Sensitivity and uncertainty in crop water footprint accounting: A case study for the Yellow River Basin

Water Footprint Assessment is a quickly growing field of research, but as yet little attention has been paid to the uncertainties involved. This study investigates the sensitivity of water footprint estimates to changes in important input variables and quantifies the size of uncertainty in water footprint estimates. The study focuses on the green and blue water footprint of producing maize, soybean, rice and wheat in the Yellow River Basin in the period 1996-2005. A grid-based daily water balance model at a 5 by 5 arc minute resolution was applied to compute green and blue water footprints of the four crops in the Yellow River Basin in the period considered. The sensitivity and uncertainty analysis focused on the effects on water footprint estimates at basin level (in m3/ton) from four key input variables: precipitation (PR), reference evapotranspiration (ET0), crop coefficient (Kc) and crop calendar. The one-at-a-time method was carried out to analyse the sensitivity of the water footprint of crops to changes in the input variables. Uncertainties in crop water footprint estimates were quantified through Monte Carlo simulations. The results show that the water footprint of crops is most sensitive to ET0 and Kc, followed by crop calendar and PR. Blue water footprints were more sensitive to input variability than green water footprints. The smaller the annual blue water footprint, the higher its sensitivity to changes in PR, ET0 and Kc. The uncertainties in the total water footprint of a crop due to combined uncertainties in climatic inputs (PR and ET0) were about ± 20% (at 95% confidence interval). The effect of uncertainties in ET0 was dominant compared to that of precipitation. The uncertainties in the total water footprint of a crop as a result of combined key input uncertainties were on average ± 26% (at 95% confidence level). The sensitivities and uncertainties differ across crop types, with highest sensitivities and uncertainties for soybean.