State Water Resources Research Institute Program

Project Id: 2010DC111B
Title: A Hierarchical Spatio-Temporal Dynamical Model for Predicting Precipitation Occurrence and Accumulation
Project Type: Research
Start Date: 3/01/2010
End Date: 2/28/2011
Congressional District: DC
Focus Categories: Floods, Methods
Keywords: precipitation prediction, probabilistic flood risk assessment, hierarchical bayesian, spatio-temporal, uncertainty, climate change
Principal Investigators: Arab, Ali (Georegtown University); Bejleri, Valbona; Deksissa, Tolessa
Federal Funds: $ 14,996
Non-Federal Matching Funds: $ 37,527
Abstract: The problem of predicting occurrence and accumulation of precipitation is of considerable interest in many disciplines such as atmospheric sciences, agriculture, and hydrology among others. The predictions based on climate models are often in a coarse resolution that can not provide accurate predictions for specific locations. Alternatively, statistical modeling of precipitation data can provide more reliable predictions at higher resolutions. There are several statistical models suggested in the literature, but most of these models ignore the spatial and/or temporal dependence of precipitation fields which results in lack of prediction accuracy.

Our goal in this project is to develop a statistical method that yields predictive distributions for precipitation occurrence and accumulation while accounting for spatial and temporal correlation in the precipitation fields. The predictive distributions for precipitation accumulation can then be used to obtain exceedance probability of rainfall accumulation beyond a threshold in order to issue flash flood warnings, and optimize evacuation management in case of flooding events.

Progress/Completion Report, 2010, PDF

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