Lake Area Changes and Their Influence on Factors in Arid and Semi-Arid Regions along the Silk Road

Responses of vegetation cover to hydro-climatic variations in Bosten Lake Watershed, NW China

Amidst the backdrop of global climate change, it is imperative to comprehend the intricate connections among surface water, vegetation, and climatic shifts within watersheds, especially in fragile, arid ecosystems. However, these relationships across various timescales remain unclear. We employed the Ensemble Empirical Mode Decomposition (EEMD) method to analyze the multifaceted dynamics of surface water and vegetation in the Bosten Lake Watershed across multiple temporal scales. This analysis has shed light on how these elements interact with climate change, revealing significant insights. From March to October, approximately 14.9-16.8% of the areas with permanent water were susceptible to receding and drying up. Both the annual and monthly values of Bosten Lake's level and area exhibited a trend of initial decline followed by an increase, reaching their lowest point in 2013 (1,045.0 m and 906.6 km2, respectively). Approximately 7.7% of vegetated areas showed a significant increase in the Normalized Difference Vegetation Index (NDVI). NDVI volatility was observed in 23.4% of vegetated areas, primarily concentrated in the southern part of the study area and near Lake Bosten. Regarding the annual components (6 < T < 24 months), temperature, 3-month cumulative NDVI, and 3-month-leading precipitation exhibited the strongest correlation with changes in water level and surface area. For the interannual components (T≥ 24 months), NDVI, 3-month cumulative precipitation, and 3-month-leading temperature displayed the most robust correlation with alterations in water level and surface area. In both components, NDVI had a negative impact on Bosten Lake's water level and surface area, while temperature and precipitation exerted positive effects. Through comparative analysis, this study reveals the importance of temporal periodicity in developing adaptive strategies for achieving Sustainable Development Goals in dryland watersheds. This study introduces a robust methodology for dissecting trends within scale components of lake level and surface area and links these trends to climate variations and NDVI changes across different temporal scales. The inherent correlations uncovered in this research can serve as valuable guidance for future investigations into surface water dynamics in arid regions.

Capillary rise induced salt deterioration on ancient wall paintings at the Mogao Grottoes

Salt deterioration has been found to be a major threat to wall paintings at culture heritage sites in arid areas along the Silk Road. However, the routes of water migration that cause the efflorescence have not been identified, and consequently, effective preservation measures have not been developed. Our microanalysis, by interrogating 93,727 individual particles collected in a Mogao cave in Dunhuang, China, revealed that capillary rise of water in the earthen plasters drives the deterioration of wall paintings. The vertical distribution of chloride and sulfate particles in the salt efflorescence and their morphologies implied a migration of salts through capillary rise and subsequent crystal growth under environmental conditions exerts sufficient pressure to cause surface decay and loss. These results indicate that blocking the water capillary rise under the porous structures is likely the most effective route to prevent rapid deterioration of the ancient wall paintings. These salt transport and deterioration mechanisms in an arid environment, suggests that a wide range of management strategies and protective measures could be developed to effectively preserve heritage sites in arid regions, especially along the Silk Road.

Identifying Seasonal and Diurnal Variations and the Most Frequently Impacted Zone of Aerosols in the Aral Sea Region

With the desiccation of the Aral Sea, salt-alkali dust storms have increased in frequency and the surrounding environment has deteriorated. In order to increase our understanding of the characteristics and potential impact zone of atmospheric aerosols in the Aral Sea region, we evaluated seasonal and diurnal variation of aerosols and identified the zone most frequently impacted by aerosols from the Aral Sea region using CALIPSO data and the HYSPLIT model. The results showed that polluted dust and dust were the two most commonly observed aerosol subtypes in the Aral Sea region with the two accounting for over 75% of observed aerosols. Occurrence frequencies of polluted dust, clean continental, polluted continental/smoke, and elevated smoke showed obvious seasonal and diurnal variations, while occurrence frequency of dust only showed obvious seasonal variation. Vertically, the occurrence frequencies of all aerosol subtypes except dust showed significant diurnal variation at all levels. The thickness of polluted dust layers and dust layers exhibited same seasonal and diurnal variations with a value of more than 1.0 km year-round, and the layer thickness of clean continental and polluted continental/smoke shared the same seasonal and diurnal variation features. The zone most severely impacted by aerosols from the Aral Sea region, covering an area of approximately 2 million km2, was mainly distributed in the vicinity of the Aral Sea region, including western Kazakhstan, and most of Uzbekistan and Turkmenistan. The results provide direct support for positioning monitoring of aeolian dust deposition and human health protection in the Aral Sea region.

Effects of climate change and anthropogenic activities on lake environmental dynamics: A case study in Lake Bosten Catchment, NW China

Arid and semiarid regions account for ∼ 40% of the world's land area. Rivers and lakes in these regions provide sparse, but valuable, water resources for the fragile environments, and play a vital role in the development and sustainability of local societies. During the late 1980s, the climate of arid and semiarid northwest China dramatically changed from "warm-dry" to "warm-wet". Understanding how these environmental changes and anthropogenic activities affect water quantity and quality is critically important for protecting aquatic ecosystems and determining the best use of freshwater resources. Lake Bosten is the largest inland freshwater lake in NW China and has experienced inter-conversion between freshwater and brackish status. Herein, we explored the long-term water level and salinity trends in Lake Bosten from 1958 to 2019. During the past 62 years, the water level and salinity of Lake Bosten exhibited inverse "W-shaped" and "M-shaped" patterns, respectively. Partial least squares path modeling (PLS-PM) suggested that the decreasing water level and salinization during 1958-1986 were mainly caused by anthropogenic activities, while the variations in water level and salinity during 1987-2019 were mainly affected by climate change. The transformation of anthropogenic activities and climate change is beneficial for sustainable freshwater management in the Lake Bosten Catchment. Our findings highlight the benefit of monitoring aquatic environmental changes in arid and semi-arid regions over the long-term for the purpose of fostering a balance between socioeconomic development and ecological protection of the lake environment.

Remote Sensing of Surface Water Dynamics in the Context of Global Change—A Review

Inland surface water is often the most accessible freshwater source. As opposed to groundwater, surface water is replenished in a comparatively quick cycle, which makes this vital resource—if not overexploited—sustainable. From a global perspective, freshwater is plentiful. Still, depending on the region, surface water availability is severely limited. Additionally, climate change and human interventions act as large-scale drivers and cause dramatic changes in established surface water dynamics. Actions have to be taken to secure sustainable water availability and usage. This requires informed decision making based on reliable environmental data. Monitoring inland surface water dynamics is therefore more important than ever. Remote sensing is able to delineate surface water in a number of ways by using optical as well as active and passive microwave sensors. In this review, we look at the proceedings within this discipline by reviewing 233 scientific works. We provide an extensive overview of used sensors, the spatial and temporal resolution of studies, their thematic foci, and their spatial distribution. We observe that a wide array of available sensors and datasets, along with increasing computing capacities, have shaped the field over the last years. Multiple global analysis-ready products are available for investigating surface water area dynamics, but so far none offer high spatial and temporal resolution.

Research on Vegetation Coverage Dynamics and Prediction in the Taitema Lake Region

The Tarim River is the largest inland river in China, which plays a crucial role in maintaining regional ecological security and carbon cycle/dynamic. However, the “green corridor” in the Taitema Lake region at the lower reaches of the Tarim River has unclear environmental changes and future dynamics due to the influence of the ecological water conveyance. Hence, protecting the “green corridor” at the lower reaches of the Tarim River in China is strategically important not only ecologically but also socially and economically. In this paper, the temporal and spatial features of the fractional vegetation coverage (FVC) dynamics in the Taitema Lake region at the lower reaches of the Tarim River in 2000–2018 are analyzed and calculated using Landsat TM/OLI remote sensing images and MODIS data products. Additionally, the future trend of FVC dynamics in the study region are predicted using trend analysis and the pixel-based Hurst index. The results show that FVC in the Taitema Lake region exhibit a positive development after the implementation of ecological water conveyance. Specifically, from 2000 to 2018, the areas of low, medium, and high FVC expanded from 1.28 km2 to 179.87 km2, resulting in an increase of 140.52%. Spatially, the regions around the lake entrance channel of the Tarim River saw a significant increase in FVC of 9.71%. The middle part of the study region, accounting for only 1.96% of the area, displayed relatively high and high fluctuations in FVC. In the future, the regions at the middle part of the lake and around the lake entrance channel of the Tarim River, accounting for 11.33% of the area, will likely show an increasing trend in FVC. The regions with either extremely low or low FVC are predicted to decrease to 14.16% of the overall area. Because the positive effects of ecological water conveyance were more significant on FVC in the study region than the influences of either temperature or precipitation, ecological water conveyance should remain the primary means of ecological restoration for Taitema Lake.

Spatial Variability of Water Resources State of Regions around the “Belt and Road”

Water resource has become a key constraint for implementing the “Belt and Road” initiative which was raised by the Chinese government. Besides the study of spatial and temporal variability of precipitation, this study created a water hazard risk map along the “Belt and Road” zone through combined flood and drought data from 1985. Our results showed that South-Eastern Asia, southern China and eastern Southern Asia are areas with the most abundant precipitations, while floods in these areas are also the most serious. Northwest China, Western Asia, Northern Africa and Southern Asia are areas highly vulnerable to drought. Furthermore, the potential influence of flood and drought were also analyzed by associating with population distribution and corridor map. It reveals that China, South-Eastern Asia, Southern Asia, Western Asia and Northern Africa have the largest population number facing potential high water hazard risk. China–India–Burma Corridor and China–Indo-China Peninsula Corridor have the largest areas facing potential high water hazard risk.

Industrial Land Change in Chinese Silk Road Cities and Its Influence on Environments

The “Belt and Road” has developed from a Chinese initiative to an international consensus, and Silk Road cities are becoming a strategic step for its high-quality development. From the perspective of industrialization, the “Belt and Road” can be regarded as a “spillover” effect of the industrialization process in China. With the spatial shift of Chinese industries along the “Belt and Road” and their clustering in Silk Road cities, the development and change of industrial land in Silk Road cities has become a new area of concern for governments and scholars. In this paper, the driving mechanism of industrial land change in 129 cities along the Silk Road in China is empirically studied by the GeoDetector method. The findings include: first, the development and changes of industrial land in Silk Road cities are significantly spatially heterogeneous, and the “Belt and Road” reshapes the town system and economic geography along the route by virtue of the differentiated configuration and changes of industrial land, changing the social, political, landscape and spatial relations in cities on the line. Second, the driving forces of industrial land change in Silk Road cities under the influence of the “Belt and Road Initiative” are increasingly diversified and differentiated, with significant two-factor enhancement and non-linear enhancement interaction between two driving factors, and growing complexity of the driving mechanisms, requiring policy makers to design policies based on key factors, comprehensive factors and their interaction. Third, the environmental effect of industrial land change is highly complex. The industrial land quantity has a direct impact on the ecological state parameter and plays a decisive role in the quality of the ecological environment and its changes in Silk Road cities. However, changes in the industrial land affect the ecological state change indirectly, mainly interacting with it through the coupling of pollutant and carbon dioxide emissions, energy use, ecological planning and landscape design and policy interventions. Finally, this study provides a new framework and method for Silk Road scholars to analyze the spatial and temporal evolution characteristics of land use and coverage in cities along the “Belt and Road” and their influence mechanisms, and provides a basis for the government to make decisions on industrial land supply and layout planning and spatial governance policy design, which is of great theoretical significance and practical value.

Construction of the Long-Term Global Surface Water Extent Dataset Based on Water-NDVI Spatio-Temporal Parameter Set

Inland surface water is highly dynamic, seasonally and inter-annually, limiting the representativity of the water coverage information that is usually obtained at any single date. The long-term dynamic water extent products with high spatial and temporal resolution are particularly important to analyze the surface water change but unavailable up to now. In this paper, we construct a global water Normalized Difference Vegetation Index (NDVI) spatio-temporal parameter set based on the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI. Employing the Google Earth Engine, we construct a new Global Surface Water Extent Dataset (GSWED) with coverage from 2000 to 2018, having an eight-day temporal resolution and a spatial resolution of 250 m. The results show that: (1) the MODIS NDVI-based surface water mapping has better performance compared to other water extraction methods, such as the normalized difference water index, the modified normalized difference water index, and the OTSU (maximal between-cluster variance method). In addition, the water-NDVI spatio-temporal parameter set can be used to update surface water extent datasets after 2018 as soon as the MODIS data are updated. (2) We validated the GSWED using random water samples from the Global Surface Water (GSW) dataset and achieved an overall accuracy of 96% with a kappa coefficient of 0.9. The producer’s accuracy and user’s accuracy were 97% and 90%, respectively. The validated comparisons in four regions (Qinghai Lake, Selin Co Lake, Utah Lake, and Dead Sea) show a good consistency with a correlation value of above 0.9. (3) The maximum global water area reached 2.41 million km2 between 2000 and 2018, and the global water showed a decreasing trend with a significance of P = 0.0898. (4) Analysis of different types of water area change regions (Selin Co Lake, Urmia Lake, Aral Sea, Chiquita Lake, and Dongting Lake) showed that the GSWED can not only identify the seasonal changes of the surface water area and abrupt changes of hydrological events but also reflect the long-term trend of the water changes. In addition, GSWED has better performance in wetland areas and shallow areas. The GSWED can be used for regional studies and global studies of hydrology, biogeochemistry, and climate models.

Responses of Seasonal Indicators to Extreme Droughts in Southwest China

Significant impact of extreme droughts on human society and ecosystem has occurred in many places of the world, for example, Southwest China (SWC). Considerable research concentrated on analyzing causes and effects of droughts in SWC, but few studies have examined seasonal indicators, such as variations of surface water and vegetation phenology. With the ongoing satellite missions, more and more earth observation data become available to environmental studies. Exploring the responses of seasonal indicators from satellite data to drought is helpful for the future drought forecast and management. This study analyzed the seasonal responses of surface water and vegetation phenology to drought in SWC using the multi-source data including Seasonal Water Area (SWA), Permanent Water Area (PWA), Start of Season (SOS), End of Season (EOS), Length of Season (LOS), precipitation, temperature, solar radiation, evapotranspiration, the Palmer Drought Severity Index (PDSI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and data from water conservancy construction. The results showed that SWA and LOS effectively revealed the development and recovery of droughts. There were two obvious drought periods from 2000 to 2017. In the first period (from August 2003 to June 2007), SWA decreased by 11.81% and LOS shortened by 5 days. They reduced by 21.04% and 9 days respectively in the second period (from September 2009 to June 2014), which indicated that there are more severe droughts in the second period. The SOS during two drought periods delayed by 3~6 days in spring, while the EOS advanced 1~3 days in autumn. All of PDSI, SWA and LOS could reflect the period of droughts in SWC, but the LOS and PDSI were very sensitive to the meteorological events, such as precipitation and temperature, while the SWA performed a more stable reaction to drought and could be a good indicator for the drought periodicity. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. Our results improved the understanding of seasonal responses to extreme droughts in SWC, which will be helpful to the drought monitoring and mitigation for different seasons in this ecologically fragile region.

Analysis of the Spatiotemporal Change in Land Surface Temperature for a Long-Term Sequence in Africa (2003–2017)

It is very important to understand the temporal and spatial variations of land surface temperature (LST) in Africa to determine the effects of temperature on agricultural production. Although thermal infrared remote sensing technology can quickly obtain surface temperature information, it is greatly affected by clouds and rainfall. To obtain a complete and continuous dataset on the spatiotemporal variations in LST in Africa, a reconstruction model based on the moderate resolution imaging spectroradiometer (MODIS) LST time series and ground station data was built to refactor the LST dataset (2003–2017). The first step in the reconstruction model is to filter low-quality LST pixels contaminated by clouds and then fill the pixels using observation data from ground weather stations. Then, the missing pixels are interpolated using the inverse distance weighting (IDW) method. The evaluation shows that the accuracy between reconstructed LST and ground station data is high (root mean square er–ror (RMSE) = 0.84 °C, mean absolute error (MAE) = 0.75 °C and correlation coefficient (R) = 0.91). The spatiotemporal analysis of the LST indicates that the change in the annual average LST from 2003–2017 was weak and the warming trend in Africa was remarkably uneven. Geographically, “the warming is more pronounced in the north and the west than in the south and the east”. The most significant warming occurred near the equatorial region in South Africa (slope > 0.05, R > 0.61, p < 0.05) and the central (slope = 0.08, R = 0.89, p < 0.05) regions, and a nonsignificant decreasing trend occurred in Botswana. Additionally, the mid-north region (north of Chad, north of Niger and south of Algeria) became colder (slope > −0.07, R = 0.9, p < 0.05), with a nonsignificant trend. Seasonally, significant warming was more pronounced in winter, mostly in the west, especially in Mauritania (slope > 0.09, R > 0.9, p < 0.5). The response of the different types of surface to the surface temperature has shown variability at different times, which provides important information to understand the effects of temperature changes on crop yields, which is critical for the planning of agricultural farming systems in Africa.

Recent Lake Area Changes in Central Asia

Using Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m spatial resolution global water product data, Least Squares Method (LSM) was applied to analyze changes in the area of 14 lakes in Central Asia from 2001 to 2016. Interannual changes in lake area, along with seasonal change trends and influencing factors, were studied for the months of April, July and September. The results showed that the total lakes area differed according to interannual variations and was largest in April and smallest in September, measuring −684.9 km²/a, −870.6 km²/a and −827.5 km²/a for April, July and September, respectively. The change rates for the total area of alpine lakes during the same three months were 31.1 km²/a, 29.8 km²/a and 30.6 km²/a, respectively, while for lakes situated on plains, the change rates were −716.1 km²/a, −900.5 km²/a, and −858 km²/a, respectively. Overall, plains lakes showed a declining trend and alpine lakes showed an expanding trend, the latter likely due to the warmer and wetter climate. Furthermore, there was a high correlation (r = 0.92) between area changes rate of all alpine lakes and the lakes basin supply coefficient, although there was low correlation (r = 0.43) between area changes rate of all alpine lakes area and glacier area/lake area. This indicates that lakes recharge via precipitation may be greater than lakes recharge via glacier meltwater. The shrinking of area changes for all plains lakes in the study region was attributable to climate change and human activities.

Geospatial Monitoring of Land Surface Temperature Effects on Vegetation Dynamics in the Southeastern Region of Bangladesh from 2001 to 2016

Land surface temperature (LST) can significantly alter seasonal vegetation phenology which in turn affects the global and regional energy balance. These are the most important parameters of surface–atmosphere interactions and climate change. Methods for retrieving LSTs from satellite remote-sensing data are beneficial for modeling hydrological, ecological, agricultural and meteorological processes on the Earth’s surface. This paper assesses the geospatial patterns of LST using correlations of the seasonally integrated normalized difference vegetation index (SINDVI) in the southeastern region of Bangladesh from 2001 to 2016. Moderate Resolution Imaging Spectroradiometer (MODIS) time series datasets for LST and SINDVI were used for estimations in the study. From 2001 to 2016, the MODIS-based land surface temperature in the southeastern region of Bangladesh was found to have gently increased by 0.2 °C (R2 = 0.030), while the seasonally integrated normalized difference vegetation index also increased by 0.43 (R2 = 0.268). The interannual average LSTs mostly increased across the study areas, except in some coastal plain and tidal floodplain areas of the study. However, the SINDVI increased in the floodplain and coastal plain regions, except for in hilly areas. Physiographically, the study area is a combination of low lying alluvial floodplains, river basin wetlands, tidal floodplains, tertiary hills, terraced lands and coastal plains in nature. The hilly areas are mostly covered by dense forests, with the exception of agricultural areas. The impacts of increased LSTs were inversely correlated for the hilly areas and areas with forest coverage; LSTs were conversely correlated for the floodplain region, and tree cover outside of the forest and agricultural crops. This study will be very helpful for the protection and restoration of the natural environment.

Global Land Surface Temperature Influenced by Vegetation Cover and PM2.5 from 2001 to 2016

Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM2.5 increased by 0.17 K, 0.04, and 1.02 μg/m3 in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72°N and 48°S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM2.5 on LST was more complex. On the whole, LST increased with a small increase in PM2.5 concentrations but decreased with a marked increase in PM2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature.

Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy

Soil organic carbon (SOC) is an important soil property that has profound impact on soil quality and plant growth. With 140 soil samples collected from Ebinur Lake Wetland National Nature Reserve, Xinjiang Uyghur Autonomous Region of China, this research evaluated the feasibility of visible/near infrared (VIS/NIR) spectroscopy data (350-2,500 nm) and simulated EO-1 Hyperion data to estimate SOC in arid wetland regions. Three machine learning algorithms including Ant Colony Optimization-interval Partial Least Squares (ACO-iPLS), Recursive Feature Elimination-Support Vector Machine (RF-SVM), and Random Forest (RF) were employed to select spectral features and further estimate SOC. Results indicated that the feature wavelengths pertaining to SOC were mainly within the ranges of 745-910 nm and 1,911-2,254 nm. The combination of RF-SVM and first derivative pre-processing produced the highest estimation accuracy with the optimal values of Rt (correlation coefficient of testing set), RMSE t and RPD of 0.91, 0.27% and 2.41, respectively. The simulated EO-1 Hyperion data combined with Support Vector Machine (SVM) based recursive feature elimination algorithm produced the most accurate estimate of SOC content. For the testing set, Rt was 0.79, RMSE t was 0.19%, and RPD was 1.61. This practice provides an efficient, low-cost approach with potentially high accuracy to estimate SOC contents and hence supports better management and protection strategies for desert wetland ecosystems.