Experimental Study on Failure Model of Tailing Dam Overtopping under Heavy Rainfall

AbstractUnusual rainfall is the primary cause of the failure of the tailing dams, and overtopping is the most representative model of the tailing dam failure. The upstream tailing dam was selected as the research object to study the whole process of breach extension and the overtopping dam-failure mechanism under the full-scale rainfall condition. The results showed that the significant size grading phenomenon in the front, middle, and end of the tailing pond was obvious due to the flow separation effect, and its average particle diameter was D50. At different moments of rainfall, the height of the infiltration line at different positions of the dam body was different; at the rainfall of 3600 s, the height of the infiltration line lagged behind the height of the tailing pond, and this phenomenon from the tail of pond to the outside of the dam slope became more obvious. After the rainfall of 3600 s, the height of the infiltration line lagging behind the water level in the pond basically disappeared, and the rate of infiltration line rise kept pace with the rate of water level. The process of overtopping dam-failure experienced dam overtopping (gully erosion), formation of a multistepped small “scarp,” breach rapid expansion, formation of large “scarp,” and burst (fan-shaped formation). The width and depth of the breach showed a positive correlation, and the widening rate of the breach was 3 to 8 times of the deepening rate, especially in the middle of the dam break, widening behavior occupied the dominant factor. The shape of the dam body after failure was parabolic, and the dam body had obvious elevation changes. These results provide the theoretical guidance and engineering application value for improving the theory and early warning model of the upstream tailing dam

Revealing Physiochemical Factors and Zooplankton Influencing <i>Microcystis</i> Bloom Toxicity in a Large-Shallow Lake Using Bayesian Machine Learning

Toxic cyanobacterial blooms have become a severe global hazard to human and environmental health. Most studies have focused on the relationships between cyanobacterial composition and cyanotoxins production. Yet, little is known about the environmental conditions influencing the hazard of cyanotoxins. Here, we analysed a unique 22 sites dataset comprising monthly observations of water quality, cyanobacterial genera, zooplankton assemblages, and microcystins (MCs) quota and concentrations in a large-shallow lake. Missing values of MCs were imputed using a non-negative latent factor (NLF) analysis, and the results achieved a promising accuracy. Furthermore, we used the Bayesian additive regression tree (BART) to quantify how Microcystis bloom toxicity responds to relevant physicochemical characteristics and zooplankton assemblages. As expected, the BART model achieved better performance in Microcystis biomass and MCs concentration predictions than some comparative models, including random forest and multiple linear regression. The importance analysis via BART illustrated that the shade index was overall the best predictor of MCs concentrations, implying the predominant effects of light limitations on the MCs content of Microcystis. Variables of greatest significance to the toxicity of Microcystis also included pH and dissolved inorganic nitrogen. However, total phosphorus was found to be a strong predictor of the biomass of total Microcystis and toxic M. aeruginosa. Together with the partial dependence plot, results revealed the positive correlations between protozoa and Microcystis biomass. In contrast, copepods biomass may regulate the MC quota and concentrations. Overall, our observations arouse universal demands for machine-learning strategies to represent nonlinear relationships between harmful algal blooms and environmental covariates

Flood Inundation Extraction and its Impact on Ground Subsidence Using Sentinel-1 Data: A Case Study of the &#x201C;7.20&#x201D; Rainstorm Event in Henan Province, China

On July 20, 2021, the northern Henan Province was hit by a historically rare, exceptionally heavy rainstorm (&#x201C;7.20&#x201D; Rainstorm Event), accompanied by severe urban flooding, flash floods, landslides, and other multiple disasters, resulting in significant casualties and property losses. On the other hand, the long-term overexploitation of groundwater since the last century has led to severe ground subsidence in the same area. We apply the intensity information of Sentinel-1 SAR images to extract the large-scale flood inundation area and their phase information to measure the ground deformation. Since heavy precipitations can recharge groundwater, the relationship between flood inundation, groundwater level change, and ground deformation is analyzed. The results show that the flood inundation areas are mainly distributed along the major rivers due to river overflowing, while heavy precipitation led to the rise of groundwater levels, and there was a significant amount of subsidence mitigation and surface uplift across the region due to the groundwater recovery. This study demonstrates the contribution of radar remote sensing to analyze the mechanism of groundwater recharge and subsidence mitigation benefited by rainstorm events and provides a technical reference to similar circumstances

Peculiarities of the inverted repeats in the complete chloroplast genome of Strobilanthes bantonensis Lindau

Strobilanthes bantonensis Lindau belongs to the family Acanthaceae. It is an antiviral herb that can be used to prevent Influenza virus infections in the border areas between China and Vietnam. Local people call it ‘Purple Ban-lan-gen’ because its root is very similar to that of Strobilanthes cusia (Nees) Kuntze, which is called ‘Southern Ban-lan-gen’ and is listed in Chinese Pharmacopeia. The two species have been used interchangeably locally. However, their pharmacological equivalence has caused concern for years. We have sequenced the chloroplast genome of S. cusia previously. In this study, we sequenced the complete chloroplast genome sequence of S. bantonensis to preform in-depth comparative genetic analysis of the two Strobilanthes species. The chloroplast genome of S. bantonensis is a circular DNA molecule with a total length of 144,591 bp and encodes 84 protein-coding, 8 ribosomes, and 37 transfer RNA genes. The chloroplast genome has a conservative quadripartite structure, including a large single-copy (LSC) region, a small single-copy (SSC) region, and a pair of inverted repeat (IR) regions, with lengths of 92,068 bp, 17,767 bp, and 17,378 bp, respectively. Phylogenetic analysis confirmed that S. bantonensis is closely related to the S. cusia. Compared with other species from Acanthaceae, S. bantonensis has a significantly shortened IR region, suggesting the occurrence of IR contraction events. This study will help future taxonomic, evolutionary, phylogenetic, and bioprospecting studies of the sizeable Strobilanthes genus, which contains over 400 species

Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake

Harmful cyanobacterial blooms (CyanoHABs) are increasingly impacting the ecosystem of lakes, reservoirs and estuaries globally. The integration of real-time monitoring and deep learning technology has opened up new horizons for early warnings of CyanoHABs. However, unlike traditional methods such as pigment quantification or microscopy counting, the high-frequency data from in-situ fluorometric sensors display unpredictable fluctuations and variability, posing a challenge for predictive models to discern underlying trends within the time-series sequence. This study introduces a hybrid framework for near-real-time CyanoHABs predictions in a cyanobacterium Microcystis-dominated lake - Lake Dianchi, China. The proposed model was validated using hourly Chlorophyll-a (Chl a) concentrations and algal cell densities. Our results demonstrate that applying decomposition-based singular spectrum analysis (SSA) significantly enhances the prediction accuracy of subsequent CyanoHABs models, particularly in the case of temporal convolutional network (TCN). Comparative experiments revealed that the SSA-TCN model outperforms other SSA-based deep learning models for predicting Chl a (R  = 0.45-0.93, RMSE = 2.29-5.89 μg/L) and algal cell density (R  = 0.63-0.89, RMSE = 9489.39-16,015.37 cells/mL) at one to four steps ahead predictions. The forecast of bloom intensities achieved a remarkable accuracy of 98.56 % and an average precision rate of 94.04 % ± 0.05 %. In addition, scenarios involving various input combinations of environmental factors demonstrated that water temperature emerged as the most effective driver for CyanoHABs predictions, with a mean RMSE of 2.94 ± 0.12 μg/L, MAE of 1.55 ± 0.09 μg/L, and R of 0.83 ± 0.01. Overall, the newly developed approach underscores the potential of a well-designed hybrid deep-learning framework for accurately predicting sensor-based algal parameters. It offers novel perspectives for managing CyanoHABs through online monitoring and artificial intelligence in aquatic ecosystems

Combined Physical Process and Deep Learning for Daily Water Level Simulations across Multiple Sites in the Three Gorges Reservoir, China

Water level prediction in large dammed rivers is an important task for flood control, hydropower generation, and ecological protection. The variations of water levels in large rivers are traditionally simulated based on hydrological models. Recently, most studies have begun applying deep learning (DL) models as an alternative method for forecasting the dynamics of water levels. However, it is still challenging to directly apply DL to the simultaneous prediction of water levels across multiple sites. This study attempts to develop a hybrid framework by combining the Physical-based Hydrological model (PHM) and Long Short-Term Memory (LSTM). This study hypothesizes that our hybrid model can enhance the predictive accuracy of water levels in large rivers, because it considers the temporal-spatial information of mainstream-tributaries relationships. The effectiveness of the proposed model (PHM-BP-LSTM) is evaluated using the daily water levels from 2012 to 2018 in the Three Gorges Reservoir (TGR), China. Firstly, we use a hydrological model to produce a large amount of water level data to solve the limited training data set. Then, we use the Back Propagation (BP) neural network to capture the mainstream-tributaries relationship. The future changes in water levels in the different mainstream stations are simultaneously predicted by the LSTM model. We reveal that our hybrid model yields satisfactory accuracy for daily water level simulations at fourteen mainstream stations of the TGR. We further demonstrate the proposed model outperforms the traditional machine learning methods in different prediction scenarios (one-day-ahead, three-day-ahead, seven-day-ahead), with RMSE values ranging from 0.793 m to 1.918 m, MAE values ranging from 0.489 m to 1.321 m, and the average relative errors at each mainstream station are controlled below 4%. Overall, our PHM-BP-LSTM, combining physical process and deep learning, can be viewed as a potentially useful approach for water level prediction in the TGR, and possibly for the rapid forecast of changes in water levels in other large rivers