Optimization of a Groundwater Monitoring Network for a Sustainable Development of the Maheshwaram Catchment, India
Groundwater is one of the most valuable resources for drinking water and irrigation in the Maheshwaram Catchment, Central India, where most of the local population depends on it for agricultural activities. An increasing demand for irrigation and the growing concern about potential water contaminati...
Ausführliche Beschreibung
Autor*in: |
Shakeel Ahmed [verfasserIn] Adrian H. Gallardo [verfasserIn] Aadil Nabi [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
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2011 |
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In: Sustainability - MDPI AG, 2009, 3(2011), 2, Seite 396-409 |
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Übergeordnetes Werk: |
volume:3 ; year:2011 ; number:2 ; pages:396-409 |
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DOI / URN: |
10.3390/su3020396 |
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Katalog-ID: |
DOAJ018274986 |
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10.3390/su3020396 doi (DE-627)DOAJ018274986 (DE-599)DOAJb3cb0d9c6d2c45b99576a31fd9bfc612 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Shakeel Ahmed verfasserin aut Optimization of a Groundwater Monitoring Network for a Sustainable Development of the Maheshwaram Catchment, India 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Groundwater is one of the most valuable resources for drinking water and irrigation in the Maheshwaram Catchment, Central India, where most of the local population depends on it for agricultural activities. An increasing demand for irrigation and the growing concern about potential water contamination makes imperative the implementation of a systematic groundwater-quality monitoring program in the region. Nonetheless, limited funding and resources emphasize the need to achieve a representative but cost-effective sampling strategy. In this context, field observations were combined with a geostatistical analysis to define an optimized monitoring network able to provide sufficient and non-redundant information on key hydrochemical parameters. A factor analysis was used to evaluate the interrelationship among variables, and permitted to reduce the original dataset into a new configuration of monitoring points still able to capture the spatial variability in the groundwater quality of the basin. The approach is useful to maximize data collection and contributes to better manage the allocation of resources under budget constrains. groundwater geostatistics monitoring network optimization India Environmental effects of industries and plants Renewable energy sources Environmental sciences Adrian H. Gallardo verfasserin aut Aadil Nabi verfasserin aut In Sustainability MDPI AG, 2009 3(2011), 2, Seite 396-409 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:3 year:2011 number:2 pages:396-409 https://doi.org/10.3390/su3020396 kostenfrei https://doaj.org/article/b3cb0d9c6d2c45b99576a31fd9bfc612 kostenfrei http://www.mdpi.com/2071-1050/3/2/396/ kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 3 2011 2 396-409 |
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10.3390/su3020396 doi (DE-627)DOAJ018274986 (DE-599)DOAJb3cb0d9c6d2c45b99576a31fd9bfc612 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Shakeel Ahmed verfasserin aut Optimization of a Groundwater Monitoring Network for a Sustainable Development of the Maheshwaram Catchment, India 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Groundwater is one of the most valuable resources for drinking water and irrigation in the Maheshwaram Catchment, Central India, where most of the local population depends on it for agricultural activities. An increasing demand for irrigation and the growing concern about potential water contamination makes imperative the implementation of a systematic groundwater-quality monitoring program in the region. Nonetheless, limited funding and resources emphasize the need to achieve a representative but cost-effective sampling strategy. In this context, field observations were combined with a geostatistical analysis to define an optimized monitoring network able to provide sufficient and non-redundant information on key hydrochemical parameters. A factor analysis was used to evaluate the interrelationship among variables, and permitted to reduce the original dataset into a new configuration of monitoring points still able to capture the spatial variability in the groundwater quality of the basin. The approach is useful to maximize data collection and contributes to better manage the allocation of resources under budget constrains. groundwater geostatistics monitoring network optimization India Environmental effects of industries and plants Renewable energy sources Environmental sciences Adrian H. Gallardo verfasserin aut Aadil Nabi verfasserin aut In Sustainability MDPI AG, 2009 3(2011), 2, Seite 396-409 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:3 year:2011 number:2 pages:396-409 https://doi.org/10.3390/su3020396 kostenfrei https://doaj.org/article/b3cb0d9c6d2c45b99576a31fd9bfc612 kostenfrei http://www.mdpi.com/2071-1050/3/2/396/ kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 3 2011 2 396-409 |
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10.3390/su3020396 doi (DE-627)DOAJ018274986 (DE-599)DOAJb3cb0d9c6d2c45b99576a31fd9bfc612 DE-627 ger DE-627 rakwb eng TD194-195 TJ807-830 GE1-350 Shakeel Ahmed verfasserin aut Optimization of a Groundwater Monitoring Network for a Sustainable Development of the Maheshwaram Catchment, India 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Groundwater is one of the most valuable resources for drinking water and irrigation in the Maheshwaram Catchment, Central India, where most of the local population depends on it for agricultural activities. An increasing demand for irrigation and the growing concern about potential water contamination makes imperative the implementation of a systematic groundwater-quality monitoring program in the region. Nonetheless, limited funding and resources emphasize the need to achieve a representative but cost-effective sampling strategy. In this context, field observations were combined with a geostatistical analysis to define an optimized monitoring network able to provide sufficient and non-redundant information on key hydrochemical parameters. A factor analysis was used to evaluate the interrelationship among variables, and permitted to reduce the original dataset into a new configuration of monitoring points still able to capture the spatial variability in the groundwater quality of the basin. The approach is useful to maximize data collection and contributes to better manage the allocation of resources under budget constrains. groundwater geostatistics monitoring network optimization India Environmental effects of industries and plants Renewable energy sources Environmental sciences Adrian H. Gallardo verfasserin aut Aadil Nabi verfasserin aut In Sustainability MDPI AG, 2009 3(2011), 2, Seite 396-409 (DE-627)610604120 (DE-600)2518383-7 20711050 nnns volume:3 year:2011 number:2 pages:396-409 https://doi.org/10.3390/su3020396 kostenfrei https://doaj.org/article/b3cb0d9c6d2c45b99576a31fd9bfc612 kostenfrei http://www.mdpi.com/2071-1050/3/2/396/ kostenfrei https://doaj.org/toc/2071-1050 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 3 2011 2 396-409 |
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Optimization of a Groundwater Monitoring Network for a Sustainable Development of the Maheshwaram Catchment, India |
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Groundwater is one of the most valuable resources for drinking water and irrigation in the Maheshwaram Catchment, Central India, where most of the local population depends on it for agricultural activities. An increasing demand for irrigation and the growing concern about potential water contamination makes imperative the implementation of a systematic groundwater-quality monitoring program in the region. Nonetheless, limited funding and resources emphasize the need to achieve a representative but cost-effective sampling strategy. In this context, field observations were combined with a geostatistical analysis to define an optimized monitoring network able to provide sufficient and non-redundant information on key hydrochemical parameters. A factor analysis was used to evaluate the interrelationship among variables, and permitted to reduce the original dataset into a new configuration of monitoring points still able to capture the spatial variability in the groundwater quality of the basin. The approach is useful to maximize data collection and contributes to better manage the allocation of resources under budget constrains. |
abstractGer |
Groundwater is one of the most valuable resources for drinking water and irrigation in the Maheshwaram Catchment, Central India, where most of the local population depends on it for agricultural activities. An increasing demand for irrigation and the growing concern about potential water contamination makes imperative the implementation of a systematic groundwater-quality monitoring program in the region. Nonetheless, limited funding and resources emphasize the need to achieve a representative but cost-effective sampling strategy. In this context, field observations were combined with a geostatistical analysis to define an optimized monitoring network able to provide sufficient and non-redundant information on key hydrochemical parameters. A factor analysis was used to evaluate the interrelationship among variables, and permitted to reduce the original dataset into a new configuration of monitoring points still able to capture the spatial variability in the groundwater quality of the basin. The approach is useful to maximize data collection and contributes to better manage the allocation of resources under budget constrains. |
abstract_unstemmed |
Groundwater is one of the most valuable resources for drinking water and irrigation in the Maheshwaram Catchment, Central India, where most of the local population depends on it for agricultural activities. An increasing demand for irrigation and the growing concern about potential water contamination makes imperative the implementation of a systematic groundwater-quality monitoring program in the region. Nonetheless, limited funding and resources emphasize the need to achieve a representative but cost-effective sampling strategy. In this context, field observations were combined with a geostatistical analysis to define an optimized monitoring network able to provide sufficient and non-redundant information on key hydrochemical parameters. A factor analysis was used to evaluate the interrelationship among variables, and permitted to reduce the original dataset into a new configuration of monitoring points still able to capture the spatial variability in the groundwater quality of the basin. The approach is useful to maximize data collection and contributes to better manage the allocation of resources under budget constrains. |
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|
score |
7.396736 |