Young Scientists Workshop “Data assimilation, dealing with

Transcription

Young Scientists Workshop on Data Assimilation
Young Scientists Workshop
“Data assimilation, dealing with uncertainties, and
the prediction capabilities of models in water research”
Seminaris Hotel Lüneburg, Germany
October 8th (evening) – 11th (morning) 2007
http://www.dfg-wasserkommission.de
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KoWa Newsletter August 2007
Contents
Aims of the workshop.........................................................................................................................4
Programme..........................................................................................................................................5
Organizing Committee.......................................................................................................................7
Workshop Venues...............................................................................................................................8
Keynote Speakers
Prof. Dr.techn. Günter Blöschl.............................................................................................................9
Dr. Matthias Drusch...........................................................................................................................10
Prof. Dr. Peter Reichert......................................................................................................................11
Prof. dr.ir. Sjoerd E.A.T.M. van der Zee............................................................................................12
Participants
Dr. Felix Ament..................................................................................................................................13
Dr. Hans-Stefan Bauer........................................................................................................................15
Dr. rer. nat. Christof Beyer.................................................................................................................17
Dr. Helge Bormann............................................................................................................................19
Dr.-Ing. Jörg Dietrich.........................................................................................................................21
Dipl. Envir. Sciences Martin Frey......................................................................................................23
Dipl.-Geoökol. Bastian Graupner.......................................................................................................25
Dipl.-Hydrol. Jens Grundmann..........................................................................................................27
Dr. Björn Gücker................................................................................................................................29
Dr. Anke Hildebrandt.........................................................................................................................31
Dr. Johan Alexander (Sander) Huisman.............................................................................................32
Dipl.-Geoökol. Sascha Christian Iden................................................................................................34
Dr. Sonja C. Jähnig.............................................................................................................................36
Dr. David Kneis..................................................................................................................................38
Dipl.-Ing. Stefan Krämer....................................................................................................................40
Dipl.-Geoökol. Tobias Krueger..........................................................................................................42
Dipl.-Geol. Christine Kübeck.............................................................................................................44
Dr. Eva Nora Müller...........................................................................................................................46
Dr. Lars Nerger...................................................................................................................................48
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Dipl.-Ing. Benjamin Nitsch................................................................................................................49
Dr. Karsten Rinke...............................................................................................................................51
Dipl.-Biomath. Stefanie Rost.............................................................................................................53
Dr. Nele Schuwirth.............................................................................................................................55
Dipl.-Ing- Leopold Stadler.................................................................................................................56
Dr. Henning Wilker............................................................................................................................59
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Aims of the workshop
The workshop is organised by the Senate Commission on Water Research (KoWa) of the German
Research Foundation (DFG) for advanced young researchers working on problems of data
assimilation, uncertainties, and the prediction capabilities of models in the (water-related) fields of
hydrology, hydrogeology, aquatic ecology and meteorology to participate in this interdisciplinary
workshop.
By data assimilation, we mean the integration of measurement data into models with which system
analysis and predictions should be made. If a model can reproduce data, we assume that it can also
predict processes. Data are treated with different approaches in various models. In some disciplines, such as meteorology, for example, scientists attempt to objectify the integration of data into
models. If measurement data and models are combined, uncertainties in the data as well as in the
models need to be considered and quantified. In the workshop, methods from various disciplines
for integrating data into models and quantifying the pertaining uncertainties will be discussed.
The general intention of the workshop is to develop new ideas for interdisciplinary research projects
where the approaches and methods of the disciplines will be exchanged and combined. We assume
that it will be easier to initiate interdisciplinary projects focusing on methods rather than having to
define the interfaces between the models of the different disciplines when addressing a thematic
topic.
Aims of the workshop
• Bringing together young postdocs and doctoral candidates in the final stage of their thesis
work on various water-related areas
• Broadening the horizon of participants by learning about methods from neighboring
disciplines
• Providing a forum for the development of further interdisciplinary research topics and
project initiatives
The river Elbe near Lüneburg
(from www.lueneburg.de)
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Programme
Monday, 8th October 2007
18:00
Arrival and mounting of posters
20:00
Welcome address by Prof. Dr. Dietrich Borchardt, spokesman of the KoWa
workgroup, presentation of KoWa and the aims of this workshop
Ice breaker
Tuesday, 9th October 2007
09:00 – 09:40
Keynote lecture Meteorology: Dr. Matthias Drusch, Reading, UK
Chair: Dr. Felix Ament
09:40 – 10:00
Discussion
10:00 – 10:40
Keynote lecture Hydrogeology: Prof. Dr. Sjoerd van der Zee, Wageningen,
The Netherlands
Chair: Dr. Anke Hildebrandt
10:40 – 11:00
Discussion
11:00 – 11:30
Coffee break
11:30 – 12:30
Poster session I
12:30 – 13:30
Lunch
13:30 – 14:10
Keynote lecture Aquatic Ecology: Prof. Dr. Peter Reichert, Dübendorf,
Switzerland
Chair: Dr. Karsten Rinke
14:10 – 14:30
Discussion
14:30 – 15:10
Keynote lecture Hydrology: Prof. Dr. Günter Blöschl, Wien, Austria
Chair: Dr. Helge Bormann
15:10 – 15:30
Discussion
15:30 – 16:00
Coffee break
16:00 – 17:00
Poster session II
17:00 – 18:30
Meeting of disciplinary workgroups
19:00 – 20:30
Dinner
20:30 –
Short presentation of the disciplinary workgroups' results
Arrangement of the interdisciplinary workgroups
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Wednesday 10th October 2007
9:00 – 10:15
First meeting of the interdisciplinary workgroups
10:15 – 10:45
Coffee break
10:45 – 12:00
Funding opportunities
Dr. Ute Weber, DFG
Experience of the Emmy-Noether Programme of the DFG
Dr. Insa Neuweiler, Group 'Effective Soil Parameters for Infiltration Processes', Universität Stuttgart
12:00 – 13:00
Lunch
13:00 – 15:00
Continued meeting of the interdisciplinary workgroups
15:00 – 15:30
Coffee break
15:30 – 18:30
Presentations of the results of the interdisciplinary workgroups
19:00 – 20:00
Dinner
20:00
Presentation of the results of the interdisciplinary workgroups (optional)
Meeting in a pub in Lüneburg (Mälzer Brau- und Tafelhaus)
Thursday 11th October 2007
9:00 – 11:00
Final discussion and feedback
Locations:
Main room: Plenarsaal 1-2
Additional rooms for workgroups Tuesday, 9th October: Konferenzraum Bad Boll, Gruppenraum 7,
Palmengarten
Additional rooms for workgroups Wednesday, 10th October: Seminarraum 3, Plenarsaal 3
Market Place in Lüneburg
(www.lueneburg.de)
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Organizing Committee
Prof. Dr. D. Borchardt
Prof. Dr. E. Günther
Helmholtz-Centre for Environmental
Technische Universität Dresden
Research – UFZ, Magdeburg
Chair for Environmental Management
Department Aquatic Ecosystem Analysis and
Management
Prof. Dr. M. Isenbeck-Schröter
Ruprechts-Karls-Universität Heidelberg
Institute of Environmental Geochemistry
Prof. Dr. K.-O. Rothhaupt
Universität Konstanz
Limnological Institute
Prof. Dr. M. Sauter
Universität Göttingen
Applied Geology
Prof. Dr. C. Simmer
Universität Bonn
Meteorological Institute
Prof. Dr. P. Werner
Technische Universität Dresden
Institute for Waste Management and
Contaminated Site Treatment
Prof. Dr. Sabine Attinger
Helmholtz-Centre for Environmental
Research – UFZ, Leipzig
Department Computational Hydrosystems
Office of the DFG-Senate Commission
on Water Research (KoWa)
German Research Foundation
Deutsche Forschungsgemeinschaft (DFG)
Dr.-Ing S. Manthey
Institut für Wasserbau
Universität Stuttgart
Pfaffenwaldring 61
70569 Stuttgart
Tel.: +49 711 685 60076
Fax: +49 711 685 60430
email: [email protected]
Dr. U. Weber
DFG-Geschäftsstelle
Kennedyallee 40
53175 Bonn
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Workshop Venues
Seminaris Hotel
Lüneburg
Soltauer Str. 3
21335 Lüneburg
Tel.: (04131) 713-0
Fax: (04131) 713-727
[email protected]
Mälzer Brau- und
Tafelhaus
Heiligengeiststraße 43
21335 Lüneburg
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Prof. Dr.techn. Günter Blöschl
keynote speaker Hydrology
Institut für Wasserbau und Ingenieurhydrologie
Vienna University of Technology
Karlsplatz 13/222
A-1040 Wien
Austria
Tel.: +43-1-58801-22315
Fax: +43-1-58801-22399
Email: [email protected]
Research interests
•
Hydrology and river basin management
•
Spatially distributed modelling and simulation, the issue of scale and scaling, and estimation
in the absence of data
•
Design of flood estimation for hydraulic structures, hydrological forecasts and flood risk
Education
1985 Diploma in Civil Engineering
1990 Doctorate in Hydrology
1997 Habilitation in Hydrology at the Vienna University of Technology
1985 – 1997 Research Assistant
1997
Associate Professor at the Vienna University of Technology
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Dr. Matthias Drusch
Keynote speaker Meteorology
Data Assimilation Section
ECMWF
Shinfield Park
Reading RG2 9AX
UK
e-mail: [email protected]
Research Interests
•
Remote sensing of the earth's surface and radiative transfer modelling.
•
Numerical Weather Prediction and hydrological applications.
•
Data assimilation and surface analyses.
Education & Professional Experience
10 / 2002 – now
Consultant in the Data Assimilation Section, European Centre for
Medium-range Weather Forecasts
06 / 2001 - 12 / 2004* Principal Investigator / Project Manager, Meteorological Institute,
University of Bonn, German Climate Research Programm DEKLIM- E:
„Hyperspektrale Satellitendaten Analyse über Landoberflächen zur
Anwendung in der Klimamodellierung“ (*on secondment from 10 / 2002)
06 / 2000 - 05 / 2001
Research Associate, Meteorological Institute, University of Bonn CLIWANet (EU-FP5)
09 / 1998 - 05 / 2000
Research Associate, Dept. Civil Engineering, Princeton University
10 / 1990 - 04 / 1994
Research Student, Institute for Marine Sciences, Kiel University
June 1998
PhD / Promotion Meteorological Institute at the University of Bonn (Prof.
Dr. C. Simmer, „Fernerkundung von Landoberflächen mit multispektralen Satellitendaten“)
1996-1998
Research Scientist, Meteorological Institute at the University of Bonn
1994-1996
Research Scientist, Institute for Marine Sciences at Kiel University
May 1994
Diploma in Meteoroloy (Prof. Dr. E. Ruprecht,„Vergleich zwischen der
optischen Dicke aus ISCCP C1 Daten und dem Flüssigwassergehalt aus
SSM/I Messungen“)
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Prof. Dr. Peter Reichert
keynote speaker Aquatic Ecology
Department of Systems Analysis, Integrated Assessment and Modeling (SIAM)
Swiss Federal Institute of Aquatic Science and Technology (Eawag)
PO Box 611
CH - 8600 Dübendorf
Switzerland
Tel +41 44 823 5281
Fax +41 44 823 5375
[email protected]
Professor at:
Department of Environmental Sciences
Swiss Federal Institute of Technology (ETH)
CH - 8092 Zürich
Switzerland
Research Interests
•
•
•
Identification of models for technical and natural aquatic systems using methods of systems
analysis. Assessment of the identifiability of model parameters and the uncertainty of model
predictions using frequentist and Bayesian techniques. Application to data from rivers (river
water quality modeling files), lakes (lake water quality modeling files), lake sediments,
biofilms, sewage treatment plants, soil columns and laboratory reactors.
Integration of knowledge from various sources (literature, data, output of more detailed
models, expert opinions) in the form of probability network models. Use of such models to
support environmental decision making.
Design and implementation of software that supports environmental scientists in modelbased evaluation of their data ( AQUASIM, UNCSIM )
Education
1981: M.Sc. in theoretical physics, University of Basel, Basel, Switzerland.
1985: Ph.D. in theoretical solid state physics, University of Basel, Basel, Switzerland.
1995: Habilitation in environmental systems analysis at the Department of Environmental Sciences,
Swiss Federal Institute of Technology (ETH), Zürich, Switzerland.
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Prof. dr.ir. Sjoerd E.A.T.M. van der Zee
keynote speaker Hydrogeology
Wageningen University
Soil physics, ecohydrology and groundwater management group
Droevendaalse steeg 4
Building ATLAS number 104
6700 AA Wageningen
The Netherlands
Tel.: +31 317 482875
Fax: +31 317-484885
Research interests
•
Heterogeneity, spatial variability, and scale problems in soil pollution research.
•
Multiphase fow and nonlinear biogeochemical interaction affecting transport.
•
Mathematical and stochastic approaches to deal with flow and transport and their
uncertainties, for natural porous media.
•
Biological availability of contaminants and nutrients for plants and other organisms.
•
Long term effects of soil management.
Scientic education
1988 Doctoral degree at Wageningen Universiteit,Wageningen
1988 - 1992 UD / Assistant professor at Wageningen Universiteit
1992 - 1998 UHD / Associate professor
1998 - present Full professor
Professional experience
1981 - 1984 Grondmechanica Delft, Delft
1981 - 1983 Project leader contaminant transport modelling
1983 - 1984 Permanent position, project leader soil and groundwater
1984 - present Wageningen Universiteit,Wageningen
1998 - 2005 Professor (Personal Title) for Soil Pollution and Soil Protection at the chair of Soil
Chemistry and Chemical Soil Quality
since 2005
Full professor Soil Physics, Ecohydrology, and Groundwater Management
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Dr. Felix Ament
Dr. Felix Ament *1975
Degree: Dipl. Meteorology (Univ. Bonn, 2001); Ph.D.,
Meteorology (Univ. Bonn, 2006).
Meteorology
Curr. Position: Scientist in the group “Numerical Models”
at the Federal Office of Meteorology and Climatology
MeteoSwiss. Appointed to become Juniorprofessor at the
Meteorological Institute, University of Hamburg
Research interests: land-atmosphere exchange, representation of the hydrological cycle in
atmospheric models, interface between models and observations (assimilation, verification,
model devolpment)
Motivation huge overlap with my research interests; interested to learn new techniques, viewpoints
or questions; gathering scientific and funding ideas to establish a new group devoted to the
combination of observations and model data at the University of Hamburg in 2008.
Current address MeteoSchweiz, Kraehbuehlstrasse 58, P.O. Box 514, CH-8044, Zurich,
Switzerland; [email protected]
Selected Publications related to the workshop:
Ament, F. and C. Simmer: Improved Representation of Land-Surface Heterogeneity in a NonHydrostatic Numerical Weather Prediction Model. Boundary-Layer Meteorology,121(1), 153ff, 2006
Abstract
MAP D-PHASE project: Demonstration of today’s capabilities to predict alpine flood events
by atmospheric and hydrological models
Keywords: flood forecasting, precipitation forecast, uncertainty
The aim of the forecast demonstration project MAP D-PHASE is to demonstrate the performance of
today’s models to forecast heavy precipitation events and floods in the Alpine region. Therefore an
end-to-end, real-time forecasting system has been installed and is operated during the demonstration
phase from June until November 2007 (www.d-phase.info). Part of this system are more than 20
numerical weather prediction models operated by weather services and research institutes, which
issue alerts if forecasted precipitation accumulations exceed critical thresholds. Additionally, all
relevant model fields of these simulations are stored in a central data archive. This comprehensive
data set allows a detailed assessment of today’s quantitative precipitation forecast (QPF)
performance. The D-PHASE system is completed by a wealth of local hydrological models using
the precipitation forecasts as forcing data. Some hydrological models are operated as ensemble
system by performing individual simulations for each member of an atmospheric ensemble system.
This technique allows studying the propagation of uncertainties from precipitation forecast to
discharge simulations.
The first part of the poster will give an overview of the D-PHASE system: its components the
interfaces and the new concepts to provide useful information to end-users. The second part is
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attributed to first results: The overall QPF performance is assessed by a verification against Swiss
Radar data both from a qualitative point of view, in terms of alerts, as well as from a quantitative
perspective, in terms of precipitation rate. This analysis is refined and extended considering
hydrological models for a severe flood event from 6 until 8 August 2007. Both exercises clearly
reveal the great uncertainty in quantitative aspects of QPF and that it might be very dangerous to
rely on a single deterministic forecasting system (see Fig. 1).
Figure 1: Accumulated precipitation observations by radar (thick black) in comparison to
deterministic model predictions for two adjacent D-PHASE warning regions in Switzerland. Note
that models being accurate in one region (e.g. COSMOCH7 (thick blue) and ISACMOL (yellow))
do not necessarily perform well in the other one.
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Dr. Hans-Stefan Bauer
Meteorology
Current affiliation:
Institute of Physics and Meteorology
University of Hohenheim
Garbenstrasse 30, 70599 Stuttgart, Germany
phone: +49 711 459 2154
E-mail: [email protected]
CV
1996 - 2000
2000 - 2001
PhD student Max Planck Institute for Meteorology / Hamburg
Scientist at the Max Planck Institute for Meteorology / Hamburg
Abstract
Assimilation of high resolution water vapour observations into the MM5 4DVAR system
H.-S. Bauer, M. Grzeschik, F. Zus, V. Wulfmeyer and A. Behrendt
Institute of Physics and Meteorology, University of Hohenheim, Garbenstrasse 30, 70599 Stuttgart
Precipitation strongly affects many aspects of our economy and general livelihood. Therefore,
efforts to improve quantitative precipitation forecasting (QPF) have high priority in meteorological
research. This is particularly challenging in the warm season where convective precipitation is more
important. However, the short-term precipitation forecast of strong local precipitation events is still
of very low quality. The reasons for this are manifold and include inaccuracies in parameterization
schemes, severe gaps and faults of observations of the pre-convective environment, and an
insufficient resolution of the forecast models. The limited availability and accuracy of observational
data needed as initial and boundary conditions for running numerical weather prediction (NWP)
models is considered as one of the most critical deficiencies in QPF.
Recent results in nowcasting and short-range weather forecasting clearly demonstrate that
variational data assimilation in combination with high-resolution modelling is essential for
optimising the quality of QPF on the mesoscale. Particularly, it is crucial to determine the initial
state of the model with respect to dynamics and water in all its phases as accurate as possible.
We are intending to make a significant contribution to quantitative precipitation forecasting (QPF)
by focusing on the development of new and the extension of existing forward operators for the
assimilation of additional types of observations into the MM5 4DVAR system. Here, the existing
radiosonde operator was generalized and combined with an observation pre-processor to be able to
assimilate data of the airborne water vapour DIAL system LASE (Wulfmeyer et al., 2006), as well
as data of ground-based Raman lidar systems (Grzeschik et al., 2007).
With recent advances in Global Positioning System (GPS) atmospheric remote sensing, groundbased GPS receivers have become an important instrument that can provide high resolution water
vapour measurements operationally at low cost with an accuracy of a few millimetres in all weather
conditions. Therefore, a new operator to assimilate GPS slant path delays was developed. At the
moment, it is in operational use in a real-time assimilation system during the WWRP Research and
Development Project COPS (Convective and Orographically-induced Precipitation Study) and the
WWRF Forecast Demonstration Project (FDP) D-PHASE (Demonstration of Probabilistic
Hydrological and Atmospheric Simulation of Flood Events in the Alpine Region).
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This contribution shall give an overview about the assimilation efforts done so far in Hohenheim as
well as an outlook to planned future activities. As an example, Figures 1 shows the impact of the
assimilation of GPS slant path data on the specific humidity and temperature fields at 850 hPa for
two different time steps from the forecast initialized at 00Z, 27th of June 2007.
Figure 1: Difference 4DVAR-CONTROL of the specific humidity [g/kg] (top row) and
temperature [K] (bottom row) at 850 hPa for the initial time 20070627 00Z (left) and 12
hours later (right).
It is clearly seen, that the assimilation of GPS data has a large-scale impact on both fields, although
only water vapour data was assimilated.
References:
Grzeschik, M. H.-S. Bauer, V. Wulfmeyer, D. Engelbart, U. Wandinger, I. Mathis, D. Althausen, R. Engelmann, M.
Tesche, and A. Riede, 2007: Four-dimensional variational analysis of water-vapor Raman lidar data and their
impact on mesoscale forecasts. J. Atmos. Oceanic Technol. Accepted.
Wulfmeyer, V., H.-S Bauer, M. Grzeschik, A. Behrendt, F. Vandenberghe, E. Browell, S. Ismail, and R. Ferrare, 2006:
4-dimensional variational assimilation of water vapor differential absorpion lidar data. The first case study within
IHOP 2002. Mon. Wea. Rev., 134, 1, 209-230.
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Dr. rer. nat. Christof Beyer
Dr. Christof Beyer *1974
Degree: Geoecologist (Diploma, TU Braunschweig, 2002);
Ph.D. (Applied Geology, University of Tübingen, 2007)
Hydrogeology
Curr. Position: Postdoc, Institute for Applied Geology,
Geohydromodeling, Christian Albrechts University of Kiel
Research interests: multi-phase flow & reactive transport modeling in heterogeneous media,
environmental assessment of recycling materials, stochastic modelling & uncertainty
propagation, geostatistics & regionalisation
Motivation: to get in contact with colleagues and scientists from neighbouring disciplines, to learn
more about how to address issues of integrating data from diverse sources into models and on
new strategies or methods to tackle data or model uncertainty and to contribute and participate
in an exchange and discussion of these concepts
Current address: Angewandte Geologie / Geohydromodellierung, Ludewig-Meyn-Str. 10
D 24118 Kiel, Tel. +49 (0)431 880 2857, email: [email protected]
Beyer, C., Altfelder, S., Duijnisveld, W.H.M., Ingwersen, J., Streck, T. (2004): Räumliche
Variabilität und statistische Unsicherheit im Kontext der flächenhaften Verlagerung von
Schadstoffen mit dem Sickerwasser. Bodenschutz, 2004, 3, 92-98.
Bauer, S., Beyer, C., Kolditz, O. (2006): Assessing measurement uncertainty of first-order
degradation rates in heterogeneous aquifers. Water Resour. Res., 42, W01420,
Beyer, C., Bauer, S., Kolditz, O. (2006): Uncertainty Assessment of Contaminant Plume Length
Estimates in Heterogeneous Aquifers. J. Contam. Hydrol., 87, 73-95,
Bauer, S., Beyer, C., Chen, C., Gronewold, J., Kolditz, O. (2006): Virtueller Aquifer (VA) Computergestützte Bewertung von Erkundungs-, Sanierungs- und Monitoringstrategien im
Hinblick auf das "Natural Attenuation" (NA) und "Enhanced Natural Attenuation" (ENA)
-Potenzial kontaminierter Böden und Grundwässer. In: Mitteilungen des DGFZ e.V., 3/2006,
93-113. Dresden.
Beyer, C., Bauer, S., Chen, C., Gronewold, J., Kolditz, O. (2007): Determination of first order
degradation rate constants for well investigated contaminant plumes. Ground Water 45(6),
774-785.
Abstract
Using Virtual Aquifers for uncertainty assessment of site investigation
strategies: Application to degradation rate estimation
Christof Beyer , Jan Gronewold , Cui Chen , Olaf Kolditz , Sebastian Bauer
1 Center for Applied Geoscience, University of Tübingen, Sigwartstr. 10, 72076 Tübingen;
[email protected]
2 Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig
3 Institute for Geosciences, University of Kiel, Ludewig-Meyn-Str. 10, 24118 Kiel
1
1
1
2
3
Keywords: Heterogeneity, site investigation, degradation rate, uncertainty,
numerical modelling.
Due to the limited accessibility of the subsurface, measurements of pollutant concentrations at
contaminated sites usually are sparse and may not be representative of the heterogeneous
hydrogeologic or geochemical conditions. As a result, any site investigation is subject to
uncertainty, for which three main sources can be identified: incomplete or wrong description of
parameter distributions due to site heterogeneity, measurement errors (e.g. in heads or
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concentrations), and conceptual model errors from an incorrect identification of the governing
processes. Due to this uncertainty, field investigation methods can hardly be verified in the field.
The Virtual Aquifer (VA) approach is particularly aimed at investigating this problem. The basic
idea is the computer based evaluation of the performance of field investigation methods by
application in heterogeneous synthetic (i.e. Virtual) aquifers. In this it resembles the concept of
“virtual realities” which is used e.g. in car industry (virtual crash test) or medicine (interactive
operation planning). Synthetic site models based on statistical properties of natural aquifers are
generated by numerical simulation of contaminant spreading in groundwater. Just like for a “real
world” site, the synthetic contamination can be investigated, e.g. by setting up observation well
networks. The unique advantage of the VA is that the “true” spatial distribution of the
contamination and all physical or geochemical parameters is exactly known, which allows to
evaluate the accuracy of the investigation methods used by comparison of their results to the “true”
parameter distribution. The VA concept is demonstrated by studying the uncertainty in field scale
contaminant degradation rate constants estimated from measured field data: Multiple realizations of
contaminant plumes under conditions of biodegradation in heterogeneous aquifers were individually
and independently investigated in the computer by individual test persons engaged in
hydrogeological research or consulting using an interactive plume investigation and mapping
software. Investigation of the plumes was achieved by installation of extensive networks of
observation wells and interpolation of measured concentrations to identify the geometry and
position of the plumes. From the data measured at the wells, i.e. Contaminant concentrations,
hydraulic conductivities and heads, first order degradation rate constants were estimated by (A)
three different one-dimensional analytical models, which only use measurements located on the
presumed plume center line, and (B) a two-dimensional model, which uses all measurements
available downgradient from the contaminant source.
Results for (A) show that the “true” rate constant (used for the numerical simulation of the plumes)
in general tends to be overestimated by the 1D analytical center line models. Overestimation is
strongest for narrow plumes from small source zones and may reach factors as high as 50 in the
worst cases. For wider plumes from larger source zones overestimation decreases, but still is
significant. The general overestimation is mainly due to three factors: i) deviations of observation
well positions from the true plume center line, ii) biased estimates of the mean transport velocity
and iii) wrong parameterization of dispersivity. For small source widths overestimation with the 2D
model (B) is comparable to or at best slightly less than for (A). For wider plumes, however,
incorporation of all observation wells in the estimation procedure yields significantly closer
estimates of the degradation rate constant than for (A) on average. Hence, this approach should
preferably be used for rate constant estimation. Overestimation of the degradation rate is a nonconservative result and thus a critical point if natural attenuation is considered as an remediation
alternative, as the overall attenuation potential is assessed too positive. The VA application thus
demonstrates the importance but also the pitfalls of a careful and accurate collection of investigation
data, if these are to be used for a model prognosis of contaminant behaviour at a site or for
comparing and optimizing alternatives of remediation measures. The main advantage of the VA is,
that individual factors, such as site heterogeneity or conceptual model errors can be studied in detail
at low costs, either individually or in combination, and under controlled conditions. As the sum of
these possibilities can neither be provided by large scale field experiments nor in the laboratory, the
VA concept can be considered a valuable contribution complementing state of the art experimental
methods. Data scarcity and parameter uncertainty due to the heterogeneity of environmental
systems rather than general system comprehension or the availability of advanced process based
numerical models is a major problem also in other applied geoscientific disciplines (e.g. surface
hydrology, climatology, geothermic reservoir engineering). Here a transfer of the VA concept may
be possible and could help in getting a better understanding of the consequences of environmental
systems management on basis of imprecise or uncertain data.
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Dr. Helge Bormann
Jun.-Prof. Dr. Helge Bormann *1967
Hydrology
Degree: Dipl. Geoecology (TU Braunschweig, 1995); Ph.D.,
Geography (Dr. rer. nat., Univ. Bonn 1999).
Curr. Position: Jun.-Prof. for Hydrology, University of Oldenburg,
Institute for Biology and Environmental Sciences
Research interests: catchment hydrology, hydrological modelling, scaling, uncertainty analysis,
model comparison, model ensembles, environmental change (climate, land use, human
impacts), coastal hydrology
Motivation: disciplinary as well as interdisciplinary research; exchange of techniques and
methods between different research fields (learning from each other); future cooperation and
project initiatives; landscape management
Current address: University Oldenburg, Institute for Biology and Environmental Sciences,
Hydrology
Working
Group,
Uhlhornsweg
84,
D-26129
Oldenburg
Tel. +49 441-798 4459, Email: [email protected]
Bormann, H., Breuer, L., Gräff, T. & Huisman, J.A. (2007): Analysing the effects of soil properties
changes associated with land use changes on the simulated water balance: A comparison of
three hydrological catchment models for scenario analysis. Ecological Modelling, 209 (1),
29-40.
Bormann, H. (2007): Analysis of the suitability of the German soil texture classification for the
regional scale application of physical based hydrological model. Advances in Geosciences,
11, 7-13.
Bormann, H, Breuer, L., Croke, B., Gräff, T., Hubrechts, L.,Huisman, J.A. Kite, G.W., Lanini, J.,
Leavesley, G.Lindström, G., Seibert, J., Viney, N.R., & Willems, P. (2007): Reduction of
predictive uncertainty by ensemble hydrological modelling of discharge and land use change
effects. IHP Technical Documents in Hydrology. In Druck.
Bormann, H. (2006): Impact of spatial data resolution on simulated catchment water balances
and model performance of the multi-scale TOPLATS model. Hydrology and Earth System
Sciences, 10, 165-179.
Bormann, H. (2006): Verbesserung der Prognosequalität hydrologischer Modelle durch den
Einsatz von Modell-Ensembles für die Simulation von Hochwasserereignissen. In: Disse, M.,
Guckenberger, K., Pakosch, S., Yörük, A. & Zimmermann, A. (Hrsg.): Risikomanagement
extremer hydrologischer Ereignisse. Forum der Hydrologie und Wasserwirtschaft. Heft 15.06,
Band 2, Vorträge 2, S. 87-98.
Bormann, H. (2005): Evaluation of hydrological models for scenario analyses: Signal-to-noiseratio between scenario effects and model uncertainty. Advances in Geosciences, 5, 43-48.
Bormann, H., Giertz, S. & Diekkrüger, B. (2005): Hydrological catchment models between
process representation, data availability and applicability for water management - case study
for Benin. IAHS Publ. 295, S: 86-93.
Stephan, K., Bormann. H. & B. Diekkrüger (Hrsg.)(2002): 5. Workshop zur Hydrologischen
Modellierung. Möglichkeiten und Grenzen für den Einsatz hydrologische Modelle in Politik,
Wirtschaft und Klimafolgeforschung. Kassel University Press, 158 S.
Bormann, H., Diekkrüger, B. & C. Renschler (1999): Regionale Simulationen in der Hydrologie Quantifikation der Fehler und der Unsicherheiten. Zusammenstellung der Beiträge des 2.
Workshops "Wasser- und Stofftransportes in großen Einzugsgebieten" in Gießen. Kassel
University Press. S. 21-29.
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Abstract
Reduction of predictive uncertainty by ensemble hydrological modelling of catchment
processes and land use change
Bormann, H., 2Breuer, L., 3Croke, B.F.W., 4Gräff, T., 5Hubrechts, L., 2Huisman, J.A., 6Kite, G. W.,
7
Lanini, J., 8Leavesley, G., 9Lindström, G., 10Seibert, J., 11Viney, N.R., 12Willems, P.
2
University of Gießen, Institute for Landscape Ecology and Resources Management, Germany,
3
ICAM, SRES, The Australian National University, Canberra, ACT, Australia, 4University of
Potsdam, Institute of Geoecology, Germany, 5Lisec NV, Genk, Belgium, 6Hydrologic Solutions,
Pantymwyn, United Kingdom, 7University of Washington, USA, 8United States Geological Survey,
Denver, USA, 9 Swedish Meteorological and Hydrological Institute, Norrköping, Sweden,
10
Stockholm University, Sweden, 12CSIRO Land and Water, Canberra, Australia, 13Katholieke
Universiteit Leuven, Belgium.
Key words: Multi-model ensemble, catchment hydrology, structural uncertainty
Ensemble modelling, whereby predictions from several models are pooled in an attempt to improve
prediction accuracy, has often been used in the climate and atmospheric sciences, but until recently,
has received little attention in hydrology. Two recent examples of model ensembles are DMIP
(Distributed model intercomparison project) and HEPEX (Hydrologic ensemble prediction
experiment). One of the key aims of the ensemble approach is to reduce uncertainty in the modelled
predictions. This abstract introduces the LUCHEM-initiative (Breuer et al., submitted; Huisman et
al., submitted, Viney et al., submitted), initiated by the Working Group on Resources Management
of the University of Gießen. Within LUCHEM, predictions of ten different catchment models
applied to the mesoscale Dill basin (693km²) in central Germany are compared for a 20 years period
(1980-1999) and used to generate ensemble predictions of current catchment characteristics and as
well as of the effects of several projected land use changes. The applied models encompass a large
range in inherent complexity and input requirements. In approximate order of decreasing
complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP,
HBV, LASCAM and IHACRES. The models are driven using the same input data sets such as
spatial data sets (digital elevation model, land use classification, soil map) and time series (daily
values of precipitation and weather data). Furthermore spatial interpolation of rainfall, adjustment
of temperature by topography and plant parameters is homogenised to achieve most comparable
model results. Streamflow at the catchment outlet is used to compare simulations to observations
and to evaluate model ensembles versus individual models using statistical quality measures.
Comparing the simulated results of the individual models, the simpler models tend to perform better
in both calibration and validation period, but while all models tend to show improved performance
during the less extreme validation period. This improvement is greatest for some of the more
complex models. Despite the disparity in model performance, ensemble predictions made up of
various combinations of the ten model predictions outperform all of the individual models.
Different model ensembles of all available models are applied such as daily mean value, daily
median value. The two mentioned ensembles are simple ensembles as others represent optimized
combinations of all (or only selected) models. Different quality measures are applied to analyse the
results of model ensembles and individual models such as Nash-Sutcliffe efficiency, square of
correlation coefficient, root mean squared deviation and bias. For the calibration period, as
expected, an ensemble based on a multi-variable regression of all models provides the best
predictions, but its prediction accuracy declines to a greater extent than all of the models in terms of
both its bias and Nash-Sutcliffe efficiency when used for the validation period. For the validation
period, the best predictions using an ensemble of all models are provided by an ensemble consisting
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of the daily median model predictions. The predictions of this median ensemble improve more
between calibration and validation than any of the other models, thus indicating its robustness.
Focusing not only on daily water flows and water balances, the study reveals also that the model
ensembles show the best performance also for peak flows and for low flow periods. After
calibration and validation, the models are then applied to different land use change scenarios. In the
scenarios, the projected patterns of land use are based on assumed average field sizes of 0.5 ha, 1.5
ha and 5.0 ha, respectively. There is broad agreement among the models on the expected
hydrological change. While only two models predict a significantly different change between
current state of land use and the scenarios, all models predict the same trend between the three land
use scenarios. The successful validation of the mean and median ensembles suggests that model
ensembles can be applied successful not only in atmospheric but also in hydrological sciences,
namely with respect to the calculation of catchment water flows, catchment water balances, peak
flows and low flow periods. In the case of the Dill catchment, there was no model performing better
than the best model ensemble in the Dill catchment. Those model ensembles which were optimized
for the calibration period performed worse for the validation period. Based on the agreement of the
different models with respect to simulated land use change effects, it seems to be possible to predict
both, the direction and the magnitude of streamflow changes associated with land use change
scenarios with an increased degree of confidence. Predictive uncertainty of individual models is
reduced significantly.
Breuer L, Huisman JA, Willems P, Bormann H, Bronstert A, Croke BFW, Frede H-G, Gräff T, Hubrechts L, Jakeman
AJ, Kite G, Leavesley G, Lanini J, Lettenmaier DP, Lindström G, Seibert J, Sivapalan M, Viney NR. Assessing the
impact of land use change on hydrology by ensemble modeling (LUCHEM) I: model intercomparison of current land
use. Adv Water Resour; submitted.
Huisman JA, Breuer L, Bormann H, Bronstert A, Croke BFW, Frede H, Gräff T, Hubrechts L, Jakeman AJ, Kite GW,
Lanini J, Leavesley G, Lettenmaier DP, Lindstr¨om G, Seibert J, Sivapalan M, Viney NR, Willems P. Assessing the
impact of land use change on hydrology by ensemble modelling (LUCHEM) III: Scenario analysis. Adv Water Resour;
submitted.
Viney NR, Bormann H, Breuer L, Bronstert A, Croke BFW, Frede H-G, Gräff T, Hubrechts L, Huisman JA, Jakeman
AJ, Kite G, Leavesley G, Lanini J, Lettenmaier DP, Lindström G, Seibert J, Sivapalan M, Willems P. Assessing the
impact of land use change on hydrology by ensemble modeling (LUCHEM) II: Deterministic and probabilistic
ensemble combinations and predictions. Adv Water Resour; submitted.
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Dr.-Ing. Jörg Dietrich
Dr.-Ing. Jörg Dietrich *1970
Degree: Ph.D., Civil Engineering (Ruhr-Univ. Bochum,
2006); Dipl. Geoecology (Tech. Univ. Braunschweig, 2000).
Hydrology
Curr. Position: Scientific assistant, project coordinator,
BMBF joint research project “Ensemble forecasts in
Operational Flood Risk Management – Mulde River Basin”,
Ruhr-University Bochum.
Research interests: hydrological modelling, decision support models and systems (strategic and
operational), integrated river basin management, flood risk management
Motivation: My working group has elaborated an ensemble based flood forecast strategy, which
combines meteorological and hydrological ensembles. I offer to share the experiences gained in
this work with others, and I’m also interested in discussion of the various methods of data
assimilation and uncertainty assessment developed or conceptualized by other young
researchers. My personal interest is in information fusion methods under uncertainty, including
information from different sources.
Current
address:
Lehrstuhl für Hydrologie, Wasserwirtschaft und Umwelttechnik (Institute of Hydrology, Water
Resources Management and Environmental Engineering), Ruhr-Universität, 44780 Bochum,
Tel. +49-234-32-25875, Email: [email protected], [email protected]
Dietrich, J., Trepte, S., Wang, Y., Schumann, A. H., Voß, F. & Hesser, F. (2007): Probabilistic flood
forecast using different types of model ensembles - Mulde case study. Submitted to Nonlinear
Processes in Geophysics.
Dietrich, J. & Funke, M. (2007): Integrated catchment modelling for strategic planning and decision
making: Werra case study. Submitted to Physics and Chemistry of the Earth.
Dietrich, J., Voß, F. & Schumann, A. (2007): Operational Flood Risk Management Based on
Ensemble Predictions. In: A. Schumann et al. (Ed), Reducing the Vulnerability of Societies to
Water Related Risks at the Basin Scale, Proceedings of the 3rd International Conference on
Integrated Water Resources Management, Bochum, September 2006. IAHS Publ. 317.
Wang, Y., Dietrich, J., Voß, F. & Pahlow, M. (2007): Identifying and reducing model structure
uncertainty based on analysis of parameter interaction. Advances in Geos-ciences Vol. 11.
Dietrich, J. (2006): Entwicklung einer Methodik zur systemanalytischen Unterstützung
adaptierbarer Entscheidungsprozesse bei der integrierten Flussgebietsbewirtschaftung.
Schriftenreihe des Lehrstuhls für Hydrologie, Wasserwirtschaft und Umwelttechnik, Band 21,
zugleich Dissertation Ruhr-Universität Bochum.
Dietrich, J., Schumann, A. H. & Lotov, A. V. (2006): Workflow oriented participatory decision
support for integrated river basin planning. In: Castelletti, A. & Soncini Sessa, R. (Eds.): Topics
on System Analysis and Integrated Water Resource Management (IWRM). Elsevier.
Dietrich, J. & Schumann, A. (Hrsg.) (2006): Werkzeuge für das integrierte Flussgebietsmanagement – Ergebnisse der Fallstudie Werra. Konzepte für die nachhaltige Entwicklung
einer Flusslandschaft Bd. 7, Weissensee-Verlag, Berlin, 470 S.
Schöniger, M., Dietrich, J., Groth, P. & Hattermann, F. (2002): Geological Reconstruction using
Conditional Stochastic Simulation for Uncertainty Analyses of Water Rescources Management
(Northwest Germany, Liebenau). In: Steenvoorden, J., Claessen, F. & Willems, J. (Hrsg.):
Agricultural Effects on Ground and Surface Waters: Research at the Edge of Science and
Society. Proceedings of an International Conference, Wageningen, The Netherlands, 1-4
October 2000, IAHS Publ. No. 273.
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Abstract
Information Fusion in Probabilistic Flood Forecasting Using Ensembles from Different
Sources
Dr.-Ing. Jörg Dietrich, Dipl.-Ing. Yan Wang ([email protected])
Keywords: ensemble forecast, data fusion, evidence theory, rainfall-runoff model
Flood forecasts are essential to issue reliable flood warnings and to initiate flood control measures
on time. The accuracy and the lead time of forecasts for the headwaters primarily depend on the
meteorological forecasts. Ensemble flood forecasts aim at framing the possible future development
of the hydro-meteorological situation, admitting and communicating the imperfectness of the
forecast (Toth et al. 2003).
This contribution presents a framework for probabilistic flood forecast simulation based on
operational meteorological ensemble prediction systems and rainfall-runoff-models. We propose an
adaptive modeling strategy. For medium to longer lead times (2-10 days, COSMO-LEPS and
ECMWF ensembles), the face-values of the ensemble members are processed by a rainfall-runoff
model or several models to account for structural uncertainty of the hydrological models. For each
of the models parameter uncertainty can also be regarded (not subject of this contribution but
covered by other researchers). For very short lead times (3 – 21 hours) SRNW-PEPS, COSMOLEPS and LMK forecasts are evaluated and processed by a single conceptual model (fig. 1). Here
parameter uncertainty is regarded by the generation of parameter ensembles from efficient solutions
obtained by calibration of historic flood events of a similar type.
One question to solve in the longer time is: when to release water from a reservoir to increase flood
retention capacities, and when to classify an alarm as false alarm and stop measures? In the very
short time, a probabilistic forecast of discharge values for vulnerable sites is a main subject of
interest.
The poster presents the application of evidence theory as a tool for information fusion and decision
support. Evidence theory has been developed as a generalization of Bayesian theory, which
introduces the concept of belief and plausibility of hypotheses (Shafer 1976, Klir & Smith 2001).
The possible contribution of information fusion to support the type of decisions mentioned above is
discussed. A challenging and possibly subjective task is the estimation of posterior distributions in
extreme situations, where no records from the past exist.
Finding a realistic sharpness of a probability distribution or a closed probability density function of
the forecasted variable (i.e. discharge) is challenging. On the one hand, the forecasters use
uncertainty bands, pdf’s etc. for communication of uncertainty, on the other hand decision makers
are often interested in deterministic or even Boolean values (evacuate – yes or no?).
As a case study we present flood forecasts for the August 2002 extreme flood event and a false
alarm in August 2006, where a flood of similar severity had been forecasted.
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5d
2d
COSMO-LEPS ensemble
meso-scale, medium range
21 h
Probabilistic weather scenario
meso-scale, short range
Driving Force
LMK-LAF ensemble
local scale, very short range
Driving Force
Driving Force
Training period: MC
historic flood events
Multi model ensemble
ArcEGMO, NASIM,
SWAT, HBV, …
Inference
Preconditions
API, snow, season
12-24 hrly comp.
Bayesian
Model Average
(BMA)
Ass. Observations
0
Parameter ensemble
ArcEGMO
3 hrly comp.
Ass.
discharge gauges
Sequential
ensemble update
Probabilistic runoff scenario for the headwaters
2007/2008
flood routing/inundation models
Figure 1. Conceptualization of an ensemble-based operational flood risk management –
hydrological ensembles.
References:
Klir, G. J. & Smith, R. M. (2001): On measuring uncertainty and uncertainty-based information: Recent developments.
Annals of Mathematics and Artificial Intelligence 32, 5-33.
Shafer, G.A. (1976): A Mathematical Theory of Evidence. Princeton, NJ: Princeton University Press.
Toth, Z., Talagrand, O., Candille, G. & Zhu, Y. (2003): Probability and Ensemble Forecasts. In: Joliffe, I. T. &
Stephenson, D. B. (eds.): Forecast Verification: A Practitioner’s Guide in Atmospheric Science. John Wiley & Sons.
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Dipl. Envir. Sciences Martin Frey
Hydrogeology
Environmental
Eawag
Überlandstrasse 133, P. O. Box 611, 8600 Dübendorf, Switzerland
Phone: +41 (0)44 823 50 71
Chemistry
CV
1997 – 2003
since 2004
Study of environmental science at the ETH Zurich with the extension in
aquatic physics
Diploma thesis: Modeling of the effect of hydropower plants on the
temperature regime of the branch river of the Rhône
PhD student in the Department of Environmental Chemistry at Eawag
in Dübendorf: Hydrological modeling of contributing areas for diffuse
herbicide losses to surface waters including an uncertainty analysis.
Abstract
Spatial prediction of contributing areas for diffuse pollution losses to surface waters Sensitivity and uncertainty analysis
Keywords: Hydrological modelling, contributing areas, sensitivity analysis, Monte Carlo
simulation
Field studies have shown that herbicide losses often originate from a limited part of a catchment
only. These areas are characterized by the occurrence of fast flow processes like surface runoff or
macropore flow to tile drains. We test the predictability of such critical source areas in a small
agricultural catchment in Switzerland, where herbicide losses were monitored in previous field
studies.
For that purpose, we modified the Soil Moisture Distribution and Routing (SMDR) model. SMDR
is a simple distributed water balance model, which predicts saturation-excess overland flow areas in
a watershed. The prediction is based on an estimation of the relative saturation of the soil by
accounting for infiltration, lateral water flow, percolation and evaporation of water in the soil
column.
The model predictions are dependent on the spatially variable hydrological parameters of the
catchment. The spatial subdivision follows soil map units. Prior estimations of the hydraulic
parameters were derived with pedotransfer functions from the soil texture. Other parameters like the
efficiency of drainage system were estimated by expert knowledge. Such estimations involve
substantial uncertainty, which affects the prediction of the spatially distributed risk areas.
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To analyze the sensitivity of the spatial prediction to the individual parameter and to quantify the
overall uncertainty of the model predictions the simulation program is linked with the system
analysis software tool UNCSIM (Reichert, 2005).
First calculations were carried with parameter estimated a priori based on literature values and data
from pedotransfer function. The predicted discharge agreed reasonably well with the measured
values. The prediction of the spatial distribution of contributing areas was more difficult to evaluate
because no explicit validation data exist. The measured herbicide losses at the outlet of three
subcatchment agreed partially with the predicted runoff processes on the herbicide application
fields.
The local sensitivity analysis revealed that the parameter describing the lower boundary of the soil
(deep percolation and depth of the soil) are the most sensitive parameters. This is critical because
classical soil maps contain only little information about these parameters.
To test whether discharge data from the catchment helps to restrict the space of acceptable
parameter values Monte Carlo calculations simulation were carried out. Simulations with a NashSutcliffe criterion > 0.6 yield significantly different parameter distributions and spatial distributions
of runoff generation. This analysis will be extended in the future by using a Bayesian inference
approach.
References:
Reichert, P, 2005, UNCSIM - A computer programme for statistical inference and sensitivity, identifiability, and
uncertainty analysis. In: Teixeira, J.M.F. and Carvalho-Brito, A.E., eds., Proceedings of the 2005 European Simulation
and Modelling Conference (ESM 2005), Oct. 24-26, Porto, Portugal, EUROSIS-ETI, pp. 51-55.
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Dipl.-Geoökol. Bastian Graupner
Hydrogeology
Dresden Groundwater Research Center (DGFZ e.V.)
Meraner Straße 10, 01217 Dresden, Germany
Phone: +49 (0)351 – 4050667
CV
1997 – 2003
Freiberg University of Technology
Diplom-Geoökologe
Diploma thesis over reactive transport modelling with MODCHEM and
column tests for sulphate reduction
Abstract
Influence of the Lausitz Post Mining Region on Groundwater Quality – a Large Scale
Approach
Keywords: sulphate, pyrite oxidation, transport modelling
In East Germany large post mining landscapes exist from open pit brown coal mining. They consist
of mining dumps, post mining lakes and branched river systems. The research area covers a whole
post mining region (about 2000 km²) located in the Lausitz southwest of Cottbus. A groundwater
cone of depression has influenced an area of around 1350 km² during the last century whereof about
10 percent in area are mining dumps and about 3 percent are post mining lakes.
Rising groundwater table and groundwater recharge releases pyrite oxidation products like iron and
sulphur within the mining dumps. Highly concentrated groundwater in these dumps is formed with
sulphate concentrations of up to 4000 mg/l. Polluted groundwater endangers surrounding
groundwater bodies as well as downstream located surface water bodies. Describing future
migration of these solutes with groundwater is necessary basis for required risk assessment and for
developing protection strategies according to EU Water Framework Directive.
A holistic approach starting with a detailed description of mining dumps as pollutant sources,
geochemical alterations over time of deposition to matter transport simulation is used to deal with
the problem dimension. Due to site dimension and large time scale questions like preparation of
large data sets, assignation of point data to area, uncertainty of data and acceptable degree of
simplification rise and have to be answered. And furthermore such a large area can’t be handled
without automated procedures. Existing data are the main source of information; therefore effort is
necessary for collection, preparation and validation of data. All calculations were carried out on a
quasi 4D GIS-grid base with the additional dimensions depth and time. Input data and results are
stored in that system providing anytime access to every processing step.
Modelling of mining discharge requires a good knowledge of the matter source itself. Mass balance
calculations based on a regional geological model were set up using borehole data from mining.
Using geochemical properties of each geological layer yield the mean content of selected reactive
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minerals in each mining dump. An average primary pyrite oxidation turnover rate of 7 percent for
each dump body was assumed representing oxidation during mining activities. Ongoing reactions
with oxygen in the mining dumps, referred to as secondary pyrite oxidation, were considered by
modelling pyrite oxidation due to oxygen diffusion through the unsaturated dump. Calculation of
secondary pyrite oxidation processes were carried out at each grid point over the depth and time.
Borehole samples from the dumps were analysed for sulphur species and showed good agreement
with calculated results.
A detailed knowledge of groundwater flow is a precondition for transport simulation. For the
research area five detailed groundwater flow models exist. They were developed and maintained
over more than one decade. The groundwater flow scenario was used as external information and
gathered in form of data files. Results are put together and used as input for transport modelling
using the tool PHT3D (Prommer, 2002) that links MT3D and PHREEQC. The release of matter
from mining dump material to the groundwater is described with a double porosity concept.
Exchange coefficients are derived from column experiments.
All mining dumps within the research area held originally about 66 Million tons of pyrite sulphur.
Approximately 15 percent of it was oxidised during and after mining and forms the source of
groundwater pollution for the whole region. Transport modelling shows continuing mining impact
on groundwater over the next decades with an area of influence of more than 100 km². Post mining
lakes are due to their close position immediately affected by polluted groundwater. Rivers are as
well influenced during the next decades. My research aims at the quantification of the impact of
mining on the aquatic environment. For similar settings (surface mining in humid climate) it may be
used in mine planning well ahead of mine closure planning. This contributes to the sustainable use
of resources.
References:
Prommer, Henning (2002): A reactive multicomponent transport model for saturated porous media, Users Manual,
www.pht3d.org
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Dipl.-Hydrol. Jens Grundmann
Jens Grundmann *1973
Hydrology
Degree: Dipl. Hydrology (TU Dresden, 2001)
Curr. Position:
Research associate at the Institute of Hydrology and Meteorology at
Dresden University of Technology
Research interests:
hydrological modelling of rainfall-runoff and watershed processes, optimisation, uncertainty
analysis, river basin management and hydrodynamics
Motivation
The topic of the workshop sounds highly interesting to me since it deals with the topic of my main
research field. At the moment I am investigating the uncertainties in rainfall-runoff modeling
within the HORIX - project “Development of an Operational Expert System for Flood Risk
Management Considering Prediction Uncertainty” founded by the BMBF-RIMAX flood research
initiative. In this context I am most of all interested in methodological questions like:
How can we minimize the effort of Monte-Carlo Simulations by the superposition of different
uncertainties?
How can we analyses in a reliable way the extrapolation ability of rainfall runoff models?
as well as technical aspects like handling of large amounts of data (storage, analysis) and
parallelization of optimization and simulation methods.
Thus, from the workshop I expect an intensive exchange of experience and ideas with theother
participants.
Current address:
Institute of Hydrology and Meteorology,
Dresden University of Technology,
Würzburger Str.46,
D- 01187 Dresden
Tel. +49 351 46335556
Abstract
Uncertainty analysis in rainfall-runoff modelling
Jens Grundmann, Gerd H. Schmitz
Institute of Hydrology and Meteorology, Dresden University of Technology, Germany
Keywords: uncertainty analysis, monte-carlo simulation, optimisation
Problem
Especially in fast responding catchments the limited reliability and wobbliness of a deterministic flood
forecast may easily jeopardise the forecast credibility. This can be overcome by considering in addition
to the meteorological forecast uncer-tainties also the uncertainty of the hydrologic modelling. These
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latter uncertainty essentially arises from:
• insuffisant catchment information (e.g. vague soil data) and
• the likelihood to obtain a similar calibration quality (goodness of fit) from different parameter
combinations, i.e. different model parameters may arise from a single calibration event (problem
of equifinality).
These uncertainties are exemplarily quantified for the flash flood-prone catchment Zöblitz in the ore
mountain.
Aims and methods
Within our study we focus on the above mentioned sources of hydrologic model uncertainties. After
analysing the uncertainties of individual parameter groups of the rainfall-runoff model (i.e. soil
parameters and model parameters), the global uncertainty of rainfall-runoff modelling is evaluated.
Finally we analyse the be-haviour pattern of these uncertainties along a growing catchment area. In this
con-text we investigate different rainfall events in order to evaluate the impact of vary-ing process
dynamics as regards the runoff formation.
The uncertainties of individual parameter groups will be quantified according to the properties and the
a-priori knowledge as regards these parameter groups. The following methods will be used:
• Monte-Carlo simulations of distributed parameters sets to analyse the influ-ence of uncertain soil data
with respect to runoff, Based on the range of soil structure information resulting from soil maps we
derive soil hydraulic parameters via a pedotransfer function. We use the con-cept of soil similarity to
describe the variability of the soil hydraulic character-istics relative to a single reference soil per soil
type by only one scaling pa-rameter. This allows reducing the large number of parameters and to
include their correlative dependencies in a Monte-Carlo simulation.
• Parameter optimizations with evolutionary algorithms and Monte-Carlo simu-lations to analyse the
uncertainty of conceptual model parameters, Based on Monte-Carlo simulations the most sensitive
parameters will be iden-tified and analysed with respect to their correlative dependencies.
Simultane-ously we use these simulations to derive task adequate objective functions. The selected
parameters and objective functions serve as an input for different state-of-the-art optimisation
algorithms (e.g. MOSCEM (Vrugt 2003), Cross entropy (Kroese 2006)) which provide the
uncertainty information. A com-parison with the common GLUE methodology (Beven 1992) serves
for verify-ing the obtained model parameter uncertainty.
• Rainfall information is only provided at a distinct times and at only a few gauges in the catchment. The
necessary interpolation results in a further com-ponent of hydrologic uncertainty. It is modelled by
an ensemble of precipita-tion scenarios. These scenarios, describing time and space variability of
rain-fall data, are generated by a turning bands method and serve as input for model simulation.
Results
Results of the above mentioned tasks will
be presented for our test and research
catchment of gauge Zöblitz,using the
distributed hydrological model WaSiMETH (Schulla 1998). For example figure 1
shows the influence of uncertain soil data of
a medium range flood event. Further investigations show, that the cor-responding
variation coeffi-cient (v) decreases with
increasing rainfall intensity.
fig 1: flood event 9/1995 gauge Zöblitz
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Literature
Beven, K.J, and Binley, A.M., 1992: The future of distributed models: model calibration and uncertainty prediction,
Hydrological Processes, 6, 279-298.
Kroese, D. P., Rubinstein, R. Y and Porotsky, S., 2006: Cross-Entropy Method for Continuous Multiextremal
Optimization, Methodology and Computing in Applied Probability, 8, pp.383-407.
Schulla, J., & Jasper, K., 1998: Modellbeschreibung WaSiM-ETH. - Geographisches Institut ETH Zürich.
Vrugt, J.A., 2003: Effective and efficient algorithm for multiobjective optimization of hydrologic models, Water
Resources Research, 39 (8), Artn 1214.
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Dr. Björn Gücker
Dr. Björn Gücker *1975
Aquatic Ecology
Degree: Dipl. Biol., Zoology and Ecology (Philipps University
Marburg, 2001); Dr. rer. nat., Freshwater Ecology (Univ. Potsdam,
2005).
Curr. Position: Postdoctoral research fellow of the German
Academy of Sciences LEOPOLDINA; researcher at the Federal
University of Minas Gerais, Brazil
Research interests: ecosystem ecology and limnology, stream ecology (nutrient and organic
carbon dynamics, energy flow, primary and secondary production, hydrodynamics)
Motivation: In my current research, I am dealing with uncertainties in simple metabolic and
biogeochemical models. Further, I am currently planning a project on the mechanistic upscaling
of stream reach-scale biogeochemical process rates to river-system scale.
Current address: Universidade Federal de Minas Gerais, Instituto de Ciências Biológicas, Depto.
Botânica, Av. Pres. Antônio Carlos 6627, Pampulha, 31270-010 Belo Horizonte, Minas Gerais,
Brazil, Phone: ++55-31-34944316, Email: [email protected], Web: http://www.bjoernguecker.net
Gücker B, Boëchat IG (2007) Impacts of agricultural land use on ecosystem structure and function
of tropical Cerrado streams. Freshwater Biology (in press)
Boëchat IG, Weithoff G, Krüger A, Gücker B, Adrian R (2007) A biochemical explanation for the
success of mixotrophy in the flagellate Ochromonas sp.. Limnology & Oceanography: 52:
1624-1632
Gücker B, Pusch MT (2006) Regulation of nutrient uptake in eutrophic lowland streams.
Limnology & Oceanography 51: 1443-1453
Gücker B, Brauns M, Pusch MT (2006) Effects of wastewater treatment plant discharge on
ecosystem structure and function of lowland streams. Journal of the North American
Benthological Society: 25: 313-329
Gücker B, Boëchat IG (2004) Stream morphology controls ammonium retention in tropical
headwaters. Ecology 85: 2818-2827
Gücker B, Fischer H (2003) Flagellate and ciliate distribution in sediments of a lowland river:
relationships with environmental gradients and bacteria. Aquatic Microbial Ecology 31: 67-76
Abstract
Uncertainties in estimating and predicting stream ecosystem services
Keywords: whole-stream primary production and respiration, nutrient spiraling
In river systems, various natural and anthropogenic compounds emitted from the catchments, such
as organic carbon (OC) and inorganic nutrients, are not only transported, but also stored,
biogeochemically transformed, and thereby partially eliminated (1). Those biogeochemical
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processes in river systems play an important role in the global geochemical budget, which is heavily
influenced by human impacts, such as eutrophication and climate change (2). Thus, biogeochemical
processing of OC and nutrients is regarded as a natural ecosystem service, which significantly
contributes to the well-being of human society by increasing the usability of limited water
resources, such as stream and river water, floodplain aquifers, riverine lakes, and estuaries.
Biogeochemical processing of OC and nutrients in running waters is most commonly estimated as
reach-scale rates of whole-stream dissolved oxygen (DO) metabolism, i.e., respiration (R) and gross
primary production (GPP), as well as rates of whole-stream gross nutrient uptake, and related subprocesses, such as nitrification, denitrification, etc. (3, 4). However, estimates of R and GPP, as
well as those of nutrient uptake can be associated with considerable uncertainties, especially when
process rates and stream water nutrient concentrations are low (5, 6). Estimates of R and GPP
appear to be most sensitive to on-site estimates of atmospheric pressure and both coefficients of
atmospheric streamwater reaeration and streamwater dilution by anaerobic groundwater. Estimates
of whole-stream nutrient uptake seem to be most sensitive to the degree of spatial and temporal
experimental replication as well as to nutrient concentrations measured at low concentration levels.
Thus, efforts to improve the precision in measuring those parameters should significantly reduce
uncertainties in estimating whole-stream R, GPP, and gross nutrient uptake. In fact, analyses
presented in this contribution will demonstrate that uncertainties in R, GPP, and gross nutrient
uptake estimates can be considerably diminished by the combined application of improvements in
experimental design, adequate degrees of replication, as well as modern measuring techniques.
A mechanistic understanding of ecosystem services, such as OC processing and nutrient uptake, at
watershed scale is essential for the integrative and costeffective management of river catchments.
This includes knowledge on the factors that control ecosystem services and their spatial and
temporal dynamics, which could in turn be used for a mechanistic upscaling of reach-scale rates of
ecosystem services to the watershed scale. Models that predict rates of ecosystem services
successfully (explaining variations between 73 to 97%) are typically non-linear and involve
multiple independent control variables (3, 7, 8). However, models differ strongly between stream
systems as well as stream orders and include different hydrogeomorphic (9, 10), chemical (3, 11),
and biotic control variables (3, 12). Though there are several approaches to predict stream
ecosystem services at small spatial and temporal scales, there is, at present, neither a unifying
mechanistic model available to predict long-term patterns of stream ecosystem services, nor to
predict patterns of ecosystem services in different watersheds or even along several stream orders.
References
1. J. D. Newbold, in The rivers handbook, P. Calow, G. E. Petts, Eds. (Blackwell Scientific, 1992), pp. 379-408.
2. P. M. Vitousek et al., Ecological Applications 7, 737 (1997).
3. B. Gücker, M. T. Pusch, Limnology and Oceanography 51, 1443 (2006).
4. B. Gücker, M. Brauns, M. T. Pusch, Journal of the North American Benthological Society 25, 313 (2006).
5. S. Hanafi, M. Grace, J. A. Webb, B. Hart, Ecosystems, in press.
6. J. R. McCutchan, W. M. Lewis, J. F. Saunders, Journal of the North American Benthological Society 17, 155 (1998).
7. R. O. Hall, J. L. Tank, Limnology and Oceanography 48, 1120 (2003).
8. P. J. Mulholland et al., Freshwater Biology 46, 1503 (2001).
9. B. Gücker, I. G. Boëchat, Ecology 85, 2818 (2004).
10. P. J. Mulholland, E. R. Marzolf, J. R. Webster, D. R. Hart, S. P. Hendricks, Limnology and Oceanography 42, 433
(1997).
11. W. K. Dodds et al., Journal of the North American Benthological Society 21, 206 (2002).
12. J. R. Webster et al., Freshwater Biology 48, 1329 (2003).
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Dr. Anke Hildebrandt
Hydrology
Helmholtz Centre for Environmental Research – UFZ
Hydrosystemmodellierung / Computational Hydrosystems
Permoserstraße 15 / 04318 Leipzig / Germany
Tel: +49 341 235 3973
CV
Since June 2005
June 2, 2005
2001-2005
at
March 23, 2000
Postdoctoral Researcher at the Helmholtz Centre for Environmental
Research UFZ.
Graduation with a PhD in the field of Hydrology (MIT, Cambridge, USA)
Graduate studies at the Department of Civil and Environmental Engineering
the Massachusetts Institute of Technology (MIT, Cambridge, USA)
Title of the PhD thesis: „On the ecohydrology of a seasonal cloud forest
in Dhofar (Oman).“
Graduation from the Technical University of Dresden (Germany) with
the degree of a „Diploma Engineer“ in the field of Water Resources
Management.
Abstract
Modelling water uptake based on a model for a single root
According to van Honert’s Model transpiration is a water flux along a potential gradient and
through a series of pathways (soil, xylem, stomata) with different resistances. The discussion about
which of those resistances, namely plant or soil resistance to water flow, dominates the transpiration
process at high atmospheric demand is still ongoing. It is often argued that the root resistance is
larger than the corresponding soil resistance to water flow, unless in very dry soils. Therefore the
plant resistance is considered the main limitation to the transpirational flux under most soil moisture
conditions. Here we argue, based on the investigation of water flow towards a single root, that the
soil hydraulic resistance to water flow might be larger than usually assumed, even under conditions
where bulk soil water content is elevated. However, plants might adapt to minimizing water
shortages stemming from limiting soil hydraulic conductivity, for example by adjusting rooting
density.
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Dr. Johan Alexander (Sander) Huisman
Dr. J.A. (Sander) Huisman *1973
Hydrology
Degree: Dipl. Soil physics and Soil chemistry (Univ. Amsterdam,
1997); Ph.D., Soil physics (Univ. Amsterdam, 2002).
Curr.
Position:
Postdoctoral
Forschungszentrum Jülich, Germany
researcher
at
the
Research interests: Hydrogeophysics, Soil physics, Watershed modelling
Motivation. Recently, I have received a grant from the DFG (‘Projektleiterstelle’) to investigate
methods to improve the use of geophysical measurements for hydrological model calibration.
This project will start in September 2007, and besides me, the research team will consist of a
postdoctoral researcher, a PhD student and a scientific programmer. In my poster presentation,
I presented a new method to integrate geophysical measurements in hydrological models. I am
aware of the fact that other disciplines are dealing with similar problems. For example, it is my
(perhaps too simplistic) view that meteorologists use a plethora of data sources to improve their
weather forecasts. Amongst these data sources, there might also be tomographic data that are
prone to similar problems as the geophysical data of interest to me. During the workshop, I
hope to find people dealing with similar problems and perhaps even to come across alternative
methods to integrate geophysical data into hydrological models.
Current address: Institute of Chemistry and Dynamics of the Geosphere (ICG), Institute 4:
Agrosphere, Forschungszentrum Jülich, 52425 Jülich, Germany, Tel:++49-(0)2461-618607. Email: [email protected]
Breuer, L., J.A. Huisman and H-G. Frede (2006). Monte Carlo assessment of uncertainty in the
simulated hydrological response to land use change. Environmental Modeling and
Assessment, 11 (3), 209-218.
Heimovaara, T.J., J.A. Huisman, J.A. Vrugt and W. Bouten (2004). Obtaining the spatial
distribution of water content along a TDR probe using the SCEM-UA Bayesian inverse
modeling scheme. Vadose Zone Journal, 3(4), 1128-1145.
Huisman, J.A., L. Breuer and H.-G. Frede (2004). The sensitivity of simulated hydrological fluxes
towards changes in soil properties in response to land use change. Physics and Chemistry of
the Earth, 29(11-12), doi: 10.1016/j.pce.2004.05.012, 749-758.
Huisman, J.A., S.S. Hubbard, J.D. Redman and P.A. Annan (2003). Measuring soil water content
with ground penetrating radar: a review. Vadose Zone Journal, 2(4), 476-491.
Huisman, J.A., J.J.J.C. Snepvangers, W. Bouten and G.B.M. Heuvelink (2003). Monitoring
temporal development of spatial soil water content variation: comparison of groundpenetrating radar and time domain reflectometry. Vadose Zone Journal, 2(4), 519-529.
Huisman, J.A., C. Sperl, W. Bouten and J.M. Verstraten (2001). Soil water content measurements
at different scales: accuracy of time domain reflectometry and ground-penetrating radar,
Journal of Hydrology, 245(1-4), 48-58.
Lambot, S., L. Weihermüller, J. A. Huisman, H. Vereecken, M. Vanclooster, and E. C. Slob
(2006). Analysis of air-launched ground-penetrating radar techniques to measure the soil
surface
water
content,
Water
Resources
Research,
42,
W11403,
doi:
10.1029/2006WR005097.
Pohlert, T., L. Breuer, J.A. Huisman and H-G. Frede (2007). Assessing the model performance of
an integrated hydrological and biogeochemical model for discharge and nitrate load
predictions. Hydrology and Earth System Sciences, 11(2), 997-1011.
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Pohlert, T., J.A. Huisman, L. Breuer and H-G. Frede (2007). Integration of a detailed
biogeochemical model into SWAT for improved nitrogen predictions – model development,
sensitivity and uncertainty analysis. Ecological Modelling, 203(3-4), 215-228.
Abstract
An integrated data inversion approach to use geophysical measurements in hydrological
models
J.A. Huisman, S. Lambot, C. Oberdörster, K.Z. Jadoon, L. Weihermüller, J. Vanderborght and H.
Vereecken.
Keywords: Geophysical measurements, Model inversion, Hydrology.
Geophysical measurements are a valuable source of information for the parameterization of
hydrological models. Traditionally, relevant information on hydrological properties and/or state
variables is obtained in a sequential approach from geophysical measurements: the geophysical
survey data are inverted first, and the information thus obtained is used within the hydrological
model calibration. When monitoring hydrological state variables, this ill-posed sequential inversion
ignores the potential constraints that are provided by available hydrological information (e.g. mass
balance, shape of wetting front). Additionally, artifacts of the geophysical data inversion (e.g. due
to lack of sensitivity, high measurement and modeling errors, etc.) will directly translate in errors of
the hydrological model calibration.
The aim of this paper is to explore an alternative method to use geophysical information in
hydrological model parameter identification. In the so-called integrated inversion approach,
geophysical measurements are directly included in the hydrological inverse problem by coupling a
forward model of the geophysical measurements with a hydrological model. The link between
hydrological and geophysical state variables is established using petrophysical relationships.
Hydrological and petrophysical parameters are obtained by minimizing the difference between
modeled and observed hydrological and geophysical data. Figure 1 represents a general example of
such integrated inversion scheme.
First tests of this integrated hydrogeophysical inversion approach involved numerical test cases with
three geophysical methods: Time Domain Reflectometry (TDR), Ground Penetrating Radar (GPR)
and Electrical Resistivity Tomography (ERT). For all three methods, the numerical experiment
investigated whether soil hydraulic parameters could be determined from time-lapse measurements
during an infiltration experiment. In case of TDR and GPR, a one-dimensional
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Figure 1. Flow chart of the integrated hydrogeophysical data inversion approach.
hydrological model based on the Richards equation with homogeneous soil hydraulic parameters
was coupled to an electromagnetic wave propagation model. In the case of the ERT measurements,
a homogeneous 2D model based on the Richards equations was coupled to a forward geophysical
model describing electrical resistivity measurements. The inversion results demonstrated that the
physical constraints provided by the hydrological model resulted in well-posed inverse problems
that allowed determination of the soil hydraulic parameters from time-lapse measurements during
infiltration. However, there are issues with the initial conditions, boundary conditions and the
petrophysical relationships that need further investigation. More information on the GPR study can
be found in Lambot et al. (2006).
Future applications of the integrated hydrogeophysical data inversion framework will focus on the
analysis of existing data sets (Krauthausen tracer experiments monitored with ERT, Selhausen
infiltration experiments monitored with ERT and GPR) and the feasibility of joint inversion of a
range of geophysical methods. Other frameworks for using geophysical data to calibrate
hydrological models, such as ensemble Kalman filtering, might also be considered.
References
Lambot, S., E.C. Slob, M. Vanclooster, and H. Vereecken. 2006. Closed loop GPR data inversion for soil hydraulic and
electric property determination. Geophysical Research Letters 33, L21405, doi:10.1029/2006GL027906.
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Dipl.-Geoökol. Sascha Christian Iden
Hydrogeology
Institut für Geoökologie
Technische Universität Braunschweig
Langer Kamp 19c, 38106 Braunschweig, Germany
phone: +49 531 391 5602
CV
2002
since 10/2002
Graduation in Geoökologie
Ph.D. Student, TU Braunschweig
Abstract
Free-Form estimation of the unsaturated soil hydraulic properties using global optimization
Models used to predict the transport and fate of pollutants in soil rely on a proper soil hydraulic
characterization because the soil hydraulic properties control the dynamics of water flow in the vadose
zone (Simunek, 2005). The term “soil hydraulic properties” (SHP) summarizes a set of two constitutive
relationships, namely the soil water retention curve relating water content to water potential, and the
unsaturated hydraulic conductivity curve relating unsaturated hydraulic conductivity to water potential.
The standard approach to simulate unsaturated water flow in porous media is the Richards equation (van
Dam, 2004) which is a nonlinear partial differential equation that needs to be solved numerically under
most initial and boundary conditions. Since the solution also requires knowledge of the SHP, their
accurate knowledge is crucial for solving many basic and applied problems in environmental research
(Vanclooster et al., 2004).
Determining the SHP in the laboratory or in the field is preferably be achieved by conducting transient
flow experiments in combination with inverse modelling (Hopmans et al., 2002; Finsterle, 2004; Vrugt
and Dane, 2005; Durner and Lipsius, 2005). This requires a combination of numerical flow simulation
and nonlinear parameter estimation. The advantages over classic steady-state methods are (i) the use of
arbitrary boundary conditions, (ii) a less time-consuming experimental analysis, and (iii) the
applicability to scales larger than typical for the laboratory. Techniques of nonlinear parameter
estimation are, although well established in engineering in science, still a topic of basic and applied
research. In particular, considerable effort is spent on improving the computational tools for solving
inverse problems, e.g. by the adaptation and application of modern global optimization techniques like
genetic algorithms (Holland, 2001), and modern hybrid optimization algorithms (Duan, 1992, Pan and
Wu, 1998) to unsaturated flow problems. The inverse modelling approach currently receives even more
attention, because the vadose zone scientific community is striving to bridge the gap between small and
regional scale simulation studies, and the application to larger scales is an alternative to the bottom-up
(upscaling) approach to derive effective properties (Harter and Hopmans, 2004; Vrugt et al., 2004;
Durner et al., 2007).
Determining SHP by inverse modelling relies on an adequate parameterization of the SHP. This enables
the estimation of a set of model parameters that mathematically describe the SHP. In soil physics, a
whole universe of functions with a relatively low number of parameters has been developed and tested
in practical simulation studies including inverse modelling. However, not all functions are sufficiently
flexible to yield adequate descriptions of the observed behaviour of unsaturated flow systems.
Moreover, the selection of an appropriate parameterization of the soil water retention and hydraulic
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conductivity function remains a challenge (Vrugt, 2003), in particular because strong parameter
correlations impede the interpretability of single estimated coefficients.
My poster contribution will present an improved algorithm for estimating SHP without assigning an a
priori shape to them. It is a further development of an idea presented by Bitterlich et al. (2004) who
introduced the term “free-form” functions to the vadose zone community. The new algorithm uses cubic
Hermite interpolation between nodal values of water content and unsaturated hydraulic conductivity and
a global evolutionary optimization strategy. A multilevel routine identifies the adequate number of
degrees of freedom by balancing model performance, the statistical interaction of the estimated model
parameters (quantified by a collinearity index inferred from the scaled sensitivity matrix; Brun et al.,
2001), and their number. A first-order uncertainty analysis provides a quantitative measure of how well
the SHP can be identified in different ranges of pressure head. This offers great potential for designing
optimal experimental procedures for identifying the hydraulic properties of porous media. I will
demonstrate the effectiveness of the algorithm for the evaluation of classic multi-step outflow
experiments by investigating synthetic data sets and real measurements. It will be shown that the freeform approach yields optimal model parameters that show only moderate correlation indicating wellposed inverse problems. Since parameterization errors are almost completely avoided, the algorithm is
well suited to identify other error sources in unsaturated flow problems, e.g. limitations in the
applicability of Richards equation or problems caused by spatial heterogeneity. The details of the
algorithm are described in Iden and Durner (2007).
The principal idea to estimate constitutive relationships by free-from functions is applicable to many
problems in hydrological and hydrochemical research, like the determination of sorption isotherms
(Knabner, 2005), arbitrary dose-response relationships, or rainfall-runoff modelling. The contribution
will emphasize the need for (i) efficient global optimization strategies, (ii) uncertainty analysis, and (iii)
identifiability analysis to avoid overparameterizations and inadequate model structures.
References
Bitterlich, S., W. Durner, S. C. Iden, and P. Knabner (2004), Vadose Zone J., 3, 971–981.
Brun, R., P. Reichert, and H. R. Künsch (2001), Water Resour. Res., 865 37, 1015– 1030.
Duan, Q., S. Sorooshian, and V. Gupta (1992), Water Resour. Res., 28, 1015– 1031.
Durner, W., U. Jansen, and S. C. Iden (in press), European Journal of Soil Science
Durner, W., and K. Lipsius (2005), in: Encyclopedia of Hydrological Sciences, pp. 1021–1144, John Wiley, Hoboken,
N. J.
Finsterle, S. (2004), Vadose Zone J., 3, 747– 762.
Harter, T., and J. W. Hopmans (2004), in: Unsaturated Zone Modeling: Progress, Applications, and Challenges,
Kluwer, Dordrecht, Netherlands.
Holland, J. H. (2001), Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, Mass.
Hopmans, J. W., J. Simunek, N. Romano, and W. Durner (2002), in: Methods of Soil Analysis Part 4: Physical
Methods, Soil Sci. Soc. Am. Book Ser., vol. 5, pp. 963–1004, Soil Sci. Soc. of Am., Madison, Wisconsin.
Iden S. C., and W. Durner (2007): Water Resour. Res., 43(7) doi:10.1029/2006WR005845.
Knabner, P., B. Igler, K. Totsche, and P. DuChateau (2005), Comput. Geosci., 9(4), 203–217, doi:
10.1007/s10596-005-9008-0.
Pan, L., and L. Wu (1998), Water Resour. Res., 34, 2261– 2269.
Simunek, J. (2005), in: Encyclopedia of Hydrological Sciences, pp. 1171–1180, John Wiley, Hoboken, N. J.
van Dam, J. C., G. H. de Rooij, M. Heinen, and F. Stagnitti (2004), in: Unsaturated Zone Modeling: Progress,
Applications, and Challenges, Kluwer, Dordrecht, Netherlands..
Vanclooster, M., J. Boesten, A. Tiktak, N. Jarvis, J. G. Kroes, R. Munoz-Carpena, B. E. Clothier, and S. R. Green
(2004), in: Unsaturated Zone Modeling: Progress, Applications, and Challenges, Kluwer, Dordrecht, Netherlands.
Vrugt, J. A., W. Bouten, H. V. Gupta, and J. W. Hopmans (2003), Vadose Zone J., 2, 98–113.
Vrugt, J. A., G. Schoups, J. W. Hopmans, C. Young, W. W. Wallender, T. Harter, and W. Bouten (2004), Water
Resour. Res., 40, W06503, doi:10.1029/2003WR002706.
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Dr. Sonja C. Jähnig
Dr. Sonja C. Jähnig *1975
Aquatic Ecology
Degree: Dipl. Environmental Sciences (Univ. Essen, 2001);
Ph.D. Hydrobiology (Univ. Duisburg-Essen, 2007).
Curr. Position: Postdoc, Department of Applied Zoology /
Hydrobiology at the University of Duisburg-Essen.
March 2008 – Feb (Aug) 2009: Postdoc, Chinese Academy
of Sciences, Institute of Hydrobiology, Wuhan.
Research interests: stream restoration, aquatic ecology, ecological modelling: effects of
restoration on biota, stream benthic invertebrates
Motivation
- Discuss and obtain feedback on the planned research project "modeling stream benthic
invertebrates in their field of dreams".
- Exchange ideas and requirements, e.g. on data, with scientists from other disciplines.
- Get a better idea about uncertainty and variability issues and various approaches to consider
them in models.
- Contact to other young scientists, start building a network.
Current address: Sonja Jähnig, Universität Duisburg-Essen, FB Biologie & Geographie,
Abteilung Angewandte Zoologie / Hydrobiologie, Raum: S05 T03 B12, 45117 Essen, Tel:
0201-183-4308, Fax: 0201-183-4442, Email: [email protected]
The question arose during my PhD-thesis: I have obtained a sound background on factors
determining invertebrate communities:
Jähnig SC, Lorenz A and Hering D. 2007. Hydromorphological parameters indicating differences
between single- and multiple-channel mountain rivers in Germany, in relation to their
modification and recovery. Aquatic Conservation: Marine and Freshwater Ecosystems: in
press.
Kail J, Jähnig SC and Hering D. 2007. Relation between floodplain land use and river
hydromorphology on different spatial scales - a case study from two lower-mountain
catchments in Germany. Fundamental & Applied Limnology: in press.
Jähnig SC, Lorenz A and Hering D. 2007. Habitat mosaics and macroinvertebrates – does channel
form determine community composition? Aquatic Conservation: Marine and Freshwater
Ecosystems (submitted).
Jähnig SC and Lorenz A. 2007. Substrate-specific macroinvertebrate diversity patterns following
stream restoration. Aquatic Sciences – Research across Boundaries (submitted).
Lorenz A, Jähnig SC, Schlachta O and Hering D. 2007. Remeandering German lowland streams –
qualitative and quantitative success of restoration measures. Restoration Ecology (submitted).
Abstract
Modeling stream benthic invertebrates in their field of dreams
Keywords: streams, benthic macroinvertebrates, aquatic habitat, scale
The 'field of dreams hypothesis' means: 'if you build it, they will come' (Palmer et al. 1997).
Consequently, to envisage effects on aquatic habitat conditions from management or restoration
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measures and predict the changes in occurrence of certain taxa or community composition of stream
benthic macroinvertebrates is an intriguing yet challenging and interdisciplinary task. It is a step
forward from recent research, which attempted to model the response of stream ecosystem
characteristics to land use changes (Johnson et al. 2007).
A model to employ for this purpose is HABITAT (WL|Delft 2003). HABITAT is a GIS-based
application, which models the availability and quality of habitats for individual species. Such modeling
might be used for prediction of suitability of habitat to flora or fauna in response to changes in
environmental factors. Stream habitat parameters influencing benthic macroinvertebrate communities
have comprehensively been described. Profound knowledge is available for the various scales, as well
as intensity of parameters (Hering et al. 2006). However, the modeling of macroinvertebrate
communities (or certain taxa for a start) according to the available habitats within a stream section has
not been attempted, due to great complexity and interaction of parameters. The description of aquatic
habitat involves the consideration of hydraulic and hydrologic information at site, section and catchment
scale (Molnar et al. 2002). Relevant habitat parameters for benthic macroinvertebrates include current
velocity, shear stress, water depth, substrate, sediment stability, shading, and physico-chemical
parameters (Oxygen, pH, Conductivity, Temperature). On a larger scale the hydromorphological
situation of a section, longitudinal, lateral and vertical continuity as well as catchment parameters (sea
level, geology, pressures) are relevant (compare Vinson and Hawkins 1998). For organisms relevant
information is available from the comprehensive freshwaterecology-database (Euro-Limpacs 2007),
which includes autecological characteristics of more than 12.000 European freshwater organisms
belonging to macroinvertebrates, fish, diatoms and macrophytes. Particularly for macroinvertebrates
information on habitat preference (current preference, substrate), saprobic preferences and life
parameters (feeding type, locomotion type, etc.) are available. These preferences need to be transferred
into rules processable for HABITAT. Prospects, possibilities, difficulties and interfaces to other aquatic
organism groups will be discussed in the workshop contribution. To build such a complex model the
expertise of hydraulic and hydrologic scientists is required for implementing the relevant hydraulic
models from catchment to habitat level, modeling aquatic habitat and changes of it due to management.
Implementing rules for presence / absence of taxa requires hydrobiologists – eventually for all aquatic
organism groups such as macroinvertebrates, fish, phytobenthos and macrophytes. Ultimately the effect
of management or restoration measures on the habitat configuration of stream sections and finally on
biota might be predicted.
References.
Euro-Limpacs. 2007. Freshwaterecology.info - The Taxa and Autecology Database for Freshwater Organisms.
Available from www.freshwaterecology.info (Version 3.1, July 2007).
Hering D, Johnson RK, Kramm S, Schmutz S, Szoszkiewicz K and Verdonschot PFM. 2006. Assessment of European
streams with diatoms, macrophytes,macroinvertebrates and fish: a comparative metric-based analysis of organism
response to stress. Freshwater Biology 51: 1757–1785.
Johnson TE, McNair JN, Srivastava P and Hart DD. 2007. Stream ecosystem responses to spatially variable land cover:
an empirically based model for developing riparian restoration strategies. Freshwater Biology 52: 680-695.
Molnar P, Burlando P and Ruf W. 2002. Integrated catchment assessment of riverine landscape dynamics. Aquatic
Sciences 64: 129-140.
Palmer MA, Ambrose RF and Poff LN. 1997. Ecological theory and community restoration ecology. Restoration
Ecology 5: 291–300.
Vinson MR and Hawkins CP. 1998. Biodiversity of stream insects: Variation at local, basin, and regional scales.
Annual Review of Entomology 43: 271–293.
WL|Delft. 2003. HABITAT – a spatial analysis tool for ecological assessments, Directorate-General of Public Works
and Water Management, Copyright © RWS, WL|Delft Hydraulics 2003.
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Dr. David Kneis
David Kneis *1977
Degree: Dipl. Geoecology (Univ.
Geoecology (Univ. Potsdam 2007).
Aquatic Ecology
Potsdam,
2002);
Ph.D.,
Curr. Position: Research associate in a project on operational
flood forecasting (Saxon flood forecasting center / University of
Potsdam)
Research interests: Development AND use of models for applications in hydrology and aquatic
ecology, GIS, data collection and processing methods, further interest: renewable energies
Motivation
I attended the YSW for several reasons:
- I wanted to learn new methods of data integration/assimilation and uncertainty analysis from
other disciplines.
- I was interested in the opinions/visions of the invited senior scientists concerning the
perspectives of modeling in their fields of specialization.
- The workshop was a great opportunity to get in contact and discuss with young scientists from
similar and other disciplines.
- I was interested in the objectives of the KoWa with respect to future water-related research
and funding opportunities offered by the DFG.
Current
address:
Sächsisches
Landesamt
für
Umwelt
und
Geologie
Landeshochwasserzentrum, Postfach 80 01 32, 01101 Dresden
Tel. +49 351 89 28 334, Email: [email protected]
Kneis, D. (2007): A water quality model for shallow river-lake systems and its application in river
basin
management;
PhD
thesis,
University
of
Potsdam;
http://nbnresolving.de/urn:nbn:de:kobv:517-opus-14647
Kneis, D., Knösche, R. & Bronstert, A. (2006): Analysis and simulation of nutrient retention and
management
for
a
lowland
river-lake
system;
Hydrology and Earth System Sciences, 10, 575-588
Bronstert, A. & Itzerott, S. (Eds.) (2006): Bewirtschaftungsmöglichkeiten im Einzugsgebiet der
Havel, Brandenburgische Umweltberichte 18
Förster, S., Kneis, D., Gocht, M., Bronstert, A. (2005): Flood Risk Reduction by the Use of
Retention Areas at the Elbe River, Intl. J. River Basin Management Vol. 3, No. 1, pp. 21-29.,
http://www.jrbm.net/pages/home/vol3.asp
Abstract
A flexible water quality model for shallow river-lake systems
Keywords: phosphorus, nitrogen, shallow lakes, model development, river basin management
In my recently finished PhD thesis (Kneis, 2006 & 2007) I developed a model for simulating mass
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transport and turnover in rivers and shallow lakes (TRAM). The simulation tool is intended to
complement eco-hydrological catchment models in studies on river basin management. Model
development and application were embedded in a research project focusing on options to ease
surface water eutrophication in the Havel River Basin (Bronstert & Itzerott, 2006). In this study,
TRAM was used for simulating nutrient concentrations in a large-scale river-lake system exhibiting
significant internal nitrogen and phosphorus turnover. For conducting scenario analyses with altered
external loading, TRAM was coupled with two catchment models specialized on the estimation of
nutrient emissions from point and non-point sources. Simulated in-river concentrations of total
phosphorus were used for assessing alternative management options in the light of the European
Water Framework Directive. The design of the newly developed model was guided by a review of
existing water quality models, recommendations published by BfG (2002), personal modeling
experience, as well as constraints arising from the targeted spatial/temporal scale of application. A
summary of some of the model’s key features is given below.
(1) TRAM relies on a strongly generalized transport scheme based on a discretization of the natural
system into fully mixed and purely advective elements. This approach proved to be suitable for
large-scale applications. Required boundary conditions such as flow rates and storage volumes are
supplied by external hydrodynamics or hydrological models.
(2) A key feature of TRAM is its flexibility with respect to turnover simulations. The number and
kinds of simulated components, reaction rates, as well as the stoichiometry matrix (Reichert et al.,
2001) are not predefined but user-supplied. Due to this ‘open-structure approach’ the model’s
complexity is adjustable to the present knowledge of processes, the amount and quality of data for
parametrization and verification, and the available time budget. This approach is considered
essential for efficient systems research since the model structure can be continuously refined by
evaluating residuals of simpler approaches and new data can be collected more specifically.
(3) Finally, TRAM aims at being efficient with respect to both setup and simulation. For example,
the capabilities of geographic information systems are used for model parametrization. The userdefined description of turnover is integrated with the model's generic kernel by automatic code
generation. The software was designed for efficient analyses of parameter sensitivities and
uncertainties. The main result of the model application to the Havel River is that the targeted "good
status" with respect to phosphorus concentrations seems to be achievable in spite of continued P
release from sediments if the full potential of emission control was tapped. Although this is a clear
statement, the simulation study revealed a number of severe limitations of the model's predictive
capabilities. For example, the chosen turnover model neither includes a mechanistic description of
the seasonal succession of the plankton nor a process-oriented model of sediment diagenesis.
However, in general, these are prerequisites for modeling the impact of altered boundary conditions
on aquatic nutrient cycling. Due to the large number of biotic interactions and abiotic control
factors, the required fully mechanistic shallow-lake ecosystems model is not (and might never
become) available. Consequently, it is believed that research should further focus on the
identification of the "appropriate" level of model complexity that balances structural and parameter
uncertainties. In this light, the use of open-structure simulation tools is recommended as they
facilitate "playing" with alternative process descriptions. Uncertainty analyses might become more
convincing by using such alternative model formulations in parallel – instead of "believing" in a
single structure and focusing on the investigation of unknown parameter values.
Bibliography
BfG (2002): Mathematisch-numerische Modelle in der Wasserwirtschaft, Handlungsempfehlung für Forschungs- und
Entwicklungsarbeiten, Bundesanstalt für Gewässerkunde, BfG-Mitteilungen 24.
Bronstert, A. & Itzerott, S. (Eds.) (2006): Bewirtschaftungsmöglichkeiten im Einzugsgebiet der Havel,
Brandenburgische Umweltberichte 18
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Kneis, D. (2007): A water quality model for shallow river-lake systems and its application in river basin management;
PhD thesis, University of Potsdam;
http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-14647
Kneis, D., Knösche, R. & Bronstert, A. (2006): Analysis and simulation of nutrient retention and management for a
lowland river-lake system; Hydrology and Earth System Sciences, 10, 575-588
Reichert, P., Borchardt, D., Henze, M., Rauch, W., Shanahan, P., Somlyody, L. & Vanrolleghem, P. A. (2001): River
water quality model No. 1, IWA Publ.
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Dipl.-Ing. Stefan Krämer
Dipl.-Ing. Stefan Krämer *1973
Meteorology
Degree: Dipl. Ing., Civil Engineering (Leibniz University of
Hanover, 2000). Ph.D. Thesis, submitted to the Faculty of Civil
Engineering and Geodesy Leibniz University of Hanover
(“Quantitative
Radardatenaufbereitung
für
die
Niederschlagsvorhersage und die Siedlungsentwässerung”)
Curr. Position: Research Scientist, Institute of
Resources Management, Leibniz University of Hanover
Water
Research interests: Radar meteorology, nowcasting and urban drainage
Motivation
My field of activity has concentrated on the measurement of radar rainfall as input for urban
drainage models. This has involved the development of algorithms for quantitative radar data
processing in real time (attenuation correction, R-Z relation) as well as the integration of rainfall
measurements from different sources (radar, microwave link, rain gauges, distrometer) for
hydrological modelling under consideration of their uncertainties. One benefit of radar rainfall data
is the possibility for extrapolation of rainfall textures in order to provide runoff and flow forecasts.
Future ideas deal with the development of an integrated rainfall model for urban water
management. For the combined control of sewer systems, waste water treatment plants and
receiving waters different timescales and forecasting horizons have to be considered as well as
various sources of rainfall information concerning their spatial resolution and their reliability
bounds.
Current address: Institut für Wasserwirtschaft, Hydrologie und landwirtschaftlichen Wasserbau,
Leibniz
Universität
Hannover,
Appelstr.
9a,
D30167
Hannover
Tel. +49 511 762 5195, Email: [email protected]
Krämer, S., M. Grum, H.-R. Verworn and A. Redder (2005): Runoff modelling using radar data
and flow measurements in a stochastic state space approach, Wat. Sci. Tech., 52 (5), 1 – 8.
Krämer, S., H.-R. Verworn and A. Redder (2005): Improvement of X-band radar rainfall estimates
using a microwave link. Atmos. Res. 77 (1-4), 278 – 299.
Grum; M., S. Krämer, H.-R. Verworn and A. Redder (2005): Combined use of point rain gauges,
radar, microwave links and level measurements in urban hydrological modelling. Atmos. Res.
77 (1-4), 313 – 321.
Verworn H.-R. and S. Krämer (2005): Aspects and effectiveness of real time control in urban
drainage systems combining radar rainfall forecasts, linear optimization and hydrodynamic
modelling. Proc. 8th Int. Conf. on Computing and Control for the Water Industry, 5–7
September 2005, University of Exeter, UK, 307 – 312.
Rahimi, A.R., A.R. Holt, G.J. Upton, S. Krämer, A. Redder and H.-R. Verworn (2006): Attenuation
calibration of an X-band weather radar using a microwave link. J. Atmos. Oceanic Tech., 23
(3) 395 – 405.
Krämer, S., H.-R. Verworn, A.Hartung, A.R. Holt, G.J.G Upton and M. Becker (2006): Uncertainty
quantification of operational X-band weather rainfall measurements. Proc. 7th Int. Workshop
on Precipitation in Urban Areas, 7-10 December, 2006, St. Moritz, Switzerland, 87 - 92.
Krämer, S., L. Fuchs and H.-R. Verworn (2007): Aspects of radar rainfall forecasts and their
effectiveness for real time control - the example of the sewer system of the city of Vienna.
Wat. Pract. Tech. 2 (2), doi10.2166/wpt.2007.042.
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Abstract
Integration of microwave link attenuation information to correct X-band weather radar
reflectivity profiles
Keywords: radar, rainfall, microwave link, attenuation correction
Rainfall results from complex atmospheric processes and is the driving force for hydrology. Its
distribution in space and time is important for meteorological and hydrological applications, (e.g.
DWD-LMK, flash flood predictions, real time control of urban drainage systems). Weather radars
provide this information highly resolved in space and time. The reliability of quantitative radar
rainfall estimation, however, is limited. This is mainly due to the effect of attenuation the radar
signal experiences when it passes a cloud of hydrometeors. As a result the radar measured
reflectivity profile Za(r) underestimates the true (unknown) reflectivity profile Z(r) with increasing
distance (r) from the radar. This error propagates when radar rainfall R is derived using any R-Z
relationship. The attenuation phenomenon is severe for X-band, but it is also acute at C-band.
Hitschfeld and Bordan (1954) addressed this problem and proposed an analytical solution for the
attenuation problem which is often used in a modified version (Delrieu et al. 1997). The specific
attenuation k(r), the radar signal experiences, is derived from measured rainfall reflectivity Za(r).
Between specific attenuation k and reflectivity Z a power law k = a × Zb is assumed. For correction
the choice of the coefficients (a, b) is crucial. They have to be estimated before the correction is
made. The common approach is to model attenuation (k) and backscattering (Z) effects on single
raindrops for different drop sizes, shapes and temperatures depending on the radar frequency. Then,
either modelled drop size distributions (DSD) or historic disdrometer measurements are the basis
for integration of the scattering characteristics for different rainfall types. This results in averaged
coefficients a,b which are uniformly / constantly used for correction. This methodology, however,
does not work for the current rainfall and its characteristics as measured by the radar. As a
consequence the Hitschfeld Bordan (HB) algorithm shows the well known instability. Therefore
attenuation correction is not applied to operational weather radar systems and the “true” rainfall is
continuously underestimated.
To investigate this problem a dual frequency microwave link was installed. The receivers were
collocated with an X-band weather radar antenna operated by Emschergenossenschaft /
Lippeverband near the City of Essen. With one frequency (10.5 GHz) operating close to the radar
frequency (9.47 GHz), the link was aimed to provide an attenuation reference (Aref) parallel to one
radar beam over a path length of 29.6 km (119 radar gates, length 0.25 km). The resulting
attenuation time series is shown in the figure left for the convective rainfall event on June 8, 2003.
The parameter space of attenuation coefficients (a,b) has been explored by calculating the radar
attenuation (Aradar) with the HB-approach. An optimal combination of a,b coefficients has been
accepted for each minute when the difference between radar and reference attenuation dmin = Aref
- Aradar(a,b) was found in 0.25 dB bounds (|dmin| ≤ 0.25 dB). The results (figure right)
demonstrate a high variability of the a,b coefficients. For each minute many a,b combinations are
found which satisfy the objective criterion. They concentrate along “minute-lines”. The
characteristics of the minute-lines show a systematic behaviour depending on the rainfall
(convective / stratiform) along the radar beam. Attenuation coefficients found in literature and
derived from event specific DSD measurements using the Mie theory are plotted for comparison. 15
events have been processed in total which confirm the results.
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The high variability of the results demonstrates that the constant and uniform use of attenuation
coefficients is non realistic and the main reason for instability of the HB-algorithm. The use of
variable a,b coefficients, in contrast, enables attenuation corrections up to 60 dB. In addition, the
systematic behaviour of coefficients defines an a priori knowledge to develop an operational
attenuation correction methodology. The attenuation correction improves significantly the
quantitative radar rainfall estimation (X and C-Band) and has a strong benefit to meteorological and
hydrological application of radar data.
Delrieu G., Caoudal S. and J.D. Creutin (1997): Feasibility of using mountain returns for the correction of ground based
X-band weather radar data. J. Atmos. Oceanic Tech., Vol. 14, 368 – 385.
Hitschfeld W. and J. Bordan (1954) Errors inherent in the radar measurement of rainfall at attenuating wavelength. J.
Meteorol., Vol. 11, 58 – 67.
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Dipl.-Geoökol. Tobias Krueger
Dipl.-Geooekol. Tobias Krueger *1978
Hydrology
Degree: Dipl. Geoecology (Tech. Univ. Braunschweig, 2005).
Curr. Position: PhD student in Environmental Science,
Lancaster University, UK; working on DEFRA funded project
PE01020 Phosphorus mobilisation with sediment and colloids
through drained and un-drained grasslands in cooperation
with IGER, University of Exeter, CEH and University of
Plymouth.
Research interests: Modelling of water, sediment and nutrient transport at plot to catchment
scale; nutrient load estimation techniques; analysis of model parameter and structural
uncertainty; quantification of data uncertainty and their incorporation into models.
Motivation: In my role as a research student in an interdisciplinary project on phosphorus
transfer processes, I am required to model the transfer of phosphorus from agricultural land to
receiving waters. Our research has shown that this inevitably needs to include model
evaluation within an uncertainty estimation framework. In water quality modelling, the data
availability with respect to process complexity will always be more limited than, for example, in
hydrological model applications. I am, therefore, actively researching the interplay between
data availability and model complexity as well as the quantification of measurement
uncertainties .I am keen to exchange ideas with related disciplines which I believe face similar
issues. An interdisciplinary discussion of model development, evaluation and uncertainty
estimation has great potential for identifying techniques that could be suitable for a given
application – especially as I think there is not one technique which is suitable for all
applications. As a contribution to the discussion, I can offer the perspective of informal
uncertainty estimation techniques along the lines of the Generalised Likelihood Uncertainty
Estimation (GLUE).
Current address: Lancaster Environment Centre, Department of Environmental Science,
Lancaster
University,
Lancaster,
LA1
4YQ,
UK
Tel. +44 (0)1524 593534, Email: [email protected]
Krueger T., Freer J., Quinton J. N. and C. J. A. Macleod (2007). Processes affecting transfer
of sediment and colloids, with associated phosphorus, from intensively farmed grasslands: A
critical note on modelling of phosphorus transfers. Hydrological Processes 21(4): 557-562.
Abstract
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Data availability, uncertainty and process identification in water quality modelling: the case
of agricultural phosphorus transfers
T. Krueger, J. Freer, J.N. Quinton
Keywords: data limitations, scale, process identification, uncertainty analysis
In this poster we will give an overview of what we think are the key issues in water quality
modelling. The focus will be placed on modelling transfers of phosphorus from agricultural land to
receiving waters (see Krueger et al. 2007), but the arguments can easily be transferred to other
water quality modelling applications were we face similar data limitations, uncertainties and gaps in
process understanding. Our points are:



Water quality modelling in practice is restricted by limitations on the amount and scope of data
that can be collected in the field to parameterise and drive models. Therefore, the scale of
application and the availability of field observations should direct model development for a
given study – different scales may require different models.
How can robust process descriptions be inferred from incomplete and imperfect field
observations? A pragmatic strategy seems to be aiming at the dominant modes of system
behaviour, where these can be supported by field observations – some model compartments may
be easier to identify than others and for some we might neither have the data nor conceptual
knowledge to say anything.
Ideally, models should be evaluated by assessing the potential uncertainties in model structure,
parameters and data. However, making assumptions about the nature of these uncertainty
components may neither be justified nor testable given the available field observations –
complexity in uncertainty analysis needs to be balanced against the information content of the
data and the level of detail required by the modelling application.
To illustrate our arguments, we will present data analyses, uncertainty analyses and modelling work
undertaken at the plot, catchment and large catchment scale within the interdisciplinary Grassland
sediment and colloid Phosphorus (GrasP) project1 funded by the UK Department for Environment,
Food and Rural Affairs as well as in collaboration with Swiss colleagues2.
Acknowledgements
1
GrasP are P.M. Haygarth, G.S. Bilotta, R. Bol, P.J. Butler, S.J. Granger, J. Hawkins, C.J.A. Macleod (Institute of
Grassland and Environmental Research, North Wyke, UK), R.E. Brazier (University of Exeter, UK), L.J. Gimbert, P.
Worsfold (University of Plymouth, UK), P.S. Naden, G. Old (Centre for Ecology and Hydrology, Wallingford, UK), J.
Freer, T. Krueger, J.N. Quinton (Lancaster University, UK).
2
M. Schaerer (Federal Office for the Environment, Bern, Switzerland).
References
Krueger T., Freer J., Quinton J.N., Macleod C.J.A., 2007. Processes affecting transfer of sediment and colloids, with
associated phosphorus, from intensively farmed grasslands: A critical note on modelling of phosphorus transfers.
Hydrological Processes 21(4), 557-562.
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Dipl.-Geol. Christine Kübeck
Dipl.-Geol. Chistine Kübeck
Degree: Dipl. Geology (Univ. TU-Clausthal, 2005)
Hydrogeology
Curr. Position: scientific co-worker, TU-Clausthal
Research interests: functional ecology of aquatic ecosystems, ecological modelling, river basin
management
Motivation: non-uniqueness of model results caused by uncertainties in data assimilation;
principle of parsimony; simplification of natural systems as ill-defined process; uncertainties of
modelling in general and its influence on model results; differences between application fields
Current address: Inst. Geology and Palaeontology, TU-Clausthal, Leibnizstr. 10, 38678,
Clausthal-Zellerfeld (Germany)
Kübeck, Ch. & Steding, T. (2006): PhreeqC Modellierung der Laborversuche zur „Bestimmung
der Basenkapazität KB 8.2 “ - Ringversuche zur Basisvalidierung und Überarbeitung der Norm
DIN 39709 7 (H7) vom Mai 1979. Clausthaler Geowissenschaften Bd. 5 S.: 115-134.
Abstract
The principle of simplicity in hydrogeochemical modelling
Keywords: simplicity, transparency, refutability, groundwater modelling
Looking at nature we find very complex systems influenced by and dependent on a huge number of
parameters. But we will also find clear structures and processes that can be used for understanding
often not the whole system but the main character of nature. Though, finding these structures or
processes is troublesome. In this context the development of computer models has been both, boon
and bane. In the first place, computer models are a great benefit in solving mathematic mo-dels with
high complexity. At the same time they are suggestive of being confident in its results. But the
uniqueness of modelling results is often not given due to the lack of system information. Thus
increasing the plausibility of modelling results requires the implementation of transparency and
refutability into modelling. Hill (2006) discusses the necessity of developing models that are both,
refutable by testing its assumptions, as well as transparent by means of understanding its dynamical
processes. Carrying on Hill´s fundamental idea of model transparency, should also include technical
data of modelling like numerical method, boundary conditions or discretization. Most of the
modellers underestimate the influence on model results by using different kinds of mathematic
approaches.
However, the purpose of modelling is to understand the structures and processes that form the
natural system by analysing them, using less complex models. As a consequence, transparency is a
major objective of modelling. Described by Oreskes (2000) paradoxically transparency and
refutability suffer with increasing complexity of a model. On that condition, modelling is a
balancing of keeping the model as simple as possible and integrating all characteristic and
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dominating processes.
For example, the strategy of modelling hydrogeochemical groundwater systems developed by
Hansen and van Berk (2004) implies a high grade of transparency, although the model attains a
relatively huge number of hydrogeochemical pro-cesses. By means of starting with a very simple
model and stepwise integrating more interacting processes, the model maintains comprehensible.
Although the 1D material flux model reproduces the raw water quality with a high conformity, it is
not meant to predict future system conditions. This is not generally a matter of non uniqueness, but
also a question of oversimplification. Oreskes and Belitz (2001) describe the increase of the
likeliness of non-uniqueness with the complexity of a model. But at the same time the validity of
the model forecast decreases with increasing level of simplification. Reflecting on that paradox, the
determination of the appropriate level of model complexity gains more importance. However, the
required determination process is ill-defined, so the level of model complexity depends on the
experience of the modeller, the task formulation and the kind of natural system.
For instance, the described 1D material flux model implies merely a primitive temporal and spatial
discretization. In order to model forecasts, this kind of discretization is non-sufficient, hence a
further development has to be implemented. To maintain transparency, the model refinement must
comply with the principle of simplification. Transferring the hydrogeochemical model into the
three-dimensional space or coupling with the geohydraulical model implies a non-reasonable
increase of complexity and modelling uncertainties.
Therefore an uncommon basic approach is developed to abstract the three-dimensional space of the
groundwater system as quasi-one-dimensional image by using stream tubes. Each stream tube
represents a specific area within the catchment area and a defined volume flow. Abstracting the
transport of groundwater within the stream tube as a covered path length, the stream tube can be
transferred into the 1D space. According to the task formulation further simplification of the
temporal and spatial discretization can be implemented by using zones of flow times or generating a
representative square section.
Keeping in mind, every model is just an abstract image of the complex nature that can be wisely
used as an instrument to formulate questions and interpret signals but should not be misused as a
verification code of nature.
References
Hansen C. and van Berk W., Retracing the development of raw water quality in water works applying reactive
controlled material flux analyses., Aqu. Sci., Vol. 66, 2004.
Hill M. C., The Practical Use of Simplicity in Developing Ground Water Models., Ground Water, Vol. 44, No. 6, pp.
775-781, 2006.
Oreskes N., Why believe a computer? Models, measures and meaning in natural world. in: Schneidermann J. S., The
Earth Around., pp. 70-82, Freeman & Co., 2000
Oreskes N. and Belitz K., Philosophical Issues in Model Assessment. in: Anderson M. G. and Bates P. D., Model
Validation: Perspectives in Hydrological Science., Wiley, 2001.
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Dr. Eva Nora Müller
Dr. Eva Nora Müller (1975)
Degree: PhD in Hydrology/Geomorphology (2004), MSc in
Nonlinear Dynamics (2001), Dipl.-Ing. (2000)
Hydrology
Curr. Position: Post-doc position within the SESAM-Project at
the Institute of Geoecology, University of Potsdam
Research interests: Modelling of catchment hydrology, sediment- and nutrient transport
processes, dryland environments, overland flow dynamics and soil erosion in arid and semiarid environments, desertification and land degradation, vegetation-change dynamics
(ecohydrology)
Motivation
Current address: Institut für Geoökologie, Universit Potsdam, Karl-Liebknecht-Str. 24-25, 14476
Potsdam, Tel. +49 (0)331-977-2975, Email: [email protected]
E. N. Mueller, J. Wainwright, A. J. Parsons (2007), The impact of connectivity on the modelling of
overland flow within semi-arid shrubland environments, Water Resources Research 43,
W09412, doi:10.1029/2006WR005006.
E. N. Mueller, J. Wainwright, A. J. Parsons (2007) The stability of vegetation boundaries and the
propagation of desertification in the American Southwest: A modelling approach, Ecological
Modelling 208, 91-101
E. N. Mueller, J. Wainwright, A. J. Parsons (in press), Spatial variability of soil and nutrient
characteristics of semi-arid grasslands and shrublands, Jornada Basin, New Mexico,
Ecohydrology
E. N. Mueller, R. J. Batalla, C. Garcia, A. Bronstert (in revision) Modelling bedload transport
during small floods in a gravel-bed river, Journal of Hydraulic Engineering
E. N. Müller, R. J. Batalla, A. Bronstert. (2006) Dryland river modelling of water and sediment
fluxes using a representative river stretch approach. Book chapter IN: Natural Systems and
Global Change, German-Polish Seminar Turew, Poznan
T. Francke, A. Güntner, A. Bronstert, G. Mamede, E. N. Müller. (2007, in press) Automated
Catena-based Discretisation of Landscapes for Semi-distributed Hydrological Modelling.
International Journal of Geographical Information Science
A. Bronstert, R. J. Batalla, J. C. de Araújo, T. Francke, A. Güntner, G. Mamede, E. N. Müller
(2007) Investigating erosion and sediment transport from head-waters to catchments to
reduce reservoir siltation in drylands. In: Reducing the Vulnerability of Societies to Water
Related Risks at the Basin Scale (Proceedings of the third International Symposium on
Integrated Water Resources Management, Bochum, Germany, September 2006). IAHS Publ.
317, Wallingford (GB)
G. L. Mamede, A. Bronstert, T. Francke, E. N. Müller, J. C. de Araújo, R. J. Batalla, A. Güntner.
(2006) 1D Process-based modelling of reservoir sedimentation: a case study for the
Barasona Reservoir in Spain. Conference Proceeding River Flow 2006. International
Conference on Fluvial Hydraulics. Lisbon Sept. 06
Abstract
The importance of vegetation dynamics in hydrological and sediment-transport modelling
from the macro to the meso-scale: the missing link
Keywords: vegetation dynamics; erosion; hydrological modelling
Vegetation dynamics plays a vital role for the redistribution processes of water and sediment
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resources. However, many current modelling approaches for water, sediment and nutrient transport
neglect the reproduction of vegetation dynamics. This is the case, for example, with inter-storm
dynamics and the functional relationship between key model parameters such as friction factor,
infiltration rate, evaporation and vegetation cover. This poster reviews the importance of vegetation
dynamics at different spatial scales, investigates its implementation in current hydrological and
erosion models and discusses the uncertainties related to the simplified implementation of
ecohydrological processes. Two case studies are presented that signify the importance of the
representation of vegetation dynamics in hydrological and erosion models at the micro- and
mesoscale. The first case study deals with degradation processes and severe vegetation changes
through shrub propagation into grassland in a semi-arid desert ecosystem in the south western part
of the US. Spatially distributed, process-based models were used to investigate the redistribution of
water and soil resources via overland flow within different grasslands and shrublands. Analysis of a
multi-scale field data set showed that shrublands are characterised by lower friction factors,
infiltration rates and nutrient content in the interconnected intershrub areas in comparison with
higher values within shrub patches. Grasslands exhibit a more homogeneous distribution of flow
and soil resource parameters with significantly higher friction factors and nutrient contents than
shrubland (Mueller et al. 2007a). Transport models were parameterised for 0.1 km2 catchments with
a grid size of 2 x 2 metres by using a connectivity approach that intrinsically represented the
pattern formations of model input parameters such as friction factor, infiltration rate and nutrient
content related to the patchiness of vegetation (Mueller et al. 2007b). Testing the model results
showed that the incorporation of connectivity patterns into model parameterisation is crucial for
model performance. The modelling study led to the formulation of a novel hypothesis regarding the
ongoing degradation of grasslands (Mueller et al. 2007c): it is hypothesised that a vegetation
boundary is stable when the following two conditions are met to balance the lower resistance of
grassland towards the existing environmental setting with the higher resistance of shrubland. First,
the soil depletion of nutrients by the action of overland flow in the grassland zone close to the
boundary is in balance with the replenishment rates of grassland by nutrient cycling. Second, the
grassland gains enough water resources from the upslope shrublands. On the contrary, a vegetation
boundary has a potential for instability when the grassland acquires a competitive disadvantage
towards shrubland regarding water benefit and nutrient depletion due to the combined effects of
overland flow dynamics and some external stresses. With reference to the ecosystem stability and
resilience theory, the modelling results provided important insights into the potential stability of the
grassland-shrubland boundaries as a function of soil-nutrient depletion and water-resource
enrichment for the grassland. To test this stability hypothesis, the implementation of vegetation
dynamics in the current modelling framework requires substantial development to include interstorm dynamics of nutrient cycling and dynamic adjustment of hydrological and soil parameters as
a function of shrub propagation. The second case study demonstrates and discusses the spatial
implementation of vegetation distribution for a meso-scale erosion model. This research is part of
the SESAM project (Sediment Export from Semi-Arid Regions: Measurement and Modelling), a
DFG-funded project of an international research consortium dealing with sediment transport
causing severe sedimentation of reservoirs in Spain and Brazil. Modelling of water and sediment
transport processes are carried out for domains of 1,200 to 2,000 km2 with a spatial representation
of sub-catchments with sizes ranging between 5 and 50 km2 that contain homogeneous landscape
units and terrain components representing individual, characteristic hillslopes. Vegetation
parameters are described for each terrain component that take into account height, root suction, LAI
and stomata resistance and vegetationdependent erosion parameters. The poster presents simulation
results for water and sediment transport from a region with different vegetation covers, ranging
from semi-arid shrublands such as Caatinga in northeast Brazil to coniferous forest and agricultural
fields in the Mediterranean pre-Pyrenees. The deficiencies of the current vegetation implementation
include the insufficient temporal variation of vegetation characteristics such as seasonal changes of
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the parameters and dynamic linkages between land-use change and flow and erosion parameters.
Finally, the poster discusses and develops ideas of how to include ecohydrological processes at the
micro- and meso-scale in process based, spatially distributed modelling frameworks to increase the
predictive ability of models in water research.
References:
E. N. Mueller, J. Wainwright, A. J. Parsons (in press 2007a), The impact of connectivity on the modelling of overland
flow within semi-arid shrubland environments, Water Resources Research
E. N. Mueller, J. Wainwright, A. J. Parsons (in press 2007b) The stability of vegetation boundaries and the propagation
of desertification in the American Southwest: A modelling approach, Ecological Modelling
E. N. Mueller, J. Wainwright, A. J. Parsons (in press 2007c), Spatial variability of soil and nutrient characteristics of
semi-arid grasslands and shrublands, Jornada Basin, New Mexico, Ecohydrology
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Dr. Lars Nerger
Aquatic Ecology
Alfred Wegener Institute for Polar and Marine Research
Am Handelshafen 12, 27570 Bremerhaven, Germany
phone: +49 (471) 4831 1558
CV
2000 M.S. in Physics, Albert Einstein Institute, Golm and University of Bremen, Germany
Thesis title: “Investigations of 3D Binary Black Hole Systems”
Advisor: Dr. Bernd Brügmann
2004 Ph.D. in Applied Mathematics, Alfred Wegener Institute, Bremerhaven and University of
Bremen, Germany
Thesis title: “Parallel Filter Algorithms for Data Assimilation in Oceanography”
Advisors: Prof. Dr. Wolfgang Hiller and Dr. Jens Schröter
Abstract
Assimilation of Satellite Ocean Chlorophyll Data with Bias Correction for Biogeochemical
State Estimation
Lars Nerger1 and Watson W. Gregg2
1) Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany
2) Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt,
Maryland, USA ([email protected])
Key words: Data assimilation, ecosystem modeling, Kalman filter, SEIK, bias correction, ocean
color
Chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) is assimilated into
the three-dimensional global NASA Ocean Biogeochemical Model (NOBM) for the period
1998-2004. The ensemble-based SEIK filter is used here with a localized analysis and simplified by
the use of a constant covariance matrix. A multivariate configuration is applied, which updates the
four phytoplankton groups of the model as well as nitrate, ammonium, and nitrogen detritus. The
SEIK
filter is combined with an online bias estimation algorithm, which estimates the bias in the model
estimate of surface chlorophyll.
The results of the daily assimilation are validated by comparison with independent in situ data. The
assimilation results in significant improvements of the chlorophyll estimates. These are superior to
both the free-run model and SeaWiFS data. The bias correction scheme shows that there are
significant model biases in the chlorophyll field. These can be estimate and improve the
assimilation compared to a bias-blind scheme.
The results are less clear for the nutrients. Without bias correction, the prediction of nitrate is
improved in wide regions. However, there are locations in which the assimilation is unstable,
resulting in strong overestimates of nitrate. Here, the bias correction stabilizes the assimilation
process, but results in smaller improvements of the non-observed fields.
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Dipl.-Ing. Benjamin Nitsch
Hydrology
Institute of Waste Management and Contaminated Sites
TU Dresden
Pratzschwitzer Straße 15, 01796 Pirna, Germany
phone: 03501-530039
CV
2000 – 2006
2004 – 2006
degreed engineer for water management at TU Dresden
Dresden Groundwater Research Centre (DGFZ e.V.), second mandatory
internship and elaboration of diploma thesis
Abstract
Using synthetic precipitation time series for transient flow model-ing in the unsaturated zone
Benjamin Nitsch & Oliver Kemmesies & Peter-Wolfgang Graeber
1 Institute of Waste Management and Contaminated Sites, Technische Universität Dresden, [email protected]
2 KP Ingenieurgesellschaft für Wasser und Boden mbH, Bahnhofstr. 37, D-91710 Gunzenhausen,
[email protected]
1
2
1
Keywords: synthetic precipitation time series, transient flow modeling
In the upper unsaturated soil layers, the water balance is mainly affected by pre-cipitation and its
intensity. Heavy precipitation produces high infiltration fronts which result in a rapid mass transfer in
the soil. This has significant effects on the dimensioning of capping systems, flood protection dams and
on the risk assess-ment of the judging of contaminated sites, where so far the approach of average water
balance values has generally been applied. For this reason over the last years a weather generator has
been developed by the authors, which supplements simulation tools with a corresponding module, for
example the program SiWaPro DSS for the simulation of flow – and mass transport processes in the
unsaturated zone. On the soil surface, precipitation, root water uptake and potential evapora-tion as
atmospheric boundary conditions have to be taken into the calculation. Background for the generation of
synthetic time series for those variables in daily values is the analysis of available long term series from
climate gauging stations (for example of the DWD). The characteristic of these real time series is
described with statistic parameters. These parameters allows firstly a generation of time se-ries for
unknown locations via regional interpolation and secondly a generation of time series of any length.
After a validation and a trend test, for example the pre-cipitation is described through Markov-chain
first order (for the precipitation con-dition of a day) and a Weibull distribution (for the amount of
precipitation of a day). These parameters are then determined for a 14-day time period respectively, so
that seasonal changes can be displayed. Using the synthetic time series, daily evapotranspiration can be
calculated with the method of TURC-WENDLING. Further modules for correction of the
evapotranspiration with data for declination, orientation and the value for Albedo value of the surface,
for modelling the inter-ception reservoir and for modelling root water uptake reduce the available potential evapotranspiration to the required parameters root water uptake, potential soil evaporation and
ground level precipitation. In the practical application, it was possibly to show that for “Süptitzer Berg”,
a former military base, only by using transient infiltrations fronts, which are generated by the weather
generator, the concentration of contaminants in soil and groundwater could be reproduced with
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sufficient accuracy. In the further development, the weather generator should gen-erate time series,
which consider the influences of forecast climate change using data from calculated climate scenarios.
With their help we will investigate ques-tions for the effect of climate change on the unsaturated soil
zone. In the future, the weather generator will be used for questions of the effects of climate change on
the water balance.
Reference
Nitsch, B., Gräber, P.-W.. 2007. Anwendung synthetischer Niederschlagszeitrei-hen bei der Strömungsmodellierung der
ungesättigten Bodenzone. Simulation in Umwelt- und Geowissenschaften, Workshop Berlin 2007. Shaker Verlag,
Bereich Umweltinformatik
Gräber, P.W., Blankenburg, R., Kemmesies, O., Krug, S.. 2006. „SiWaPro DSS – Beratungssystem zur Simulation von
Prozessen der unterirdischen Zone“; in: Si-mulation in Umwelt- und Geowissenschaften, Workshop Leipzig 2006;
Shaker Verlag, Bereich Umweltinformatik
Richter, Gunnar. 2003. Zeitliche Beschreibung der Sickerwassereinträge in den Boden. Diplomarbeit. Technische
Universität Dresden. Institut für Abfallwirt-schaft und Altlasten
Geng, S., Penning de Vries, F.W.T., Supit, L., 1985. A Simple method for gene-rating rainfall data. Agricultural and
Forest Meteorology, 36: 363-376
Richardson, C.W. 1981. Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resource
Research 17, pp. 182-190
Matalas, N.C. 1967. Mathematical assessment of synthetic hydrology. Water Re-source Research 3, pp. 937-945
Richardson, C.W., and D.A. Wright. 1984. WGEN: A model for generating daily weather variables. U.S. Department of
Agriculture, Agriculture Research Service, ARS-8. p. 235
Richardson, C.W. 2000. Data Requirement for Estimation of Weather Generation Parameters. Trans. of ASAE 43(4),
pp. 877-882
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Dr. Karsten Rinke
Dr. Karsten Rinke *1975
Aquatic Ecology
Degree: Biologist (Dipl. Biol., TU Dresden, 2001); Ph.D., Faculty
of Forest-, Geo-, and Hydrosciences (TU Dresden 2006).
Curr. Position: Postdoc at the Limnological Institute (University
of Konstanz, Prof. Rothhaupt).
Research interests: Evolutionary ecology of zooplankton, functional ecology of phytoplankton,
water quality management models, ecological modelling
Motivation: Learning techniques about uncertainty analysis and stochastic simulations, meeting
scientists from other disciplines for possible interdisciplinary project ideas
Current address: University of Konstanz, Limnological Institute, Mainaustr. 252, 78464 Konstanz
Tel. +49 7531 882930, Email: [email protected]
Schalau, K., Rinke, K., Straile, D. & Peeters, F. (submitted). Temperature is the key factor
explaining
interannual
variability
of
Daphnia
development
in
spring
a modelling study.
Rinke, K., Hülsmann, S. & Mooij, W.M. (accepted). Adaptive value, energetic costs, and
underlying resource allocation patterns of predator-induced life-history shifts in Daphnia. Oikos
Petzoldt, T. & Rinke, K. (accepted). Simecol: an object-oriented framework for ecological modeling
in R. Journal of Statistical Software.
Rinke,K., Hübner, I., Petzoldt, T., Rolinski, S., König-Rinke, M., Post, J., Lorke, A. & Benndorf, J.
(2007). How internal waves influence the vertical distribution of zooplankton. Freshwater
Biology 52: p137-144.
Rinke, K & Vijverberg, K. (2005). A model approach to evaluate the effect of temperature and food
concentration on individual life-history and population dynamics of Daphnia. Ecological
Modelling 186: p326-344.
Hülsmann, S., Rinke, K. & Mooij, W.M. (2005). A quantitative test of the size efficiency hypothesis
by means of a physiologically structured model. Oikos 110: p43-54.
Rinke, K., Vijverberg, J., Petzoldt, T. & Benndorf, J. (2005). Individual and population level
dynamics of Daphnia at varying food, temperature, and fish predation: a model approach.
Verhandlungen der Internationalen Vereinigung für Limnologie 29: p310-314.
Rinke, K. & Petzoldt, T. (2003). Effects of temperature and food on individual growth and
reproduction of Daphnia and their consequences on the population level. Limnologica 33:
p293-304.
Rinke, K., Robinson, C.T. & Uehlinger, U. (2001). A note on abiotic factors that constrain
periphyton growth in alpine glacier streams. International Review of Hydrobiology 86:
p361-366.
Abstract
Simulating phytoplankton community dynamics in Lake Constance with a coupled
hydrodynamic-ecological model
Rinke, Karsten ; Eder, Magdalena , Peeters, Frank , Gal, Gideon and Rothhaupt, Karl-Otto
1 Limnological Institute, University of Konstanz, Germany
1
2
1
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1
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2 Institute
of Hydraulic Engeneering, University of Stuttgart, Germany
3 Kinneret Limnological Institute, Migdal, Israel
Email addresses: [email protected], [email protected], [email protected],
[email protected]
keywords: hydrodynamics, limnology, phytoplankton dynamics, water quality management
models, functional phytoplankton groups, longterm dynamics
Lake Constance is among the largest lakes in central Europe and represents the most important
drinking water reservoir in south-western Germany. In total, about 4 million people depend on this
lake in terms of their drinking water supply and considerable efforts have been carried out to protect
the high water quality of the
lake. Besides drinking water supply the lake is also the receiving water body of numerous wastewater plants within its catchment and is moreover used for recreational purposes and fisheries. The
pelagic zone of Lake Constance has been studied extensively in the past and detailed data about
nutrient and plankton dynamics as well as physical properties are available.
In an attempt to provide a model-based decision support system for water managerns of Lake
Constance the BodenseeOnline-project was initiated. Within this project a complex model system
(Fig.1) is developed enabling to perform online-simulations and scenario analyses with a coupled
hydrodynamic-ecological model. The ecological model CAEDYM (Fig. 2) is applied to this
purpose, which can either be coupled with a one-dimensional hydrodynamic (DYRESM) or a threedimensional hydrodynamic model (ELCOM). For the application of CAEDYM (Fig. 2) an
ecosystem analysis of Lake Constance was carried out and simulations of historical data were
conducted in order to find an appropriate model configuration and parameterisation. To account for
the functional diversity of phytoplankton four functional phytoplankton groups were established
and parameterised. Long-term simulations with the coupled model DYRESM-CAEDYM showed
that the model is able to reproduce the typical succession patterns of the phytoplankton and the
dynamics of total chlorophyll. However, besides uncertainties in the meteorological and
hydrological forcing factors the physiological characterisation of the functional phytoplankton
groups remains puzzling. Problems arise due to high variability in measurements of physiological
rates and physiological plasticity of the organisms. The current realisation with four groups may
represent a compromise between accounting for the prevalent diversity and keeping the model
tractable.
Fig. 1: Structure of the BodenseeOnline system (for Fig. 2: State variables of and included proceses in
detailed information refer to our webpage at
the ecological model CAEDYM.
www.bodenseeonline.de).
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Dipl.-Biomath. Stefanie Rost
Hydrogeology
Potsdam Institute for Climate Impact Research
Earth System Analysis Research Domain
P.O. Box 60 12 03, 14412 Potsdam, Germany
phone: +49-331-288-24 22
CV
since 07.2005
10.2004 – 05.2005
10.2000 – 09.2004
Ph.D. Student, Department of Global Change and Natural Systems, Potsdam
Institut of Climate Impact Research
Master Thesis (Diplomarbeit) prepared at the TU Dresden Institute for
Hydrobiology: „Methodology for the qualitative evaluation of water quality
models by means of measured data“ („Methodik zur qualitativen Beurteilung
von Gewässergütemodellen anhand von Messdaten“)
Diploma Study of Natural Science Biomathematic at the Ernst-Moritz-ArndtUniversität Greifswald major subjects: Stochastic/Statistic, Ecology Degree:
Master of Biomathematic (Dipl.-Biomath.Univ.)
Abstract
Modeling the agricultural green and blue water consumption and determining the
anthropogenic influence on the global water system
Global food production relies on both "blue" water (available in rivers, lakes and aquifers) and
"green" water (soil water evaporating or transpiring through plants). This study quantifies for the
first time, spatially explicitly and consistently, how much water currently is consumed by the
Earth’s vegetation, differentiated between water consumption by irrigation, rainfed agriculture, and
natural ecosystems. We use the dynamic global vegetation and water balance model LPJmL (LundPotsdam-Jena managed land), which simulates the growth and abundance of natural and agricultural
vegetation and the related water fluxes in a single framework (Sitch et al, 2003; Gerten et al., 2004;
Bondeau et al., 2007). For the present study, the model was enhanced by a river routing model,
lakes and reservoirs as additional water storage pools, and modules to consider blue water
withdrawals for households and industry (Jachner et al., subm.). Since on irrigated land agricultural
plants use both blue water stemming from irrigation and green water as infiltrated from
precipitation, a method was developed to quantify their individual fractions. We performed three
different simulations: 1) natural and agricultural vegetation with irrigation as dependent solely on
computed blue water stored in rivers, lakes and reservoirs; 2) natural and agricultural vegetation
with a potential irrigation assuming that enough water is available in every irrigated place; and 3)
potential natural vegetation, for which the green water flux compared to 1) and 2) is determined.
We show that global agricultural water use is by far dominated by green water (though the blue
water component predominates in certain regions). We also find that the conversion of land cover
for agricultural use has reduced the global green water flow by ca. 5%. The simulation results
highlight the importance of green water in global food production and ecosystem conservation, as
well as the need for a focus on better management of green water resources. On this basis the virtual
water content of crops (i.e. the amount of water required to produce a unit of crop yield)
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differentiated between green and blue will be estimated and the virtual water trade between nations
and regions will be assessed in a consistent modeling framework. To evaluate the uncertainties the
model results as simulated by two different models (LPJmL and WaterGAP) will be compared.
Despite the important role of green water existing water stress indicators considering only blue
water resources. For a consistent assessment of human dependencies on the global water system
new water stress indicators combining green and blue water consumption as well as virtual water
transport will be developed.
References
Bondeau, A., P. C. Smith, S. Zaehle, S. Schaphoff, W. Lucht, W. Cramer, D. Gerten, H. Lotze-Campen, C. Müller, M.
Reichstein, and B. Smith (2007), Modelling the role of agriculture for the 20th century, Glob. Change Biol., 13(3),
679-706.
Gerten, D., S. Schaphoff, U. Haberlandt, W. Lucht, and S. Sitch (2004), Terrestrial vegetation and water balance hydrological evaluation of a dynamic global vegetation model, J. Hydrol., 286(1), 249-270.
Jachner, S., D. Gerten, A. Bondeau, W. Lucht, J. Rohwer, and S. Schaphoff, Agricultural green and blue water
consumption and its influence on the global water system, Submitted to Water Resources Research. In July 2007.
Sitch, S., B. Smith, I. C. Prentice, A. Arneth, A. Bondeau, W. Cramer, J. O. Kaplan, S. Levin, W. Lucht, M. T. Sykes,
K. Thonicke, and S. Venensky (2003), Evaluationof ecosystem dynamics, plant geography and terrestrial carbon
cycling in the LPJ dynamic global vegetation model, Glob. Chang. Biol., 9(2), 161-185.
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Dr. Nele Schuwirth
Dr. Nele Schuwirth *1977
Aquatic Ecology
Degree: Dipl. Geology (Univ. Mainz, 2002); Ph.D., Geosciences
(Univ. Mainz 2006)
Curr. Position: Postdoc at the Department of Systems Analysis,
Integrated Assessment and Modelling, Eawag: Swiss Federal
Institute of Aquatic Science and Technology, Lecturer at ETH
Zurich, Institute of Environmental Sciences
Research interests: Construction of mechanistic models of biogeochemical and ecological
processes in river ecosystems, Application of Bayesian techniques for the assessment of the
identifiability of model parameters.
Soil and groundwater protection, behaviour of inorganic contaminants in the environment,
thermodynamic modelling of hydrochemical processes, application of soil water sampling
techniques and leaching tests for the assessment of contaminated sites
Motivation: meeting people, learning to know other system analytical strategies, getting new
ideas, cooperation perspectives,
Current address: Department of Systems Analysis, Integrated Assessment and Modelling,
Eawag: Swiss Federal Institute of Aquatic Science and Technology, Ueberlandstrasse 133, P. O.
Box 611, CH-8600 Duebendorf/Switzerland, Phone: +41 (0)44 823 55 28, Fax: +41 (0)44 823 53
75, e-mail: [email protected]
Schuwirth, N., Kühni, M., Schweizer, S., Uehlinger, U., and Reichert, P.: A mechanistic model of
benthos
community
dynamics
in
the
River
Sihl,
Switzerland.
Freshwater
Biology (in revision).
Abstract
Combining data analysis and expert knowledge in a mechanistic river model using Bayesian
inference
Schuwirth, Nele & Reichert, Peter
Keywords: Mechanistic models, Bayesian inference, river ecosystems
The intention of the development of mechanistic models of aquatic ecosystems is to test hypotheses
on ecosystem functionality and to asses the influence of driving forces. With the help of
mechanistic models it is possible to estimate transformation rates from substance concentration and
organism density data. Mechanistic models can be used to support the communication of
quantitative knowledge about processes, to optimize the design of field measurements or laboratory
studies, and finally to predict the future development of an ecosystem under changing
environmental conditions including an estimation of the prediction uncertainty.
A difficulty with the development of mechanistic models for aquatic ecosystems is the requirement
of a detailed knowledge on structural relationships and external influence factors. Since these
relationships are of complex nature mechanistic models very often tend to be overparameterized so
that it is not possible to identify all model parameter from the available measured data. Frequentist
parameter estimation procedures require a good identifiability of all calibration parameters. This
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prerequisite is often not fulfilled so that significantly different parameter sets lead to a very similar
model output. This is known as the equifinality problem (Beven and Binley, 1992) occurring in
many fields of environmental modeling.
Therefore, it can be advantageous to include prior knowledge on parameter distributions from
further experience and literature in the parameter estimation process and to update this prior
knowledge with new measured data to calibrate the model. This can be done with the application of
Bayesian inference (e.g. Gelman & Carlin, 1995). For this method identifiability is not required and
it allows a consideration of uncertainty of model parameters and results.
In this study we used Bayesian inference to calibrate a mechanistic river benthos community model,
ERIMO (Ecological River Model), developed to simulate the dynamics of the most important
functional feeding groups of benthic organisms (periphyton and invertebrates) (Schuwirth et al.,
submitted). We used process formulations at the lowest level of complexity consistent with a
mechanistic, and as far as possible universal, description of the ecosystem. As a first step this model
was applied to the River Sihl, Switzerland. The strategy for model development is summarized in
Figure 1. Since processes determining the development of the benthic community are highly
complex and due to multiple interferences with adjacent ecosystems many influencing factors are
difficult to quantify.
Figure 1: Concept for model
development
Despite the availability of a comprehensive data set, the
complexity of processes to be considered even in a relatively
simple model leads to an overparameterized model that cannot
be calibrated uniquely with the available data. We therefore
combine expert knowledge, literature data, and the experience
from other model studies with the information contained in the
data to confine the model parameters as good as possible and to
consider uncertainty by applying Bayesian inference. This led to
a successful calibration of the model, an analysis of the gain of
information about the values of the model parameters, and an
analysis of the uncertainty of model predictions. For validation
and extension of the range of applicability, data from more
investigation sites and more rivers would be required.
The method of Bayesian calibration of process-based models
turned out to be a promising method for application in aquatic
ecology, but it can be easily transferred to a wide variety of disciplines, e.g., hydrology,
ecotoxicology, air pollution (Arhonditsis et. al, 2007). We hope that the demonstration of the
capabilities of our methodological approach, which leads to a deeper analysis of what we can learn
from model-based data analyses, stimulates the use of such techniques in ecological studies.
References:
Arhonditsis, G.B., Qian, S.S., Stow, C.A., Lamon, C., and Reckhow, K.H. (2007): Eutrophication risk assessment using
Bayesian calibration of process-based models: Application to a mesotrophic lake, Ecological Modeling (in press, doi:
10.1016/j.ecolmodel.2007.05.020).
Beven, K., Binley, A., 1992. The future of distributed models–model calibration and uncertainty prediction.
Hydrological Processes 6, 279–298.
Gelman, S., Carlin, J.B., Stren, H.S. & Rubin, D.B. (1995) Bayesian Data Analysis, Chapman and Hall, New York,
USA.
Schuwirth, N., Kühni, M., Schweizer, S., Uehlinger, U., and Reichert, P. (submitted): A mechanistic model of benthos
community dynamics in the River Sihl, Switzerland. Freshwater Biology (submitted).
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Dipl.-Ing- Leopold Stadler
Dipl.- Ing. Leopold Stadler *1980
Hydrogeology
Degree: Dipl.-Ing. Umweltschutztechnik, Universität Stuttgart
Curr. Position:
Fachgebiet für Wasserwirtschaft und Hydrosystemmodellierung,
Institut für Bauingenieurwesen, TU - Berlin
Research interests:
- Multiphase flow in porous media
- Coupling of software / models
- Macropores
- Unsaturated Zone + Groundwater + Transport
- Model concepts
Motivation
I am working as a young scientist in the DFG researcher’s group "Natural Slopes, Coupling of Flow and Deformation Processes for Modelling the Movement of Natural
Slopes” at the TU-Berlin (Technical University).
The interdisciplinary workgroup consists of several sub-projects. In my sub-project
“Subsurface Hydraulics” we simulate the infiltration and flow of rainwater in natural slopes.
The complex structure of natural slopes, the interaction between macropores and the soil
matrix, the geometry of macropores and the soil parameters are mostly unknown. This
makes modelling to a challenging work, and the dealing with uncertainties plays an
important role. To use models for simulation, we have to know the capabilities and
limitations of our models and the uncertainties in the used model parameters, and boundary
conditions.
Current address:
TU-Berlin, Institut für Bauingenieurwesen,
Fachgebiet Wasserwirtschaft und Hydrosystemmodellierung,
Sekr. TIB1-B14, Gustav-Meyer-Allee 25, 13355 Berlin
email: [email protected],
phone: +49 30 314 72428
2006
PHAM VAN,S., STADLER, L. & HINKELMANN,R.: Comparison of a Micro-scale and a Mesoscale Model Concept for Two-phase Flow in Fractured-porous Media. CMWR XVI Computational Methods in Water Resources, XVI International Conference, Copenhagen, Denmark
STADLER,L., HELMIG,R., HINKELMANN,R. & PHAM VAN,S.: A Comparison of Model
Concepts for Macropore Infiltration.. 6. Workshop "Porous Media", Blaubeuren
Abstract
Simulation of the rainwater infiltration via macropores into natural slopes
Stadler L., Hinkelmann R., Germer K., Braun J., Helmig R.
Keywords: Macropore; Landslides; Double continuum; Two-phase flow, Porous media,
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The failure of natural slopes consists of several coupled and complex processes, so that for a
prediction hydrological, soil-hydraulic and soil-mechanical processes must be considered together
with deterministical and statistical methods in order to take into account the available data on the
one hand and the very different scales involved on the other hand. Since there is no overall
simulation tool to describe all these processes, the coupling and exchange between different models
is necessary. The DFG research’s group 581 Natural Slopes, “Coupling of Flow and Deformation
Processes for Modeling the Movement of Natural Slopes” investigates the movement and
deformation of natural slopes. Goal of the research is to develop new methods and simulation tools
to predict the failure of slopes.
The group investigates the movement and deformation of the slope Heumöser Hang in Ebnit
(Austria). Therefore, a high number of hydrological and geophysical measurements have been
carried out by the group.
The field measurement showed that the fast infiltration of rainwater in macropores at the upper
parts of the hill and the resulting pressure increase in the lower parts of the slope plays a dominant
role for the deformation, which might lead to a failure. Macropores can generate a fast infiltration
via the bypassing of rain water through mainly dry parts of a slope. This feature was captured by the
group during tracer experiments with Brilliant Blue. The flow regime in macropores is very
complex, in literature it is often distinguished between film and pulse flow. To simulate the fast
infiltration we investigate different model concepts for two-phase flow (water/air) in porous media,
using the numerical simulator MUFTE-UG (Multipahse Flow, Transport and Energy Model Unstructured Grids).
The first studies with an easy example of a natural slope have shown that the existing concepts are
not suitable to describe the fast infiltration and bypassing via macropores on a small scale.
Simulating flow through a single macropore using the two-phase flow equations leads to extremely
small time-steps and numerical problems. Also the flow regime is not described correctly by using
the extended Darcy’s law. Models that use simple boundary conditions to describe macropores
often overestimate the infiltration because the missing limitation of the water inflow into the
macropore.
The group has carried out a number of experiments to investigate the infiltration over a single
macropore. To simulate the experiments with a two-phase flow model, we developed an extended
boundary condition model. Therefore we use an external program that simulates the distribution of
water in the macropore and the exchange to the soil matrix. The simulation for the macropore is
similar to a cascade of storage cells and requires an interface length for the exchange with the soil
matrix.
For larger scales this concept is not suitable, thus we started to implement a double continuum
model. The model consists of two continua, one that describes the soil matrix and a second
describing the macropores. For both continua we use the two-phase flow equation with the extended
Darcy’s law. The exchange between both continua is formulated with a function similar to the
extended Darcy’s law. Therefore, we use a so called “interface length” to be able to compute the
pressure gradient between both continua. Primary results of the double continuum model at the
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larger scale look very promising; the model can reproduce the fast bypassing of infiltrating water.
But for a detailed evaluation benchmarks at different scales are necessary.
We plan to transfer the knowledge from the experimental scale continuously to larger scales, till the
scale of the natural slope. During this, the number of unknown variables increases rapidly. Not only
the structure of the soil and the soil parameters like porosity, permeability and the corresponding
soil-function parameters are often unknown, also the boundary conditions are more complex.
The infiltration is coupled with the highly variable rainfall and the generated surface runoff. This
makes it necessary to couple and exchange the data of different models to get realistic boundary
conditions. At the large scale of a natural slope, the assimilation of data will be the main challenge.
Since it is not possible to measure the parameters for the whole domain, geostatistical methods will
be used. The unknowns of the larger scale create additional uncertainties. To handle the
uncertainties arising from the different scales and processes an intelligent use of different methods
is important. Without the estimation of the range of the different variables and parameters the use
and calibration of the model is not possible. The prediction and the results of the model will be
compared with measurements on different scales. Thereby it is important to distinguish between
differences of the simulation results and the measurements. The differences can be caused by the
limitation of the used model concept, and the uncertainties in the used simulation parameters.
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Dr. Henning Wilker
Meteorology
Meteorological Institute
University of Bonn
Auf dem Hügel 20, 53121 Bonn, Germany
phone: +49 228 735101
CV
1991 – 1998
2001 – 04/2007
Since 04/2007
Higher education in Meteorology, University of Kiel.
Diploma thesis: The effects of clouds on the radiation budget over the Baltic
Sea.
Ph.D. student at the Meteorological Institute of the University of Bonn.
Dissertation: Soil moisture analysis based on microwave brightness
temperatures: A study on systematic and random errors.
Projects: ELDAS, LandSAF, geoland.
Research associate at the Meteorological Institute of the University of Bonn.
Research focus: Soil moisture analysis in numerical weather prediction
models based on passive microwave remote sensing data.
Abstract
Soil Moisture Analysis Based On Microwave Brightness Temperatures: Single-Column
Studies with the ECMWF Weather Prediction Model
Henning Wilker1, Matthias Drusch2, Clemens Simmer1
1 Meteorological Institute, University of Bonn Auf dem H¨ugel 20, 53121 Bonn, Germany
2 European Centre for Medium-Range Weather Forecasts Shinfield Park, Reading, RG2 9AX,
United Kingdom
Keywords: Land Data Assimilation, Numerical Weather Prediction, Root-Zone Soil Moisture,
Passive Microwave Remote Sensing
Root-zone soil moisture governs the partitioning of available energy at the land surface into latent
and sensible heat fluxes and thus plays an important role for the energy and water budget in the
atmospheric boundary layer. Consequently, the soil water content can significantly affect the
development of the local to regional weather. Numerical weather prediction therefore requires an
accurate specification of the horizontal and vertical soil moisture distribution. Due to deficiencies in
the soil moisture simulation of current operational weather prediction models (e.g. caused by
imperfect parameterizations, uncertainties in radiation and precipitation predictions, inaccurately
known soil and vegetation characteristics), modeled soil moisture values can drift away from reality
and therefore need to be regularly adjusted by observations. Since a global soil moisture
measurement network does not exist at present, proxy observations have to be used. Many current
operational weather prediction systems use air temperature and humidity measured at a height of 2
m above ground (screen level) within the framework of the global meteorological observation
network. These two variables are coupled to soil moisture through the sensible and latent heat
fluxes at the land surface. The proxy observations are assimilated into the forecast models by means
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of an objective soil moisture analysis which merges the information of both observations and
modeled values by taking into account their respective uncertainties. However, the use of air
temperature and humidity requires a sufficient coupling between the land surface and the
atmospheric boundary layer (e.g. high insolation and weak winds).
Another proxy for soil moisture independent of the weather situation and therefore increasingly
under consideration is the land surface brightness temperature at low microwave frequencies. The
Soil Moisture and Ocean Salinity (SMOS; Kerr et al., 2001) mission will, for the first time, provide
satellite observations at the L-band microwave frequency, which is best suited for monitoring nearsurface soil moisture. In preparation for the launch of the SMOS satellite scheduled for the year
2008, the European Centre for Medium-Range Weather Forecasts developed an soil moisture
analysis system (based on an extended Kalman filter) which is able to assimilate both screen-level
variables and microwave brightness temperatures (Seuffert et al., 2004).
The goal of this new analysis system is to improve weather prediction by more realistic soil
moisture values in the surface model. Positive effects are e.g. expected for the precipitation
forecasts. Accurate precipitation forecasts are particularly important for hydrologic applications like
water discharge prediction in order to assess the possibility and intensity of floodings. The
combination of the proxy nearsurface soil moisture information retrieved from SMOS with
appropriate coupled soil-atmosphere models can also help in assessing the water absorption
capacity of the soil which also highly affects the risk of floodings in case of heavy rain. Single
column-studies with the new soil moisture analysis system based on hydrological and
meteorological data from field measurements and aircraft-borne brightness temperature
observations over parts of the U.S. state of Oklahoma revealed promising results but demonstrate
that an accurate specification of the model and observation uncertainties is fundamental (Wilker,
2007). The studies indicate that the new assimilation system is able to improve modeled root-zone
soil moisture in comparison to observations. At the same time, however, the modeled surface
fluxes partly get worse when compared to the flux observations. Reasons for this seem to be
imperfect surface scheme parameterizations, some only roughly known vegetation and soil
characteristics and difficulties in deriving exact observations of soil moisture and surface fluxes
arising from the measuring techniques applied.
References:
Kerr, Y. H., P. Waldteufel, J.-P. Wigneron, J.-M. Martinuzzi, J. Font, and M. Berger, 2001: Soil moisture retrieval from
space: The Soil Moisture and Ocean Salinity (SMOS) mission. IEEE Transactions on Geoscience and Remote Sensing,
39, 1729–1735.
Seuffert G., H. Wilker, P. Viterbo, M. Drusch, and J.-F. Mahfouf, 2004: The usage of screen-level parameters and
microwave brightness temperature for soil moisture analysis. Journal of Hydrometeorology, 5, 516–531.
Wilker, H., 2007: Soil Moisture Analysis Based On Microwave Brightness Temperatures: A Study on Systematic and
Random Errors. Ph.D. thesis, University of Bonn.
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Young Scientists Workshop “Data assimilation, dealing with

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