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[Display omitted] • The VS-TOPSIS model was used to conduct groundwater quality assessment. • PCA and PMF model were used to quantitatively identity sources of water chemistry. • Water rock interaction and evaporation mainly controlled groundwater chemistry. • Fluoride contamination in Songyuan City impacted the groundwater quality. • Pumping and irrational well-forming ways partly worsen the quality of the deep water. A detailed and thorough study of the quality and its distribution, source identification and apportionment of groundwater, as the major or even sole selection of clean water in Songyuan City (a typical arid and semi-arid district in China), is needed for local sustainable development. The groundwater quality was evaluated using a vague set (VS)-technique for order of preference by similarity to ideal scheme (TOPSIS) model to determine its contamination status. Additionally, to explore the source and quantitative analysis of the driving factors of the groundwater chemistry in Songyuan City, a hydrochemical graphical method and a Principle Component Analysis (PCA) and Positive Matrix Factorization (PMF) model were employed. Regarding the quality of the groundwater in Songyuan, 74.5% of the samples were categorized into Grade I–III (Excellent, Good, and Moderate) water, indicating that the majority of the groundwater can be used directly. Moreover, the confined water tended to be cleaner and more appropriate for use in comparison with the phreatic water in Songyuan City. The PCA and PMF model and hydrochemical graphs demonstrated water–rock interactions and the evaporation process acted as the main factor, which was followed by anthropogenic activities as they contributed 50.1% and 20.4% of the total, respectively. Besides, the fluoride and iron contamination mainly caused by geogenic processes cannot be ignored as they were identified as the two driving factors that controlled the water quality, accounting for 20.2% and 9.3%, respectively. The mutual influence of the natural geological processes and human interventions contribute to the groundwater deterioration in Low Plain. Furthermore, the inappropriate well-forming techniques (without water-stop processes) in Qianan County was the main reason for the low-quality of deep Neogene confined water. This research provides useful information and directions for the local government to carry out corresponding and effective groundwater remediation strategies. [ABSTRACT FROM AUTHOR]

• Inspiration on the heterogeneous nature of water adsorption in bentonites. • Noval (continuum) SWRC model considering adsorption and capillarity. • New model contains only 5 parameters, with high efficiency in parameter estimation. • A graphical method was suggested to estimate maximum adsorption capacity. Understanding soil–water retention behavior is a longstanding topic. Water retained in soils can be decomposed into adsorptive and capillary components, controlled by different physicochemical mechanisms. The capillary water retention was frequently discussed in literatures, but the adsorption role was rarely considered, especially for high active clays (e.g., bentonite). In this study, two novel equations for quantifying adsorptive and capillary water retentions are proposed, generating a twofold model to continuously simulate soil–water retention behavior of bentonites. Only 5 parameters, i.e., the maximum adsorption capacity, characteristic adsorptive and capillary suctions, and uniformities of adsorptive and capillary pores, are defined with clear physical meanings. A graphical method was suggested to firstly determine the maximum adsorption capacity, and the remaining parameters are efficiently estimated by non-linear curve fitting. The water retention data for various bentonites, representing a variety of hydration conditions, initial compactness, montmorillonite content and suction range, are used to assess the model performance. The predictions agree well with the measured total, adsorptive and capillary water contents of a Wyoming bentonite, and the fitting curves also match well with test data for other bentonites over the full suction range. Adsorption parameters are distributed within a narrow range, while the characteristic capillary suction is also distributed within a limited range under constant volume condition. The proposed model allows reasonable predictions about the capillary onset and the transition from adsorption to capillarity, revealing obvious superiority by comparison with other hybrid models. This work offers a new pathway to quantitively assess the soil–water retention curve of high active clays. [ABSTRACT FROM AUTHOR]

• A comprehensive decision support system to characterize tails of probability distributions is proposed. • The robustness of proposed DSS is evaluated through a simulation experiment. • The utility of the DSS is demonstrated through its application to Indian precipitation data. • Precipitation analysis (4801 stations) shows around 98% of records have heavy tails over India. A conventional Decision Support System (DSS) can be used to characterize tails of probability distributions into distribution families using various graphical methods. Existing DSSs lack efficient segregation of the Lognormal distribution from the Regularly varying and Subexponential distribution families. Also, they lack the ability to identify the distributions from the hyper-exponential distributions. Recently developed graphical diagnostic tools, such as concentration profile, concentration adjusted expected shortfall, discriminant moment ratio plot, maximum-to-sum plot, and Zenga plot can classify the tails of distributions into various classes if used in an appropriate order in combination with tools of conventional DSS. The present study proposes a comprehensive DSS that alleviates the shortcomings associated with the conventional DSS and characterizes the tails of distributions into classes B\A (Pareto type), C\B (regularly varying), D\C (subexponential), E (Exponential type), hyper-exponential class (outside class E) and LN (Lognormal) distribution (the limiting case between class C and D). The robustness of the proposed DSS over the conventional DSS is established through a simulation experiment. Further, this study also evaluates the influence of the sample size on the effective implementation of the proposed DSS. Finally, the proposed DSS is applied to characterize the tails of daily gridded precipitation data over India. Results indicate that precipitation data from about 98% of grids over India exhibit distributions from heavy-tailed families. The study recommends the use of heavy-tailed distributions to model daily precipitation data over India. The study also suggests that one should rely on more than one graphical method for deducing rational conclusions regarding tail characterization. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an artificial neural network (ANN) approach to the determination of aquifer parameters is developed. The approach is based on the combination of an ANN and the Theis solution. The proposed ANN approach has advantages over the existing ANN approach. It avoids inappropriate setting of a trained range. It also determines the aquifer parameters more accurately and needs less required training time. Testing the existing and the proposed ANN approaches by 1000 sets of synthetic data also demonstrates these advantages. As to the comparison between the proposed ANN approach and the type-curve graphical method, an application to actual time-drawdown data shows that the proposed ANN approach determines the aquifer parameters more precisely. The proposed ANN approach is recommended as an alternative to the type-curve graphical method and the existing ANN approach. [Copyright &y& Elsevier]

Summary: Climate change has become the central question in numerous fields of life. Precipitation is one of the important characteristics of climate. Nowadays, several methods have been developed to present changes of climate and its elements. In this article a new visualization (graphical) method is presented which is suitable to demonstrate changes of different parameters of weather. We used two interpolation methods to prepare the plots, namely artificial neural networks (ANN) and the surface joint method using the least squares model. Our method is presented by means of the examination of time series consisting of the daily amount of precipitation for a 100years period; but we have applied it for temperature and evaporation data as well. [Copyright &y& Elsevier]

Summary: Most previous investigations to evaluate the “effective” or average transmissivity in heterogeneous environments have used calculations based on areas to weight the effects of each heterogeneity. Analysis of spatial volumetric variations within the cone of depression expressed at the potentiometric surface offers a more general solution to evaluate the meaning of transmissivity (T) and storativity (S) values derived from aquifer tests in these environments. The [Cooper Jr., H.H., Jacob, C.E. 1946. A generalized graphical method for evaluating formation constants and summarizing well-field history. Eos Trans. AGU, 27(4), 526–534] method is used to demonstrate that T variations reflected in slope changes in plots of the pumping well drawdown data correspond to changes in the volumetric weighted mean transmissivity (VWMT) over time. Lognormal distributions of transmissivity are represented by block heterogeneities within two simulated aquifers, for both spatially random and spatially correlated data sets. By analyzing the volumetric evolution of the cone of depression observed in the potentiometric surface, the nature of T averaging within the cone of depression as a function of time is illustrated. Volumetric analysis shows that the average aquifer T varies with time as the cone of depression progressively envelops different heterogeneities. The initial trend is controlled primarily by the heterogeneities directly surrounding the pumping center. If steady-shape conditions are not achieved, late-time VWMT values do not approach an asymptotic limit. [Copyright &y& Elsevier]

Abstract: Sensitivity analyses are valuable tools for identifying important model parameters, testing the model conceptualization, and improving the model structure. They help to apply the model efficiently and to enable a focussed planning of future research and field measurement. Two different methods were used for sensitivity analyses of the complex process-oriented model TACD (tracer aided catchment model, distributed) that was applied to the meso-scale Brugga basin (40km2) and the sub-basin St Wilhelmer Talbach (15.2km2). Five simulations periods were investigated: two summer events, two snow melt induced events and one summer low flow period. The model was applied using 400 different parameter sets, which were generated by Monte Carlo simulations using latin hypercube sampling. The regional sensitivity analysis (RSA) allowed determining the most significant parameters for the complete simulation periods using a graphical method. The results of the regression-based sensitivity analysis were more detailed and complex. The temporal variability of the simulation sensitivity could be observed continuously and the significance of the parameters could be determined in a quantitative way. A dependency of the simulation sensitivity on initial- and boundary conditions and the temporal and spatial variability of the sensitivity to some model parameters was revealed by the regression-based sensitivity analysis. Thus, the difficulty of transferring the results to different time periods or model applications in other catchments became obvious. The analysis of the temporal course of the simulation sensitivity to parameter values in conjunction with simulated and measured additional data sets (precipitation, temperature, reservoir volumes etc.) gave further insight into the internal model behaviour and demonstrated the plausibility of the model structure and process conceptionalizations. [Copyright &y& Elsevier]

• An analytical model for imbibition mechanism in dual porosity system is proposed. • Diverse matrix block distributions are used to consider the heterogeneity. • Imbibition dimensionless shape factors are derived based on a typical fluid transfer process. • Several fractured reservoir characteristics are estimated via the proposed model. Naturally fractured carbonate reservoirs which possess most of the remaining oil in the world, can fulfill the needs of widely nowadays energy demand. Most of the oil remains in the matrix block during production from naturally fractured reservoirs. Water flooding is a technique implemented in naturally fractured reservoirs to recover additional amounts of oil from the matrix blocks. In this paper, for the first time, an analytical model for water flooding in the naturally fractured carbonate reservoirs is developed that describes the transient behavior of the imbibition mechanism in the dual porosity models. Using the fracture-matrix fluid transfer of the dual porosity model, the presented analytical model takes the account of viscous-capillary and capillary imbibition mechanisms in fracture and matrix system, respectively. The analytical solution is validated using both laboratory data and a numerical solution. In order to consider the effect of the heterogeneity in fractured reservoirs, the proposed analytical model is developed for various distributions of block sizes. In addition, a graphical method is developed to estimate capillary diffusion coefficient and matrix block size distribution based on the water saturation data in the monitoring well during water injection. The monitoring well is used to monitor and evaluate reservoir during water flooding. The proposed methodology which is based on water saturation data in the monitoring well, does not call for a specific test in the reservoir. Moreover, the relating shape factor is calculated with the aid of dual porosity model equipped with variable fracture intensity distribution. The findings imply that the fluid transfer is highly dependent on the distribution of matrix block size. [ABSTRACT FROM AUTHOR]

• Varzeqan aquifer is exposed to nitrate, fluoride & arsenic risks and mining. • Risk exposures stem from anthropogenic or geogenic origins at multiple sources. • Total Information Management present a framework with 5 dimensions with sparse data. • Dimensions includes perceptual, conceptual models, risk cells and soft modelling. • The study gives an insight into the hotspots and a direction for the next phase. The aquifer at Varzeqan plain, with multiple confined/unconfined and hard-rock boundaries, is exposed to risks from several contaminants (nitrate-N, fluoride and arsenic) originated by anthropogenic and/or geogenic activities, which are possibly accelerating by anthropogenic activities. The study is a research initiative driven by impacts of poor or non-existent planning/ regulation practices to produce insights despite the sparsity of the available data and the unknown baseline. A methodology is given, which seeks 'total information management' by pooling together the following five dimensions: (i) a perceptual model to collect existing knowledge-base; (ii) a conceptual model to analyse a sample of ion-concentrations by a set of existing techniques (e.g. statistical, graphical and multivariate analysis); (iii) risk cells to contextualise each contaminant; (iv) "soft modelling" to firm up information by learning from convergences and/or divergences within the conceptual model; and (v) study the processes within each risk cell through the OSPRC framework (Origins , Sources, Pathways, Receptors and Consequence). The research caters for inherent variabilities in the study area by 15 risk cells delineated within the boundaries of confined, unconfined and hard-rock aquifers as follows: 4 risk cells account for minor ions of nitrate-N pollution of anthropogenic origins; 6 for minor ions of fluoride and 5 for trace ions of geogenic arsenic anomalies. It further identifies the possibility of anthropogenic activities encouraging geogenic anomalies. The findings are presented as a descriptive model but this will be transformed into quantitative models in due course when more data become available. [ABSTRACT FROM AUTHOR]