Groundwater systems of the Indian Sub-Continent

Mukherjee, Abhijit; Saha, Dipankar; Harvey, Charles F.; Taylor, Richard G.; Ahmed, Kazi Matin; Bhanja, Soumendra N.

doi:10.1016/j.ejrh.2015.03.005

Cited by 163 publications

(113 citation statements)

References 29 publications

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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: 80%

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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Section: Introductionmentioning

confidence: 80%

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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“…Abstraction is widespread and prolific due to groundwater accessibility and low cost of development. The aquifer is often represented on hydrogeological maps as a single highly permeable homogenous aquifer (Struckmeier and Richts 2008;CGWB 2012;Mukherjee et al 2015); however, in practice the aquifer system is highly variable with significant spatial variability in groundwater recharge, permeability, storage and water chemistry.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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The Indo-Gangetic aquifer is one of the world's most important transboundary water resources, and the most heavily exploited aquifer in the world. To better understand the aquifer system, typologies have been characterized for the aquifer, which integrate existing datasets across the IndoGangetic catchment basin at a transboundary scale for the first time, and provide an alternative conceptualization of this aquifer system. Traditionally considered and mapped as a single homogenous aquifer of comparable aquifer properties and groundwater resource at a transboundary scale, the typologies illuminate significant spatial differences in recharge, permeability, storage, and groundwater chemistry across the aquifer system at this transboundary scale. These changes are shown to be systematic, concurrent with large-scale changes in sedimentology of the Pleistocene and Holocene alluvial aquifer, climate, and recent irrigation practices. Seven typologies of the aquifer are presented, each having a distinct set of challenges and opportunities for groundwater development and a different resilience to abstraction and climate change. The seven typologies are: (1) the piedmont margin, (2) the Upper Indus and Upper-Mid Ganges, (3) the Lower Ganges and Mid Brahmaputra, (4) the fluvially influenced deltaic area of the Bengal Basin, (5) the Middle Indus and Upper Ganges, (6) the Lower Indus, and (7) the marine-influenced deltaic areas.

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“…Lower recharge rates are found in central and southern parts of India (Figure 4, 5). The crystalline aquifers in these regions (Mukherjee et al, 2015) are not conducive enough for precipitation-based infiltration through subsurface. The observation is consistent with Sukhija et al (1996), who also found lower recharge rate in fractured regions.…”

Section: Groundwater Recharge Estimates As a Function Of Climate Hydmentioning

confidence: 99%

Long-term groundwater recharge rates across India by in situ measurements

Bhanja¹,

Mukherjee²,

Ramaswamy³

et al. 2018

Preprint

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Abstract. Groundwater recharge sustains groundwater discharge, including natural discharge through springs and base flow 15 to surface water as well as anthropogenic discharge through pumping wells. Here, for the first time, we compute long-term (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) groundwater recharge rates using data retrieved from several groundwater level monitoring locations across India (3.3 million km 2 area), the most groundwater-stressed region globally. Spatial variations in groundwater recharge rates (basin-wide mean: 17 to 960 mm/yr) were estimated in the 22 major river basins across India. The extensive plains of the Indus-Ganges-Brahmaputra (IGB) river basins are subjected to prevalence of comparatively higher recharge. This is mainly 20 attributed to occurrence of coarse sediments, higher rainfall, and intensive irrigation-linked groundwater abstraction inducing recharge by increasing available groundwater storage and return flows. Lower recharge rates (<200 mm/yr) in most of the central and southern study areas occur in cratonic, crystalline fractured aquifers. Estimated recharge rates have been compared favorably with field-scale recharge estimates (n= 52) based on tracer (tritium) injection tests. Results show precipitation rates are not significantly influencing groundwater recharge in most of the river basins across India, indicating 25 human influence in prevailing recharge rates. The spatial variability in recharge rates could provide critical input to policy makers to develop more sustainable groundwater management in India.

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Groundwater systems of the Indian Sub-Continent

Cited by 163 publications

References 29 publications

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

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

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

Long-term groundwater recharge rates across India by in situ measurements

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