Intentional contamination of water distribution networks: developing indicators for sensitivity and vulnerability assessments

Performing a comprehensive risk analysis is primordial to ensure a reliable and sustainable water supply. Though the general framework of risk analysis is well established, specific adaptation seems needed for systems such as water distribution networks (WDN). Understanding of vulnerabilities of WDN against deliberate contamination and consumers’ sensitivity against contaminated water use is very vital to inform decision-maker. This paper presents an innovative step-by-step methodology for developing comprehensive indicators to perform sensitivity, vulnerability and criticality analyses in case of absence of early warning system (EWS). The assessment and the aggregation of these indicators with specific fuzzy operators allow identifying the most critical points in a WDN. Intentional intrusion of contaminants at these points can potentially harm both the consumers as well as water infrastructure. The implementation of the developed methodology has been demonstrated through a case study of a French WDN unequipped with sensors.     

Hybrid denoising-jittering data pre-processing approach to enhance multi-step-ahead rainfall–runoff modeling

Successful modeling of stochastic hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. On the other hand in training phase of any Artificial Intelligence based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole population. Accordingly in the present article first, wavelet-based denoising method was used in order to smooth hydrological time series and then small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smoothed time series to form different denoised-jittered training data sets, for Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling of daily and multi-step-ahead rainfall–runoff process of the Milledgeville station of the Oconee River and the Pole Saheb station of the Jighatu River watersheds, respectively located in USA and Iran. The proposed hybrid data pre-processing approach in the present study is used for the first time in modeling of time series and especially in modeling of hydrological processes. Furthermore, the impacts of denoising (smoothing) and noise injection (jittering) have been simultaneously investigated neither in hydrology nor in any other engineering fields. To evaluate the modeling performance, the outcomes were compared with the results of multi linear regression and Auto Regressive Integrated Moving Average models. Comparing the achieved results via the trained ANN and ANFIS models using denoised-jittered data showed that the proposed data pre-processing approach which serves both denoising and jittering techniques could improve performance of the ANN and ANFIS based single-step-ahead rainfall–runoff modeling of the Milledgeville station up to 14 and 12% and of the Pole Saheb station up to 22 and 16% in the verification phase. Also the results of multi-step-ahead modeling using the proposed data pre-processing approach showed improvement of modeling for both watersheds.     

Statistical evaluation and probabilistic modeling of aftershock sequences of Iranian plateau

The present research focuses on the statistical evaluation of Iranian plateau aftershocks from an engineering perspective and presents probabilistic models applicable for generating random earthquake scenarios. Accordingly, a comprehensive earthquake data catalog including the period from 1964 to 2016 is prepared. Data are declustered into 37 separate mainshock-aftershock sequences by considering the completeness moment magnitude of the database. The well-known modified Omori occurrence rate formula is adopted to determine the recurrence time of the events, considering the effect of secondary aftershocks. In addition to computing the probability density functions of the parameters of the Omori formula, the joint probability distribution of the aftershock occurrence versus magnitude and occurrence time is obtained for modeling their magnitude sequences. The obtained results are applicable for producing randomly generated mainshock-aftershock scenarios.     

BARENTS16: a 1-D velocity model for the western Barents Sea

A minimum 1-D seismic velocity model for routine seismic event location purposes was determined for the area of the western Barents Sea, using a modified version of the VELEST code. The resulting model, BARENTS16, and corresponding station corrections were produced using data from stations at regional distances, the vast majority located in the periphery of the recorded seismic activity, due to the unfavorable land–sea distribution. Recorded seismicity is approached through the listings of a joint bulletin, resulting from the merging of several international and regional bulletins for the region, as well as additional parametric data from temporary deployments. We discuss the challenges posed by this extreme network-seismicity geometry in terms of velocity estimation resolution and result stability. Although the conditions do not facilitate the estimation of meaningful station corrections at the farthermost stations, and even well-resolved corrections do not have a convincing contribution, we show that the process can still converge to a stable velocity average for the crust and upper mantle, in good agreement with a priori information about the regional structure and geology, which reduces adequately errors in event location estimates.     

Allocating river water in a cooperative way: a case study of the Dongjiang River Basin,South China

Water resources provide the foundation for human development and environmental sustainability. Water shortage occurs more or less in some regions, which usually causes sluggish economic activities, degraded ecology, and even conflicts and disputes over water use sectors. Game theory can better reflect the behaviors of involved stakeholders and has been increasingly employed in water resources management. This paper presents a framework for the allocation of river basin water in a cooperative way. The proposed framework applies the TOPSIS model combined with the entropy weight to determine stakeholders’ initial water share, reallocating water and net benefit by using four solution concepts for crisp and fuzzy games. Finally, the Fallback bargaining model was employed to achieve unanimous agreement over the four solution concepts. The framework was demonstrated with an application to the Dongjiang River Basin, South China. The results showed that, overall, the whole basin gained more total benefits when the players participated in fuzzy coalitions rather than in crisp coalitions, and \(\left\{ {NHS_{Fuzzy} \,and\, SV_{Crisp} } \right\}\) could better distribute the total benefit of the whole basin to each player. This study tested the effectiveness of this framework for the water allocation decision-making in the context of water management in river basins. The results provide technical support for water right trade among the stakeholders at basin scale and have the potential to relieve water use conflicts of the entire basin.     

Estimation of carbon stock for greenhouse gas emissions from hydropower reservoirs

The identification and accurate quantification of sources or sinks of greenhouse gas (GHG) have become a key challenge for scientists and policymakers working on climate change. The creation of a hydropower reservoir, while damming a river for power generation, converts the terrestrial ecosystems into aquatic and subsequently aerobic and anaerobic decomposition of flooded terrestrial soil organic matter resulting in the emission of significant quantity of GHG to the atmosphere. Tropical/subtropical hydropower reservoirs are more significant sources of GHG compared to boreal or temperate one. This paper aims to estimate the emission factor (gCO₂eq./kWh) and net GHG emission from Koteshwar hydropower reservoir in Uttarakhand, India. Further, estimated GHG are compared with those from global reservoirs located in the same eco-region so that its impact could be timely minimized/mitigated. Results have shown that emission factor and net GHG emission of Koteshwar reservoir are, respectively, estimated as 13.87 gCO₂eq./kWh and 167.70 Gg C year^?1 which are less than other global reservoirs located in the same eco-region. This information could be helpful for the hydropower industries to construct reservoirs in tropical eco-regions.     

A direct scaling analysis for the sea level rise

The estimation of long-term sea level variability is of primary importance for a climate change assessment. Despite the value of the subject, no scientific consensus has yet been reached on the existing acceleration in observed values. The existence of this acceleration is crucial for coastal protection planning purposes. The absence of the acceleration would enhance the debate on the general validity of current future projections. Methodologically, the evaluation of the acceleration is a controversial and still open discussion, reported in a number of review articles, which illustrate the state-of-art in the field of sea level research. In the present paper, the well-proven direct scaling analysis approach is proposed in order to describe the long-term sea level variability at 12 worldwide-selected tide gauge stations. For each of the stations, it has been shown that the long-term sea level variability exhibits a trimodal scaling behaviour, which can be modelled by a power law with three different pairs of shape and scale parameters. Compared to alternative methods in literature, which take into account multiple correlated factors, this simple method allows to reduce the uncertainties on the sea level rise parameters estimation.     

A catastrophe progression approach based index sensitivity analysis model for the multivariate flooding process

Flood mitigation should deal with those most sensitive flooding elements to very efficiently release risks and reduce losses. Present the most concerns of flood control are peak level or peak discharge which, however, may not always be the most sensitive flooding element. Actually, along with human activities and climate change, floods bring threats to bear on human beings appear in not only peak level and peak discharge, but also other elements like maximum 24-h volume and maximum 72-h volume. In this paper, by collecting six key flooding intensity indices (elements), a catastrophe progression approach based sensitivity analysis algorithm model is developed to identify the indices that mostly control over the flood intensity. The indices sensitivity is determined through a selected case study in the Wujiang River, South China, based on half a century of flow record. The model results indicate that there is no evident relationship of interplay among the index sensitivities, but the variability of the index sensitivity is closely related to the index variability and the index sensitivity increases with the decrease of index value. It is found that peak discharge is not the most influential flooding factor as is generally thought in this case. The sensitivity value of the maximum 24-h volume is the greatest influential factor among all the other indices, indicating that this index plays a leading role in the flood threat of the Wujiang River, South China. It is inferred that, for the purpose of flood warning and mitigation, the peak flood discharge is not always the most sensitive and dominant index as opposed to the others, depending on the sensitivity.     

Declustering experimental variograms by global estimation with fourth order moments

The variogram is a key parameter for geostatistical estimation and simulation. Preferential sampling may bias the spatial structure and often leads to noisy and unreliable variograms. A novel technique is proposed to weight variogram pairs in order to compensate for preferential or clustered sampling . Weighting the variogram pairs by global kriging of the quadratic differences between the tail and head values gives each pair the appropriate weight, removes noise and minimizes artifacts in the experimental variogram. Moreover, variogram uncertainty could be computed by this technique. The required covariance between the pairs going into variogram calculation, is a fourth order covariance that must be calculated by second order moments. This introduces some circularity in the calculation whereby an initial variogram must be assumed before calculating how the pairs should be weighted for the experimental variogram. The methodology is assessed by synthetic and realistic examples. For synthetic example, a comparison between the traditional and declustered variograms shows that the declustered variograms are better estimates of the true underlying variograms. The realistic example also shows that the declustered sample variogram is closer to the true variogram.     

Distributions-oriented wind forecast verification by a hidden Markov model for multivariate circular–linear data

Winds from the North–West quadrant and lack of precipitation are known to lead to an increase of PM10 concentrations over a residential neighborhood in the city of Taranto (Italy). In 2012 the local government prescribed a reduction of industrial emissions by 10% every time such meteorological conditions are forecasted 72 h in advance. Wind forecasting is addressed using the Weather Research and Forecasting (WRF) atmospheric simulation system by the Regional Environmental Protection Agency. In the context of distributions-oriented forecast verification, we propose a comprehensive model-based inferential approach to investigate the ability of the WRF system to forecast the local wind speed and direction allowing different performances for unknown weather regimes. Ground-observed and WRF-forecasted wind speed and direction at a relevant location are jointly modeled as a 4-dimensional time series with an unknown finite number of states characterized by homogeneous distributional behavior. The proposed model relies on a mixture of joint projected and skew normal distributions with time-dependent states, where the temporal evolution of the state membership follows a first order Markov process. Parameter estimates, including the number of states, are obtained by a Bayesian MCMC-based method. Results provide useful insights on the performance of WRF forecasts in relation to different combinations of wind speed and direction.