VIEWS AND NEWS

International viewpoint and news

� Springer-Verlag 2012

Data and modelling platforms in environmental Earth

sciences

Olaf Kolditz � Karsten Rink � Haibing Shao �Thomas Kalbacher � Steffen Zacharias � Peter Dietrich

Currently, tremendous efforts are undertaken to establish

long-term observatories for monitoring environmental sys-

tems. Terrestrial Environmental Observatories (TERENO)

is an example for such an observation network. TERENO

(http://www.tereno.net) is an interdisciplinary and long-

term research programme involving six research centres of

the Helmholtz Association. Its goal is the observation and

exploration of long-term ecological, social, and economic

impacts of global change at regional level. Innovative

monitoring concepts based on current state-of-the-art mea-

surements technologies as well as remote sensing are uti-

lised to analyse and record processes in different terrestrial

compartments ranging from groundwater, soils, surface

water, vegetation up to the lower atmosphere (Zacharias

et al. 2011). For optimal use of the amount and variety of

collected information from the TERENO network, a suit-

able data and modelling platform is being developed.

The TERENO observatory operated by the Helmholtz

Centre for Environmental Research UFZ is located in the

Central German Lowlands. A key element of the moni-

toring concept is a hydrological observatory covering the

catchment of the Bode River (Fig. 1, centre).

To cover and integrate the variety of research activities

in the hydrological observatory, a data management con-

cept that combines monitoring, mapping and modelling is

required (Fig. 1, lower right).

Monitoring activities at catchment field scale are

embedded in a hierarchical monitoring concept covering

several intensively instrumented test sites (Rein et al. 2011).

The SoilCan project, a network of lysimeters, is being

installed in order to investigate the effect of changing cli-

mate on water and solute fluxes in the soil (Fig. 1, upper

left; Zacharias et al. 2011). The question is how those local

measurements and scientific findings can be translated into

A German-wide Earth observation network, TERENO, was launched

3 years ago by the Helmholtz Association and now brings together

climate and environmental research from the Alps to the Baltic coast.

UFZ researchers from the Department of Environmental Informatics,

Karsten Rink, Haibing Shao, Thomas Kalbacher, and Olaf Kolditz

together with colleagues from the Department of Monitoring and

Exploration Technologies, Steffen Zacharias and Peter Dietrich

outline how their collaborative work embarks on new paths with this

long-term environmental observation system.

Dr. Olaf Kolditz heads UFZ’s Department of Environmental

Informatics and chairs the Department of Applied Environmental

Systems Analysis at Technical University of Dresden. Dr. Peter

Dietrich heads UFZ’s Department of Monitoring und Exploration

Technologies and is also the Professor for Environmental and

Engineering Geophysics at the Eberhard-Karls-University of

Tubingen.

O. Kolditz � K. Rink � H. Shao � T. Kalbacher � S. Zacharias �P. Dietrich

Helmholtz Centre for Environmental Research-UFZ,

Permoserstrasse 15, 04318 Leipzig, Germany

O. Kolditz (&)

Applied Environmental Systems Analysis,

Technische Universitat Dresden, 01062 Dresden, Germany

e-mail: olaf.kolditz@ufz.de

P. Dietrich

Environmental and Engineering Geophysics,

Eberhard-Karls-University of Tubingen,

Holderlinstraße 12, 72074 Tubingen, Germany

123

Environ Earth Sci (2012) 66:1279–1284

DOI 10.1007/s12665-012-1661-8

larger scales relevant for water management purposes.

Figure 1 (upper right) depicts a model projection of future

groundwater recharge for all of Germany. With modern

remote sensing and novel measurement technologies (e.g.

direct push; Fig. 1, lower left; Leven et al. 2011), a com-

pletely new scan of both the Earth surface and the subsur-

face can be obtained. In order to handle the huge amounts of

information provided by integrative monitoring projects

like TERENO, appropriate data management systems have

to be developed that include tools for data integration,

assimilation as well as visualisation (Fig. 1, lower right).

A technical challenge is how to make observation data

optimally and instantly available to the community with

high quality standards, e.g. for history matching and/or

predictive analysis. After many years of research, well-

developed model concepts for hydrosystem analysis are

available today. Most of the existing hydrological models,

however, are very specialised, single-purposed and focussed

on particular environmental compartments such as surface

water, soil and groundwater systems, and use a rather small

portion of available data from observatories. Challenges

increase considerably, the greater the interdisciplinarity of

research becomes. Therefore, the development of cross-

sectional competences is essential for the development of

symbiotic data and modelling platforms with efficient

streams of information.

Workflows

Currently, a large variety of tools for data processing and

environmental modelling is available from different sour-

ces as commercial, scientific or open source software. For

optimal and efficient use of acquired data provided by

novel observation and exploration strategies, linking those

tools is a challenging task. For this purpose, the next

generation of data and modelling tools must be embedded

into entire workflows, incorporating data acquisition, data

management, integrative modelling and visualisation

instruments to prevent bottlenecks for data streams. In

order to achieve this objective, common purpose interface

protocols and new methods for heterogeneous code inte-

gration must be developed.

To prevent loss of information, the basic idea is the

development of continuous workflows. Figure 2 shows the

different components of workflow concepts, starting with

the acquisition of data from the different observatories.

Fig. 1 Concept of developing cross-sectional competences for the TERENO data and modelling platform

1280 Environ Earth Sci (2012) 66:1279–1284

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Data portal

TEODOOR, the TERENO data portal, is hosted at the

Research Centre Julich and can be accessed via

https://teodoor.icg.kfa-juelich.de/. TEODOOR is a unique

entry point—the door to a network of terrestrial observa-

tories. Each participating centre is responsible for its own

data, organisation, storage, backup etc. In this way, the

concept for data storage is decentralised on the one hand,

but on the other hand, the data management system is

centralised. Most important is the definition of standards

for the metadata layer. A common description of datasets

based on the INSPIRE directive for spatial data infra-

structure (http://inspire.jrc.ec.europa.eu) is allowing for

search throughout the entire data base. Standard protocols

for accessing the data (such as OGC webservices,

http://www.opengeospatial.org/) are used to guarantee

compatibility to related data bases (e.g. CUASHI initia-

tive). The TEODOOR portal allows versatile community

access to data sources. Property rights for different data

levels are regulated. Three different levels have been

established for data acquired within the project: Logger

outputs (level 0) and raw data (level 1) can only be

accessed by contacting the data owner using contact

information provided via metadata. Quality controlled data

(level 2) can be accessed directly. Algorithms for auto-

mated processing to provide this data layer are currently

under development. It is also planned to issue document

object identifiers (DOI) to datasets for unique referencing

and citation purposes.

Data integration

The huge amount, as well as the heterogeneity and diver-

sity of data that is typical for interdisciplinary research,

makes the development of data integration methods,

a substantial part of the entire workflow. The OpenGeoSys

(OGS) data explorer allows for combining a large variety

of data from very different sources, such as geographical,

geological, hydrological, and pedological information in a

versatile geometrical context (Rink et al. 2012). Such

methods are especially necessary for visualisation of

comprehensive datasets and particularly for data validation.

In a native way, it allows detecting inconsistencies between

different datasets and displaying data layers in different

contexts (e.g. hydrological units, climate data, and land

use) (Rink et al. 2011).

As an example, Fig. 3 shows the data integration for the

Selke subcatchment in the TERENO Bode basin. Datasets

from various disciplines can be combined in a 3D virtual

environment:

– Land use data (i.e. forest, agricultural and urban areas

from CORINE) are mapped on the digital elevation

model (DEM),

– Geological information from the borehole data base of

Saxony-Anhalt is included with appropriate search

functionalities, e.g. for wellbore positions, depth and

stratigraphy,

– Hydrological and hydrogeological data are available,

e.g. river network, measured water levels, precipitation,

– Climate data and modelling results can be incorporated,

e.g. surface temperatures, soil moisture, drought indi-

ces, etc.

This comprehensive integration of various data is an

important prerequisite for model preparation, i.e. for pro-

cess-based simulation (e.g. grid generation purposes,

assignment of boundary conditions, model parameterisa-

tion). The present concept of data integration has been

successfully applied recently in a number of hydrological

and geotechnical projects (IWAS (Kalbus et al. 2012),

SMART (Wu et al. 2011), NANKOU (Sun et al. 2011),

CLEAN and WESS).

Data modelling analysis of hydrological processes

Based on measured and observed data, multi-compartment

models have been established to describe natural systems.

This is related to both structures (e.g. land surface and

geological structure models) and processes (e.g. physical,

chemical, and biological behaviour). Modelling of inter-

acting flow, transport and transformation processes between

terrestrial compartments, such as lower atmosphere, land

surface, vegetation, soil, and groundwater systems, is

essential for a holistic understanding of the terrestrial sys-

tems, including the complex feedback mechanisms between

the different environmental compartments. It is certainly

true that for each environmental compartment, well-estab-

lished simulation codes are available. However, their

Fig. 2 Development of continuous workflows for efficient data

processing

Environ Earth Sci (2012) 66:1279–1284 1281

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development has been traditionally somehow isolated from

related disciplines.

A new generation of comprehensive tools based on a

profound scientific background is needed for integrated

modelling of coupled hydrosystems. The objective is to

combine and extend existing modelling software to address

coupled processes in all relevant compartments related to

the hydrosphere. This includes the provision of models for

the prediction of water availability, water quality or the

ecological situation under changing natural and socio-

economic boundary conditions such as climate change,

land use or population growth in the future (Kalbacher

et al. 2012). To meet those challenges, the development of

multi-scale approaches to address processes at, as well as

through, compartment interfaces is critical. Furthermore,

utilising high-performance computing (HPC) capabilities is

necessary for hyperresolution simulations (Wood et al.

2011; Beven and Cloke 2012). The OpenGeoSys initiative

provides a scientific framework for both comprehensive

simulation of coupled hydrosystems and modern compu-

tational strategies (Kolditz et al. 2012).

Coupling concepts

In general, two different coupling concepts can be distin-

guished. The global implicit (GI) approach is in particular

suited for simulation of strongly coupled processes, e.g. in

porous media, which are related to similar length scales but

different time scales. Those multi-fields processes are

mathematically described by partial differential equations

(PDEs), which are assembled into one global equation

system. The resulting algebraic equation systems are often

huge and require parallel solution algorithms (e.g. Wang

et al. 2011). Two of the most difficult and demanding topics

in hydrosystem analysis are bridging scales and coupling

processes across compartment interfaces such as land sur-

face, soil, and groundwater. To overcome these difficulties,

compartment approaches (CA) have been developed. The

hydrologic compartments individually host the different

physico-chemical processes, which are coupled at their

common compartment interfaces by state variables or

exchange fluxes (Delfs et al. 2009). The compartment

approach allows the consideration of each process appro-

priately at its specific time and space scales. To keep flex-

ibility in the spatial resolution, each process is solved with a

discretisation optimised for its geological and hydrological

structures, and numerical constraints. Alternatively GI and

CA concepts can be combined into hybrid approaches.

Computational infrastructure

Integrative modelling of terrestrial systems is computa-

tionally extremely expensive and therefore requires high

computational power. Basically, this is caused by the

Fig. 3 Application of OGS data explorer for visual data integration of the TERENO Bode observatory

1282 Environ Earth Sci (2012) 66:1279–1284

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numerical analysis at multiple temporal and spatial scales.

Use of high performance computing offers a unique

opportunity for hyperresolution modelling, and thus,

increasing to a large extent the precision of terrestrial

modelling. In general, two concepts for parallel computa-

tion exist: OpenMP is easier to implement into existing

codes but only works in shared memory environments. The

message passing interface (MPI) concept often requires

complete restructuring of existing codes, but it is well

suited for super computer architectures. Both OpenMP and

MPI concepts can be combined in hybrid mode, which is

particularly suited for hybrid codes. Recently, the Helm-

holtz Association launched an initiative to foster super-

computing and data science in various disciplines including

environmental Earth sciences as one of the priority areas.

Data visualisation

In the last years, scientific visualisation has become an

emerging area in environmental Earth sciences. The

availability of high-resolution data from terrestrial obser-

vatories, as well as from remote sensing, requires adequate

methods, tools and facilities for assessing such data. As an

example, Fig. 4 depicts the visualisation of a subsurface

model for the development of remediation scenarios of

nitrate contaminations in groundwater in a 3D virtual

environment. To present complex hydrogeological struc-

tures and results of the Nankou project to stakeholders and

decision-makers of the Beijing district, 3D visualisation

techniques have been used to advance both understanding

and discussion of the invoked problems, data and simula-

tion results (Sun et al. 2011).

The use of supercomputers makes scientific visualisa-

tion indispensable for the analysis of hyperresolution

numerical models and of uncertainties in environmental

systems. The scientific ‘‘market’’ for visual data explora-

tion is increasing as more and more visualisation facilities

become available to environmental research. This encour-

ages the development of special software tools for visual-

isation of environmental data (Billen et al. 2008).

Moreover, scientific visualisation is an appropriate instru-

ment for interdisciplinary research, knowledge transfer to

the public and authorities as well as for educational

purposes.

Community efforts

In climate research, the development of community models

has a long tradition. As an example, the CLM-community

(http://www.clm-community.eu\) is an open international

network of scientists working on the development of a

community climate model. In this regard, COSMO–CLM

is an operational model for numerical weather prediction

and regional climate simulations. In contrast, in water

science a wide range of individual modelling tools exists

for different purposes, designed for different scales and

various levels of complexity as mentioned above. A com-

munity effort working towards coupled hydrosystem model

development and data integration has been selected as one

of the priority research fields in water science recently

(Teutsch and Krueger 2010). Benchmarking projects are

considered as one of the means for community networking.

Benchmarking is meant as a procedure to develop sys-

tematic test cases (benchmarks) that serve as standards on

which scientists can verify/falsify their models (hypothe-

ses) and compare their results. Both conceptual and pro-

cess-based approaches will be considered. One idea is to

identify optimal model structures for different problems

under defined boundary conditions and according to lim-

ited data availability (Samaniego et al. 2010). On the one

hand, benchmark tests are an approved tool for model and

code comparisons. Several benchmarking projects have

been conducted in the past for both process-based and

conceptual models, such as HYDROCOIN, DMIP, and

MOPEX, to mention a few. Ongoing initiatives for process-

based approaches are, e.g., in hydrogeology DECOVALEX

(Rutqvist et al. 2008) and in hydrology HM-INTERCOMP

(Sulis et al. 2010). On the other hand, increasing model

complexities require appropriate verification and validation

methods to ensure reliability of model predictions.

Recently in the framework of the Water Science Alliance,

the new benchmarking initiative ‘‘HydroBench’’ has been

started to overcome the shortcomings in linking the

process-based communities with conceptual modellers, as

well as in developing field scale test beds based on the

TERENO observatories. HydroBench should also act as a

‘‘community hub’’ to improve the reliability as well as

predictability of hydrological models.Fig. 4 Visual analysis of the NANKOU groundwater model in a

virtual 3D environment—TESSIN VISLab at UFZ

Environ Earth Sci (2012) 66:1279–1284 1283

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