Spatial Bayesian hierarchical modeling of precipitation extremes over a large domain

By C. Bracken, Balaji Rajagopalan, L. Cheng, William Kleiber, S. Gangopadhyay. 2015

47 citations recorded by the source corpus.

We propose a Bayesian hierarchical model for spatial extremes on a large domain. In the data layer a Gaussian elliptical copula having generalized extreme value (GEV) marginals is applied. Spatial dependence in the GEV parameters is captured with a latent spatial regression with spatially varying coefficients. Using a composite likelihood approach, we are able to efficiently incorporate a large precipitation data set, which includes stations with missing data. The model is demonstrated by application to fall precipitation extremes at approximately 2600 stations covering the western United States, −125°E to −100°E longitude and 30°N–50°N latitude. The hierarchical model provides GEV parameters on a 1/8° grid and, consequently, maps of return levels and associated uncertainty. The model results indicate that return levels and their associated uncertainty have a well‐defined spatial structure. Maps of return levels provide information about the spatial variations of the risk of extreme precipitation in the western US and is expected to be useful for infrastructure planning.

Subfields: Economics and Econometrics, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/3c148d2913616049e62337da2daf85bc6c6ead0c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. C. Bracken
2. K. Holman
3. Hamid Moradkhani
4. Balaji Rajagopalan

In Environmental Engineering

1. C. Bracken
2. K. Holman
3. Hamid Moradkhani
4. Balaji Rajagopalan
5. Hoshin V. Gupta

In Global and Planetary Change

1. C. Bracken
2. K. Holman
3. Hamid Moradkhani
4. Balaji Rajagopalan
5. Hoshin V. Gupta

Citations in the corpus

• A Bayesian Hierarchical Approach to Multivariate Nonstationary Hydrologic Frequency Analysis (2018, methodology): “…to regional frequency analysis using a Bayesian hierarchical approach allow for estimation of streamflow quantiles at ungaged sites (Chebana et al., 2014; Lima et al., 2015; Yan & Moradkhani, 2014), precipitation quantiles (Bracken et al., 2016; Renard, 2011), all with attendant uncertainties.”