Jiahua Wei

Lacustrine estuarine aquifers play an essential role in regulating solute transport to terminal lakes and maintaining downstream water supplies in endorheic basins. This research takes the Buha river watershed, the primary tributary to Lake Qinghai, as a case study to explore hydrochemical signatures, evolutionary characteristics, and water quality suitability of lacustrine estuarine groundwater using isotopic and modified hydrochemical tools. The results reveal that 37.50% of groundwater samples exceeded the WHO guideline value for Ca ²⁺ (75 mg/L) and 43.75% surpassed the geogenic nitrate threshold of 10 mg/L, with the HCO ₃ –Ca facies dominating the estuarine aquifer. Although overall water suitability was classified as excellent to good, this macroscopic assessment masked localized toxicological threats, particularly noncarcinogenic risks to infants from nitrate exposure. These exposure hazards are primarily linked to the rapid downward percolation of synthetic crop fertilizers and domestic wastewater. The discrepancy between overall water suitability and localized health risks is driven by the interaction of regional land use and hydrogeological architecture. In the upper reaches, concentrated agricultural and residential activities overlie highly permeable coarse alluvial deposits, causing localized contamination. Conversely, finer sediments in the lower reach promote natural lithological attenuation, which prevents downstream nitrate accumulation and protects the terminal Lake Qinghai ecosystem. These findings provide critical insights into groundwater quality dynamics in endorheic basins, highlighting the necessity of implementing targeted groundwater protection to safeguard domestic drinking water safety in vulnerable lacustrine estuarine plains.

Graph showing NICP research framework and main results in the study (including Study Region, InVEST Model, NICP Distribution, the relationship of temperature, LULC, NDVI and NICP). Focusing on the Natural Intercept Coefficient of Precipitation (NICP) is crucial for fully leveraging nature's capacity to regulate water resources and buffer extreme hydrological disasters. Based on hydro-meteorological reanalysis data from 1980 to 2020, the InVEST model was applied to simulate water retention in the upper Yellow River. The concept of NICP was introduced, and NICP was evaluated alongside changes in temperature, vegetation cover, and land use across five clustered regions in the upper Yellow River. The results indicate that natural retention can store about 10% of precipitation, making it a significant water resource, although it lacks interannual regulation capacity. From 1980 to 2020, NICP showed substantial spatial heterogeneity, with major retention areas concentrated upstream of Lanzhou. NICP was significantly negatively correlated with temperature (TMP) and positively correlated with the Normalized Difference Vegetation Index (NDVI), with increased vegetation identified as the primary factor driving changes in NICP. This may reflect increased soil porosity from root development, enhanced root zone water storage capacity, and improved deep infiltration pathways that replenish groundwater. These results improve understanding of vegetation-hydrology interactions.

Accurate three‐dimensional (3D) lake bathymetry reconstruction is critical for water resources assessment and hydrological modeling yet remains constrained by data scarcity and oversimplified geometric assumptions. To address these challenges, we propose the Geomorphologically informed deep learning (GIDL) framework for high‐resolution 3D lake bathymetry reconstruction. GIDL resolves the critical data bottleneck by constructing abundant virtual lake samples from terrestrial river valleys and depressions, which serve as geomorphological analogs to underwater terrain. The framework integrates geomorphology‐based mathematical priors, which encode terrain continuity, slope‐related depth variation, and basin‐shape regularities, to establish a preliminary basin skeleton. Trained on the geomorphologically consistent virtual samples, the network learns to identify and correct the systematic deviations of the mathematical approximation, thereby recovering high‐fidelity morphological details. To further refine performance, lakes are classified using a Rectanglarity and Ellipticity Index, enabling morphology‐specific learning. Validated in High Mountain Asia (HMA) using 18,753 training and 4,688 independent virtual samples for validation and testing, GIDL achieved reliable bathymetric reconstructions (mean NRMSE: 16.14%, R ² : 0.48), reducing errors by ∼35% compared to mathematical priors, and outperforming deep learning baselines. Extensive testing on 30 real HMA lakes demonstrated more than a 60% reduction in depth estimation errors compared to existing state‐of‐the‐art models. Furthermore, application to 605 lakes yielded robust Area‐Depth‐Volume relationships ( R ² = 0.75). By explicitly incorporating geomorphic knowledge into data‐driven modeling, GIDL offers a transferable methodological paradigm for reconstructing ungauged lake terrains, significantly advancing global water resource management and climate monitoring.

We present an experimental study on the evolution of microdroplets in acoustic traveling wavefields. The experimental setup comprises a 4 m-long tunnel with loudspeakers installed at both ends to generate plane traveling acoustic wavefields. A humidifier produces microdroplets that enter the tunnel and are propelled by a velocity regulator. A laser and a high-speed camera were employed to observe the motion of the microdroplets. Using Gaussian filtering and the Hough transform, we identified the microdroplets and their trajectories, and derived their diameters for further statistical analysis. Eight experimental sets were conducted at two acoustic frequencies, 200 and 500 Hz, under varying acoustic pressure conditions, and we analyzed microdroplets' evolution of those different sets from number density, probability density function (PDF), and cumulative mass distribution (CMD). The results revealed the effects of the acoustic wavefield on the condensation and coalescence of microdroplets. The condensation is primarily reflected in a slight rightward shift of the PDF within the first 30 s of the acoustic action, while the coalescence is evidenced by a decrease in the number density and an increased proportion of large microdroplets in both the PDF and the CMD. Specifically, acoustic pressure influences the rate of microdroplet evolution: The higher the acoustic pressure, the faster the evolution. Frequency influences the preferred coalescence pairs. The 200 Hz acoustic wavefields promote coalescence between large (d>20 μm) and small (d≤20 μm) microdroplets more easily. In comparison, the 500 Hz acoustic wavefields are more likely to facilitate coalescence between small microdroplets.