Situated in the hinterland of Eurasia, Central Asia is characterized by an arid climate and sparse rainfall. The uneven spatial distribution of water and land resources across the region has pressured economic and social development. An accurate understanding of Central Asia's water resources carrying capacity (WRCC) is vital for enhancing the sustainability of water resources utilization and guiding regional economic and social activities. This study aims to facilitate the sustainability of water resources utilization by evaluating the region's WRCC from the viewpoints of economic and technological conditions and social welfare. A concise yet effective model with relatively fewer parameters was established by adopting water resources data from the Food and Agriculture Organization (FAO) and socioeconomic data from the World Bank. The results indicated that the WRCC of all five Central Asian countries showed an increasing trend with improved water use efficiency from 1995 to 2020. Kazakhstan's WRCC was significantly higher than the other four countries, reaching 54.03 million people in 2020. The water resources carrying index (WRCI) of the five Central Asian countries varied considerably, with the actual population sizes of Turkmenistan and Uzbekistan highly overloaded. Although there has been a decrease in Central Asian countries’ WRCI between 1995 and 2020, water resources utilization problems in the region remain prominent. Based on the water resources carrying capacity evaluation system, to increase available water resources and improve production water use efficiency are key to address these issues. In light of this, this study offers practical and feasible solutions at the policy level: (1) The implementation of signed multilateral agreements on transboundary water resources allocation must proceed through joint governmental efforts. (2) Investments in advancing science and technology need to be increased to improve water use efficiency in irrigation systems. (3) The output of water-intensive crops should be reduced. (4) The industrial structure could be further optimized so that non-agricultural uses are the primary drivers of gross domestic product (GDP) growth.
Rural decline is a global issue accompanied by the regional imbalanced development and dysfunction in rural areas. Coordinated interaction among production, living, and ecological functions is essential for the sustainability of rural regional systems. Based on the framework of “element-structure-function”, an indicator system was constructed to explore the evolution characteristics and driving factors of rural regional functions in the farming-pastoral ecotone of northern China (FPENC) using the models of entropy-based TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), revised vertical and horizontal comparison, and GeoDetector. The results indicated a gradual synergy of rural production, living, and ecological functions during the period 2000-2020. Improvements were observed in production and living functions, and higher ecological function was found in Hebei, Inner Mongolia, Liaoning, and Shaanxi. However, conflicts between ecological function and production and living functions were evident in Shanxi, Gansu, and Ningxia. The spatial structure played a dominant role in determining rural production, living, and ecological functions, with ratios of 38%, 56%, and 84%, respectively. Land and industry emerged as the main driving factors influencing the evolution of rural regional functions. Notably, combined interactions of rural permanent population and primary industry output (0.73), grassland area and tertiary industry output (0.58), and forest area and tertiary industry output (0.72) were responsible for the changes observed in rural production, living, and ecological functions, respectively. The findings suggest that achieving coordinated development of rural regional functions can be accomplished by establishing differentiated rural sustainable development strategies that consider the coupling of population, land, and industry in FPENC.
Global extreme hydrological events pose considerable challenges to the sustainable development of human society and river ecology. Land use/cover change (LUCC) is a visible manifestation of human activity and has caused substantial alterations in extreme hydrological regimes across rivers worldwide. The Jinsha River lies upstream of the Yangtze River and its hydrological variability has had profound socioeconomic and environmental effects. In this study, we developed Hydrological Simulation Program-FORTRAN (HSPF) and land-use simulation models of the entire watershed to simulate the effects of LUCC on hydrological extremes and quantify the inter-relationships among them. The main land-use changes between 1995 and 2015 were those associated with cropland, forest land, and grassland. Between 2015 and 2030, it is estimated that the coverage of forest land, grassland, construction land, and unused land will increase by 0.64%, 0.18%, 69.38%, and 45.08%, respectively, whereas that of cropland, water bodies, and snow- and ice-covered areas will decline by 8.02%, 2.63%, and 0.89%, respectively. LUCC has had irregular effects on different hydrological regimes and has most severely altered stream flows. The responses of hydrological extremes to historical land-use change were characterized by spatial variation. Extreme low flows increased by 0.54%-0.59% whereas extreme high flows increased by 0%-0.08% at the lowest outlet. Responses to future land-use change will be amplified by a 0.72%-0.90% reduction in extreme low flows and a 0.08%-0.12% increase in extreme high flows. The hedging effect caused by irregular changes in tributary stream flow was found to alleviate the observed flow in mainstream rivers caused by land-use change. The extreme hydrological regimes were affected mainly by the net swap area transferred from ice and snow area to forest (NSAIF) and thereafter to cultivated land (NSAIC). Extreme low flows were found to be positively correlated with NSAIF and NSAIC, whereas extreme high flows were positively correlated with NSAIC and negatively correlated with NSAIF.
The mass elevation effect (MEE) is a thermal effect, in which heating produced by long wave radiation on a mountain surface generates atmospheric uplift, which has a profound impact on the hydrothermal conditions and natural geographical processes in mountainous areas. Based on multi-source remote sensing data and field observations, a spatial downscaling inversion of temperature in the Tianshan Mountains in China was conducted, and the MEE was estimated and a spatio-temporal analysis was conducted. The GeoDetector model (GDM) and a geographically weighted regression (GWR) model were applied to explore the spatial and temporal heterogeneity of the study area. Four key results can be obtained. (1) The temperature pattern is complex and diverse, and the overall temperature presented a pattern of high in the south and east, but low in the north and west. There were clear zonal features of temperature that were negatively correlated with altitude, and the temperature difference between the internal and external areas of the mountains. (2) The warming effect of mountains was prominent, and the temperature at the same altitude increased in steps from west to east and north to south. Geomorphological units, such as large valleys and intermontane basins, weakened the latitudinal zonality and altitudinal dependence of temperature at the same altitude, with the warming effect of mountains in the southern Tianshan Mountains. (3) The dominant factors affecting the overall pattern of the MEE were topography and location, among which the difference between the internal and external areas of the mountains, and the absolute elevation played a prominent role. The interaction between factors had a greater influence on the spatial differentiation of mountain effects than single factors, and there was a strong interaction between terrain and climate, precipitation, the normalized difference vegetation index (NDVI), and other factors. (4) There was a spatial heterogeneity in the direction and intensity of the spatial variation of the MEE. Absolute elevation was significantly positively correlated with the change of MEE, while precipitation and the NDVI were dominated by negative feedback. In general, topography had the largest effect on the macroscopic control of MEE, and coupled with precipitation, the underlying surface, and other factors to form a unique mountain circulation system and climate characteristics, which in turn enhanced the spatial and temporal heterogeneity of the MEE. The results of this study will be useful in the further analysis of the causes of MEE and its ecological effects.
Karst depressions are common negative topographic landforms formed by the intense dissolution of soluble rocks and are widely developed in Guizhou province. In this work, an inventory of karst depressions in Guizhou was established, and a total of approximately 256,400 karst depressions were extracted and found to be spatially clustered based on multidistance spatial cluster analysis with Ripley's K function. The kernel density (KD) can transform the position data of the depressions into a smooth trend surface, and five different depression concentration areas were established based on the KD values. The results indicated that the karst depressions are clustered and developed in the south and west of Guizhou, while some areas in the southeast, east and north have poorly developed or no clustering. Additionally, the random forest (RF) model was used to rank the importance of factors affecting the distribution of karst depressions, and the results showed that the influence of lithology on the spatial distribution of karst depressions is absolutely dominant, followed by that of fault tectonics and hydrological conditions. The research results will contribute to the resource investigation of karst depressions and provide theoretical support for resource evaluation and sustainable utilization.
Accurately identifying the dominant factor of karst ecosystem services (ESs) is a prerequisite for the rocky desertification control. However, the explanatory power of environmental factors on the spatial distribution of ESs is affected by scaling, and the quantitative research on the scale effect still needs to be further strengthened. This study used the geographical detector to access the explanatory power of environmental factors on soil erosion and water yield at different spatial resolutions, and then explored its differences in three geomorphological-type areas. Results showed that slope and vegetation coverage were the dominant factors of soil erosion, and the interactive explanatory power between the two factors was stronger. Affected by the universality of topographic relief and landscape fragmentation in the study area, the explanatory power of slope and land use type on soil erosion was optimal at low resolution. Precipitation, elevation, and land use type were the dominant factors for the spatial heterogeneity of water yield, and the interaction between precipitation and land use type explained more than 95% of water yield. The spatial variability of elevation in different geomorphological-type areas affected its optimal explanatory power, specifically, in the terrace and hill-type areas, the spatial variability of elevation was weak, its explanatory power was optimal at high resolution. While in the mountain-type areas, the spatial variability of elevation was strong, and its explanatory power was optimal at low resolution. This study quantitatively identified the optimal explanatory power of ES variables through multi-scale analysis, which aims to provide a way and basis for accurate identification of the dominant factors of karst mountain ESs and zoning optimization.
There is a great uncertainty in generation and formation of non-point source (NPS) pollutants, which leads to difficulties in the investigation of monitoring and control. However, accurate calculation of these pollutant loads is closely correlated to control NPS pollutants in agriculture. In addition, the relationships between pollutant load and human activity and physiographic factor remain elusive. In this study, a modified model with the whole process of agricultural NPS pollutant migration was established by introducing factors including rainfall driving, terrain impact, runoff index, leaching index and landscape intercept index for the load calculation. Partial least squares path modeling was applied to explore the interactions between these factors. The simulation results indicated that the average total nitrogen (TN) load intensity was 0.57 t km-2 and the average total phosphorus (TP) load intensity was 0.01 t km-2 in Chengdu Plain. The critical effects identified in this study could provide useful guidance to NPS pollution control. These findings further our understanding of the NPS pollution control in agriculture and the formulation of sustainable preventive measures.
Effective soil particle size composition can more realistically reflect the particle size sorting process of erosion. To reveal the individual contributions of rainfall intensity and slope to splash erosion, and to distinguish the enrichment ratio of each size and the critical size in splash, loessial soil collected on the Loess Plateau in May 2019 was tested under different rainfall intensities (60, 84, 108, 132, 156 mm h-1) and slopes (0°, 5°, 10°, 15°, 20°). The results demonstrated that 99% of splash mass was concentrated in 0-0.4 m. Rainfall intensity was the major factor for splash according to the raindrop generation mode by rainfall simulator nozzles. The contributions of rainfall intensity to splash erosion were 82.72% and 93.24%, respectively in upslope and downslope direction. The mass percentages of effective clay and effective silt were positively correlated with rainfall intensity, while the mass percentages of effective very fine sand and effective fine sand were negatively correlated with rainfall intensity. Opposite to effective very fine sand, the mass percentages of effective clay significantly decreased with increasing distance. Rainfall intensity had significant effects on enrichment ratios, positively for effective clay and effective silt and negatively for effective very fine sand and effective fine sand. The critical effective particle size in splash for loessial soil was 50 μm.
Land subsidence is a geohazard phenomenon caused by the lowering of land elevation due to the compression of the sinking land soil body, thus creating an excessive constraint on the safe construction and sustainable development of cities. The use of accurate and efficient means for land subsidence prediction is of remarkable importance for preventing land subsidence and ensuring urban safety. Although the current time-series prediction method can accomplish relatively high accuracy, the predicted settlement points are independent of each other, and the existence of spatial dependence in the data itself is lost. In order to unlock this problem, a spatial convolutional long short-term memory neural network (ConvLSTM) based on the spatio-temporal prediction method for land subsidence is constructed. To this end, a cloud platform is employed to obtain a long time series deformation dataset from May 2017 to November 2021 in the understudied area. A convolutional structure to extract spatial features is utilized in the proposed model, and an LSTM structure is linked to the model for time-series prediction to achieve unified modeling of temporal and spatial correlation, thereby rationally predicting the land subsidence progress trend and distribution. The experimental results reveal that the prediction results of the ConvLSTM model are more accurate than those of the LSTM in about 62% of the understudied area, and the overall mean absolute error (MAE) is reduced by about 7%. The achieved results exhibit better prediction in the subsidence center region, and the spatial distribution characteristics of the subsidence data are effectively captured. The present prediction results are more consistent with the distribution of real subsidence and could provide more accurate and reasonable scientific references for subsidence prevention and control in the Beijing-Tianjin-Hebei region.
