Risk assessment of failure of irrigation rules for parallel "reservoir-pumping station" system under the background of climate change

Against the backdrop of continuously intensifying global climate change, extreme hydro meteorological events occur frequently, posing a severe challenge to irrigation systems that rely on joint scheduling of multiple types of water source projects. This study constructed a risk assessment framework: firstly, an optimized irrigation model considering the water demand characteristics of multiple crops in the irrigation area was constructed for the irrigation system with reservoir groups and pump station groups operating in coordination, and the irrigation rules of the parallel "reservoir-pumping station" system were extracted based on the machine learning model; Secondly, based on the runoff data output from the Global Climate Model (GCM), a multivariate Copula method is used to generate multiple long series runoff samples under future climate scenarios to characterize the impact of climate uncertainty on hydrological processes; Finally, based on the assessment of future climate drought risks, fuzzy set analysis and probability change point analysis are used to comprehensively evaluate the failure risk and probability of irrigation rules under future climate scenarios. The research results indicate that under future climate scenarios, the risk of failure of current irrigation rules significantly increases, manifested in a sustained decline in the economic benefits of irrigation areas. Furthermore, it was found that there is a positive correlation between the dry and wet changes in future climate scenarios and the risk of irrigation rule failure: the drier the climate, the higher the risk of failure, and the earlier the occurrence of failure events. The risk assessment framework constructed in this study not only helps irrigation area managers identify vulnerable links and critical thresholds of irrigation systems in the face of climate change, but also provides theoretical basis and decision support for enhancing the climate adaptation capacity of agricultural infrastructure.