Three decades of depth-dependent groundwater response to climate variability and human regime in the transboundary Indus-Ganges-Brahmaputra-Meghna mega river basin aquifers

Malakar, Pragnaditya; Mukherjee, Abhijit; Bhanja, Soumendra N.; Ganguly, Auroop R.; Ray, R. K.; Zahid, Anwar; Sarkar, Sudeshna; Saha, Dipankar; Chattopadhyay, Siddhartha

doi:10.1016/j.advwatres.2021.103856

Cited by 34 publications

(20 citation statements)

References 67 publications

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“…Our results suggest that seasonal and interannual GWS changes (neglecting longterm trends) are controlled more by climate variations than by pumping, as also observed in the US (Russo & Lall, 2017;Thomas & Famiglietti, 2019). EOF1 also identifies a part of south India where groundwater levels are explained better by pumping effects (Figure 7a), in addition to the previously identified pockets of north India (Asoka et al, 2017;Malakar et al, 2021). But, even in the most depleted northwest India, rainfall is found to have indirectly influenced pumping effects (Asoka et al, 2017).…”

Section: Influence Of Climate Variabilitysupporting

confidence: 69%

Groundwater Variability Across India, Under Contrasting Human and Natural Conditions

2022

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Characterizing local to regional scale water cycles and water resources will be crucial for achieving the United Nations' water‐related Sustainable Developmental Goals. However, quantification and understanding of groundwater extraction across scales have been hampered by inadequate water usage reporting and limited information on irrigation practices. Here we analyze observations from ∼15,000 groundwater monitoring wells and the Gravity Recovery and Climate Experiment satellites together with irrigation, agricultural, and meteorological datasets to show how drought‐induced coupling between natural and anthropogenic groundwater storage variations has caused sustainability challenges in India, the world's biggest consumer of groundwater for irrigation. Notably, the mechanisms and consequences of such coupling differ significantly depending on aquifer types. In Andhra Pradesh's hard rock aquifer, groundwater declines have been limited, despite the nearly constant water scarcity that its farmers face. Moreover, its free farm power policy involves an annual irrigation energy consumption of 26 billion kWh that costs US$ 2.5 billion, possibly unparalleled compared to any other part of the world of similar size (0.27 million km2). In West Bengal's highly permeable alluvial aquifer, the water table is declining rapidly (15 cm/yr) due to a policy that encourages irrigation. Situated between these two states, Odisha's aquifer shows substantial resilience to drought, owing to the state's relatively natural landscape and forest restoration policy. The findings of this study provide new insights to understand the divergent aspects of groundwater irrigation in north versus south India, which can enable development of adaptation and mitigation strategies to avert the looming water crisis.

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Section: Influence Of Climate Variabilitysupporting

confidence: 69%

Groundwater Variability Across India, Under Contrasting Human and Natural Conditions

2022

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“…This could be explained by the fact that local scale deep GWW from the irrigation, industrial, or drinking water pumping wells influences the near vicinity observation wells. The deep groundwater abstraction through deep irrigation wells (∼15-fold higher output to shallow irrigation wells) is a common practice to avoid shallow contaminated water and due to continuous dependence of GWL in the region, particularly in the Indus–Ganges–Brahmaputra basin of north India. , The greater deep groundwater abstraction has led to higher deep GWW in the region. Thus, the observation wells in vicinity of the abstraction wells could be highly affected by the local GWW.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning-Based Forecasting of Groundwater Level Trends in India: Implications for Crop Production and Drinking Water Supply

et al. 2021

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Despite numerous studies in recent times, there is no consensus on the primary drivers for groundwater storage (GWS) changes over India. Thus, predicting future groundwater level trends seems remote. In this context, using Gravity Recovery and Climate Experiment (GRACE)-derived GWS, WaterGap model-based groundwater recharge (GWR), and groundwater withdrawal (GWW), we show that GWW exhibits a stronger dominance than GWR on GWS change over India. Furthermore, we developed feed-forward neural network (FNN), recurrent neural network (RNN), and deep learning-based long short-term memory network (LSTM) models using multidepth in situ observations from a dense network of monitoring wells (n = 5367, 1996−2018), to simulate and forecast groundwater levels (GWL) in India. The result demonstrates the better performance of LSTM (>84% of observation wells showing r > 0.6, RMSE n < 0.7) across India, outperforming both FNN and RNN in the testing period of 5 years (2014−2018). Our estimates also reveal that besides the prevailing long-term (1996−2018) statistically significant (p < 0.1) declining GWL trends in northwest India and the Ganges river basin, higher declining trends will potentially be observed in parts of north-central and south India in the forecasting period of 5 years (2019−2023). We envisage that the forecasting approach presented in the study can contribute toward an improved urban−rural drinking water supply and sustainable crop production for 1.3 billion people in India.

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“…Lessard et al, 2013). Other local activities can have more gradual in uences, for example, the effect of groundwater abstraction on rivers ows can be delayed due to temporal lags in groundwater systems (Malakar et al, 2021). Landcover change can have an important in uence on river ows through changes in evaporation and surface runoff (Mark and Dickinson, 2008), but these may occur rapidly due to forest harvesting, or gradually due to spread of invasive species or ecological succession (Guzha et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Climate change and local anthropogenic activities have altered river flow regimes across Canterbury, New Zealand

Booker

Snelder²

2022

Water Resour Manage

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River ow regimes in uence ecologic, cultural, social, aesthetic, and economic values. Detecting changes in river ows and attributing their causes is important but challenging due to the combined in uence of climate and relevant local activities, and the lack of data on water abstraction, drainage modi cation or land use management. This study assessed the degree to which trends in river ows could be attributed to changes in climate versus local anthropogenic activities across Canterbury, Aotearoa New Zealand. Trends were assessed for a period that started immediately after a change in regulatory regime in 1991 and ended in 2020, that coincided with increases in water abstraction and changes in water management practices. Trends in observed summer conditions indicated that rainfall was stable, temperature increased, and ows decreased for many sites during the assessed period. Models representing ow as a function of rainfall and temperature were trained and tested using cross-validation for an earlier baseline period. Predictions for the 1991-2020 period made with the models were used to account for the effect of change in climate. The difference between predicted and observed ows were attributed to changes in local activities. Decreases in summer ows were partially associated with changes in climate, but changes in summer ows in several catchments were also associated with local activities. The ndings indicate changes to both climate and local activities have combined to alter ow regimes, suggesting that hydrological impacts of local activities should be considered alongside climate change when making river ow management decisions.

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Three decades of depth-dependent groundwater response to climate variability and human regime in the transboundary Indus-Ganges-Brahmaputra-Meghna mega river basin aquifers

Cited by 34 publications

References 67 publications

Groundwater Variability Across India, Under Contrasting Human and Natural Conditions

Groundwater Variability Across India, Under Contrasting Human and Natural Conditions

Deep Learning-Based Forecasting of Groundwater Level Trends in India: Implications for Crop Production and Drinking Water Supply

Climate change and local anthropogenic activities have altered river flow regimes across Canterbury, New Zealand

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