S.W. Jin

Modeling for regional ecosystem sustainable development under uncertainty--A case study of Dongying, China.

In this study, a superiority-inferiority two-stage stochastic programming (STSP) method is developed for planning regional ecosystem sustainable development. STSP can tackle uncertainties expressed as fuzzy sets and probability distributions; it can be used to analyze various policy scenarios that are associated with different levels of economic penalties when the promised targets are violated. STSP is applied to a real case of planning regional ecosystem sustainable development in the City of Dongying, where ecosystem services valuation approaches are incorporated within the optimization process. Regional ecosystem can provide direct and indirect services and intangible benefits to local economy. Land trading mechanism is introduced for planning the regional ecosystems sustainable development, where wetlands are buyers who would protect regional ecosystem components and self-organization and maintain its integrity. Results of regional ecosystem activities, land use patterns, and land trading schemes have been obtained. Results reveal that, although large-scale reclamation projects can bring benefits to the local economy development, they can also bring with negative effects to the coastal ecosystem; among all industry activities oil field is the major contributor with a large number of pollutant discharges into local ecosystem. Results also show that uncertainty has an important role in successfully launching such a land trading program and trading scheme can provide more effective manner to sustain the regional ecosystem. The findings can help decision makers to realize the sustainable development of ecological resources in the process of rapid industrialization, as well as the integration of economic and ecological benefits.

Identifying water resources management strategies in adaptation to climate change under uncertainty

In this study, an integrated simulation-based allocation modeling system (ISAMS) is developed for identifying water resources management strategies in response to climate change. The ISAMS incorporates global climate models (GCMs), a semi-distributed land use-based runoff process (SLURP) model, and a multistage interval-stochastic programming (MISP) approach within a general framework. The ISAMS can not only handle uncertainties expressed as probability distributions and interval values but also reveal climate change impacts on water resources allocation under different projections of GCMs. The ISAMS is then applied to the Kaidu-kongque watershed with cold arid characteristics in the Tarim River Basin (the largest inland watershed basin in China) for demonstrating its efficiency. Results reveal that different climate change models corresponding to various projections (e.g., precipitation and temperature) would lead to changed water resources allocation patterns. Variations in water availability and demand due to uncertainties could result in different water allocation targets and shortages. A variety of decision alternatives about water allocations adaptive to climate change are generated under combinations of different global climate models and ecological water release plans. These findings indicate that understanding the uncertainties in water resources system, building adaptive methods for generating sustainable water allocation patterns, and taking actions for mitigating water shortage problems are key adaptation strategies responding to climate change.