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Editorial Dear Reader, The 2nd Biennial Meeting of the International Environmental Modelling and Software Society (iEMSS 2004) was dedicated to the theme: Complexity and Integrated Resources Management”, a very topical theme given the increasing complexity of contemporary resource management problems and the increasing uncertainties from global change. The meeting assembled nearly 300 researchers from all over the globe and from a wide range of disciplines. Presentations discussed latest developments in modelling methodologies and software tools applied to different areas of resources management. Contributions provided evidence of the important role of models to improve our understanding of the complexity of socio-ecological systems and to develop appropriate management strategies. Increasing attention was paid to the role of stakeholders in model development and application and to a new role for models in processes of social learning in participatory resources management. The conference took place in the facilities of the German Environmental Foundation in Osnabrück. The ambience of these low-energy buildings, designed to minimise their impact on the environment, was well suited to the conference theme and their open and flexible structure facilitated intense discussions and exchange not only during but also between sessions. I hope that readers will share the excitement of conference participants when browsing through the conference proceedings and reading some of the papers in more detail. Interested readers are advised to consult the journals Environmental Modelling and Software and Ecological Modelling and Advances in Geosciences where special issues emanating from this conference will be published. We also look forward to the third biennial meeting, iEMSs 2006, which will be held in Burlington, Vermont, USA (see http://www.iemss.org/iemss2006). October 2004 Claudia Pahl-Wostl The International Environmental Modelling and Software Society acknowledges gratefully the assistance of the following people in realizing the iEMSs 2004 conference: • Claudia Pahl-Wostl for convening the conference • Sonja Schmidt for organising the conference • Andrea Rizzoli for the web-based conference management tool, creating and updating the conference website and for expert and technical advice • all session organizers and reviewers • Antje Braeuer and Georg Johann for supporting the organisation whenever and wherever necessary • all members of the Institute of Environmental Systems Research and the department of resource flow management for supporting iEMSs 2004, especially Ilke Borowski, Frank Hilker, Maja Schlüter and Dominik Reusser • all members of ZUK “Zentrum für Umweltkommunikation“ of the German Environmental Foundation IV
TABLE OF CONTENTS iEMSs 2004 sessions (part two) Environmental Informatics Towards Citizen-centred Electronic Information Services: the Urban Environment Example Using FLOSS towards Building Environmentale Information K. Karatzas, A. Masouras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .p. 525 Applying agent technologyin Environmental Management Systems underreal-time constraints I.N. Athanasiadis, P.A. Mitkas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .p. 531 Supporting the Strategic Objectives of Participative Water Resources Management; an Evaluation of the Performance of Four ICT Tools A. Swinford, B. McIntosh, P. Jeffrey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .p. 537 Web Services for Environmental Informatics E. Arauco, L. Sommaruga . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .p. 543 Environmental Decision Support Systems Concepts of Decision Support for River Rehabilitation P. Reichert, M. Borsuk, M. Hostmann, S. Schweizer, C. Spörri, K. Tockner, B. Truffer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .p. 550 Decision Making under Uncertainty in a Decision Support System for the Red River Inge A.T. de Kort, M.J. Booij . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .p. 556 Development of a GIS-based Decision Support Tool for Integrated Water Resources Management in Southern Africa M. Märker, K.Bongartz, W.A. Flügel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .p. 562 Possible Courses: Multi-Objective Modelling and Decision Support Using a Bayesian Network Approximation to a Nonpoint Source Pollution Model D. Swayne, J. Shi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .p. 568 V
Page 3 and 4: Complexity and Integrated Resources
Page 5: IEMSs 2004 - 14-17 June 2004, Unive
Page 9 and 10: Some Methodological Concepts to Ana
Page 11 and 12: A Model of the Biocomplexity of Def
Page 13: River Basin Management The Utility
Page 16 and 17: detail [Ghosh], [Shah], [Lerner 200
Page 18 and 19: of the APNEE regional server was ba
Page 20 and 21: European Parliament, European Counc
Page 22 and 23: 2 BACKGROUND 2.1 Environmental Mana
Page 24 and 25: Domain Knowledge Application Domain
Page 26 and 27: sible for suppling the O 3 RTAA wit
Page 28 and 29: 1.2 Bridging the knowledge gap It i
Page 30 and 31: schools in the Bedfordshire area. A
Page 32 and 33: the respondents did not feel confid
Page 34 and 35: • To control the effects on the e
Page 36 and 37: A001 7:00:00 150 Figure 3. Web
Page 38 and 39: Figure 6. Air information measures
Page 40 and 41: Concepts of Decision Support for Ri
Page 42 and 43: 4. PREDICTION OF OUTCOMES The outco
Page 44 and 45: Number of nat. tributaries p.r.l. D
Page 46 and 47: Decision Making under Uncertainty i
Page 48 and 49: Reforestation is a sustainable floo
Page 50 and 51: The results of the uncertainty anal
Page 52 and 53: Development of a GIS-based Decision
Page 54 and 55: distributed hydrological model, the
Page 56 and 57:
Fig. 5 Water availability/deficienc
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Possible Courses: Multi-Objective M
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arrows or arcs, with conditional pr
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of that result, productive years re
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A Spatial DSS for South Australia's
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framework to be developed, however,
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• efficiency and • comprehensiv
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Optimum Sustainable Water Managemen
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factors in the basin, each model th
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Treated wastewater from households
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Application of a GIS-based Simulati
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3. THE TUGAI SIMULATION TOOL The TU
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Figure 3 depicts the results of the
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Schlüter M., Savitsky A.G., McKinn
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temporal scale for a large river ba
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Some model components such as proce
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In the next step all indented measu
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states to conduct a disaggregate an
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• Water demand is quantified by p
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much higher, confirming the intensi
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- the optimization module, defining
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2.3 A proposal of dynamic risk defi
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heavily interested by hazmat traffi
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2. APPROPRIATENESS FRAMEWORK Figure
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inputs and parameters are arbitrari
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wrong decision and have counteracti
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community of the 2002 World Summit
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framework (Driving Force - Pressure
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are assessed in terms of their sust
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scales are required to identify con
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consumption requirements, limits on
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possibilities to adoption of sustai
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Several attempts in this direction
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With concern to the technologies se
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also political and economic impact
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Submit interim report on the implem
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ies within the river basin district
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complexity of WFD Decision Support
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7. REFERENCES [1] CIS (Common Strat
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tion of the pricing policies. 1.2 A
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3 DAWN MODELS 3.1 Econometric model
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5 DEMONSTRATION The DAWN platform h
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2001]. A combined definition of ver
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decision support system. When one v
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model evaluation. F. D’Erchia pro
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An integrated modelling approach to
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2.1 Framework requirements The fram
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patterns). Outputs also need to ind
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Some Methodological Concepts to Ana
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The term “communication” can be
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The stake is to convince all partic
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Tools to Think With? Towards Unders
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expected to prevent our knowledge a
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1. What impact at the work-package
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Uncertainty in the Water Framework
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asin without any additional provisi
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3.3 Uncertainty due to Prediction -
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. An Interactive Spatial Optimisati
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those available in BIOCLIM (variabl
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study area and any scale to solve u
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Assessing the Feasibility of Using
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sense but in an geographical sense.
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misclassifications can cause supply
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6. REFERENCES [1] Leuven, R.S.E.W.
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algorithms (GAs) to find optimal so
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demand dissatisfaction, minimizatio
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1 10 C 8 0 [mg/l] 5 0 3 0 1 0 0 Fig
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In other words, given that the only
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£ male have only 40% of chance of
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1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0
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dissolution of rock salt in the Mah
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YIELD PRICE RECEIPTS AREA CASH COST
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4. DISCUSSION AND CONCLUSIONS ICHAM
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Management Strategies for Cities an
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generated is very similar in these
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elationship between the average hou
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− the representation of resources
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dr j is the spread speed of the fir
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Referring to Scenario 1 (see figure
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to evaluate field scale environment
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An increase in LFA support is assum
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oilseeds and winter cereals with hi
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Simulation of Water and Carbon Flux
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latter is separated into 5 fraction
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is accounted for through the separa
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An integrated geomorphological and
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The recession coefficient of the sl
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etween modeled and observed dischar
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Anticipated Effects of Re-Allocatio
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Shrinkage in livestock was 11% (exp
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3.3. Integration in other spatial p
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An Integrated System for the Forest
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Topography and territorial data (GI
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The first information on which the
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Linking Narrative Storylines and Qu
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and structured for each of these th
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Figure 3. Examples of maps of the G
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Integrated Assessment of Water Stre
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1999). This can easily lead to inco
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influence on the regional vulnerabi
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methodological guidelines for a suc
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The next item—illustrating a part
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eflects a knowledge of the relevant
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management and in Hoyland and Walla
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that scenario g will occur, he coul
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period t and the value x b , identi
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2 MATERIAL 3 METHODS The basis for
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Figure 1 Distance between scenarios
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Therefore the proposed approach see
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system dynamics allow studying inte
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the IPCC marker scenarios and the c
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Essential elements of the storyline
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ise and water availability. This st
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how legal or economic restrictions
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into environmental quality (from th
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control were carried out as they id
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5) by number of years since HIV inf
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upon the incidence of diarrhea, whe
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Yates, D.N. 1996. WatBal: an integr
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equires not so much better understa
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1.2.2 Present opportunities Chronic
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progress in application of Conceptu
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Linking Hydrologic Modeling and Eco
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25°25´ 80°50´ 80° 45´ 80° 40
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Figure 7: Hydrology of dwarf mangro
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On the Local Coexistence of Species
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(1) Initially, we randomly distribu
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Figure 4. A snapshot of typical sta
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Ecosystems as Evolutionary Complex
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network such as predation, mutualis
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Table 2. Example of 2 connectance m
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Benthic Macroinvertebrates Modellin
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the error being calculated for the
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the percentage of success and the c
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Interspecific Segregation and Phase
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The equations (4a) and (4b) are cal
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when the steady-state densities app
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A Model of the biocomplexity of def
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1. The Government neither interacts
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software integrates simulation tech
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Developing Tools for Adaptive Integ
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stability are contested (e.g. see P
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formed to manage the resource more
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as Stability Analyses of the 50/50
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e The parameter m represents the mo
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Figure 4. Same as Fig .2 , but the
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Reproductive Strategies of Marine G
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studies on gamete size and swimming
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ecause they often possess mechanism
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Predicting predation efficiency of
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where Γ(k ) is a gamma function [K
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pupate at an earlier age [Tenhumber
Page 360 and 361:
The Coexistence of Plankton Species
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Figure 1. A typical result of popul
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Figure 3. Snapshots of a temporal p
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ÅØÑØÐ ÑÓÐÐÒ Ó ÖÑÙÐ
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878
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880
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882
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and these differences are categoris
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2a) a) subsurface flow. This can be
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kindly provided by the Finnish Mete
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2 MODEL APPROACHES In this section
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vival of individuals and seeds in t
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Shannon diversity index 2.2 2.4 2.6
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and Amblyomma limbatum are ectopara
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Predation on ticks within refuges b
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Relative width of overlap zone [km]
Page 392 and 393:
How to Compare Different Conceptual
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¥ ¥ ¡ ¥ tions are assumed to oc
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Table 2. Relative errors [variation
Page 398 and 399:
Simulation of Dynamic Tree Species
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Lotter et al., 2003]. The species w
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4. DISCUSSION The large-scale spati
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Aphid Population Dynamics in Agricu
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3.3 Reproduction Aphid agents becom
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peaked around 40 days later. Therea
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Integrating Wetlands and Riparian Z
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is can has is α = 10 * T S * L 2 (
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Q [m 3 /s] basin is strongly influe
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Williams, J.R., Renard, K.G., Dyke,
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are mapped as areas that have disti
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A general approach for implementing
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fit mixture models to the existing
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conditions and biotic response have
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these maps may be transformed to ma
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variable in space and time, Verhand
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͌ single 2. MODEL DESCRIPTION The
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wavelengths and low sun, where the
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Solar Energy Center, Rep. FSEC-PF-2
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¥ ¢ £ ¤ s , ¡ parameter of the
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A N ( φ ) f φ N 0.15 0.1 0.05 A N
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A 160 180 200 220 0.02 0.015 0.01 0
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Genuchten and Wierenga, 1976] is of
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Cum. Flux [mm] 600 400 200 Pot. ET
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¢ ¡ The influence of the averagin
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Month Hour Hourly averages ten min
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4 CONCLUSIONS The differences in ca
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Interaction Between Hydrodynamics a
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˽ [2002]). Also, this interface is
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Reynolds number Re*=1200. The S-H m
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0.5 Sh = c2 Re* Sc (15) where c 2 i
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A Spatially-Distributed Conceptual
Page 462 and 463:
saturated hydraulic conductivity of
Page 464 and 465:
For routing of the channel water, t
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Bouraoui, F. and T.A. Dillaha, ANSW
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analyst to understand the main cont
Page 470 and 471:
- the annual maximum discharge of t
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The approach is therefore very well
Page 474 and 475:
Parameters Estimation Using Some An
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typical numerical errors of the PDE
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In Table 2 the results of the param
Page 480 and 481:
Soil Hydraulics Properties Estimati
Page 482 and 483:
¥¤££ ©© ¨ § ¢ ¡ ¥¤££
Page 484 and 485:
Soil sample 1 2 3 4 5 6 7 8 Bulk de
Page 486 and 487:
An Approach for Calculating the Tur
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˻ ̌ ̌ ˻ ˻ ̌ ̌ ̌ ̌ that wil
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[2004]. Using the above parameter v
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The Utility of GIS Delivered Enviro
Page 494 and 495:
water body of groundwater abstracti
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100 pressures and the quality of me
Page 498 and 499:
A Tool for Evaluating Risk to Surfa
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and Binley 1992). However, WaterRAT
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Figure 2 shows the same for P load
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Spatially Distributed Investment Pr
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mapping of the controlling environm
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Total Cost ( Millions $ ) Total Cos
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erosion source rates can potentiall
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River Basin Catchment Module urban
Page 514 and 515:
R 2 = 1 N N tot ∑ t tot t = 1 1
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Fuchs E., Giebel H., Hettrich A., H
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connected to unusual events. The me
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Criteria Analysis functionalities s
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price, thus experiencing the implem
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The final product of the study is a
Page 526 and 527:
Summing the scores for each variabl
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Florence Impact from the QUAL2E mod
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loads. CatchMODS links several comp
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N 1 C hi S hi g = ( − ). gG hi +
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4. DISCUSSION AND CONCLUSIONS This
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additional sources of uncertainty b
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unbiased simulations included in th
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major source of predictive uncertai
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contributing over 50% of the phosph
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(snow pack temperature lag factor).
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increase the understanding of proce
Page 548 and 549:
Lake Simojärvi . 4 Water quality m
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Table 1. Used max. N fertilization
Page 552 and 553:
4. CONCLUSIONS This study shows tha
Page 554 and 555:
Implications of Complexity and Unce
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for regional applications). They ar
Page 558 and 559:
Elbe basin and its four subregions:
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esources in a large irrigation dist
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0.55 0.5 0.45 a 0.4 0.35 0.3 0.25 1
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5. CONCLUDING REMARKS In the paper
Page 566 and 567:
e used to compare an observation wi
Page 568 and 569:
4. COMPARISON DEMONSTRATION This se
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IMSE pixel values for observed and
Page 572 and 573:
2. THE INCA-N MODEL The INCA-N is a
Page 574 and 575:
include any transformation or immob
Page 576 and 577:
Moreover, we found that, almost reg
Page 578 and 579:
3. METHODS To reduce the overall un
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literature ranges. The ranges for t
Page 582 and 583:
first one being ‘exploring’, wh
Page 584 and 585:
Granlund K. .......................
Page 586:
Tymoshevska L. ................III
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