- Department of Simulation and Graphics

Transcription

Noname manuscript No.
(will be inserted by the editor)
Visual Data Exploration for Hydrological Analysis
Karsten Rink · Thomas Kalbacher · Olaf Kolditz
Environmental Earth Sciences, 65(5), pp 1395–1403
http://dx.doi.org/10.1007/s12665-011-1230-6
Abstract Hydrological research projects for integrated water resources management such as the IWAS initiative often accumulate large amounts of heterogeneous
data from different sources. Given the number of partners taking part in such projects, surveying and accessing the available data sets as well as searching for a defined subset becomes increasingly difficult. We propose
an integrated approach for a system combining visual
data management and numerical simulation which allows to survey and select data sets based on keywords
such as a region of interest or given indicators. An adequate 3D visualisation of such subsets helps to convey
information and significantly supports the assessment
of relations between different types of data. Furthermore, the interface between the visual data management system and finite element codes allows for the
straightforward integration of information into the numerical simulation process and the subsequent visualisation of simulation results in a geographical context.
We demonstrate typical workflows for integration and
processing within the system based on data from the
IWAS model region in Saudi Arabia and the TERENO
Bode Observatory in the Harz Mountains in Germany.
Additionally, we present examples for data import and
export based on established standard file formats.
Keywords Data exploration · 3D visualisation · Hydrological processes · IWAS · TERENO · OpenGeoSys
Karsten Rink, E-mail: [email protected]
Thomas Kalbacher, E-mail: [email protected]
Olaf Kolditz, E-mail: [email protected]
Deparment of Environmental Informatics,
Helmholtz Centre for Environmental Research,
Leipzig, Germany
1 Introduction
The IWAS project – the International Water Alliance
Saxony – is aiming at integrated water resources research in different hydro-climate sensitive regions [27].
These well-selected model regions include the Ukraine
[5,8,11,40,47,55]; the Middle East, with focus on Saudi
Arabia [42] and Oman [19, 48]; Brazil [35, 58] and Mongolia [20, 59]. Cross-cutting activities are focussing on
integrated modelling [25], socio-economic aspects [21,
46, 51] as well as capacity building [32].
Joint research projects such as the IWAS initiative
often accumulate huge amounts of heterogeneous data
from very different sources. This information might be
as diverse as raster data from digital terrain models,
land use, etc.; time series data such as temperature,
precipitation or ground water gauges and of course also
documents from state offices, scientific reports or surveys. Large scale projects are often handled by cooperating research facilities, universities and other project
partners. Therefore, it is mandatory to establish a wellstructured and easy-to-use data management system
for storage of collected as well as provided data. Lacking
such a system will make it unfeasible for any party to
get an overview of the available data. Given that such a
system exists, other important issues can be addressed
such as the assessment of data quality, completeness,
the need for pre- or postprocessing, etc. Either of these
may be performed manually or by defining adequate
algorithms. Scientific visualisation of the data for visual inspection by experts is an easy way for evaluating
the information prior and after error correction or signal processing algorithms have been applied to the data
sets.
There exists a large number of possibilities for processing and subsequent visualisation of geoscientific data,
most notably various types of geographic information
systems (GIS). These have been employed for many research activities dealing with integrated water resource
management (IWRM). GIS-based tools have been adopted to combine hydrological problems with related issues
from different areas such as land use [34], climate research [7, 9] and ecology [2] as well as addressing economical questions [13, 33]. Unfortunately, many GIS
just offer a 2D view of the selected data or provide
only limited possibilities for 3D object generation such
as ArcScene or the ArcGIS 3D Analyst extension [1].
However, the resulting objects do not represent a domain discretisation but rather (a set of) 3D surfaces
and are therefore not adequate for a subsequent FEM
simulation.
The creation and handling of complex 3D subsurface
data is not straightforward and further pre-processing
and adjustments are often required. Most of the graphical user environments for performing groundwater simulations and data management, e.g. Feflow [12], GMS
[17] or Hydro GeoAnalyst [22], use a conceptual modelling approach to create and manage numerical models
using the GIS based objects.
A number of commercial geoscientific CAD tools
such as Petrel [41], Rockworks [44], Earth Vision [10] or
GOCAD [18] are often used in the mining or petroleum
industry and allow for 3D modelling and visualisation
of geological data. Frequently, if the geometric modeling of subsurface systems becomes more complicated,
CAD or GIS tools provide unstructured meshes. However, these meshes often have an insufficient element
quality for the numerical analysis via finite element or
volume methods. Furthermore, the element sizes often
do not correspond with the numerical requirements.
This poses a problem for the analysis of hydrogeological processes, for instance if fracture or fault structures have to be taken into account. Additional discretisation strategies have to be prepared to provide
high resolution meshes along fracture intersections with
appropriate quality as shown in [24] and [36] for the
numerical modelling of transport processes in fracture
networks with matrix diffusion.
More detailed information about the integration of
CAD tools into the work flow for numerical analysis can
be found e.g. in [4], [23] and [24].
Integrated hydrosystem analysis, e.g. for catchment
hydrology, relies on the geometric coupling of surface
and subsurface compartments. Similar to the fracture
network modelling mentioned above, the interfaces between adjacent compartments require an adequate geometric and numerical representation. Corresponding
concepts have been presented recently by [3, 29, 30, 39,
52, 53]. For the coupling of surface and subsurface domains, data frequently has to be interpolated on different meshes, such as the combination of unstructured triangulations for groundwater models and rastered quadrilaterals for mesoscale hydrological models. Coupling
codes from different disciplines, e.g. subsurface hydrogeology and surface hydrology is much more complex
than simple geometric coupling of varying triangulations. It is also related to coupling of different concepts,
e.g. calculation of deterministic and stochastic quantities as in mesoscale hydrological modelling [45].
As a result of high resolution measurement technologies (i.e. airborne remote sensing) and the advances
in computational power (e.g. high-performance-computation on super computers), more and more complex
numerical models can be constructed. This increasing
model complexity requires the development of adequate
methods for data visualisation, in particular the use of
3D stereoscopic projection. As an example, [65] presented a workflow for scientific visualisation of process
uncertainty in geothermal reservoirs.
We propose an integrated approach that allows integration and exploration of geological, hydrological and
geographic information as well as the simulation of complex hydrological processes, e.g. the analysis of groundwater dynamics, recharge pattern and abstraction from
well fields. An overview of the functionality of our system is described in section 2, its application based on
case studies from an IWAS model region as well as
an TERENO observatory is demonstrated in section
3. The most important findings and technical developments are summarised in section 4.
2 Visual Data Management
We employ a framework called the OpenGeoSys Data
Explorer for the detailed visualisation of geoscientific
data in 3D space (see figure 1. The Data Explorer is part
of the interdisciplinary software project OpenGeoSys
(OGS) originally developed for the simulation of THMC
processes within fractured-porous media [60]. In contrast to most of the software mentioned in section 1,
OGS is available free of charge for scientific use. Furthermore, our software is platform independent and has
been tested on various Linux- and Windows-based operating systems as well as on Mac-OS.
Geo-referenced data loaded into OGS will be arranged in 3D space. This is also done for 2D data such
as maps or other data without actual height information. The z-dimension of any data set can be adjusted
or scaled once it has been loaded.
Environmental Earth Sciences, 65(5), pp 1395–1403, http://dx.doi.org/10.1007/s12665-011-1230-6
Fig. 1: User Interface of OpenGeoSys Data Explorer showing the integration of a typical data set for hydrological
modeling. A raster file and the boundary of the model region have been imported into the software along
with boreholes and a precipitation event. Also shown are detailed views of a time series curve and a borehole
stratigraphy.
Fig. 2: Interfaces for data-input (top row), data-output (bottom row) and native OpenGeoSys files. Native files
are currently in the process of being converted to XML to ensure easy validation of files and conversion to other
file formats. Furthermore, OGS has a number of interfaces to other simulation software also emplyed in the
hydrological research projects IWAS and TERENO.
The assembly of data in this way allows for full visual inspection and exploration in three dimensions.
This makes an assessment of the existing data possible and will support the user in estimating data quality
and finding inconsistencies between data sets. Quite a
number of artefacts can be easily detected in an user
controlled 3D space by simple visual inspection, for examples see figure 3. In the absence of ground truth data
this is often a necessary process and important for making decisions for a given case studies. Our programme
also offers an optional stereo mode for this process if a
stereo-capable monitor is used.
The software supports standard file formats such
as GeoTiff, ESRI ASCII and popular image formats
for raster data as well as ESRI shape files for vector
data. In addition a number of hydrological and geological software formats can be imported as well. Files generated by the Aquaveo Groundwater Modeling System
(GMS), the seismic simulation software Petrel, the geological modelling software GOCAD and the scientific
open source data format netCDF [37] can also be handled. For a complete overview of supported file formats
see figure 2.
The native file formats of our software are largely
compliant to the XML specification. While taking into
account existing standards such as the GML-standard
by the Open Geospatial Consortium [16] or Google’s
KML [28], the file format used by OGS is much more
compact since it is basically reduced to geometrical information (i.e. points, lines, surfaces and a small subset
of additional information). Furthermore, it includes information needed for the simulation of processes such
as boundary conditions (and their properties) or material properties, which are not covered in the standards
mentioned above. This way, we keep all the advantages
of the XML specification such as the easy validation
of files, extensibility, and mapping to other standards
via XSLT, etc. while using a file format specific to our
needs.
Furthermore, OGS employs a database interface that
supports all established systems. As a standard for data
concerning the IWAS and TERENO project database
(see section 3) the DublinCore Metadata Standard [62]
is used.
For the support of the wide spectrum of file formats
a number of software libraries have been integrated into
OpenGeoSys, all of which also comply to the requirements of platform independence and free distribution.
Examples are shapelib [49] or libgeotiff [14] for the import of geoscientific data or the application framework
Qt [43] for composition of the user interface as well
as 2d visualisation. Likewise we employ the Visualisa-
tion Toolkit (VTK) [57] for 3D visualisation. This allows for the use of a wide range of graphic- and signal
processing-filters as well as the straightforward definition of new functionality for illustration of the characteristics of geoscientific data or the simulation of processes. We implemented a number of visualisation filters that enhance certain aspects of data sets or provide an adequate visualisation of the region of interest
for a given problem. Examples are the mapping of a
3D mesh based on a given raster data set, the selection
of a user-specified aquifer or geological formation in a
subsurface model or the enhancement of stream lines or
parameter maps for a specified simulation.
Since VTK is employed for the rendering of data, an
interface for the import and export of VTK datasets has
also been integrated into the software. This allows for
a visualisation of OGS-projects in other graphic programmes and–vice versa–the import of data or visualisation results created by other software for a direct
comparison. In our framework this process is further
assisted by techniques such as crossfading of datasets
or assigning specific colours to given objects.
Furthermore, it is possible to export rendered data
to popular graphic formats VRML and OpenSG. The
latter enables the user to visualise the data in virtual
reality environments, such as the one installed at the
Helmholtz Centre for Environmental Research.
As the Data Explorer is now part of the original
OGS programme, any data that has been imported can
subsequently also be processed by the aforementioned
simulation methods as both modules of the software
access the same data structures. This way, OGS constitutes the combination of an advanced simulation tool
for a wide range of applications with a user interface
for the management of project data from files as well
as databases and the subsequent 3D visualisation of
geoscientific data and simulation results.
3 Examples
In this section we will illustrate the process of data visualisation with OGS. We will first explain the construction of a typical model based on the IWAS model region
in the Middle East and then briefly show the application
for the TERENO Bode Observatory to demonstrate the
portability of the concept to an arbitrary model region.
In this case study, the IWAS Middle East model region covers large parts of the Arabian Peninsula (2.2
mio km2 ) and is located mainly in the Kingdom of
Saudi Arabia and the Sultanate of Oman. The main objective here is the development of a sustainable manage-
Fig. 3: Examples for inconsistencies within the data. The left figure depicts two superelevated meshes of the same
region. Mesh A (yellow) and Mesh B (red) have roughly the same elevations but variations are easily visible and
may have effects on a subsequent simulation. The figure on the right shows a surface mesh and a number of
boreholes visualised as coloured tubes whose length is based on the borehole’s depth. It can be easily seen that
the coordinates of one borehole include an incorrect z-coordinate resulting in a positioning of the borehole head
above ground.
ment scheme for the limited groundwater resources [26].
A model for the simulation of the groundwater recharge
and -discharge based on precipitation, groundwater extraction through wells and other parameters therefore
needs to cover the whole model region but requires a
different level of detail for certain parts of this area.
While large regions are covered by uninhabited dessert,
locations with significant water withdrawal, e.g. agricultural centres or well fields, are of special interest
(figure 4a). All the necessary data for this case study
has been kindly provided by the Ministry of Water and
Electricity (MoWE) of the Kingdom of Saudi-Arabia
and the Gesellschaft für Technische ZusammenarbeitInternational Services (GTZ-IS).
A common starting point for a new project is the
acquisition of a digital elevation model (DEM) and a
boundary for the region of interest (ROI). Thanks to
the comprehensive exploration of our planet with satellites, high-quality DEMs are available from various sources at no charge [56]. The definition of ROIs on the
other hand can be accomplished either with standard
GIS software or by defining a boundary manually.
Given these two components, a surface mesh of the
ROI can be created (see figure 4b). It should be kept
in mind that this mesh might later be needed for numerical simulations of such processes as overland flow
or groundwater recharge and should therefore not contain distorted triangles. Obviously, it is possible to create a triangle-mesh from the DEM by regarding each
pixel of the raster as a square which can be divided
into two right-angled triangles. However, at this scale
a mesh this fine is unnecessarily complex and will only
slow down loading times, visualisation and the simulation process. However, the fast and reliable construction of 3D finite element meshes in a non-trivial problem and generation of such meshes has been a topic in
mathematics and computer graphics for the past twenty
years [50]. Therefore, we are incorporating the open
source software Gmsh [15], a generator for 2d or 3d
finite element meshes, for this task. Additional information can be incorporated into the mesh in the form
of either point data such as observation stations or boreholes; or polylines such as fault systems or rivers.
Given a surface mesh, supplemental data can be
mapped onto it in the form of either textures (e.g. land
use data) or scalar data (such as parameter maps for
precipitation, etc.).
One of the focus points of the IWAS initiative is the
simulation of certain hydrogeological processes. Therefore it is necessary to add information for the construction of a hydrogeological model (figure 5). If DEMs
of aquifers in the region of interest are available these
might be created in much the same way as the surface
mesh. Alternatively, a model may also be interpolated
based on borehole data. In the first case, the surface
mesh can be expanded into 3D by adding a given numbers of layers where neighbouring layers are connected
by prism- or tetrahedra-elements. Each layer can then
be mapped based on a given DEM raster file [63]. The
second case cannot be handled by OGS itself and we of-
Fig. 4: Visualisation of GIS data imported into the OGS Data Explorer. The digital elevation model of the Arabian
Peninsula, country borders (red) and the model region (white) are depicted in the left figure. Further information
for hydrogeological modeling is provided by well fields (green) and agricultural centres (blue). Based on the model
domain, a surface mesh has been created and mapped using a DEM of the region (figure 4b). For better visibility,
colour-coded elevation information has been mapped onto the surface. (Data source: MoWE/GTZ-IS)
Fig. 5: Visualisation of a hydrogeological model. The left figure shows boreholes and wells providing stratigraphic
information. Based on this information and additional digital layers map a hydrogeological model including aquifers
and aquicludes has been constructed. (Data source: MoWE/GTZ-IS)
ten incorporate programs such as the Aquaveo Groundwater Modeling System (GMS) for this task.
For simulation purposes (see figure 6 for an example) it is now possible to define boundary conditions,
source terms, etc. on existing geometric objects such
as points (e.g. well positions), lines (e.g. boundaries,
rivers) or surfaces (e.g. the ocean floor or faults). Note
that additional information such as borehole data, geographic information and much more can also be added
at any point for either visual reference or inclusion in
the model. For more information on simulation with
OpenGeoSys the interested reader is referred to [24,29,
38, 61].
The OGS Data Explorer has been successfully applied for an initial simulation of groundwater recharge
and -discharge in the IWAS Middle East region and is
of practical importance for stake holders in Saudi Arabia. GTZ-IS is now employing the software to complete
the data basis for regional water resources management
purposes.
Fig. 6: Demonstration of the visualisation of distributed parameter fields, in this case annual precipitation in
the Arabian Peninsula. Different parameters can be assigned to the length and colour of the tubes. The right
figure shows the calculated hydraulic head distribution in the model area (coloured isosurfaces) as well as the
corresponding streamlines (red lines).
Fig. 7: Data integration for the Bode catchment as part of the TERENO project. A semitransparent view of the
underlying model has been superimposed on the DEM (left). For hydrological analysis a number of objects needed
as boundary conditions for a subsequent simulation are depicted as well, such as the river network within the
catchment, precipitation- (yellow) and ground water stations (light blue) and a subsurface model of the catchment
of the river Selke, a branch of the Bode river. On the right hand side is a close-up of the Selke catchment which
covers an area of 468 km2 . Precipitation gauging stations (red) and the over 5.000 boreholes (pink) in the region
have been superimposed on the data along with a mapping of accumulated precipitation per km2 .
To demonstrate the general application of the framework for hydrological problems, another example for
data integration in a different model region and at a
much smaller scale is presented: The Bode catchment
in central Germany (figure 7a) is one of the hydrological
observatories for the TERENO project [6, 30, 64]. The
catchment has an area of 3100 km2 with the Bode river
itself at a length of 169 km (see figure 7a). The catch-
ment of the Selke (figure 7b), a branch of the Bode river,
is of special interest for the project because of its gradient in land use between the agriculture in the lowlands
in the northeast of the catchment and the forests in the
low mountain ranges in the southwest. The region has
been intensively equipped with hydrologic instrumentation. There exists also a huge number of boreholes
because of the former use of this region for the mining
Fig. 8: 3D view of the Bode catchment including the boreholes in the Selke catchment (left). Again, all data has
been superelevated for better visibility. The right figure shows additional land use information mapped onto the
DEM. Furthermore, the subsurface model of the Selke catchment is visualised beneath the surface mesh. (Data
source boreholes: Landesbohrdatenbank Sachsen-Anhalt; Land use data: CORINE Land Cover 2000)
industry (see figure 8a). Based on the stratigraphic information from these boreholes a hydrogeological model
of the Selke catchment has been interpolated (figure
8b) and is used as the basis for a subsequent simulation
of groundwater discharge and recharge with boundary
conditions and source terms derived from the information gained from the instrumentation in the area. As
the project is still in the beginning stage, little or no
data as been acquired so far by the sensor network so
far and the simulation is currently still limited to a
proof of concept. Once data from a large number of
heterogeneous sources is recorded it will be challenging
to visualise the multitude of available data in a manner
that still allows experts to understand the illustrated
information and draw conclusions based on the visualisation. Relevant data sets can be static as well as
time-dependent, discrete or dense measurements, and
are available in differing spacial and temporal resolutions. The same challenge exists for the simulation of
(coupled) processes since as much of the relevant data
as possible should be integrated.
detection of inaccuracies within data or inconsistencies
across data sets.
The additional integration of data structures and
viewers for 2D data such as time series from data loggers
or stratigraphic profiles of boreholes is supporting the
process of model validation, i.e. visual comparison of
measured data and simulated results. For this purpose,
tools for automatic parameter estimation [54] will be
integrated into the framework.
The visual data management framework has been
applied to various case studies in different regions of the
world, showing the transferability of the methodology
independent of the specific scale of consideration (that
is, several million km2 for the Middle East to less than
500 km2 for the Selke catchment).
The presented methodology is providing valuable
tools for the straightforward development process of
numerical simulation models from geoscientific data. A
generally applicable continuous workflow has been developed for the construction of high quality finite element meshes for accurate numerical simulations which
are at the same time addressing the geometric complexity for real world applications.
4 Concluding Remarks
We presented an integrating approach for visual data
management for the analysis and simulation of hydrological processes in real world applications. The visual
framework has interfaces to standardised databases as
well as established file formats and allows for the exploration of geoscientific data in 3D space. This way an
easy to use tool is provided for data validation, i.e. the
Acknowledgements This work was supported by funding from
the Federal Ministry for Education and Research (BMBF) in the
framework of the project “IWAS - International Water Research
Alliance Saxony” (grant 02WM1027) and by TERENO (Terrestrial Environmental Observatories). We would like to thank the
Ministry of Water and Electricity (MoWE) of the Kingdom of
Saudi Arabia and the Gesellschaft für technische ZusammenarbeitInternational Services (GTZ-IS)/Dornier Consulting for the data
of the IWAS Middle East model region. The IWAS Middle East
groundwater case study is a result of the research collaboration
between MoWE, GIZ, TU Darmstadt and UFZ. Other data sets
shown in this paper have been kindly provided by Edda Kalbus,
Jan Friesen, Luis Samaniego, Christian Schmidt and Andreas
Musolff. Furthermore, we would like to thank everyone participating in the OpenGeoSys project, especially Thomas Fischer,
Lars Bilke, Feng Sun and Wenqing Wang.
References
1. ArcGIS 3D Analyst. http://www.esri.com/software/
arcgis/extensions/3danalyst/index.html
2. Bangert U, Vater G, et al.: Ökologische Risikoanalyse für
Feuchtgebiete mit Hilfe einer GIS-gestützten Modellierung
der Vegetationsentwicklung. IALE Struktur Workshop 2003,
Salzburg (2003)
3. Anders F-C, Ostländer, et al.: Designing Service Architectures
for Distributed Geoprocessing. Challenges and Future Directions Transactions in GIS, 11(6): 799–818 (2007)
4. Blessent D, Therrien R and MacQuarrie K: Coupling geological and numerical models to simulate groundwater flow
and contaminant transport in fractured media. Comp Geosci,
35(9):1897–1906 (2009)
5. Blumensaat F, Wolfram M, Krebs P: Sewer model development under minimum data requirements. Environ Earth Science, DOI: 10.1007/s12665-011-1146-1 (2011)
6. Bogena H, Schulz K, Vereecken H: Towards a Network of Observatories in Terrestrial Environmental Research, ADGEO, 9:
109–114 (2006)
7. Colditz R, Conrad C, et al.: TiSeG – A Flexible Software
Tool for Time Series Generation of MODIS Data Utilizing the
Quality Assessment Science Data Set. IEEE Trans Geosci Rem
Sens, 46(10): 3296–3308 (2008)
8. Delfs J-O, Blumensaat F, et al.: Coupling hydrogeological
with surface runoff model in a Poltva case study in Western Ukraine. Environ Earth Science, DOI: 10.1007/s12665-0111285-4 (2011)
9. Dietrich O, Redetzky M, Schwarzel K: Wetlands with controlled drainage and sub-irrigation systems - modelling of
the water balance. Hydrological Processes, 21(14): 1814–1828
(2007)
10. Earth Vision. http://www.dgi.com/
11. Ertel A, Lupo A, et al.: Heavy load and high potential: Anthropogenic pressures and their impacts on the water quality
along a lowland river (Western Bug, Ukraine). Environ Earth
Science, DOI: 10.1007/s12665-011-1289-0 (2011)
12. Feflow. http://www.feflow.info/
13. Georgiadou Y, Bernhard L, Sahay S: Implementation of Spatial Data Infrastructures in Transitional Ecomomics (II). Journal of Information Technology for Development, 13(1): 1–6
(2007)
14. The GeoTIFF file format. http://geotiff.osgeo.org/
15. Geuzaine C, Remacle J-F: Gmsh – a three-dimensional finite
element mesh generator with built-in pre- and post-processing
facilities. http://geuz.org/gmsh/
16. Open Geospatial Consortium, Inc: Geography Markup Language, http://www.opengeospatial.org/standards/gml
17. GMS. http://www.aquaveo.com/gms
18. GOCAD. http://www.gocad.org/
19. Grundmann J, Schütze N, et al.: Towards an integrated
arid zone water management using simulation based optimisation. Environ Earth Science, DOI: 10.1007/s12665-011-1253-z
(2011)
20. Hartwig M, Theuring P, et al.: Sources of suspended
sediments and its implications on ecosystem functions in
the Kharaa river (Mongolia). Environ Earth Science, DOI:
10.1007/s12665-011-1198-2 (2011)
21. Horlemann L, Dombrowsky I: Institutionalizing IWRM in
developing and transition countries - The case of Mongolia. Environ Earth Science, DOI: 10.1007/s12665-011-1213-7 (2011)
22. HydroGeo Analyst. www.water.slb.com
23. Kalbacher T, Wang W, McDermott C, Kolditz O, Taniguchi
T: Development and application of a CAD interface for fractured rock. Environ Geol, 47(7):1017–1027 (2005)
24. Kalbacher T, Mettier R, et al.: Geometric modelling and
object-oriented software concepts applied to a heterogeneous
fractured network from the grimsel rock laboratory. Comput
Geosci, 11(1):9–26 (2007)
25. Kalbacher T, Delfs J-O, et al.: The IWAS-ToolBox: Software
Coupling for an Integrated Water Resources Management. Environ Earth Science, DOI: 10.1007/s12665-011-1270-y (2011)
26. Kalbus E, Oswald S, et al.: Large-scale modelling of groundwater resources in an arid region. Groundwater Quality Management in a Rapidly Changing World, GQ2010, (2010)
27. Kalbus E, Kalbacher T, et al.: IWAS - Integrated Water
Resources Management under different hydrological, climatic
and socio-economic conditions. Environ Earth Science, DOI:
10.1007/s12665-011 (2011)
28. The KML file format. http://code.google.com/apis/kml
29. Kolditz O, Delfs J-O, et al.: Numerical analysis of coupled
hydrosystems based on an object-oriented compartment approach. Journal of Hydroinformatics, 10(3):227–244 (2008)
30. Kollet S J, Maxwell R M: Integrated surface-groundwater
flow modeling: a freesurface overland flow boundary condition in a parallel groundwater flow model. Adv Water Resour,
29(7):945–958 (2006)
31. Kunkel R, Sorg J: Management of heterogeneous data from
terrestrial observatories using a coupled sstem of content management, relational database and Web-GIS-services. Data Management Workshop, Cologne, Germany (2009)
32. Leidel M, Niemann S, Hagemann N: Capacity Development as key factor for Integrated Water Resources Management (IWRM) -Improving water management in the Western Bug River Basin, Ukraine. Environ Earth Science, DOI:
10.1007/s12665-011-1223-5 (2011)
33. Lienhoop N, Messner F: The Economic Value of Allocating
Water to Post-Mining Lakes in East Germany. Water Recources
Management, 23(5): 965–980 (2009)
34. Le B Q, Park S, et al.: Land Use Dynamic Simulator (LUDAS): A multi-agent system model for simulating spaciotemporal of coupled humand-landscape system. Ecol Informat,
5(3): 203–221 (2008)
35. Lorz C, Abbt-Braun G, et al: Challenges of an Integrated
Water Resource Management for the Distrito Federal, Western
Central Brazil - Climate, Land Use and Water Resources. Environ Earth Science, DOI: 10.1007/s12665-011-1219-1 (2011)
36. Mettier R, Kosakowski G, Kolditz O: Infuence of small-scale
heterogeneities on contaminant transport in fractured crystalline rock. Ground Water, 44(5):687–696 (2006)
37. The network Common Data Form file format.
http://www.unidata.ucar.edu/software/netcdf/
38. OpenGeoSys – scientific software for coupled processes in
porous media, http://www.opengeosys.net
39. Panday S, Huyakorn PS.: A fully coupled physically-based
spatially-distributed model for evaluating surface/subsurface
flow Adv Water Resour, 27:361–382 (2004)
40. Pavlik D, Söhl D, et al.: Dynamic downscaling of global climate projections for Eastern Europe with a horizontal resolution of 7 km. Environ Earth Science, DOI: 10.1007/s12665-0111081-1 (2011)
41. Petrel.
http://www.slb.com/services/software/geo/
petrel.aspx
42. Pfletschinger H, Engelhardt I, et al.: Soil column experiments
to quantify vadose zone water fluxes in arid settings. Environ
Earth Science, DOI: 10.1007/s12665-011-1257-8 (2011)
43. Qt – Cross platform application and UI framework.
http://qt.nokia.com/
44. Rockworks. http://www.rockware.com/
45. Samaniego L, Bárdossy A: Exploratory modelling applied
to integrated water resources management. In: Schumann A,
Pahlow M (eds.): Reducing the Vulnerability of Societies to
Water Related Risks at the Basin Scale. IAHS Publ. 317:197–
203 (2007)
46. Schanze J, Trümper J, et al.: A methodology for dealing with
regional change in integrated water resources management. Environ Earth Science, DOI: 10.1007/s12665-011-1311-6 (2011)
47. Scheifhacken N, Haase U, et al.: The comparison and suitability of assessment methods to identify the hydro-morphological
status of a transboundary river in the Ukraine. Environ Earth
Science, DOI: 10.1007/s12665-011-1218-2 (2011)
48. Schütze N, Kloss S, et al.: Optimal planning and operation
of irrigation systems under water resource constraints in Oman
considering climatic uncertainty. Environ Earth Science, DOI:
10.1007/s12665-011-1135-4 (2011)
49. Shapefile C Library. http://shapelib.maptools.org/
50. Shewchuk J R: What is a Good Linear Finite Element? Interpolation, Conditioning, Anisotropy, and Quality Measures.
Technical Report Department of Electrical Engineering and
Computer Science, University of Berkeley (2002)
51. Sigel K, Altantuul K, Basandorj D: Household needs and demand for improved water supply and sanitation in peri-urban
ger areas: The case of Darkhan, Mongolia. Environ Earth Science, DOI: 10.1007/s12665-011-1221-7 (2011)
52. Sudicky E, Jones J, Park Y: Simulating complex flow and
transport dynamics in an integrated surfacesubsurface modeling framework. Geosci J, 12(2):107–122 (2008)
53. Sulis M, Mayerhoff S, et al.: A comparison of two
physics-based numerical models for simulating surface watergroundwater interactions. Adv Water Resour, 33(4):456–467
(2010)
54. Sun F, Shao H, et al.: Change of subsurface flow regime in
the Nankou Area, Beijing. Environ Earth Sci (accepted) (2011)
55. Tavares T, Tarasiuk M, et al.: Estimation of spatially
distributed soil information. Dealing with data shortages
in the Western Bug Basin. Environ Earth Science, DOI:
10.1007/s12665-011-1197-3 (2011)
56. US Geological Survey: Earth Resources Observation and Science Center. http://eros.usgs.gov
57. The Visualizaton Toolkit. http://www.vtk.org/
58. Vasyukova E, Uhl W, et al.: Challenges of Drinking Water Production from Surface Water Sources in Brasilia DF,
Brazil. Environ Earth Science, DOI: 10.1007/s12665-011-13081 (2011)
59. Vetter S, Schaffrath D, Bernhofer C: Spatial Simulation
of evapotranspiration of semi-arid Inner Mongolian grassland
based on MODIS and eddy covariance data. Environ Earth
Science, DOI: 10.1007/s12665-011-1187-5 (2011)
60. Wang W, Kolditz O: Object-oriented finite element analysis
of thermo-hydro-mechanical (THM) problems in porous media.
Int J Numer Meth Engng, 69:162–201 (2007)
61. Wang W, Kosakowski G, Kolditz O: A parallel finite element
scheme for thermo-hydro-mechanical (THM) coupled problems
in porous media Comput Geosci, 35(8):1631–1641 (2009)
62. Weibel S L: Dublin Core Metadata Initiative: A Personal
History. Encyclopedia of Library and Information Science, 2nd
Edition. Taylor and Francis, London (2009)
63. Wu Y, Toll M, et al.: Development of a high-precision groundwater model with scarce data: The Wadi Kafrein area. Environmental Earth Sciences, DOI: 10.1007/s12665-010-0898-3
64. Zacharias S, Bogena H, et al.: A network of terrestrial environmental observatories in Germany. Vadose Zone Journal,
(submitted) (2010)
65. Zehner B, Watanabe N, Kolditz O: Visualization of gridded
scalar data with uncertainty in geosciences. Comput Geosci,
36(10):1268–1275 (2010)

- Department of Simulation and Graphics

Transcription

Similar documents

Heft 168 Wei Yang Discrete-Continuous Downscaling Model for