Enhancing the water accounting plus framework to evaluate field-level water use and availability in rice-dominant cropping system and potential for mapping GHG emissions at scale: a case study for the eastern Gangetic Plains, Bihar, India - Mahapatra, Smaranika; Velpuri, Naga Manohar; Schmitter, Petra S.; Pandey, Shivam; Sikka, Alok (International Water Management Institute (IWMI). CGIAR Initiative on Excellence in Agronomy, 2024)
Agriculture is a major contributor to total global methane (CH4 ) and Nitrous oxide (N2O) emissions, accounting for 50% and 60% share, respectively. Rice cultivation accounts for approximately 20% and 10% of the global CH4 and N2O emissions, respectively. There is a strong positive correlation between water use in rice cultivation and greenhouse gas (GHG) emissions, primarily because flooded rice creates ideal (anaerobic) conditions for methane-producing bacteria. This study applies the conventional water accounting plus (WA+) framework that uses remotely sensed data products for the Eastern Gangetic Plains (EGP) region to estimate total water use, irrigation water use, and other hydrologic variables. These variables are estimated and assessed for entire cropland areas and selected rice fields across Bihar, India. Results indicated that the conventional WA+ approach ran at monthly time scales and using 250 m spatial resolution data. The results indicate that conventional WA+ was able to capture large-scale variations in water use but failed to capture field-level variations. To address this problem, the WA+ framework was refined to account better for water use in paddy fields. The new framework – Paddy WA+, uses high-resolution remote sensing data (at 30 m), improved runoff, and percolation estimation routines to track water use in the rice paddy fields. This new framework tracks the ponding depth on a daily time step so the amount of water in each rice field is estimated. The next steps include modelling efforts that will correlate the ponding depth with the GHG emissions at the field scale and scaling the emissions based on the empirical relationships between ponding depth and the emissions. The proposed framework is scalable and applicable to paddy-dominated regions worldwide because open-access remote sensing products are easily available.