Crop Water Footprint Assessment: A Review of Methodological Approaches, Scale Variability, and Data Constraints

Ashish Koradia, Basant Yadav (), Ashish Pandey, V. M. Chowdary and K. Chandrasekar
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Ashish Koradia: Indian Institute of Technology Roorkee, Department of Water Resources Development and Management
Basant Yadav: Indian Institute of Technology Roorkee, Department of Water Resources Development and Management
Ashish Pandey: Indian Institute of Technology Roorkee, Department of Water Resources Development and Management
V. M. Chowdary: National Remote Sensing Centre, ISRO, Agriculture Sciences and Applications Group
K. Chandrasekar: National Remote Sensing Centre, ISRO, Water Resources Group

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 14, No 1, 7384 pages

Abstract: Abstract Freshwater availability is decreasing day by day, with the agriculture sector being the primary contributor. Therefore, understanding water consumption and reducing excessive use of freshwater in agriculture through accurate estimation is crucial. The water footprint (WF) concept has emerged as a suitable assessment method. However, WF estimates vary across studies due to differences in estimation approaches, spatial scales, agro-climatic conditions, farming practices, data availability, and quality. These factors affect the accuracy and consistency of green, blue, and grey WF component estimations, leading to over- or underestimation and affecting water-use decisions. This study investigates the variability in WF components and proposes ways to address it across methods, scales, and data constraints. It is the first to examine all three WF components using five approaches: field crop water requirement (FCWR), field soil water balance (FSWB), regional water balance (RWB), remote sensing (RS), and field-measured water balance (FMWB), based on global data (2002–2023) for wheat, rice, maize, potato, and sugarcane. Results show FSWB has less variability than FCWR, with CVs for rice and wheat at 45.25% and 61.16%. RWB and RS suit large scales, while FMWB gives accurate field-level estimates. Green WF variability is influenced by rainfall patterns (CVs of 45.2% for wheat in India, 52.5% for rice in China). Blue WF depends on irrigation practices (CVs of 50.78% for wheat in India, 16.83% in Iran). Grey WF is the most variable, reflecting agrochemical pollution (CVs of 57.38% for wheat in India). This study also suggests potential recommendations for improving WF estimation under different approaches. Graphical Abstract

Keywords: Water footprint; Crops; Water uses; WF estimation approaches; Variability (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-025-04323-2

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