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Natural disasters, such as floods, may damage power system assets and lead to widespread and long outages. The impact of flood can be alleviated by preventive actions such as installing tiger dams around power substations before the flood. In this regard, it is imperative that critical substations are identified in terms of the connected load and imposed costs to the system. This article presents a stochastic resource allocation approach for protecting power substations against flood events a day ahead of the event. Flood probability distribution functions are used to generate several flood scenarios at each substation. Using flood scenarios and substations' fragility, damage, and repair time curves obtained from historical data, the failure probability, damage percentage, damage cost, and repair time of substations are estimated. A day-ahead risk-aware stochastic scheduling model is proposed to identify the critical substations whose protection by tiger dams maximizes grid resilience. The risk-aware approach prevents high cost and low resilience if a particular scenario with a low probability is realized. A scenario reduction method is developed to generate representative substation failure scenarios and reduce the computational cost of the optimization problem. The simulation results on a realistic 30-substation system show the effectiveness of the proposed model.