Area-based scenario development in land-use change modeling: A system dynamics-assisted approach for mixed agricultural-residential landscapes |
| |
| Affiliation: | 1. PhD Student, Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, Iran;2. Department of Natural Resources, Isfahan University of Technology, Isfahan 84156-83111, Iran;3. Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran;4. Faculty of Environment and Natural Resources, University of Freiburg,Tennenbacherstr. 4, 79106 Freiburg, Germany;1. Faculty of Statistics at Complutense University, Avenida Puerta de Hierro, s/n, Madrid 28040, Spain;2. Institute of Artificial Intelligence, De Montfort University, Gateway House, Leicester LE1 9BH, United Kingdom;3. Department of Computer Science and Artificial Intelligence, University of Granada, Avenida de la Fuente Nueva S/N, Granada 18071, Spain;1. Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China;2. Department of Neurosurgery, Peking Union Medical College Hospital, Beijing 100044, China;3. Department of Otorhinolaryngology Head and Neck Surgery, Hainan General Hospital, Haikou 570100, China;1. College of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China;2. Collaborative Innovation Center for Grassland Ecological Security, Ministry of Education of China, Hohhot 010021, China;3. School of Ecology and Environment, Inner Mongolia University, Hohhot 010021, China |
| |
| Abstract: | This study aimed to enhance land use and land cover (LULC) change models by addressing their main limitations, which include the lack of accountability and temporal stability of driving forces. Additionally, the study aimed to create area-based scenarios to forecast future LULCs, rather than solely relying on distribution-based scenarios. To accomplish this goal, the study developed a coupled System Dynamics (SD) and Cellular Automata (CA) modeling system to simulate possible LULC changes in the Gavkhooni Basin, central Iran. The study utilized LULC maps from Landsat images in 2001, 2011, and 2021 to analyze spatio-temporal land use changes in the region. Agricultural and residential transition suitability layers were produced using a spatial Multi-Criteria Evaluation procedure and applied to inform the CA model in the proper allocation of LULC changes. Three interconnected water supply, agricultural, and residential area projection subsystems were developed using system dynamics method to determine land requirements for LULC conversions from 2020 to 2041, taking into account factors such as water availability, land suitability, agricultural labor force, and economic development. Ten scenarios were developed based on changes in the key variables affecting the limiting factors, such as climatic conditions and water management policies, to project agricultural and residential areas in the future. The CA's spatial allocation informed by transition suitability layers was found to be satisfactory with a Kappa-location value of 0.85. The subsystems were competent in projecting water supply with Mean Absolute Error (MAE) values of 6.57% and the dynamics of agricultural and residential areas with MAE values of 2.94%, whereas those of the Markovian Chain model were found to be 23.02% and 7.5% for agricultural and residential areas, respectively. The study found that available agricultural areas varied significantly between 86.53 and 1480 sq.km under different climatic conditions, irrigation efficiency, and agricultural water assignment coefficients between 2024 and 2033. Residential area demand was found to be increasing with different rates under the scenarios between 47.40 and 73.01 sq.km. The SD-CA coupled framework presented in this research can be viewed as a decision support system to develop compensatory strategies for better management and planning of agricultural and residential lands. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
