Recognition of district-wise groundwater stress zones using the GLDAS-2 catchment land surface model during lean season in the Indian state of West Bengal

Chakraborty, Subha; Pham, Quoc Bao; Sharifi, Ehsan; Sammen, Saad Sh.; Vojtek, Matej; Vojteková, Jana; Elkhrachy, Ismail; Costache, Romulus; Linh, Nguyễn Thị Thùy

doi:10.1007/s11600-020-00509-x

Cited by 25 publications

(7 citation statements)

References 57 publications

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“…Agriculture in this region relies heavily on groundwater resources for irrigation due to the limited availability of surface water (Jain et al 2021). Groundwater, primarily extracted from shallow aquifers, raises concerns about its suitability for irrigation due to its complex hydrogeological characteristics and natural and anthropogenic contaminants in the area (Sahoo et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Watering Sundarban's fields: a systematic review of groundwater and surface water suitability for irrigation

Monika,

Dimple,

Kumar

et al. 2024

Appl Water Sci

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The Sundarban region—located from the Hooghly River in India's state of West Bengal to the Baleswar River in Bangladesh—renowned for its distinctive ecosystem and agricultural techniques, heavily depends on groundwater for irrigation purposes. However, concerns arise regarding the suitability of groundwater for sustainable agriculture in this vulnerable coastal zone. This systematic review aims to comprehensively evaluate the quality and appropriateness of groundwater and surface water for irrigation in the Sundarban region. By extensively searching scientific databases and relevant literature, we identified eighteen studies meeting our inclusion criteria. These studies encompassed hydrological, hydrochemical, and agronomic parameters, facilitating a thorough assessment of groundwater quality and its potential impacts on crop productivity. The review evaluates key parameters such as total hardness, residual sodium carbonate, potential salinity, permeability index, Kelly's ratio, sodium absorption ratio, corrosivity ratio, and chloroalkaline indices. The results revealed that irrigation water displays an alkaline nature, and both surface water and groundwater were unsuitable for irrigation due to exceedingly high-quality parameters beyond the standard limits. Nevertheless, groundwater exhibited favorable physiochemical properties. The review identifies critical research gaps and proposes future directions to enhance the understanding of groundwater suitability for irrigation in the Sundarban region. The findings emphasize the necessity of a multidisciplinary approach to ensure sustainable agricultural practices and safeguard the delicate ecosystem of the Sundarban region.

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Section: Introductionmentioning

confidence: 99%

Watering Sundarban's fields: a systematic review of groundwater and surface water suitability for irrigation

Monika,

Dimple,

Kumar

et al. 2024

Appl Water Sci

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“…In the Terai agro-climatic region of North Bengal in India, a massive amount of groundwater is used for irrigation, especially during the rabi season (November to May). Due to the lack of knowledge for precise estimation of irrigation water, most layman farmers use traditional flood irrigation methods and waste a considerable amount of precious groundwater, which creates a major problem of moderate to extreme groundwater stress during the rabi season ( Sahoo et al, 2021 ). Therefore, a model for precise estimation of crop water requirement for efficient irrigation practices is now very significant for conserving groundwater resources and sustaining agriculture.…”

Section: Introductionmentioning

confidence: 99%

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

Banerjee,

Sarkar,

Sarkar

et al. 2024

PeerJ

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Reference evapotranspiration (ET0 ) is a significant parameter for efficient irrigation scheduling and groundwater conservation. Different machine learning models have been designed for ET0 estimation for specific combinations of available meteorological parameters. However, no single model has been suggested so far that can handle diverse combinations of available meteorological parameters for the estimation of ET0. This article suggests a novel architecture of an improved hybrid quasi-fuzzy artificial neural network (ANN) model (EvatCrop) for this purpose. EvatCrop yielded superior results when compared with the other three popular models, decision trees, artificial neural networks, and adaptive neuro-fuzzy inference systems, irrespective of study locations and the combinations of input parameters. For real-field case studies, it was applied in the groundwater-stressed area of the Terai agro-climatic region of North Bengal, India, and trained and tested with the daily meteorological data available from the National Centres for Environmental Prediction from 2000 to 2014. The precision of the model was compared with the standard Penman-Monteith model (FAO56PM). Empirical results depicted that the model performances remarkably varied under different data-limited situations. When the complete set of input parameters was available, EvatCrop resulted in the best values of coefficient of determination (R2 = 0.988), degree of agreement (d = 0.997), root mean square error (RMSE = 0.183), and root mean square relative error (RMSRE = 0.034).

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“…Even though the Indian subcontinent is surrounded by water on three sides, the country's demand for freshwater accelerates steadily. Both surface and groundwater levels have been decreasing over the past decades due to population thresholds, rapid urbanization, irregularity in precipitation, and intensive use for agricultural activity (Chatterjee et al, 2020;Sahoo et al, 2021). The limited availability of freshwater resources is a major hurdle for many sectors, particularly agriculture and its allied sectors (Sheidaei et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Need to educate farmers about the benefits of using treated wastewater for agriculture

Baanu¹,

Babu²,

Anuradha³

2022

Water Policy

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Treated wastewater (TWW) reuse is one of the strategic options for preserving available freshwater resources and ensuring sustainability. The success of any utility project is centered on public acceptance and utilization. A questionnaire is randomly distributed to 397 farmers to study the willingness to use TWW reuse for agriculture and pay for the reuse. The socio-economic profile of the farmers, satisfaction of water supply, and agricultural factors are considered as the influential parameters. Descriptive analysis, χ2 test, Mann–Whitney U test, and binary logistic method are used for statistical analysis. The analyses reveal that more than 90% of the farmers have willingness to use TWW due to water scarcity. However, most of them are unwilling to pay for the reuse due to their straitened economic conditions. The physicochemical characteristics of the TWW from a wastewater treatment plant are analyzed and possible reductions in the application of fertilizer quantity and cost are quantified. An attempt to improve the percentage of willingness to pay for the TWW reuse is made by detailing the economic benefits of TWW reuse. A significant percentage of the farmers who are contrary to pay for the TWW reuse initially have budged to pay for TWW at a subsidized cost.

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Recognition of district-wise groundwater stress zones using the GLDAS-2 catchment land surface model during lean season in the Indian state of West Bengal

Cited by 25 publications

References 57 publications

Watering Sundarban's fields: a systematic review of groundwater and surface water suitability for irrigation

Watering Sundarban's fields: a systematic review of groundwater and surface water suitability for irrigation

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

Need to educate farmers about the benefits of using treated wastewater for agriculture

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