The general consensus on climate change appears to suggest a higher frequency and intensity of extreme events such as those in Fukushima, Japan and the current (2016) extreme flooding in Louisiana, USA. These activities present significant modeling challenges. In this paper, I will briefly review some approaches to the modeling of unexpected events where a more compressed temporal dimension (daily, monthly, quarterly) is required in contrast to the usual annual-based systems that we construct. I will present findings from a continuous time econometric-input-output model, a sequential input-output system and the temporal Leontief Inverse developed by Sonis and Hewings. I will also review some of the other approaches including the inoperability input-output system and finally present some initial findings on work modifying the T-EURO method of IO accounts adjustment for temporal disaggregation.