Quantification of Uncertainty in Spatial Return Levels of Urban Precipitation Extremes

Abstract

Variations in precipitation extremes over the relatively small spatial scales of urban areas could be significantly different from those over larger regions. An understanding of such variations is critical for urban infrastructure design and operation. Urban climatology and sparse spatial data lead to uncertainties in the estimates of spatial precipitation. In this study, a Bayesian hierarchical model is used to obtain the spatial distribution of return levels of precipitation extremes in urban areas and quantify the associated uncertainty. The generalized extreme value (GEV) distribution is used for modeling precipitation extremes. A spatial component is introduced in the parameters of the GEV through a latent spatial process by considering geographic and climatologic covariates. A Markov-chain Monte Carlo algorithm is used for sampling the parameters of the GEV distribution and latent-process model. Applicability of the methodology is demonstrated with data from 19 telemetric rain-gauge stations in Bangalore city, India. For this case study, it is inferred that the elevation and mean monsoon precipitation are the predominant covariates for annual maximum precipitation. Variation of seasonal extremes are also examined in the study. For the monsoon maximum precipitation, it is observed that the geographic covariates dominate, whereas for the summer maximum precipitation, elevation and mean summer precipitation are the predominant covariates. A significant variation in spatial return levels of extreme precipitation is observed over the city.