Inland water bodies in China: Features discovered in the long-term satellite data

A high-resolution dataset of water bodies distribution over the Tibetan Plateau

Water body (WB) extraction is the basic work of water resources management. Tibetan Plateau is one of the largest alpine lake systems in the world. However, research on the characteristics of water bodies (WBs) is mainly focused on large and medium WBs due to spatial resolution. This research presents a dataset containing a 2-m resolution map of WBs in 2020 based on Gaofen-1 data, and morphometric and landscape indices of WBs across the Tibetan Plateau. The Swin-UNet model is well performed with overall accuracy at 98%. The total area of WBs is 56354.6 km2 across Tibetan Plateau in 2020. The abundance compared with that from size-abundance relationship indicate WBs in the Tibetan Plateau conformed to the classic power scaling law. We evaluate the influence of spatial-resolution in WB extraction, which shows the dataset could be valuable to fill the gap of existing WBs map, especially for small waters. The dataset is valuable for revealing the spatial patterns of WBs, and understanding the impacts of climate change on water resources in Plateau.

An optical mechanism-based deep learning approach for deriving water trophic state of China's lakes from Landsat images

Widespread eutrophication has been considered as the most serious environment problems in the world. Given the critical roles of lakes in human society and serious negative effects of water eutrophication on lake ecosystems, it is thus fundamentally important to monitor and assess water trophic status of lakes. However, a reliable model for accurately estimating the trophic state index (TSI) of lakes across a large-scale region is still lacking due to their high complexity. Here, we proposed an optical mechanism-based deep learning approach to remotely estimate TSI of lakes based on Landsat images. The approach consists of two steps: (1) determining the optical indicators of TSI and modeling the relationship between them, and (2) developing an approach for remotely deriving the determined optical indicator from Landsat images. With a large number of in situ datasets measured from lakes (2804 samples from 88 lakes) across China with various optical properties, we trained and validated three machine learning methods including deep neural network (DNN), k-nearest neighbors (KNN) and random forest (RF) to model TSI with the optical indicators and TSI and derive the determined optical indicator from Landsat images. The results showed that (1) the total absorption coefficients of optically active constituents at 440 nm (at-w(440)) performs best in characterizing TSI, and (2) DNN outperforms other models in the inversion of both TSI and at-w(440). Overall, our proposed optical mechanism-based deep learning approach demonstrated a robust and satisfactory performance in assessing TSI using Landsat images (root mean squared error (RMSE) = 5.95, mean absolute error (MAE) = 4.81). This highlights its merit as a nationally-adopted method in lake water TSI estimation, enabling the convenience of the acquisition of water eutrophic information in large scale, thereby assisting us in managing lake ecology. Therefore, we assessed water TSI of 961 lakes (>10 km2) across China using the proposed approach. The resulting at-w(440) and TSI ranged from 0.01 m-1 to 31.42 m-1 and from 6 to 96, respectively. Of all these studied lakes, 96 lakes (11.40 %) were oligotrophic, 338 lakes were mesotrophic (40.14 %), 360 lakes were eutrophic (42.76 %), and 48 were hypertrophic (5.70 %) in 2020.

Forecasting environmental water availability of lakes using temporal fusion transformer: case studies of China's two largest freshwater lakes

Preserving lacustrine ecosystems is vital for sustainable watershed development, and forecasting the environmental water availability of lakes would support policymakers in developing sound management strategies. This study proposed a methodology that merges the lake water level prediction and environmental water availability evaluation. The temporal fusion transformer (TFT) model forecasted the lake water levels for the next 7 days by inputting the streamflow and lake water level data for the past 30 days. The environmental water availability was assessed by comparing the forecasted lake water levels with the environmental water requirements, resulting in adequate, regular, scarce, and severely scarce environmental water availability. The methodology was tested in two case studies: Poyang Lake and Dongting Lake, the two largest freshwater lakes in the Yangtze River Basin, China. The TFT model performed well in forecasting the lake water levels, as shown by the high coefficient of determination and finite root mean square error. The coefficients of determination exceeded 0.98 during the model training, validation, and test for both Poyang Lake and Dongting Lake, and the root mean square errors ranged from 0.06 to 0.46 m. The accurate prediction of lake water level promoted the precise forecasting of the environmental water availability with the high Kappa coefficient exceeding 0.90. Results indicated the rationality and effectiveness of integrating the lake water level prediction and environmental water availability evaluation. Future research includes the applicability of the TFT model to other lakes worldwide to test the proposed approach and investigate strategies to cope with environmental water scarcity.

Satellites reveal hotspots of global river extent change

Rivers are among the most diverse, dynamic, and productive ecosystems on Earth. River flow regimes are constantly changing, but characterizing and understanding such changes have been challenging from a long-term and global perspective. By analyzing water extent variations observed from four-decade Landsat imagery, we here provide a global attribution of the recent changes in river regime to morphological dynamics (e.g., channel shifting and anabranching), expansion induced by new dams, and hydrological signals of widening and narrowing. Morphological dynamics prevailed in ~20% of the global river area. Booming reservoir constructions, mostly skewed in Asia and South America, contributed to ~32% of the river widening. The remaining hydrological signals were characterized by contrasting hotspots, including prominent river widening in alpine and pan-Arctic regions and narrowing in the arid/semi-arid continental interiors, driven by varying trends in climate forcing, cryospheric response to warming, and human water management. Our findings suggest that the recent river extent dynamics diverge based on hydroclimate and socio-economic conditions, and besides reflecting ongoing morphodynamical processes, river extent changes show close connections with external forcings, including climate change and anthropogenic interference.

Combining historical maps and landsat images to delineate the centennial-scale changes of lake wetlands in Taihu Lake Basin, China

Lake wetlands (LWs) are essential components of the ecosystem and play an irreplaceable role in flood regulation, carbon fixation, and biodiversity maintenance. Continuous monitoring of LWs' change is necessary in the context of increased human disturbance and climate change, particularly in Taihu Lake Basin, China, an area exposed to early human exploitation. Yet, long-time series of LWs detection in this region is still unavailable due to the data limitation. To quantify the spatiotemporal dynamics of LWs and the associated driving forces, we combined 236 historical topographic maps and thousands of Landsat satellite images from the 1910s to 2021 to delineate the centennial-scale changes of lake wetlands for the first time in this region. We also applied land use transitions and statistical analyses to quantitively explore the climatic and anthropogenic factors behind LWs variations. Our results document a dramatic decline in the area and number of LWs in the Taihu Lake Basin over the last century and a shift in the 2000s: Taihu Lake Basin has seen a total of 89.15% loss in lake littoral wetlands and a decrease of 14.5% in the whole lake wetlands area, with a net reduction of 68 (from 156 in the 1910s to 88 in the 2021) lakes. This decrease has been especially predominant during the 1910s-2000s, because of the policy initiatives for reclamation and aquacultural industries. The area and number of LWs have gradually been recovered since the 2000s as the country strengthened concern on the ecological restoration and sustainable development. The statistical results suggested that human activities played a dominant role in the LWs changes, with GDP and population explained 80.74% of the changes, coupled with climatic contribution of only around 20%. This long-term investigation will provide baseline information for future lake wetlands monitoring. Our findings could also provide a guidance for decision makers regarding water resources management, environmental protection and land-use planning in urban areas.

High-resolution circa-2020 map of urban lakes in China

Urban lakes provide important ecological services to local communities, such as flood mitigation, biodiversity, and recreation. With rapid urbanization, urban lakes are significantly affected by socio-economic development and urgently need attention. Yet there is still a lack of datasets that include tiny urban lakes on a global or national scale. This study aims to produce a high-resolution circa-2020 map of urban lakes (≥0.001 km²) in China. The 10-m-resolution Sentinel-2 imagery and a simple but robust water extraction method was used to generate waterbodies. The accuracy of this national-scale dataset was evaluated by comparing it with manually sampled urban units, with the average accuracy of 81.85% in area and 93.35% in count. The database totally inventories 1.11 × 10⁶ urban lakes in China, with a net area of ~2.13 × 10³ km². Overall, the spatial distribution of urban lakes in China showed strongly heterogeneous characteristics. This dataset will enhance our understanding of the distribution pattern of China's urban lakes and contribute to better ecological and environmental management as well as sustainable urban development planning.

Long-term detection and spatiotemporal variation analysis of open-surface water bodies in the Yellow River Basin from 1986 to 2020

Accurately investigating long-term information about open-surface water bodies can contribute to water resource protection and management. However, due to the limits of big-data calculations for remote sensing, there has been no specific study on the long-term changes in the water bodies in the Yellow River Basin. Thus, in this study, we developed a new combined extraction rule to build an entire annual-scale open-surface water body dataset for 1986-2020 with excellent effectiveness in eliminating the interference of shadows in the Yellow River Basin using all of the available Landsat images. For the first time, the spatial distribution, change trends, conversion processes, and the heterogeneity of the surface water bodies in the Yellow River Basin were analyzed comprehensively to the best of our knowledge. The extraction results had an overall accuracy of 99.70 % and a kappa coefficient of 0.90, which were validated using 34,073 verification points selected on high-resolution Google Earth images and random Landsat images. The total area of water bodies initially decreased (1986-2000) and then increased (2001-2020); however, only the size of the permanent water bodies increased in most areas, while the size of most of the seasonal water bodies decreased. In regions with human-made water bodies, the non-water areas were substantially converted to seasonal and permanent water bodies; however, in areas with natural water bodies, many permanent and seasonal water bodies were gradually converted to non-water areas. Thus, most of the increases in the water bodies occurred in the form of artificial lakes and reservoirs, while most of the decreases in the water body area occurred in natural wetlands and lakes. The areas of both the permanent and seasonal water bodies were positively correlated with precipitation, but only the area of the seasonal water bodies was negatively correlated with temperature.

Artificial and Natural Water Bodies Change in China, 2000–2020

Artificial and natural water bodies, such as reservoirs, ponds, rivers and lakes, are important components of water-related ecosystems; they are also important indicators of the impact of human activities and climate change on surface water resources. However, due to the global and regional lack of artificial and natural water bodies data sets, understanding of the changes in water-related ecosystems under the dual impact of human activities and climate change is limited and scientific and effective protection and restoration actions are restricted. In this paper, artificial and natural water bodies data sets for China are developed for the years 2000, 2005, 2010, 2015 and 2020 based on satellite remote sensing surface water and artificial water body location sample data sets. The characteristics and causes of the temporal and spatial distributions of the artificial and natural water bodies are also analyzed. The results revealed that the area of artificial and natural water bodies in China shows an overall increasing trend, with obvious differences in spatial distribution during the last 20 years, and that the fluctuation range of artificial water bodies is smaller than that of natural water bodies. This research is critical for understanding the composition and long-term changes in China’s surface water system and for supporting and formulating scientific and rational strategies for water-related ecosystem protection and restoration.

Amount of and proximity to blue spaces and general health among older Chinese adults in private and public housing: A national population study

A growing body of research indicates that exposure to outdoor blue spaces is associated with better physical and mental health. However, few studies have explored the associations between different blue space indicators (e.g., amount of and proximity to freshwater and seawater) and general health. Moreover, research has rarely attempted to address the residential selection bias associated with the salutogenic effect of access to blue spaces. Therefore, this study explores the associations between the amount (percentage of blue space within a 1 km circular buffer) of and proximity (Euclidean distance to the edge of the nearest blue space) to blue space and older adults' general health across the entire country of China using the micro-data sample of one-percent national population sample survey in 2015. It adds to the existing literature by taking into account the neighbourhood selection mechanism for different housing tenures and examining the salutogenic effect of blue spaces separately for public housing residents and private housing residents. The results indicated that greater neighbourhood seawater coverage and living near a coastline were associated with better general health among older adults in both private and public housing, while the percentage of freshwater blue spaces within neighbourhoods and the distance to freshwater blue spaces were associated with better general health among private housing residents only. The blue spaces-general health associations were stronger among urban participants, participants in deprived neighbourhoods, males, participants aged under 80 years, and low- and medium-educated participants. Our findings indicated that living near the coast was beneficial to older adults' health, and residential selection bias confounded the association between freshwater blue spaces and health.

An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China

Earth Observation (EO) data is a critical information source for mapping and monitoring water resources over large inaccessible regions where hydrological in-situ networks are sparse. In this paper, we present a simple yet robust method for fusing optical and Synthetic Aperture Radar (SAR) data for mapping surface water dynamics over mainland China. This method uses a multivariate logistic regression model to estimate monthly surface water extent over a four-year period (2017 to 2020) from the combined usages of Sentinel-1, Sentinel-2 and Landsat-8 imagery. Multi-seasonal high-resolution images from the Chinese Gaofen satellites are used as a reference for an independent validation showing a high degree of agreement (overall accuracy 94%) across a diversity of climatic and physiographic regions demonstrating potential scalability beyond China. Through inter-comparison with similar global scale products, this paper further shows how this new mapping technique provides improved spatio-temporal characterization of inland water bodies, and for better capturing smaller water bodies (< 0.81 ha in size). The relevance of the results is discussed, and we find this new enhanced monitoring approach has the potential to advance the use of Earth observation for water resource management, planning and reporting.

Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

Long-Term Dynamics of Different Surface Water Body Types and Their Possible Driving Factors in China

Various surface water bodies, such as rivers, lakes and reservoirs, provide water and essential services to human society. However, the long-term spatiotemporal dynamics of different types of surface water bodies and their possible driving factors over large areas remain very limited. Here, we used unprecedented surface water data layers derived from all available Landsat images and further developed two databases on China’s lakes and reservoirs larger than 1 km2 to document and understand the characteristics of changes in different water body types during 2000 to 2019 in China. Our results show that China is dominated by permanent water bodies. The areas of permanent and seasonal water bodies in China increased by 16,631.02 km2 (16.72%) and 16,994.95 km2 (25.14%), respectively, between 2000 and 2019, with permanent and seasonal water bodies exhibiting divergent spatial variations. Lakes and artificial reservoirs larger than 1 km2, which collectively represent a significant proportion of the permanent water bodies in China, displayed net increases of 6884.52 km2 (10.71%) and 4075.13 km2 (36.10%), respectively, from 2000 to 2019; these increases accounted for 41.40% and 24.50%, respectively, of the total permanent water body increment. The expanding lakes were mainly distributed on the Tibetan Plateau, whereas the rapidly growing reservoirs were mainly located on the Northeast Plain and Eastern Plain. Statistical analyses indicated that artificial reservoirs were an important factor controlling both permanent and seasonal water body changes in most of provinces. Climate factors, such as precipitation and temperature, were the main influencing factors affecting the changes in different water bodies in the sparsely populated Tibetan Plateau.

The Decrease in Lake Numbers and Areas in Central Asia Investigated Using a Landsat-Derived Water Dataset

Although Central Asia has a strong continental climate with a constant moisture deficit and low relative humidity, it is covered by thousands of lakes that are critical to the sustainability of ecosystems and human welfare in the region. Vulnerability to climate change and anthropogenic activities have contributed to dramatic inter-annual and seasonal changes of the lakes. In this study, we explored the high spatio–temporal dynamics of the lakes of Central Asia using the terraPulse™ monthly Landsat-derived surface water extent dataset from 2000 to 2015 and the HydroLAKES dataset. The results identified 9493 lakes and significant linear decreasing trends were identified for both the number (rate: −85 lakes/year, R2: 0.69) and area (rate: −1314.1 km2/year, R2: 0.84) of the lakes in Central Asia between 2000 and 2015. The decrease rate in lake area accounted for 1.41% of the total lake area. About 75% of the investigated lakes (7142 lakes), mainly located in the Kazakh steppe (especially in the north) and the Badghyz and Karabil semi-desert terrestrial ecological zones, experienced a decrease in the water area. Lakes with increasing water area were mainly distributed in the Northern Tibetan Plateau–Kunlun Mountains alpine desert and Qaidam Basin semi-desert zones in the east-south corner of Central Asia. The possible driving factors of lake decreases in Central Asia were explored for the Aral Sea and Tengiz Lake on yearly and monthly time scales. The Aral Sea showed the greatest decrease in the summer months because of increased evaporation and massive irrigation, while the largest decrease for Tengiz Lake was observed in early spring and was linked to decreasing snowmelt.

Gainers and losers of surface and terrestrial water resources in China during 1989-2016

Data and knowledge of the spatial-temporal dynamics of surface water area (SWA) and terrestrial water storage (TWS) in China are critical for sustainable management of water resources but remain very limited. Here we report annual maps of surface water bodies in China during 1989-2016 at 30m spatial resolution. We find that SWA decreases in water-poor northern China but increases in water-rich southern China during 1989-2016. Our results also reveal the spatial-temporal divergence and consistency between TWS and SWA during 2002-2016. In North China, extensive and continued losses of TWS, together with small to moderate changes of SWA, indicate long-term water stress in the region. Approximately 569 million people live in those areas with deceasing SWA or TWS trends in 2015. Our data set and the findings from this study could be used to support the government and the public to address increasing challenges of water resources and security in China.