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There are an estimated 800,000 small reservoirs globally with a range of uses. Given the collective importance of these reservoirs to water resource management and wider society, it is essential that we can monitor and understand the hydrological dynamics of ungauged reservoirs, particularly in a changing climate. However, unlike large reservoirs, continuous and systematic hydrological observations of small reservoirs are often unavailable. In response, this study has developed a retrieval framework for water levels of small reservoirs using a deep learning algorithm and remotely sensed satellite data. Demonstrated at four reservoirs in California, satellite imagery from both Sentinel-1 and Sentinel-2 along with corresponding water level field measurements was collected. Post-processed images were fed into a water level inversion convolutional neural network model for water level inversion, while different combinations of these satellite images, sampling approaches for training/testing data, and attention modules were used to train the model and evaluated for accuracy. The results show that random sampling of training data coupled with Sentinel-2 satellite imagery was generally the most accurate initially. Performance is improved by incorporating a channel attention mechanism, with the average R2 increasing by 8.6% and the average RMSE and MAE decreasing by 15.5% and 36.4%, respectively. The proposed framework was further validated on three additional reservoirs in different regions. In conclusion, the retrieval framework proposed in this study provides a stable and accurate methodology for water level estimation of small reservoirs and can be a powerful tool for small reservoir monitoring over large spatial scales. [ABSTRACT FROM AUTHOR]

The eco-toxicities of different crystalline phases of TiO 2 -NPs are controversial, and the effects and mechanisms on activated sludge are unclear. Therefore, we assessed the acute-toxicities (8-h exposure) of P25, anatase, and rutile TiO 2 -NPs in activated sludge using flow cytometry under simulated sunlight (hereafter-sun) and evaluated the relationship between sludge dewatering and bacterial cell death modes using Pearson's correlation coefficients ( r). Additionally, the response of the microbial community structure was examined by high throughput sequencing. Bacterial survival and death were observed by confocal laser scanning microscopy. Toxicity indicators (e.g., lactate dehydrogenase (LDH) and reactive oxygen species (ROS)) were determined. Overall, TiO 2 -NPs toxicity was concentration-dependent and crystalline-phase-dependent. The responses of bacterial communities to crystalline phases were more obvious than that of dosage. P25-sun and anatase-sun caused necrosis-like cell death via strong photo-oxidation confirmed by 131%/123% (1 mg/L) and 301%/254% (50 mg/L) LDH released by the control, while rutile-sun induced apoptosis-like death via intracellular ROS production increased to 165% (1 mg/L) and 420% (50 mg/L) of the control. P25 and anatase NPs had higher protein and polysaccharide affinities, while rutile NPs exhibited stronger attachment onto phospholipids. TiO 2 -NPs-sun reduced activated sludge dewaterability. Specific resistance to filtration (SRF) showed the strongest positive correlation with tightly bound extracellular polymeric substances (EPS) and total soluble microbial byproducts ( r = 0.974, p < 0.01) and was closely related to EPS content and composition, especially the increased bound water (BW) content and sludge protein concentrations. High Pearson correlation coefficients were observed between early apoptotic cells and BW content ( r = 0.952, p < 0.01) resulting from massive polysaccharides and between necrotic (including late apoptotic) cells and SRF ( r = 0.959, p < 0.01) resulting from high protein and EPS concentrations. Thus, in response to TiO 2 -NPs, bacterial cell death modes differentially weakened sludge dewatering.