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Short-term (e.g., hourly) urban water consumption (or demand) prediction is of great significance for the optimal operation of the intelligent water distribution pump stations. In this study, three single models (autoregressive integrated moving average (ARIMA), back-propagation (BP), support vector machine (SVM)) and three hybrid models (ensemble empirical mode decomposition (EEMD)-ARIMA, EEMD-BP and EEMD-SVM) were developed and compared in terms of prediction accuracy and application convenience. 31-day (1 month) hourly flow series from a water distribution division in Shanghai were used for the demonstration case study, among which 30-day data used for model training and 1-day data used for model verification. Finally, the effects of historical data length on the prediction accuracy of three hybrid models were also analyzed, and the optima of the historical data length for three hybrid models were obtained. Results reveal that (1) the mean absolute percentage errors (MAPE) of EEMD-ARIMA, EEMD-BP, EEMD-SVM, ARIMA, BP and SVM are 5.2036, 1.4460, 1.3424, 5.7891, 4.3857 and 3.8470%, respectively. (2) In terms of prediction accuracy and actual practice convenience, EEMD-SVM performs best among the above six models. (3) The EEMD algorithm is effective for improving the prediction accuracy of six models. (4) The optimal historical data length of EEMD-ARIMA, EEMD-BP and EEMD-SVM are 11, 11 and 10 days, respectively.

Climate change significantly influences water resources and flood hazards in global watersheds. This study focuses on predicting the impact of climate change on the streamflow of the Qinglong River situated in northern China. The streamflow of the Qinglong River (2021-2100) under two climate change scenarios (RCP 4.5 and RCP 8.5) was mainly synthesized over multiple timescales. Meteorological data from 31 Global Climate Models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) served as inputs for the Hydrological Simulation Program-Fortran (HSPF) to conduct hydrological simulations. Results show that: (1) The peak flood flow and average daily streamflow for the RCP4.5 scenario are at least 101.15% and 110.14% of the historical phase, and at least 108.89% and 121.88% of the historical phase for the RCP8.5 scenario. (2) Under both scenarios, the proportion of summer streamflow to the annual total is expected to increase from 61.46% (historical phase) to over 85%, while the proportion of winter streamflow to the annual total is expected to decrease from 8.84% (historical phase) to below 0.5%. (3) Compared to the historical period, the maximum increase in future multi-year average annual streamflow for the RCP4.5 and RCP8.5 scenarios is 30.34%, 31.48%, respectively.

The identification of rainfall catastrophe characteristics is important for rainfall consistency testing in hydrostatistical analysis. In this study, a new classification method (trend, mean and change-rate catastrophe) was proposed and applied to the Qinglong River watershed, Northern China. Two groups of algorithms were compared to obtain the optimal algorithm: the Cumulative-anomaly method and the Sequential Mann-Kendall (SQ-MK) algorithm, the Pettitt algorithm and the Bernaola-Galvan heuristic segmentation (B-G) algorithm. Its parameters were optimized and its robustness was tested. Results revealed that: (1) The SQ-MK algorithm was suitable for trend catastrophe and sensitive to the length of the time series. The most significant point of trend catastrophe in the Qinglong River watershed was in 2012. (2) The sensitivity of parameter P0 (Range value (R) = 4.333) in the B-G algorithm was greater than that of parameter l0 (R = 2.889). (3) The B-G algorithm was suitable for identifying mean catastrophes and insensitive to the length of the time series. In the Qinglong River watershed, mean catastrophe points were identified in 1997, 2002, 2004, 2006, 2009, 2012, and 2018. (4) There was no change-rate catastrophe point in the Qinglong River watershed. Trend catastrophe and mean catastrophe do not necessarily lead to change-rate catastrophe.

Understanding daily water consumption patterns is crucial for efficient management and distribution of water resources, as well as for promoting energy conservation and achieving carbon peaking and neutrality targets. It compares performance of three clustering algorithms, K-means Clustering (KC), Agglomerative Hierarchical Clustering (AHC), and Spectral Clustering (SC), using Silhouette Coefficient Index (SCI) and Calinski–Harabasz Index (CHI) as evaluation metrics. We conducted a case study using original hourly flow series of a water distribution division. It aims to identify typical daily water consumption patterns and explore factors that influence them. Findings are as follows: (1) among the three algorithms, KC demonstrates the best, with SCI of 0.6315, 0.5922, and 0.6272, and CHI of 305.9207, 274.1120, and 302.4738 for KC, AHC, and SC, respectively. (2) KC successfully identifies three distinct typical daily water consumption patterns. (3) Results indicate a significant impact of seasons on daily water consumption patterns. (4) Conversely, weekdays and holidays have minimal effect on daily water consumption patterns. It highlights the importance of comprehending daily water consumption patterns and underscores the effectiveness of KC in identifying such patterns. Furthermore, it emphasizes the significant influence of seasons while revealing limited impact of weekdays and holidays on daily water consumption patterns.

A new parameter optimization and uncertainty assessment procedure using the Bayesian inference with an adaptive Metropolis–Hastings (AM-H) algorithm is presented for extreme rainfall frequency modeling. An efficient Markov chain Monte Carlo sampler is adopted to explore the posterior distribution of parameters and calculate their uncertainty intervals associated with the magnitude of estimated rainfall depth quantiles. Also, the efficiency of AM-H and conventional maximum likelihood estimation (MLE) in parameter estimation and uncertainty quantification are compared. And the procedure was implemented and discussed for the case of Chaohu city, China. Results of our work reveal that: (i) the adaptive Bayesian method, especially for return level associated to large return period, shows better estimated effect when compared with MLE; it should be noted that the implementation of MLE often produces overy optimistic results in the case of Chaohu city; (ii) AM-H algorithm is more reliable than MLE in terms of uncertainty quantification, and yields relatively narrow credible intervals for the quantile estimates to be instrumental in risk assessment of urban storm drainage planning.

Background Although several studies have revealed that the sensitivity of ducklings to duck Tembusu virus (DTMUV) was related to age, however, DTMUV was originally isolated from egg-laying ducks, and the ovary was the target organ of this virus. Cherry Valley breeding ducks aged 15- and 55-week-old (they are reserve breeding ducks and the normal egg-laying breeding ducks, respectively) were infected with DTMUV, using intramuscular injection, to study the effect of age-related difference on the pathogenicity of DTMUV in breeding ducks. Results Examinations of clinical symptoms, gross and microscopic lesions, viral loads, cytokines and serum neutralizing antibodies were performed. Results showed that obvious clinical symptoms, such as depression, ruffled feathers, ataxia and egg-laying drop were observed in the 55-week-old laying ducks, with five ducks dying at 5–7 days post infection (dpi). The 15-week-old ducks showed slight symptoms during infection. Gross lesions were severe and characterized by the congestion, hemorrhage and swelling of some organs in the 55-week-old ducks, including the hemorrhage of endocardium, hepatomegaly, splenomegaly, oviduct hemorrhage, hyperemia and deformation of the ovary. Mild endocardial hemorrhage and hepatosplenomegaly were observed in the 15-week-old ducks. Similarly, there was a significant difference in microscopic lesions between the two groups. The older ducks displayed severe microscopic lesions, specifically in the hemorrhage, interstitial inflammatory cell infiltration of the endocardium, typical viral encephalitis and hemorrhage in the ovary. But on the whole, the 15-week-old ducks showed milder lesions. Viral loads in tissues of the older group were significantly higher than those of the younger group. The levels of interferon (IFN)-γ, interleukin (IL)-2 and neutralizing antibody in the 15-week-old ducks were higher than in the 55-week-old ducks at the early stage of the DTMUV infection, suggesting the immune response in the younger ducks to DTMUV was stronger than in the older ducks. Conclusions These results demonstrated that age-related differences in susceptibility to DTMUV in breeding ducks was significant, with 55-week-old egg-laying ducks being more susceptible to DTMUV than 15-week-old reserve breeding ducks.

In this research, a novel framework is proposed for temporal downscaling rainfall records to address the problem of insufficient temporal resolution of rainfall during the refined hydrological simulation. This framework consists of three main steps: (1) The existence of the fractal features of the original rainfall series with different temporal scales (month, week, day, h) can be judged by the dominant frequency of Hilbert marginal spectrum, and geometric shape similarity of different time scales on the basis of the ensemble empirical mode decomposition (EEMD). (2) Based on the previous fractal interpolation theory, an improved algorithm that couples with a threshold to control the number of interpolation points for the specific interpolation intervals and a parameter to captures the high intermittentness of rainfall series is developed, which can achieve the conservation of the total rainfall quantity. (3) EEMD method is applied again to compare and evaluate the fit of the downscaled records to observed records from the perspective of IMF components, and various statistical indicators (e.g., root mean square error, correlation coefficient, Nash–Sutcliffe efficiency coefficient, fractal dimension) are applied to quantify the performance of improved fractal interpolation algorithm. Three sets of observed hourly rainfall series (from 2016 to 2018) of Chaohu city, Anhui, China, were investigated using the new framework, EF (EEMD + improved fractal interpolation algorithm). First, in order to validate the applicability of the EF framework, the observed hourly rainfall sets were aggregated into daily, weekly, and monthly sets and subsequently, the EF framework was employed to downscale above sets back into daily and hourly scale. Then, a long-duration rainfall event with hourly scale in early July 2016 was downscaled to 5-min time series using EF framework and equipartition method, respectively. Finally, simulation results based on MIKE URBAN software were compared for EF framework and equipartition method. The results of our work reveal that: (a) EEMD is a reasonable identification tool for fractal characteristics of rainfall records. (b) the improved fractal interpolation algorithm not only reasonably preserves key statistical indices (e.g., low-order moments, autocorrelation, power spectra), but also captures the overall trends and inherent details of the rainfall. (c) Based on the digital simulation and model analysis, the storm sewer peak flows obtained by the EF framework are larger than that of equipartition method.

Outbreaks of Tembusu virus (TMUV) infection have caused huge economic losses to the poultry industry in China since 2010. However, the potential threat of TMUV to mammals has not been well studied. In this study, a TMUV HB strain isolated from diseased ducks showed high virulence in BALB/c mice inoculated intranasally compared with the reference duck TMUV strain. Further studies revealed that the olfactory epithelium is one pathway for the TMUV HB strain to invade the central nervous system of mice. Genetic analysis revealed that the TMUV HB virus contains two unique residues in E and NS3 proteins (326K and 519T) compared with duck TMUV reference strains. K326E substitution weakens the neuroinvasiveness and neurovirulence of TMUV HB in mice. Remarkably, the TMUV HB strain induced significantly higher levels of IL-1β, IL-6, IL-8, and interferon (IFN)-α/β than mutant virus with K326E substitution in the brain tissue of the infected mice, which suggested that TMUV HB caused more severe inflammation in the mouse brains. Moreover, application of IFN-β to infected mouse brain exacerbated the disease, indicating that overstimulated IFN response in the brain is harmful to mice upon TMUV infection. Further studies showed that TMUV HB upregulated RIG-I and IRF7 more significantly than mutant virus containing the K326E mutation in mouse brain, which suggested that HB stimulated the IFN response through the RIG-I-IRF7 pathway. Our findings provide insights into the pathogenesis and potential risk of TMUV to mammals.

Construction of the initial rainwater storage tank is crucial for managing urban first-flush pollution. Initial rainwater storage tank is designed to maximize pollutant capture efficiency while minimizing storage volume requirement. Considering the random nature of rainfall process, a method is proposed for determining the design volume of initial rainwater storage tank with its specific design return period. First, rainfall events were obtained by dividing the original rainfall series, setting the minimum inter-event time (MIET) as the emptying time of storage facilities. The rainfall events for design purpose were selected from the above rainfall events according to three indicators (the initial rainfall amount, maximum rainfall intensity of the specified period, and antecedent dry periods (ADP)). Then the annual multi-event-maxima (AMEM) method was used for the sampling of initial rainfall period on the basis of the selected rainfall events. Second, samples were arranged in descending order and the empirical frequency of the samples was calculated according to the mathematical expectation formula (Weibull formula). Thus, the theoretical probability distribution function was estimated based on Markov Chain Monte Carlo (MCMC) algorithm and the initial rainfall intensity formula was obtained according to the Horner formula. Finally, for the targeted catchment and the selected return period, the initial rainfall intensity can be calculated setting the initial period equals to overland time of concentration. Afterward, the design rainfall amount (design volume) can be obtained by the initial rainfall intensity, the initial period, initial loss and the catchment area. It was concluded that: (1) A method for the initial rainfall intensity sampling and the initial rainfall intensity formula were proposed for design volume of the initial rainwater storage tank. (2) The return period of the design volume can be taken into consideration in this method. (3) The proposed method is suitable for scenarios of collecting urban first-flush pollutants from small urban catchments.

Rainfall intensity at a specific time is an important factor affecting the occurrence of combined sewer overflow (CSO). In this study, a statistical analysis of rainfall intensity frequency considering rainfall time (or cross-section) in the diurnal cycle were conducted based on the original 10-year rainfall intensity time series (the temporal resolution is 5 min). First, the stationarity of two different types of time series was evaluated by Augmented Dickey-Fuller (ADF) test and Phillips-Perron (PP) test, including the original rainfall time series and the diurnal cycle time series of five statistical characteristics (mean value (Mean), standard deviation (Std), coefficient of variation (Cv), skewness coefficient (Cs) and kurtosis coefficient (Kurt)). Moreover, the cumulative distribution function (CDF) of rainfall intensity at different cross-sections was analyzed. Finally, the best-fitting CDF of cross-section was used to quantify the CSO overflow frequency in the diurnal cycle under different thresholds. Results revealed that: (1) The original 10-year rainfall time series was second-order stationary time series. (2) The diurnal cycle time series of rainfall intensity statistics (Mean and Std) were non-stationary while those of rainfall intensity statistics (Cv, Cs and Kurt) were second-order stationary. (3) CDF of rainfall intensity at different cross-sections can be elaborated by the Generalized exponential distribution (Genexpon) and Generalized Pareto distribution (GPD) (R2 > 0.914). (4) CSO overflow has a high probability of occurring in three time intervals: (4:0–5:25), (15:35 − 16:40), and (20:30 − 22:55).HighlightsThe original rainfall time series was second-order stationary.The diurnal cycle time series of Mean and Std were non-stationary.The diurnal cycle time series of Cv, Cs and Kurt were second-order stationary.CDF of rainfall intensity can be expressed as the generalized exponential distribution and generalized pareto distribution.CSO overflow has a high probability of occurring in three time intervals.