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Modelling the Nicobar Islands in the aftermath of the 2004 Sumatra tsunami
1. RECOVER ( re search on co ping with v ulnerability to e nvironmental r isk) Waves of change Modelling the Nicobar Islands in the Aftermath of the 2004 Sumatra Tsunami Martin Wildenberg , Simron J. Singh, Willi Haas , Marina Fischer - Kowalski
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12. Model Components I Model Interface to users R E A L W O R L D Interface to users “ Hard “ Data Output Access Database Excel GIS Assumptions about who the real system works / structure of the system Easy to change i.e. to make model of another Island of the region Change requiers going “ into “ the model Theories Module 1 Module 2 Module 3 Posibility to use the model for given context Input
18. Figure 7: Available, required and desired working times per adult. Required working time includes time for subsistence and market oriented activities. Results I
19. Figure 9: change in forest, grass- & shrub land, coconut plantations and garden area in the next thirty years. Results II
20. Figure: 10 Kamorta land-cover and land-use – 2005, 2015, 2025 and 2035. Results III
(material and energy flow and land use analysis) ethnographic tools and stakeholder processes). This case study is particularly relevant in consideration of the (destructive) fate foraging people commonly suffer when they become subject to outside influences. develop methods to relate emic and etic descriptions in participative decision making. Provide a further case study for an emerging theory of sustainability transitions by using the new dynamics triggered as a consequence of the tsunami disaster
The Nicobars are a group of twenty-four islands belonging to India with a total area of 1841 km2. Twelve of the islands are inhabited. The population consists of mainland Indians how are either working for the administration or military or how are legal respectively illegal settlers and of two distinct ideginous groups: the Nicobarese and the . The latter living as hunter-gatheres only on great nicobar the southern most island. The nicobarese are of mongoloid origin and belong to the sout-east asian cultural complex. They are the only indigenous group of the andaman-nicobar archipelago with growing population numbers. Today about …. Live on the islands. The Islands are protected by the ANPTR which strictly regulates the access to the islands. Not only to shield the indigenous population from outside exploitation (which is the official reason) but also due to strategic and military interests of India. This makes it virtually impossible for non-Indians to visit the islands for what reason ever.
Due to their location in the equatorial belt the islands have a warm and humid tropical climate with a mean annual temperature of about 26° C. Mean annual perception ranges between 3000 and 4000 mm (Indian Institute for Remote Sensing, 2003). Rainfall is influenced by the southwest and northeast monsoon winds. The soil is composed of sandstones, slates and clay is immature, poor in drainage, with low moisture retaining capacity (P.S. Roy et al. 2003). Soil cover is rather thin, varying between two and five meters. Vegetation on the Islands bears significant resemblance to phytogeoraphical Malaysian and Indonesian species And also the fauna is influenced by the Indo-Chinese and Indo-Malayan regions. The main terrestrial mammals of the central nicobars are wild boars, civets, and several species of rats, bats, and shrews large mammals are not found on the islands. Prominent ecosystems of the central nicobars are the evergreen and mixed evergreen forests – which already suffer degradation on some of the islands. grasslands which could be of anthropogenic origin and which are occasionally burned . Before the Tsunami the Islands harbored large areas of intact mangrove forests which are now partly destroyed The corals surrounding the islands consist mainly of fringing reefs with a barrier reef only on the western side (Andrews et al 2002). Only used for subsistence fishing they are still fairly pristine and in good condition. They are one of the species-richest found in the Indian Ocean Altogether the islands are characterized by a high diversity and high rates of endemism (xxx species of endemites) and are recognized by the The World Conservation Union (IUCN) as a hot spot of biodiversity.
Challenges to modelling Social-ecological systems or other complex adaptive systems are characterised by a large number of interacting components, nonlinear and emergent behaviour, multi-scale interactions, ability to selforganise, multiple stable states and path-dependent development (Levin 1998). Aditionally Modelling SES systems must include human behaviour and their actions that affect these systems. However, they pose additional difficulties due to the complex and diverse motivations and processes driving human behaviour. However, the goal of modelling is not so much in reproducing the entire social-ecological system in all its complexity, but the exercise is rather driven by questions that need to be answered by the model. In other words, a model needs to be complex enough incorporating relevant attributes of the socialecological system and its dynamics insofar as it helps to answer the question which is being asked. In doing so, emphasis is on finding a balance between the choice of modelled components and processes while yet maintaining the relevant details and holism necessary to address the questions Modelling the social-ecological system of Kamorta Island required a distinct set of elements from both natural and social systems to be included. Here the notion of social metabolism was useful that allowed us to focus on the exchange of materials and energy between society and nature. We followed these flows and explicitly modelled them together with their endogenous and exogenous drivers.
Model grundlage für intervention Aufbau auf parti daten erfassung Welche optionen haben welches risiko Guidance: resourcen nutzung, monitoring…
Marine resources Trophic model Terrestrial resources Land Use / Cover Change Model Driven by internal dynamics & Inputs / Demand from society Output: Effort per unit of resource Availability of resource Interface with other components: Scenarios of comercila species Implemented with Ecopath & Ecosim. Trophic model of marine system Both Implemented in AnyLogic – directly coupled in runtime (= “one model“) Agent based Model Input – Output Internal Dynamics: Changes in consumption patterns Changes in production Changes in Livestyle
Model Structure II The model as a whole consists of four interlinked modules: (a) socioeconomic, (b) agent-based (c) ecosystem, and (d) output (see figure 1). The socio-economic module interacts with the ecosystem module via the agent that simulates human decisions. The combined effect of these decisions are then exhibited or displayed over the output module in terms of social and environmental indicators. The model has a user-friendly interface that allows the stakeholders to engage in an interactive process and play around with their decisions and test their assumptions on system behaviour. Let us know look into a bit more detail into the four modules.
The Socio-economic module This module contains three submodules, namely, demography, household life-style patterns and the economy. It can be run as an independant unit Population module -> IIASA Housholde module -> HH are charactreized by 3 HH types. speed of transitions between HH types can be set Output = population per HH-type which is then used to calculate the money demand together with the available time. The central element is a Leontief Input-output matrix to calculate total maoney and resource demand.
The Agent based module The Agent-based module receives as an input from the society module the total amount of natural resources that need to be harvested for the reproduction of the population for a given lifestyle composition. The basic decision an agent has to take in the K -SES model is whether it wants to maintain, increase or decrease the amount of land under cultivation. To reach this decision the harvest of the last year is compared to the actual resource demand. If there is more demand than the potential harvest the area under cultivation is increased, and vice versa. In this case, the agent does not have a choice to intensify productivity per land area, since we assume that the Nicobarese may not be able to switch to another technology for a long time. Factors which the model considers when calculating the required area of land are the decline of yields due to loss of fertility in vegetable gardens, stochastic fluctuation of yields, and changes in coconut yields over time. The agent perceives two variables of the LANDUNIT which influence his decision.
The Landunit The terrestrial sub-module consists of a grid of cells which are termed LANDUNITs in the model. Each LANDUNIT has x- and y-coordinates, which define its exact position and a number of parameters and variables describing its land-cover, its land-cover change history, and the probability of fire. A rule based ‘state-chart’ controls the dynamics of the LANDUNITs and the changes between different land-cover types. In our model we use 15 land-cover and land-use states from which four are colonized states: vegetable fields, permanent fruit cultures, coconut plantations and settlement. LANDUNITs can shift from one land cover state to another either through internal time driven processes which represent succession, through fire, or through a colonisation process by humans initiated through the Agent Based Module (figure 4). Most transitions are influenced to some extent by the landcover states of the neighbouring LANDUNITS. Fire outbreaks are stochastic in the model depending on the land-cover state, and occur with a higher probability in proximity to roads or to other human influenced LANDUNITs. Fire spreads from neighbour to neighbour LANDUNIT with a probability depending on its land-cover type and the time since last fire (representing the fuel-load). Yields and working time required to maintain the colonized LANDUNITS, that is of vegetable fields, coconut plantation and permanent fruit cultures are dependent on the internal condition of each LANDUNIT (such as age of crops and trees, factors affecting soil, proximity to human settlement). The required time for all economic and subsistence activities on each LANDUNIT are calculated based on field data. We also accounted for other factors affecting time-use such as accessibility and neighbourhood conditions. The suitability indicator, which is represented as a number between 0-8 is calculated according to the current land-cover, preferences for certain neighbourhood conditions and the land-use history of the LANDUNIT. Through changing the weights the Suitability Index can be adjusted to the preferences (and perception) of the actors. Ownership, which is at the moment only considered in the small-plot version of the model, or other factors at the LANDUNIT level, can additionally be introduced to constrain land-use decisions. When choosing LANDUNITs the agent randomly iterates through them and chooses those that fulfil the predefined suitability criteria (e.g. transform this LANDUNIT into a garden when it’s Suitability Index >= 6.5). After each round of iteration the criteria for choosing LANDUNITs can be lowered until the requirements are met. This mimics a situation were the agents do not have a complete information over the conditions of every single LANDUNIT but rather investigate each piece of land for its usability and decide to use it if it fulfils the minimum criteria – regardless if there might be a better piece of land somewhere else. If excessive production occurs, the least efficient LANDUNITS are abandoned first. Efficiency can be either calculated as money earned per hour of work invested or also in biophysical units such as material or energy gained per hour of work. The use of energy and money as a currency in the model allows linking subsistence, monetary and mixed economic systems. Required working time is contingent on how accessible the selected LANDUNITS are and their land-use types. In contrast to the method used for choosing LANDUNITS (where we assume that the agent does not have complete knowledge of all LANDUNITS), but in case of abandonment we assume that the agent does know about the efficiency of all the LANDUNITS he is maintaining and will first abandon the least efficient. Also a ‘minimum efficiency value’ can be set which will lead to the agent abandoning each LANDUINT that exhibits ‘efficiency’ below this value. As both suitability and efficiency of a LANDUNIT are influenced positively by neighbouring colonized LANDUNITS, patterns of aggregation emerge on the landscape level. Different weights on neighbourhood relations can produce different patterns of land-use on the landscape level ranging from aggregated to dispersed patterns of land-use.
The Output Module The Output Module displays the combined effects of human decisions and changes in framework conditions on the various socio-economic and ecosystem variables, thereby providing information on the system performance. The results calculated by this module are automatically fed back to the computer agent that influences its decision and actions according to a set of pre-defined rules derived from assumptions from the field. However, since the model also provides a user interface to make it possible for real agents (stakeholders) to interact with the model, they may directly intervene into the model by playing around with a set of sliders representing various system parameters and human preferences. Presently, we have introduced a set of five (land-use preference) sliders and check-boxes for the users to influence agent behaviour during run-time of the model. The model surface displays a map of Kamorta Island showing land-use and landcover Additionally, the surface also displays outputs of some relevant variables such as income and yield per unit of land or hour, loss of forest to agriculture, and required working time for meeting the needs of a given mix of lifestyles. This allows the agent or the user to react to benchmarks like maximum available time or comparing activities like gardening with fishing in terms of money or yield per hour of work invested.
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