Work

A comparative assessment of grey water footprint estimation methods in paddy fields

Year: 2024

Type: article

Abstract: Reliable estimation of the gray water footprint (GWF), a key indicator for assessing freshwater pollution, remains limited due to uncertainties in estimating leaching-runoff fractions and other assumptions in conventional methods. To address this gap, we applied three different methods for estimating the GWF during the rice production process in a subsurface-drained paddy field under mid-season drainage (MSD) and alternate wetting and drying (AWD) strategies: (i) a simple tier-1 approach (T1M), (ii) a field-based estimation method (FBM), and (iii) a modeling approach using the HYDRUS-2D model (HM). During four rice growing seasons in 2011–2015, nitrate concentrations were monitored in drainage water of three subsurface drainage systems with drain spacings and depths of 30 m and 0.90 m (D0.90L30), respectively, 30 m and 0.65 m (D0.65L30), and 15 m and 0.65 m (D0.65L15). The HYDRUS performance was evaluated using the root mean square error ( RMSE ), normalized root mean square error ( nRMSE ), normalized mean bias error ( nMBE ), and model efficiency ( EF ). The values of RMSE (0.25–0.99 mg L −1 ), nRMSE (5.3–19.1 %), nMBE (-2.14–10.47 %), and EF (0.6–1.0) were within the acceptable range in the calibration and validation periods under MSD and AWD. The GWF determined using FBM ranged from 17 to 2995 m 3 t −1 , from 701 to 1368 m 3 t −1 using T1M, and from 19 to 2770 m 3 t −1 using HM. The average GWFs determined using FBM, T1M, and HM were 158, 783, and 171 m 3 t −1 under MSD, and 1808, 1053, and 1840 m 3 t −1 under AWD, respectively. While a difference of up to 3961 % was observed between GWFs estimated by FBM and T1M, HM provided closer values to FBM, with relative errors ranging from 5 % to 12 %. The results indicate the HYDRUS-2D model performed well in simulating nitrate losses under MSD and AWD. Therefore, HYDRUS-2D can provide acceptable estimations of the GWF compared to the conventional and widely used T1M. • HYDRUS-2D predicted GWF more precisely than the tier-1 method (T1M). • Alternate wetting and drying had a substantially greater GWF than mid-season drainage. • Under both AWD and MSD, increasing the drainage intensity raised the GWF. • Using GWF obtained by T1M is associated with substantial uncertainties in practice.

Source: Agricultural Water Management

DOI: 10.1016/j.agwat.2024.109269

Authors: Mahsa Naderi, Abdullah Darzi-Naftchali, Fatemeh Karandish, Hadi Razaghian, Jiří Šimůnek

Institutions: Sari Agricultural Sciences and Natural Resources University, Wageningen University & Research, Payame Noor University, University of California, Riverside

Language: English

Topic: Rice Cultivation and Yield Improvement

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Clean water and sanitation, Zero hunger, Responsible consumption and production, Climate action

Open Access status: gold

Estimated APC paid: $2,780