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With global warming and intensified human activities, extreme convective precipitation has become one of the most frequent natural disasters. An accurate and reliable assessment of severe convective precipitation events can support social stability and economic development. In order to investigate the accuracy enhancement methods and data fusion strategies for the assessment of severe convective precipitation events, this study is driven by the horizontal reflectance factor (ZH) and differential reflectance (ZDR) of the dual-polarization radar. This research work utilizes microphysical information of convective storms provided by radar variables to construct the precipitation event assessment model. Considering the problems of high dimensionality of variable data and low computational efficiency, this study proposes a dual-polarization radar echo-data-layering strategy. Combined with the results of mutual information (MI), this study constructs Bayes–Kalman filter (KF) models (RF, SVR, GRU, LSTM) for the assessment of severe convective precipitation events. Finally, this study comparatively analyzes the evaluation effectiveness and computational efficiency of different models. The results show that the data-layering strategy is able to reduce the data dimensions of 256 × 256 × 34,978 to 5 × 2213, which greatly improves the computational efficiency. In addition, the correlation coefficient of interval III–V calibration period is increased to 0.9, and the overall assessment accuracy of the model is good. Among them, the Bayes–KF-LSTM model has the best assessment effect, and the Bayes–KF-RF has the highest computational efficiency. Further, five typical precipitation events are selected for validation in this study. The stratified precipitation dataset agrees well with the near-surface precipitation, and the model's assessment values are close to the observed values. This study completely utilizes the microphysical information offered by dual-polarized radar ZH and ZDR in precipitation event assessment, which provides a wide range of application possibilities for the assessment of severe convective precipitation events. [ABSTRACT FROM AUTHOR]

Water temperature plays a crucial role in water ecological environment both in the reservoir and downstream area. Three Gorges Project (TGP) is the largest hydraulic engineering in the world, and changes of water quality attract much more attention, especially in the thermal structure since impoundment. In order to clearly understand water temperature distribution after impoundment in the Three Gorges Reservoir (TGR), we monitored the temperature distribution of the Taipingxi section which was not far from the dam from early April to the end of July. According to the analyzing of the monitoring data of transverse and vertical temperature variation, we could find that the temperature mixed uniformly in transverse direction. Excepting April, there was basically had no temperature difference in vertical direction, even in April, the maximum temperature difference was only 1.39°C within 100 meters (April 22), the average temperature gradient was only 0.014°C/m; Both the temperature of water and atmosphere have similar variation tendency, but the response of the water temperature to atmosphere is delayed, especially the bottom water temperature. The result indicate that the reservoir has mixed thermal structure during impoundment phase, so the release temperature had little change after the impoundment, the work provide a scientific basis for the development of pollution control and ecological protection measure.

Accurate monthly runoff prediction is significant to extreme flood control and water resources management. However, traditional statistical models without multi-variable input may fail to capture runoff changes effectively due to the dual effect of climate change and human activities. Here, we used five multi-input machine learning (ML) models to predict monthly runoff, where multiple global circulation indexes and surface meteorological indexes were selected as explanatory variables by the stepwise regression or copula entropy methods. Moreover, four univariate models were adopted as benchmarks. The multi-input ML models were tested at two typical hydrological stations (i.e., Gaochang and Cuntan) in the Upper Yangtze River. The results indicate that the LSTM_Copula (long short-term memory model combined with copula entropy method) model outperformed other models in both hydrological stations, while the GRU_Step (gate recurrent unit model combined with stepwise regression method) model and the RF_Copula (random forest model combined with copula entropy method) model also showed satisfactory performances. In addition, the ML models with multi-variable input provided better predictability compared with four univariate statistical models, and the MAPE (mean absolute percentage error), RMSE (root mean square error), NSE (Nash–Sutcliffe efficiency coefficient), and R (Pearson's correlation coefficient) values were improved by 5.10, 4.16, 5.34, and 0.43% for the Gaochang Station, and 10.84, 17.28, 13.68, and 3.55% for the Cuntan Station, suggesting the proposed ML approaches are practically applicable to monthly runoff forecasting in large rivers. [ABSTRACT FROM AUTHOR]

Deep learning models are essential tools for mid- to long-term runoff prediction. However, the influence of the input time lag and output lead time on the prediction results in deep learning models has been less studied. Based on 290 schemas, this study specified different time lags by sliding windows and predicted the runoff process by RNN (Recurrent Neural Network), LSTM (Long–short-term Memory), and GRU (Gated Recurrent Unit) models at five hydrological stations in the upper Yangtze River during 1980–2018 at daily, ten-day, and monthly scales. Different models have different optimal time lags; therefore, multiple time lags were analyzed in this paper to find out the relationship between the time intervals and the accuracy of different river runoff predictions. The results show that the optimal time-lag settings for the RNN, LSTM, and GRU models in the daily, ten-day, and monthly scales were 7 days, 24 ten days, 27 ten days, 24 ten days, 24 months, 27 months, and 21 months, respectively. Furthermore, with the increase of time lags, the simulation accuracy would stabilize after a specific time lag at multiple time scales of runoff prediction. Increased lead time was linearly related to decreased NSE at daily and ten-day runoff prediction. However, there was no significant linear relationship between NSE and lead time at monthly runoff prediction. Choosing the smallest lead time could have the best prediction results at different time scales. Further, the RMSE of the three models revealed that RNN was inferior to LSTM and GRU in runoff prediction. In addition, RNN, LSTM, and GRU models could not accurately predict extreme runoff events at different time scales. This study highlights the influence of time-lag setting and lead-time selection in the mid- to long-term runoff prediction results for the upper Yangtze River basin. It is recommended that researchers should evaluate the effect of time lag before using deep learning models for runoff prediction, and to obtain the best prediction, the shortest lead-time length can be chosen as the best output for different time scales. [ABSTRACT FROM AUTHOR]

Fluoride-based lanthanide-doped nanoparticles (LDNPs) featuring second near-infrared (NIR-II, 1000-1700 nm) downconversion emission for bioimaging have attracted extensive attention. However, conventional LDNPs cannot be degraded and eliminated from organisms because of an inert lattice, which obstructs bioimaging applications. Herein, the core-shell LDNPs of Na 3 HfF 7 :Yb,Er@CaF 2 :Ce,Zr(Hf) [labeled as Zr(Hf)Ce-HC] with pH-selective and tunable degradability were synthesized for dual-modal bioimaging. Notably, the "softening" lattice of the Na 3 HfF 7 matrix and different Zr 4+ (Hf 4+ ) doping amounts in the shell enable Zr(Hf)Ce-HC with acidity-dependent and tunable degradability. After coating of an optimized Ce 3+ -doped CaF 2 :Zr shell, the near-infrared-IIb (NIR-IIb, 1500-1700 nm) luminescence intensity of ZrCe-HC is enhanced by 5.2 times compared with that of Na 3 HfF 7 :Yb,Er. The Hf element with high X-ray attenuation allows ZrCe-HC as the contrast agent for computed tomography (CT) bioimaging. The modification of oxidized sodium alginate endows ZrCe-HC with satisfying biocompatibility for NIR-IIb/CT dual-modal bioimaging. These findings would benefit the bioimaging applications of degradable fluoride-based LDNPs.