Spatial distribution of regionalized variables on reservoirs and groundwater resources based on geostatistical analysis using GIS: case of Rmel-Oulad Ogbane aquifers (Larache, NW Morocco)

Hamidi, Mohamed Jalal El; Larabi, Abdelkader; Faouzi, Mohamed; Souissi, M.

doi:10.1007/s12517-018-3430-9

Cited by 23 publications

(18 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Over the past decade, several research initiatives have been aimed at creating and enhancing water quality prediction models [45][46][47]. Recently, machine learning and data-driven results have shown promising results in the development of precise water quality estimation models [48,49]. Therefore, multivariable statistical regression methods, including the PCR, SVMR and SMLR, were tested as alternative approaches for predicting WQIs for irrigation.…”

Section: Introductionmentioning

confidence: 99%

Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

Elsayed

Hussein

Moghanm

et al. 2020

Water

View full text Add to dashboard Cite

Under sustainable development conditions, the water quality of irrigation systems is a complex issue which involves the combined effects of several surface water management parameters. Therefore, this work aims to enhance the surface water quality assessment and geochemical controlling mechanisms and to assess the validation of surface water networks for irrigation using six Water Quality Indices (WQIs) supported by multivariate modelling techniques, such as Principal Component Regression (PCR), Support Vector Machine Regression (SVMR) and Stepwise Multiple Linear Regression (SMLR). A total of 110 surface water samples from a network of surface water cannels during the summers of 2018 and 2019 were collected for this research and standard analytical techniques were used to measure 21 physical and chemical parameters. The physicochemical properties revealed that the major ions concentrations were reported in the following order: Ca2+ > Na+ > Mg2+ > K+ and alkalinity > SO42− > Cl− > NO3− > F−. The trace elements concentrations were reported in the following order: Fe > Mn > B > Cr > Pb > Ni > Cu > Zn > Cd. The surface water belongs to the Ca2+-Mg2+-HCO3− and Ca2+-Mg2+-Cl−-SO42− water types, under a stress of silicate weathering and reverse ion exchange process. The computation of WQI values across two years revealed that 82% of samples represent a high class and the remaining 18% constitute a medium class of water quality for irrigation use with respect to the Irrigation Water Quality (IWQ) value, while the Sodium Percentage (Na%) values across two years indicated that 96% of samples fell into in a healthy class and 4% fell into in a permissible class for irrigation. In addition, the Sodium Absorption Ratio (SAR), Permeability Index (PI), Kelley Index (KI) and Residual Sodium Carbonate (RSC) values revealed that all surface water samples were appropriate for irrigation use. The PCR and SVMR indicated accurate and robust models that predict the six WQIs in both datasets of the calibration (Cal.) and validation (Val.), with R2 values varying from 0.48 to 0.99. The SMLR presented estimated the six WQIs well, with an R2 value that ranged from 0.66 to 0.99. In conclusion, WQIs and multivariate statistical analyses are effective and applicable for assessing the surface water quality. The PCR, SVMR and SMLR models provided robust and reliable estimates of the different indices and showed the highest R2 and the highest slopes values close to 1.00, as well as minimum values of RMSE in all models.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

Elsayed

Hussein

Moghanm

et al. 2020

Water

View full text Add to dashboard Cite

show abstract

“…Water quality parameters were affected in a complex way with seasons and geographical locations 29 . Those studies, mainly focusing on the autocorrelation of water quality parameters in the water coastal aquifers, groundwater and urban aquatic environments, observed no autocorrelation in the water chemical parameters 40 , high spatial autocorrelation with sodium ions 41 , or most significant autocorrelation in bicarbonate 42 . Those results differed much from ours, since environmental parameters, especially geographical conditions, water pollutants and water salinity, could affect ionic content, resulting in variations in spatial distribution of water quality parameters across study areas.…”

Section: Discussionmentioning

confidence: 99%

A novel assessment considering spatial and temporal variations of water quality to identify pollution sources in urban rivers

Yang

Liang

Qin³

et al. 2021

Sci Rep

View full text Add to dashboard Cite

It’s vital to explore critical indicators when identifying potential pollution sources of urban rivers. However, the variations of urban river water qualities following temporal and spatial disturbances were highly local-dependent, further complicating the understanding of pollution emission laws. In order to understand the successional trajectory of water qualities of urban rivers and the underlying mechanisms controlling these dynamics at local scale, we collected daily monitoring data for 17 physical and chemical parameters from seven on-line monitoring stations in Nanfeihe River, Anhui, China, during the year 2018. The water quality at tributaries were similar, while that at main river was much different. A seasonal ‘’turning-back” pattern was observed in the water quality, which changed significantly from spring to summer but finally changed back in winter. This result was possibly regulated by seasonally-changed dissolved oxygen and water temperature. Linear mixed models showed that the site 2, with the highest loads of pollution, contributed the highest (β = 0.316, P < 0.001) to the main river City Water Quality Index (CWQI) index, but site 5, the geographically nearest site to main river monitoring station, did not show significant effect. In contrast, site 5 but not site 2 contributed the highest (β = 0.379, P < 0.001) to the main river water quality. Therefore, CWQI index was a better index than water quality to identify potential pollution sources with heavy loads of pollutants, despite temporal and spatial disturbances at local scales. These results highlight the role of aeration in water quality controlling of urban rivers, and emphasized the necessity to select proper index to accurately trace the latent pollution sources.

show abstract

“…Mapping groundwater quality parameters through using geostatistics has become well known. There are numerous studies on groundwater quality evaluation using geostatistical techniques all around the world (Nur et al 2012;Shamsudduha 2007;Goovaerts et al 2005;Zehtabian et al 2013). Sarath Prasanth et al (2012) conducted a study to assess the spatial distribution of groundwater quality in the coastal city of Alappuzha in India.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of some groundwater quality parameters using geostatistics in the urban coastal aquifer of Bosaso plain, Somalia

Said¹,

Yurtal²,

Çetin³

et al. 2021

Tarım Bilimleri Dergisi

View full text Add to dashboard Cite

Groundwater is a major drinking water resource in arid coastal regions. The groundwater quality of Bosaso city experienced degradation due to rapid urbanization and industrialization. This study was carried out to delineate the spatial distribution of groundwater quality parameters and evaluate groundwater suitability for drinking and irrigation uses. The groundwater samples were collected from Bosaso Plain to determine the pH, electrical conductivity (EC), total dissolved solids (TDS), and sodium and chloride concentrations. To categorize water quality for irrigation purposes, sodium adsorption ratio (SAR) was calculated. Ordinary kriging procedure was performed in order to map the spatial distribution of groundwater quality parameters. The interpretation of laboratory analysis results revealed that the most of groundwater wells in the study area is unsafe for drinking purposes due to high salinity, except for the central area (Biyo Kulule). In terms of irrigation uses, the minor area may be under the risk of alkalinity or sodium hazard. However, all groundwater supply points are not suitable for irrigation due to the salinization risk and can be only used to irrigate high salttolerant crops. The final maps show that the groundwater quality decreases from southeast to the north of the plain. This indicates that the groundwater is probably subjected to the seawater intrusion. In this regard, the implementation of a groundwater monitoring program is necessary to achieve concrete results. Nevertheless, the most suitable groundwater quality is found to be at the central part of the Bosaso plain.

show abstract

Spatial distribution of regionalized variables on reservoirs and groundwater resources based on geostatistical analysis using GIS: case of Rmel-Oulad Ogbane aquifers (Larache, NW Morocco)

Cited by 23 publications

References 21 publications

Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

A novel assessment considering spatial and temporal variations of water quality to identify pollution sources in urban rivers

Evaluation of some groundwater quality parameters using geostatistics in the urban coastal aquifer of Bosaso plain, Somalia

Contact Info

Product

Resources

About