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Sketch map of the Tarim River Basin, China. The water and elevation information comes from the Resources and Environment Data Center of the Chinese Academy of Science (http://www.resdc.cn (accessed on 26 May 2022)). (a) Average annual temperature of the Tarim River basin. (b) Average annual precipitation in the Tarim River Basin.
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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 ligh...
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... This region is located in the continental arid climate zone, with hot and dry weather, average temperature 10.7 °C, large annual and daily temperature differences, scarce precipitation (average annual average 17.4~42.8 mm), and high evapotranspiration intensity (1800~2900 mm), which is prone to desertification [49]. The altitude range of the study area is 702 m~3526 m, and the total area is 3.294 × 10 7 ha. ...
The problem of desertification in the Tarim Basin, an area with a unique geography and climatic conditions, has received extensive research attention not only in China but also around the world. Between natural factors and human activities, the latter are considered the main cause of desertification, with the excessive use of land resources accelerating its risk. This study classified the degree of desertification into five types, no, light, moderate, severe, and extremely severe desertification, and focused on the spatio-temporal changes in LULC, desertification development, and their relationship in the Circum-Tarim Basin during the period of 1990–2020, and the results indicated the following. (1) Over the 30-year study period, farmland development was frequent in the basin. The total farmland area increased significantly by 1.40 × 104 km2, which resulted from the occupation of grassland (mainly low-covered and medium-covered grassland) and unused land (mainly saline–alkali land). (2) There was a general alleviation of the effects of desertification, but also local deterioration. The area of no-desertification land has significantly increased (an increase of 2.10 × 104 km2), and the degree of desertification has shifted significantly to adjacent lighter degrees, but the area of extremely severe desertification in certain regions has increased (an increase of 7.89 × 104 km2). (3) There was an inseparable relationship between LULC and desertification. Oasisization and desertification were two processes that interacted and were interrelated. There was an approximately 54.42% increase in no-desertification land area mainly occurring in the region where LULC types changed (Region II), although this area only accounted for 9.71% of the total area of the basin. There was an approximately 98.28% increase in the area of extremely severe desertification occurring where there were no changes in LULC types (Region I). Region II demonstrated the best effects of desertification prevention and control in the 30-year study period in the Circum-Tarim Basin. Land development and oasis expansion have led to concentrated water use, resulting in water scarcity in certain areas, which cannot support the needs of vegetation growth, thus aggravating the degradation. Hence, “adapting measures to local conditions, rational planning, zoning policies, precise prevention and control” will be the way forward for desertification control in the future in the Circum-Tarim Basin.
... 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. ...
... Where � is the carbon emission from construction land (t); * is the output value of the second and third industries in the Yangtze River Delta region (million yuan); is the energy consumed per 10,000 yuan of GDP generated, measured in standard coal (t standard coal / million yuan); is the carbon emission coefficient of coal consumption [3], K=0.7476 (t C / million t standard coal). ...
The article investigated the characteristics of land use and land cover and their effects on carbon emissions in the Yangtze River Delta region from 2005 to 2020 and predicted carbon emissions in the next ten years. The results show that the land use in the region is spatially territorial and quantitatively stable, with the area of cultivated land and forest land decreasing, the area of construction land and unused land increasing, and the area of grassland, forest land, and water not changing much; the degree of land use in the region tends to increase, and the areas with high degree are the economically developed eastern urban agglomerations, while the low degree is the western mountainous areas; The spatial variability of regional carbon emission intensity is changing, with the total amount showing an upward trend, the distribution direction converging toward coastal areas, and the spatial development direction of “northwest-southeast” expanding more intensely than “northeast-southwest”; the model predicts that carbon emissions will still be on an upward trend in the next ten years. Based on this, measures such as optimizing the land use structure and comprehensive development of woodland-grassland agglomerations are necessary to achieve carbon reduction targets.
... Among them, the Aksu River is the main source of the TRB, contributing about 73.2% of the water supply [59]. In such an ecological fragile area, with the long-term development and utilization, streamflow processes and land-use patterns have changed greatly [60,61]. The contradiction between economic development and ecological protection poses an unprecedented challenge to the management of the river basin. ...
Climate change and land use/cover change (LUCC) are two major factors that alter hydrological processes. The upper reaches of the Tarim River, situated in the northwest region of China, experience a dry and less rainy climate and are significantly influenced by human activities. This study comprehensively assessed the impacts of individual and combined climate changes and LUCCs on streamflow. Three general circulation models (GCMs) were utilized to predict future climate changes under three shared socioeconomic pathways (SSP119, SSP245, and SSP585). Cellular Automata–Markov (CA–Markov) was employed to predict future LUCC under three scenarios (i.e., ecological protection, historical trend, and farmland development). Streamflow for the period 2021–2050 was simulated using the calibrated MIKE SHE model with multiple scenarios. The results showed that from 2021 to 2050, increments in both average annual precipitation and average annual temperature under the three SSPs were predicted to lead to an increased streamflow. In comparison to the conditions observed in 2000, under three LUCC scenarios for 2030, the grassland area decreased by 1.04% to 1.21%, while the farmland area increased by 1.97% to 2.26%, resulting in reduced streamflow. The related changes analysis indicated that the variation in streamflow during winter is most significant, followed by spring. The study predicted that climate change would increase streamflow, while LUCC would decrease it. Due to the greater impact of LUCC, considering the combined effect of both factors, runoff would decrease. The contribution analysis indicated that climate change contributed between −7.16% and −18.66%, while LUCC contributed between 107.16% and 118.66%.
... 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.; additionally, the evaluation scale evolves from a single scale to multiple scales, and numerous researchers have looked into the LER's multi-scale changes [39]. ...
... 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]. ...
... 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]. Table 2 show the spatial and temporal changes in the composition of the six LULC types in the KRB. ...
To comprehend the potential impacts of both natural phenomena and human activities on ecological risk, a thorough examination of the spatial and temporal evolution characteristics of Landscape Ecological Risk (LER) in arid river basins is imperative. This investigation holds paramount importance for the proactive prevention and mitigation of LER, as well as for the preservation of ecological security within these basins. In this scholarly inquiry, the Kriya River Basin (KRB) serves as the focal point of analysis. Leveraging three historical land use and land cover (LULC) images and incorporating a diverse array of drivers, encompassing both natural and anthropogenic factors, the study employs the PLUS model to forecast the characteristics of LULC changes within the basin under three distinct scenarios projected for the year 2030. Concurrently, the research quantitatively assesses the ecological risks of the basin through the adoption of the Landscape Ecological Risk Assessment (LERA) methodology and the Spatial Character Analysis (SCA) methodology. The results showed the following: (1) The study area is primarily composed of grassland and unused land, which collectively account for over 97% of the total land. However, there has been a noticeable rise in cropland and considerable deterioration in grassland between 2000 and 2020. The key observed change in LULC involves the transformation of grassland and unused land into cropland, forest, and construction land. (2) The overall LER indices for 2000, 2010, and 2020 are 0.1721, 0.1714, and 0.16696, respectively, showing strong positive spatial correlations and increasing autocorrelations over time. (3) Over time, human activities have come to exert a greater influence on LER compared to natural factors between 2000 and 2020. (4) In the natural development scenario (NDS), cropland protection scenario (CPS), and ecological priority scenario (EPS), the LER of KRB experienced notable variations in the diverse 2030 scenarios. Notably, the CPS exhibited the highest proportion of low-risk areas, whereas Daryaboyi emerged as the focal point of maximum vulnerability. These findings offer theoretical and scientific support for sustainable development planning in the watershed.
... For example, Mubako et al. [53] evaluated past and future LULC changes in the semi-arid Dodoma, Tanzania, by combining artificial neural networks and CA models. Hou et al. [54] simulated LULC changes in the Tarim River Basin with the support of the CA-Markov model, and the results revealed the spatiotemporal pattern of land use in the future. Mamitimin et al. [55] analyzed the spatiotemporal LULC change in Urumqi, Xinjiang, during 1980-2020, and then projected future LULC change in 2050 under several scenarios by using a CA model. ...
Arid and semi-arid areas are facing severe land degradation and desertification due to water scarcity. To alleviate these environmental issues, the Chinese government has launched a “water conveyance” project for environmental protection along the Tarim River. While previous studies have mainly focused on environmental conditions, the influence of these policies on land use conditions remains less explored. Therefore, this study first simulated the land use and land cover (LULC) changes in a major city (Korla) around the Tarim River. We found that the water conveyance routes have exerted notable influences on surrounding LULC changes. Next, we primarily focused on the LULC changes among different reaches of the Tarim River. We found that water and forest areas in the lower reaches have increased at the expense of a slight decrease in such areas in the upper and middle reaches, which suggests that the water conveyance policy may also have unintended consequences. These findings could attract the attention of decision makers in many other arid and semi-arid areas, and they could provide practical policy implications for other similar inter-basin water conveyance projects. The benefits and risks of these man-made projects should be carefully balanced.
... Water demand is mainly concentrated in food production, and more than 90% of water use in the Tarim Basin is used for agricultural irrigation [10]. The cropland area has significantly expanded in the Tarim Basin in recent decades, owing to the rapid rise in population and land demand [20]. In the past 30 years, the uncontrolled exploitation of land and water resources associated with the growing population in the Tarim Basin has led to rapidly increased water consumption and a great change in the water regime, and the agriculture and desert vegetation relied on groundwater owing to scarce rainfall and limited surface water, resulting in regional groundwater overexploitation for irrigation and groundwater level decline [10,15,21]. ...
... Bai et al. [18] reported that groundwater overexploitation was the dominant factor of groundwater level decline in the Yarkand River irrigation districts in China. Furthermore, some researchers have simulated and analyzed the dynamic land-cover patterns in the Tarim Basin using the CA-Markov model, PLUS model, and remote sensing data [20]. Wang et al. [15] found that the cropland area and actual evapotranspiration were the dominant controlling factors for the groundwater level decline in the Weigan-Kuqa Oasis of Tarim Basin. ...
Groundwater is essential to residents, ecology, agriculture, and industry. The depletion of groundwater impacted by climatic variability and intense human activities could threaten water, food, and socioeconomic security in arid regions. A thorough understanding of groundwater level dynamics and its response to land-cover change is necessary for groundwater management and ecosystem improvement, which are poorly understood in arid desert regions due to a scarcity of field monitoring data. In our study, spatiotemporal characteristics of groundwater level impacted by land-cover change and its relationship with vegetation were examined using 3-years in-situ monitoring data of 30 wells in the desert regions of Tarim Basin during 2019–2021. The results showed that the depth to groundwater level (DGL) exhibited obvious spatial and seasonal variations, and the fluctuation of DGL differed significantly among the wells. The cultivated land area increased by 1174.6, 638.0, and 732.2 km2 during 2000–2020 in the plains of Yarkand, Weigan-Kuqa, and Dina Rivers, respectively, mainly transferring from bare land and grassland. Annual average Normalized Difference Vegetation Index (NDVI) values increased with time during the period in the plains. DGL generally exhibited a weakly increasing trend from 2019 to 2021, mainly due to human activities. Land-cover change significantly affected the groundwater level dynamic. Generally, the groundwater system was in negative equilibrium near the oasis due to agricultural irrigation, was basically in dynamic equilibrium in the desert region, and was in positive equilibrium near the Tarim River Mainstream due to irrigation return water and streamflow. NDVI of natural desert vegetation was negatively correlated with DGL in the desert regions (R2 = 0.78, p < 0.05). Large-scale land reclamation and groundwater overexploitation associated with water-saving irrigation agriculture development have caused groundwater level decline in arid oasis-desert regions. Hence, controlling groundwater extraction intensity, strengthening groundwater monitoring, and promoting water-saving technology would be viable methods to sustainably manage groundwater and maintain the ecological environment in arid areas.
... It should be noted that the patch number of building land increased the most, from 3346 in 2000 to 10,201 in 2020 (more than 2.0 times), and the area of the building land also increased ( Table 2). This indicates that with the acceleration of urbanization [35], the landscape types are very disturbed by human beings, the landscape fragmentation degree increases, and the stability of the landscape structure declines. For the other indices, the water-body area significantly increased but its Ci was reduced significantly. ...
Land-use variation indicates the spatial differentiation of regional ecological risk. Landscape ecological risk assessment (LERA) has been used for the measurement and prediction of environmental quality. In the present study, the land-use dynamics of the Tarim River Basin from 2000 to 2020 were quantitatively analyzed using ENVI 5.6 software based on Landsat TM and ETM+ images (2000, 2010, and 2020). Moreover, the ecological risk level and its spatiotemporal differentiation features were explored using geostatistical methods based on landscape pattern indices. The results show that: (1) From 2000 to 2020, the arable land area increased the most (12,130.272 km2), and the woodland, wetland, water bodies, and building-land areas increased by 2416.541 km2, 4103.789 km2, 3331.230 km2, and 2330.860 km2, respectively. However, the bare-land area decreased the most (18,933.943 km2). (2) From 2000 to 2020, a decrease was detected in the landscape ecological risk index (LERI) of the basin, and the very low-, low-, and moderate-risk areas had the largest decrease. In addition, the area of the low- and moderate-risk areas gradually increased, while that of the high-risk areas was reduced. (3) The conversion rate of low-risk areas to very low-risk areas was the largest (5144.0907 km2/a), followed by that of high-risk areas to moderate-risk areas (4994.4765 km2/a). Therefore, the overall landscape ecological risk (LER) of the basin was reduced from 2000 to 2020, but the ecological risk of some areas, especially that of the glaciers and permanent snow-covered areas, still needs close attention.
... Simulation predictions are also discussed (Yonaba et al., 2021). Researchers have investigated urban areas (Gao et al., 2021;Karunaratne et al., 2022;Zhang et al., 2022c) and river basins (da Cunha et al., 2021;Hou et al., 2022a;Hou et al., 2022b) as well as ecological protection areas . There are several research methods used to analyze, land use transfer matrix Wang et al., 2022a), principal component analysis , landscape pattern index , and prediction models Mungai et al., 2022;Wang et al., 2022c;Yonaba et al., 2021). ...
... Kappa coefficient is widely used to verify the rationality of PLUS model simulation (Han et al. 2022;Hou et al. 2022) and has high reliability above 0.6 (Zhang et al. 2020). We used the real data of the study area in 2010 and 2020 to verify the reliability of the prediction data and calculated Kappa coefficient, overall accuracy, and user's accuracy. ...
The rapid economic development and intense human activities have seriously restricted the sustainable development of ecology and the maintenance of ecosystem services. Ecological network can effectively connect fragmented habitats and is an important way to couple landscape structure, ecological process, and function. This study proposes a multimodel coupling framework to explore the ecological security status of Ningxia Hui Autonomous Region (NHAR) under different development scenarios from the perspective of ecological networks. The conclusions are as follows: (1) From 2000 to 2030, grassland and arable land were the main land types of NHAR. Grassland is the main expansion land type under the ecological land protection (ELP) scenario, while construction land is the main expansion land type in two other scenarios. (2) The main gather and change of the ecological sources occurred in the central region, and the ecological expansion should develop from the middle to the south. (3) The average area of ecological sources under BAU and RED scenarios is smaller than that under ELP scenario, and more ecological corridors are needed to connect. (4) The centrality of the ecological sources under the BAU scenario is generally high, but the ecological sources under ELP and RED scenarios have undergone spatial migration. In addition, the urbanization trend of NHAR is different under different scenarios, and more attention should be paid to the maintenance and protection of ecological networks in typical areas. This study can provide important reference for NHAR’s ecological space planning and ecological protection policy formulation.
... Land use dynamics can quantify the change rates of various LULC types within a certain timespan and plays an active role in projecting future change trend in LULC (Hou et al., 2022). The single/synthetic land use dynamic degree (D si /D sy ) was employed to describe the change rate of LULC in different periods (Zhang et al., 2009). ...
