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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]

A detailed knowledge of structural evolution during mechanical alloying (MA) is of interest and critical for understanding of synthesis mechanism, optimization of milling process, and control of milling products. The structural evolution of the 2Si‐BN‐3C mixture with a molar ratio of Si:BN:C = 2:1:3 during MA was studied by investigating the changes of phases, morphologies, elemental distributions, microstructures, and chemical bond states using XRD, SEM‐EDS, TEM, XPS, and Raman. With the increases in milling time, the particle sizes of the milled 2Si‐BN‐3C powders first decrease to submicrometers (~1 hour), slightly increase afterward (~3 hours), and eventually decrease to nanoscales gradually reaching equilibrium (≥10 hours). Depending on the intrinsic crystal structures of themselves, h‐BN and graphite are almost amorphized after milling of 10 hours, while amorphization of c‐Si takes at least 20 hours. The same milling parameters can provide amorphous Si2BC3N powders, but incompletely amorphous (partially crystalline) SiC powders, which is mainly due to the dilution effect of B and N atoms on the atomic concentration of Si and C hindering the microdiffusion and subsequent mechanochemical reaction of Si and C. Crystallite refinement–induced amorphization is the major synthesis mechanism of amorphous Si2BC3N powders. This work would offer more insight into the MA synthesis of multiple component materials, especially Si‐based brittle systems. [ABSTRACT FROM AUTHOR]

Abstract: Amorphous Si2BC3N nanopowders (NPs) were heated at 1000‐1700°C temperatures for 30 minutes in 1 atm N2. The changes in phases, chemical bonds, and microstructures were investigated by XRD, XPS, NMR, TEM, and first‐principles calculation. Increases in heating temperatures lead to the nucleation and growth of SiC and BN(C) grains, along with partial transformation of SiC4 units from amorphous to β/α‐SiC, and collapse of B–C–N bonds. SiC nucleates prior to BN(C) at 1 atm, while it goes in the opposite order at high pressures (≥1 GPa). High pressures also shift the initial temperatures of crystallization of amorphous Si2BC3N NPs from 1400°C (1 atm) to 1150°C (5 GPa). [ABSTRACT FROM AUTHOR]