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Study region: Luanhe River Basin in North China Study focus: Global warming exacerbates hydrological cycles, leading to increased intensity and frequency of floods. Regional studies on the impacts of climate change and land cover changes on flood events in recent decades are particularly important for flood prevention and risk assessment. In this study, the Peak Over Threshold- Generalized Pareto Distribution (POT-GPD) method is used to fit extreme runoff values in the Luanhe River Basin from 1961 to 2018 to identify floods with different return periods. Six flood behavior indicators are adopted to analyze the flood characteristics and extreme climate indices are also used for breakpoint testing and trend analysis. Various scenarios are established as the input of the SWAT model to study the impacts of climate change and land-use change on the flood events. New hydrological insights for the region: The results indicate that the 2-year and 20-year return period floods are primarily rapid-rise single-peak floods, dominated by short-duration intense precipitation. The 5-year and 10-year return period floods are mainly multi-peak floods, primarily caused by long-duration precipitation. Floods in the Luanhe River Basin are primarily influenced by climate change, however, flood events do not directly correspond to the return periods of precipitation events. Forest is the most effective land-use type for flood retention. It could reduce flood magnitudes, especially for the 20-year return period floods. However, increasing forest cover does not effectively reduce the occurrence of the 5-year or 10-year floods, i.e. multi-peak floods. With more extreme rainfall events triggered by future climate change, particular attention should be paid to long-duration precipitation events, which may result in more severe disasters and economic losses.

The temporal and spatial resolutions of rainfall data directly affect the accuracy of hydrological simulation. Weather radar has been used in business in China, but the uncertainty of data is large. At present, research on radar data and fusion in small and medium-sized basins in China is very weak. In this paper, taking the Duanzhuang watershed as an example, based on station data, Shijiazhuang's radar data are preprocessed, optimized and fused. Eleven rainfall events are selected for fusion by three methods and quality evaluation, and three flood simulations are used to test their effect. The results show that preprocessing and initial optimization have poor effects on radar data improvement. The rainfall proportional coefficient fusion method performs best in rainfall spatial estimation, where the R2 values of the three inspection stations are increased to 0.51, 0.78 and 0.82. Three fusion datasets in the peak flow and flood volume of flood simulation perform better than station data. For example, in the No.20210721 flood, the NSE of the three fusion data increased by 39, 30 and 48%. This shows that the fusion method can effectively improve the data accuracy of radar and can obtain high temporal and spatial resolution rainfall data. HIGHLIGHTS Preprocessing and initially optimizing Shijiazhuang's S-band weather radar.; Using three fusion methods to optimize radar rainfall data and station data.; Evaluating and comparing fusion data on the different scales and by the HEC-HMS model simulation.;

Considering the sensitivity and vulnerability of vegetation to precipitation changes, exploring the response of vegetation to standardized precipitation index (SPI) is essential for ecosystem stability. Based on observed data from 624 stations and Moderate Resolution Imaging Spectroradiometer (MODIS) dataset from 2000 to 2018, Spearman's rank correlation coefficient (rs) of SPI and vegetation condition index (VCI) in Chinese mainland was analysed, response time of vegetation to SPI was investigated, and random forest models were constructed to explore the drivers of vegetation response. The results revealed that: (1) SPI and VCI exhibit a loop distribution. The correlation between VCI and 1-month SPI is mostly positive, accounting for 77.58% of the total; rs shows an increasing-decreasing distribution pattern, and the response time of VCI to 1-month SPI indicates an overall decreasing-increasing-decreasing trend. (2) Precipitation, temperature, altitude, vegetation, land use, landform and soil types all have influence on the correlation between 1-month SPI and VCI. Under various factors, rs is concentrated in the range of -0.1 ∼ 0.2, and the response time of VCI to SPI under various factors primarily falls into the ranges of 0 ∼ 1 month and 6 ∼ 9 months. Meanwhile, rs generally decreases with the increasing latitude, ranging between -0.1 and 0.2, whereas the mean response time increases with latitude, within a 0 ∼ 6 months range. (3) Precipitation, temperature and altitude are the top three important factors that affect both maximum rs of VCI and 1-month SPI and response time of VCI to 1-month SPI. The rs is positive in areas with low precipitation, high temperature and low altitude, whereas response time is generally long in areas with low precipitation, low temperature and high altitude. This study is significant for better understanding the interactions between vegetation and SPI changes, and provides reference for vegetation irrigation and vegetation management.

Flood disasters occur frequently in semi-arid and sub-humid mountain watersheds, and their formation mechanism is affected by many factors, resulting in low simulation accuracy. The main purpose of this study is to evaluate the impact of adding actual river channel data on the accuracy of flood simulation. In order to obtain higher-resolution terrain data, an unmanned aerial vehicle (UAV) was used to survey the study area. Taking the Liulin Watershed in Xingtai City, Hebei Province as the research area, the HEC-HMS hydrological model and the HEC-HMS and HEC-RAS coupling model were constructed, respectively, to simulate 26 flood events during the period from 1982 to 2016. The results indicate that the coupled model can reflect the evolution process of river floods. When simulating certain major flood events, the percentage of flood peak error and Nash–Sutcliffe efficiency coefficient (NSE) have improved, such as the NSE of No. 160812 floods increasing from 0.9 to 0.95. However, the simulation accuracy of these two models for small floods in the watershed is relatively low. Future research should focus on how to accurately evaluate the parameter curve number (CN) of the watershed before rainfall and obtain more accurate runoff yield. HIGHLIGHTS This paper analyzes the difference in flood simulation accuracy between the hydrological model and the hydrological–hydrodynamic coupling model.; The high-precision DEM data in this study was obtained based on UAV.; This method can also be used for other similar hilly small watersheds.; The results are helpful in optimizing flood forecasting methods for small watersheds in mountainous areas.;

Rainfall nowcasting is the basis of extreme rainfall monitoring, flood prevention, and water resource scheduling. Based on the structural features of the U-Net model, we proposed the Double Recurrent Residual Attention Gates U-Net (DR2A-UNet) deep-learning model to carry out radar echo extrapolation. The model was trained with mean square error (MSE) and balanced mean square error (BMSE) as loss functions, respectively. The dynamic Z-R relationship was applied for quantitative rainfall estimation. The reference U-Net model, U-Net++, and the ConvLSTM were used as control experiments to carry out radar echo extrapolation. The results showed that the model trained by BMSE had better extrapolation. For 1 h lead time, the rainfall nowcasted by each model could reflect the actual rainfall process. DR2A-UNet performed significantly better than other models for intense rainfall, with a higher extrapolation accuracy for echo intensity and variability processes. At the 2 h lead time, the nowcast accuracy of each model was significantly reduced, but the echo extrapolation and rainfall nowcasting of DR2A-UNet were better.

In statistical modeling, it is crucial to have consistent variables that are the most relevant to the outcome variable(s) of interest in the model. With the increasing richness of data from multiple sources, the size of the pool of potential variables is escalating. Some variables, however, could provide redundant information, add noise to the estimation, or waste the degrees of freedom in the model. Therefore, variable selection is needed as a parsimonious process that aims to identify a minimal set of covariates for maximum predictive power. This study illustrated the variable selection methods considered and used in the small area estimation (SAE) modeling of measures related to the proficiency of adult competency that were constructed using survey data collected in the first cycle of the PIAAC. The developed variable selection process consisted of two phases: phase 1 identified a small set of variables that were consistently highly correlated with the outcomes through methods such as correlation matrix and multivariate LASSO analysis; phase 2 utilized a k-fold cross-validation process to select a final set of variables to be used in the final SAE models.

Drought is an important factor that limits economic and social development due to its frequent occurrence and profound influence. Therefore, it is of great significance to make accurate predictions of drought for early warning and disaster alleviation. In this paper, SPEI-1 was confirmed to classify drought grades in the Guanzhong Area, and the autoregressive integrated moving average (ARIMA), random forest (RF) and support vector machine (SVM) model were established. Meteorological data and remote sensing data were used to derive the prediction models. The results showed the following. (1) The SVM model performed the best when the models were developed using meteorological data, remote sensing data and a combination of meteorological and remote sensing data, but the model's corresponding kernel functions are different and include linear, polynomial and Gaussian radial basis kernel functions, respectively. (2) The RF model driven by the remote sensing data and the SVM model driven by the combined meteorological and remote sensing data were found to perform better than the model driven by the corresponding other data in the Guanzhong Area. It is difficult to accurately measure drought with the single meteorological data. Only by considering the combined factors can we more accurately monitor and predict drought. This study can provide an important scientific basis for regional drought warnings and predictions. HIGHLIGHTS SPEI-1 was used to analyze the temporal distribution characteristics of drought and the main driving factors in Guanzhong Area, China.; Drought grades were selected as the dependent variable, and the meteorological, geographical and vegetative factors were selected as the independent variables to establish an autoregressive integrated moving average (ARIMA) model, random forest (RF) model and support vector machine model.; Meteorological data and remote sensing data were used as independent variables to derive prediction models, respectively.; Comparing the models driven by remote sensing data only and the combination of meteorological and remote sensing data, the RF model driven by the remote sensing data and the SVM model driven by the combined meteorological and remote sensing data were found to perform better than the model driven by the corresponding other data in Guanzhong Area.; This study can provide an important scientific basis for regional drought warning and prediction.;

Flood scaling theory is important for flood predictions in data-scarce regions but is often applied to quantile-based floods that have no physical mechanisms. In this study, we propose a framework for flood prediction in data-scarce regions by event-based flood scaling. After analyzing the factors controlling the flood scaling, flood events are first simulated by a hydrological model with different areally averaged rainfall events and curve number (CN) values as inputs, and the peak discharge of each subcatchment is obtained. Then, the flood scaling is analyzed according to the simulated peak discharge and subcatchment area. Accordingly, the relationship curves between the scaling exponent and the two explanatory factors (rainfall intensity and CN) can be drawn. Assuming that the flood and the corresponding rainfall event have the same frequency, the scaling exponent with a specific flood frequency can be interpolated from these curves. HIGHLIGHTS Hydrological modeling was used to conduct flood scaling analysis.; Rainfall intensity and curve number were identified to be the two main factors affecting flood scaling.; A framework of flood prediction by event-based flood scaling in data-scarce regions was proposed.;

La(Mg1/2Sn1/2)O3 (LMS) ceramic was synthesized via the conventional solid-state reaction method. The main phase of the sample is LMS with a double perovskite structure (monoclinic P121/n1 symmetry), which is confirmed by X-ray diffraction. Scanning electron microscopy shows the sample is well-crystallized with dense and uniform grains as well as clear grain boundaries. The Raman scattering and Fourier transform far-infrared reflection spectroscopies were employed to analyze the lattice vibrational modes of the sample. The Raman active modes were fitted by the Lorentz function, and the lattice vibrational modes were assigned and illustrated accurately. The four-parameter semiquantum model was applied to simulate the intrinsic dielectric properties, which agree well with the data calculated from the microscopic polarizability & damping angles. The A1g(La) Raman mode in A-site has a great impact on the dielectric loss, and F3u(2) mode makes the largest contribution to the dielectric constant and the dielectric loss. Keywords: Microwave dielectric ceramic, Double perovskite, Crystal structures, Intrinsic dielectric properties, Vibration characteristics

BaMoO4 ceramic was prepared using a conventional solid-sintering method. It was observed that the sample is a pure BaMoO4 with a tetragonal scheelite structure by analyzing X-ray diffraction data. Scanning electron microscopy characterized the dense sample with uniform grains. The phonon modes were analyzed by Raman and Far-infrared reflection spectra, and the phonon characteristics were studied. The intrinsic properties of the sample were calculated by the four-parameter semi-quantum model as well as the Clausius-Mosotti & damping equations, and the results were of εr = 9.388 and tanδ = 4.760 × 10−4, εr = 9.798 and tanδ = 6.445 × 10−4, respectively, which agrees well with the experimental values (εr = 9.84, tanδ = 5.0 × 10−4). The contributions to dielectric properties of each mode were investigated, and the results indicate that the external mode (Eu) yield greatest contribution to intrinsic permittivity and loss. Keywords: Microwave dielectric ceramic, BaMoO4, Crystal structure, Dielectric properties, Phonon characteristics

Global climate change and rapid urbanization have caused increases in urban floods. Urban flood risk assessment is a vital method for preventing and controlling such disasters. This paper takes the central region of Cangzhou city in Hebei Province as an example. Detailed topographical information, such as the buildings and roads in the study area, was extracted from GF-2 data. By coupling the two models, the SWMM and MIKE21, the spatial distribution of the inundation region, and the water depth in the study area under different return periods, were simulated in detail. The results showed that, for the different return periods, the inundation region was generally consistent. However, there was a large increase in the mean inundation depth within a 10-to-30-year return period, and the increase in the maximum inundation depth and inundation area remained steady. The comprehensive runoff coefficient in all of the scenarios exceeded 0.8, indicating that the drainage system in the study area is insufficient and has a higher flood risk. The flood risk of the study area was evaluated based on the damage curve, which was obtained from field investigations. The results demonstrate that the loss per unit area was less than CNY 250/m2 in each return period in the majority of the damaged areas. Additionally, the total loss was mainly influenced by the damaged area, but, in commercial areas, the total loss was highly sensitive to the inundation depth.

This study investigates the water supply risk of Panjiakou reservoir in the Luanhe River basin of China during 1956–2016 under environmental change. Since the monthly runoff series during 1956–2016 is a time series with change points, it is necessary to find a new stochastic streamflow series generation approach to preserve the statistical characteristics of the original series and to refine the reliability of water supply risk analysis. This paper improves a known stochastic streamflow simulation method of previous research to better reflect the characteristics of series with change points. And this paper simulates the monthly runoff series with change point of Panjiakou reservoir during 1956–2016 by three different methods, including Thomas–Fiering model, copula function stochastic simulation method, and copula function stochastic simulation method with the mixed distribution model. Among the three methods, the copula function stochastic simulation method with the mixed distribution model which is improved on the basis of copula function stochastic simulation method in this study performs best in simulating the observed monthly runoff series during 1956–2016, and the water supply risk indices including reliability (time-based and volume-based), resilience, vulnerability, drought risk index (DRI), and sustainability index (SUI) are evaluated for Panjiakou reservoir and analyzed by using the stochastic simulation results. By comparing with the previous studies, all indicators are between the corresponding results of 1956–1979 and 1980–2016 with stationary inflows; it can be seen that change point seriously affects the water supply risk of Panjiakou reservoir. These results make it easy to formulate water supply strategies and schemes in changing environment for water resources managers.

As for CaZrO3 (CZ) ceramic, the reported dielectric property values, especially dielectric constants, were much different from 23 to 32, which is reliable and credible? Without precise property data, CZ can’t be further developed and utilized accurately. Herein, CZ ceramic was fabricated by a traditional two-step sintering process, then simulated and calculated the dielectric properties precisely at a microscopic polarization angle using the lattice vibrational spectra and the Clausius–Mossotti (C–M) as well as damping equations. The Raman and Fourier transform far-infrared modes were analyzed and used to predict the intrinsic properties, which were consistent well with the values calculated from C–M and damping equations. The intrinsic permittivity, after precise prediction, is about 20, which is reliable and credible. As for the dielectric loss, the value of about 6×10−4 was obtained after precise calculation, which is similar to other results.

Drought propagation pattern forms a basis for establishing drought monitoring and early warning. Due to its regional disparity, it is necessary and significant to investigate the pattern of drought propagation in a specific region. With the objective of improving understanding of drought propagation pattern in the Luanhe River basin, we first simulated soil moisture and streamflow in naturalized situation on daily time scale by using the Soil and Water Assessment Tool (SWAT) model. The threshold level method was utilized in identifying drought events and drought characteristics. Compared with meteorological drought, the number of drought events was less and duration was longer for agricultural and hydrological droughts. The results showed that there were 3 types of drought propagation pattern: from meteorological drought to agricultural/hydrological drought (M-A/H), agricultural/hydrological drought without meteorological drought (NM-A/H), and meteorological drought only (M). To explain the drought propagation pattern, possible driven factors were determined, and the relations between agricultural/hydrological drought and the driven factors were built using multiple regression models with the coefficients of determination of 0.4 and 0.656, respectively. These results could provide valuable information for drought early warning and forecast.

Historical extraordinary floods are an important factor in non-stationary flood frequency analysis and they may occur at any time, regardless of whether the environment is changing or not. Based on mixed distribution (MD) modeling, this paper proposed an improved mixed distribution (IMD) model to consider the discontinuity and non-stationarity of flood samples simultaneously, which adds historical extraordinary floods in both sub-series divided by a change point. As a case study, the annual maximum peak discharge and volume series of Ankang hydrological station, located in the upper Hanjiang River Basin of China, were selected to identify non-stationarity by using the variation diagnosis system. MD and IMD were used to fit the flood characteristic series and a genetic algorithm was employed to estimate the optimal parameters. Compared with the design flood values fitted by the stationary Pearson type-III distribution, the results computed by IMD decreased at low return periods and increased at high return periods, with the difference varying from −6.67% to 7.19%. The results highlighted that although the design flood values of IMD are slightly larger than those of MD with different return periods, IMD provided a better result than MD. IMD provides a new perspective for non-stationary flood frequency analysis.

Satellite precipitation products are unique sources of precipitation measurement that overcome spatial and temporal limitations, but their precision differs in specific catchments and climate zones. The purpose of this study is to evaluate the precipitation data derived from the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, TRMM 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products over the Luanhe River basin, North China, from 2001 to 2012. Subsequently, we further explore the performances of these products in hydrological models using the Soil and Water Assessment Tool (SWAT) model with parameter and prediction uncertainty analyses. The results show that 3B42 and 3B42RT overestimate precipitation, with BIAs values of 20.17% and 62.80%, respectively, while PERSIANN underestimates precipitation with a BIAs of −6.38%. Overall, 3B42 has the smallest RMSE and MAE and the highest CC values on both daily and monthly scales and performs better than PERSIANN, followed by 3B42RT. The results of the hydrological evaluation suggest that precipitation is a critical source of uncertainty in the SWAT model, and different precipitation values result in parameter uncertainty, which propagates to prediction and water resource management uncertainties. The 3B42 product shows the best hydrological performance, while PERSIANN shows unsatisfactory hydrological performance. Therefore, 3B42 performs better than the other two satellite precipitation products over the study area.

Accurate soil moisture information is vital for flood and drought predictions, crop growth and agricultural water management. Reanalysis soil moisture products with multi-decadal temporal coverage are gradually becoming a good alternative for providing global soil moisture data in various applications compared to in-situ measurements and satellite products. Much effort has been devoted to evaluating the performance of soil moisture products, yet the scale discrepancy between point measurements and grid cell soil moisture products limits the assessment quality. As the land surface and hydrological modelling community evolve towards the next generation of (sub)kilometer resolution models, Cosmic Ray Neutron Sensors (CRNS) that provide estimates of root-zone soil moisture at the field scale (~250 m radius from the sensor and up to 0.7 m deep), may consequently be more suitable for soil moisture product evaluation as they cover a relatively larger footprint, when compared to traditional methods. In this study, we perform a comprehensive evaluation of seven widely-used reanalysis soil moisture products (ERA5-Land, CFSv2, MERRA2, JRA55, GLDAS-Noah, CRA40 and GLEAM datasets) against 135 CRNS sites from the UK, Europe, USA and Australia. We evaluate the products using six metrics capturing different aspects of soil moisture dynamics. Results show that all reanalysis products exhibit good temporal correlation with the measurements, with the median of temporal correlation coefficient (R) values spanning from 0.69 to 0.79, though large deviations are found at sites with seasonally varying vegetation cover. Poor performance is observed across products for soil moisture anomalies timeseries, with R values varying from 0.49 to 0.70. The performance of reanalysis products differs greatly across regions, climate, land covers and topographic conditions. In general, all products tend to overestimate in arid climates and underestimate in humid regions as well as grassland. Most reanalysis products perform poorly in steep terrain. Relatively low temporal correlation and high Bias are detected in some sites from west of the UK, which might be associated with relatively low bulk density and high soil organic carbon. Overall, ERA5-Land, CFSv2, CRA40, GLEAM exhibit superior performance compared to MERRA2, GLDAS-Noah and JRA55. We recommend ERA5-Land and CFSv2 should be used in humid climates, whereas CRA40 and GLEAM perform better in arid regions. GLEAM is more effective in shrubland regions. Our findings also provide insights on directions for improvement of soil moisture products for product developers. [ABSTRACT FROM AUTHOR]

ABSRTACT: Resistance of Escherichia coli (E.coli) to antibiotics has steadily increased over time; hence, there is an urgent need to develop safer alternatives to antibiotics. The present study aimed to evaluate the effect of luteolin (Lut) on E. coli from chicken. The bioactive compound Lut from Humulus scandens was selected by network pharmacology and molecular docking analyses. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), and confocal laser scanning microscopy (CLSM) were used to observe the effects of Lut on the morphology and structure of E. coli cells. The data-independent acquisition (DIA) method was used to analyze protein expression level of E. coli before and after Lut treatment. The in vivo evaluation of the antibacterial, anti-inflammatory, and oxidative effects of Lut on E.coli was conducted using E.coli isolated strains infected the SPF chicken model. The network pharmacology analysis revealed 19 distinctive bioactive compounds such as Lut and β-sitosterol in H. scandens; furthermore, 30 core targets were selected from H. scandens. The KEGG enrichment analysis showed that the PI3K-Akt, TNF, MAPK, IL-17, JAK-STAT, and HIF-1 pathways were related from H. scandens. Based on the results of the network pharmacology analysis, Lut was subjected to screening by molecular docking analysis to determine its antibacterial effect on E. coli and the associated mechanism of action. The minimum inhibitory concentration (MIC) of Lut against E. coli standard strains was 500 µg/mL. SEM, TEM, and CLSM results indicated that Lut damaged the cell wall and cell membrane of E. coli strains and destroyed the cell structure, leading to cell death.The expression level of membrane structure, Phenylalanine metabolism and some other metabolic pathways in E.coli changed after treatment with Lut (P < 0.05). In vivo experiments in the SPF chicken model showed that Lut treatment alleviated the decline in the growth performance of chickens (P < 0.05), prevented pathological changes in the correspond ding organs and suppressed the inflammatory response induced by E. coli infection (P < 0.05), improved the immunity and antioxidant capacity of chickens (P < 0.05), and protected them against infection with E. coli strains. To summarize, Lut from H. scandens can inhibit E. coli growth by damaging the cell membrane structureand affecting the expression level of some metabolic proteins. In vivo experiments also showed that Lut can significantly reduce the damage caused by E. coli isolates on SPF chickens, improve their antioxidant capacity and immunity, and reduce inflammatory responses following E. coli infection.