Interpreting land subsidence impacts due to groundwater depletion using remote sensing-based GRACE gravity anomaly and DInSAR technique: a study on north-western parts of India

Pranjal, Pranshu; Kadiyan, Neha; Chatterjee, R. S.; Kumar, Devendra; Sati, M. S.

doi:10.1007/s12665-021-09905-y

Cited by 12 publications

(2 citation statements)

References 33 publications

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“…NW part of ISC has been selected for this study, as it is primarily among the most populated and highly GW extracted regions around the globe (Rodell et al 2009;Bhanja et al 2018;Pranjal et al 2021). The NWI has an area of 11,70,553 km 2 (shown in Fig.…”

Section: Study Areamentioning

confidence: 99%

Investigating Spatio-temporal behaviour for Groundwater in North-West India: A Deep Learning Approach

Pranjal,

Kumar,

Soni

et al. 2023

Preprint

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Sustainability and conservation of natural resources require amalgamation of the novel approaches with the current usages and weather conditions. Groundwater resources is one of the natural resources which is varying annually and requires regular attention for the prediction. Our proposed deep learning (DL) approach, namely Convolution Neural Network-Long short term memory (ConvLSTM) has been implemented for the groundwater level (GWL) prediction model. The model is designed based on U-Net framework with up-sampling and down-sampling modules and also induces non-linearity using the ReLU activation function. Each module in the LSTM unit is responsible for pattern recognition based on the temporal information of GWL. The assessment of the groundwater in North-West India (NWI) has been carried out using several fundamental factors such as precipitation, soil moisture, evapotranspiration and satellite-based groundwater storage. In addition, in-situ groundwater has been used to get groundwater fluctuation scenarios (i.e., categorised into four cycles PrePre, PrePost, PostPre, and PostPost) w.r.t monsoon season to understand the difference (Δh) in GWL. The proposed model has been tested with other DL frameworks such as; Artificial neural network (ANN) and Convolution neural network (CNN). The model has been trained using the stochastic gradient method to optimise the internal parameter and validated using several geo-locations information of NWI, where ConvLSTM outperformed compared to the benchmark method. The proposed model has shown consistent least error in terms of root mean square root (RMSE) and mean square error (MAE) for the year 2014-17 with an overall score of 0.0957 and 0.0520, respectively.

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Section: Study Areamentioning

confidence: 99%

Investigating Spatio-temporal behaviour for Groundwater in North-West India: A Deep Learning Approach

Pranjal,

Kumar,

Soni

et al. 2023

Preprint

Self Cite

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“…The main cause of groundwater depletion is excessive extraction for irrigation, especially in cases where an aquifer is only replenished gradually, while climate change and variability could potentially exacerbate the phenomenon in some regions [8,9]. Global food production has increased dramatically since 1970 due to advances in well-drilling equipment and electrical pumping [10], and numerous wells have been operated in a largely unregulated manner, often replacing surface-water resources.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Depletion. Are Environmentally Friendly Energy Recharge Dams a Solution?

Kazakis,

Karakatsanis,

Ntona

et al. 2024

Water

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Groundwater is a primary source of drinking water; however, groundwater depletion constitutes a common phenomenon worldwide. The present research aims to quantify groundwater depletion in three aquifers in Greece, including the porous aquifers in the Eastern Thermaikos Gulf, Mouriki, and the Marathonas basin. The hypothesis is to reverse the phenomenon by adopting an environmentally acceptable methodology. The core of the suggested methodology was the simulation of groundwater using MODFLOW-NWT and the application of managed aquifer recharge (MAR) by using water from small dams after the generation of hydropower. Surface run-off and groundwater recharge values were obtained from the ArcSWAT simulation. The predicted future climatic data were obtained from the Coordinated Regional Climate Downscaling Experiment (CORDEX), considering the Representative Concentration Pathway (RCP) 4.5 and the climate model REMO2009. Groundwater flow simulations from 2010 to 2020 determined the existing status of the aquifers. The simulation was extended to the year 2030 to forecast the groundwater regime. In all three sites, groundwater depletion occurred in 2020, while the phenomenon will be exacerbated in 2030, as depicted in the GIS maps. During 2020, the depletion zones extended 11%, 28%, and 23% of the aquifers in Mouriki, the Eastern Thermaikos Gulf, and the Marathonas basin, respectively. During 2030, the depletion zones will increase to 50%, 42%, and 44% of the aquifers in Mouriki, the Eastern Thermaikos Gulf, and the Marathonas basin, respectively. The simulation was extended to 2040 by applying MAR with the water from the existing dams as well as from additional dams. In all sites, the application of MAR contributed to the reversal of groundwater depletion, with a significant amount of hydropower generated. Until 2040, the application of MAR will reduce the depletion zones to 0.5%, 9%, and 12% of the aquifers in Mouriki, the Eastern Thermaikos Gulf, and the Marathonas basin, respectively. Apart from over-pumping, climatic factors such as long periods of drought have exacerbated groundwater depletion. The transformation of dams to mini-scale hydropower facilities combined with MAR will benefit clean energy production, save CO2 emissions, and lead to an economically feasible strategy against groundwater depletion.

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Assessment of groundwater level using satellite-based hydrological parameters in North-West India: A deep learning approach

Pranjal,

Kumar,

Soni

et al. 2024

Earth Sci Inform

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Interpreting land subsidence impacts due to groundwater depletion using remote sensing-based GRACE gravity anomaly and DInSAR technique: a study on north-western parts of India

Cited by 12 publications

References 33 publications

Investigating Spatio-temporal behaviour for Groundwater in North-West India: A Deep Learning Approach

Investigating Spatio-temporal behaviour for Groundwater in North-West India: A Deep Learning Approach

Groundwater Depletion. Are Environmentally Friendly Energy Recharge Dams a Solution?

Assessment of groundwater level using satellite-based hydrological parameters in North-West India: A deep learning approach

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