Land Use Dynamic Changes in an Arid Inland River Basin Based on Multi-Scenario Simulation

Abstract

The Tarim River Basin is the largest inland river basin in China. It is located in an extremely arid region, where agriculture and animal husbandry are the main development industries. The recent rapid rise in population and land demand has intensified the competition for urban land use, making the water body ecosystem increasingly fragile. In light of these issues, it is important to comprehensively grasp regional land structure changes, improve the degree of land use, and reasonably allocate water resources to achieve the sustainable development of both the social economy and the ecological environment. This study uses the CA-Markov model, the PLUS model and the gray prediction model to simulate and validate land use/cover change (LUCC) in the Tarim River Basin, based on remote sensing data. The aim of this research is to discern the dynamic LUCC patterns and predict the evolution of future spatial and temporal patterns of land use. The study results show that grassland and barren land are currently the main land types in the Tarim River Basin. Furthermore, the significant expansion of cropland area and reduction in barren land area are the main characteristics of the changes during the study period (1992–2020), when about 1.60% of grassland and 1.36% of barren land converted to cropland. Over the next 10 years, we anticipate that land-use types in the basin will be dominated by changes in grassland and barren land, with an increasing trend in land area other than for cropland and barren land. Grassland will add 31,241.96 km2, mainly in the Dina River and the lower parts of the Weigan-Kuqu, Kashgar, Kriya, and Qarqan rivers, while barren land will decline 2.77%, with significant decreases in the middle and lower reaches of the Tarim River Basin. The findings of this study will provide a solid scientific basis for future land resource planning.

Landscape Ecological Risk Assessment of Kriya River Basin in Xinjiang and Its Multi-Scenario Simulation Analysis

“…Conversely, the FOM coefficient is computed as the ratio of the intersection of the projected and actual land changes to the total of the two. Higher values of this coefficient, which likewise has a range of 0 to 1, denote increased simulation accuracy [36,59]. Analysis of LULC Structure Change Figure 4 shows the conversion relationship of the LULC for the three phases from 2000 to 2020, and it is represented visually by a chord diagram.…”

“…The rising demand for agricultural water and the decreasing ecological water demand of vegetation have significantly altered the water demand structure of the river basin, as evident in the spatial distribution of the high-risk area of LER from 2000 to 2020. The key ecological LULC in the KRB region is grassland, which, compared to other areas, displays heightened susceptibility and plays a critical role in mitigating ecological risks [9,36].…”

“…Paukert et al (2011) [31] evaluated the ecological health of the Colorado River Basin using an LERI constructed using the landscape index method. Research on LER is increasing again both at home and abroad as we enter the 21st century, and most of the evaluation objects are ecologically fragile and sensitive areas as well as areas with high intensity of human activities, which are mainly centered around watersheds, cities, mines, nature reserves, and ecologically fragile areas [23,[32][33][34][35][36]. The methods of evaluation include landscape pattern index method [9], entropy value method [37], exposure-response method [38], etc.…”

“…Furthermore, in order to support future high-quality economic and social development in watersheds, strategies for optimizing LULC structure in arid watersheds must be proposed [40,41]. Multi-scenario LULC change models can be classified into quantitative predictive models, spatial predictive models and coupled models [36]. Currently, commonly used quantitative forecasting models include Markov, system dynamics (SDs), grey forecasting models (GMs), and artificial neural network (ANN) models [42,43].…”

Study on Carbon Emission Impacts and Carbon Emission Reduction of Land Use Changes in the Yangtze River Delta Region from 2005-2020

“…The study of LUCC (land use and cover change) has good explanatory and reference significance for solving the current global climate change problems and environmental evolution research field, as economic and social development, human-land conflict and a series of social problems [2]caused by land use change are becoming more and more prominent. Hou et al [3] used the CA-Markov model, PLUS model, and gray prediction model to simulate the Tarim River basin based on remote sensing data to identify dynamic LUCC patterns and predict the evolution of future spatial and temporal land use patterns. Therefore, the study of LUCC in the Yangtze River Delta region can directly reflect the impact of human activities on land resources and is of great significance in solving a series of social problems such as regional environment and ecology.…”