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Study region: The Amur River Basin (ARB), a transboundary river basin comprising four sub-regions: the Northeast Plain of China, Mongolian Plateau, Siberian Federal region and Far East Federal region. Study focus: This research presents a comprehensive analysis on the lake dynamics in both lake area and number across the entire ARB from 1999 to 2021, using multi-source remote sensing datasets. The driving mechanisms are analyzed, considering the gradients in climatic conditions and varying levels of anthropogenic activities in different sub-regions. New hydrological insights for the region: The study found that both the total area and number of lakes in ARB decreased before 2009, whereafter increased significantly until 2021. Specifically, lakes in the Mongolian Plateau shrank seriously (-46.6 km2 yr−1), while those in the Northeast Plain of China, Siberian Federal region, and Far Eastern Federal region all showed overall expanding trends. Lake changes in the entire ARB were predominantly influenced by precipitation (R2:0.54,) and soil moisture (R2:0.55). Increasing irrigation and grazing were the main forces in the Mongolian Plateau, while hydro-climatic factors played more important roles in Northeast Plain, Siberian Federal region, and Far Eastern Federal region. This study provides a comprehensive cross-regional analysis of lake area changes and driving forces, which is of great value in ensuring lake ecological safety in ARB and other regions with densely distributed lakes.

The existing management frameworks were not precisely tailored to effectively reduce nutrient pollution in specific watersheds, where both nonpoint source (NPS) and point source (PS) discharges necessitate simultaneous treatment. Such situations are prevalent in developing countries, where the spatial distribution of PS is often characterized by fragmentation and overlapping with NPS. To bridge the gap, an Integrated Watershed Water Quality Management (IWWQM) framework was developed in this study. The critical areas (CAs) for priority governance of PS and NPS were identified separately, thus generating candidate plans at different size scales of CAs. Optimal plans were further obtained under various future climate scenarios, designed to address the impact of future climate uncertainties on water quality. Three robustness metrics, namely, expected value (EV), second worst case (SW) and regret index (RI), were introduced to examine the effectiveness of the optimal plans. On the premise of meeting the water quality thresholds, the three packed metrics and the total cost were used to select representative plans indicative of both climate-robustness and cost-effectiveness. This framework was implemented and validated in a typical watershed in China. The case study revealed that out of 1,149 candidate plans, a total of 19 plans were determined to meet water quality thresholds. Among these plans, 2 were ultimately recommended due to their relatively low-cost budget (

The estuary and its nearshore waters are of great ecological value. As one of the important water quality evaluation indicators, suspended particulate matter (SPM) is also relevant to the ecology of the estuary. However, the current research on SPM of surface water is quite well established. It is urgent to study the vertical distribution of SPM due to its significance for further studies of biogeochemical processes in the water column. This study developed remote estimation model of SPM for five different depth water layers in the Liaohe estuary based on in situ Rrs(λ) data and support vector regression (SVR) methods, and the model performed well (RMSE = 24.32 mg/L, MAPE = 26.11%, MAE = 18.37 mg/L, N = 115). After applying the model to 10,090 MODIS images collected in 2000–2021, results show that: (1) Vertical distribution of SPM concentration in the Liaohe estuary could be divided into three types: top-down decreasing type (Type Ⅰ), uniform type (Type Ⅱ) and top-down increasing type (Type III). (2) Seasonal distribution of SPM concentration in the Liaohe estuary is: medium in spring (76.30 ± 10.34 mg/L), low in summer (61.80 ± 6.98 mg/L) and high in autumn (82.28 ± 19.12 mg/L). There is a significant decreasing trend of SPM from 2000 to 2021 (p

The water footprint is a widely used indicator for water resource assessment, among which quantifying the grey water footprint (GWF) is essential in assessing the basin-scale water resources. Due to the lack of accurate data and parameters in calculating the GWF by statistical methods, a few studies have used the Soil and Water Assessment Tool (SWAT) model to assess the GWF. This study aims to explore the applicability of statistical and modeling methods for GWF accounting of a basin. The Fuzhou River basin (FRB) in Dalian, China, was used as a case study; the GWF in 2015 was assessed using the statistical and modeling methods, respectively. Results showed that the GWF calculated by the statistical method and the SWAT model were 4158.44 million m3 and 4494.62 million m3, respectively. Both spatially exhibited a rise from the upstream to the downstream reaches, and a decrease from the mainstream river to both sides of the river. In comparison, the modeling method lowered the difficulties of data acquisition and generated a more refined spatial distribution of GWF; whereas the statistical method can provide a more comprehensive analysis of the GWF from each pollution source. Furthermore, the water pollution level of FRB was calculated as 11.88 and 12.84, respectively, by the statistical method and the SWAT model, suggesting that the current water resources in the FRB are seriously polluted, which may threaten the sustainable use of local water resources.

Study Region: The Upper Taoer River Watershed (UTRW), Northeastern China Study Focus: This study presents a comprehensive hydrometeorological evaluation of six satellite precipitation products (SPPs) over a sparsely gauged semi-arid watershed, including the adjusted and unadjusted versions of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM products (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERISIANN) series. The effectiveness of monthly bias correction in improving SPPs estimation accuracy and in enhancing their feasibility in hydrological applications was also analyzed. New hydrological insights for the region: The SPPs, especially bias-corrected SPPs, adequately provide complementary precipitation information for UTRW where ground measurement is insufficient. The adjusted SPPs after bias correction obtained a higher Nash-Sutcliffe Efficiency (NSE) than gauge data when driving streamflow simulation. The unadjusted SPPs hydrological applicability was significantly enhanced after bias correction, with NSE 0.34 before and after correction, respectively. Performance of the TMPA series improved the most through bias correction, making it the preferred choice for daily and monthly simulations. Additionally, SPPs were more applicable in wet and normal years and require improvement in dry years. This study provides valuable references for identifying better alternative precipitation sources for local water resource management and exploring an effective SPP utilization approach in practical applications.