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Download Luận văn A methodology for validation of integrated systems models with an application to coastal-zone management in south-west sulawesi
Contents
Preface .
1. Introduction . . .
1.1. General introduction . .
1.2. Background . .
1.2.1. Systems approach .
1.2.2. Integrated approach and Integrated Assessment
1.2.3. Integrated management and policy analysis . .
1.3. The problem of validating Integrated Systems Models .
1.4. Research aim and research questions .
1.5. Case study description
1.5.1. RaMCo .
1.5.2. Study area .
1.6. Outline of the thesis
2. Methodology . . . .
2.1. Introduction. .
2.2. Literature review .
2.3. Concept definition. .
2.4. Conceptual framework of analysis .
2.5. Procedure for validation .
2.6. Conclusion .
3. Validation of an integrated systems model for coastal-zone management
using sensitivity and uncertainty analyses .
3.1. Introduction. .
3.2. Methodology . .
3.2.1. Basics for the method
3.2.2. The testing procedure
3.2.3. The sensitivity analysis .
3.2.4. The elicitation of expert opinions .
3.2.5. The uncertainty propagation .
3.2.6. The validation tests
3.3. Results .
3.3.1. Sensitivity analysis .
3.3.2. Elicitation of expert opinions .
Contents 8
3.3.3. Uncertainty analysis .
3.3.4. Parameter-Verification test .
3.3.5. Behaviour-Anomaly test .
3.3.6. Policy-Sensitivity test .
3.4. Discussion and conclusions .
3.5. Appendices .
4. A new approach to testing an integrated water systems model using
qualitative scenarios .
4.1. Introduction . .
4.2. Validation methodology. . .
4.2.1. Overview of the new approach .
4.2.2. The detail procedure .
4.3. The RaMCo model .
4.3.1. Land-use/land-cover change model
4.3.2. Soil loss computation .
4.3.3. Sediment yield
4.4. Formulation of scenarios for testing
4.4.1. Structuring scenarios .
4.4.2. Developing qualitative scenarios for testing .
4.5. Translation of qualitative scenarios .
4.5.1. Fuzzification .
4.5.2. Formulation of inference rules .
4.5.3. Application of the inference rules .
4.5.4. Calculation of the output value .
4.5.5. Testing the consistency of the scenarios
4.6. Results .
4.7. Discussion and conclusions
5. Validation of a fisheries model for coastal-zone management in
Spermonde Archipelago using observed data .
5.1. Introduction . .
5.2. Case study. . . .
5.2.1. Fisheries in the Spermonde Archipelago, Southwest Sulawesi .
5.2.2. Fisheries modelling in RaMCo .
5.2.3. Data source and data processing .
5.3. Validation methodology .
5.3.1. Sate of the art .
5.3.2. The proposed method .
5.3.3. Fishery production models .
5.4. Results .
5.4.1. Calibration .
5.4.2. The pattern test .
5.4.3. The accuracy test .
5.4.4. The extreme condition test .
5.5. Discussion and conclusions .
Contents 9
6. Discussions, conclusions and recommendations .
6.1. Introduction . .
6.2. Discussions. . . .
6.2.1. Innovative aspects . . .
6.2.2. Generic applicability of the methodology .
6.2.3. Limitations .
6.3. Conclusions .
6.3.1. Concept definition .
6.3.2. Methodology .
6.4. Recommendations .
6.4.1. Other directions for the validation of integrated systems models .
6.4.2. Proper use of integrated systems models .
References .
Symbols
Acronyms and abbreviations .
Summary .
Samenvatting .
About the author .
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sponsors of the scenario building exercise and experts, should include around three to
six members. The latter, which consists of stakeholders, policymakers and additional
experts, should include around fifteen to twenty-five members. For the purpose of
testing ISMs, we propose to distinguish two groups in the scenario building team. The
first group includes model developers (they are also interdisciplinary scientists), experts
(scientists who may have different views about the model system) and additional
analysts (scientists who are not involved in the model building). The second group
consists of multidisciplinary experts, resource managers and stakeholders. The second
group can play a role both as the fact-contributor and scenario evaluator in the scenario
building for the testing of ISMs. Preferably, the stakeholders and resource managers
should participate at the beginning of the scenario building process (steps 1 to 3).
In the iterative scenario building process, the consistency of the scenarios need to be
tested. Van der Heijden (1996) and Alcamo (2001) recommend two similar approaches
to establishing the consistency of scenarios, which include two supplementary tests:
scenario-quantification testing and actor-testing. Quantification testing comprises
quantifying the scenarios and examining the quantitative projections of the system
indicators (management variables). Actor-testing diagnoses the inconsistencies by
confronting the internal logic of the qualitative scenarios with the intuitive human
ability to guess at the logic of the various actors (stakeholders, resource managers and
Validation using qualitative scenarios 69
additional experts). We propose to use physical, biological constraints (e.g. the total
available area of a watershed) to check the quantitative projections (e.g. the projections
of the areas of different land-use types) for quantification testing. In actor-testing, both
the narrative descriptions of the scenarios and the quantitative projections of the system
indicators should be communicated to the second group (stakeholders, resource
managers and additional experts) by means of report papers, workshops and the internet.
Translating qualitative scenarios
For the translation of qualitative scenarios, the application of fuzzy set theory is
proposed. Fuzzy set theory was originally developed by Zadeh (1973), based on the
concepts of classical set theory. The essential motivation, as he claimed, for the
development of fuzzy set theory is the inadequacy and inappropriateness of
conventional quantitative techniques for the analysis of mechanistic systems (e.g.
physical systems governed by the laws of mechanics) to analyse humanistic systems.
The design of a fuzzy system comprises five steps (Mathworks, 2005), which can be
reduced to four main steps (De Kok et al., 2000):
1) Translation of the independent and dependent variables from numerical into the fuzzy
domain (fuzzification)
2) Formulation of the conditional inference rules
3) Application of these rules to determine the fuzzy outputs
4) Translation of the fuzzy outputs back into the numerical domain (defuzzification)
In order to test the internal consistency of scenarios, scenario quantification-testing
needs to be conducted. Therefore, the process of scenario translation is extended to
include step 5 (testing the internal consistency of scenarios). These five steps are
demonstrated by the application described in Section 4.5.
Conducting simulations by the ISM and comparing the results
After translating the qualitative scenarios to get the quantitative projections of the
output variable, simulations made by the IWS model are conducted. Comparison of the
output behaviours produced by the two systems in terms of trend lines is carried out.
This phase will be demonstrated in Section 4.6.
It is recommended that the interactive communication within the first group should be
carried out in all three phases (qualitative scenario building, scenario translating and
comparing results). In doing so, any possible disagreements between model developers
and experts can be brought out for discussion at every step. Thus, the biasness or
inconsistency of the expert(s) can be minimised.
4.3. The RaMCo Model
The study area for RaMCo occupies a total area of about 8000 km2 (80km x100km), of
which more than half is on the mainland (De Kok and Wind, 2002). The offshore part
covers the Spermonde archipelago where multi-ecosystems such as coral reef,
mangrove and seagrass can be found. On the mainland, the city of Makassar has a fast-
growing population of 1.09 million (1995), which is expected to double in twenty years.
In the upland rural area, the forest area is rapidly declining, due to the increase in
Chapter 4 70
cultivated land. The expansions of urban areas and the conversion of uncultivated to
cultivated land are imposing a strong demand on the effective management of water and
other ecological systems in the coastal area.
To meet the rapidly increasing demand for water supplies for domestic uses, industry,
irrigation, shrimp culture and the requirements for flood defence of the city of
Makassar, the construction of a multi-purpose storage lake started in 1992. The dam
was closed for water storage in November 1997 (Suriamihardja et al., 2001). The
watershed of the Bili-Bili dam covers the total area of 384 km2, which represents the
upper part of the Jeneberang river catchment. The dam was designed to have its
effective storage capacity of 346 million m3 and dead storage capacity of 29 million m3
(CTI, 1994). Its expected lifetime of 50 years was determined by computing the total
soil loss due to erosion of the watershed surface. The computation was carried out using
the Universal Soil Loss Equation (USLE) in combination with the land cover map
surveyed in 1992. No future dynamic development of land-use in the watershed area
was taken into consideration. Analyses of recently measured sediment transport rates at
the inlet of the Bili-Bili dam and land-use maps show an obvious decrease in the storage
capacity of the dam, due to increasing sediment input (CTI, 1994; Suriamihardja et al.,
2001). This calls for a proper land-use management strategy to minimise the sediment
eroded from the watershed surface that runs into the reservoir.
RaMCo quantitatively describes the future dynamic land-use and land-cover changes
under the combined influence of socio-economic factors. Then, the resulting soil losses
from the watershed surface and the resulting sediment yields at the inlet of the Bili-Bili
dam are computed. The following are conceptual and mathematical descriptions of this
chain-model.
4.3.1. Land-use/land-cover change model
Land-use types
During the design stage, a problem-based approach was followed to select relevant
land-use-types (De Kok et al., 2001). In RaMCo, a distinction was made between static
land-use types (land-use features) and active land-use types (land-use functions). Land-
use features such as beach, harbour and airport are expected to be relatively stable in
their size and location over the time frame considered. Land-use functions such as
industry, tourism, brackish pond culture, rice culture and others are expected to change
both in space and over time under the influence of various internal and external driving
factors (drivers). In this paper, attention is paid to the two land-use types: nature and
mixed agriculture. The model treats the “nature” land-use type as the uncultivated land
which is a combination of natural forest, production forest, shrubs and grasses. Mixed
agriculture represents food crop culture (other than rice culture) such as maize, cassava
and cash crops such as coffee and cacao. These types of land-use predominate in the
Bili-Bili catchment and are expected to change rapidly, affecting the amount of
sediment transported into the reservoir. In addition to the two defined categories, three
other land-use types exist in RaMCo: rural residential, rice culture and inland water.
Drivers of land-use changes: temporal dynamics versus spatial dynamics
The drivers of land-use changes in the RaMCo model can be separated into three
categories: i) socio-economic drivers, such as price, cost, yield, technology development
Validation using qualitative scenarios 71
and demography; ii) management measures, such as reservoir building and
reforestation; and iii) biophysical attributes, such as soil types and road networks. The
first two groups of drivers, in combination with the availability of irrigated water and
suitable land, determine the rate of land-use cha...
Download miễn phí Luận văn A methodology for validation of integrated systems models with an application to coastal-zone management in south-west sulawesi
Contents
Preface .
1. Introduction . . .
1.1. General introduction . .
1.2. Background . .
1.2.1. Systems approach .
1.2.2. Integrated approach and Integrated Assessment
1.2.3. Integrated management and policy analysis . .
1.3. The problem of validating Integrated Systems Models .
1.4. Research aim and research questions .
1.5. Case study description
1.5.1. RaMCo .
1.5.2. Study area .
1.6. Outline of the thesis
2. Methodology . . . .
2.1. Introduction. .
2.2. Literature review .
2.3. Concept definition. .
2.4. Conceptual framework of analysis .
2.5. Procedure for validation .
2.6. Conclusion .
3. Validation of an integrated systems model for coastal-zone management
using sensitivity and uncertainty analyses .
3.1. Introduction. .
3.2. Methodology . .
3.2.1. Basics for the method
3.2.2. The testing procedure
3.2.3. The sensitivity analysis .
3.2.4. The elicitation of expert opinions .
3.2.5. The uncertainty propagation .
3.2.6. The validation tests
3.3. Results .
3.3.1. Sensitivity analysis .
3.3.2. Elicitation of expert opinions .
Contents 8
3.3.3. Uncertainty analysis .
3.3.4. Parameter-Verification test .
3.3.5. Behaviour-Anomaly test .
3.3.6. Policy-Sensitivity test .
3.4. Discussion and conclusions .
3.5. Appendices .
4. A new approach to testing an integrated water systems model using
qualitative scenarios .
4.1. Introduction . .
4.2. Validation methodology. . .
4.2.1. Overview of the new approach .
4.2.2. The detail procedure .
4.3. The RaMCo model .
4.3.1. Land-use/land-cover change model
4.3.2. Soil loss computation .
4.3.3. Sediment yield
4.4. Formulation of scenarios for testing
4.4.1. Structuring scenarios .
4.4.2. Developing qualitative scenarios for testing .
4.5. Translation of qualitative scenarios .
4.5.1. Fuzzification .
4.5.2. Formulation of inference rules .
4.5.3. Application of the inference rules .
4.5.4. Calculation of the output value .
4.5.5. Testing the consistency of the scenarios
4.6. Results .
4.7. Discussion and conclusions
5. Validation of a fisheries model for coastal-zone management in
Spermonde Archipelago using observed data .
5.1. Introduction . .
5.2. Case study. . . .
5.2.1. Fisheries in the Spermonde Archipelago, Southwest Sulawesi .
5.2.2. Fisheries modelling in RaMCo .
5.2.3. Data source and data processing .
5.3. Validation methodology .
5.3.1. Sate of the art .
5.3.2. The proposed method .
5.3.3. Fishery production models .
5.4. Results .
5.4.1. Calibration .
5.4.2. The pattern test .
5.4.3. The accuracy test .
5.4.4. The extreme condition test .
5.5. Discussion and conclusions .
Contents 9
6. Discussions, conclusions and recommendations .
6.1. Introduction . .
6.2. Discussions. . . .
6.2.1. Innovative aspects . . .
6.2.2. Generic applicability of the methodology .
6.2.3. Limitations .
6.3. Conclusions .
6.3.1. Concept definition .
6.3.2. Methodology .
6.4. Recommendations .
6.4.1. Other directions for the validation of integrated systems models .
6.4.2. Proper use of integrated systems models .
References .
Symbols
Acronyms and abbreviations .
Summary .
Samenvatting .
About the author .
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ario team and a scenario panel. The former, which consists of thesponsors of the scenario building exercise and experts, should include around three to
six members. The latter, which consists of stakeholders, policymakers and additional
experts, should include around fifteen to twenty-five members. For the purpose of
testing ISMs, we propose to distinguish two groups in the scenario building team. The
first group includes model developers (they are also interdisciplinary scientists), experts
(scientists who may have different views about the model system) and additional
analysts (scientists who are not involved in the model building). The second group
consists of multidisciplinary experts, resource managers and stakeholders. The second
group can play a role both as the fact-contributor and scenario evaluator in the scenario
building for the testing of ISMs. Preferably, the stakeholders and resource managers
should participate at the beginning of the scenario building process (steps 1 to 3).
In the iterative scenario building process, the consistency of the scenarios need to be
tested. Van der Heijden (1996) and Alcamo (2001) recommend two similar approaches
to establishing the consistency of scenarios, which include two supplementary tests:
scenario-quantification testing and actor-testing. Quantification testing comprises
quantifying the scenarios and examining the quantitative projections of the system
indicators (management variables). Actor-testing diagnoses the inconsistencies by
confronting the internal logic of the qualitative scenarios with the intuitive human
ability to guess at the logic of the various actors (stakeholders, resource managers and
Validation using qualitative scenarios 69
additional experts). We propose to use physical, biological constraints (e.g. the total
available area of a watershed) to check the quantitative projections (e.g. the projections
of the areas of different land-use types) for quantification testing. In actor-testing, both
the narrative descriptions of the scenarios and the quantitative projections of the system
indicators should be communicated to the second group (stakeholders, resource
managers and additional experts) by means of report papers, workshops and the internet.
Translating qualitative scenarios
For the translation of qualitative scenarios, the application of fuzzy set theory is
proposed. Fuzzy set theory was originally developed by Zadeh (1973), based on the
concepts of classical set theory. The essential motivation, as he claimed, for the
development of fuzzy set theory is the inadequacy and inappropriateness of
conventional quantitative techniques for the analysis of mechanistic systems (e.g.
physical systems governed by the laws of mechanics) to analyse humanistic systems.
The design of a fuzzy system comprises five steps (Mathworks, 2005), which can be
reduced to four main steps (De Kok et al., 2000):
1) Translation of the independent and dependent variables from numerical into the fuzzy
domain (fuzzification)
2) Formulation of the conditional inference rules
3) Application of these rules to determine the fuzzy outputs
4) Translation of the fuzzy outputs back into the numerical domain (defuzzification)
In order to test the internal consistency of scenarios, scenario quantification-testing
needs to be conducted. Therefore, the process of scenario translation is extended to
include step 5 (testing the internal consistency of scenarios). These five steps are
demonstrated by the application described in Section 4.5.
Conducting simulations by the ISM and comparing the results
After translating the qualitative scenarios to get the quantitative projections of the
output variable, simulations made by the IWS model are conducted. Comparison of the
output behaviours produced by the two systems in terms of trend lines is carried out.
This phase will be demonstrated in Section 4.6.
It is recommended that the interactive communication within the first group should be
carried out in all three phases (qualitative scenario building, scenario translating and
comparing results). In doing so, any possible disagreements between model developers
and experts can be brought out for discussion at every step. Thus, the biasness or
inconsistency of the expert(s) can be minimised.
4.3. The RaMCo Model
The study area for RaMCo occupies a total area of about 8000 km2 (80km x100km), of
which more than half is on the mainland (De Kok and Wind, 2002). The offshore part
covers the Spermonde archipelago where multi-ecosystems such as coral reef,
mangrove and seagrass can be found. On the mainland, the city of Makassar has a fast-
growing population of 1.09 million (1995), which is expected to double in twenty years.
In the upland rural area, the forest area is rapidly declining, due to the increase in
Chapter 4 70
cultivated land. The expansions of urban areas and the conversion of uncultivated to
cultivated land are imposing a strong demand on the effective management of water and
other ecological systems in the coastal area.
To meet the rapidly increasing demand for water supplies for domestic uses, industry,
irrigation, shrimp culture and the requirements for flood defence of the city of
Makassar, the construction of a multi-purpose storage lake started in 1992. The dam
was closed for water storage in November 1997 (Suriamihardja et al., 2001). The
watershed of the Bili-Bili dam covers the total area of 384 km2, which represents the
upper part of the Jeneberang river catchment. The dam was designed to have its
effective storage capacity of 346 million m3 and dead storage capacity of 29 million m3
(CTI, 1994). Its expected lifetime of 50 years was determined by computing the total
soil loss due to erosion of the watershed surface. The computation was carried out using
the Universal Soil Loss Equation (USLE) in combination with the land cover map
surveyed in 1992. No future dynamic development of land-use in the watershed area
was taken into consideration. Analyses of recently measured sediment transport rates at
the inlet of the Bili-Bili dam and land-use maps show an obvious decrease in the storage
capacity of the dam, due to increasing sediment input (CTI, 1994; Suriamihardja et al.,
2001). This calls for a proper land-use management strategy to minimise the sediment
eroded from the watershed surface that runs into the reservoir.
RaMCo quantitatively describes the future dynamic land-use and land-cover changes
under the combined influence of socio-economic factors. Then, the resulting soil losses
from the watershed surface and the resulting sediment yields at the inlet of the Bili-Bili
dam are computed. The following are conceptual and mathematical descriptions of this
chain-model.
4.3.1. Land-use/land-cover change model
Land-use types
During the design stage, a problem-based approach was followed to select relevant
land-use-types (De Kok et al., 2001). In RaMCo, a distinction was made between static
land-use types (land-use features) and active land-use types (land-use functions). Land-
use features such as beach, harbour and airport are expected to be relatively stable in
their size and location over the time frame considered. Land-use functions such as
industry, tourism, brackish pond culture, rice culture and others are expected to change
both in space and over time under the influence of various internal and external driving
factors (drivers). In this paper, attention is paid to the two land-use types: nature and
mixed agriculture. The model treats the “nature” land-use type as the uncultivated land
which is a combination of natural forest, production forest, shrubs and grasses. Mixed
agriculture represents food crop culture (other than rice culture) such as maize, cassava
and cash crops such as coffee and cacao. These types of land-use predominate in the
Bili-Bili catchment and are expected to change rapidly, affecting the amount of
sediment transported into the reservoir. In addition to the two defined categories, three
other land-use types exist in RaMCo: rural residential, rice culture and inland water.
Drivers of land-use changes: temporal dynamics versus spatial dynamics
The drivers of land-use changes in the RaMCo model can be separated into three
categories: i) socio-economic drivers, such as price, cost, yield, technology development
Validation using qualitative scenarios 71
and demography; ii) management measures, such as reservoir building and
reforestation; and iii) biophysical attributes, such as soil types and road networks. The
first two groups of drivers, in combination with the availability of irrigated water and
suitable land, determine the rate of land-use cha...