A new study has provided the clearest picture yet of how much water is stored in China's lakes, finding that the country's natural lakes contain about 1,174 km3 of water, including around 335 km3 of freshwater, while eastern population centers have direct access to only a limited share of these natural freshwater resources.The study, published in the journal National Science Review, was conducted by a research team led by researchers from the Nanjing Institute of Geography and Limnology under the Chinese Academy of Sciences (NIGLAS).Using nationwide lake survey data collected over the past three decades and advanced spatial statistical methods, the researchers carried out the first systematic nationwide assessment of lake depth and freshwater–saline lake types across China, quantifying water storage in 2,713 natural lakes larger than 1 km2.To strengthen the reliability of the assessment, the team integrated data from nationwide lake surveys, scientific expeditions and water resource investigations. The researchers compiled high-precision depth and bathymetric measurements from hundreds of lakes across the country and combined them with spatial statistical modeling.The findings reveal a striking geographical imbalance in freshwater distribution. Nearly 65% of China's natural freshwater lake storage is concentrated in a small number of deep lakes in western inland basins, particularly on the Tibetan Plateau. This challenges the traditional perception that freshwater lakes are mainly concentrated in eastern China.By contrast, lakes in eastern plains are generally shallower and store far less water. About 81% of China's population lives in eastern regions, yet these areas have direct access to only around 23% of the country's natural freshwater lake storage. Water quality degradation in some eastern lakes further reduces the amount of safely usable freshwater.The study also noted that China's extensive reservoir network plays an important role in supporting water availability and easing regional disparities in natural freshwater resources.“This study provides the most comprehensive assessment to date of China's natural lake freshwater resources,” said Song Chunqiao, a researcher at NIGLAS and first author of the study.“By combining large-scale field investigations with advanced statistical methods, we were able to move beyond traditional surface observations and better understand how much water China's lakes actually store and where freshwater resources are concentrated,” Song said. “The findings provide an important scientific basis for water resource management and long-term water security planning.”

According to a study published in Science Advances on May 15, global rivers are undergoing widespread and sustained deoxygenation driven by climate warming, among which tropical rivers are the most vulnerable ecosystems, with an urgent need to combat oxygen loss.A research team led by Prof. SHI Kun from the Nanjing Institute of Geography and Limnology (NIGLAS) of the Chinese Academy of Sciences conducted this study, with Dr. GUAN Qi serving as the first author, in collaboration with a researcher from Tongji University.Oxygen is a fundamental foundation of river ecosystems, sustaining ecological health, supporting aquatic organisms, and regulating biogeochemical cycles. Its decline poses threats to river biodiversity.To investigate long-term trends in river dissolved oxygen, the team employed a machine-learning stacking algorithm to analyze data from 21,439 river reaches across the globe over a nearly 40-year period (1985–2023).Key findings from the study indicated that river ecosystems are losing oxygen at a rate of -0.045 mg L-1 decade-1, with 78.8% of the studied rivers experiencing deoxygenation.The most severe deoxygenation occurred in tropical rivers (between 20°S and 20°N), such as those in India. This contradicts prior expectations that high-latitude rivers, which face amplified climate warming, would be the primary deoxygenation hotspots. The study found that low oxygen levels coupled with faster deoxygenation make tropical rivers more vulnerable to hypoxia events.The researchers further quantified the impacts of flow regimes and dam impoundment on river deoxygenation. Results indicate that both low- and high-flow conditions can partially mitigate river deoxygenation, leading to an 18.6% lower deoxygenation rate in low-flow conditions compared with normal conditions. On the other hand, high-flow conditions led to a 7.0% lower deoxygenation rate compared with normal-flow conditions. Furthermore, dam impoundment also altered deoxygenation in its impoundment area: negative in shallow reservoirs but positive in deep reservoirs. That is to say, dam impoundment can accelerate deoxygenation in shallow reservoirs, but mitigate deoxygenation in deep reservoirs.Further analysis identified climate-driven declines in oxygen solubility as the major cause of river deoxygenation, accounting for 62.7% of the observed decline. Ecosystem metabolism—reflected by factors such as temperature, light, and flow—was responsible for 12% of the deoxygenation.Heatwave events were also specifically analyzed, with their impacts on river deoxygenation quantitatively assessed. The results show that heatwaves were responsible for 22.7% of global river deoxygenation, with an increase of 0.01 mg L-1 decade-1 in the deoxygenation rate, relative to conditions under average climatological temperatures.These findings underscore the negative consequences of climate warming on lotic ecosystems and identify tropical rivers as the ecosystems in greatest need of effective action and mitigation strategies to combat deoxygenation crises. The study provides a systematic baseline for policymakers in formulating measures to mitigate river deoxygenation worldwide.

For over 40 years, the Landsat satellite series has acted as Earth's “photographer,” archiving a massive library of global surface imagery. Recently, with the rise of cloud-based platforms like Google Earth Engine (GEE), scientists have been able to process these valuable imagery archives to assess the health of rivers and lakes worldwide.However, the accuracy of these assessments has long been compromised by a hidden “visual error”. The standard Landsat Surface Reflectance products typically used in GEE rely on atmospheric correction algorithms developed for land surfaces. When applied to water with very low water-leaving signals, these algorithms often fail, leading to substantial misestimation of water quality.A research team led by Prof. DUAN Hongtao from the Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences (NIGLAS), with doctoral student YANG Chen as the first author and Dr. SHEN Ming and Prof. DUAN Hongtao as co-corresponding authors, has successfully “corrected” this error on a global scale. By analyzing over 1,000 sets of global field measurements, the researchers identified the root causes of these inaccuracies and proposed an optimized GEE-based solution. The findings were published in the ISPRS Journal of Photogrammetry and Remote Sensing.Through global comparisons, the researchers pointed two critical issues with Landsat surface reflectance data over inland waters.Firstly, when water bodies are near bright objects like clouds or snow, land-based “aerosol correction” algorithms tend to over-compensate. This results in unrealistic “black patches” (artifacts) on the imagery. The hallmark of this error is a negative reflectance value at 443 nm. This issue affects an average of 13% of global inland water pixels. If ignored, relative error in retrieving key indicators, such as suspended particulate matter (SPM) concentration, can exceed 100%.Secondly, cross-sensor inconsistency causes data gaps. Analyzing decades of water quality requires stitching together data from Landsat-5, 7, 8, and 9. However, differences in sensor configuration and algorithms across generations create significant reflectance shifts over inland waters. These inconsistencies introduce significant uncertainties into long-term environmental trend analysis.After testing various optimized algorithms on GEE (including SIAC and MAIN), the researchers concluded that the MAIN algorithm, based on the “Short-Wave Infrared black pixel” assumption, is currently the best choice.By avoiding over-correction, MAIN removes artifacts in images, restoring true spatial patterns. Also, it provides a consistent framework across different satellites, improving temporal consistency by over 90% and enabling a seamless 40-year data record. Already deployed on GEE, the algorithm allows researchers to generate high-quality water reflectance products directly in the cloud without downloading massive datasets.For those who must continue using official products, the researchers suggest a simple preprocessing fix: Discard pixels where CA < 0. This low-cost step significantly reduces artifact contamination.This study presents the first global-scale systematic quantification of inherent errors in official Landsat products for inland water monitoring. By recommending the ready-to-use MAIN algorithm, the research provides a quality-control benchmark and an efficient tool for the scientific community. “Our study ensures that 40 years of Earth archives can be used reliably to monitor water resources and safeguard the global environment.” Said by Prof. Duan