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Complexity and Integrated Resources Management - Transactions of the 2nd Biennial Meeting of the International Environmental Modelling and Software Society Volume Editors Claudia Pahl-Wostl Sonja Schmidt Institut für Umweltsystemforschung Universität Osnabrück Artilleriestr. 34 D 49076 Osnabrück, Germany Andrea E. Rizzoli IDSIA Istituto Dalle Molle di studi sull'intelligenza artificiale Galleria 2 CH 6928 Manno, Switzerland Anthony J. Jakeman The Centre for Resource and Environmental Studies (Bldg 43) The Australian National University ACT 0200, Canberra, Australia Each paper in this volume was refereed by an Editor, a member of the Editorial Board and two anonymous referees. The copyright of all papers is an exclusive right of the authors. No work can be reproduced without written permission of the authors. Responsibility for the contents of these papers rests upon the authors and not on the International Environmental Modelling and Software Society. ISBN 88-900787-1-5 iEMSs 2004 Published by the International Environmental Modelling and Software Society (iEMSs) President: Anthony J. Jakeman. Address: iEMSs, c/- IDSIA, Galleria 2, 6928 Manno, Switzerland Email: secretary@iemss.org Website: http://www.iemss.org II Typeset in Como (Italy) by SEA, Servizi Editoriali Associati
IEMSs 2004 – 14-17 June 2004, University of Osnabrück, Germany Complexity and Integrated Resources Management Transactions of the 2nd Biennial Meeting of the International Environmental Modelling and Software Society Claudia Pahl-Wostl, Sonja Schmidt, Andrea E. Rizzoli, Anthony J. Jakeman ( E d i t o r s ) Co-editors David Batten Keith Jeffery Dale S. Rothman Michel Blind Kostas Karatzas Miquel Sànchez-Marrè Felix Chan Markus Knoflacher Dragan Savic Barry Croke Peter Krause Huub Scholten Wolfgang-Albert Flügel Christine Lim Boris Schröder Carlo Giupponi Ian Littlewood Jan Sendzimir Romy Greiner Michael Matthies Ralf Seppelt Carlo Gualtieri Michael McAleer Achim Sydow Nigel Hall Dragutin T. Mihailovic Hilde Passier Matt Hare Les Oxley David Post Reinout Heijungs Jens C. Refsgaard Peter Vanrolleghem Stefanie Hellweg Otto Richter Suhejla Hoti Michela Robba Organizers The conference has been organized by iEMSs (the International Environmental Modelling and Software Society) in cooperation with: Harmoni-CA (Concerted Action on Harmonizing Modelling Tools for River Basin Management) TIAS (Integrated Assessment Society) IAHS (International Association of Hydrological Sciences) BESAI (Binding Environmental Sciences and Artificial Intelligence) MODSS International Conference on Multi-objective Decision Support Systems for Land, Water and Environmental Management ISESS International Symposium on Environmental Software Systems ERCIM the European Research Consortium for Informatics and Mathematics Local Organizers The local organization of the conference has been managed by: DBU (German Environmental Foundation) USF (Institute of Environmental Systems Research, University of Osnabrück) III
Page 3: Complexity and Integrated Resources
Page 7 and 8: TABLE OF CONTENTS iEMSs 2004 sessio
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
Page 58 and 59:
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
Page 76 and 77:
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
Page 340 and 341:
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
Page 394 and 395:
¥ ¥ ¡ ¥ tions are assumed to oc
Page 396 and 397:
Table 2. Relative errors [variation
Page 398 and 399:
Simulation of Dynamic Tree Species
Page 400 and 401:
Lotter et al., 2003]. The species w
Page 402 and 403:
4. DISCUSSION The large-scale spati
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Aphid Population Dynamics in Agricu
Page 406 and 407:
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
Page 424 and 425:
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
Page 442 and 443:
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
Page 466 and 467:
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
Page 476 and 477:
typical numerical errors of the PDE
Page 478 and 479:
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
Page 488 and 489:
˻ ̌ ̌ ˻ ˻ ̌ ̌ ̌ ̌ that wil
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[2004]. Using the above parameter v
Page 492 and 493:
The Utility of GIS Delivered Enviro
Page 494 and 495:
water body of groundwater abstracti
Page 496 and 497:
100 pressures and the quality of me
Page 498 and 499:
A Tool for Evaluating Risk to Surfa
Page 500 and 501:
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
Page 506 and 507:
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
Page 516 and 517:
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
Page 528 and 529:
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
Page 550 and 551:
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
Page 556 and 557:
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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