TY - JOUR
T1 - Projecting Flood-Inducing Precipitation with a Bayesian Analogue Model
AU - Bopp, Gregory P.
AU - Shaby, Benjamin A.
AU - Forest, Chris E.
AU - Mejía, Alfonso
N1 - Funding Information:
This research was supported in part by the National Science Foundation (Grant No. DMS-1752280) and seed grants from the Institute for Computational and Data Sciences and the Institute for Energy and the Environment at the Pennsylvania State University. Computations for this research were performed on the Institute for Computational and Data Sciences Advanced CyberInfrastructure (ICDS-ACI).
Funding Information:
This research was supported in part by the National Science Foundation (Grant No. DMS-1752280) and seed grants from the Institute for Computational and Data Sciences and the Institute for Energy and the Environment at the Pennsylvania State University. Computations for this research were performed on the Institute for Computational and Data Sciences Advanced CyberInfrastructure (ICDS-ACI).
Publisher Copyright:
© 2020, International Biometric Society.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - The hazard of pluvial flooding is largely influenced by the spatial and temporal dependence characteristics of precipitation. When extreme precipitation possesses strong spatial dependence, the risk of flooding is amplified due to catchment factors such as topography that cause runoff accumulation. Temporal dependence can also increase flood risk as storm water drainage systems operating at capacity can be overwhelmed by heavy precipitation occurring over multiple days. While transformed Gaussian processes are common choices for modeling precipitation, their weak tail dependence may lead to underestimation of flood risk. Extreme value models such as the generalized Pareto processes for threshold exceedances and max-stable models are attractive alternatives, but are difficult to fit when the number of observation sites is large, and are of little use for modeling the bulk of the distribution, which may also be of interest to water management planners. While the atmospheric dynamics governing precipitation are complex and difficult to fully incorporate into a parsimonious statistical model, non-mechanistic analogue methods that approximate those dynamics have proven to be promising approaches to capturing the temporal dependence of precipitation. In this paper, we present a Bayesian analogue method that leverages large, synoptic-scale atmospheric patterns to make precipitation forecasts. Changing spatial dependence across varying intensities is modeled as a mixture of spatial Student-t processes that can accommodate both strong and weak tail dependence. The proposed model demonstrates improved performance at capturing the distribution of extreme precipitation over Community Atmosphere Model (CAM) 5.2 forecasts. Supplementary materials accompanying this paper appear online.
AB - The hazard of pluvial flooding is largely influenced by the spatial and temporal dependence characteristics of precipitation. When extreme precipitation possesses strong spatial dependence, the risk of flooding is amplified due to catchment factors such as topography that cause runoff accumulation. Temporal dependence can also increase flood risk as storm water drainage systems operating at capacity can be overwhelmed by heavy precipitation occurring over multiple days. While transformed Gaussian processes are common choices for modeling precipitation, their weak tail dependence may lead to underestimation of flood risk. Extreme value models such as the generalized Pareto processes for threshold exceedances and max-stable models are attractive alternatives, but are difficult to fit when the number of observation sites is large, and are of little use for modeling the bulk of the distribution, which may also be of interest to water management planners. While the atmospheric dynamics governing precipitation are complex and difficult to fully incorporate into a parsimonious statistical model, non-mechanistic analogue methods that approximate those dynamics have proven to be promising approaches to capturing the temporal dependence of precipitation. In this paper, we present a Bayesian analogue method that leverages large, synoptic-scale atmospheric patterns to make precipitation forecasts. Changing spatial dependence across varying intensities is modeled as a mixture of spatial Student-t processes that can accommodate both strong and weak tail dependence. The proposed model demonstrates improved performance at capturing the distribution of extreme precipitation over Community Atmosphere Model (CAM) 5.2 forecasts. Supplementary materials accompanying this paper appear online.
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U2 - 10.1007/s13253-020-00391-6
DO - 10.1007/s13253-020-00391-6
M3 - Article
AN - SCOPUS:85082926868
VL - 25
SP - 229
EP - 249
JO - Journal of Agricultural, Biological, and Environmental Statistics
JF - Journal of Agricultural, Biological, and Environmental Statistics
SN - 1085-7117
IS - 2
ER -