New modeling paradigms for assessing future irrigation storage requirements: a case study of the Western irrigation district in Alberta. Issue 2 (2nd April 2020)

• Record Type: Journal Article
• Authors: Ilich, Nesa; Davies, Evan G. R.; Gharib, Amr
• Is Part Of: Canadian water resources journal =. Volume 45:Issue 2(2020)
• Page Start: 172
• Page End: 185
• Publication Date: 2020-04-02
• Subjects: Reservoir management -- irrigation supply -- multiple time step optimization; Gestion des réservoirs -- approvisionnement en eau pour l'irrigation -- optimisation par pas de temps multiples; Water resources development -- Canada -- Periodicals; Water conservation -- Canada -- Periodicals; 333.9100971
• DOI: 10.1080/07011784.2020.1737237
• Languages: English
• ISSNs: 0701-1784

Abstract

Abstract: River basin planning in Alberta has relied on the use of computer modeling since the early 1980s. Typical modeling studies rely on a single time step operational framework, where water allocation decisions are made for individual model time steps, without taking into account seasonal forecasts or the corresponding demand hedging rules that are often implemented by farming communities. This kind of modeling often leads to premature depletion of reservoir storage during dry years, producing model results that represent worse decisions than those that irrigators would make by using the rule-of-thumb. This paper critically reviews the current modeling practice, and provides insight into possible improvements in modeling through the use of multiple time step optimization in combination with optimal demand hedging, which is found as part of the model solution. A case study focuses on potential storage expansions in the Western Irrigation District of Southern Alberta. Improvements with the multiple time step optimization approach also shed new light on important water management decisions made in the past and the value of a revised definition of irrigation failure criteria. Finally, the selected modeling approach reveals significant potential for capital cost savings related to future infrastructure development, and suggests that investing in digital infrastructure – better forecasting and reservoir management tools – may be more productive than investment in additional physical infrastructure.