number of parameters can be adjusted
for displaying vector data in such a way, the radius r
of glyphs and tubes is by far the most important for an
adequate visualisation. The extend of model regions we
have worked with in the past varies significantly, rang-
ing from less than one hectare (ca. 2.5 acres) to more
than a million square kilometres [28]. Also, large differ-
ences of the E in different dimensions of a macroscale
hydrological modelmax{Ex,Ey}
Ez> 100 are quite com-
mon while for geothermal applications Ez might easily
be the largest extent. In our framework we have set the
default radius r = maxE150 , which has proven a good size
for structures to be instantly visible without appearing
too large or concealing to much of the surrounding area.
The user can easily adjust a number of parameters for
each data set individually, including the radius, colour,
opacity, etc. An application of these techniques is de-
picted in figure 3 where lines and points would be barely
visible without enlarging the data and using noticeable
colours. An application for the comparision of two geo-
metric data sets using different glyph size is illustrated
in figure 3c.
In contrast, raster data can contain a far wider range
of information. In general, it can be mapped as texture
onto surfaces. This is especially useful if the information
is of statistical nature such as the classification of land
use classes, or the distribution of social or economic
parameters. If the information can be interpreted in a
geometric sense, we typically use our framework to con-
struct geometric objects to represent this information.
Examples include creating surface meshes from digital
elevation models as well as cloud or humidity distribu-
tions from climate data [13]. DEMs in combination with
interpolation of borehole stratigraphies are the basis for
3D subsurface models [27] that are also used later for
the actual simulation of phenomena. Thus, simulation
related data forms a third class of data to be considered
for visualisation purposes.
As such, volume data can easily occlude a lot of
other information within the subsurface domain. We
employ transparency to avoid this issue or reduce the
visualisation to layer boundaries only. Another solution
utilises clipping-planes which can be set to see cross sec-
tions of subsurface models or simulation results. To fo-
cus on interesting subdomains within a data volume, it
is also possible to apply thresholds to a mesh based on
any parameter associated with nodes or elements, such
as materials, groundwater recharge, temperature, etc.
To intuitively represent various kinds of flow stream-
lines, iso-surfaces or arrow-glyphs can be employed (see
figure 4a). All of the above techniques can also be com-
bined to ensure a maximum of flexibility for presenting
case studies and their results.
The complete scene containing combinations of all
the types of data sets described above can be continu-
ously rotated and zoomed, each data set can be individ-
ually scaled and translated to account for differences in
offset or extent. Colours or lookup tables can be indi-
vidually chosen and opacity can be adjusted to ensure
The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-013-2970-2
basic visibility and make comparison between data sets
as simple as possible. Many of the potential problems
described in the last section become apparent instantly,
the most common examples being different coordinate
systems, missing elevation information in shape files,
false offsets or intersecting subsurface layers.
We employ the Visualization Toolkit for 3D visu-
alisation as it offers a wide range of functionality for
the transformation and modification of objects. In ad-
dition, the VTK library is extendable and allows for the
implementation of new functionality as well as the defi-
nition of pipelines for applying a workflow consisting of
a sequence of modifications to a given data set. While
the library implements a large number of visualisation
techniques, the algorithms described above for the var-
ious types of geoscientific data sets are the ones applied
most frequently.
3.3 Data Mapping
If a data set that might potentially present a prob-
lem during model creation or simulation, that data set
should be modified in such a way that the potential is-
sue is less likely to occur. Such modifications to the data
are always considered if experts expect the simulation
result to benefit from the change. That includes not
only modifications to data sets containing artefacts (as
shown in section 3.1) but might also result in modify-
ing actually accurate input files if they are inconsistent
with the rest of the data or might otherwise present
a problem later in the workflow. Examples are surface
triangles with a bad edge ratio that might result in os-
cillations as well as geometric information that does not
fit the finite element mesh, resulting in an incomplete
application of boundary conditions during the simula-
tion. The detection of degenerated mesh elements has
been thoroughly discussed in [27], the focus here will
be on a number mapping algorithms for various cases
to map points and lines or surfaces based on another
data set to remove inconsistencies or artefacts.
For distinction we will in the following refer to the
data set that is modified (i.e. mapped) as A and the
data set that A is mapped on as B. In general, a map-
ping of data set A can be performed if a data set B
exists such that Pxy(A)⋂Pxy(B) 6= ∅, where Pxy(X)
denotes the projection of X onto the xy-plane. The re-
sult of such a mapping operation is the modified data
set PB(A). In the algorithms proposed below a point
p ∈ A is mapped only if Pxy(p) ∈ Pxy(B). Otherwise
no value for p can be deducted from B and the point
is ignored (i.e. not modified). This allows to perform a
mapping of A unto B even if Pxy(A) 6⊂ Pxy(B) or even
Pxy(B) ⊂ Pxy(A).
Algorithm 1 - Mapping data on a raster: The ele-
vation information for data sets contained in a model is
usually extracted from a digital elevation model B. The
pixel size of DEMs can vary considerably. While free
DEMs such as NASA SRTM [7] or ASTER GDEM [37]
have a pixel size of 90 metres and 30 metres, respec-
tively, high resolution DEMs with a pixel size of one or
two metres are sometimes available from state offices or
other administrative organisations. Geometric points or
mesh nodes p ∈ A are assigned an elevation based on
the pixel q ∈ B with Pxy(p) ∈ q. To account for steep
gradients or artefacts in the DEM we do not assign the
value of q directly but use a 2D linear interpolation be-
tween q and its neighbours for calculating the actually
assigned elevation value (fig. 2a).
While Algorithm 1 correctly maps each point of
the data set based on the DEM, elevation of data sets
PB(A1) and PB(A2) need not be consistent. Polyline
data A1 representing observation sites, streams or bore-
holes might not match the surface mesh A2, even if both
have been mapped based on the same DEM B (see fig-
ure 3a). The position and local distribution of points
pi ∈ A1 may vary considerably from qj ∈ A2, therefore
the interpolated elevation might differ and the slopes
of line segment and triangle edges might vary consider-
ably depending on the position of their end points. To
minimise this effect, we generally try to integrate all rel-
evant geometric information (such as provided by A1)
during the generation of the surface mesh A2 (see [27]
for details on mesh generation). However, to get a con-
sistent result requires the typical segment length of
polylines in A1 to be roughly similar to the desired edge
length of triangles forming surface A2. If the local num-
ber of geometry points is significantly smaller or larger
than that of the mesh nodes, geometry points will form
plateaus on or span bridges across surface triangles, re-
spectively. This disparity grows with larger gradients
on the surface as well as with the difference between
the number of local geometric points and mesh nodes.
Algorithm 2 - Mapping data on a mesh: The pre-
ferred workflow consists of mapping the surface mesh
A2 based on the DEM B and afterwards mapping the
geometry A1 on C = PB(A2). A collision detection al-
gorithm will set the elevation of point p ∈ A1 to the
intersection of a vertical line l through p with C, i.e.
PB(p) = l(p)⋂
PB(A2) (fig. 2b). That way, all geomet-
ric points are matched exactly onto the surface, even if
the position of geometric points varies from the posi-
tion of mesh nodes or there are locally more geometric
points than mesh nodes. The only potential problem left
occurs if the distance of consecutive points in a poly-
line is larger than the edge length of the local triangles
The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-013-2970-2
(a) Mapping based on DEM (b) Mapping based on mesh (c) Additional geometric points
Fig. 2: Schematic of mapping algorithms implemented in the OpenGeoSys Data Explorer. (a) Calculation of
geometric point elevation based on weighted DEM pixels. (b) Projection of points onto mesh surfaces via triangle
line intersection. (c) Inserting additional points into geometry at intersections of geometric lines with mesh element
edges and nodes. Newly inserted points are marked in red.
forming the surface (see fig. 3b). Again, the disparity
is larger in the presence of large gradients. Therefore,
our algorithm will optionally insert additional points
into a polyline whenever the line intersects with trian-
gle edges or nodes and these additional points A+1 will
be mapped along with the original points in A1 (fig. 2c).
The resulting geometry A1 = PC(A1
⋃A+
1 ) is guaran-
teed to fit the surface perfectly (see figure 3) and the
subsequent increase of the number of geometric points
has in all test cases been insignificant in comparison to
the number of mesh nodes.
In the same way, a mesh A can also be mapped
based on a second mesh B. The mesh nodes p ∈ A are
handled the same way as geometric points in the pre-
vious example. This has been useful in the past if reli-
able information exists locally with Pxy(B) ⊂ Pxy(A).
While A is still required to represent the model area,
the topography of A can be improved by integrating in-
formation from B. An application for this algorithm is
shown in figure 8, where the values of a DEM acquired
via satellite are modified based on the much more reli-
able data of a bathymetry aquired via sonar.
Low pass filtering of surfaces: While not techni-
cally a mapping algorithm, we would like to add that
our framework also allows to apply a low pass filter on
surface meshes. If a surface triangulation A is signif-
icantly finer than the DEM B it is mapped on using
Algorithm 1, the result is an oversampling of B which
gives rise to step artefacts in PB(A) (see for example
figure 6c). This effect can be reduced or even avoided
by low pass filtering over the elevation of mesh nodes
p ∈ PB(A). The new elevation of p is given by
pz =1
|N |+ 2
(2pz +
∑x∈N
xz
)(1)
where N is the set of all nodes x directly connected to
p by a triangle edge. By weighting pz twice as much
as the neighbours of p the smoothing effect is approx-
imately that of a binomial or gaussian low pass filter.
Adjusting the influence of neighbours by distance to
p has been neglected since A is a finite element mesh,
including the common constraint that the size of neigh-
bouring elements must not vary in size by more than
30%. Application of such a low pass filter will reduce
outliers as well as step artefacts in PB(A), thus given
a better representation of the surface for presentation
purposes. The difference of simulation results using the
mapped surfaces PB(A) and the filtered surface PB(A)
have not been investigated yet but since step artefacts
are not a good representation of the actual surface of
the model region, results using A are expected to give
at least as good an approximation of simulated param-
eters as using A.
3.4 Presentation
We employ various methods for presentation of virtual
scenes of case studies that have progressed to a point
where it makes sense to discuss them in a larger group
to gain further insight or to formally present its re-
sults. The focus of our work is presentation in a vir-
tual reality centre with a 6x3 metre video wall using
stereoscopic projection and an optical tracking system
(Fig. 4a). Presentations within this environment are
used frequently for discussions between scientists with
The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-013-2970-2
(a) Geometry mapped based on DEM (b) Geometry mapped based on mesh
(c) Additional geometric points (d) Concurrent view of all algorithms
Fig. 3: Comparison of geometry mapping on a high-gradient mesh. The green geometry G is mapped on the
underlying DEM, also used to create the depicted surface mesh via Algorithm 1. The blue geometry B is mapped
directly on the surface via Algorithm 2. The red geometry R is mapped via the extended version of Algorithm
2 : Additional points have been inserted into R where the polyline intersects with triangle edges or nodes before
it has been mapped on the mesh. When projected into the xy-plane, the polylines of all three geometries are
identical. Given the underlying mesh, only R fits perfectly due to the mapping of the additionally inserted points.
(a) Comparison of geometries G and R, (b) comparison of geometries B and R, (c) top-down view for illustration
of additional points inserted into R in comparison to B, (d) a wider view of the data with a concurrent visualisation
of all three mappings on a semitransparent mesh. Note, that for surfaces with low gradients all proposed algorithms
give very similar results. The large differences visible in this example are due to the model being a mountainous
region with sudden changes in elevation.
a different background. We also present research results
to stakeholders during project meetings or inform the
general public during open day events.
For navigation within this environment and demon-
stration of models and simulation results we employ two
respective software systems: an OpenSG framework as
well as the Unity game engine. Special input devices
allow users to move within the data displayed in the
virtual reality environment. We integrated support for
3D mouse and flystick to make navigation within the
scene as intuitive as possible.
For both programmes we implemented functionality
typically needed during such presentations. Most im-
portantly, predefined viewpoints can be accessed that
take the user to a specific point in the scene, with a
predefined view of a certain interesting aspect in the
data. While such viewpoints could also be accessed us-
ing the aforementioned input devices, a smooth and tar-
geted transition to that point usually looks more pro-
fessional and finding that specific position and perspec-
tive is much more reliable. Viewpoints can be connected
via paths, so by defining a sequence of notable view-
points and connecting them accordingly, a presentation
can be conducted without actually using interaction de-
vices. While the OpenSG framework only supports di-
rect connections between viewpoints, Unity supports
the definition of arbitrary camera paths via polylines
or splines for convenient transit between two specific
points or the rotation around a certain object. At any
point on such a path objects can be set to appear or
The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-013-2970-2
Fig. 4: (left) Stereoscopic visualisation of an OpenFOAM and Min3P simulation [40] of stream flow in the hyporheic
zone in a virtual reality environment. (Data kindly provided by N. Trauth), (right) Visualisation of the Rappbode
reservoir in Unity, including camera paths and visual effects, employed for interactive presentations.
fade away. This allows to focus on data sets that are
particularly interesting at this part of the presentation
and to hide objects or groups of objects when they are
not needed. The velocity of movement along the camera
path as well as the time delay for fading objects can be
adjusted individually.
For data sets containing geometric information that
have been enhanced using glyphs and tubes (i.e. Obser-
vations sites, streams, boreholes, etc. – see section 3.2)
we often include more than one representation. When
looking at the complete scene it is necessary that, for
instance, river networks can be seen right away and are
therefore represented using a rather large radius. When
the user zooms into the scene to see details, these thick
tubes would look rather chunky. Having multiple rep-
resentations of one data set allows to switch to another
representation consisting of tubes with a smaller radius
during the zoom process. The cross-fading possibilities
of Unity are again very helpful here, but the basic con-
cept also works for general frameworks.
Similar techniques can be applied to mesh represen-
tations, e.g. of surfaces. Meshes with fewer triangles can
still adequately capture the basic layout of surface from
further afar while refined meshes are needed for a closer
look at the data. In contrast to the use of multiple rep-
resentations detailed before to ensure an optimal visual
impression for geometric data, the governing factor for
surfaces is not one of visibility but performance. As all
data is loaded locally via a fast local network, adding
multiple resolutions of meshes to a scene only makes
sense for very large data sets. In our experience these
are rare, as the quality of the input data often does not
justify an exceedingly fine discretisation of the domain.
Interaction tools allow users to select 3D objects to
see additional information. This includes the placement
of clipping planes within data sets as well as the opening
of context menus to display additional information or
even photographs of the real-world location to get a
better impression of what kind of environment the data
set actually represents.
In addition, the frameworks we employ for presenta-
tion also allow adding “cosmetic” effects to the presen-
tations. These do not enlarge the actual amount of in-
formation but make the visualisation easier to compre-
hend for the audience by employing familiar metaphors
for certain aspects of the scene. Examples include the
display of a sky with clouds or sunshine, or a realistic
display of reflecting surfaces of water bodies by assigned
adequate shaders (Fig. 4b), etc.
4 Case Study
As an example for the proposed techniques we present
a visualisation project containing more than 70 data
sets acquired within the TERENO initiative [43] in
Germany. TERENO is concerned with the prediction
of possible impacts of climate change. Four areas in
Germany have been selected and are now heavily in-
strumented to allow extensive studies and simulations
for researchers from many different disciplines. Hydro-
geological analysis in central Germany is being con-
ducted in the catchment of the River Bode with a size of
3,100 km2. Within that area a number of intensive test
sites have been selected, such as the catchment of the
Rappbode reservoir system, used as a source of drink-
ing water and electricity generation. The data sets con-
The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-013-2970-2
Fig. 5: Overview over the
Rappbode catchment and
reservoir system. Details
on regions of interest (a)-
(c) can be found in fig-
ure 6, region (d) is dis-
cussed in figure 7. (Stream
information from ATKIS
DLM [1])
(a) (b) (c)
(d) (e) (f)
Fig. 6: Results of geometry mapping and lowpass filtering on unstructured surface meshes based on algorithms
presented in section 3.3.
The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-013-2970-2
Fig. 7: (left) Contrast-enhanced DEM containing artefacts due to reflections of the surface of the water body
outlined in blue. (centre) The resulting surface representation distorts the actual surface of the water body, the
coast line seems different and a number of “islands” are visible. (right) After correcting the surface based on sonar
data (see figure 8) the reservoir is depicted correctly.
Fig. 8: Example for removal of artefacts using mesh-on-mesh mapping: (left) The surface mesh generated from
the original DEM includes prominent artefacts. (centre) Bathymetry of the Rappbode reservoir with a resolution
of 5 m acquired via sonar. (right) Corrected mesh using sonar data and lowpass filter on mesh node elevation.
(Bathymetry provided by Landesbetrieb fur Hochwasserschutz und Wasserwirtschaft, Sachsen Anhalt).
cerned with the project range from raster data (DEM,
soil moisture maps, land use classification, etc.) cover-
ing the complete area to observation sites covering only
a few square metres or a small number of points (i.e.
sensors). A surface mesh of the whole region consisting
of 1.08 million triangles (max. edge length 90 m) has
been created based on the ASTER-DEM [37], acquired
via satellite and with a pixel size of 30×30 m. For inten-
sive test sites refined surface representations have been
created. The mesh for the catchment of the Rappbode
reservoir system has 930 000 triangles with a maximum
edge length of 30 m. At such a small scale the inaccu-
racies of data sets are easily noticeable. Figure 5 gives
an overview over the region. The default colour lookup
table (LUT) used by our framework (Fig. 1) has been
replaced with a customised LUT more suited for this
region. The river network in the north-western part ap-
pears more prominent than the rivers in south-eastern
part due to a larger radius of the tubes representing
the stream network, to ensure better visibility amid the
mountainous region. Observation sites are marked by
red dots, also enlarged to be visible at this scale. A num-
ber of regions of interest have been in selected figure 5 to
demonstrate the application of algorithms proposed in
section 3. Detailed illustrations of these regions can be
found in figure 6. While generally the simple mapping of
geometry on meshes gives good results (see Algorithm 2
in section 3.3), there are a number of problems visible
in figures 6a and 6b. Due to the large surface gradients
in the mountains and the irregular distribution of geo-
The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-013-2970-2
metric points forming the polylines, parts of the rivers
represented by those polylines are occluded by the sur-
face (Fig. 6a). This effect is even more noticeable when
the river course does not quite match the river valley ex-
tracted from the DEM (Fig. 6b). Therefore, additional
points have been inserted into the river geometry before
mapping at the intersections of geometric lines with the
edges of surface triangles. This fixes the problem in both
cases (Figs. 6d and 6e). The original stream geometry
data set contains 529 points. Including the additional
points results in a data set with 6505 points. Although
the number of points has increased by a magnitude,
the actual amount of data is negligible in comparison
to the ca. 470 000 surface mesh nodes. Even though the
river in figure 6e does still not match the valley, it is
clearly visible now and will not be overlooked during
assessment of the data. Fixing this remaining problem
of the river not fitting the valley is not straightforward,
however, as the other streams from this data set match
their respective valleys and it is not clear if the error is
originating in the DEM or the stream data set.
Since the edge length of triangle elements forming
the surface is equal or smaller than the resolution of the
underlying DEM, step artefacts are visible when map-
ping the surface mesh (Fig. 6c). Lowpass filtering using
the direct neighbours of each node almost completely
remove this effect and results in a much smoother sur-
face that allows a better assessment of topological fea-
tures such as ridges and valleys (Fig. 6f).
Figure 7 illustrates the difficulty of creating a sur-
face representation from a DEM in the presence of arte-
facts, in this case due to reflection on the water surface
of the reservoir. Figure 8 shows the effect of re-mappingparts of the surface based on sonar data. These data sets
have a resolution of three to five metres, respectively,
and are much more reliable than the satellite imagery.
In addition, a low pass filter has been applied to the
surface, removing step artefacts and, again, resulting
in a smoother surface.
Besides the DEM and stream data, this case study
includes a number of sensor positions at the in- and
outlet of each reservoir (see figure 5). These sensors
are constantly monitoring water levels, temperatures
at various depths, as well as the spectral absorption
for determining the amount of dissolved organic carbon
within the water bodies [29]. For instance, the diagram
in figure 1 shows time series data that is attached to
an observation site. The selected time interval depicts
the increase in dissolved organic carbon (measured via
spectral absorption coefficient at 256 nm (SAK254)) af-
ter rising water levels due to heavy rainfall. Correla-
tions such as this are important for the quality of drink-
ing water extracted from the reservoir. Simulations are
planned based on these and other data sets to make
predictions about the development of the water qual-
ity. Data sets on the interaction of streams with the
groundwater are measured for small sections of rivers
in this region and employed for separate visualisation
studies (see figure 4a).
5 Conclusions
We presented a number of visualisation strategies for
the concurrent visualisation of geoscientific and mod-
elling data using the OpenGeoSys Data Explorer frame-
work . The proposed system renders heterogeneous data
sets in 3D space and offers functionality for meshing,
mapping and filtering data sets. Visualisation supports
users prior to model generation or simulation when check-
ing input data sets for potential problems due to arte-
facts or inconsistencies and while re-analysing the data
after modification based on previously found issues. Af-
terwards simulation results can be added to that same
visualisation for checking plausibility of simulation re-
sults as well as presenting the progress or results of
this particular study to other scientists or stakehold-
ers. The 3D visualisation is implemented using VTK
and applying additional algorithms for mapping and en-
hancement of aspects within the data. For presentations
in a virtual reality environment an OpenSG framework
and the Unity engine are employed. Additional func-
tionality has been implemented in both environments
to allow navigation and interaction with specific data
sets using specialised input devices. Visual Effects are
added to those presentations for making them more ap-
pealing to the audience. Future enhancements of the
system are added in coordination with experts and in-
clude user guidance during the modelling process to
allow semi-automatic modification of data sets as well
as additional scripting for a more straightforward han-
dling of the Unity engine.
Acknowledgements The research has been supported by theHelmholtz Association with the programme “Earth and En-vironment” and the TERENO initiative (Terrestrial Envi-ronmental Observatories). The authors would also like tothank Karsten Rinke, Nico Trauth, Christian Schmidt andUte Wollschlager for providing the data sets presented in thecase study.
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