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"IRRIGATION WATER SUPPLY"
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Groundwater quality assessment for agricultural utilizing indexical and machine learning techniques in Ouled Djellal Aquifer, Southern Algeria
Groundwater represents the main water resource for irrigation in the Ouled Djellal region (southeast of Algeria). Despite the importance of groundwater in this area, its quality and sustainability remain insufficiently studied. Therefore, this study aimed to introduce an integrated analytical framework by combining multivariate statistical techniques i.e., Principal Component Analysis (PCA) and Hierarchical Ascending Classification (HAC), irrigation indices (IWQI, SAR, Na%, SSP, PS, and RSC), and machine learning (ML) models such as Artificial Neural Network (ANN), Support Vector Machine (SVM), and Multiple Linear Regression (MLR) to assess and predict groundwater quality for irrigation. The main difference with previous studies is the fact that this work applied Empirical Bayesian Kriging Regression Prediction (EBKRP) to spatialize irrigation indices derived from ML with higher precision. The approach enables cross-validation of model performance and captures complex nonlinear interactions among hydrochemical parameters. The attained results revealed that groundwater quality was varied from moderate to poor for irrigation, driven mainly by salinity and sodicity effects. In addition, the ANN model achieved the highest predictive accuracy (R² = 0.97, RMSE = 1.50), confirming its superiority in modelling complex hydrochemical behavior. The proposed modelling framework represents a methodological advancement for data-scarce arid regions, serving as a practical tool adaptable to groundwater monitoring and irrigation planning in similar regions.
Journal Article
Geochemical characteristics, mechanisms, and suitability of groundwater resource for sustainable water supply in Quetta valley
Groundwater is a crucial water resource for various usages worldwide. The Quetta Valley of Pakistan was investigated regarding its groundwater quality sustainability based on integrated approaches of hydrochemistry, geographic information system, and multivariate statistics. A total of 29 groundwater samples were collected from monitoring wells to get insights into the hydrochemical suitability of groundwater for sustainable irrigation and drinking utilization. The results indicate groundwater is mainly featured by the hydrochemical facies of HCO3·Cl-Ca. Groundwater hydrochemical composition is dominantly governed by the dissolution of carbonates and silicate minerals in combination with positive cation exchange in the valley. Principal component analysis reveals a significant influence of geogenic factors on groundwater chemistry, further supported by PHREEQC simulation that detects a supersaturation of calcite, dolomite, and sulphate minerals in the aquifer. The irrigation water quality index divides groundwater in the study area into three zones, which signify low restriction and no restriction, except for a severe restriction in the southwestern part of the valley. Groundwater is generally suitable for irrigation across the valley. The entropy-weighted water quality index classifies groundwater as excellent and good quality for drinking. This study can provide crucial insights for authorities on groundwater suitability in Quetta Valley and similar regions worldwide.
Journal Article
Spatiotemporal monitoring of groundwater supply and active energy for irrigation practice in semi-arid regions of Tunisia with machine learning
Semiarid regions are facing overexploitation of groundwater resources to meet irrigation needs. Monitoring the water-energy nexus allows for optimal management of extracted water volumes and consumed energy. The Nabeul region of Tunisia was selected where 14 farmers, whose wells were equipped with smart electricity and water meters (SWEMs), for instant monitoring of pumped water volumes and the electrical energy required for irrigation. Monthly data over a period of eight months were used to study the variations in water volumes and active energy. The analysis of variance classified farmers into four groups based on water volumes and five groups based on active energy. Spatial variability analysis using kriging showed that the northeast zone is the most solicited in terms of water pumping and energy consumption with water volume exceeding 4,000 m3/month and active energy reaching 2,500 kWh/month. The prediction of energy based on water volume using machine learning techniques such as random forest and support vector machine was successfully conducted. The tools generated by the methodology were applied to a chosen case in the region to estimate active energy and validate the results obtained. The implemented framework allows for better management of groundwater resources for irrigation.
Journal Article
Assessing irrigation water shortage in the middle reaches of the Heihe River Basin under future climate scenarios
【Objective】Climate change is projected to increase the frequency of extreme weather events such as flooding and drought. This paper evaluates the risk of agricultural water shortage in the middle reaches of the Heihe River basin under different climate change scenarios.【Method】Using the CMIP6 SSP scenarios (SSP126, SSP245, and SSP585), downscaled meteorological data were integrated with the BP neural network, the Hargreaves model, and crop coefficients to predict changes in irrigation water availability and demand in the middle reaches of the Heihe River. Copula functions were applied to model the joint distribution of water supply and demand and assess the risk of water scarcity.【Result】Calculations for the period from 2024 to 2100 show that average annual irrigation demand is expected to increase by 10.21%, 11.73%, and 14.59% under SSP126, SSP245, and SSP585, respectively. The associated average annual growth rates will be 0.17%, 0.16%, and 0.18%, with the maximum-to-minimum annual runoff volume ratio being 1.21, 1.27, and 1.34, respectively. SSP126 will see an increased probability of wet-dry and wet-wet alternation, while SSP245 will exhibit the highest probability of wet-dry and normal-normal alternation. SSP585 will experience a decrease in the probability of wet-dry alternation and an increase in the probability of normal-normal alternation. Water scarcity risk is below 0.6 under the SSP126 scenario, below 0.6 in some years under SSP245, and consistently above 0.6 under the SSP585 scenario.【Conclusion】Runoff in the upstream regions and irrigation demands in the midstream regions of the Heihe River basin are both expected to increase under all SSP scenarios. Water scarcity risks are projected to decrease under the SSP126 and SSP245 scenarios, while SSP585 scenarios presents the highest water scarcity risk and SSP126 presents the lowest. These findings can inform the development of strategies to mitigate the impact of climate change on water scarcity in the catchment.
Journal Article
Construction of a water resource suitability index for agricultural production and matching analysis of cultivated land in Lhasa, Tibet
Water resources are essential for agriculture. In the spatial layout of agricultural production, quantitatively identifying the spatial differences in water resource conditions, including precipitation and irrigation water supply factors, is necessary. Here, a water resource suitability index for agricultural production (WRSIA) was constructed for agricultural development using irrigation water supply convenience (IWSC) and precipitation conditions. Considering Lhasa as the study area, water resource suitability index for agricultural production was calculated on a 100 m grid scale, and the spatial distribution relationship between water resource suitability index for agricultural production and cultivated land was analyzed using geographically weighted regression (GWR). The results showed that irrigation water supply convenience severely restricted agricultural production in Lhasa, and the high water resource suitability index for agricultural production values were mainly distributed in the valleys of the Lhasa River and its tributaries. Moreover, 47.7% of the cultivated land was distributed in 5% of the area having the highest water resource suitability. According to geographically weighted regression, the cultivated land area and water resource availability were strongly correlated (R 2 = 0.904). The distribution of the cultivated land was well explained by water resource suitability index for agricultural production, which could describe the differences in water resource suitability for agricultural production. Furthermore, the suitability of agricultural production was better evaluated when water resource suitability index for agricultural production was coupled with the land resource suitability index. Overall, water resource suitability index for agricultural production showed high applicability in Lhasa and other regions, thereby providing a scientific basis and technical support for the spatial layout of agricultural production.
Journal Article