Modeling Probabilistic-Based Reliability Analysis for Irrigation Water Supply Due to Uncertainties in Hydrological and Irrigation Factors

Abstract

This study aims to model a probabilistic-based reliability analysis, named the RA_IWS_Canal model, for calculating the probability of the irrigation water supply exceeding the water demand (i.e., reliability) within a multi-canal irrigation zone due to variations in hydrological and irrigation uncertainty factors. The proposed RA_IWS_Canal model is developed by coupling uncertainty and risk analysis with a logistic regression equation. The Zhudong irrigation zone, located within the Touqian River watershed in northern Taiwan, was selected as the study area, with the inflow from Shanping Weir, water supplies at 15 irrigation canals, and water intakes of two reservoirs (Baoshan and Baoshan II) and a water treatment plant (Yuandon); 1000 simulations of 10-day irrigation water allocations and resulting exceedance probabilities of the water supplies at the 15 canals were achieved using the multivariate Monte Carlo simulation and the uncertainty with the water allocation model (RIBASIM), and employed in the development of the proposed RA_IWS_Canal model. The model development and application results indicate that the uncertainty factors and the inflow from Shanping Weir markedly and positively influence the exceedance probability of the canal-based irrigation water supply to boost the corresponding reliability (about 0.8). The water intake of the Baoshan Reservoir has a lower relationship (by 0.19) than the Yuandon water treatment plant with the reliabilities of the irrigation water supplies at its downstream canals. As a result, the proposed RA_IWS_Canal model can evaluate the effect of not only the canal-based uncertainty factors, but also the regional features on the irrigation water supply reliability. In addition, using the proposed RA_IWS_Canal model, the planned irrigation water demands at various canals within a multi-canal irrigation zone could be accordingly formulated based on acceptable reliability.