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Regional patterns of the seasonal weather and atmospheric moisture origins can impact the seasonal activities of extreme precipitation in the eastern United States (eUS). At many locations, tracks of atmospheric moisture can have different influence on timing of extreme precipitation based on the moisture origin source. In this study, we evaluate the contribution of atmospheric rivers (ARs) and their moisture origin sources to the distribution and seasonal effectivity of annual maximum precipitation (AMP) across the eUS during 1950–2021. Our results suggest that AR is a dominant mechanism of AMP in the eUS as it contributes to 75% (31 438 out of 41 976) of total AMP events recorded between 1950 and 2021. The seasonal analysis based on the circular density approach shows that spring, summer, and fall seasons display strong signals of seasonality of AMP-AR events. The spatial patterns of AMP associated with the four major moisture sources (the Pacific Ocean, the Atlantic Ocean, the combined source of the Caribbean Sea and the Gulf of Mexico, and the local source of moisture) reinforce the key role ARs play in transporting water vapor to the eUS from both oceanic and inland originated moisture. The results additionally highlight the importance of moisture subsources (major source subregions) in modulating the seasonality of extreme precipitation in the eUS. [ABSTRACT FROM AUTHOR]

Temporal changes in the seasonality of extreme precipitation, and possible teleconnections between the seasonality of extreme precipitation and large‐scale climate patterns are not well understood. In this study, we investigated temporal changes in seasonality of annual daily maximum (ADM) and monthly maximum (MM) precipitation indices over the period 1951–2014 for 1,108 stations across the contiguous USA. We also examined seasonality of extreme precipitation during negative and positive phases of three major oscillations: the El Niño–Southern Oscillation, the Northern Atlantic Oscillation, and the Pacific Decadal Oscillation. Our results show that many climate regions within the contiguous USA display distinct seasonality for both ADM and MM. Comparison of seasonality between two historical records of equal length, that is, before and after 1981, shows great spatial variability across the contiguous USA. While a spatial coherence of change in the mean date of occurrence of extreme precipitation across a large area is not visible, a cluster of stations showing decrease in strength of seasonality for the recent period is concentrated in the eastern Gulf Coast and coastal sites of Northeast and Northwest regions. Extreme precipitation seasonality during negative and positive phases of three climate indices revealed that large‐scale climate variabilities have a strong influence on the mean date of occurrence of extreme precipitation but generally weak influence on the strength of seasonality in the contiguous USA. Results from our study might be helpful for sustainable water resource management, flood risk mitigation, and prediction of future precipitation seasonality. [ABSTRACT FROM AUTHOR]

Global warming is likely to provoke extreme storms in the eastern United States (eUS), ultimately affecting the probabilistic distribution of the dates of daily maximum precipitation. In this study, probabilistic properties of timing of annual maximum precipitation (AMP) were studied using circular statistics at 583 sites in the eUS (1950–2019). A kernel circular density method was applied to examine distributional modes of timing of AMP. The results of circular median show that seasonality is pronounced across the eUS with many locations having their median date of occurrence in summer, and AMP seasonality is strong in the East North Central region. Similarly, results of circular density method applied to the distribution of AMP timing shows that around 90% of the sites have two or three modes of AMP seasonality in the eUS. Comparison of seasonality between two historical records of equal length (1950–1984 and 1985–2019) shows great spatial variability across the eUS. Temporal changes in seasonal modes for AMP dates revealed four different cases of seasonality changes: (i) weakening of seasonality, (ii) strengthening of seasonality, (iii) strong seasonality for both the old and recent periods, (iv) or uniform or no preferred seasonality for both periods. While a spatial coherence of seasonality changes was not observed, majority of sites showed strong seasonality (case iii) for old and recent periods mainly during summer and fall seasons. [ABSTRACT FROM AUTHOR]

In the eastern United States, the empirical probability distribution of extreme daily precipitation comprises heavy rainfall events stemming from North Atlantic tropical cyclones (TCs). At many locations, these events influence estimates of extreme value statistics (e.g., 100‐year event), thus have important bearing on the sizing of flood protection infrastructure and, in general, flood risk management and preparedness. Consequently, a characterization of location specific and regional patterns in precipitation extremes and changes therein has salience for both scientific and engineering concerns. To this end, analysis of seasonal and annual maximum daily precipitation at 667 long‐term stations across the eastern United States was pursued to ascertain recent changes in the extreme events over the 1950–2011 period. Three key results from this study illuminate less understood facets of recent changes in precipitation extremes: (a) an overall increase in the fraction of seasonal and annual maximum precipitation events linked to TCs, (b) a dramatic increase in the correlation between Accumulated Cyclone Energy Index and the leading principal component of extreme precipitation, and (c) changes in the spatial patterns of regions with highest TC‐related risk for heavy precipitation. [ABSTRACT FROM AUTHOR]

The current global issue of water scarcity has demanded for over-abstraction of conventional freshwater resources. The states of water scarcity are anticipated to worsen, as by 2050 the population is estimated to reach 9 billion worldwide. Desalination is considered a solution to solve the water scarcity issues, as it is considered a drought-proof water source, which does not depend on climate change, river flows or reservoir levels. Moreover, membrane fouling is still the main "Achilles heel" for the effective operation of desalination systems. This makes the technology chemically, energetically and operationally intensive and requires a considerable infusion of capital. The application of an artificial neural network (ANN), the computing model inspired by the human brain, and its variants, have been developed that can optimize the operation of membrane-based desalination system through analyzing the complex experimental and real-time data. This review paper presents the recent trends and developments focussed primarily on the modelling and simulation of reverse osmosis (RO) plant using ANN to solve the challenging problem in membrane-based desalination systems. The literature review suggested that ANN has a potential application in predicting linear, nonlinear, complicated complex systems with high accuracy and with better control, prediction of membrane fouling, cost analysis. Therefore, ANN considered a strong basis to attract and motivate the researchers to work in this field in the future. [ABSTRACT FROM AUTHOR]

Amongst the present water treatment technologies membrane separation is gaining focus nowadays. Emulsion liquid membrane is one of the advanced strategies used for pollution removal from waste water in which the use of biosurfactant makes this approach eco-friendly technique. The norfloxacin belongs to the fluoroquinolones class of antibiotic and extensively used for urinary tract infections, which is the model pollutant in this study. Saponin was solvent extracted from soapnuts and used as biosurfactant. The main and interactive effects of parameters such as saponin concentration (0.01-0.03 g/100 mL), NaOH concentration (0.25-0.75 M) and Initial Norfloxacin concentration (25-75 mg/L) were investigated using Response Surface Methodology (RSM) - Box Behnken Design (BBD) and Artificial Neural Networks (ANN) design. The results suggested that developed 3-8-1 ANN model evaluated in terms of performance measure (R2: 0.9806, Chi square: 0.02) and error functions (RMSE: 0.11, MAE: 0.09, SEP: 1.16, MPE: 1.76) demonstrated superiority more precisely than the RSM model in terms of both data fitting and prediction capability. The optimal conditions for ELM were found to be: initial norfloxacin concentration 67.65 mg/L, saponin concentration 0.021 mg/100 mL, and NaOH concentration 0.36 M and the extraction efficiency at these parameter settings would be 91.27%. [ABSTRACT FROM AUTHOR]

The FEWsOnt ontology models major structural and dynamic concepts of the food–energy–water (FEW) systems from the complex system perspective by defining the emergent, nonlinear, and scale-invariant state transitions and behaviors of the network elements that result from natural and planned processes. The model represents the semantics of concepts such as security, footprint, challenge, risk, impact, and uncertainty in relation to governance and assessment of the level of sustainability of the FEW systems in varied domains of usage. The ontology will allow stakeholders working with the FEW systems' data to draw new inferences using semantic facts and discover insights and relationships among the systems' elements to make improved assessment and decisions toward sustainable growth. The knowledge-based model will lead users to optimize the tradeoffs and identify and prevent adverse changes to the FEW systems in relation to the interacting natural and social systems. The annotated terminology and formalized interactions in the ontology will facilitate the integration of the diverse FEW data types, improve communication among researchers, and help to reduce environmental stresses. [ABSTRACT FROM AUTHOR]

Changes in extreme precipitation are associated with changes in their probability distributions and the characteristics of quantiles derived from fitted distributions. In this study, the linear quantile regression method is employed to analyse spatio‐temporal trends of extreme precipitation and to study the impact of North Atlantic tropical cyclones in the distribution of extreme precipitation for the southeastern United States. Daily annual maximum precipitation over the period of 64 years (1950–2013) for 107 sites was used for the analysis. Our results show that changes in upper quantiles of the distributions of the extreme precipitation have occurred in the southeastern United States. Analysis of the potential changes in the distribution of the extreme precipitation by separating the historical record into two periods, that is, before and after 1981, reveals that upper‐quantile trends have increasing magnitude in most of the sites for the latest time period. Analysis of the impact of tropical cyclones in the extreme precipitation distribution shows that overall the heavy rainfall events in the recent decades may have been caused by tropical cyclones. Such results are particularly useful for water managers who are more concerned with extreme values rather than the averaged one. Hence, our study has significant implication in environmental and infrastructural assessment as well as disaster risk management. [ABSTRACT FROM AUTHOR]

Controlling fouling in seawater reverse osmosis and ultrafiltration systems is a major challenge during algal blooms. This study investigates UF fouling potential of four marine algae and their algal organic matter (AOM): Chaetoceros affinis (Ch), Rhodomonas balthica (Rh), Tetraselmis suecica (Te), and Phaeocystis globulosa (Ph). Batch culture monitoring of the four different marine algal species showed remarkable differences in their production of biopolymers, transparent exopolymer particles (TEP) and their membrane fouling potential (MFI-UF 10 kDa ). MFI-UF 10 kDa was linearly related to biopolymer concentration, and TEP during the growth and stationary/death phase of all four algal species. But the linear relation of MFI-UF 10 kDa with algal cell density and chlorophyll-a concentration did not continue during the stationary/death phase. In experiments with capillary UF membranes, non-backwashable fouling of UF membranes varied strongly for the four different AOM solutions tested, and was linked to the presence of polysaccharides (stretching-OH) and sugar ester (stretching S˭O) groups in the AOM. The non-backwashable fouling coincided with MFI-UF 150 kDa and TEP concentration. Therefore, determination of these parameters (MFI and TEP) and correlating with MODIS satellite data may generate useful information about the fouling potential of seawater at different locations during an algal bloom. [ABSTRACT FROM AUTHOR]

Changes in seasonality of extreme storms have important implications for public safety, storm water infrastructure, and, in general, adaptation strategies in a changing climate. While past research on this topic offers some approaches to characterize seasonality, the methods are somewhat limited in their ability to discern the diversity of distributional types for extreme precipitation dates. Herein, we present a comprehensive approach for assessment of temporal changes in the calendar dates for extreme precipitation within a circular statistics framework which entails: (a) three measures to summarize circular random variables (traditional approach), (b) four nonparametric statistical tests, and (c) a new nonparametric circular density method to provide a robust assessment of the nature of probability distribution and changes. Two 30 year blocks (1951-1980 and 1981-2010) of annual maximum daily precipitation from 10 stations across the state of Maine were used for our analysis. Assessment of seasonality based on nonparametric approach indicated nonstationarity; some stations exhibited shifts in significant mode toward Spring season for the recent time period while some other stations exhibited multimodal seasonal pattern for both the time periods. Nonparametric circular density method, used in this study, allows for an adaptive estimation of seasonal density. Despite the limitation of being sensitive to the smoothing parameter, this method can accurately characterize one or more modes of seasonal peaks, as well as pave the way toward assessment of changes in seasonality over time. [ABSTRACT FROM AUTHOR]

This study aimed to quantify the effect of membrane surface porosity on particulate fouling predicted by the MFI-UF method at constant flux. Firstly, the surface porosity of polyethersulfone UF membranes (5–100 kDa) was determined using ultra-high resolution SEM. Thereafter, the MFI-UF was measured using suspensions of polystyrene particles (75 nm), which were pre-washed to remove surfactant and particle fractions smaller than the pores of MFI-UF membranes, thus ensuring complete retention of particles during MFI-UF measurements. Consequently, the MFI-UF values of washed polystyrene particle suspensions were independent of the pore size and depended only on the surface porosity of MFI-UF membrane. The results showed that the membrane surface porosity decreased with MWCO from 10.5% (100 kDa) to 0.6% (5 kDa), and consequently the MFI-UF increased from 3700 to 8700 s/L2, respectively. This increase in MFI-UF was attributed to the non-uniform distribution of membrane pores, which is exacerbated as surface porosity decreases. Consequently, preliminary correction factors of 0.4–1.0 were proposed for MFI-UF measured with UF membranes in the range 5–100 kDa. Finally, the surface porosity correction was applied to predict particulate fouling in a full-scale RO plant. However, additional research is required to establish correction factors for different types of feed water. [Display omitted] • Novel approach allows quantification of membrane surface porosity effect on MFI-UF. • Washed polystyrene particles were used to investigate the surface porosity effect. • Effect of surface porosity depends on how uniform the pores are distributed. • Low membrane surface porosity can result in overestimation of measured MFI-UF value. • Surface porosity correction improved particulate fouling prediction in an RO plan. [ABSTRACT FROM AUTHOR]

The modified rational method (MRM) is an extension of the rational method to develop triangular and trapezoidal runoff hydrographs. A trapezoidal unit hydrograph (UH) was developed from the MRM for the case when the duration of rainfall is less than the time of concentration of the watershed and is called the modified rational unit hydrograph (MRUH). The MRUH method was applied to 1400 rainfall-runoff events at 80 watersheds in Texas. Application of the MRUH method involved three steps: () determination of rainfall excess using the runoff coefficient; () determination of the MRUH using drainage area and time of concentration; and () simulating event runoff hydrographs. The MRUH performed as well as the Gamma function UH, Clark-HEC-1 UH and NRCS curvilinear UH methods when the same rainfall loss model was used. The MRUH method can be applied to time-variable rainfall distributions and at watersheds with drainage areas greater than typically used for the rational method (a few hundred acres). [ABSTRACT FROM AUTHOR]

The runoff coefficient, , of the rational method is an expression of rate proportionality between rainfall intensity and peak discharge. Values of were derived for 80 developed and undeveloped watersheds in Texas using two distinct methods. First, the rate-based runoff coefficient, , was estimated for each of about 1,500 rainfall-runoff events. Second, the frequency-matching approach was used to derive a runoff coefficient, , for each watershed. Published values, , or literature-based runoff coefficients were compared to those obtained from the methods investigated here. Using the 80 Texas watersheds, comparison of the two methods shows that about 75% of literature-based runoff coefficients are greater than and the watershed-median , but for developed watersheds with more impervious cover, literature-based runoff coefficients are less than and . An equation applicable to many Texas watersheds is proposed to estimate as a function of impervious area. [ABSTRACT FROM AUTHOR]

The rational method for peak discharge () estimation was introduced in the 1880s. The runoff coefficient () is a key parameter for the rational method that has an implicit meaning of rate proportionality, and the has been declared a function of the annual return period by various researchers. Rate-based runoff coefficients as a function of the return period, , were determined for 36 undeveloped watersheds in Texas using peak discharge frequency from previously published regional regression equations and rainfall intensity frequency for return periods of 2, 5, 10, 25, 50, and 100 years. The values and return period adjustments determined in this study are most applicable to undeveloped watersheds. The return period adjustments determined for the Texas watersheds in this study and those extracted from prior studies of non-Texas data exceed values from well-known literature such as design manuals and textbooks. Most importantly, the return period adjustments exceed values currently recognized in Texas Department of Transportation design guidance when . [ABSTRACT FROM AUTHOR]

The rational method for peak discharge (Qp) estimation was introduced in the 1880s. Although the rational method is considered simplistic, it remains an effective method for estimating peak discharge for small watersheds. The runoff coefficient (C) is a key parameter for the rational method and can be estimated in various ways. Literature-based C values (Clit) are listed for different land-use/land cover (two words, no hyphen) (LULC) conditions in various design manuals and textbooks; however, these Clit values were developed with little basis on observed rainfall and runoff data. In this paper, Clit values were derived for 90 watersheds in Texas by using LULC data for 1992 and 2001; the Clit values derived from the two data sets were essentially the same. Also for this study, volumetric runoff coefficients (Cv) were estimated by using observed rainfall and runoff depths from more than 1,600 events observed in the watersheds. Watershed-median and watershed-average Cv values were computed, and both are consistent with data from the National Urban Runoff Program. In addition, Cv values were estimated by using rank-ordered pairs of rainfall and runoff depths (i.e., frequency matching). As anticipated, C values derived by all three methods (literature based, event totals, and frequency matching) consistently had larger values for developed watersheds than for undeveloped watersheds. Two regression equations of Cv versus percent impervious area were developed and combined into a single equation that can be used to rapidly estimate Cv values for similar Texas watersheds. [ABSTRACT FROM AUTHOR]

Measuring the bacterial growth potential of seawater reverse osmosis (SWRO) feed water is an issue that is receiving growing attention. This study developed and demonstrated the applicability of the flow-cytometry (FCM)-based bacterial growth potential (BGP) method to assess the biofouling potential in SWRO systems using natural microbial consortium. This method is relatively fast (2–3 days) compared to conventional bioassays. The effect of the potential introduction of nutrients during measurement has been studied thoroughly to achieve the lowest measure value of about 45,000 cells/mL, which is equivalent to about (10 µg-C glucose/L). The BGP method was applied in two full-scale SWRO plants that included (i) dissolved air flotation (DAF) and ultra-filtration (UF); (ii) dual-media filtration (DMF) and cartridge filter (CF), which were compared with the cleaning frequency of the plants. A significant reduction (54%) in BGP was observed through DAF–UF as pre-treatment (with 0.5 mg Fe3+/L), while there was a 40% reduction by DMF–CF (with 0.8 mg Fe3+/L). In terms of the absolute number, the SWRO feed water after DAF–UF supports 1.5 × 106 cells/mL, which is 1.25 times higher than after DMF–CF. This corresponds to the higher cleaning-in-place (CIP) frequency of SWRO with DAF–UF compared to DMF–CF as pre-treatment, indicating that the BGP method has an added value in monitoring the biofouling potential in SWRO systems. [ABSTRACT FROM AUTHOR]

Climate change is likely to impact precipitation as well as snow accumulation and melt in the Northeastern and Upper Midwest United States, ultimately affecting the quantity and seasonal distribution of streamflow. The objective of this study is to analyze seasonality of long-term daily annual maximum streamflow (AMF) records and its changes for 158 sites in Northeastern and Upper Midwest Unites States. A comprehensive circular statistical approach comprising a kernel density method was used to assess the seasonality of AMF. Temporal changes were analyzed by separating the AMF records into two 30-year sub-periods (1951–1980 and 1981–2010). Results for temporal change in seasonality showed mixed pattern/trend across the stations. While for majority of stations, the distribution of AMF timing is strongly unimodal (concentrated around spring season) for the period 1951–1980, the seasonal modes have weakened during the period 1981–2010 for several stations along the coastal region with simultaneous emergence of multiple modes indicating changes of seasonality therein. The fresh statistical approach based on non-parametric circular density estimates reduces some of the limitations of previous studies to detect and model event timing distributions with multiple seasons and addresses issues of non-stationarity in the data records of extreme events. [ABSTRACT FROM AUTHOR]

Almost every year, north Atlantic tropical cyclones (TCs) are responsible for significant socioeconomic losses across the Mid-Atlantic USA. However, the extent to which TC activity contributes to the changes in the probability distributions of the extreme precipitation have not yet been comprehensively characterized for this region. In this study, a quantile regression method was used to investigate the trends of the lower (τ = 0.2) and upper (τ = 0.8) quantiles of annual and seasonal daily maximum precipitation series for the region using the station-based daily precipitation data for the period 1950–2011. Results show that the rates of changes in the upper quantile have greatly strengthened for the region. Analysis of the spatial pattern of the lower and upper quantile trends for TC and non-TC extreme precipitation series shows that trends have larger magnitudes in most of the sites for TC precipitation series as compared with the non-TC precipitation series for both the lower and upper quantiles. Additionally, the highest trends are observed in the upper quantile for TC time series indicating that TC precipitation is contributing more to the upper tails of the extreme precipitation distribution as compared to the non-TC precipitation. Results from this study have implications for the improved design and reassessment of flood-controlling infrastructure. [ABSTRACT FROM AUTHOR]