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Complexity and Integrated Resources Management Transactions of the 2nd Biennial Meeting of the International Environmental Modelling and Software Society Editors Claudia Pahl-Wostl Sonja Schmidt Andrea E. Rizzoli Anthony J. Jakeman IEMSs 2004 – 14-17 June 2004, University of Osnabrück, Germany
Page 4 and 5: Complexity and Integrated Resources
Page 6 and 7: Editorial Dear Reader, The 2nd Bien
Page 8 and 9: A Spatial DSS for South Australia's
Page 10 and 11: Scenario Development and Integrated
Page 12 and 13: Integrating Wetlands and Riparian Z
Page 15 and 16: Using FLOSS towards Βuilding Envir
Page 17 and 18: that can be utilized in building su
Page 19 and 20: automatic build and deployment scri
Page 21 and 22: Applying agent technology in Enviro
Page 23 and 24: Agent-based EMS development is conc
Page 25 and 26: Case 1 Deterministic Strategies The
Page 27 and 28: Supporting the Strategic Objectives
Page 29 and 30: perspectives, understandings, or kn
Page 31 and 32: Strategic objectives achieved (%) S
Page 33 and 34: Web Services for Environmental Info
Page 35 and 36: How a web service generally works i
Page 37 and 38: on the mobile operators in relation
Page 39 and 40: 3) Presentation level: developing t
Page 41 and 42: 1. DEFINITION OF THE DECISION PROBL
Page 43 and 44: River hydraulics and morphology Fig
Page 45 and 46: 7. ASSESSMENT OF RESULTS The prefer
Page 47 and 48: hydraulic model, which has a spatia
Page 49 and 50: 4.3 Ranking procedure The ranking p
Page 51 and 52: The second step has been the assess
Page 53 and 54:
2. OBJECTIVES The overall objective
Page 55 and 56:
considerable contribution to the ov
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final phase of the project in the f
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social and environmental aspects. C
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The STRAT watershed is rural, prima
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context: case study. J. Multi-Crit.
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management and describe the decisio
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the main port at Wallaroo. The flee
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closures are an important “input
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Japan Tokyo Taguri-River basin Rive
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Table 2. Indirect and direct evalua
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concrete from the river bottom and
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There are different sources of unce
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Environmental Models A statistical
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tion measures other factors than th
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Integration of MONERIS and GREAT-ER
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Climate change external scenarios H
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(Figure 4a) based on this evaluatio
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An integrated tool for water policy
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price elasticity of demand 2 , whic
Page 93 and 94:
summers. The analysis compares two
Page 95 and 96:
Towards a Decision Support System f
Page 97 and 98:
subject to several external/interna
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2.4 A comprehensive decision suppor
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Appropriate Modelling in DSSs for R
Page 103 and 104:
outputs. There are several techniqu
Page 105 and 106:
Table 3: The risks of making a wron
Page 107 and 108:
Water Management, Public Participat
Page 109 and 110:
Another reason for public participa
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structure can support the establish
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A Dual-scale Modelling approach to
Page 115 and 116:
in the southwest to more than 2,400
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and groups using different sets of
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The role of Multi-Criteria Decision
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a Multi-criteria Decision Analysis
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Socio - economic analysis Socio - e
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ICT Requirements for an ‘evolutio
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Planning process (e.g. WFD) Start I
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the other hand because due to the f
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HarmonIT IT Frameworks (2002-2005)
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DAWN: A platform for evaluating wat
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simulation procedure is initialized
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Application User Designed Scenario
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Empirical Evaluation of Decision Su
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conflict, circularity, non-used or
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Test MVPTMP p-value Interpretation
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Springer-Verlag, New York, New York
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flexibility and resilience within a
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Hartwood Montepulciano Homogeneous
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emphasis then falls on the quality
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Several types of public can be dist
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This function complements the previ
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studies will allow to verify our hy
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existing formal models to address (
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user then receptivity to the innova
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initially assessing the utility of
Page 165 and 166:
which they are collected to the hyd
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gravity. A more precise algorithm i
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prediction, the forecast of future
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fundamentally at odds with the data
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2.3 Spatial Decision Support System
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Bryan, B.A., Reserve selection for
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estimation and the vulnerability of
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Fig. 3: Floodplain Krosno Odra skie
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may be the growth of crops, a chang
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Optimal Groundwater Exploitation an
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pollutant. Since in many applicatio
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h(i,j,t) > B (13) where B is the aq
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¡ Towards an Environmental DSS bas
Page 191 and 192:
for each of them. Then, we chose th
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£ ¢ * ¢ M ¤ D ¡1¢¥¤5¦¨>¨
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Land Use and Hydrological Managemen
Page 197 and 198:
comparative shortage of data and of
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¢¡ ¢¢ ¡¢£¤¥ ¨© © 30 yea
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Forecasting Municipal Solid Waste G
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materials, only 45 data sets from 3
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well-known problem in waste managem
Page 207 and 208:
Real Time Optimal Resource Allocati
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w jl ( t) j ∈V l ∈ S( j) ≥ 0
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een performed. The overall graph of
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Combining Dynamic Economic Analysis
Page 215 and 216:
evaluation of medium and long term
Page 217 and 218:
Table 1 Development of the number o
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soils. Trans. ASAE, 44(2), 297-307,
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y a new module for the turnover of
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2.4 Verification sites description
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6. REFERENCES Berg, B. and H. Staaf
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Tabant JBEL 6°20 WAOUGOULZAT 3763
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4. MMS MODEL 4.1 GIS Weasel The pre
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and residence times of water in the
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(www.cbs.nl). With respect to natur
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costs for settlement of damage (Dri
Page 237 and 238:
more revenues by spatial connection
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system to the Italian territory are
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variation of K 1 and K 2 ). However
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ASCII format and is composed by 26
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Table 1. Main results and methods e
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Table 2. Summary of the formalised
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We therefore need to focus future e
Page 251 and 252:
Figure 1. Location of the study are
Page 253 and 254:
esolutions were made dependent on t
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From Narrative to Number: A Role fo
Page 257 and 258:
3. QUANTITATIVE MODELS AS A RESPONS
Page 259 and 260:
contentious cases. In the approach
Page 261 and 262:
Scenario Reoptimisation under Data
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ules guarantee that the solution in
Page 265 and 266:
manager would have made if he had a
Page 267 and 268:
Reliable and Valid Identification o
Page 269 and 270:
* add n i−1 mi m j k = ∑∑ add
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4.4 Differences between scenarios f
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SIMULATING GLOBAL FEEDBACKS BETWEEN
Page 275 and 276:
structures towards a service and in
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0. 0 0 selected 20 [cm]. The impact
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0.00 0.00 SRES storylines A2 and B1
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Principles of Human-Environment Sys
Page 283 and 284:
Within each hierarchy level, insigh
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is not considered see Schleiss and
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Addressing Sustainability, HIV-AIDS
Page 289 and 290:
where K1 and K2 represent capital v
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Hellmuth, M.E. and Sanderson, W.C.
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Modelling Biocomplexity in the Tisz
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(1934 - 1964) to almost every other
Page 297 and 298:
Adaptive Management (AM), offers a
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4. ACKNOWLEDGMENTS This paper benef
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Directly or indirectly, small estua
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80° ´ 25°25´ 80°50´ 45 80° 4
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ary conditions for SICS, the combin
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eplacement, local coexistence is im
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ate. This figure shows the distinct
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promote the coexistence by promotin
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asic premise of direct energy-matte
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ecombination, if the randomly-chose
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parameter value 2.5 2 1.5 1 0.5 0 0
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and for agricultural irrigation. Th
Page 321 and 322:
The relationship between the occurr
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Junqueira, I. C. 1995. Aplicação
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extensively studied from mathematic
Page 327 and 328:
(c x and c y ), and fix the other p
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population: the lattice Lotka-Volte
Page 331 and 332:
hectares, and it has been divided o
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• Moore Neighborhood (Zeigler et
Page 335 and 336:
parameter estimation from gap model
Page 337 and 338:
This paper describes the challenges
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proportional historical changes for
Page 341 and 342:
Dale, M., Dale, P., & Edgoose, T.,
Page 343 and 344:
steady-state densities. In the simu
Page 345 and 346:
population size of male in stable e
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were necessary for persistence. Hen
Page 349 and 350:
significant advantages, especially
Page 351 and 352:
species (Figure 1e). Therefore, in
Page 353 and 354:
Advantages of positive phototaxis m
Page 355 and 356:
during the growing season of winter
Page 357 and 358:
expected value [Conover, 1980]. Thi
Page 359 and 360:
6. REFERENCES Blower, S.M., and H.
Page 361 and 362:
usually show that the outcomes are
Page 363 and 364:
Figure 2. Temporal dynamics of ten
Page 365 and 366:
The lattice size (500 × 500) in ou
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877
Page 369 and 370:
879
Page 371 and 372:
881
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Implications of processing spatial
Page 375 and 376:
4. RESULTS AND DISCUSSION 4.1. Poss
Page 377 and 378:
exfiltration runoff, which is initi
Page 379 and 380:
Generic process-based plant models
Page 381 and 382:
2.3 Process-based models In process
Page 383 and 384:
Vertical extent (m) 0 2 4 6 8 10 Ex
Page 385 and 386:
The role of local spatial heterogen
Page 387 and 388:
At the beginning of a model day, al
Page 389 and 390:
elative to mortality during the act
Page 391 and 392:
MacArthur, R.H., Geographical ecolo
Page 393 and 394:
a) IBM simulations Snapshot occupan
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¢ (egg - larva - adult) and non-ov
Page 397 and 398:
J i can be calculated as a function
Page 399 and 400:
2.1. The spatio-temporal tree migra
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[van der Knaap and Ammann, 1997] fr
Page 403 and 404:
e.g. the overwhelming dominance of
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taken into account, including repro
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4. SIMULATION RESULTS A simulation
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Hunter, M.D., Landscape structure,
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is three-level scheme of spatial di
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50.5 Jun- Jun- Jun- Jun- Jun- Jun-
Page 415 and 416:
Figure 7 shows a map of the additio
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Ecoregion Classification Using a Ba
Page 419 and 420:
3. 1 Models In many situations stat
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out in a GIS with custom tools to a
Page 423 and 424:
Assessing management systems for th
Page 425 and 426:
3. CASE STUDY: LEY LANDSCAPE IN THE
Page 427 and 428:
foliatum would benefit from rototil
Page 429 and 430:
Forecasting UV Index by NEOPLANTA M
Page 431 and 432:
̄ ̄ is SCIAMACHY spectrometer mea
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10 8 YANKEE UVB-1 NEOPLANTA in Figu
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Mathematical Models for Gene Flow f
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function f ( u, φ) fϕ ( φ) f ( u
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c wind A 1 0.75 0.5 0.25 0 0 20 40
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Simulation of Herbicide Transport i
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umn. From the water table to the so
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6. CONCLUSIONS In conclusion, the m
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2 THE WIND EXTRAPOLATION Following
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Figure 3: Concentration of pollutio
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tional Symposium and exhibition on
Page 453 and 454:
˺ is and water interface. Therefor
Page 455 and 456:
has is =8·τ K m−t 2 = ⋅ n1
Page 457 and 458:
Glud data were collected on a rough
Page 459 and 460:
Gualtieri C. (2001). Dimensionless
Page 461 and 462:
This paper presents a spatially-dis
Page 463 and 464:
transport capacity of the rill flow
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kgP/ha/d kgP/ha/d kgP/ha/d kgP/ha/d
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A probabilistic modelling concept f
Page 469 and 470:
necessary to generate flood hydrogr
Page 471 and 472:
emphasises the key role of upstream
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scenarios. Natural Hazards, Special
Page 475 and 476:
monodimensional advection-dispersio
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C r,0, t) = C H( t); ( 0 C( r, x,0)
Page 479 and 480:
Factor comparison Fischer distribut
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PTF [Hodnett and Tomasella (2002)].
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Survey measurements showed that soi
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Different geographic regions around
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̊ ̍ with ̊ ̌ as Section 2 inclu
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̌ ̅ ˻ iterative procedure for th
Page 491 and 492:
proposed method using more specific
Page 493 and 494:
Pollution Act 1974. The Water Act o
Page 495 and 496:
ecological sensitivity at each AP i
Page 497 and 498:
Environment Agency. Managing Water
Page 499 and 500:
own data processing modules can be
Page 501 and 502:
(α). This is based on the simple,
Page 503 and 504:
1 Headwater P load KS statistic Phy
Page 505 and 506:
were used to examine the cost chang
Page 507 and 508:
In the Goulburn and Murrumbidgee ca
Page 509 and 510:
Fig. 5. Spatial distribution of inv
Page 511 and 512:
Appropriate Accuracy of Models for
Page 513 and 514:
are 2 MODEL BASE 2.1 Floodplain Eco
Page 515 and 516:
Figure 3. Percentage of years with
Page 517 and 518:
River Basin Management Plans and De
Page 519 and 520:
the multidimensional data stored in
Page 521 and 522:
the form of DPS chains and used by
Page 523 and 524:
Introducing River Modelling in the
Page 525 and 526:
Impacts computed. This augmented DP
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2) Quality data, either from the ri
Page 529 and 530:
Sensitivity Analysis of a Network-B
Page 531 and 532:
approximately by noting the output
Page 533 and 534:
45 40 35 30 Analytical Value 10% Pe
Page 535 and 536:
Dealing with unidentifiable sources
Page 537 and 538:
is The threshold value can be defin
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(a) Figure 5: Confidence regions fo
Page 541 and 542:
Assessing SWAT model performance in
Page 543 and 544:
Load reduction target Yläneenjoki
Page 545 and 546:
nutrient leaching should occur, whi
Page 547 and 548:
Assessing the Effects of Agricultur
Page 549 and 550:
fields 2.7% of the catchment area [
Page 551 and 552:
Q [m 3 s -1 ] 600 400 Observed Simu
Page 553 and 554:
vironment being sidelined? Land Use
Page 555 and 556:
nutrient cycling (nitrogen, N and p
Page 557 and 558:
egional evapotranspiration. Here on
Page 559 and 560:
Coupling Surface And Ground Water P
Page 561 and 562:
influential, both SWRRB and ALHyMUS
Page 563 and 564:
30 25 20 SWAP SWRRB ALHyMUS a 15 10
Page 565 and 566:
Investigating Spatial Pattern Compa
Page 567 and 568:
The size of the neighbourhood is re
Page 569 and 570:
variance as the event, simulations
Page 571 and 572:
Reduced Models of the Retention of
Page 573 and 574:
́ ́ forcings, while the anthropog
Page 575 and 576:
independent of the mineralisation r
Page 577 and 578:
The Evaluation of Uncertainty Propa
Page 579 and 580:
experience of the modeller. Automat
Page 581 and 582:
Figure 2 and 3. Simulation of nitra
Page 583 and 584:
Index of authors Ablan M. .........
Page 585 and 586:
Mijatovic ′ Z. ..................
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