Hydrogeological typologies of the Indo-Gangetic basin alluvial aquifer, South Asia

Bonsor, H.C.; MacDonald, Alan; Ahmed, Kazi Matin; Burgess, W.; Basharat, Muhammad; Calow, Roger; Dixit, Ankur; Foster, Stephen; Gopal, K. Rama; Lapworth, Dan; Moench, Marcus; Mukherjee, Abhijit; Rao, M. A.; Shamsudduha, Mohammad; Smith, Linda S.; Taylor, Richard G.; Tucker, Josephine; Steenbergen, Frank van; Yadav, S. K.; Zahid, Anwar

doi:10.1007/s10040-017-1550-z

Cited by 140 publications

(70 citation statements)

References 72 publications

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“…Recent models for As‐affected aquifers in Southeast Asia have elucidated similar influential factors and variables (groundwater age, organic carbon, aquifer, depth, etc. ; Biswas et al, ; Biswas, Bhattacharya, et al, ; Biswas, Neidhardt, et al, ; Desbarats et al, ; Gillispie et al, ; McArthur et al, , , ; Mihajlov et al, ) related to modeled As hazard in aquifers (Biswas, Neidhardt, et al, ; Bonsor et al, ; Hoque et al, , , ; Mahmud et al, ; Mukherjee et al, ; Podgorski et al, ).…”

Section: Resultsmentioning

confidence: 99%

“…Naturally occurring, geogenic arsenic (As) adversely affects drinking water quality of groundwater in geologically diverse aquifers in Europe, Africa, and North and South America, and particularly Southeast Asia (Amini et al, ; Bonsor et al, ; McArthur et al, ; Smedley & Kinniburgh, ; Wen et al, ). Within North America, naturally occurring As is widespread in groundwater throughout Late Quaternary glacial aquifers in the northern United States (Ayotte et al, ; Erickson, ; Root et al, ; Thomas, , ; Thomas et al, ; Warner, ; Warner & Ayotte, ; Welch et al, ; Ying et al, ).…”

Section: Introductionmentioning

confidence: 99%

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Predicting geogenic Arsenic in Drinking Water Wells in Glacial Aquifers, North‐Central USA: Accounting for Depth‐Dependent Features

Erickson

Elliott

Christenson

et al. 2018

Water Resources Research

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Chronic exposure to arsenic (As) via drinking groundwater is a human health concern worldwide. Probabilities of elevated geogenic As concentrations in groundwater were predicted in complex, glacial aquifers in Minnesota, north-central USA, a region that commonly has elevated As concentrations in well water. Maps of elevated As hazard were created for depths typical of drinking water supply and with well construction attributes common for domestic wells. Conventional variables describing aquifer properties and materials, position on the hydrologic landscape, and soil geochemistry were among the most influential for predicting the probability of elevated As. We also found that certain well construction attributes were influential in predicting As hazard. Smaller distances between the top of the well screen and overlying aquitard (proximity) and shorter well screen lengths were each associated with higher probabilities of elevated As. Influential predictor variables, which are either mapped across the region or are well construction attributes, are proxies in the model for measurable physical or geochemical causes of elevated As (e.g., redox condition, till or aquifer sediment chemistry, and water chemistry), which are not mapped across the region. Our setting shares some important characteristics with deltaic and other high-As aquifers in Southeast Asia: late Quaternary age, complex layering of coarse-and fine-grained materials, low-As sediment concentrations, and geochemical controls on As mobilization. Translating three-dimensional geologic and geochemical understanding of As mobility to quantifiable variables for modeling with powerful, flexible statistical tools could improve predictions and help identify safer groundwater supply options in the USA, Southeast Asia, and elsewhere.Plain Language Summary This study demonstrates that certain well construction attributes are influential in predicting arsenic (As) concentrations in drinking water wells. Smaller distances between the top of the well screen and overlying aquitard, and shorter well screen lengths, were each associated with higher probabilities of elevated As. Chronic exposure to As via drinking groundwater is a human health concern worldwide, and Minnesota, USA, commonly has elevated As concentrations in well water. This study describes a new, novel, and important finding from an As probability model: Controllable well construction choices (not just location or depth) influence As concentrations in drinking water from wells.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting geogenic Arsenic in Drinking Water Wells in Glacial Aquifers, North‐Central USA: Accounting for Depth‐Dependent Features

Erickson

Elliott

Christenson

et al. 2018

Water Resources Research

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“…On the other hand, no clear trend appears in South India [9,10]. These two contrasted results are mainly driven by the size of the groundwater resources: the presence in the north of the highest-yielding deep aquifers known in the world [11], contrasts with the southern aquifers, which consist of shallow to moderately shallow fractured hard rocks with low porosity and storage capacities [12]. Whereas in the north water extraction for irrigation purposes leads to a continuous decline in groundwater stocks, South Indian farmers experience recurring shortages, as shallow aquifers are temporarily emptied during periods of overexploitation when extraction is higher than recharge, and are partially refilled after heavy monsoon rainfalls.…”

Section: Introductionmentioning

confidence: 99%

Detection of Irrigated Crops from Sentinel-1 and Sentinel-2 Data to Estimate Seasonal Groundwater Use in South India

et al. 2017

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Indian agriculture relies on monsoon rainfall and irrigation from surface and groundwater. The interannual variability of monsoon rainfalls is high, which forces South Indian farmers to adapt their irrigated areas to local water availability. In this study, we have developed and tested a methodology for monitoring these spatiotemporal variations using Sentinel-1 and -2 observations over the Kudaliar catchment, Telangana State (~1000 km 2 ). These free radar and optical data have been acquired since 2015 on a weekly basis over continental areas, at a high spatial resolution (10-20 m) that is well adapted to the small areas of South Indian field crops. A machine learning algorithm, the Random Forest method, was used over three growing seasons (January to March and July to November 2016 and January to March 2017) to classify small patches of inundated rice paddy, maize, and other irrigated crops, as well as surface water stored in the small reservoirs scattered across the landscape. The crop production comprises only irrigated crops (less than 20% of the areas) during the dry season (Rabi, December to March), to which rain-fed cotton is added to reach 60% of the areas during the monsoon season (Kharif, June to November). Sentinel-1 radar backscatter provides useful observations during the cloudy monsoon season. The lowest irrigated area totals were found during Rabi 2016 and Kharif 2016, accounting for 3.5 and 5% with moderate classification confusion. This confusion decreases with increasing areas of irrigated crops during Rabi 2017. During this season, 16% of rice and 6% of irrigated crops were detected after the exceptional rainfalls observed in September. Surface water in small surface reservoirs reached 3% of the total area, which corresponds to a high value. The use of both Sentinel datasets improves the method accuracy and strengthens our confidence in the resulting maps. This methodology shows the potential of automatically monitoring, in near real time, the high short term variability of irrigated area totals in South India, as a proxy for Remote Sens. 2017, 9, 1119; doi:10.3390/rs9111119 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 1119 2 of 21 estimating irrigated water and groundwater needs. These are estimated over the study period to range from 49.5 ± 0.78 mm (1.5% uncertainty) in Rabi 2016, and 44.9 ± 2.9 mm (6.5% uncertainty) in the Kharif season, to 226.2 ± 5.8 mm (2.5% uncertainty) in Rabi 2017. This variation must be related to groundwater recharge estimates that range from 10 mm to 160 mm·yr −1 in the Hyderabad region. These dynamic agro-hydrological variables estimated from Sentinel remote sensing data are crucial in calibrating runoff, aquifer recharge, water use and evapotranspiration for the spatially distributed agro-hydrological models employed to quantify the impacts of agriculture on water resources.

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“…They found that CHM structure uncertainty dominated over parameter uncertainty when the models were used for extrapolation. Many studies have recently suggested that uncertainty derived from the definition of alternative CHMs is one of the major sources of total uncertainty, and the parameter uncertainty does not cover the entire uncertainty range (Bredehoeft, 2005;Neuman, 2003;Refsgaard et al, 2006;Rojas et al, 2008;Troldborg et al, 2007). Therefore, neglecting the CHM uncertainty may result in un-reliable prediction and underestimate the total predictive uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Multi-model approach to quantify groundwater-level prediction uncertainty using an ensemble of global climate models and multiple abstraction scenarios

Mustafa

Hasan

Saha

et al. 2019

Hydrol. Earth Syst. Sci.

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Abstract. Worldwide, groundwater resources are under a constant threat of overexploitation and pollution due to anthropogenic and climatic pressures. For sustainable management and policy making a reliable prediction of groundwater levels for different future scenarios is necessary. Uncertainties are present in these groundwater-level predictions and originate from greenhouse gas scenarios, climate models, conceptual hydro(geo)logical models (CHMs) and groundwater abstraction scenarios. The aim of this study is to quantify the individual uncertainty contributions using an ensemble of 2 greenhouse gas scenarios (representative concentration pathways 4.5 and 8.5), 22 global climate models, 15 alternative CHMs and 5 groundwater abstraction scenarios. This multi-model ensemble approach was applied to a drought-prone study area in Bangladesh. Findings of this study, firstly, point to the strong dependence of the groundwater levels on the CHMs considered. All groundwater abstraction scenarios showed a significant decrease in groundwater levels. If the current groundwater abstraction trend continues, the groundwater level is predicted to decline about 5 to 6 times faster for the future period 2026–2047 compared to the baseline period (1985–2006). Even with a 30 % lower groundwater abstraction rate, the mean monthly groundwater level would decrease by up to 14 m in the southwestern part of the study area. The groundwater abstraction in the northwestern part of Bangladesh has to decrease by 60 % of the current abstraction to ensure sustainable use of groundwater. Finally, the difference in abstraction scenarios was identified as the dominant uncertainty source. CHM uncertainty contributed about 23 % of total uncertainty. The alternative CHM uncertainty contribution is higher than the recharge scenario uncertainty contribution, including the greenhouse gas scenario and climate model uncertainty contributions. It is recommended that future groundwater-level prediction studies should use multi-model and multiple climate and abstraction scenarios.

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Hydrogeological typologies of the Indo-Gangetic basin alluvial aquifer, South Asia

Cited by 140 publications

References 72 publications

Predicting geogenic Arsenic in Drinking Water Wells in Glacial Aquifers, North‐Central USA: Accounting for Depth‐Dependent Features

Predicting geogenic Arsenic in Drinking Water Wells in Glacial Aquifers, North‐Central USA: Accounting for Depth‐Dependent Features

Detection of Irrigated Crops from Sentinel-1 and Sentinel-2 Data to Estimate Seasonal Groundwater Use in South India

Multi-model approach to quantify groundwater-level prediction uncertainty using an ensemble of global climate models and multiple abstraction scenarios

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