Keywords
adaptive management, vulnerability, resilience, system dynamic modelling
Start Date
1-7-2004 12:00 AM
Abstract
The erosion of biocomplexity in the Tisza River Basin developed slowly and incrementally over the past 130 years since implementation of the original Vásárhelyi river engineering plan. The Hungarian public view, blinded by flood and toxic spill catastrophes, missed the slow and subtle changes to natural, social and human capital precipitated by the reshaping of the TRB landscape and its agriculture for flood defence and grain production. While conversion of the TRB from a fruit/nut/ fishery polyculture to a wheat monoculture produced a great deal of financial capital for an aristocratic minority, the gradual drain of alternatives forms of capital left the region less and less resilient in the face of ecological (floods), economic (globalization) and political (war) shocks. Domination by central authorities over the past 50 years reduced local civic capacity to levels of passivity that make most communities incapable of innovating to find sustainability solutions, and this trend is reinforced by ongoing paternalistic attitudes in the Hungarian national government. Poverty, passivity, apathy and the severe consequences of failure in the event of flooding have severely reduced Adaptive Capacity, the potential to innovate and adapt to uncertainty. Both Nature and Society have evolved considerably since 1870, so simple reverse engineering futilely aims to resurrect a system that no longer exists. Since the knowledge to un-straighten and reflood a river basin is in its infancy, we must learn as we go along, humble in the knowledge that management interventions often only increase uncertainty and can push the system further into a degraded state. This paper describes an initiative to use conceptual and formal modelling within an Adaptive Management framework to facilitate a regional discussion on how to manage the TRB while inventing a pathway back to a more resilient socio-ecosystem, linking natural and social processes.
Modelling Biocomplexity in the Tisza River Basin within a Participatory Adaptive Framework
The erosion of biocomplexity in the Tisza River Basin developed slowly and incrementally over the past 130 years since implementation of the original Vásárhelyi river engineering plan. The Hungarian public view, blinded by flood and toxic spill catastrophes, missed the slow and subtle changes to natural, social and human capital precipitated by the reshaping of the TRB landscape and its agriculture for flood defence and grain production. While conversion of the TRB from a fruit/nut/ fishery polyculture to a wheat monoculture produced a great deal of financial capital for an aristocratic minority, the gradual drain of alternatives forms of capital left the region less and less resilient in the face of ecological (floods), economic (globalization) and political (war) shocks. Domination by central authorities over the past 50 years reduced local civic capacity to levels of passivity that make most communities incapable of innovating to find sustainability solutions, and this trend is reinforced by ongoing paternalistic attitudes in the Hungarian national government. Poverty, passivity, apathy and the severe consequences of failure in the event of flooding have severely reduced Adaptive Capacity, the potential to innovate and adapt to uncertainty. Both Nature and Society have evolved considerably since 1870, so simple reverse engineering futilely aims to resurrect a system that no longer exists. Since the knowledge to un-straighten and reflood a river basin is in its infancy, we must learn as we go along, humble in the knowledge that management interventions often only increase uncertainty and can push the system further into a degraded state. This paper describes an initiative to use conceptual and formal modelling within an Adaptive Management framework to facilitate a regional discussion on how to manage the TRB while inventing a pathway back to a more resilient socio-ecosystem, linking natural and social processes.
