- Page 3 and 4: Complexity and Integrated Resources
- Page 5 and 6: IEMSs 2004 - 14-17 June 2004, Unive
- 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 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
- Page 60 and 61: arrows or arcs, with conditional pr
- Page 62 and 63: of that result, productive years re
- Page 64 and 65: A Spatial DSS for South Australia's
- Page 66 and 67: framework to be developed, however,
- Page 68 and 69: • efficiency and • comprehensiv
- Page 70 and 71: Optimum Sustainable Water Managemen
- Page 72 and 73: factors in the basin, each model th
- Page 74 and 75: Treated wastewater from households
- Page 76 and 77: Application of a GIS-based Simulati
- Page 78 and 79: 3. THE TUGAI SIMULATION TOOL The TU
- Page 80 and 81: Figure 3 depicts the results of the
- Page 82 and 83: Schlüter M., Savitsky A.G., McKinn
- Page 84 and 85: temporal scale for a large river ba
- Page 86 and 87: Some model components such as proce
- Page 88 and 89: 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
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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]
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How to Compare Different Conceptual
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¥ ¥ ¡ ¥ tions are assumed to oc
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Table 2. Relative errors [variation
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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
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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
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- the annual maximum discharge of t
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The approach is therefore very well
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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
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Soil Hydraulics Properties Estimati
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¥¤££ ©© ¨ § ¢ ¡ ¥¤££
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Soil sample 1 2 3 4 5 6 7 8 Bulk de
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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
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water body of groundwater abstracti
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100 pressures and the quality of me
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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
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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
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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
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Lake Simojärvi . 4 Water quality m
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Table 1. Used max. N fertilization
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4. CONCLUSIONS This study shows tha
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Implications of Complexity and Unce
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for regional applications). They ar
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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
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e used to compare an observation wi
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4. COMPARISON DEMONSTRATION This se
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IMSE pixel values for observed and
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2. THE INCA-N MODEL The INCA-N is a
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include any transformation or immob
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Moreover, we found that, almost reg
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3. METHODS To reduce the overall un
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literature ranges. The ranges for t
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first one being ‘exploring’, wh
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Granlund K. .......................
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Tymoshevska L. ................III