Training a hidden Markov model with PMDI and temperature to create climate informed scenarios

Understanding the nature of climatic change impacts on spatial and temporal hydroclimatic patterns is important to the development of timely and spatially explicit adaptation options. However, regime-switching behavior of hydroclimatic variables complicates the modelling process as many traditional time series methods do not capture this behavior. Accurately representing spatial correlation across hydroclimatic regimes is particularly important for water resources planning in large watersheds such as the Colorado River, and regions where interbasin transfers and shared demand nodes link multiple watersheds. Here, we developed a hidden Markov model (HMM) with covariates that generates an ensemble of plausible future regional scenarios of the Palmer modified drought index (PMDI) for any projected temperature sequence. The resulting spatially explicit scenarios represent the historical spatial and temporal patterns of the training data while incorporating non-stationarity by conditioning on temperature. These ensembles can aid water resources managers, infrastructure planners, and government policymakers tasked with building of more resilient water systems. Moreover, these ensembles can be used to generate streamflow ensembles, which, in turn, will be a valuable input to study the impact of climate change on regional hydrology.