Modification of the DRASTIC Framework for Mapping Groundwater Vulnerability Zones

The DRASTIC technique is commonly used to assess groundwater vulnerability. The main disadvantage of the DRASTIC method is the difficulty associated with identifying appropriate ratings and weight assignments for each parameter. To mitigate this issue, ratings and weights can be approximated using different methods appropriate to the conditions of the study area. In this study, different linear (i.e., Wilcoxon test and statistical approaches) and nonlinear (Genetic algorithm [GA]) modifications for calibration of the DRASTIC framework using nitrate (NO₃) concentrations were compared through the preparation of groundwater vulnerability maps of the Meshqin-Shahr plain, Iran. Twenty-two groundwater samples were collected from wells in the study area, and their respective NO₃ concentrations were used to modify the ratings and weights of the DRASTIC parameters. The areas found to have the highest vulnerability were in the eastern, central, and western regions of the plain. Results showed that the modified DRASTIC frameworks performed well, compared to the unmodified DRASTIC. When measured NO₃ concentrations were correlated with the vulnerability indices produced by each method, the unmodified DRASTIC method performed most poorly, and the Wilcoxon–GA–DRASTIC method proved optimal. Compared to the unmodified DRASTIC method with an R² of 0.22, the Wilcoxon–GA–DRASTIC obtained a maximum R² value of 0.78. Modification of DRASTIC parameter ratings was found to be more efficient than the modification of the weights in establishing an accurately calibrated DRASTIC framework. However, modification of parameter ratings and weights together increased the R² value to the highest degree.     

Hydrological appraisal of rainfall estimates from radar,satellite, raingauge and satellite–gauge combination on the Qinhuai River Basin,China

ABSTRACT

Multisource rainfall products can be used to overcome the absence of gauged precipitation data for hydrological applications. This study aims to evaluate rainfall estimates from the Chinese S-band weather radar (CINRAD-SA), operational raingauges, multiple satellites (CMORPH, ERA-Interim, GPM, TRMM-3B42RT) and the merged satellite–gauge rainfall products, CMORPH-GC, as inputs to a calibrated probability distribution model (PDM) on the Qinhuai River Basin in Nanjing, China. The Qinhuai is a middle-sized catchment with an area of 799 km². All sources used in this study are capable of recording rainfall at high spatial and temporal resolution (3 h). The discrepancies between satellite and radar data are analysed by statistical comparison with raingauge data. The streamflow simulation results from three flood events suggest that rainfall estimates using CMORPH-GC, TRMM-3B42RT and S-band radar are more accurate than those using the other rainfall sources. These findings indicate the potential to use satellite and radar data as alternatives to raingauge data in hydrological applications for ungauged or poorly gauged basins.     

Investigating the effect of complexity on groundwater flow modeling uncertainty

Considering complexity in groundwater modeling can aid in selecting an optimal model, and can avoid over parameterization, model uncertainty, and misleading conclusions. This study was designed to determine the uncertainty arising from model complexity, and to identify how complexity affects model uncertainty. The Ajabshir aquifer, located in East Azerbaijan, Iran, was used for comprehensive hydrogeological studies and modeling. Six unique conceptual models with four different degrees of complexity measured by the number of calibrated model parameters (6, 10, 10, 13, 13 and 15 parameters) were compared and characterized with alternative geological interpretations, recharge estimates and boundary conditions. The models were developed with Model Muse and calibrated using UCODE with the same set of observed data of hydraulic head. Different methods were used to calculate model probability and model weight to explore model complexity, including Bayesian model averaging, model selection criteria, and multicriteria decision-making (MCDM). With the model selection criteria of AIC, AICc and BIC, the simplest model received the highest model probability. The model selection criterion, KIC, and the MCDM method, in addition to considering the quality of model fit between observed and simulated data and the number of calibrated parameters, also consider uncertainty in parameter estimates with a Fisher information matrix. KIC and MCDM selected a model with moderate complexity (10 parameters) and the best parameter estimation (model 3) as the best models, over another model with the same degree of complexity (model 2). The results of these comparisons show that in choosing between models, priority should be given to quality of the data and parameter estimation rather than degree of complexity.     

Comparison of machine learning models for predicting fluoride contamination in groundwater

Groundwater is an especially important freshwater source for water supplies in the Maku area of northwest Iran. The groundwater of the area contains high concentrations of fluoride and is, therefore, important in predicting the fluoride contamination of the groundwater for the purpose of planning and management. The present study aims to evaluate the ability of the extreme learning machine (ELM) model to predict the level of fluoride contamination in the groundwater in comparison to multilayer perceptron (MLP) and support vector machine (SVM) models. For this purpose, 143 water samples were collected in a five-year period, 2004–2008. The samples were measured and analyzed for electrical conductivity, pH, major chemical ions and fluoride. To develop the models, the data set—including Na⁺, K⁺, Ca²⁺ and HCO₃ ^? concentrations as the inputs and fluoride concentration as the output—was divided into two subsets; training/validation (80% of data) and testing (20% of data), based on a cross-validation technique. The radial basis-based ELM model resulted in an R ² of 0.921, an NSC of 0.9071, an RMSE of 0.5638 (mg/L) and an MABE of 0.4635 (mg/L) for the testing data. The results showed that the ELM models performed better than MLP and SVM models for prediction of fluoride contamination. It was observed that ELM models learned faster than the other models during model development trials and the SVM models had the highest computation time.     

Seismic modelling for reservoir studies: a comparison between convolutional and full-waveform methods for a deep-water turbidite sandstone reservoir

Two seismic modelling approaches, that is, two-dimensional pre-stack elastic finite-difference and one-dimensional convolution methods, are compared in a modelling exercise over the fluid-flow simulation model of a producing deep-water turbidite sandstone reservoir in the West of Shetland Basin. If the appropriate parameterization for one-dimensional convolution is used, the differences in three-dimensional and four-dimensional seismic responses from the two methods are negligible. The key parameters to ensure an accurate seismic response are a representative wavelet, the distribution of common-depth points and their associated angles of incidence. Conventional seismic images generated by the one-dimensional convolutional model suffer from lack of continuity because it only accounts for vertical resolution. After application of a lateral resolution function, the convolutional and finite-difference seismic images are very similar. Although transmission effects, internal multiples and P-to-S conversions are not included in our convolutional modelling, the subtle differences between images from the two methods indicates that such effects are of secondary nature in our study. A quantitative comparison of the (normalized root-mean-square) amplitude attributes and waveform kinematics indicates that the finite-difference approach does not offer any tangible benefit in our target-oriented seismic modelling case study, and the potential errors from one-dimensional convolution modelling are comparatively much smaller than the production-induced time-lapse changes.     

Performance evaluation of land change simulation models using landscape metrics

This study proposes a landscape metrics-based method for model performance evaluation of land change simulation models. To quantify model performance at both landscape and class levels, a set of composition- and configuration-based metrics including number of patches, class area, landscape shape index, mean patch area and mean Euclidean nearest neighbour distance were employed. These landscape metrics provided detailed information on simulation success of a cellular automata-Markov chain (CA-Markov) model standpoint of spatial arrangement of the simulated map versus the corresponding reference layer. As a measure of model simulation success, mean relative error (MRE) of the metrics was calculated. At both landscape and class levels, the MRE values were accounted for 22.73 and 10.2%, respectively, which are further categorised into qualitative measurements of model simulation performance for simple and quick comparison of the results. Findings of the present study depict a hierarchical and multi spatial level assessment of model performance.     

Enhancement of Carbon Dioxide Capture by Amine‐Functionalized Multi‐Walled Carbon Nanotube

In this study, crude multi‐walled carbon nanotubes (MWCNT) was functionalized by a two‐step process; first using strong mixed acids (H₂SO₄/HNO₃) and then treatment with 1,3‐phenylenediamine (mPDA). The equilibrium adsorption of CO₂ on pristine MWCNT and amine functionalized MWCNT (MWCNT‐NH₂) were investigated. Experiments were preformed via application of volumetric method in a dual sorption vessel at temperature range of 298–318 K and pressures up to 40 bars. The results obtained indicated that the equilibrium uptake of CO₂ increased after functionalizing of MWCNT. The increase in CO₂ capture by MWCNT‐NH₂ was attributed to the existence of great affinity between CO₂ molecules and amine sites on this adsorbent at low pressures. The experimental data were analyzed by means of Freundlich and Langmuir adsorption isotherm models. The data obtained revealed a fast kinetics for the adsorption of CO₂ in which most of adsorption occurred at initial period of adsorption experiments. This renders MWCNT as a suitable adsorbent for practical applications. Values of isosteric heat of adsorption were evaluated based on Clausius–Clapeyron equation. The results demonstrated that both chemisorption and physisorption played important role in CO₂ adsorption on MWCNT‐NH₂, whereas the physisorption process was dominant for CO₂ adsorption on MWCNT.     

Water Consumption of a Six-Year-Old River Red Gum Plantation in the Southern Zagros Mountains, Iran

Water is the most important limiting factor in the development of arid environments; therefore, optimization of water use is a necessity in desert reclamation projects. Evapotranspiration of a 6-year-old river red gum (Eucalyptus camaldulensis Dehnh.) plantation, located in a sedimentation basin of an artificial recharge of the Gareh Bygone Plain groundwater system in Iran’s southern Zagros Mountains, was studied during a 7-month period. A neutron moisture probe was used to monitor soil water changes in the root zone. Soil water depletion following rapid drainage for blocks receiving relatively high, medium and low volumes of floodwater were 809.6, 312.4, and 203.1 mm, respectively, for a 150-day period. Soil water potential during most of the study period was below -1.5 MPa. This study proves that river red gum in not a suitable species for desertification control where water conservation is of vital importance. Flood irrigation of trees on 8 Aug. 1991 provided the opportunity to throw doubt upon the claim of summer dormancy of river red gum.     

Modeling the impact of groundwater harvesting on the hydro-salinity of a saline catchment in southeastern Australia

Recharge to a saline, unconfined shallow-water-table aquifer is normally considered as an irrecoverable loss of water, but such thinking could be reviewed empirically. The use of an appropriate groundwater harvesting system does not only provide an opportunity to recover this lost water, but can also help in catchment salinity management and improvement. Agricultural-based land-drainage systems such as those that use serial biological concentration (SBC) of salts, provide examples of such harvesting methods. The impact of groundwater harvesting has been assessed on the hydro-salinity of a saline catchment in southeastern Australia through modeling. For both the below average rainfall and very wet years, the “do nothing” scenario resulted in increasing salinization in the catchment. However, after introducing a SBC system, groundwater salinity showed a decreasing trend while hydraulic heads tended to stabilize around the depth of subsurface collector wells. However, for a successful groundwater harvesting system, proper understanding of the groundwater flows and salt mobilization associated with a catchment is necessary. The outcomes of this modelling study have the potential to address similar issues (salinization) and/or needs (water harvesting) existing elsewhere in the world, particularly in semi-arid regions.