Shixiong Song

Context: Effectively estimating the influences of urban expansion on multiple ecosystem services (ESs) is of great importance for improving urban planning in drylands. However, there are some shortcomings in the existing urban expansion models, which lead to great uncertainties in the assessment of the influences of urban expansion on multiple ESs. Objectives: This study sought to effectively estimate the influences of urban expansion on multiple ESs in drylands. Methods: We combined the land use scenario dynamics-urban model and ES models to estimate the influences of urban expansion on habitat quality, food production, water retention, air quality regulation, sand fixation, and aesthetics. Results: The results showed that (1) our method can effectively evaluate the influences of urban expansion on multiple ESs in drylands, and the accuracy increased by more than 20% on average. (2) Under the impacts of future urban expansion, the annual average loss per unit area of habitat quality, food production, water retention, air quality regulation, sand fixation, and aesthetics will increase. (3) Future urban expansion will occupy more cropland and grassland, which will be the main reason for the intensified degradation of multiple ESs. Conclusions: We suggest that the local government should focus on protecting key ecosystems, especially grasslands, from urban expansion to mitigate the potential influences on multiple ESs in drylands.

Effective evaluations of the future urban expansion impacts (UEI) on surface runoff in alpine basins are full of challenges due to the lack of reliable methods. Our objective was to provide a new approach by coupling the Land Use Scenario Dynamics-urban (LUSD-urban) and Soil Conservation Service-Curve Number (SCS-CN) models to estimate the future UEI on surface runoff. Taking the Qinghaihu-Huangshui basin (QHB) in the Tibetan Plateau, China, as an example, we first applied the SCS-CN model to quantify the surface runoff in 2000 and 2018 and analyzed the changes in surface runoff. Next, we applied the LUSD-urban model to simulate urban expansion under five localized shared socioeconomic pathways (SSPs) from 2018 to 2050. Finally, we assessed the UEI on surface runoff in the QHB from 2018 to 2050. We found that coupling the LUSD-urban and SCS-CN models could effectually evaluate the future UEI on surface runoff. Compared with the combination of the Future Land Use Simulation (FLUS) and SCS-CN models, our method reduced the absolute evaluation errors from 3.40% and 11.78% to 0.18% and 4.23%, respectively. In addition, the results showed that future urban expansion will have severe impacts on surface runoff in the valley region. For example, as a result of urban expansion, the surface runoff in the Huangzhong, Xining, and Datong catchments will increase by 4.90–9.01%, 4.25–7.36%, and 2.33–3.95%, respectively. Therefore, we believe that the coupled model can be utilized to evaluate the future UEI on surface runoff in alpine basins. In addition, the local government should pay attention to flood risk prevention, especially in the valley region, and adopt reasonable urban planning with soft and hard adaptation measures to promote the sustainable development of alpine basins under rapid urban expansion.

The belief-desire-intention (BDI) model has been widely used to construct reasoning systems for complex tasks in dynamic environments. We have designed a capabilities and abilities (CA)-BDI farmer decision-making model, which is an extension of the BDI architecture and includes internal representations for farmer household Capabilities and Abilities. This model is used to explore farmer learning mechanisms and to simulate the bounded rational decisions made by farmer households. Our case study focuses on the Gaoqu Commune of Mizhi County, Shaanxi Province, China, where scallion is one of the main cash crops. After comparing the differences between actual land-use changes from 2007 to 2009 and the simulation results, we analyze the validity of the model and discuss the potential and limitations of the farmer land-use decision-making model under three scenarios. Based on the design and implementation of the model, the following conclusions can be drawn: (1) the CA-BDI framework is an appropriate model for exploring learning mechanisms and simulating bounded rational decisions; and (2) local governments should encourage scallion planting by assisting scallion farmer cooperatives and farmers to understand the market risk, standardize the rules of their cooperation, and supervise the contracts made between scallion cooperatives and farmers.