Global irrigation modeling relies more on pragmatic than empirical assumptions

Many global water models contain an irrigation module that simulates irrigation water withdrawals based on several assumptions on climate conditions, crop management, soil moisture, irrigation practices or water source. However, we do not know how many of these assumptions are grounded on empirical data and how many are pragmatic; that is, based on practical considerations rather than on observational evidence. Given that pragmatic assumptions are more flexible and can be altered, replaced or removed without compromising the model's representational capacity, this knowledge gap constrains our ability to delineate the uncertainties in these models and assess how reliable their results are. Here we address this issue through the lens of sensitivity auditing and philosophy of science and analyze 50 studies across nine global irrigation models (GIM), identifying a total of 102 model assumptions. Our results suggest that 70% of these assumptions are pragmatic, 35% are shared by multiple models and that most of these in turn are pragmatic. This indicates that the uncertainty space of GIMs may be larger than currently addressed using traditional uncertainty analyses. Our findings underscore the need for systematic appraisal of model assumptions to enhance transparency and improve the robustness of GIMs for decision-making in water resource management and policy.