Modification of the GALDIT framework using statistical and entropy models to assess coastal aquifer vulnerability

Recently, groundwater vulnerability assessment of coastal aquifers using the GALDIT framework has been widely used to investigate the process of groundwater contamination. This study proposes multi-attribute decision-making (MADM) entropy and Wilcoxon non-parametric statistical test methods to improve the vulnerability index of coastal aquifers. The rates and weights of this framework were modified using Wilcoxon non-parametric and entropy methods, respectively, and a combined framework of GALDIT-entropy, Wilcoxon-GALDIT, and Wilcoxon-entropy was obtained. Pearson correlation coefficients between the mentioned vulnerability indices and total-dissolved solids (TDS) of 0.51, 0.66 and 0.75, respectively, were obtained. According to the results, the Wilcoxon-entropy index had the highest correlation with TDS. Generally, it can be concluded that the proposed frameworks provide a more accurate estimation of vulnerability distribution in coastal aquifers.     

Sensitivity analysis in groundwater vulnerability assessment based on GIS in the Mahdia-Ksour Essaf aquifer,Tunisia: a validation study

Abstract

The assessment of groundwater vulnerability to pollution has proved to be an effective tool for water resource management, especially in arid and semi-arid regions like Mahdia and Ksour Essaf. The main objective of this study is to assess the aquifer vulnerability by applying the DRASTIC method as well as using sensitivity analysis to evaluate the effect of each DRASTIC parameter on the final vulnerability map. An additional objective is to demonstrate the role of the GIS techniques in the vulnerability assessment. The DRASTIC method assigns a high vulnerability to the coast of the Mahdia-Ksour Essaf. The lowest values are observed in the southern part of the study area. A sensitivity analysis applied in this study suggests that net recharge, aquifer media and depth of groundwater are the key factors determining vulnerability. The model is validated with groundwater quality data and the results have shown strong relationships between modified DRASTIC Vulnerability Index and nitrate and chloride concentrations.

Citation Saidi, S., Bouri, S. & Ben Dhia, H. (2011) Sensitivity analysis in groundwater vulnerability assessment based on GIS in the Mahdia-Ksour Essaf aquifer, Tunisia: a validation study. Hydrol. Sci. J. 56(2), 288–304.     

A field demonstration of the entropy-weighted fuzzy DRASTIC method for groundwater vulnerability assessment

Abstract

Groundwater vulnerability assessment based on the DRASTIC index has been widely used since the 1980s to map potential risks of groundwater contamination. However, its applicability and usefulness are affected by two uncertain and subjective factors. One is the discretization of continuous input variables and the other is the assignment of different weights to the index variables. In this study, an entropy-weighted fuzzy-optimization approach was developed to augment and improve the classic DRASTIC method by reducing the uncertainties associated with variable discretization and weight assignment. The modified DRASTIC method was applied to a study site in Shandong, north China. The entropy-weighted fuzzy-optimization approach is shown to provide a more rigorous delineation of the relative vulnerability distribution. Meanwhile, the new approach does not require the use of more parameters. The results suggest that this approach significantly improves and enhances the ability of the classic DRASTIC method in a more systematic and rigorous way.

Editor D. Koutsoyiannis

Citation Yu, C., Zhang, B.X., Yao, Y.Y., Meng, F.H., and Zheng, C.M., 2012. A field demonstration of the entropy-weighted fuzzy DRASTIC method for groundwater vulnerability assessment. Hydrological Sciences Journal, 57 (7), 1420–1432.     

SOM-DRASTIC: using self-organizing map for evaluating groundwater potential to pollution

Groundwater is considered as the most important water resource, especially in arid and semi-arid regions, so it is crucial to impede this source of water to be contaminated. One of the most common methods to assess groundwater vulnerability is DRASTIC method. However, the subjectivity existing in defining DRASTIC weights and ratings as well as inadaptability of the parameters involved in this method with special geology, hydrogeology, land use and climatic conditions have urged researchers to modify this method. In this paper, a new method combining a special type of the neural networks called Self-Organizing Map (SOM) and the traditional DRASTIC model resulting in the hybrid SOM-DRASTIC model is applied to modify and improve DRASTIC Model. The traditional DRASTIC method holds a summation among all negative effects of different factors contributing to vulnerability, while the proposed hybrid method is able of classifying the groundwater vulnerability and deriving the real relation existing between the DRASTIC parameters as the inputs and the vulnerability class as the output of the method. The vulnerability assessment process was performed on the Zayandeh-Rud river basin aquifers in Iran. The SOM-DRASTIC identified the northern parts of the study area as the most vulnerable areas with a drastically fractured structure, while the traditional DRASTIC ranked the western parts as the most vulnerable regions with a high rate of net recharge. The results demonstrate that the proposed method can be used by managers and decision-makers as an alternative robust tool for vulnerability-based classification and land use planning.     

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer,Iran

Vulnerability maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydrogeological conditions and human impacts. The objective of this research is (1) to assess the groundwater vulnerability using DRASTIC method and (2) to improve the DRASTIC method for evaluation of groundwater contamination risk using AI methods, such as ANN, SFL, MFL, NF and SCMAI approaches. This optimization method is illustrated using a case study. For this purpose, DRASTIC model is developed using seven parameters. For validating the contamination risk assessment, a total of 243 groundwater samples were collected from different aquifer types of the study area to analyze \( {\text{NO}}_{ 3}^{ - } \) concentration. To develop AI and CMAI models, 243 data points are divided in two sets; training and validation based on cross validation approach. The calculated vulnerability indices from the DRASTIC method are corrected by the \( {\text{NO}}_{3}^{ - } \) data used in the training step. The input data of the AI models include seven parameters of DRASTIC method. However, the output is the corrected vulnerability index using \( {\text{NO}}_{3}^{ - } \) concentration data from the study area, which is called groundwater contamination risk. In other words, there is some target value (known output) which is estimated by some formula from DRASTIC vulnerability and \( {\text{NO}}_{3}^{ - } \) concentration values. After model training, the AI models are verified by the second \( {\text{NO}}_{3}^{ - } \) concentration dataset. The results revealed that NF and SFL produced acceptable performance while ANN and MFL had poor prediction. A supervised committee machine artificial intelligent (SCMAI), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of vulnerability. The performance of SCMAI was also compared to those of the simple averaging and weighted averaging committee machine intelligent (CMI) methods. As a result, the SCMAI model produced reliable estimates of groundwater contamination risk.     

Transfer function noise modelling of groundwater level fluctuation using threshold rainfall-based binary-weighted parameter estimation approach

Considerable uncertainty occurs in the parameter estimates of traditional rainfall–water level transfer function noise (TFN) models, especially with the models built using monthly time step datasets. This is due to the equal weights assigned for rainfall occurring during both water level rise and water level drop events while estimating the TFN model parameters using the least square technique. As an alternative to this approach, a threshold rainfall-based binary-weighted least square method was adopted to estimate the TFN model parameters. The efficacy of this binary-weighted approach in estimating the TFN model parameters was tested on 26 observation wells distributed across the Adyar River basin in Southern India. Model performance indices such as mean absolute error and coefficient of determination values showed that the proposed binary-weighted approach of fitting independent threshold-based TFN models for water level rise and water level drop scenarios considerably improves the model accuracy over other traditional TFN models.

EDITOR D. Koutsoyiannis

ASSOCIATE EDITOR A. Fiori     

Improving streamflow estimation in ungauged basins using a multi-modelling approach

ABSTRACT

In this study, a multi-modelling approach is proposed for improved continuous daily streamflow estimation in ungauged basins using regionalization—the process of transferring hydrological data from gauged to ungauged watersheds. Four regionalization models, two data-driven and two hydrological, were used for continuous daily streamflow estimation. Comparison of the individual models reveals that each of the four models performed well on a limited number of ungauged basins while none of them performed well for the entire 90 selected watersheds. The results obtained from the four models are evaluated and reported in a deterministic way by a model combination approach along with its uncertainty range consisting of 16 ensemble members. It is shown that a combined model of the four individual models performed well on all 90 watersheds and the ensemble range can account for the uncertainty of models. The combined model was more efficient and appeared more robust compared to the individual models. Furthermore, continuous ranked probability scores (CRPS) calculated for the ensemble model outputs indicate better performance compared to individual models and competitive with the combined model.

EDITOR A. Castellarin ASSOCIATE EDITOR G. Di Baldassarre     

Variation of hydrogeochemical characteristics of water in surface flows,shallow wells,and boreholes in the coastal city of Douala (Cameroon)

ABSTRACT

Groundwater is used by 3?million inhabitants in the coastal urban city of Douala, Cameroon, but comprehensive data are too sparse for it to be managed in a sustainable manner. Hence this study aimed to (1) assess the potability of the groundwater; (2) evaluate the spatial variation of groundwater composition; and (3) assess the interaction and recharge mechanisms of different water bodies. Hydrogeochemical tools and methods revealed the following results in the Wouri and Nkappa formations of the Douala basin, which is beneath Douala city: 30% of water samples from hand-dug wells in the shallow Pleistocene alluvium aquifer were saline and highly mineralized. However, water from boreholes in the deeper (49–92 m depth) Palaeocene aquifer was saline-free, less mineralized and potable. Water in the shallow aquifer (0.5–22 m depth) was of Na⁺-K⁺-Cl^?-NO₃^? type and not potable due to point source pollution, whereas Ca⁺-HCO₃^? unpolluted water dominates in the deeper aquifer. Water in the deep and shallow aquifers indicates the results of preferential flow pass and evaporative recharge, respectively. Possible hydrogeochemical processes include point source pollution, reverse ion exchange, remote recharge areas and mixing of waters with different chemical signatures.

EDITOR D. Koutsoyiannis ASSOCIATE EDITOR M.D. Fidelibus     

Estimation of groundwater vulnerability to pollution based on DRASTIC in the Niipele sub-basin of the Cuvelai Etosha Basin,Namibia

Surface water is a scarce resource in Namibia with about sixty percent of Namibia's population dependent on groundwater for drinking purposes. With increasing population, the country faces water challenges and thus groundwater resources need to be managed properly. One important aspect of Integrated Water Resources Management is the protection of water resources, including protection of groundwater from contamination and over-exploitation. This study explores vulnerability mapping as a basic tool for protecting groundwater resources from pollution. It estimates groundwater vulnerability to pollution in the upper Niipele sub-basin of the Cuvelai-Etosha in Northern Namibia using the DRASTIC index. The DRASTIC index uses GIS to estimate groundwater vulnerability by overlaying different spatially referenced hydrogeological parameters that affect groundwater contamination. The study assesses the discontinuous perched aquifer (KDP) and the Ohangwena multi-layered aquifer 1 (KOH-1). For perched aquifers, point data was regionalized by a hydrotope approach whereas for KOH-1 aquifer, inverse distance weighting was used. The hydrotope approach categorized different parts of the hydrogeological system with similar properties into five hydrotopes. The result suggests that the discontinuous perched aquifers are more vulnerable than Ohangwena multi-layered aquifer 1. This implies that vulnerability increases with decreasing depth to water table because contaminants have short travel time to reach the aquifer when they are introduced on land surface. The nitrate concentration ranges between 2 and 288 mg/l in perched aquifers while in Ohangwena multi-layered aquifer 1, it ranges between 1 and 133 mg/l. It was observed that perched aquifers have high nitrate concentrations than Ohangwena 1 aquifer, which correlates well with the vulnerability results.     

A comparative study for water temperature modelling in a small basin,the Fourchue River,Quebec, Canada

Analysis and forecasting of water temperature are important for water ecological management. The objective of this study is to compare models for water temperature during the summer season for an impounded river. In a case study, we consider hydro-climatic and water temperature data for the Fourchue River (St-Alexandre-de-Kamouraska, Quebec, Canada) between 2011 and 2014. Three different models are applied, which are broadly characterized as deterministic (CEQUEAU), stochastic (Auto-regressive Moving Average with eXogenous variables or ARMAX) and nonlinear (Nonlinear Autoregressive with eXogenous variables or NARX). The efficiency of each model is analysed and compared. The results show that the ARMAX is the best performing water temperature model for the Fourchue River and the CEQUEAU model also simulates water temperature adequately without the overfitting issues that seem to plague the autoregressive models.

EDITOR M.C. Acreman

ASSOCIATE EDITOR R. Hirsch     

Analysis of recharge conceptualization in inverse groundwater modelling

ABSTRACT

This study presents a systematic illustration quantifying how misleading the calibration results of a groundwater simulation model can be when recharge rates are considered as the model parameters to be estimated by inverse modelling. Three approaches to recharge estimation are compared: autocalibration (Model 1), the empirical return coefficient method (Model 2), and distributed hydrological modelling using the Soil and Water Assessment Tool, SWAT (Model 3). The methodology was applied in the Dehloran Plain, western Iran, using the MODFLOW modular flow simulator and the PEST method for autocalibration. The results indicate that, although Model 1 performed the best in simulating water levels at observation wells in the calibration stage, it did not perform satisfactorily in real future scenarios. Model 3, with SWAT-based recharge rates, performed better than the other models in the validation stage. By not evaluating the model performance solely on calibration results, we demonstrate the relative significance of using more accurate recharge estimates when calibrating groundwater simulation models.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR M. Besbes     

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.     

Heterogeneity influences on stream water–groundwater interactions in a gravel-dominated floodplain

ABSTRACT

Floodplains are composed of complex depositional patterns of ancient and recent stream sediments, and research is needed to address the manner in which coarse floodplain materials affect stream–groundwater exchange patterns. Efforts to understand the heterogeneity of aquifers have utilized numerous techniques typically focused on point-scale measurements; however, in highly heterogeneous settings, the ability to model heterogeneity is dependent on the data density and spatial distribution. The objective of this research was to investigate the correlation between broad-scale methodologies for detecting heterogeneity and the observed spatial variability in stream/groundwater interactions of gravel-dominated alluvial floodplains. More specifically, this study examined the correlation between electrical resistivity (ER) and alluvial groundwater patterns during a flood event at a site on Barren Fork Creek, in the Ozark ecoregion of Oklahoma, USA, where chert gravels were common both as streambed and as floodplain material. Water table elevations from groundwater monitoring wells for a flood event on 1–5 May 2009 were compared to ER maps at various elevations. Areas with high ER matched areas with lower water table slope at the same elevation. This research demonstrated that ER approaches were capable of indicating heterogeneity in surface water–groundwater interactions, and that these heterogeneities were present even in an aquifer matrix characterized as highly conductive. Portions of gravel-dominated floodplain vadose zones characterized by high hydraulic conductivity features can result in heterogeneous flow patterns when the vadose zone of alluvial floodplains activates during storm events.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR X. Chen     

Developing a reliable model for aquifer vulnerability

The assessment of aquifer vulnerability to pollution is crucial for planning a sound management strategy of groundwater quality protection and farmland fertilizer use. This study establishes a reliable model for aquifer vulnerability assessment with an excellent performance for predicting groundwater nitrate-N contamination in the Choushui River alluvial fan, Taiwan based on the DRASTIC method. To promote the prediction performance of aquifer vulnerability assessment, discriminant analysis (DA) was applied to determine the weights of factors in the DRASTIC model by comparing the model results with the observed nitrate-N data. Key factors influencing the presence of groundwater nitrate-N pollution were characterized for different concentration thresholds. The results of analysis reveal that the modified DRASTIC model using DA significantly improves prediction performance for aquifer vulnerability assessment, and groundwater protection zones can be determined correctly based on the modified DRASTIC index. Furthermore, the sensitivity of the factors in the modified DRASTIC model indicates that the depth to the groundwater and aquifer media are critical when the nitrate-N concentration is less than 3 mg/L, while the impact of the vadose zone plays a vital role in controlling nitrate-N pollution of over 5 mg/L.     

Assessment of water resources to support the development of irrigation in northwest Cambodia: a water budget approach

Water availability is the primary constraint on the improvement of food security in rural areas in northwestern Cambodia. A 4-year study was carried out in the upper Stung Sreng watershed to assess water resources. Four sub-watersheds with different land cover types, ranging in size from 1.5 to 185 km², were monitored using dedicated weather stations and rain- and streamgauges. Geophysics and observation boreholes were used to characterize aquifers. Rainwater is mostly split into evapotranspiration (annual mean of 54% rainfall) and streamflow components (49%), because groundwater recharge is low (1%). Thus, rainwater and streamflow are the main sources for irrigation development. Groundwater can be used only in specific locations for low water-demand crops. A total of 186 household ponds and three village-scale dams were built and 31 wells were installed. The household pond was determined to be the best solution for irrigation development because of its simple management.

EDITOR A. Castellarin ASSOCIATE EDITOR M. Piniewski     

Comparison of an artificial neural network and a conceptual rainfall–runoff model in the simulation of ephemeral streamflow

ABSTRACT

The rainfall–runoff process is governed by parameters that can seldom be measured directly for use with distributed models, but are rather inferred by expert judgment and calibrated against historical records. Here, a comparison is made between a conceptual model (CM) and an artificial neural network (ANN) for their ability to efficiently model complex hydrological processes. The Sacramento soil moisture accounting model (SAC-SMA) is calibrated using a scheme based on genetic algorithms and an input delay neural network (IDNN) is trained for variable delays and hidden layer neurons which are thoroughly discussed. The models are tested for 15 ephemeral catchments in Crete, Greece, using monthly rainfall, streamflow and potential evapotranspiration input. SAC-SMA performs well for most basins and acceptably for the entire sample with R² of 0.59–0.92, while scoring better for high than low flows. For the entire dataset, the IDNN improves simulation fit to R² of 0.70–0.96 and performs better for high flows while being outmatched in low flows. Results show that the ANN models can be superior to the conventional CMs, as parameter sensitivity is unclear, but CMs may be more robust in extrapolating beyond historical record limits and scenario building.

EDITOR M.C. Acreman; ASSOCIATE EDITOR not assigned     

Application of Groundwater Vulnerability Overlay and Index Methods to the Jijel Plain Area (Algeria)

Today, scientists are deeply concerned by the vulnerability of groundwater reservoirs to pollution. Relatively simple overlay and index methods can be used to produce groundwater vulnerability maps in geographic information system. In addition, this study deals with contamination from nonpoint sources. In this study, two such models, DRASTIC and GOD, were applied in the Jijel Plain area of northeast Algeria and compared with measured groundwater nitrate concentrations. This showed that results from DRASTIC were better than GOD, 69% correlation with nitrate compared to 56%. DRASTIC was better able to identify vulnerable zones along the river valleys. The DRASTIC model was then modified using the nitrate concentrations to optimize the rating score given within each parameter range and sensitivity analysis to change the weighting given for each parameter. These combined changes gave a final Pearson's correlation of 83% with nitrate. This showed that recharge, aquifer type, and topography were the key factors in controlling vulnerability to nitrate pollution.     

Integration of gauge and radar rainfall to enable best simulation of hydrological parameters

This study is about use of spatially distributed rain in physically based hydrological models. In recent years, spatially distributed radar rainfall data have become available. The distributed radar rain is used to precisely model hydrologic processes and it is more realistic than the past practice of distribution methods like Thiessen polygons. Radar provides a highly accurate spatial distribution of rainfall and greatly improves the basin average rainfall estimates. However, quantification of the exact amount of rainfall from radar observation is relatively difficult. Thus, the fundamental idea of this study is to apply hourly gauge and radar rainfall data in a distributed hydrological model to simulate hydrological parameters. Hence the comparison is made between the outcomes of the WetSpa model from radar rainfall distribution and gauge rainfall distributed by the Thiessen polygon technique. The comparative plots of the hydrograph and the results of hydrological components such as evapotranspiration, surface runoff, soil moisture, recharge and interflow, reflect the spatially distributed radar input performing well for model outflow simulation.

EDITOR D. Koutsoyiannis

ASSOCIATE EDITOR F. Pappenberger     

Simultaneous hydrological prediction at multiple gauging stations using the NARX network for Kemaman catchment,Terengganu, Malaysia

ABSTRACT

This paper presents a neural network model capable of catchment-wide simultaneous prediction of river stages at multiple gauging stations. Thirteen meteorological parameters are considered in the input, which includes rainfall, temperature, mean relative humidity and evaporation. The NARX model is trained with a representative set of hourly data, with optimal time delay for both the input and output. The network trained using 120-day data is able to produce simulations that are in excellent agreement with field observations. We show that for application with one-step-ahead predictions, the loss in network performance is marginal. Inclusion of additional tidal observations does not improve predictions, suggesting that the river stage stations under consideration are not sensitive to tidal backwater effects despite the claim commonly made.

EDITOR D. Koutsoyiannis ASSOCIATE EDITOR F. Pappenberger