Modelling Driving Forces of Urban Growth with Fuzzy Sets and GIS

Urban growth occurs in conjunction with a series of decision-making processes and is, on the whole, not deterministic but rather is the outcome of competing local demands and uncontrolled, chaotic processes. Fuzzy sets theory is ideally suited to treat the complexity and uncertainties in the decision-making process. This chapter presented an example of how fuzzy sets can be applied to model urban growth driving forces within geographical information system environment. The mathematical models for measuring, computing and defining 10 fuzzy urban growth factors to form fuzzy driving forces of urban growth in Riyadh City, Saudi Arabia, were discussed. Four factors were considered as the driving forces for urban growth in Riyadh City: the transport support factor, urban agglomeration and attractiveness factor, topographical constraints factor, and planning policies and regulations factor. The urban growth factors were established using fuzzy set theory, which quantified the effect of distance decay using fuzziness. This approach is a transparent method of interpreting the curve of distance decay using linguistic variables. This feature does not exist in the linear, negative exponential or inverse power functions. The results indicate that fuzzy accessibility and fuzzy urban density factors are capable of mimicking and representing the uncertainty in the behaviour of the human decision-making process in land development in a very efficient manner

Modelling and simulating land development processes in Shanghai

This thesis groups four papers to investigate the growth and evolution mechanisms behind urban land use change through multiple computer-based modelling and simulation approaches. The first paper theorizes land development in Shanghai into five modes and then delineates the location and estimates the magnitude of each of the land development modes in Shanghai. This paper lays down the groundwork for the following papers on land use change modelling and simulation in Shanghai. The second paper develops the Population-Driven Urban Land Development (PDULD) to simulate the land development and population dynamics of Jiading New City, one of the suburban districts in Shanghai. The third paper develops the Location-based Firm Profit (LbFP) model to delineate how urban land use growth may lead to the spatial structural transformation of industries. The fourth paper then furthers the industrial land use study and proposes the Industrial and Residential Land Use Competition Model (IRLUCM) to simulate the dynamic spatial transformation of industrial land use in Shanghai.ATIC, TECTERRA, AMETHYST, University of Lethbridge, Mitac

Scenarios of Urban Growth in Kenya Using Regionalised Cellular Automata based on Multi temporal Landsat Satellite Data

The exponential increase of urban areas in Africa during the last decade has become a major concern in the context of local climatic change and the increasing amount of impervious surface. Major African cities such as Nairobi and Nakuru have undergone rapid urban growth in comparison to the rest of the world. In this research we investigated the land-use changes and used the results in urban growth modelling which integrates cellular automata (CA), remote sensing (RS) and geographic information systems (GIS) in order to simulate urban growth up to the year 2030. We used multi-temporal Landsat imageries for the years 1986, 2000 and 2010 to map urban land-use changes in Nairobi and Nakuru. The use of multi-sensor imageries was also explored incorporating World view 2, and Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data for urban land-use mapping in Nakuru. We conducted supervised classification using support vector machine (SVM) which performed better than maximum likelihood classification. Land-use change estimates were obtained indicating increased urban growth into the year 2010. We used the land-use change analysis information to model urban growth in Nairobi and Nakuru. Our urban growth model (UGM) utilised various datasets in modelling urban growth namely urban land-use extracted from land-use maps, road network data, slope data and exclusion layer defining areas excluded from development. The Monte-Carlo technique was used in model calibration. The model was validated using Multiple Resolution Validation (MRV) technique. Prediction of urban land-use was done up to the year 2030 when Kenya plans to attain Vision 2030. Three scenarios were explored in the urban modelling process; unmanaged growth with no restriction on environmental areas, managed growth with moderate protection, and a managed growth with maximum protection on forest, agricultural areas, and urban green. Furthermore, we explored the spatial effects of varying UGM parameters using the city of Nairobi. The objective here was to investigate the contribution of each model parameter in simulating urban growth. The results obtained indicate that varying model coefficients leads to urban growth in different directions and magnitude. However, several model parameters were observed to be highly correlated namely; spread, breed and road. The lowest spatial effect was achieved by at least maintaining spread, breed and road while varying the other parameters. The highest spatial effect was observed by at least keeping slope constant while varying the other four parameters. Additionally, we used kappa statistics to compare the simulation maps. High values of Khisto indicated high similarity between the maps in terms of quantity and location thus indicating the lowest spatial effect obtained. Kenya plans to achieve Vision 2030 in the year 2030 and information on spatial effects of our UGM can help in identifying different scenarios of future urban growth. It is thus possible to discover areas that are likely to experience; spontaneous growth, edge growth, road influenced growth or new spreading centres growth. Policy makers can see the influence of establishing new infrastructure such as housing and road in new areas compared to existing settlements. Moreover, the outcome of this research indicates that Nairobi and Nakuru are experiencing fast urban sprawl with urban land-use consuming the available land. The results obtained illustrate the possibility of urban growth modelling in addressing regional planning issues. This can help in comprehensive land-use planning and an integrated management of resources to ensure sustainability of land and to achieve social equity, economic efficiency and environmental sustainability. Hence, cellular automata are a worthwhile approach for regional modelling of African cities such as Nairobi and Nakuru. This provides opportunities for other cities in Africa to be studied using UGM and its adaptability noted accordingly.Das exponentielle Wachstum afrikanischer Städte im letzten Jahrzehnt ist mit Blick auf die lokalen klimatischen Veränderungen und der zunehmenden Menge an versiegelten Oberflächen von besonderer Tragweite. Im Vergleich zu anderen Metropolen erfuhren afrikanische Städte wie Nairobi und Nakuru ein extensives Wachstum der urbanen Flächen. Die vorliegende Arbeit setzt sich mit dem urbanen Landnutzungswandel auseinander und modelliert die Siedlungsflächenausdehnung für das Jahr 2030 mit Hilfe eines Zellulären Automaten (CA), Fernerkundungsdaten (RS) sowie Geographischen Informationssystemen (GIS). Zur Kartierung der Siedlungsflächenausdehnung von Nairobi und Nakuru wurden multitemporale Landsat-Daten der Jahre 1986, 2000 und 2010 verwendet. Zusätzlich wurden multisensorale Daten von World View 2 und ALOS PALSAR für Nakuru eingesetzt. Die Landnutzungsklassifikation erfolgte mit support vector machines (SVM). Dieses Verfahren zeigte bessere Ergebnisse als eine Maximum-Likelihood-Klassifikation. Auf Basis der klassifizierten Satellitendaten erfolgte die Landnutzungsmodellierung für Nairobi und Nakuru. Hierzu wurde die von Goetzke (2011) modifizierte Version von Clarke’s Urban Growth Model (Clarke, Hoppen, & Gaydos, 1997) benutzt. Neben den Landnutzungskarten fungieren Informationen zum Verkehrsnetz, zur Hangneigung und zu Ausschlussflächen als Hauptinputdaten. Die Kalibration erfolgte mit Hilfe von Monte Carlo Iterationen. Zur Validation des Modells wurde eine Multiple Resolution Validation (MRV) durchgeführt. Die Siedlungsflächenausdehnung wurde für das Jahr 2030 simuliert. Zu diesem Zeitpunkt plant das Land Kenia die Umsetzung des Vision 2030 Programmes. Es wurden insgesamt drei Szenarien mit dem Wachstumsmodell gerechnet: (1) Wachstum ohne Planungszwänge, so dass auch Siedlungsflächen in Naturschutzgebieten entstehen dürfen. (2) Siedlungsflächenausdehnung unter moderaten Planungsbedingungen. (3) Wachstum mit sehr restriktiven Planungsbedingungen, unter Einschluss des Schutzes von Wald-, Grün- und- Agrarflächen. Des Weiteren wurde eine Sensitivitätsanalyse der modelleigenen Wachstumsparameter am Beispiel von Nairobi durchgeführt. Es konnte gezeigt werden, welchen Einfluss die Parameter auf die Intensität und das Muster der modellierten Siedlungsflächenausdehnung ausüben. Dabei zeigten die Wachstumskoeffizienten „spread“, „breed“ und „road“ eine signifikante Korrelation. Zur weiteren Analyse der erzielten Modellierungsergebnisse und zum Vergleich der räumlichen Muster wurden Kappa-Statistiken herangezogen. Die Arbeit sieht sich als Beitrag zum Vision 2030 Diskurs der kenianischen Regierung. Die simulierten Szenarien der Siedlungsflächenausdehnung von Nairobi und Nakuru identifizieren die für eine Urbanisierung wahrscheinlich in Frage kommenden Regionen. Die Studie zeigt zudem, dass sich die Siedlungsflächenausdehnung von Nairobi und Nakuru schnell und mit hohen Wachstumsraten vollzieht. Der Einsatz von CA Modellen ist ein wertvoller Ansatz zur regionalen Modellierung nicht nur von kenianischen sondern auch von afrikanischen Städten. Die Arbeit kann somit Entscheidungsträger aus Politik und Verwaltung unterstützen, indem sie die räumlichen Auswirkungen des zukünftigen Ausbaus der Infrastruktur und von Wohnflächen aufzeigt. Eine umfassende Planung von Landnutzungswandel und ein integriertes Management sind essentiell auf dem Weg zu einem bewussteren Umgang mit der Ressource Land sowie zu einer sozialen Gleichheit, wirtschaftlichen Effizienz und einer ökologischen Nachhaltigkeit

Modelling spatial and temporal urban growth

Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery

Assessing the impact of land use changes on hydropower production and erosion in the Coca River basin. A contribution towards Integrated Water Resources Management in Ecuador

Die Region mit der weltweit höchsten Rate an Landnutzungsänderungen sind die feuchten Tropen. Es ist ein weit verbreiteter Prozess in diesen Regionen durch Entwaldung Raum für landwirtschaftliche Flächen und Weiden zu schaffen. Darüber hinaus ist diese Region für ihre große Wasserverfügbarkeit und ihr Potenzial zur Erzeugung von Wasserkraft bekannt. Daher sind in den letzten Jahrzehnten Bemühungen zur Erhaltung und zum Schutz der natürlichen Waldbedeckung der tropischen Wassereinzugsgebiete zu einer Priorität innerhalb der Prozesse des integrierten Wasserressourcenmanagements (IWRM) geworden. Landnutzungsänderungen (LUC) beeinflussen den Wasserhaushalt eines Einzugsgebiets, indem sie das verfügbare Wasser zusammen mit der Veränderung der anderen Wasserhaushaltskomponenten beeinflussen. Das Verständnis der LUC und ihrer Auswirkungen auf die Hydrologie eines Einzugsgebiets ist für das Management und die Nutzung der Wasserressourcen in einem Einzugsgebiet von entscheidender Bedeutung. Daher ist es wichtig, die Auswirkungen von Landnutzungsänderungen in den letzten Jahrzenten auf die Abflussmenge eines Wassereinzugsgebiets zu verstehen, um in Zukunft - innerhalb eines IWRM-Rahmens - ein ordnungsgemäßes Wassermanagement und eine Wasserressourcenplanung durchführen zu können. Diese Studie bewertet die historischen Trends von Niederschlag und Stromfluss und analysiert die Reaktionen des Stromflusses auf Landnutzungsänderungen unter verschiedenen Szenarien und Zukunftsprojektionen im oberen Coca-Einzugsgebiet. Dieses befindet sich am Osthang der ecuadorianischen Anden und ist Teil der oberen ecuadorianischen Amazonasregion. Die Ergebnisse des Mann-Kendall-Tests (MK) zeigen, dass kein statistisch signifikanter Trend in den täglichen Niederschlags- und monatlichen Flussabflussmessungen im Wassereinzugsgebiet existiert. Der Pettitt-Test kann keinen Sprungpunkt in den einzugsgebietsweiten Niederschlagsreihen feststellen. Die Landnutzungskarten von 1990, 2000, 2008 und 2016 werden für die LUC-Erkennungsanalyse verwendet, sowie das CA_Markov-Modell, um die zukünftigen LUC-Projektionen unter drei verschiedenen Szenarien vorherzusagen: Trendszenario, “Best-Case-Szenario”, “Worst-Case-Szenario”. Die Vorhersagen für die Jahre 2026 und 2036 werden unter Berücksichtigung der physischen und sozioökonomischen Treiber der LUC-Dynamik im Einzugsgebiet berechnet. Das Trendszenario behält die für die Jahre 2026 und 2036 prognostizierten Wahrscheinlichkeiten für Landnutzungsänderungen bei. Das Best-Case-Szenario befasst sich mit den Wahrscheinlichkeiten für Änderungen der LUC in Richtung eines ausgewogenen Szenarios, zwischen der Erhaltung natürlicher Ökosysteme und produktiven Aktivitäten innerhalb des Einzugsgebiets. Das “Worst-Case-Szenario” befasst sich mit den Wahrscheinlichkeiten einer Änderung der LUC in Richtung eines Szenarios, in dem Rohstoffaktivitäten vorherrschen und die Produktionsbereiche in der Wasserscheide zunehmen. Die LUC-Erkennungsergebnisse zeigen eine Zunahme der landwirtschaftlichen Flächen und eine Abnahme der Waldbedeckung zwischen 1990 und 2016. Statistisch gesehen, verringerte sich die natürliche Waldbedeckung von 61,2% im Jahr 1990 auf 57,12% im Jahr 2016, während der Anteil der landwirtschaftlichen Flächen von 2,9% auf 7,23% zwischen die Jahren 1990 und 2016 zunahm. Die Ergebnisse der LUC- Projektion für die Jahre 2026 und 2036 in Bezug auf das Jahr 2016 deuten darauf hin, dass die landwirtschaftlichen Flächen im Jahr 2026 voraussichtlich um 9,3% und im Jahr 2036 um 19,2% im Trendszenario zunehmen werden. Für das “Best-Case-Szenario” wird eine Zunahme der landwirtschaftlichen Flächen um 1,1% bzw. 3% im Jahr 2026 bzw. 2036 prognostiziert. Die Ergebnisse des “Worst-Case–Szenarios” für die Jahre 2026 und 2036 prognostizieren eine Zunahme der landwirtschaftlichen Flächen um 26,1% bzw. 54,3%. Darüber hinaus wird für das Trendszenario im Vergleich zu 2016 ein Rückgang der natürlichen Waldbedeckung im Einzugsgebiet um 0,6% (2026) und um 1,5% (2036) prognostiziert. Für das “Best-Case-Szenario” wird prognostiziert, dass die Waldbedeckung um 0,2% (2026) und um 0,4% (2036) abnehmen wird. Das “Worst-Case-Szenario” prognostiziert für die Jahre 2026 und 2036 einen Rückgang der natürlichen Waldbedeckung um 2,6% bzw. 5,8% gegenüber 2016. Die Ergebnisse der hydrologischen Modellierung zeigen, dass aufgrund der Auswirkungen von LUC der durchschnittliche tägliche Stromfluss für das Trendszenario im Vergleich zu 2016 um 1,04% (2026) und 1,45% (2036) anstieg. Für das “Best-Case– Szenario” verringerte sich der durchschnittliche tägliche Stromfluss in den Jahren 2026 und 2036 gegenüber 2016 um 4,91% (-24,8 m³/s) bzw. 6,10% (-30,8 m³/s). Für das Szenario “Worst-Case” wird in Bezug auf das Jahr 2016 ein Anstieg des durchschnittlichen täglichen Stromflusses um 2,08% (2026) und um 2,37% (2036) prognostiziert. Die Ergebnisse zu den Auswirkungen von LUC auf den Stromfluss unter den verschiedenen vorgeschlagenen Szenarien zeigen, dass die Änderungen des Stromflusses kein Faktor sind, der die Wasserkrafterzeugung im Einzugsgebiet beeinflussen könnte. Die Ergebnisse zeigen jedoch, dass die Wasserhaushaltskomponenten durch die räumliche und zeitliche Verteilung von LUC im Untersuchungsgebiet beeinflusst werden, was für ein einzugsgebietsweites integriertes Wasserressourcenmanagement nützlich ist. Das Ausmaß dieser Effekte kann jedoch durch Unsicherheiten verdeckt werden, die sich aus den hydrologischen und LUC-Modellierungsprozessen ergeben. Daher sind weitere Studien zur Optimierung von Landnutzungsänderungen und Untersuchungen zur Bewertung von Niederschlag-Abfluss-Prozessen im Untersuchungsgebiet unerlässlich. Nichtsdestotrotz sollten Nachhaltigkeitsaspekte, die mit dem Vorhandensein der Wasserkraftanlage im Untersuchungsgebiet verbunden sind, nicht vernachlässigt werden. Um eine nachhaltige Entwicklung im Einzugsgebiet gewährleisten zu können (die die langfristige Wasserkraftproduktion, die Erhaltung der Ökosysteme und das sozioökonomische Wohlergehen der Bevölkerung im Einzugsgebiet umfasst), müssen in weiteren Arbeiten innerhalb eines IWRM-Rahmens weitere Variablen und Prozesse analysiert werden, die in dieser Studie nicht behandelt wurden

Cellular automata simulations of field scale flaming and smouldering wildfires in peatlands

In peatland wildfires, flaming vegetation can initiate a smouldering fire by igniting the peat underneath, thus, creating a positive feedback to climate change by releasing the carbon that cannot be reabsorbed by the ecosystem. Currently, there are very few models of peatland wildfires at the field-scale, hindering the development of effective mitigation strategies. This lack of models is mainly caused by the complexity of the phenomena, which involves 3-D spread and km-scale domains, and the very large computational resources required. This thesis aims to understand field-scale peatland wildfires, considering flaming and smouldering, via cellular automata, discrete models that use simple rules. Five multidimensional models were developed: two laboratory-scale models for smouldering, BARA and BARAPPY, and three field-scale models for flaming and smouldering, KAPAS, KAPAS II, and SUBALI. The models were validated against laboratory experiments and field data. BARA accurately simulates smouldering of peat with realistic moisture distributions and predicts the formation of unburned patches. BARAPPY brings physics into BARA and predicts the depth of burn profile, but needs 240 times more computational resources. KAPAS showed that the smouldering burnt area decreases exponentially with higher peat moisture content. KAPAS II integrates daily temporal variation of moisture content, and revealed that the omission of this temporal variation significantly underestimates the smouldering burnt area in the long term. SUBALI, the ultimate model of the thesis, integrates KAPAS II with BARA and considers the ground water table to predict the carbon emission of peatland wildfires. Applying SUBALI to Indonesia, it predicts that in El Niño years, 0.40 Gt-C in 2015 (literature said 0.23 to 0.51 Gt-C) and 0.16 Gt-C in 2019 were released, and 75% of the emission is from smouldering. This thesis provides knowledge and models to understand the spread of flaming and smouldering wildfires in peatlands, which can contribute to efforts to minimise the negative impacts of peatland wildfires on people and the environment, through faster-than-real-time simulations, to find the optimum firefighting strategy and to assess the vulnerability of peatland in the event of wildfires.Open Acces