Stochastic Analysis and Modeling of Velocity Observations in Turbulent Flows

Highly turbulent water flows,often encountered near human constructions like bridge piers,spillways,and weirs,display intricate dynamics characterized by the formation of eddies and vortices.These formations,varying in sizes and lifespans,significantly influence the distribution of fluid velocities within the flow.Subsequently,the rapid velocity fluctuations in highly turbulent flows lead to elevated shear and normal stress levels.For this reason,to meticulously study these dynamics,more often than not,physical modeling is employed for studying the impact of turbulent flows on the stability and longevity of nearby structures.Despite the effectiveness of physical modeling,various monitoring challenges arise,including flow disruption,the necessity for concurrent gauging at multiple locations,and the duration of measurements.Addressing these challenges,image velocimetry emerges as an ideal method in fluid mechanics,particularly for studying turbulent flows.To account for measurement duration,a probabilistic approach utilizing a probability density function(PDF)is suggested to mitigate uncertainty in estimated average and maximum values.However,it becomes evident that deriving the PDF is not straightforward for all turbulence-induced stresses.In response,this study proposes a novel approach by combining image velocimetry with a stochastic model to provide a generic yet accurate description of flow dynamics in such applications.This integration enables an approach based on the probability of failure,facilitating a more comprehensive analysis of turbulent flows.Such an approach is essential for estimating both short-and long-term stresses on hydraulic constructions under assessment.

Prioritization of Water Resources Management Problems in North Central Nigeria Using Rapid Impact Assessment Matrix (RIAM)

This study employed the Rapid Impact Assessment Matrix (RIAM) to prioritize the water resources management problems in the North Central Nigeria. This was done through the assessment of the status of water resources management in the region, evaluation of existing policy and strategy of water management, identification of the management problems and the prioritization with RIAM. The stakeholders identified water resources management problems, ranked them in other of severity in different categories and also evaluated them using the RIAM techniques in the administered questionnaire. Eleven problems were analyzed based on the physical/chemical, biological/ecological, social/cultural and economic/operational factors using several impact indicators. Scores were assigned, the RIAM models applied and the averages taken to arrive at the final assessment scores. The two major water resources management problems identified are: 1) inadequate funds for further agricultural, hydroelectric, navigation and industrial development;2) poor data collection and banking. These problems were prioritized by RIAM in order of severity for urgent intervention. The RIAM technique has made a key contribution to the prioritization of water resources management by providing insights into urgent problems according to stakeholders and thus guides the policy maker in appropriate decision making.

On the Use of Agent Based Modelling for Addressing the Social Component of Urban Water Management in Europe

The paper aimed to provide a review of different tools that estimate how human behavior changes by water management strategies and quantify this change to support the decisions of urban water managers. To support decision makers, it is essential to be able to model the urban water system’s human part explicitly and link it to the hydro system’s response, rather than only explore the reaction of the system based on scenarios. To do so, tools are needed that can model the human part of the system, explore its reaction to potential changes and dynamically link back this to the techno-environmental model of the water system. This work reviews state-of-the-art ABMs that are publicly available focusing on the human part of the urban water system in Europe. The review leads to the proposals of three pillars for future development of ABMs for urban water management in Europe: end-user enablement;Machine Learning and Artificial Intelligence integration and adversaries modelling.

Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale

Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability.Despite the scientific interest suggested by such assumptions,the relationships between descriptive time series features(e.g.,temporal dependence,entropy,seasonality,trend and linearity features)and actual time series forecastability(quantified by issuing and assessing forecasts for the past)are scarcely studied and quantified in the literature.In this work,we aim to fill in this gap by investigating such relationships,and the way that they can be exploited for understanding hydroclimatic forecastability and its patterns.To this end,we follow a systematic framework bringing together a variety of–mostly new for hydrology–concepts and methods,including 57 descriptive features and nine seasonal time series forecasting methods(i.e.,one simple,five exponential smoothing,two state space and one automated autoregressive fractionally integrated moving average methods).We apply this framework to three global datasets originating from the larger Global Historical Climatology Network(GHCN)and Global Streamflow Indices and Metadata(GSIM)archives.As these datasets comprise over 13,000 monthly temperature,precipitation and river flow time series from several continents and hydroclimatic regimes,they allow us to provide trustable characterizations and interpretations of 12-month ahead hydroclimatic forecastability at the global scale.We first find that the exponential smoothing and state space methods for time series forecasting are rather equally efficient in identifying an upper limit of this forecastability in terms of Nash-Sutcliffe efficiency,while the simple method is shown to be mostly useful in identifying its lower limit.We then demonstrate that the assessed forecastability is strongly related to several descriptive features,including seasonality,entropy,(partial)autocorrelation,stability,(non)linearity,spikiness and heterogeneity features,among others.We further(i)show that,if such descriptive information is available for a monthly hydroclimatic time series,we can even foretell the quality of its future forecasts with a considerable degree of confidence,and(ii)rank the features according to their efficiency in explaining and foretelling forecastability.We believe that the obtained rankings are of key importance for understanding forecastability.Spatial forecastability patterns are also revealed through our experiments,with East Asia(Europe)being characterized by larger(smaller)monthly temperature time series forecastability and the Indian subcontinent(Australia)being characterized by larger(smaller)monthly precipitation time series forecastability,compared to other continental-scale regions,and less notable differences characterizing monthly river flow from continent to continent.A comprehensive interpretation of such patters through massive feature extraction and feature-based time series clustering is shown to be possible.Indeed,continental-scale regions characterized by different degrees of forecastability are also attributed to different clusters or mixtures of clusters(because of their essential differences in terms of descriptive features).

Climate Change Impact on Sub-Surface Hydrology of Kaduna River Catchment

This paper applied the use of a stochastic weather generator at the site of a hydrological model to simulate the impact of climate change on the sub-surface hydrological response of Kaduna River as a basis for a sustainable ground water development plan. Average time series of rainfall, temperature and gauge height observed readings were downscaled to the watershed flow volume and applied as forcing to simulate the ground water response as spatially lumped, ignoring the heterogeneous nature of the ground water aquifer. Future simulations indicate increase in base flow, ground storage and decrease soil storage throughout the summer and autumn months for the catchment.

Calibration of Hydrognomon Model for Simulating the Hydrology of Urban Catchment

Due to chaotic nature of flow in natural open channels and the physical processes and the unknown in a river basin variable, the parameters to be used in studying the behavior of river basin to a given rainfall data cannot be measured directly for this reason, a hydrological model was calibrated and applied to simulate the hydrology of Kaduna River (7112 sq miles) North West Nigeria. Prior to the model calibration, a sensitivity analysis of the hydrognomon model to the parameters was carried out to gain a better understanding of the correspondence between the data and the physical processes being modeled.

Electrochemical determination of catechol in tea samples using anthraquinone modified carbon paste electrode

Electrochemical investigation of catechol using square wave voltammetry with anthraquinone modified carbon paste electrode was found to be very sensitive. Compared with the unmodified carbon paste electrode, the anthraquinone modified electrode remarkably increases the peak currents of catechol, and greatly lowers the peak potential separation. Two varieties of tea, namely green, and black variety: Wush Wush tea, from Ethiopia, known by its brand name were investigated. Responses for the extracts using ethanol: water (1:4) % v/v showed green tea to be superior in catechol content. Optimization of different variables such as pH of working solution, modifier composition and square wave parameters such as frequency, amplitude and step potential were made to improve the method efficiency during the experiment. The reproducibility for the nine repeated analysis of 80 μmol·L-1 of catechol gave a relative standard deviation of 3.65% and linear calibration plots were obtained in the range 6 to 80 μmol·L-1 with (R = 0.998) and the detection limit with (S/N = 3) was as low as 2.155 x 10-7 mol·L-1.

Effectiveness of tertiary treatment processes in removing different classes of emerging contaminants from domestic wastewater

In this work,38 different organic emerging contaminants(ECs),belonging to various chemical classes such as pharmaceuticals(PhCs),endocrine-disrupting chemicals(EDCs),benzotriazoles(BTRs),benzothiazoles(BTHs),and perfluorinated compounds(PFCs),were initially identified and quantified in the biologically treated wastewater collected from Athens’(Greece)Sewage Treatment Plant(STP).Processes already used in existing STPs such as microfiltration(MF),nanofiltration(NF),ultrafiltration(UF),UV radiation,and powdered activated carbon(PAC)were assessed for ECs’removal,under the conditions that represent their actual application for disinfection or advanced wastewater treatment.The results indicated that MF removed only one out of the 38 ECs and hence it was selected as pretreatment step for the other processes.UV radiation in the studied conditions showed low to moderate removal for 5 out of the 38 ECs.NF showed better results than UF due to the smaller pore sizes of the filtration system.However,this enhancement was observed mainly for 8 compounds originating from the classes of PhCs and PFCs,while the removal of EDCs was not statistically significant.Among the various studied technologies,PAC stands out due to its capability to sufficiently remove most ECs.In particular,removal rates higher than 70%were observed for 9 compounds,22 were partially removed,while 7 demonstrated low removal rates.Based on our screening experiments,future research should focus on scaling-up PAC in actual conditions,combining PAC with other processes,and conduct a complete economic and environmental assessment of the treatment.

GIS-based optimisation of the hydrometeorological network in Greece

The operation of a network of hydrometeorological stations constitutes basic infrastructure for the management of water resources.Despite the increased need for rational water management in Greece,there is not a single network of hydrometeorological stations.Various State Services have established sectional networks of limited coverage,resulting in many disadvantages.This study aims at the optimisation of the existing network,using GIS methods.An optimum network of hydrometeorological stations is designed,which constitutes the base for the optimisation of the present network.The optimum network consists of 1379 hydrometeorological stations and is based on the guidelines of international organisations.The uniform spatial distribution of the stations and criteria concerning the selection of the appropriate site,such as the low terrain slope,easy accessibility and proximity to special interest points,were of special importance for its development.Eventually,only the 20%of the existing stations remain at their locations and are included in the optimised network.The rest of the stations should be redistributed according to the proposed sites of the optimum network.