Hydroclimate extreme events, especially precipitation and streamflow; extremes during wet seasons, pose severe threats to life, livelihoods,; and infrastructure. Therefore, timely and skillful projections of; attributes of seasonal streamflow extremes are imperative to plan; mitigation strategies. In particular, the number of ‘events’ – i.e.,; exceedances of flow thresholds that result in flooding and the magnitude; of such extremes during the season, will be of immense use to; policymakers for early planning and implementation of flood risk; mitigation and adaptation strategies. However, predicting seasonal; extremes is challenging, particularly under spatial and temporal; non-stationarity. To address this need, we develop a space-time model to; project seasonal flow risk attributes using a Bayesian hierarchical; modeling (BHM) framework in this study. In this model, the number of; events exceeding a threshold during a season at a suite of gauge; locations on a river network are modeled as Poisson margins. The; seasonal daily maximum flows are modeled as a generalized extreme value; (GEV). The rate parameters of the Poisson distribution and scale and; shape parameters of the GEV are modeled as a linear function of suitable; covariates. Gaussian Elliptical Copulas are applied to capture the; spatial dependence. The best set of covariates is selected using the; leave-one-out cross-validation information criteria (LOOIC). The; modeling framework results in the posterior distribution of the risk; attributes for each season and, thus, the uncertainties. We demonstrate; the utility of this modeling framework to project the flood risk; attributes during the summer peak monsoon season (July-August) at five; gauges in the Narmada River basin of West-Central India. As potential; covariates, we consider climate indices such as El Niño–Southern; Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Pacific Warm; Pool Region (PWPR) from the precedent season, which have shown strong; teleconnections with the Indian monsoon. This spatiotemporal modeling; framework helps in the planning of seasonal adaptation and preparedness; measures as predictions of monsoon high flow risk occurrence become; available up to 3 months before actual flood occurrence.
