A simple method to evaluate groundwater vulnerability in urbanizing agricultural regions

Olivares, Edwin Antonio Ojeda; Jiménez, Samantha; Sandoval-Torres, Sadoth; Enríquez, José Oscar Campos; Tiefenbacher, John P.; Takaro, Tim K.

doi:10.1016/j.jenvman.2020.110164

Cited by 14 publications

(5 citation statements)

References 29 publications

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“…Literature examining vulnerability to groundwater stress has been much more limited, with only a few studies examining this link in Bolivia [32], Mexico [33], the United States [34], and at a global scale [22]. These studies often use socioeconomic data and the results of groundwater models to demonstrate the spatial variability of vulnerability.…”

Section: Plos Onementioning

confidence: 99%

An integrated framework for examining groundwater vulnerability in the Mekong River Delta region

Powlen,

Haider,

Davis

et al. 2023

PLoS ONE

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The Mekong River provides water, food security, and many other valuable benefits to the more than 60 million Southeast Asian residents living within its basin. However, the Mekong River Basin is increasingly stressed by changes in climate, land cover, and infrastructure. These changes can affect water quantity and quality and exacerbate related hazards such as land subsidence and saltwater intrusion, resulting in multiple compounding risks for neighboring communities. In this study, we demonstrate the connection between climate change, groundwater availability, and social vulnerability by linking the results of a numerical groundwater model to land cover and socioeconomic data at the Cambodia-Vietnam border in the Mekong River Delta region. We simulated changes in groundwater availability across 20 years and identified areas of potential water stress based on domestic and agriculture-related freshwater demands. We then assessed adaptive capacity to understand how communities may be able to respond to this stress to better understand the growing risk of groundwater scarcity driven by climate change and overextraction. This study offers a novel approach for assessing risk of groundwater scarcity by linking the effects of climate change to the socioeconomic context in which they occur. Increasing our understanding of how changes in groundwater availability may affect local populations can help water managers better plan for the future, leading to more resilient communities.

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Section: Plos Onementioning

confidence: 99%

An integrated framework for examining groundwater vulnerability in the Mekong River Delta region

Powlen,

Haider,

Davis

et al. 2023

PLoS ONE

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“…The water matrix suction by the crops has been a recent topic of research in environmental science and agriculture engineering (Xu and Yang 2018 ; Rahardjo et al 2019 ; Tian et al 2020 ). Measurement of SWCs in specific soil types is limited to detection techniques whether recent or advanced (Wang et al 2021 ; Karakan 2022 ; Ojeda Olivares et al 2020 ). These studies have presented the idea of hydraulic retention and SWC strength using quantitative analytical techniques.…”

Section: Introductionmentioning

confidence: 99%

AI and machine learning for soil analysis: an assessment of sustainable agricultural practices

Awais,

Naqvi,

Zhang

et al. 2023

Bioresour. Bioprocess.

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Sustainable agricultural practices help to manage and use natural resources efficiently. Due to global climate and geospatial land design, soil texture, soil–water content (SWC), and other parameters vary greatly; thus, real time, robust, and accurate soil analytical measurements are difficult to be developed. Conventional statistical analysis tools take longer to analyze and interpret data, which may have delayed a crucial decision. Therefore, this review paper is presented to develop the researcher’s insight toward robust, accurate, and quick soil analysis using artificial intelligence (AI), deep learning (DL), and machine learning (ML) platforms to attain robustness in SWC and soil texture analysis. Machine learning algorithms, such as random forests, support vector machines, and neural networks, can be employed to develop predictive models based on available soil data and auxiliary environmental variables. Geostatistical techniques, including kriging and co-kriging, help interpolate and extrapolate soil property values to unsampled locations, improving the spatial representation of the data set. The false positivity in SWC results and bugs in advanced detection techniques are also evaluated, which may lead to wrong agricultural practices. Moreover, the advantages of AI data processing over general statistical analysis for robust and noise-free results have also been discussed in light of smart irrigation technologies. Conclusively, the conventional statistical tools for SWCs and soil texture analysis are not enough to practice and manage ergonomic land management. The broader geospatial non-numeric data are more suitable for AI processing that may soon help soil scientists develop a global SWC database. Graphical Abstract

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“…A few studies have used remote sensing, GIS, and AHP for monitoring and assessing GWPZs in Mexico. Studies realized by [28][29][30][31][32] have been reported.…”

Section: Introductionmentioning

confidence: 99%

Delineation of Groundwater Potential Zones (GWPZs) in a Semi-Arid Basin through Remote Sensing, GIS, and AHP Approaches

Uc-Castillo

Cruz

Ramos-Leal

et al. 2022

Water

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Groundwater occurrence in semi-arid regions is variable in space and time due to climate patterns, terrain features, and aquifer properties. Thus, accurate delineation of Groundwater Potential Zones (GWPZs) is essential for sustainable water resources management in these environments. The present research aims to delineate and assess GWPZs in a semi-arid basin of San Luis Potosi (SLP), Mexico, through the integration of Remote Sensing (RS), Geographic Information System (GIS), and Analytic Hierarchy Process (AHP). Seven thematic layers (geology, lineament density, land use and land cover, topographic wetness index (TWI), rainfall, drainage density, and slope) were generated in raster format. After the AHP procedure and rank assignment, the thematic layers were integrated using the raster calculator to obtain the GWPZs map. The results indicated that 68.21% of the area is classified as low groundwater potential, whereas 26.30% is classified as moderate. Validation was done by assessing the water residence time data from 15 wells distributed in the study area. Furthermore, the Receiver Operating Characteristics (ROC) curve was obtained, indicating a satisfactory accuracy prediction (AUC = 0.677). This study provides valuable information for decision-makers regarding the conservation and sustainable management of groundwater resources.

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A simple method to evaluate groundwater vulnerability in urbanizing agricultural regions

Cited by 14 publications

References 29 publications

An integrated framework for examining groundwater vulnerability in the Mekong River Delta region

An integrated framework for examining groundwater vulnerability in the Mekong River Delta region

AI and machine learning for soil analysis: an assessment of sustainable agricultural practices

Delineation of Groundwater Potential Zones (GWPZs) in a Semi-Arid Basin through Remote Sensing, GIS, and AHP Approaches

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