Abstract

The hydrologically based crop model PolyCrop (PC) is used here to investigate crop productivity and water consumption (i.e., water footprint) of irrigated rice (Oryza sativa L.), in the case study area of Landriano, in the Po valley of Italy. Water consumption for rice is explicitly investigated by way of water footprint indicators (green water footprint, WFG, and blue water footprint, WFB), defined as the absolute, specific (per kg yield), and relative (i.e., to precipitation) amount of water evapotranspired during the growing season, and by total irrigation WFI. To subsequently investigate potential effect of future climate change upon rice production and water consumption, climate scenarios from two GCMs (CCSM4, ECHAM6) from the IPCC panels, and three RCPs (RCP2.6, 4.5, 8.5) are downscaled for Landriano, and fed to the PC model. Two reference periods are studied, at half century, and end of the century, and a correlation analysis is used to highlight the potential effect of climate variables. At half century, increased CO2 concentration may lead to increased rice yield (especially under CCSM4), at the cost of increased WFB, and WFI, under decreasing precipitation, and increasing summer temperature. Under ECHAM6 scenarios, rice production would remain constant in practice, or decreasing (RCP2.6), as due to largely increasing temperature during spring, and anticipated harvest date, again with largely increasing WFB. At the end of the century, only CCSM4 under RCP2.6 and 4.5 would provide increased rice yield (and WFB, WFI), with all other scenarios providing largely decreased rice yield, and largely increasing WFB, WFI, due to increasing summer temperature. Under all scenarios, increased yearly yield variability is seen, and WFI largely exceeds WFB, i.e., a surplus of irrigation water is used with respect to evapotranspiration demand. According to the results here adaptation strategies will need to be implemented in the future to maintain acceptable rice yield, and water consumption, under an increasing demand scenario, and potentially modified climate conditions.

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