Water footprint concept, approaches, and applications: A comprehensive review for the agricultural sector

Water and Environment Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 6, 2025

Abstract Water footprint (WF) assessment has emerged as a vital tool for understanding and managing agricultural water consumption amid increasing scarcity. This review synthesizes current knowledge methodological developments in WF research, examining both volumetric Life Cycle Assessment approaches. We analyze key challenges, including component differentiation, effective precipitation estimation, calculation standardization across spatial scales. Recent trends show shift from global assessments toward regional analyses, enabling precise management strategies. The evaluates reduction methods, demonstrating that optimized crop patterns improved practices can significantly reduce consumption. Integration of hydrological models with shows promise improving accuracy. Climate change, varying impacts, emerges critical factor influencing future calculations. While methodology offers valuable insights sustainable management, its practical application requires careful consideration contexts limitations.

Language: Английский

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Partitioning Green and Blue Evapotranspiration by Improving Budyko Equation Using Remote Sensing Observations in an Arid/Semi-Arid Inland River Basin in China

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(4), P. 612 - 612

Published: Feb. 11, 2025

The estimation of water requirements constitutes a critical prerequisite for delineating scarcity hotspots and mitigating intersectoral competition, particularly in endorheic basins arid or semi-arid regions where hydrological closure exacerbates resource allocation conflicts. Under conditions scarcity, supplied locally by precipitation shallow groundwater bodies should be taken into account to estimate the net met with conveyed from off-site sources. This concept is embodied distinction blue ET (BET) green (GET). In this study, Budyko hypothesis (BH) method was optimized partition total GET BET during 2001–2018 Heihe River Basin. region, better knowledge even more important due policies which reduced supply irrigated lands last 15 years. study proposes modified BH based on new vegetation-specific parameter (ωv) different vegetation types using actual data obtained remote sensing observations. results show that partitioned reasonably well, percent bias 23.8% 37.4% root mean square error 84.8 mm/a 113.6 mm/a, respectively, when compared reported data, are superior deficit soil balance methods. A sensitivity experiment showed exhibits low uncertainties input data. documented differences contribution across land cover As expected, rainfed forest grassland ecosystems predominantly governed GET, 81.3% 87.2% ET, respectively. contrast, croplands shrublands primarily regulated BET, contributions 61.5% 84.3% ET. improved developed paves way further analyses regions.

Language: Английский

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Interprovincial agricultural water footprint in China: Spatial pattern, driving forces and implications for water resource management

Sustainable Production and Consumption, Journal Year: 2023, Volume and Issue: 43, P. 264 - 277

Published: Nov. 10, 2023

Language: Английский

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Dynamic simulation of the water-land-food nexus for the sustainable agricultural development in the North China Plain

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 912, P. 168771 - 168771

Published: Nov. 28, 2023

Language: Английский

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Quantification of wheat water footprint based on data assimilation of remote sensing and WOFOST model

Agricultural and Forest Meteorology, Journal Year: 2024, Volume and Issue: 347, P. 109914 - 109914

Published: Feb. 6, 2024

Language: Английский

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Burgeoning food demand outpaces sustainable water supply in China

Agricultural Water Management, Journal Year: 2024, Volume and Issue: 301, P. 108936 - 108936

Published: July 5, 2024

China's agricultural sector has long realized innovation to meet burgeoning food demand, yet often occurred without concurrent consideration of trade-offs on blue water security. We contend that security and should be given equal priority if sustainable production consumption policies are defined. Here, we quantify the interplay between in China from 1990 2020 using footprint (BWF) boundary as lenses. find national agri-food BWF grown over time, being only 4 % below sustainability 2020, with number provinces unsustainable system states increasing nine 1990s 17 2010s. While minimum have been achieved since 2012, surpassed by 32 26 respectively. Assuming trends continue ceteris paribus, project will transgress 2025. Even so, appropriate intervention production, supply chains, guidelines, demand/water conundrum could equitable sustainable, notwithstanding higher 2030. This study clearly addressed contradictions potential balancing at scale, articulate a simple, scalable approach for nations facing scarcity deconstruct drivers of, develop alternative futures for,

Language: Английский

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Machine learning modeling of water footprint in crop production distinguishing water supply and irrigation method scenarios

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 625, P. 130171 - 130171

Published: Sept. 13, 2023

Language: Английский

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An integrated approach through controlled experiment and LCIA to evaluate water quality and ecological impacts of irrigated paddy rice

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(32), P. 45264 - 45279

Published: July 4, 2024

Language: Английский

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Analysis of factors affecting the water footprint of major grain crops in the Qingtongxia irrigation area

Ecohydrology & Hydrobiology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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A framework for assessing the circular economy potential in the water and agriculture sectors in Türkiye through the water-energy-food-ecosystem nexus

Sustainable Production and Consumption, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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