Coastal management in China is confronted with an urgent choice between natural restoration and maintenance of existing seawalls and reclaimed land for economic development. A key criterion for making this decision is the resilience to coastal flooding, which depends on the ability to predict tidal level. Tidal duration asymmetry (TDA) is a key parameter in determination of the arrival and duration of flood tides. This study selected the western inner shelf of the Yellow Sea (WYS) as the study area and investigated the responses of TDA to different shoreline configurations and relative sea level rise. The responses of TDA to shoreline reconstruction yielded spatial variability locally and remotely. In the nearshore area, the responses of TDA to the complex ocean environment mainly originated from the combined functions of reflection, bottom friction, and advection, which controlled the energy transfer from M2 or S2 constituents to their overtides or compound tides. The sensitivity of TDA to coastline typologies was not limited to coastal waters but could stretch over the entire inner shelf. The vulnerability of tidal responses was due to the displacement of the M2 amphidrome of the Kelvin wave on the WYS, which in turn changed tidal energy fluxes over the regime. The relative sea level rise could intensify the feedback of TDA to seawalls and land reclamation.
Copula functions have been widely used in stochastic simulation and prediction of streamflow. However, existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months. To address this limitation, this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations. This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas. The up-to-down sequential method, which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach, was used to determine the structures of multivariate D-vine copulas. The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station, the inflow control station of the Longyangxia Reservoir in the Yellow River Basin. The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow. This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.
Increasing bacteria levels in the Lower Neches River caused by Hurricane Harvey has been of a serious concern. This study is to analyze the historical water sampling measurements and real-time water quality data collected with wireless sensors to monitor and evaluate water quality under different hydrological and hydraulic conditions. The statistical and Pearson correlation analysis on historical water samples determines that alkalinity, chloride, hardness, conductivity, and pH are highly correlated, and they decrease with increasing flow rate due to dilution. The flow rate has positive correlations with Escherichia coli, total suspended solids, and turbidity, which demonstrates that runoff is one of the causes of the elevated bacteria and sediment loadings in the river. The correlation between E. coli and turbidity indicates that turbidity greater than 45 nephelometric turbidity units in the Neches River can serve as a proxy for E. coli to indicate the bacterial outbreak. A series of statistical tools and an innovative two-layer data smoothing filter are developed to detect outliers, fill missing values, and filter spikes of the sensor measurements. The correlation analysis on the sensor data illustrates that the elevated sediment/bacteria/algae in the river is either caused by the first flush rain and heavy rain events in December to March or practices of land use and land cover. Therefore, utilizing sensor measurements along with rainfall and discharge data is recommended to monitor and evaluate water quality, then in turn to provide early alerts on water resources management decisions.
Sodium hypochlorite has significant potential as a sanitation solution in hard-to-reach areas. Few studies have investigated the optimal electrolysis parameters for its production with volumes greater than 10 L. This study evaluated sodium hypochlorite production through electrolysis in a 22-L prototype and identified the optimal operating parameters. Tests were performed using graphite electrodes with areas of 68.4 cm2 at the laboratory scale and 1 865.0 cm2 at the prototype scale. A design for experiments with different operating times, chloride concentrations, and electric current intensities was developed. The optimal operating time, sodium chloride concentration, and current intensity at the laboratory scale were 120 min, 150 g of chloride per liter, and 3 A, respectively, leading to the production of 5.02 g/L of the disinfectant with an energy efficiency of 12.21 mg of Cl2 per kilojoule. At the prototype scale, the maximum sodium hypochlorite concentration of 3.99 g of chloride per liter was achieved with an operating time of 120 min, a sodium chloride concentration of 100 g of chloride per liter, and a current intensity of 70 A, reaching an energy efficiency of 42.56 mg of Cl2 per kilojoule. In addition, this study evaluated the influences of the chloride concentration, current intensity, and operating time on the production of sodium hypochlorite at the two scales, and formulated the equations showing the trends of sodium hypochlorite production and energy efficiency in the electrochemical systems. The 22-L prototype model for production of this oxidizing substance is promising for disinfection of large volumes of water in areas that are difficult to access.
Pollution of rivers is mainly caused by anthropogenic activities such as discharge of effluent from industrial facilities, maintenance of sewage/effluent treatment plants, and dumping of solid waste on river banks. This study dealt with the pollution issues of the Cooum River in the well-known city of Chennai in South India. Water samples from 27 locations were collected and analyzed for 12 elements, including Ba, B, and Al, as well as heavy metals such as Pb, Cr, Mn, Fe, Co, Ni, Cu, Zn, and Cd. The samples showed levels of these elements that exceeded World Health Organization recommendations. Pearson correlation analysis revealed the inter-dependency among elements, and the contribution of each element based on factor loadings showed its percentage contribution compared to others. Water samples from six significant locations were chosen for remediation with three algae: Chlorella vulgaris, Scenedesmus dimorphus, and Phormedium sp. The uptake of pollutants led to the continuous growth of algae during the incubation period of 15 d, effectively removing heavy metals from the river water. The increasing levels of algal counts and the chlorophyll a content confirmed the algal growth during the incubation period, followed by a declining stage after the incubation period. The scanning electron microscopic images of algae before and after the remediation showed no remarkable modification of morphological patterns. This study showed that the uptake of heavy metals using algae is an effective water pollution remediation measure, making the process practicable in the field on a large scale in the near future.
Increased urbanisation, economic growth, and long-term climate variability have made both the UK and China more susceptible to urban and river flooding, putting people and property at increased risk. This paper presents a review of the current flooding challenges that are affecting the UK and China and the actions that each country is undertaking to tackle these problems. Particular emphases in this paper are laid on (1) learning from previous flooding events in the UK and China, and (2) which management methodologies are commonly used to reduce flood risk. The paper concludes with a strategic research plan suggested by the authors, together with proposed ways to overcome identified knowledge gaps in flood management. Recommendations briefly comprise the engagement of all stakeholders to ensure a proactive approach to land use planning, early warning systems, and water-sensitive urban design or redesign through more effective policy, multi-level flood models, and data driven models of water quantity and quality.
This paper presents a study on the improvement of wind field hindcasts for two typical tropical cyclones, i.e., Fanapi and Meranti, which occurred in 2010. The performance of the three existing models for the hindcasting of cyclone wind fields is first examined, and then two modification methods are proposed to improve the hindcasted results. The first one is the superposition method, which superposes the wind field calculated from the parametric cyclone model on that obtained from the Cross-Calibrated Multi-Platform (CCMP) reanalysis data. The radius used for the superposition is based on an analysis of the minimum difference between the two wind fields. The other one is the direct modification method, which directly modifies the CCMP reanalysis data according to the ratio of the measured maximum wind speed to the reanalyzed value as well as the distance from the cyclone center. Using these two methods, the problem of underestimation of strong winds in reanalysis data can be overcome. Both methods show considerable improvements in the hindcasting of tropical cyclone wind fields, compared with the cyclone wind model and the reanalysis data.
In this experiment, cobalt ferrite-supported activated carbon (CF-AC) was developed and characterized via the wet impregnation method for the removal of Cr and Pb(II) ions from tannery wastewater. Batch adsorption was carried out to evaluate the effect of experimental operating conditions (pH of solution, contact time, adsorbent dose, and temperature), and the removal efficiencies of Cr and Pb(II) ions by the developed adsorbents were calculated and recorded for all experimental conditions. These variables were estimated and reported as removal efficiencies of 98.2% for Cr and 96.4% for Pb(II) ions at the optimal conditions of 5, 0.8 g, 80 min, and 333 K for pH, adsorbent dose, contact time, and temperature, respectively. The equilibrium for the sorption of Cr and Pb(II) ions was studied using four widely used isotherm models (the Langmuir, Freundlich, Dubinin-Radushkevich, and Temkin isotherm models). It was found that the Freundlich isotherm model fit better with the coefficient of determination (R2) of 0.948 4 and a small sum of square error of 0.000 6. The maximum adsorption capacities (Qm) of Pb(II) and Cr adsorbed onto CF-AC were determined to be 6.27 and 23.6 mg/g, respectively. The adsorption process conformed well to pseudo-second order kinetics as revealed by the high R2 values obtained for both metals. The thermodynamic parameters showed that adsorption of Cr and Pb(II) ions onto CF-AC was spontaneous, feasible, and endothermic under the studied conditions. The mean adsorption energy (E) values revealed that the adsorption mechanism of Cr and Pb(II) by CF-AC is physical in nature. The results of the study showed that adsorbent developed from CF-AC can be efficiently used as an environmentally friendly alternative adsorbent, for removal of Cr and Pb(II) ions in tannery wastewater.
Hydraulic models for the generation of ?ood inundation maps are not commonly applied in mountain river basins because of the dif?culty in modeling the hydraulic behavior and the complex topography. This paper presents a comparative analysis of the performance of four twodimensional hydraulic models (HEC-RAS 2D, Iber 2D, Flood Modeller 2D, and PCSWMM 2D) with respect to the generation of ?ood inundation maps. The study area covers a 5-km reach of the Santa Barbara River located in the Ecuadorian Andes, at 2330 masl, in Gualaceo. The model's performance was evaluated based on the water surface elevation and ?ood extent, in terms of the mean absolute difference and measure of ?t. The analysis revealed that, for a given case, Iber 2D has the best performance in simulating the water level and inundation for ?ood events with 20- and 50-year return periods, respectively, followed by Flood Modeller 2D, HEC-RAS 2D, and PCSWMM 2D in terms of their performance. Grid resolution, the way in which hydraulic structures are mimicked, the model code, and the default value of the parameters are considered the main sources of prediction uncertainty.
This study aimed to investigate the biosorption potential of Na2CO3-modified Aloe barbadensis Miller (Aloe vera) leaf (MABL) powder for removal of Ni(II) ions from a synthetic aqueous solution. Effects of various process parameters (pH, equilibrium time, and temperature) were investigated in order to optimize the biosorptive removal. The maximum biosorption capacity of MABL was observed to be 28.986 mg/g at a temperature of 303 K, a biosorbent dose of 0.6 g, a contact time of 90 min, and a pH value of 7. Different kinetic models (the pseudo-first-order, pseudo-second-order, Elovich, and intraparticle diffusion models) were evaluated. The pseudo-second-order kinetic model was found to be the best fitted model in this study, with a coefficient of determination of R2 = 0.974. Five different isotherm models (the Langmuir, Freundlich, Temkin, Dubinin-Radushkevich, and Brunauer-Emmett-Teller (BET) models) were investigated to identify the best-suited isotherm model for the present system. Based on the minimum chi-square value (χ2 = 0.027) and the maximum coefficient of determination (R2 = 0.996), the Langmuir isotherm model was found to represent the system well, indicating the possibility of monolayer biosorption. The sticking probability (S*) was found to be 0.41, suggesting a physisorption mechanism for biosorption of Ni(II) on MABL. The biosorbent was characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), zeta potential, and BET surface area, in order to understand its morphological and functional characteristics.
The southern coast of the Gulf of Maine in the United States is prone to flooding caused by nor’easters. A state-of-the-art fully-coupled model, the Simulating WAves Nearshore (SWAN) model with unstructured grids and the ADvanced CIRCulation (ADCIRC) model, was used to study the hydrodynamic response in the Gulf of Maine during the Patriot’s Day storm of 2007, a notable example of nor’easters in this area. The model predictions agree well with the observed tide-surges and waves during this storm event. Waves and circulation in the Gulf of Maine were analyzed. The Georges Bank plays an important role in dissipating wave energy through the bottom friction when waves propagate over the bank from offshore to the inner gulf due to its shallow bathymetry. Wave energy dissipation results in decreasing significant wave height (SWH) in the cross-bank direction and wave radiation stress gradient, which in turn induces changes in currents. While the tidal currents are dominant over the Georges Bank and in the Bay of Fundy, the residual currents generated by the meteorological forcing and waves are significant over the Georges Bank and in the coastal area and can reach 0.3 m/s and 0.2 m/s, respectively. In the vicinity of the coast, the longshore current generated by the surface wind stress and wave radiation stress acting parallel to the coastline is inversely proportional to the water depth and will eventually be limited by the bottom friction. The storm surge level reaches 0.8 m along the western periphery of the Gulf of Maine while the wave set-up due to radiation stress variation reaches 0.2 m. Therefore, it is significant to coastal flooding.
The main objective of this study was to evaluate four latest global high-resolution satellite precipitation products (TMPA 3B42RT, CMORPH, TMPA 3B42V7, and CMORPH_adj) against gauge observations of the Yellow River Basin from March 2000 to December 2012. The assessment was conducted with several commonly used statistical indices at daily and monthly scales. Results indicate that 3B42V7 and CMORPH_adj perform better than the near real-time products (3B42RT and CMORPH), particularly the 3B42V7 product. The adjustment by gauge data significantly reduces the systematic biases in the research products. Regarding the near real-time datasets, 3B42RT overestimates rainfall over the whole basin, while CMORPH presents a mixed pattern with negative and positive values of relative bias in low- and high-latitude regions, respectively, and CMORPH performs better than 3B42RT on the whole. According to the spatial distribution of statistical indices, these values are optimized in the southeast and decrease toward the northwest, and the trend is similar for the spatial distribution of the mean annual precipitation during the period from 2000 to 2012. This study also reveals that all the four products can effectively detect rainfall events. This study provides useful information about four mainstream satellite products in the Yellow River Basin, and the findings can facilitate the use of global precipitation measurement (GPM) data in the future.
Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling
water quality. The evolutionary algorithm (EA) is a new technique for improving the performance of artificial intelligence models such as the
adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN). Attempts have been made to make the models more
suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being
trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization (PSO)
and ant colony optimization for continuous domains (ACOR) in estimating water quality parameters at three stations along the Zayandehrood
River, in Iran. The ANFIS-PSO and ANFIS-ACOR methods were also compared with the classic ANFIS method, which uses least squares and
gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity (EC), total dissolved
solids (TDS), the sodium adsorption ratio (SAR), carbonate hardness (CH), and total hardness (TH). Correlation analysis was performed using
SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with
ANFIS-ACOR. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters.
The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and higher accuracy. Our approach was to first apply the single-channel brightness radiometric algorithm to estimate soil moisture from the respective brightness temperature observations of the SMAP, SMOS, AMSR2, FY3B, and FY3C satellites on the same day and then produce a daily composite dataset by averaging the individual satellite-retrieved soil moisture. We further evaluated our product, the official soil moisture products of the five satellites, and the ensemble mean (i.e., arithmetic mean) of the five official satellite soil moisture products against ground observations from two networks in Central Tibet and Anhui Province, China. The results show that our product outperforms the individual released products of the five satellites and their ensemble means in the two validation areas. The root mean square error (RMSE) values of our product were 0.06 and 0.09 m3/m3 in Central Tibet and Anhui Province, respectively. Relative to the ensemble mean of the five satellite products, our product improves the accuracy by 9.1% and 57.7% in Central Tibet and Anhui Province, respectively. This demonstrates that jointly using brightness temperature observations from multiple satellites to retrieve soil moisture not only improves the spatial coverage of daily observations but also produces better daily composite products.
To assess the magnitude of water quality decline in the Turag River of Bangladesh, this study examined the seasonal variation of physicochemical parameters of water, identified potential pollution sources, and clustered the monitoring months with similar characteristics. Water samples were collected in four distinct seasons to evaluate temperature, pH, dissolved oxygen (DO) concentration, five-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), electrical conductivity (EC), chloride ion (Cl−) concentration, total alkalinity (TA), turbidity, total dissolved solids (TDS) concentration, total suspended solids (TSS) concentration, and total hardness (TH) using standard methods. The analytical results revealed that 40% of water quality indices were within the permissible limits suggested by different agencies, with the exception of EC, Cl− concentration, TA, turbidity, DO concentration, BOD5, and COD in all seasons. Statistical analyses indicated that 52% of the contrasts were significantly different at a 95% confidence interval. The factor analysis presented the best fit among the parameters, with four factors explaining 94.29% of the total variance. TDS, BOD5, COD, EC, turbidity, DO, and Cl− were mainly responsible for pollution loading and were caused by the significant amount of industrial discharge and toxicological compounds. The cluster analysis showed the seasonal change in surface water quality, which is usually an indicator of pollution from rainfall or other sources. However, the values of different physicochemical properties varied with seasons, and the highest values of pollutants were recorded in the winter. With the change in seasonal temperature and increase in rainfall, the seasonal Turag River water followed a self-refining trend as follows: rainy season > pre-winter > summer > winter.
- Top Download
- Top Click
Editor-in-ChiefChao Wang
Edited byEditorial Board of Water Science and Engineering
Distributed byEditorial Office of Water Science and Engineering
News
- WSE Special Issue on Security and Sustainability for Hydraulic Structures November 01，2021
- WSE Special Issue on Water Security and Sustainability April 14，2021
- WSE Special Issue for CORE2021 March 09，2021
Sponsor
Publisher
